diff --git a/carbon/tests/nbtest/test_063_CPC.py b/carbon/tests/nbtest/test_063_CPC.py index abde2572..e15a19e7 100644 --- a/carbon/tests/nbtest/test_063_CPC.py +++ b/carbon/tests/nbtest/test_063_CPC.py @@ -11,15 +11,175 @@ from carbon.helpers.stdimports import * -from carbon import ConstantProductCurve as CPC, CarbonOrderUI +from carbon import CarbonOrderUI +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter +from carbon.tools.optimizer import CPCArbOptimizer, F +import carbon.tools.tokenscale as ts plt.style.use('seaborn-dark') plt.rcParams['figure.figsize'] = [12,6] print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonOrderUI)) -print_version(require="2.3.3") +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ts.TokenScaleBase)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) +print_version(require="2.4.2") +try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") +except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") +CCmarket = CPCContainer.from_df(df) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment P +# ------------------------------------------------------------ +def test_p(): +# ------------------------------------------------------------ + + c = CPC.from_pk(pair="USDC/WETH", p=1, k=100, params=dict(exchange="univ3", a=dict(b=1, c=2))) + assert c.P("exchange") == "univ3" + assert c.P("a") == {'b': 1, 'c': 2} + assert c.P("a:b") == 1 + assert c.P("a:c") == 2 + assert c.P("a:d") is None + assert c.P("b") is None + assert c.P("b", "meh") == "meh" + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment TVL +# ------------------------------------------------------------ +def test_tvl(): +# ------------------------------------------------------------ + + c = CPC.from_pk(pair="WETH/USDC", p=2000, k=1*2000) + assert c.tvl(incltkn=True) == (4000.0, 'USDC', 1) + assert c.tvl("USDC", incltkn=True) == (4000.0, 'USDC', 1) + assert c.tvl("WETH", incltkn=True) == (2.0, 'WETH', 1) + assert c.tvl("USDC", incltkn=True, mult=2) == (8000.0, 'USDC', 2) + assert c.tvl("WETH", incltkn=True, mult=2) == (4.0, 'WETH', 2) + assert c.tvl("WETH", incltkn=False) == 2.0 + assert c.tvl("WETH") == 2.0 + assert c.tvl() == 4000 + assert c.tvl("WETH", mult=2000) == 4000 + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment estimate prices +# ------------------------------------------------------------ +def test_estimate_prices(): +# ------------------------------------------------------------ + + CC = CPCContainer() + CC += [CPC.from_univ3(pair="WETH/USDC", cid="uv3", fee=0, descr="", + uniPa=2000, uniPb=2010, Pmarg=2005, uniL=10*sqrt(2000))] + CC += [CPC.from_pk(pair="WETH/USDC", cid="uv2", fee=0, descr="", + p=1950, k=5**2*2000)] + CC += [CPC.from_pk(pair="USDC/WETH", cid="uv2r", fee=0, descr="", + p=1/1975, k=5**2*2000)] + CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="USDC", yint=1000, y=1000, pa=1850, pb=1750)] + CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="WETH", yint=1, y=0, pb=1/1850, pa=1/1750)] + CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="USDC", yint=1000, y=500, pa=1870, pb=1710)] + #CC.plot() + + assert CC.price_estimate(tknq=T.WETH, tknb=T.USDC, result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" + assert CC.price_estimate(pair=f"{T.USDC}/{T.WETH}", result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" + assert raises(CC.price_estimate, tknq="a", result=CC.PE_PAIR) + assert raises(CC.price_estimate, tknb="a", result=CC.PE_PAIR) + assert raises(CC.price_estimate, tknq="a", tknb="b", pair="a/b", result=CC.PE_PAIR) + assert raises(CC.price_estimate, pair="ab", result=CC.PE_PAIR) + assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, + unwrapsingle=False)[0][0] == f"{T.USDC}/{T.WETH}" + assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, + unwrapsingle=True)[0] == f"{T.USDC}/{T.WETH}" + assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True)[0] == f"{T.USDC}/{T.WETH}" + r = CC.price_estimates(tknqs=list("ABC"), tknbs=list("DEFG"), pairs=True) + assert r.ndim == 2 + assert r.shape == (3,4) + r = CC.price_estimates(tknqs=list("A"), tknbs=list("DEFG"), pairs=True) + assert r.ndim == 1 + assert r.shape == (4,) + + assert CC[0].at_boundary == False + assert CC[1].at_boundary == False + assert CC[2].at_boundary == False + assert CC[3].at_boundary == True + assert CC[3].at_xmin == True + assert CC[3].at_ymin == False + assert CC[3].at_xmax == False + assert CC[3].at_ymax == True + assert CC[4].at_boundary == True + assert CC[4].at_ymin == True + assert CC[4].at_xmin == True + assert CC[4].at_ymax == True + assert CC[4].at_xmax == True + assert CC[5].at_boundary == True + + r = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_CURVES) + assert len(r)==3 + + p,w = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_DATA) + assert len(p) == len(r) + assert len(w) == len(r) + assert iseq(sum(p), 5930) + assert iseq(sum(w), 894.4271909999159) + pe = CC.price_estimate(tknq="USDC", tknb="WETH") + assert pe == np.average(p, weights=w) + + O = CPCArbOptimizer(CC) + Om = CPCArbOptimizer(CCmarket) + assert O.price_estimates(tknq="USDC", tknbs=["WETH"]) == CC.price_estimates(tknqs=["USDC"], tknbs=["WETH"]) + CCmarket.fp(onein="USDC") + r = Om.price_estimates(tknq="USDC", tknbs=["WETH", "WBTC"]) + assert iseq(r[0], 1820.89875275) + assert iseq(r[1], 28351.08150121) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment price estimates in optimizer +# ------------------------------------------------------------ +def test_price_estimates_in_optimizer(): +# ------------------------------------------------------------ + + prices = {"USDC":1, "LINK": 5, "AAVE": 100, "MKR": 500, "WETH": 2000, "WBTC": 20000} + CCfm, ctr = CPCContainer(), 0 + for tknb, pb in prices.items(): + for tknq, pq in prices.items(): + if pb>pq: + pair = f"{tknb}/{tknq}" + pp = pb/pq + k = (100000)**2/(pb*pq) + CCfm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{ctr}") + ctr += 1 + + O = CPCArbOptimizer(CCfm) + assert O.MO_PSTART == O.MO_P + tknq = "WETH" + df = O.margp_optimizer(tknq, result=O.MO_PSTART) + rd = df[tknq].to_dict() + assert len(df) == len(prices)-1 + assert df.columns[0] == tknq + assert df.index.name == "tknb" + assert rd == {k:v/prices[tknq] for k,v in prices.items() if k!=tknq} + df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=df)) + assert np.all(df == df2) + df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=rd)) + assert np.all(df == df2) + df + # ------------------------------------------------------------ # Test 063 @@ -29,7 +189,7 @@ def test_assertions_and_testing(): # ------------------------------------------------------------ - c = CPC.from_px(p=2000,x=10, pair="eth/usdc") + c = CPC.from_px(p=2000,x=10, pair="ETH/USDC") assert c.pair == "ETH/USDC" assert c.tknb == c.pair.split("/")[0] assert c.tknx == c.tknb @@ -46,6 +206,8 @@ def test_assertions_and_testing(): assert c == CPC.from_px(c.p, c.x) assert c == CPC.from_py(c.p, c.y) + c + c = CPC.from_px(p=2, x=100, x_act=10, y_act=20) assert c.y_max*c.x_min == c.k assert c.x_max*c.y_min == c.k @@ -161,6 +323,974 @@ def test_carbonorderui_integration(): assert iseq(o.p_end, c.p_min) +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment New CPC features in v2 +# ------------------------------------------------------------ +def test_new_cpc_features_in_v2(): +# ------------------------------------------------------------ + + # + + p = CPCContainer.Pair("ETH/USDC") + assert str(p) == "ETH/USDC" + assert p.pair == str(p) + assert p.tknx == "ETH" + assert p.tkny == "USDC" + assert p.tknb == "ETH" + assert p.tknq == "USDC" + + pp = CPCContainer.Pair.wrap(["ETH/USDC", "WBTC/ETH"]) + assert len(pp) == 2 + assert pp[0].pair == "ETH/USDC" + assert pp[1].pair == "WBTC/ETH" + assert pp[0].unwrap(pp) == ('ETH/USDC', 'WBTC/ETH') + # - + + pairs = ["A", "B", "C"] + assert CPCContainer.pairset(", ".join(pairs)) == set(pairs) + assert CPCContainer.pairset(pairs) == set(pairs) + assert CPCContainer.pairset(tuple(pairs)) == set(pairs) + assert CPCContainer.pairset(p for p in pairs) == set(pairs) + + pairs = [f"{a}/{b}" for a in ["ETH", "USDC", "DAI"] for b in ["DAI", "WBTC", "LINK", "ETH"] if a!=b] + CC = CPCContainer() + fp = lambda **cond: CC.filter_pairs(pairs=pairs, **cond) + assert fp(bothin="ETH, USDC, DAI") == {'DAI/ETH', 'ETH/DAI', 'USDC/DAI', 'USDC/ETH'} + assert fp(onein="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} + assert fp(onein="ETH") == fp(contains="ETH") + assert fp(notin="WBTC, ETH, DAI") == {'USDC/LINK'} + assert fp(tknbin="WBTC") == set() + assert fp(tknqin="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} + assert fp(tknbnotin="WBTC") == set(pairs) + assert fp(tknbnotin="WBTC, ETH, DAI") == {'USDC/DAI', 'USDC/ETH', 'USDC/LINK', 'USDC/WBTC'} + assert fp(notin_0="WBTC", notin_1="DAI") == fp(notin="WBTC, DAI") + assert fp(onein = "ETH") == fp(anyall=CC.FP_ANY, tknbin="ETH", tknqin="ETH") + + P = CPCContainer.Pair + ETHUSDC = P("ETH/USDC") + USDCETH = P(ETHUSDC.pairr) + assert ETHUSDC.pair == "ETH/USDC" + assert ETHUSDC.pairr == "USDC/ETH" + assert USDCETH.pairr == "ETH/USDC" + assert USDCETH.pair == "USDC/ETH" + assert ETHUSDC.isprimary + assert not USDCETH.isprimary + assert ETHUSDC.primary == ETHUSDC.pair + assert ETHUSDC.secondary == ETHUSDC.pairr + assert USDCETH.primary == USDCETH.pairr + assert USDCETH.secondary == USDCETH.pair + assert ETHUSDC.primary == USDCETH.primary + assert ETHUSDC.secondary == USDCETH.secondary + + assert P("BTC/ETH").isprimary + assert P("WBTC/ETH").isprimary + assert P("BTC/WETH").isprimary + assert P("WBTC/ETH").isprimary + assert P("BTC/USDC").isprimary + assert P("XYZ/USDC").isprimary + assert P("XYZ/USDT").isprimary + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment Real data and retrieval of curves +# ------------------------------------------------------------ +def test_real_data_and_retrieval_of_curves(): +# ------------------------------------------------------------ + + try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") + except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") + CC = CPCContainer.from_df(df) + assert len(CC) == 459 + assert len(CC) == len(df) + assert len(CC.pairs()) == 326 + assert len(CC.tokens()) == 141 + assert CC.tokens_s + assert CC.tokens_s()[:60] == '1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARM' + print("Num curves:", len(CC)) + print("Num pairs:", len(CC.pairs())) + print("Num tokens:", len(CC.tokens())) + #print(CC.tokens_s()) + + assert CC.bypairs(CC.fp(onein="WETH, WBTC")) == CC.bypairs(CC.fp(onein="WETH, WBTC"), asgenerator=False) + assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"))) == 254 + assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"), ascc=True)) == 254 + CC1 = CC.bypairs(CC.fp(onein="WBTC"), ascc=True) + assert len(CC1) == 29 + cids = [c.cid for c in CC.bypairs(CC.fp(onein="WBTC"))] + assert len(cids) == len(CC1) + assert CC.bycid("bla") is None + assert not CC.bycid(191) is None + assert raises(CC.bycids, ["bla"]) + assert len(CC.bycids(cids)) == len(cids) + assert len(CC.bytknx("WETH")) == 46 + assert len(CC.bytkny("WETH")) == 181 + assert len(CC.bytknys("WETH")) == len(CC.bytkny("WETH")) + assert len(CC.bytknxs("USDC, USDT")) == 41 + assert len(CC.bytknxs(["USDC", "USDT"])) == len(CC.bytknxs("USDC, USDT")) + assert len(CC.bytknys(["USDC", "USDT"])) == len(CC.bytknys({"USDC", "USDT"})) + cs = CC.bytknx("WETH", asgenerator=True) + assert raises(len, cs) + assert len(tuple(cs)) == 46 + assert len(tuple(cs)) == 0 # generator empty + + CC2 = CC.bypairs(CC.fp(bothin="USDC, DAI, BNT, SHIB, ETH, AAVE, LINK"), ascc=True) + tt = CC2.tokentable() + assert tt["ETH"].x == [] + assert tt["ETH"].y == [0] + assert tt["DAI"].x == [1,4,8] + assert tt["DAI"].y == [3,6] + tt + + assert CC2.tknxs() == {'AAVE', 'BNT', 'DAI', 'LINK'} + assert CC2.tknxl() == ['BNT', 'DAI', 'LINK', 'LINK', 'DAI', 'LINK', 'LINK', 'AAVE', 'DAI'] + assert set(CC2.tknxl()) == CC2.tknxs() + assert set(CC2.tknyl()) == CC2.tknys() + assert len(CC2.tknxl()) == len(CC2.tknyl()) + assert len(CC2.tknxl()) == len(CC2) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment TokenScale tests +# ------------------------------------------------------------ +def test_tokenscale_tests(): +# ------------------------------------------------------------ + + TSB = ts.TokenScaleBase() + assert raises (TSB.scale,"ETH") + assert TSB.DEFAULT_SCALE == 1e-2 + + TS = ts.TokenScale.from_tokenscales(USDC=1e0, ETH=1e3, BTC=1e4) + TS + + assert TS("USDC") == 1 + assert TS("ETH") == 1000 + assert TS("BTC") == 10000 + assert TS("MEH") == TS.DEFAULT_SCALE + + TSD = ts.TokenScaleData + + tknset = {'AAVE', 'BNT', 'BTC', 'ETH', 'LINK', 'USDC', 'USDT', 'WBTC', 'WETH'} + assert tknset - set(TSD.scale_dct.keys()) == set() + + cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") + assert cc1.tokenscale is cc1.TOKENSCALE + assert cc1.tknx == "ETH" + assert cc1.tkny == "USDC" + assert cc1.scalex == 1 + assert cc1.scaley == 1 + cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") + assert cc2.tknx == "BTC" + assert cc2.tkny == "MEH" + assert cc2.scalex == 1 + assert cc2.scaley == 1 + assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE + + cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") + cc1.set_tokenscale(TSD) + assert cc1.tokenscale != cc1.TOKENSCALE + assert cc1.tknx == "ETH" + assert cc1.tkny == "USDC" + assert cc1.scalex == 1e3 + assert cc1.scaley == 1e0 + cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") + cc2.set_tokenscale(TSD) + assert cc2.tknx == "BTC" + assert cc2.tkny == "MEH" + assert cc2.scalex == 1e4 + assert cc2.scaley == 1e-2 + assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment dx_min and dx_max etc +# ------------------------------------------------------------ +def test_dx_min_and_dx_max_etc(): +# ------------------------------------------------------------ + + cc = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110) + assert iseq(cc.x_act, 4.653741075440777) + assert iseq(cc.y_act, 513.167019494862) + assert cc.dx_min == -cc.x_act + assert cc.dy_min == -cc.y_act + assert iseq( (cc.x + cc.dx_max)*(cc.y + cc.dy_min), cc.k) + assert iseq( (cc.y + cc.dy_max)*(cc.x + cc.dx_min), cc.k) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment xyfromp_f and dxdyfromp_f +# ------------------------------------------------------------ +def test_xyfromp_f_and_dxdyfromp_f(): +# ------------------------------------------------------------ + + # + + c = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") + + assert c.pair == 'WETH-6Cc2/USDC-eB48' + assert c.pairp == 'WETH/USDC' + assert c.p == 100 + assert iseq(c.x_act, 4.653741075440777) + assert iseq(c.y_act, 513.167019494862) + assert c.tknx == T.ETH + assert c.tkny == T.USDC + assert c.tknxp == "WETH" + assert c.tknyp == "USDC" + assert c.xyfromp_f() == (c.x, c.y, c.p) + assert c.xyfromp_f(withunits=True) == (100.0, 10000.0, 100.0, 'WETH', 'USDC', 'WETH/USDC') + + x,y,p = c.xyfromp_f(p=85, ignorebounds=True) + assert p == 85 + assert iseq(x*y, c.k) + assert iseq(y/x,85) + + x,y,p = c.xyfromp_f(p=115, ignorebounds=True) + assert p == 115 + assert iseq(x*y, c.k) + assert iseq(y/x,115) + + x,y,p = c.xyfromp_f(p=95) + assert p == 95 + assert iseq(x*y, c.k) + assert iseq(y/x,p) + + x,y,p = c.xyfromp_f(p=105) + assert p == 105 + assert iseq(x*y, c.k) + assert iseq(y/x,p) + + x,y,p = c.xyfromp_f(p=85) + assert p == 85 + assert iseq(x*y, c.k) + assert iseq(y/x,90) + + x,y,p = c.xyfromp_f(p=115) + assert p == 115 + assert iseq(x*y, c.k) + assert iseq(y/x,110) + + # + + assert c.dxdyfromp_f(withunits=True) == (0.0, 0.0, 100.0, 'WETH', 'USDC', 'WETH/USDC') + + dx,dy,p = c.dxdyfromp_f(p=85, ignorebounds=True) + assert p == 85 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx),p) + + dx,dy,p = c.dxdyfromp_f(p=115, ignorebounds=True) + assert p == 115 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx),p) + + dx,dy,p = c.dxdyfromp_f(p=95) + assert p == 95 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx),p) + + dx,dy,p = c.dxdyfromp_f(p=105) + assert p == 105 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx),p) + + dx,dy,p = c.dxdyfromp_f(p=85) + assert p == 85 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx), 90) + assert iseq(dy, -c.y_act) + + dx,dy,p = c.dxdyfromp_f(p=115) + assert p == 115 + assert iseq((c.x+dx)*(c.y+dy), c.k) + assert iseq((c.y+dy)/(c.x+dx), 110) + assert iseq(dx, -c.x_act) + + assert iseq(c.x_min*c.y_max, c.k) + assert iseq(c.x_max*c.y_min, c.k) + assert iseq(c.y_max/c.x_min, c.p_max) + assert iseq(c.y_min/c.x_max, c.p_min) + # - + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment CPCInverter +# ------------------------------------------------------------ +def test_cpcinverter(): +# ------------------------------------------------------------ + + c = CPC.from_pkpp(p=2000, k=10*20000, p_min=1800, p_max=2200, pair=f"{T.ETH}/{T.USDC}") + c2 = CPC.from_pkpp(p=1/2000, k=10*20000, p_max=1/1800, p_min=1/2200, pair=f"{T.USDC}/{T.ETH}") + ci = CPCInverter(c) + c2i = CPCInverter(c2) + curves = CPCInverter.wrap([c,c2]) + assert c.pairo == c2i.pairo + assert ci.pairo == c2.pairo + + #print("x_act", c.x_act, c2i.x_act) + assert iseq(c.x_act, c2i.x_act) + xact = c.x_act + dx = -0.1*xact + c_ex = c.execute(dx=dx) + assert isinstance(c_ex, CPC) + assert iseq(c_ex.x_act, xact+dx) + assert iseq(c_ex.x, c.x+dx) + c2i_ex = c2i.execute(dx=dx) + assert iseq(c2i_ex.x_act, xact+dx) + assert iseq(c2i_ex.x, c.x+dx) + assert isinstance(c2i_ex, CPCInverter) + + assert len(curves) == 2 + assert set(c.pair for c in curves) == {'WETH-6Cc2/USDC-eB48'} + assert len(set(c.pair for c in curves)) == 1 + assert len(set(c.tknx for c in curves)) == 1 + assert len(set(c.tkny for c in curves)) == 1 + + assert c.tknx == ci.tkny + assert c.tkny == ci.tknx + assert c.tknxp == ci.tknyp + assert c.tknyp == ci.tknxp + assert c.tknb == ci.tknq + assert c.tknq == ci.tknb + assert c.tknbp == ci.tknqp + assert c.tknqp == ci.tknbp + assert f"{c.tknq}/{c.tknb}" == ci.pair + assert f"{c.tknqp}/{c.tknbp}" == ci.pairp + assert c.x == ci.y + assert c.y == ci.x + assert c.x_act == ci.y_act + assert c.y_act == ci.x_act + assert c.x_min == ci.y_min + assert c.x_max == ci.y_max + assert c.y_min == ci.x_min + assert c.y_max == ci.x_max + assert c.k == ci.k + assert iseq(c.p, 1/ci.p) + assert iseq(c.p_min, 1/ci.p_max) + assert iseq(c.p_max, 1/ci.p_min) + + + assert c.pair == c2i.pair + assert c.tknx == c2i.tknx + assert c.tkny == c2i.tkny + assert c.tknxp == c2i.tknxp + assert c.tknyp == c2i.tknyp + assert c.tknb == c2i.tknb + assert c.tknq == c2i.tknq + assert c.tknbp == c2i.tknbp + assert c.tknqp == c2i.tknqp + assert iseq(c.p, c2i.p) + assert iseq(c.p_min, c2i.p_min) + assert iseq(c.p_max, c2i.p_max) + assert c.x == c2i.x + assert c.y == c2i.y + assert c.x_act == c2i.x_act + assert c.y_act == c2i.y_act + assert c.x_min == c2i.x_min + assert c.x_max == c2i.x_max + assert c.y_min == c2i.y_min + assert c.y_max == c2i.y_max + assert c.k == c2i.k + + assert iseq(c.xfromy_f(c.y), c2i.xfromy_f(c2i.y)) + assert iseq(c.yfromx_f(c.x), c2i.yfromx_f(c2i.x)) + assert iseq(c.xfromy_f(c.y*1.05), c2i.xfromy_f(c2i.y*1.05)) + assert iseq(c.yfromx_f(c.x*1.05), c2i.yfromx_f(c2i.x*1.05)) + assert iseq(c.dxfromdy_f(1), c2i.dxfromdy_f(1)) + assert iseq(c.dyfromdx_f(1), c2i.dyfromdx_f(1)) + + assert c.xyfromp_f() == c2i.xyfromp_f() + assert c.dxdyfromp_f() == c2i.dxdyfromp_f() + assert c.xyfromp_f(withunits=True) == c2i.xyfromp_f(withunits=True) + assert c.dxdyfromp_f(withunits=True) == c2i.dxdyfromp_f(withunits=True) + assert iseq(c.p, c2i.p) + x,y,p = c.xyfromp_f(c.p*1.05) + x2,y2,p2 = c2i.xyfromp_f(c2i.p*1.05) + assert iseq(x,x2) + assert iseq(y,y2) + assert iseq(p,p2) + dx,dy,p = c.dxdyfromp_f(c.p*1.05) + dx2,dy2,p2 = c2i.dxdyfromp_f(c2i.p*1.05) + assert iseq(dx,dx2) + assert iseq(dy,dy2) + assert iseq(p,p2) + + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment simple_optimizer +# ------------------------------------------------------------ +def test_simple_optimizer(): +# ------------------------------------------------------------ + + CC = CPCContainer(CPC.from_pk(p=2000+i*10, k=10*20000, pair=f"{T.ETH}/{T.USDC}") for i in range(11)) + c0 = CC.curves[0] + c1 = CC.curves[-1] + CC0 = CPCContainer([c0]) + assert len(CC) == 11 + assert iseq([c.p for c in CC][-1], 2100) + assert len(CC0) == 1 + assert iseq([c.p for c in CC0][-1], 2000) + + # + + O = CPCArbOptimizer(CC) + O0 = CPCArbOptimizer(CC0) + func = O.simple_optimizer(result=O.SO_DXDYVECFUNC) + func0 = O0.simple_optimizer(result=O.SO_DXDYVECFUNC) + funcs = O.simple_optimizer(result=O.SO_DXDYSUMFUNC) + funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC) + funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC) + x,y = func0(2100)[0] + xb, yb, _ = c0.dxdyfromp_f(2100) + assert x == xb + assert y == yb + x,y = func(2100)[-1] + xb, yb, _ = c1.dxdyfromp_f(2100) + assert x == xb + assert y == yb + assert np.all(sum(func(2100)) == funcs(2100)) + + p = 2100 + dx, dy = funcs(p) + assert iseq(dy + p*dx, funcvy(p)) + assert iseq(dy/p + dx, funcvx(p)) + + p = 1500 + dx, dy = funcs(p) + assert iseq(dy + p*dx, funcvy(p)) + assert iseq(dy/p + dx, funcvx(p)) + + assert iseq(float(O0.simple_optimizer(result=O.SO_PMAX)), c0.p) + assert iseq(float(O.simple_optimizer(result=O.SO_PMAX)), 2049.6451720862074, eps=1e-3) + # - + + O.simple_optimizer(result=O.SO_PMAX) + + # ### global max + + r = O.simple_optimizer() + r_ = O.simple_optimizer(result=O.SO_GLOBALMAX) + assert raises(O.simple_optimizer, targettkn=T.WETH, result=O.SO_GLOBALMAX) + assert iseq(float(r), float(r_)) + assert len(r.curves) == len(CC) + assert np.all(r.dxdy_sum == sum(r.dxdy_vec)) + dx, dy = r.dxdy_vecs + assert tuple(tuple(_) for _ in r.dxdy_vec) == tuple(zip(dx,dy)) + assert r.result == r.dxdy_valx + for dp in np.linspace(-500,500,100): + assert r.dxdyfromp_valx_f(p) < r.dxdy_valx + assert r.dxdyfromp_valy_f(p) < r.dxdy_valy + + CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) + # CC.plot() + # CC_ex.plot() + prices = [c.p for c in CC] + prices_ex = [c.p for c in CC_ex] + assert iseq(np.std(prices), 31.622776601683707) + assert iseq(np.std(prices_ex), 4.547473508864641e-13) + #prices, prices_ex + + # ### target token + + r = O.simple_optimizer(targettkn=T.WETH) + r_ = O.simple_optimizer(targettkn=T.WETH, result=O.SO_TARGETTKN) + assert raises(O.simple_optimizer,targettkn=T.DAI) + assert raises(O.simple_optimizer, result=O.SO_TARGETTKN) + assert iseq(float(r), float(r_)) + assert abs(sum(r.dyvalues) < 1e-6) + assert sum(r.dxvalues) < 0 + assert iseq(float(r),sum(r.dxvalues)) + + r = O.simple_optimizer(targettkn=T.USDC) + assert abs(sum(r.dxvalues) < 1e-6) + assert sum(r.dyvalues) < 0 + assert iseq(float(r),sum(r.dyvalues)) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment optimizer plus inverted curves +# ------------------------------------------------------------ +def test_optimizer_plus_inverted_curves(): +# ------------------------------------------------------------ + + CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) + CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"{T.USDC}/{T.ETH}") for i in range(11)) + CC = CCr.bycids() + assert len(CC) == len(CCr) + CC += CCi + assert len(CC) == len(CCr) + len(CCi) + + # + + # CC.plot() + # - + + O = CPCArbOptimizer(CC) + r = O.simple_optimizer() + print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") + assert iseq(r.result, -1.3194573866437527) + + CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) + # CC.plot() + # CC_ex.plot() + + prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] + assert iseq(np.std(prices_ex), 5.130242014436283e-13) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment posx and negx +# ------------------------------------------------------------ +def test_posx_and_negx(): +# ------------------------------------------------------------ + + O = CPCArbOptimizer + a = O.a + + assert O.posx([0,-1,2]) == (0, 0, 2) + assert O.posx((-1,-2, 3)) == (0, 0, 3) + assert O.negx([0,-1,2]) == (0, -1, 0) + assert O.negx((-1,-2, 3)) == (-1, -2, 0) + assert np.all(O.posx(a([0,-1,2])) == a((0, 0, 2))) + assert O.t(a((-1,-2))) == (-1,-2) + + for v in ((1,2,3), (1,-1,5-10,0), (-10.5,8,2.34,-17)): + assert np.all(O.posx(a(v))+O.negx(a(v)) == v) + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment TradeInstructions +# ------------------------------------------------------------ +def test_tradeinstructions(): +# ------------------------------------------------------------ + + TI = CPCArbOptimizer.TradeInstruction + + ti = TI.new(curve_or_cid="1", tkn1="ETH", amt1=1, tkn2="USDC", amt2=-2000) + print(f"cid={ti.cid}, out={ti.amtout} {ti.tknout}, , out={ti.amtin} {ti.tknin}") + assert ti.tknin == "ETH" + assert ti.amtin > 0 + assert ti.tknout == "USDC" + assert ti.amtout < 0 + assert ti.price_outperin == 2000 + assert ti.price_inperout == 1/2000 + assert ti.prices == (2000, 1/2000) + assert ti.price_outperin == ti.p + assert ti.price_inperout == ti.pr + assert ti.prices == ti.pp + + assert not raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=-1) + assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=1) + assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=-1) + assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=1) + assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=0) + assert raises(TI, cid="1", tknin="USDC", amtin=0, tknout="ETH", amtout=-1) + assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=-1) + assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=1) + assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=1) + assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=-1) + assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=0, tkn2="ETH", amt2=1) + assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=0) + + til = [ + TI.new(curve_or_cid=f"{i+1}", tkn1="ETH", amt1=1*(1+i/100), tkn2="USDC", amt2=-2000*(1+i/100)) + for i in range(10) + ] + tild = TI.to_dicts(til) + tildf = TI.to_df(til) + assert len(tild) == 10 + assert len(tildf) == 10 + assert tild[0] == {'cid': '1', 'tknin': 'ETH', 'amtin': 1.0, 'tknout': 'USDC', 'amtout': -2000.0} + assert dict(tildf.iloc[0]) == { + 'pair': '', + 'pairp': '', + 'tknin': 'ETH', + 'tknout': 'USDC', + 'ETH': 1.0, + 'USDC': -2000.0 + } + + tildf + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment margp_optimizer +# ------------------------------------------------------------ +def test_margp_optimizer(): +# ------------------------------------------------------------ + + # ### no arbitrage possible + + CCa = CPCContainer() + CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") + CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") + CCa += CPC.from_pk(pair="USDC/USDT", p=1.0, k=200000*200000, cid="c2") + O = CPCArbOptimizer(CCa) + + r = O.margp_optimizer("WETH", result=O.MO_DEBUG) + assert isinstance(r, dict) + prices0 = r["price_estimates_t"] + assert not prices0 is None, f"prices0 must not be None [{prices0}]" + r1 = O.arb("WETH") + r2 = O.SelfFinancingConstraints.arb("WETH") + assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) + assert r1 == r2 + assert r["sfc"] == r1 + assert r1.is_arbsfc() + assert r1.optimizationvar == "WETH" + + r + + prices0 + + f = O.margp_optimizer("WETH", result=O.MO_DTKNFROMPF, params=dict(verbose=True, debug=False)) + r3 = f(prices0, islog10=False) + assert np.all(r3 == (0,0)) + r4, r3b = f(prices0, asdct=True, islog10=False) + assert np.all(r3==r3b) + assert len(r4) == len(r3)+1 + assert tuple(r4.values()) == (0,0,0) + assert set(r4) == {'USDC', 'USDT', 'WETH'} + + r = O.margp_optimizer("WETH", result=O.MO_MINIMAL, params=dict(verbose=True)) + rd = r.asdict + assert abs(float(r)) < 1e-10 + assert r.result == float(r) + assert r.method == "margp" + assert r.curves is None + assert r.targettkn == "WETH" + assert r.dtokens is None + assert sum(abs(x) for x in r.dtokens_t) < 1e-10 + assert r.p_optimal is None + assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) + assert set(r.tokens_t) == {'USDC', 'USDT'} + assert r.errormsg is None + assert r.is_error == False + assert r.time > 0 + assert r.time < 0.1 + + # + + r = O.margp_optimizer("WETH", result=O.MO_FULL) + rd = r.asdict() + r2 = O.margp_optimizer("WETH") + r2d = r2.asdict() + for k in rd: + #print(k) + if not k in ["time", "curves"]: + assert rd[k] == r2d[k] + assert r2.curves == r.curves # the TokenScale object fails in the dict + + assert abs(float(r)) < 1e-10 + assert r.result == float(r) + assert r.method == "margp" + assert len(r.curves) == 3 + assert r.targettkn == "WETH" + assert set(r.dtokens.keys()) == set(['USDT', 'WETH', 'USDC']) + assert sum(abs(x) for x in r.dtokens.values()) < 1e-10 + assert sum(abs(x) for x in r.dtokens_t) < 1e-10 + assert iseq(0.0005, r.p_optimal["USDC"], r.p_optimal["USDT"]) + assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) + assert tuple(r.p_optimal.values()) == r.p_optimal_t + assert set(r.tokens_t) == set(('USDC', 'USDT')) + assert r.errormsg is None + assert r.is_error == False + assert r.time > 0 + assert r.time < 0.1 + # - + + # ### arbitrage + + CCa = CPCContainer() + CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") + CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") + CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=200000*200000, cid="c2") + O = CPCArbOptimizer(CCa) + + r = O.margp_optimizer("WETH", result=O.MO_DEBUG) + assert isinstance(r, dict) + prices0 = r["price_estimates_t"] + r1 = O.arb("WETH") + r2 = O.SelfFinancingConstraints.arb("WETH") + assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) + assert r1 == r2 + assert r["sfc"] == r1 + assert r1.is_arbsfc() + assert r1.optimizationvar == "WETH" + + f = O.margp_optimizer("WETH", result=O.MO_DTKNFROMPF) + r3 = f(prices0, islog10=False) + assert set(r3.astype(int)) == set((17425,-19089)) + r4, r3b = f(prices0, asdct=True, islog10=False) + assert np.all(r3==r3b) + assert len(r4) == len(r3)+1 + assert set(r4) == {'USDC', 'USDT', 'WETH'} + + r = O.margp_optimizer("WETH", result=O.MO_FULL) + assert iseq(float(r), -0.03944401129301944) + assert r.result == float(r) + assert r.method == "margp" + assert len(r.curves) == 3 + assert r.targettkn == "WETH" + assert abs(r.dtokens_t[0]) < 1e-6 + assert abs(r.dtokens_t[1]) < 1e-6 + assert r.dtokens["WETH"] == float(r) + assert tuple(r.p_optimal.values()) == r.p_optimal_t + assert tuple(r.p_optimal) == r.tokens_t + assert iseq(r.p_optimal_t[0], 0.0005421803152482512) or iseq(r.p_optimal_t[0], 0.00045575394031021585) + assert iseq(r.p_optimal_t[1], 0.0005421803152482512) or iseq(r.p_optimal_t[1], 0.00045575394031021585) + assert tuple(r.p_optimal.values()) == r.p_optimal_t + assert set(r.tokens_t) == set(('USDC', 'USDT')) + assert r.errormsg is None + assert r.is_error == False + assert r.time > 0 + assert r.time < 0.1 + + abs(r.dtokens_t[0]) + + + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment simple_optimizer demo [NOTEST] +# ------------------------------------------------------------ +def notest_simple_optimizer_demo(): +# ------------------------------------------------------------ + + CC = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+i*10000), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) + O = CPCArbOptimizer(CC) + c0 = CC.curves[0] + CC0 = CPCContainer([c0]) + O = CPCArbOptimizer(CC) + O0 = CPCArbOptimizer(CC0) + funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC) + funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC) + funcvx0 = O0.simple_optimizer(result=O.SO_DXDYVALXFUNC) + funcvy0 = O0.simple_optimizer(result=O.SO_DXDYVALYFUNC) + #CC.plot() + + xr = np.linspace(1500, 3000, 50) + plt.plot(xr, [funcvx(x)/len(CC) for x in xr], label="all curves [scaled]") + plt.plot(xr, [funcvx0(x) for x in xr], label="curve 0 only") + plt.xlabel(f"price [{c0.pairp}]") + plt.ylabel(f"value [{c0.tknxp}]") + plt.grid() + plt.show() + plt.plot(xr, [funcvy(x)/len(CC) for x in xr], label="all curves [scaled]") + plt.plot(xr, [funcvy0(x) for x in xr], label="curve 0 only") + plt.xlabel(f"price [{c0.pairp}]") + plt.ylabel(f"value [{c0.tknyp}]") + plt.grid() + plt.show() + + r = O.simple_optimizer() + print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") + + CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) + CC.plot() + CC_ex.plot() + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment MargP Optimizer Demo [NOTEST] +# ------------------------------------------------------------ +def notest_margp_optimizer_demo(): +# ------------------------------------------------------------ + + CCa = CPCContainer() + CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") + CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") + CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=20000*20000, cid="c2") + O = CPCArbOptimizer(CCa) + + CCa.plot() + + r = O.margp_optimizer("WETH", params=dict(verbose=True)) + rd = r.asdict + r + + rd + + CCa1 = O.adjust_curves(r.dxvalues) + CCa1.plot() + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment Optimizer plus inverted curves [NOTEST] +# ------------------------------------------------------------ +def notest_optimizer_plus_inverted_curves(): +# ------------------------------------------------------------ + + CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) + CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"{T.USDC}/{T.ETH}") for i in range(11)) + CC = CCr.bycids() + assert len(CC) == len(CCr) + CC += CCi + assert len(CC) == len(CCr) + len(CCi) + CC.plot() + + O = CPCArbOptimizer(CC) + r = O.simple_optimizer() + print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") + CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) + prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] + print("prices post arb:", prices_ex) + print("stdev", np.std(prices_ex)) + #CC.plot() + CC_ex.plot() + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment Operating on leverage ranges [NOTEST] +# ------------------------------------------------------------ +def notest_operating_on_leverage_ranges(): +# ------------------------------------------------------------ + + N = 10 + + # + + CCc, CCm, ctr = CPCContainer(), CPCContainer(), 0 + U, U1 = CPCContainer.u, CPCContainer.u1 + tknb, tknq = T.ETH, T.USDC + pb, pq = 2000, 1 + pair = f"{tknb}/{tknq}" + pp = pb/pq + k = 100000**2/(pb*pq) + CCm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{pair}", params=dict(xc="market")) + #print("\n***PAIR:", tknb, pb, tknq, pq, pair, pp) + for i in range(N): + p = pp * (1+0.2*U(-0.5, 0.5)) + p_min, p_max = (p, U(1.001, 1.5)*p) if U1()>0.5 else (U(0.8, 0.999)*p, p) + amtusdc = U(10000, 200000) + k = amtusdc**2/(pb*pq) + #print("*curve", int(amtusdc), p, p_min, p_max, int(k)) + CCc += CPC.from_pkpp(p=p, k=k, p_min=p_min, p_max=p_max, + pair=pair, cid = f"carb-{ctr}", params=dict(xc="carbon")) + ctr += 1 + + CC = CCc.bycids().add(CCm) + CC.plot() + # - + + O = CPCArbOptimizer(CC) + r = O.simple_optimizer() + print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") + CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) + prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] + print("prices post arb:", prices_ex) + print("stdev", np.std(prices_ex)) + #CC.plot() + CC_ex.plot() + + r.dxvalues + + +# ------------------------------------------------------------ +# Test 063 +# File test_063_CPC.py +# Segment Arbitrage testing [NOTEST] +# ------------------------------------------------------------ +def notest_arbitrage_testing(): +# ------------------------------------------------------------ + + c1 = CPC.from_pkpp(p=95, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") + c2 = CPC.from_pkpp(p=105, k=90*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") + CC = CPCContainer([c1,c2]) + CC.plot() + + a = lambda x: np.array(x) + pr = np.linspace(70,130,200) + dx1, dy1, p = zip(*(c1.dxdyfromp_f(p) for p in pr)) + assert np.all(p == pr) + dx2, dy2, p = zip(*(c2.dxdyfromp_f(p) for p in pr)) + assert np.all(p == pr) + v1 = a(dy1)+a(p)*a(dx1) + v2 = a(dy2)+a(p)*a(dx2) + plt.plot(p, v1, label="Value curve c1") + plt.plot(p, v2, label="Value curve c2") + plt.plot(p, v1+v2, label="Value combined curves") + plt.legend() + plt.grid() + + + def vfunc(p): + + dx1, dy1, _ = c1.dxdyfromp_f(p) + dx2, dy2, _ = c2.dxdyfromp_f(p) + v1 = dy1 + p*dx1 + v2 = dy2 + p*dx2 + v = v1+v2 + #print(f"[v] v({p}) = {v}") + return -v + + + O = CPCArbOptimizer + O.findmin(vfunc, 100, N=100) + + func1 = lambda x: (x-2)**2 + O.findmin(func1, 1) + + func2 = lambda x: 1-(x-3)**2 + O.findmax(func2, 2.5) + + val = tuple(float(O.findmin(func1, 100, N=n)) for n in range(100)) + val = tuple(abs(v-val[-1]) for v in val) + val = tuple(v for v in val if v > 0) + plt.plot(val) + plt.yscale('log') + plt.grid() + + val = tuple(float(O.findmin(func2, 100, N=n)) for n in range(100)) + val = tuple(abs(v-val[-1]) for v in val) + val = tuple(v for v in val if v > 0) + plt.plot(val) + plt.yscale('log') + plt.grid() + + val0 = tuple(float(O.findmin(vfunc, 99, N=n)) for n in range(100)) + val = tuple(abs(v-val0[-1]) for v in val0) + val = tuple(v for v in val if v > 0) + print(val0[-1]) + plt.plot(val) + plt.yscale('log') + plt.grid() + + val0 = tuple(float(O.findmin_gd(vfunc, 99, N=n)) for n in range(100)) + val = tuple(abs(v-val0[-1]) for v in val0) + val = tuple(v for v in val if v > 0) + print(val0[-1]) + plt.plot(val) + plt.yscale('log') + plt.grid() + + O.findmin(vfunc, 99, N=700) + + # ------------------------------------------------------------ # Test 063 # File test_063_CPC.py @@ -306,4 +1436,7 @@ def notest_charts(): plt.grid() # - + + + \ No newline at end of file diff --git a/carbon/tests/nbtest/test_064_Serialization.py b/carbon/tests/nbtest/test_064_Serialization.py new file mode 100644 index 00000000..1824f60d --- /dev/null +++ b/carbon/tests/nbtest/test_064_Serialization.py @@ -0,0 +1,399 @@ +# ------------------------------------------------------------ +# Auto generated test file `test_064_Serialization.py` +# ------------------------------------------------------------ +# source file = NBTest_064_Serialization.py +# source path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# target path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# test id = 064 +# test comment = Serialization +# ------------------------------------------------------------ + + + +from carbon.helpers.stdimports import * +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer +from carbon.tools.optimizer import CPCArbOptimizer, cp, time + +import json +import time +import pandas as pd +import numpy as np +from math import sqrt +from matplotlib import pyplot as plt +plt.style.use('seaborn-dark') +plt.rcParams['figure.figsize'] = [12,6] + +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCContainer)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) +print_version(require="2.4.2") + + + + +# ------------------------------------------------------------ +# Test 064 +# File test_064_Serialization.py +# Segment Optimizer pickling [NOTEST] +# ------------------------------------------------------------ +def notest_optimizer_pickling(): +# ------------------------------------------------------------ + + N=5 + curves = [ + CPC.from_xy(x=1, y=2000, pair="ETH/USDC"), + CPC.from_xy(x=1, y=2200, pair="ETH/USDC"), + CPC.from_xy(x=1, y=2400, pair="ETH/USDC"), + ] + # note: the below is a bit icky as the same curve objects are added multiple times + CC = CPCContainer(curves*N) + O = CPCArbOptimizer(CC) + O.CC.asdf() + + O.pickle("delme") + O.pickle("delme", addts=False) + + # !ls *.pickle + + O.unpickle("delme") + + +# ------------------------------------------------------------ +# Test 064 +# File test_064_Serialization.py +# Segment Creating curves +# ------------------------------------------------------------ +def test_creating_curves(): +# ------------------------------------------------------------ + # + # Note: for those constructor, the parameters `cid` and `descr` as well as `fee` are mandatory. Typically `cid` would be a field uniquely identifying this curve in the database, and `descr` description of the pool. The description should neither include the pair nor the fee level. We recommend using `UniV3`, `UniV3`, `Sushi`, `Carbon` etc. The `fee` is quoted as decimal, ie 0.01 is 1%. If there is no fee, the number `0` must be provided, not `None`. + + # ### Uniswap v2 + # + # In the Uniswap v2 constructor, $x$ is the base token of the pair `TKNB`, and $y$ is the quote token `TKNQ`. + # + # By construction, Uniswap v2 curves map directly to CPC curves with the following parameter choices + # + # - $x,y,k$ are the same as in the $ky=k$ formula defining the AMM (provide any 2) + # - $x_a = x$ and $y_a = y$ because there is no leverage on the curves. + # + + c = CPC.from_univ2(x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") + c2 = CPC.from_univ2(x_tknb=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") + c3 = CPC.from_univ2(y_tknq=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") + assert c.k == 10000 + assert c.x == 100 + assert c.y == 100 + assert c.x_act == 100 + assert c.y_act == 100 + assert c == c2 + assert c == c3 + assert c.fee == 0 + assert c.cid == "1" + assert c.descr == "UniV2" + c + + c.asdict() + + assert c.asdict() == { + 'k': 10000, + 'x': 100, + 'x_act': 100, + 'y_act': 100, + 'pair': 'TKNB/TKNQ', + 'cid': "1", + 'fee': 0, + 'descr': 'UniV2', + 'constr': 'uv2', + 'params': {} + } + + assert not raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, fee=0, cid=1, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", cid=1, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, descr="UniV2") + assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1) + + # ### Uniswap v3 + # + # Uniswap V3 uses an implicit virtual token model. The most important relationship here is that $L^2=k$, ie the square of the Uniswap pool constant is the constant product parameter $k$. Alternatively we find that $L=\bar k$ if we use the alternative pool invariant $\sqrt{xy}=\bar k$ for the constant product pool. The conventions are as in the Uniswap v2 case, ie $x$ is the base token `TKNB` and $y$ is the quote token `TKNQ`. The parameters are + # + # - $L$ is the so-called _liquidity_ parameter, indicating the size of the pool at this particular tick (see above) + # - $P_a, P_b$ are the lower and upper end of the _current_ tick range* + # - $P_{marg}$ is the current (marginal) price of the range; we have $P_a \leq P_{marg} \leq P_b$ + # + # *note that for Uniswap v3 curves we _only_ usually model the current tick range as crossing a tick boundary is relatively expensive and most arb bots do not do that; in principle however nothing prevents us from also adding inactive tick ranges, in which case every tick range corresponds to a single, out of the money curve. + + c = CPC.from_univ3(Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV3") + assert c.x == 1000 + assert c.y == 1000 + assert c.k == 1000*1000 + assert iseq(c.p_max, 1.1) + assert iseq(c.p_min, 0.9) + assert c.fee == 0 + assert c.cid == "1" + assert c.descr == "UniV3" + + assert not raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") + assert raises(CPC.from_univ3, Pmarg=2, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") + assert raises(CPC.from_univ3, Pmarg=0.5, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") + assert raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=1.1, uniPb=0.9, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") + + # ### Carbon + # + # First a bried reminder that the Carbon curves here correspond to Carbon Orders, ie half a Carbon strategy. Those order trade unidirectional only, and as we here are only looking at a single trade we do not care about collateral moving from an order to another one. We provide slightly more flexibility here in terms of tokens and quotes: $y$ corresponds to `tkny` which must be part of `pair` but which can be quote or base token. + # + # - $y, y_{int}$ are the current amounts of token y and the y-intercept respectively, in units of `tkny` + # + # - $P_a, P_b$ are the prices determining the range, either quoted as $dy/dx$ is `isdydx` is True (default), or in the natural direction of the pair* + # + # - $A, B$ are alternative price parameters, with $B=\sqrt{P_b}$ and $A=\sqrt{P_a}-\sqrt{P_b}\geq 0$; those must _always_ be quoted in $dy/dx$* + # + # *The ranges must _either_ be specificed with `pa, pb, isdydx` or with `A, B` and in the second case `isdydx` must be True. There is no mix and match between those two parameter sets. + + c = CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert c.y_act == 1 + assert c.x_act == 0 + assert iseq(1/c.p_min, 2200) + assert iseq(1/c.p_max, 1800) + assert iseq(1/c.p, 1/c.p_max) + + c = CPC.from_carbon(yint=1, y=1, A=1/256, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) + assert c.y_act == 1 + assert c.x_act == 0 + assert iseq(1/c.p_min, 2000) + print("pa", 1/c.p_max, 1/(1/256+sqrt(c.p_min))**2) + assert iseq(1/c.p_max, 1/(1/256+sqrt(c.p_min))**2) + assert iseq(1/c.p, 1/c.p_max) + + c = CPC.from_carbon(yint=3000, y=3000, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert c.y_act == 3000 + assert c.x_act == 0 + assert iseq(c.p_min, 2900) + assert iseq(c.p_max, 3100) + assert iseq(c.p, c.p_max) + + c = CPC.from_carbon(yint=2000, y=2000, A=10, B=sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert c.y_act == 2000 + assert c.x_act == 0 + assert iseq(c.p_min, 3000) + print("pa", c.p_max, (10+sqrt(c.p_min))**2) + assert iseq(c.p_max, (10+sqrt(c.p_min))**2) + assert iseq(1/c.p, 1/c.p_max) + + CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + CPC.from_carbon(yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) + CPC.from_carbon(yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="3", descr="Carbon", isdydx=True) + CPC.from_carbon(yint=1, y=1, A=10, B=sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="4", descr="Carbon", isdydx=True) + + assert not raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", fee=0, cid="1", descr="Carbon", isdydx=False) + #assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", cid="1", descr="Carbon", isdydx=False) + #assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, descr="Carbon", isdydx=False) + #assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="LINK", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pb=1800, pa=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + + assert not raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=False) + assert raises(CPC.from_carbon, yint=1, y=1, pa=1000, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert raises(CPC.from_carbon, yint=1, y=1, pb=1000, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert raises(CPC.from_carbon, yint=1, y=1, A=-1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + + assert not raises(CPC.from_carbon, yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + assert raises(CPC.from_carbon, yint=1, y=1, pb=3100, pa=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + + +# ------------------------------------------------------------ +# Test 064 +# File test_064_Serialization.py +# Segment Charts [NOTEST] +# ------------------------------------------------------------ +def notest_charts(): +# ------------------------------------------------------------ + + curves_uni =[ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="U2/1", descr="UniV2"), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="U2/2", descr="UniV2"), + CPC.from_univ3(Pmarg=2000, uniL=100, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), + CPC.from_univ3(Pmarg=2010, uniL=75, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), + ] + CC = CPCContainer(curves_uni) + + curves_carbon = [ + CPC.from_carbon(yint=3000, y=3000, pa=3500, pb=2500, pair="ETH/USDC", tkny="USDC", fee=0, cid="C1", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=3000, y=3000, A=20, B=sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C2", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=3000, y=3000, A=40, B=sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C3", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="C4", descr="Carbon", isdydx=False), + CPC.from_carbon(yint=1, y=1, pa=1/1800, pb=1/2000, pair="ETH/USDC", tkny="ETH", fee=0, cid="C5", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, A=1/500, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C6", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, A=1/1000, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C7", descr="Carbon", isdydx=True), + ] + + curves = curves_uni + curves_carbon + CC = CPCContainer(curves) + CC.plot(params=CC.Params()) + + +# ------------------------------------------------------------ +# Test 064 +# File test_064_Serialization.py +# Segment Serializing curves +# ------------------------------------------------------------ +def test_serializing_curves(): +# ------------------------------------------------------------ + # + # The `CPCContainer` and `ConstantProductCurve` objects do not strictly have methods that would allow for serialization. However, they allow conversion from an to datatypes that are easily serialized. + # + # - on the `ConstantProductCurve` level there is `asdict()` and `from_dicts(.)` + # - on the `CPCContainer` level there is also `asdf()` and `from_df(.)`, allowing conversion from and to pandas dataframes + # + # Recommended serialization is either dict to json via the `json` library, or any of the serialization methods inherent in dataframes, notably also pickling (Excel formates are not recommended as they are slow and heavy). + # + # + # + + curves = [ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="1", descr="UniV2", params={"meh":1}), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="2", descr="UniV2"), + CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid="3", descr="UniV2"), + ] + + c0 = curves[0] + assert c0.params.__class__.__name__ == "AttrDict" + assert c0.params == {'meh': 1} + + CC = CPCContainer(curves) + assert raises(CPCContainer, [1,2,3]) + assert len(CC.curves) == len(curves) + assert len(CC.asdicts()) == len(CC.curves) + assert CPCContainer.from_dicts(CC.asdicts()) == CC + ccjson = json.dumps(CC.asdicts()) + assert CPCContainer.from_dicts(json.loads(ccjson)) == CC + CC + + df = CC.asdf() + assert len(df) == 3 + assert tuple(df.reset_index().columns) == ('cid', 'k', 'x', 'x_act', 'y_act', + 'pair', 'fee', 'descr', 'constr', 'params') + assert tuple(df["k"]) == (2000, 8040, 1970) + assert CPCContainer.from_df(df) == CC + df + + +# ------------------------------------------------------------ +# Test 064 +# File test_064_Serialization.py +# Segment Saving curves [NOTEST] +# ------------------------------------------------------------ +def notest_saving_curves(): +# ------------------------------------------------------------ + # + # Most serialization methods we use go via the a pandas DataFram object. To create a dataframe we use the `asdf()` method, and to instantiate curve container from a dataframe we use `CPCContainer.from_df(df)`. + + N=5000 + curves = [ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid=1, descr="UniV2"), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid=2, descr="UniV2"), + CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid=3, descr="UniV2"), + ] + CC = CPCContainer(curves*N) + df = CC.asdf() + #CC + + # ### Formats + # #### json + # + # Using `json.dumps(.)` the list of dicts returned by `asdicts()` can be converted to json, and then saved as a textfile. When loaded back, the text can be expanded into json using `json.loads(.)` and the new object can be instantiated using `CPCContainer.from_dicts(dicts)`. + + start_time = time.time() + cc_json = json.dumps(CC.asdicts()) + print("len", len(cc_json)) + CC2 = CPCContainer.from_dicts(json.loads(cc_json)) + assert CC == CC2 + print(f"elapsed time: {time.time()-start_time:.2f}s") + #CC2 + + # #### csv + # + # `to_csv` converts a dataframe to a csv file; this file can also be zipped; this format is ideal for maximum interoperability as pretty much every software allows dealing with csvs; it is very fast, and the zipped files are much smaller than everything else + + start_time = time.time() + df.to_csv(".curves.csv") + df_csv = pd.read_csv(".curves.csv") + assert CPCContainer.from_df(df_csv) == CC + print(f"elapsed time: {time.time()-start_time:.2f}s") + df_csv[:3] + + # #### tsv + # + # `to_csv` can be used with `sep="\t"` to create a tab separated file + + start_time = time.time() + df.to_csv(".curves.tsv", sep="\t") + df_tsv = pd.read_csv(".curves.tsv", sep="\t") + assert CPCContainer.from_df(df_tsv) == CC + print(f"elapsed time: {time.time()-start_time:.2f}s") + + # #### compressed csv + # + # `to_csv` can be used with `compression = "gzip"` to create a compressed file. This is by far the smallest output available, and takes little more time compared to uncompressed. + + start_time = time.time() + df.to_csv(".curves.csv.gz", compression = "gzip") + df_csv = pd.read_csv(".curves.csv.gz") + assert CPCContainer.from_df(df_csv) == CC + print(f"elapsed time: {time.time()-start_time:.2f}s") + + + # #### Excel + # + # `to_excel` converts the dataframe to an xlsx file; older versions of pandas may allow to also save in the old xls format, but this is deprecated; note that Excel files can be rather big, and saving them is very slow, 10-15x(!) longer than csv. + + start_time = time.time() + df.to_excel(".curves.xlsx") + df_xlsx = pd.read_excel(".curves.xlsx") + assert CPCContainer.from_df(df_xlsx) == CC + print(f"elapsed time: {time.time()-start_time:.2f}s") + df_xlsx[:3] + + # #### pickle + # + # `to_pickle` pickles the dataframe; this format is rather big, but it is the fastest to process, albeit not at a significant margin + + start_time = time.time() + df.to_pickle(".curves.pkl") + df_pickle = pd.read_pickle(".curves.pkl") + assert CPCContainer.from_df(df_pickle) == CC + print(f"elapsed time: {time.time()-start_time:.2f}s") + df_pickle[:3] + + # ### Benchmarking + # + # below a comparison of the different methods in terms of size and speed; the benchmark run used **300,000 curves** + # + # 33000000 .curves.json -- 5.2s (without read/write) + # 11100035 .curves.csv -- 3.4s + # 37817 .curves.csv.gz -- 3.4s + # 15602482 .curves.pkl -- 2.6s + # 11100035 .curves.tsv -- 3.2s + # 8031279 .curves.xlsx -- 45.0s (!) + # + # Below are the figures for the current run (timing figures inline above) + + print(f"{len(df_xlsx)} curves") + print(f" {len(cc_json)} .curves.json", ) + # !ls -l .curves* + + \ No newline at end of file diff --git a/carbon/tests/nbtest/test_065-GraphCode_.py b/carbon/tests/nbtest/test_065-GraphCode_.py new file mode 100644 index 00000000..c31c20b5 --- /dev/null +++ b/carbon/tests/nbtest/test_065-GraphCode_.py @@ -0,0 +1,856 @@ +# ------------------------------------------------------------ +# Auto generated test file `test_065-GraphCode_.py` +# ------------------------------------------------------------ +# source file = NBTest_065-GraphCode.py +# source path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# target path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# test id = 065-GraphCode +# test comment = +# ------------------------------------------------------------ + + + +from carbon.helpers.stdimports import * +#from carbon import CarbonOrderUI +import carbon.tools.arbgraphs as ag +from carbon.tools.arbgraphs import np, pd, plt # convenience imports +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer +import math + +plt.style.use('seaborn-dark') +plt.rcParams['figure.figsize'] = [12,6] +#print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonOrderUI)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ag.ArbGraph)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) +print_version(require="2.4.2") + + + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment ArbGraphs test and demo +# ------------------------------------------------------------ +def test_arbgraphs_test_and_demo(): +# ------------------------------------------------------------ + + nodes = lambda: ag.create_node_list("ETH, USDC, WBTC, BNT") + assert [str(n) for n in nodes()] == ['ETH(0)', 'USDC(1)', 'WBTC(2)', 'BNT(3)'] + nodes() + + AG = ag.ArbGraph(nodes=nodes()) + N = AG.node_by_tkn + assert str(N("ETH")) == "ETH(0)" + assert str(N("BNT")) == "BNT(3)" + assert str(AG.node_by_ix(1)) == "USDC(1)" + assert str(AG.node_by_tkn("USDC")) == "USDC(1)" + AG + + assert str(N("ETH")) == "ETH(0)" + + edge = ag.Edge(N("ETH"), 1, N("USDC"), 2000) + edge1 = ag.Edge(N("ETH"), 1, N("USDC"), 2000, inverse=True, ix=10) + assert (edge.pair(), edge.price(), edge.convention()) == ('ETH/USDC', 2000.0, 'USDC per ETH') + assert (edge1.pair(), edge1.price(), edge1.convention()) == ('USDC/ETH', 0.0005, 'ETH per USDC') + edge, str(edge), str(edge1) + + assert (edge+0).asdict() == edge.asdict() + assert (edge+0) != edge # == means objects are the same + assert not edge+0 is edge + assert (2*edge).asdict() == (edge*2).asdict() + assert (edge + 2*edge).asdict() == (3*edge).asdict() + assert sum([edge,edge,edge]).asdict() == (3*edge).asdict() + + (edge+0).asdict() + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Paths and cycles +# ------------------------------------------------------------ +def test_paths_and_cycles(): +# ------------------------------------------------------------ + + C = ag.Cycle([1,2,3,4,5]) + assert len(C) == 5 + assert [x for x in C.items()] == [1, 2, 3, 4, 5, 1] + assert [x for x in C.items(start_ix=3)] == [4, 5, 1, 2, 3, 4] + assert [x for x in C.items(start_val=3)] == [3, 4, 5, 1, 2, 3] + assert [p for p in C.pairs()] == [(1, 2), (2, 3), (3, 4), (4, 5), (5, 1)] + + c1 = ag.Cycle([1,2,3,4,5,6], "c1") + assert ag.Cycle([8,9]).is_subcycle_of(c1) == False + assert ag.Cycle([1,5,6]).is_subcycle_of(c1) == True + assert ag.Cycle([1,6,5]).is_subcycle_of(c1) == False + assert c1.filter_subcycles([ag.Cycle([8,9]), ag.Cycle([1,5,6]), ag.Cycle([1,6,5])]) == (ag.Cycle([1, 5, 6]),) + assert c1.filter_subcycles(ag.Cycle([1,5,6])) == (ag.Cycle([1, 5, 6]),) + assert str(c1) == 'cycle [c1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6 ->...' + + assert c1.asdict() == {'data': [1, 2, 3, 4, 5, 6], 'uid': 'c1', 'graph': None} + assert c1.astuple() == ([1, 2, 3, 4, 5, 6], 'c1', None) + assert (c1.asdf().set_index("uid")["data"] == c1.asdf(index="uid")["data"]).iloc[0] + assert list(c1.asdf(exclude=["data"]).columns) == ['uid', 'graph'] + assert list(c1.asdf(include=["data", "graph"], exclude=["graph"]).columns) == ['data'] + + import types + nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") + c2 = ag.Cycle(nodes, "c2") + assert c2.uid == "c2" + assert str(c2) == 'cycle [c2]: ETH->USDC->WBTC->BNT->...' + print(nodes) + print(c2) + gc2 = (c for c in c2.items()) + assert isinstance(gc2, types.GeneratorType) + tc2 = tuple(gc2) + assert str(tc2) == "(ETH(0), USDC(1), WBTC(2), BNT(3), ETH(0))" + assert tuple(gc2) == tuple() # generator spent + pc2 = (p for p in c2.pairs()) + assert isinstance(pc2, types.GeneratorType) + tpc2 = tuple(pc2) + assert len(tpc2) == 4 + assert str(tpc2[0]) == '(ETH(0), USDC(1))' + assert str(tpc2[-1]) == '(BNT(3), ETH(0))' + assert c2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT', 'BNT/ETH'] + + p1 = ag.Path([1,2,3,4,5,6], "p1") + assert p1.uid == "p1" + assert (str(p1)).strip() == 'path [p1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6' + gp1 = (p for p in p1.items()) + assert isinstance(gp1, types.GeneratorType) + tp1 = tuple(gp1) + assert tp1 == (1, 2, 3, 4, 5, 6) + + nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") + p2 = ag.Path(nodes, "p2") + assert p2.uid == "p2" + assert str(p2) == 'path [p2]: ETH->USDC->WBTC->BNT' + gp2 = (c for c in p2.items()) + assert isinstance(gp2, types.GeneratorType) + tp2 = tuple(gp2) + assert str(tp2) == "(ETH(0), USDC(1), WBTC(2), BNT(3))" + assert tuple(gp2) == tuple() # generator spent + pp2 = (p for p in p2.pairs()) + assert isinstance(pp2, types.GeneratorType) + tpp2 = tuple(pp2) + assert len(tpp2) == 3 + assert str(tpp2[0]) == '(ETH(0), USDC(1))' + assert str(tpp2[-1]) == '(WBTC(2), BNT(3))' + assert p2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT'] + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Arbgraph transport test and demo +# ------------------------------------------------------------ +def test_arbgraph_transport_test_and_demo(): +# ------------------------------------------------------------ + + n = ag.Node("ETH") + assert isinstance(n.state, n.State) + assert n.state == n.State(amount = 0) + + try: + ag.Edge("ETH", 1, "USDC", 2000) + raise + except: + pass + + ETH = ag.Node("ETH") + USDC = ag.Node("USDC") + assert ETH != n # nodes are only equal if they are the same object! + assert ETH.asdict() == n.asdict() + edge = ag.Edge(ETH, 1, USDC, 2000) + edge2 = ag.Edge(ETH, 1, USDC, 2000) + edge3 = ag.Edge(ETH, 2, USDC, 3500) + assert (edge == edge2) == False + assert edge != ag.Edge(ETH, 1, USDC, 2000) + assert edge.asdict() == ag.Edge(ETH, 1, USDC, 2000).asdict() + assert edge.node_in == ETH + assert edge.node_out == USDC + assert edge.amount_in == 1 + assert edge.amount_out == 2000 + assert edge.state == ag.Edge.State(amount_in_remaining=1) + + ETH.reset_state() + USDC.reset_state() + edge.reset_state() + ETH.state.amount_.set(1) + assert ETH.state.amount == 1 + edge.transport(1, record=True) + assert ETH.state.amount == 0 + assert USDC.state.amount == 2000 + assert edge.state.amount_in_remaining == 0 + + ETH.reset_state() + USDC.reset_state() + edge.reset_state() + ETH.state.amount_.set(1) + edge.transport(0.25, record=True) + assert ETH.state.amount == 0.75 + assert USDC.state.amount == 500 + assert edge.state.amount_in_remaining == 0.75 + edge.transport(0.25, record=True) + assert ETH.state.amount == 0.5 + assert USDC.state.amount == 1000 + assert edge.state.amount_in_remaining == 0.50 + + ETH.reset_state() + USDC.reset_state() + edge.reset_state() + ETH.state.amount = 1 + try: + edge.transport(2, record=True) + except Exception as e: + print(e) + + ETH.reset_state() + USDC.reset_state() + edge.reset_state() + ETH.state.amount = 0.5 + try: + edge.transport(1, record=True) + except Exception as e: + print(e) + + ETH.reset_state() + USDC.reset_state() + edge.reset_state() + ETH.state.amount = 2 + edge.transport(0.5, record=True) + try: + edge.transport(1, record=True) + except Exception as e: + print(e) + + ETH.state.amount = 10 + edge.state.amount_in_remaining = 10 + AG = ag.ArbGraph(nodes=[ETH, USDC], edges=[edge, edge2, edge3]) + assert AG.nodes == [ETH, USDC] + assert AG.edges == [edge, edge2, edge3] + assert AG.nodes[0].state.amount == 10 + assert AG.edges[0].state.amount_in_remaining == 10 + AG.reset_state() + assert AG.nodes[0].state.amount == 0 + assert AG.edges[0].state.amount_in_remaining == 1 + assert AG.state.nodes[0] == ETH.state + assert AG.state.edges[0] == edge.state + + assert AG.node_by_tkn("ETH") is ETH + assert AG.node_by_tkn(ETH) is ETH + try: + AG.node_by_tkn(ag.Node("ETH")) + raise + except Exception as e: + print(e) + + AG.reset_state() + ETH.state.amount = 4 + r = AG.transport(2, "ETH", "USDC", record=True) + assert ETH.state.amount == 2 + assert r.amount_in.amount == 2 + assert r.amount_in.tkn == "ETH" + capacity_in = sum([e_.amount_in for e_ in r.edges]) + assert capacity_in == 4 + capacity_out = sum([e_.amount_out for e_ in r.edges]) + assert capacity_out == 7500 + assert r.amount_out.amount == r.amount_in.amount * capacity_out / capacity_in + assert sum(r.amounts_in) == r.amount_in.amount + assert sum(r.amounts_out) == r.amount_out.amount + assert AG.has_capacity("ETH", "USDC") + assert AG.has_capacity() + AG.transport(2, "ETH", "USDC", record=True) + assert AG.has_capacity() == False + r + + rs = AG.edge_statistics(edges=r.edges) + assert rs.len == 3 + assert rs.edges is r.edges + assert rs.amounts_in == (1, 1, 2) + assert rs.amounts_in_remaining == (0.0, 0.0, 0.0) + assert rs.amounts_out == (2000, 2000, 3500) + assert rs.prices == (2000.0, 2000.0, 1750.0) + assert rs.utilizations == (1.0, 1.0, 1.0) + assert rs.amount_in.amount == 4 + assert rs.amount_in_remaining.amount == 0.0 + assert rs.amount_out.amount == 7500 + assert rs.amount_in.tkn == "ETH" + assert rs.amount_in_remaining.tkn == "ETH" + assert rs.amount_out.tkn == "USDC" + assert rs.utilization == 1.0 + assert rs.price == 1875.0 + rs + + rns = AG.node_statistics("ETH") + assert len(rns.edges_out) == 3 + assert len(rns.edges_in) == 0 + assert rns.amount_in.amount == 0 + assert rns.amount_out.amount == 4 + assert rns.amount_out_remaining.amount == 0 + assert rns.nodes_in==set() + assert rns.nodes_out=={"USDC"} + rns + + rns2 = AG.node_statistics("USDC") + assert len(rns2.edges_out) == 0 + assert len(rns2.edges_in) == 3 + assert rns2.amount_in.amount == 7500 + assert rns2.amount_out.amount == 0 + assert rns2.amount_out_remaining.amount == 0 + assert rns2.nodes_in==set(["ETH",]) + assert rns2.nodes_out==set() + rns2 + + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Arbgraph transport test and demo 2 +# ------------------------------------------------------------ +def test_arbgraph_transport_test_and_demo_2(): +# ------------------------------------------------------------ + + @ag.dataclass + class MyState(): + myval_: ag.TrackedStateFloat = ag.field(default_factory=ag.TrackedStateFloat, init=False) + myval: ag.InitVar=None + + def __post_init__(self, myval): + self.myval = myval + + @property + def myval(self): + return self.myval_.value + + @myval.setter + def myval(self, value): + self.myval_.set(value) + + + mystate = MyState(0) + mystate.myval_.set(10) + assert mystate.myval == 10 + mystate.myval += 5 + assert mystate.myval == 15 + mystate.myval -= 4 + assert mystate.myval == 11 + assert mystate.myval_.history == [0, 0, 10, 15, 11] + + mystate = MyState(10) + assert mystate.myval == 10 + assert mystate.myval_.history == [0,10] + mystate.myval = 20 + assert mystate.myval == 20 + assert mystate.myval_.history == [0,10,20] + mystate.myval += 5 + assert mystate.myval == 25 + mystate.myval -= 4 + assert mystate.myval == 21 + assert mystate.myval_.history == [0,10,20,25,21] + assert mystate.myval_.reset(42) + assert mystate.myval == 42 + assert mystate.myval_.history == [42] + + n = ag.Node("MEH") + n.state.amount = 10 + n.state.amount += 5 + n.state.amount -= 4 + assert n.state.amount == 11 + assert n.state.amount_.history == [0, 10, 15, 11] + n.reset_state() + assert n.state.amount_.history == [0] + + nodes = ag.Node.create_node_list("USDC, LINK, ETH, WBTC") + assert len(nodes)==4 + assert nodes[0].tkn == "USDC" + AG = ag.ArbGraph(nodes) + AG.add_edge("USDC", 10000, "ETH", 5) + AG.add_edge_obj(AG.edges[-1].R()) + AG.add_edge("USDC", 10000, "WBTC", 1) + AG.add_edge_obj(AG.edges[-1].R()) + AG.add_edge("USDC", 10000, "LINK", 1000) + AG.add_edge_obj(AG.edges[-1].R()) + AG.add_edge("LINK", 1000, "ETH", 5) + AG.add_edge_obj(AG.edges[-1].R()) + AG.add_edge("ETH", 5, "WBTC", 1) + AG.add_edge_obj(AG.edges[-1].R()) + assert len(AG.edges)==10 + assert len(AG.cycles())==11 + ns = AG.node_statistics("USDC") + assert ns.amount_in.amount == 30000 + assert ns.amount_out.amount == 30000 + assert ns.amount_out_remaining == ns.amount_out + assert ns.nodes_out==set(['WBTC', 'ETH', 'LINK']) + assert ns.nodes_in==set(['WBTC', 'ETH', 'LINK']) + #_=AG.plot() + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Transport 3 and prices +# ------------------------------------------------------------ +def test_transport_3_and_prices(): +# ------------------------------------------------------------ + + AG = ag.ArbGraph() + prices = dict(USDC=1, LINK=5, AAVE=100, WETH=2000, BTC=10000) + for t1,p1 in prices.items(): + for t2,p2 in prices.items(): + if t1 2000 USDC(1)' + + assert raises (lambda: e1+e3) + assert raises (lambda: -2*e1) + assert raises (lambda: e3*(-2)) + try: + e1 += e3 + raise + except ValueError as e: + pass + + assert not raises (lambda: e4+e5) + assert not raises (lambda: 2*e4) + assert not raises (lambda: e4*2) + e4 += e5 + + assert e6.amount_in == 1 + assert e1.transport() == e6.transport() + assert e1.transport(amount_in=1e6) == 1e6*e1.transport() + + AG = ag.ArbGraph(nodes = [ETH, USDC]) + assert AG.edgetype is None + AG.add_edge_obj(e1) + assert AG.edgetype == AG.EDGE_CONNECTION + assert AG.edgetype == e1.EDGE_CONNECTION + AG.add_edge_obj(e2) + assert raises(AG.add_edge_obj, e4) + assert AG.edgetype == e1.EDGE_CONNECTION + + AG = ag.ArbGraph(nodes = [ETH, USDC]) + assert AG.edgetype is None + AG.add_edge_obj(e4) + assert AG.edgetype == AG.EDGE_AMOUNT + assert AG.edgetype == e1.EDGE_AMOUNT + AG.add_edge_obj(e5) + assert raises(AG.add_edge_obj, e1) + assert AG.edgetype == e1.EDGE_AMOUNT + + AG = ag.ArbGraph() + AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000) + AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5) + AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000) + assert AG.edgetype == AG.EDGE_CONNECTION + assert len(AG) == 6 + #_=AG.plot() + + AG = ag.ArbGraph() + AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000, symmetric=False) + AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5, symmetric=False) + AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000, symmetric=False) + assert AG.edgetype == AG.EDGE_CONNECTION + assert len(AG) == 3 + #_=AG.plot() + + AG = ag.ArbGraph() + assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", price=2000, price_outperin=2000) + assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", inverse = True, price_outperin=2000) + assert AG.add_edge_connectiontype == AG.add_edge_ct + + AG = ag.ArbGraph() + for i in range(5): + mul = 1+i/50 + AG.add_edge_ct(tkn_in="ETH", tkn_out="USDC", price=2000*mul) + AG.add_edge_ct(tkn_in="WBTC", tkn_out="USDC", price=10000*mul) + AG.add_edge_ct(tkn_in="ETH", tkn_out="WBTC", price=0.2/mul) + assert AG.len() == (2*3*5, 3) + assert len(AG.cycles()) == 5 + assert np.array_equal(AG.A.toarray(), np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])) + print(AG.A) + AG2 = AG.duplicate() + assert AG2.len() == (6,3) + edges = AG.filter_edges("ETH", "USDC") + assert len(edges) == 5 + edges2 = AG2.filter_edges("ETH", "USDC") + assert len(edges2) == 1 + assert [e.p_outperin for e in edges] == [2000.0, 2040.0, 2080.0, 2120.0, 2160.0] + assert edges2[0].p_outperin == np.mean([e.p_outperin for e in edges]) + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Interaction with CPC +# ------------------------------------------------------------ +def test_interaction_with_cpc(): +# ------------------------------------------------------------ + + c1 = CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0, cid="1", descr="UniV2") + c2 = CPC.from_univ2(x_tknb=1, y_tknq=10000, pair="WBTC/USDC", fee=0, cid="2", descr="UniV2") + c3 = CPC.from_univ2(x_tknb=1, y_tknq=5, pair="WBTC/ETH", fee=0, cid="3", descr="UniV2") + assert c1.p == 2000 + assert c2.p == 10000 + assert c3.p == 5 + + AG = ag.ArbGraph() + AG.add_edges_cpc(c1) + AG.add_edges_cpc(c2) + AG.add_edges_cpc(c3) + #_=AG.plot() + + AG = ag.ArbGraph() + AG.add_edges_cpc([c1, c2, c3]) + #_=AG.plot() + + AG = ag.ArbGraph() + AG.add_edges_cpc(c for c in [c1, c2, c3]) + #_=AG.plot() + + AG = ag.ArbGraph() + CC = CPCContainer([c1,c2,c3]) + AG.add_edges_cpc(CC) + #_=AG.plot() + + print(AG.A) + + AG.cycles() + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment With real data from CPC +# ------------------------------------------------------------ +def test_with_real_data_from_cpc(): +# ------------------------------------------------------------ + + try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") + except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") + CC0 = CPCContainer.from_df(df) + print("Num curves:", len(CC0)) + print("Num pairs:", len(CC0.pairs())) + print("Num tokens:", len(CC0.tokens())) + print(CC0.tokens_s()) + + AG0 = ag.ArbGraph().add_edges_cpc(CC0) + #AG0.plot() + assert AG0.len() == (918, 141) + + assert str(AG0.A)[:60] ==' (0, 1)\t1\n (1, 0)\t1\n (2, 3)\t1\n (2, 4)\t1\n (2, 5)\t1\n (2,' + + pairs = CC0.filter_pairs(bothin="WETH, USDC, UNI, AAVE, LINK") + CC = CC0.bypairs(pairs, ascc=True) + AG = ag.ArbGraph().add_edges_cpc(CC) + #AG.plot() + AG.len() == (24, 5) + + assert np.all(AG.A.toarray() == np.array( + [[0, 1, 1, 0, 0], + [1, 0, 1, 1, 1], + [1, 1, 0, 1, 1], + [0, 1, 1, 0, 0], + [0, 1, 1, 0, 0]])) + + assert raises(AG.edge_statistics,"WETH", "USDC") + + AG.edgedf(consolidated=False) + + df = AG.edgedf(consolidated=True) + df + + dx,dy = ((71.22, -0.28, 3.4, -10.82, 755278.31, -65.01, -5.93, -3.38, -0.02, 60.27, -49.45, 1507698.66, -2263343.63), + (-0.3, 1.99, -0.14, 0.04, -393.48, 0.27, 46.42, 0.13, 1.41, -0.2, 316.84, -786.1, 833.78)) + AG2 = ag.ArbGraph() + for cpc, dx_, dy_ in zip(CC, dx, dy): + print(dx_, cpc.tknx, dy_, cpc.tkny, cpc.cid) + AG2.add_edge_dxdy(cpc.tknx, dx_, cpc.tkny, dy_, uid=cpc.cid) + #print("---") + + #_=AG2.plot() + assert AG2.len() == (12,5) + + assert np.all(AG2.A.toarray() == np.array( + [[0, 1, 0, 0, 0], + [1, 0, 0, 1, 1], + [1, 1, 0, 1, 1], + [0, 1, 0, 0, 0], + [0, 1, 0, 0, 0]])) + print(AG2.A.toarray()) + + assert AG2.edge_statistics("WETH", "USDC", bothways=False) is None + assert len(AG2.edge_statistics("WETH", "USDC", bothways=True)) == 2 + assert AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict()["amounts_in_remaining"] == (755278.31, 1507698.66) + AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict() + + assert AG2.filter_edges("WETH", "USDC") == [] + assert AG2.filter_edges("WETH", "USDC", bothways=True)[0].amount_in == 755278.31 + assert AG2.filter_edges("WETH", "USDC", bothways=True) == AG2.filter_edges("USDC", "WETH") + assert AG2.filter_edges(pair="WETH/USDC", bothways=False) == [] + assert AG2.filter_edges(pair="WETH/USDC") == AG2.filter_edges("WETH", "USDC", bothways=True) + assert AG2.filter_edges == AG2.fe + assert AG2.fep("WETH/USDC") == AG2.filter_edges(pair="WETH/USDC") + assert AG2.fep("WETH/USDC", bothways=False) == AG2.filter_edges(pair="WETH/USDC", bothways=False) + assert tuple(AG2.edgedf(consolidated=True, resetindex=False).iloc[0]) == (1.41, 0.02) + assert len(AG2.edgedf(consolidated=False)) == len(AG2) + + assert len(AG2.edgedf(consolidated=False)) == 12 + AG2.edgedf(consolidated=False) + + assert len(AG2.edgedf(consolidated=True, resetindex=False)) == 10 + AG2.edgedf(consolidated=True, resetindex=False) + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Amount algebra +# ------------------------------------------------------------ +def test_amount_algebra(): +# ------------------------------------------------------------ + + A = ag.Amount + nodes = lambda: ag.create_node_list("ETH, USDC") + ETH, USDC = nodes() + + ae1, ae2, au1 = A(1, ETH), A(2, ETH), A(1, USDC) + + assert ae1 + ae2 == 3*ae1 + assert ae2 - ae1 == ae1 + assert -ae1 + ae2 == ae1 + assert 2*ae1 == ae2 + assert ae1*2 == ae2 + assert ae1/2 +ae1/2 == ae1 + assert round(ae1/9,2) == round(1/9,2)*ae1 + assert round(ae1/9,4) == round(1/9,4)*ae1 + assert math.floor(ae1/9) == math.floor(1/9)*ae1 + assert math.ceil(ae1/9) == math.ceil(1/9)*ae1 + assert (ae1 + 2*ae1)/ae1 == 3 + + assert raises (lambda: ae1 + 1) + assert raises (lambda: ae1 - 1) + assert raises (lambda: 1 + ae1) + assert raises (lambda: 1 - ae1) + + assert 2*ae1 > ae1 + assert 2*ae1 >= ae1 + assert .2*ae1 < ae1 + assert .2*ae1 <= ae1 + assert ae1 <= ae1 + assert ae1 >= ae1 + assert not ae1 < ae1 + assert not ae1 > ae1 + + +# ------------------------------------------------------------ +# Test 065-GraphCode +# File test_065-GraphCode_.py +# Segment Specific Arb examples +# ------------------------------------------------------------ +def test_specific_arb_examples(): +# ------------------------------------------------------------ + + # ### USDC/ETH + + AG = ag.ArbGraph() + AG.add_edge("ETH", 1, "USDC", 2000) + AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) + G = AG.as_graph() + print(AG.cycles()) + #_=AG.plot() + + for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("ETH")): + print(c) + + c, AG.filter_edges(*c) + + AG.A.toarray() + + # ### USDC/LINK to ETH (oneway) + + AG = ag.ArbGraph() + AG.add_edge("USDC", 100, "ETH", 100/2000) + AG.add_edge("LINK", 100, "USDC", 1000) + AG.add_edge("USDC", 900, "LINK", 100, inverse=True) + G = AG.as_graph() + print(AG.cycles()) + #_=AG.plot() + + # _=AG.duplicate().plot() + + for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("USDC")): + print(c) + + c, AG.filter_edges(*c) + + AG.A.toarray() + + # ### USDD, LINK, ETH cycle + + AG = ag.ArbGraph() + AG.add_edge("ETH", 1, "USDC", 2000) + AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) + AG.add_edge("LINK", 200, "ETH", 1, inverse=True) + G = AG.as_graph() + print(AG.cycles()) + #_=AG.plot() + + for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("USDC")): + print(c) + + c, AG.filter_edges(*c) + + AG.A.toarray() + + # ### USDD, LINK, ETH cycle plus ETH/USDC + + AG = ag.ArbGraph() + AG.add_edge("ETH", 1, "USDC", 2000) + AG.add_edge("ETH", 1, "USDC", 2000) + AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) + AG.add_edge("LINK", 200, "ETH", 1, inverse=True) + AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) + G = AG.as_graph() + print(AG.cycles()) + #_=AG.plot() + + # + + #_=AG.duplicate().plot() + # - + + AG.edges + + AG.duplicate().edges + + AG.A.toarray() + + for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("ETH")): + print(c) + + cycle = AG.cycles()[1] + cycle + + for cycle in AG.cycles(): + result = AG.run_arbitrage_cycle(cycle=cycle, verbose=True) + print(result) + print("---") + + assert raises(AG.price, AG.nodes[0], AG.nodes[1]) + raises(AG.price, AG.nodes[0], AG.nodes[1]) + + + \ No newline at end of file diff --git a/carbon/tests/nbtest/test_066_Uniswap.py b/carbon/tests/nbtest/test_066_Uniswap.py new file mode 100644 index 00000000..e4b66ab0 --- /dev/null +++ b/carbon/tests/nbtest/test_066_Uniswap.py @@ -0,0 +1,195 @@ +# ------------------------------------------------------------ +# Auto generated test file `test_066_Uniswap.py` +# ------------------------------------------------------------ +# source file = NBTest_066_Uniswap.py +# source path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# target path = /Users/skl/REPOES/Bancor/CarbonSimulator/resources/NBTest/ +# test id = 066 +# test comment = Uniswap +# ------------------------------------------------------------ + + + +from carbon.helpers.stdimports import * +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter +from carbon.tools.univ3calc import Univ3Calculator as U3 +from dataclasses import dataclass, asdict +plt.style.use('seaborn-dark') +plt.rcParams['figure.figsize'] = [12,6] +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(U3)) +print_version(require="2.4.2") + + + + +# ------------------------------------------------------------ +# Test 066 +# File test_066_Uniswap.py +# Segment u3 standalone +# ------------------------------------------------------------ +def test_u3_standalone(): +# ------------------------------------------------------------ + + data = { + "token0": "0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48", # USDC + "token1": "0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2", # WETH + "sqrt_price_q96": "1725337071198080486317035748446190", + "tick": "199782", + "liquidity": "36361853546581410773" + } + + u1 = U3( + tkn0="USDC", + tkn0decv=6, + tkn1="WETH", + tkn1decv=18, + sp96=data["sqrt_price_q96"], + tick=data["tick"], + liquidity=data["liquidity"], + fee_const = U3.FEE500, + ) + u2 = U3.from_dict(data, U3.FEE500) + assert u1 == u2 + u = u2 + assert asdict(u) == { + 'tkn0': 'USDC', + 'tkn1': 'WETH', + 'sp96': int(data["sqrt_price_q96"]), + 'tick': int(data["tick"]), + 'liquidity': int(data["liquidity"]), + 'fee_const': U3.FEE500 + } + assert u.tkn0 == "USDC" + assert u.tkn1 == "WETH" + assert u.tkn0dec == 6 + assert u.tkn1dec == 18 + assert u.decf == 1e-12 + assert u.dec_factor_wei0_per_wei1 == u.decf + assert iseq(u.p, 0.00047422968986928404) + assert iseq(1/u.p, 2108.6828205033694) + assert u.p == u.price_tkn1_per_tkn0 + assert 1/u.p == u.price_tkn0_per_tkn1 + assert u.price_convention == 'USDC/WETH [WETH per USDC]' + assert iseq(u._price_f(1725337071198080486317035748446190), 474229689.86928403) + assert iseq(u._price_f(u.sp96), 474229689.86928403) + assert u.ticksize == 10 + ta, tb = u.tickab + par, pbr = u.papb_raw + pa, pb = u.papb_tkn1_per_tkn0 + pai, pbi = u.papb_tkn0_per_tkn1 + assert ta <= u.tick + assert tb >= u.tick + assert ta % u.ticksize == 0 + assert tb % u.ticksize == 0 + assert tb-ta == u.ticksize + assert iseq(par, 474134297.0246954) + assert iseq(pbr, 474608644.73905975) + assert iseq(pbr/par, 1.0001**u.ticksize) + assert iseq(pa, 0.0004741342970246954) + assert iseq(pb, 0.00047460864473905973) + assert iseq(pbr/par, pb/pa) + assert iseq(pbr/par, pai/pbi) + assert papbi + assert pa == par * u.decf + assert pb == pbr * u.decf + assert iseq(pai, 2109.1070742514007) + assert iseq(pbi, 2106.999126722188) + assert pai == 1/pa + assert pbi == 1/pb + assert u.p >= pa + assert u.p <= pb + assert u.fee_const == 500 + assert u.fee == 0.0005 + assert u.info() + print(u.info()) + + assert u.liquidity == int(data["liquidity"]) + assert u.L == 36361853.54658141 + assert u.liquidity/u.L == 1e18/1e6 + assert u.L2 == u.L**2 + assert u.Lsquared == u.L**2 + assert u.k == u.L2 + assert u.kbar == u.L + u.tkn0reserve(incltoken=True), u.tkn1reserve(incltoken=True), u.tvl(incltoken=True) + + +# ------------------------------------------------------------ +# Test 066 +# File test_066_Uniswap.py +# Segment with cpc +# ------------------------------------------------------------ +def test_with_cpc(): +# ------------------------------------------------------------ + + data = { + "token0": "0xA0b86991c6218b36c1d19D4a2e9Eb0cE3606eB48", + "token1": "0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2", + "sqrt_price_q96": "1727031172247131125466697684053376", + "tick": "199801", + "liquidity": "37398889145617323159" + } + u = U3.from_dict(data, U3.FEE500) + + pa, pb = u.papb_tkn1_per_tkn0 + curve = CPC.from_univ3( + Pmarg = u.p, + uniL = u.L, + uniPa = pa, + uniPb = pb, + pair = u.pair, + fee = u.fee, + descr = "", + params = dict(uv3raw=asdict(u)), + cid = "0", + ) + curve + + c = curve + print(f"Reserve: {c.x_act:,.3f} {c.tknx}, {c.y_act:,.3f} {c.tkny}") + print(f"TVL = {c.tvl(tkn=c.tknx):,.3f} {c.tknx} = {c.tvl(tkn=c.tkny):,.3f} {c.tkny}") + assert iseq(c.x_act, 716877.5715601444) + assert iseq(c.y_act, 66.88731140131131) + assert iseq(c.tvl(tkn=c.tknx), 857645.1222000704) + assert iseq(c.tvl(tkn=c.tkny), 407.51988721569177) + + print(f"Reserve: {u.tkn0reserve():,.3f} {c.tknx}, {u.tkn1reserve():,.3f} {c.tkny}") + print(f"TVL = {u.tvl(astkn0=True):,.3f} {c.tknx} = {u.tvl(astkn0=False):,.3f} {c.tkny}") + assert iseq(u.tkn0reserve(), c.x_act) + assert iseq(u.tkn1reserve(), c.y_act) + assert iseq(u.tvl(astkn0=False), c.tvl(tkn=c.tkny)) + assert iseq(u.tvl(astkn0=True), c.tvl(tkn=c.tknx)) + assert u.tkn0reserve(incltoken=True)[1] == u.tkn0 + assert u.tkn1reserve(incltoken=True)[1] == u.tkn1 + assert u.tvl(astkn0=True, incltoken=True)[1] == u.tkn0 + assert u.tvl(astkn0=False, incltoken=True)[1] == u.tkn1 + u.tkn0reserve(incltoken=True), u.tkn1reserve(incltoken=True), u.tvl(incltoken=True) + + curve = CPC.from_univ3( + **u.cpc_params(), + descr = "", + params = dict(uv3raw=asdict(u)), + cid = "0", + ) + curve + + c = curve + print(f"Reserve: {c.x_act:,.3f} {c.tknx}, {c.y_act:,.3f} {c.tkny}") + print(f"TVL = {c.tvl(tkn=c.tknx):,.3f} {c.tknx} = {c.tvl(tkn=c.tkny):,.3f} {c.tkny}") + + curve = CPC.from_univ3( + **u.cpc_params( + cid = "0", + descr = "", + #params = dict(uv3raw=asdict(u)), + ), + ) + curve + + + c = curve + print(f"Reserve: {c.x_act:,.3f} {c.tknx}, {c.y_act:,.3f} {c.tkny}") + print(f"TVL = {c.tvl(tkn=c.tknx):,.3f} {c.tknx} = {c.tvl(tkn=c.tkny):,.3f} {c.tkny}") + + \ No newline at end of file diff --git a/carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz b/carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz new file mode 100644 index 00000000..486f5046 Binary files /dev/null and b/carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz differ diff --git a/resources/NBTest/NBTEST_063_Curves.csv.gz b/resources/NBTest/NBTEST_063_Curves.csv.gz new file mode 100644 index 00000000..486f5046 Binary files /dev/null and b/resources/NBTest/NBTEST_063_Curves.csv.gz differ diff --git a/resources/NBTest/NBTest_063_CPC.ipynb b/resources/NBTest/NBTest_063_CPC.ipynb index 6655e534..02eeac66 100644 --- a/resources/NBTest/NBTest_063_CPC.ipynb +++ b/resources/NBTest/NBTest_063_CPC.ipynb @@ -11,20 +11,27 @@ "output_type": "stream", "text": [ "[stdimports] imported np, pd, plt, os, sqrt, exp, log\n", - "ConstantProductCurve v1.0 (15/Mar/2023)\n", - "CarbonOrderUI v1.9.1 (15/Mar/2023)\n", - "Carbon v2.3.3-BETA7 (14/Mar/2023)\n" + "ConstantProductCurve v2.6.1 (18/Apr/2023)\n", + "CarbonOrderUI v1.9.2 (30/Mar/2023)\n", + "TokenScaleBase v1.0 (07/Apr/2022)\n", + "CPCArbOptimizer v3.4 (18/Apr/2023)\n", + "Carbon v2.4.2-BETA2 (09/Apr/2023)\n" ] } ], "source": [ "from carbon.helpers.stdimports import *\n", - "from carbon import ConstantProductCurve as CPC, CarbonOrderUI\n", + "from carbon import CarbonOrderUI\n", + "from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter\n", + "from carbon.tools.optimizer import CPCArbOptimizer, F\n", + "import carbon.tools.tokenscale as ts\n", "plt.style.use('seaborn-dark')\n", "plt.rcParams['figure.figsize'] = [12,6]\n", "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPC))\n", "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CarbonOrderUI))\n", - "print_version(require=\"2.3.3\")" + "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(ts.TokenScaleBase))\n", + "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCArbOptimizer))\n", + "print_version(require=\"2.4.2\")" ] }, { @@ -32,7 +39,314 @@ "id": "b3f59f14-b91b-4dba-94b0-3d513aaf41c7", "metadata": {}, "source": [ - "# Constant product curve [NBTest063]" + "# Constant product curve and Optimizer [NBTest063]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "c9c9fa8b-b7be-4381-a4e1-4a2f60a08c14", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " df = pd.read_csv(\"NBTEST_063_Curves.csv.gz\")\n", + "except:\n", + " df = pd.read_csv(\"carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz\")\n", + "CCmarket = CPCContainer.from_df(df)" + ] + }, + { + "cell_type": "markdown", + "id": "f338198f-370f-4d51-9f0d-dec2af191c1a", + "metadata": {}, + "source": [ + "## P" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "214e8e25-2139-4755-aea7-4db9c136be77", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pk(pair=\"USDC/WETH\", p=1, k=100, params=dict(exchange=\"univ3\", a=dict(b=1, c=2)))\n", + "assert c.P(\"exchange\") == \"univ3\"\n", + "assert c.P(\"a\") == {'b': 1, 'c': 2}\n", + "assert c.P(\"a:b\") == 1\n", + "assert c.P(\"a:c\") == 2\n", + "assert c.P(\"a:d\") is None\n", + "assert c.P(\"b\") is None\n", + "assert c.P(\"b\", \"meh\") == \"meh\"" + ] + }, + { + "cell_type": "markdown", + "id": "6fab917d-113a-407f-9c2e-c5bec9fcaa5b", + "metadata": {}, + "source": [ + "## TVL" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4055cb6e-5fda-4970-87dd-c201b706a993", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=1*2000)\n", + "assert c.tvl(incltkn=True) == (4000.0, 'USDC', 1)\n", + "assert c.tvl(\"USDC\", incltkn=True) == (4000.0, 'USDC', 1)\n", + "assert c.tvl(\"WETH\", incltkn=True) == (2.0, 'WETH', 1)\n", + "assert c.tvl(\"USDC\", incltkn=True, mult=2) == (8000.0, 'USDC', 2)\n", + "assert c.tvl(\"WETH\", incltkn=True, mult=2) == (4.0, 'WETH', 2)\n", + "assert c.tvl(\"WETH\", incltkn=False) == 2.0\n", + "assert c.tvl(\"WETH\") == 2.0\n", + "assert c.tvl() == 4000\n", + "assert c.tvl(\"WETH\", mult=2000) == 4000" + ] + }, + { + "cell_type": "markdown", + "id": "26d538f0-072f-4d07-b701-2b98b2e65ec4", + "metadata": {}, + "source": [ + "## estimate prices" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "726a4312-33d3-4ff3-b7f6-951877ddf8d8", + "metadata": {}, + "outputs": [], + "source": [ + "CC = CPCContainer()\n", + "CC += [CPC.from_univ3(pair=\"WETH/USDC\", cid=\"uv3\", fee=0, descr=\"\",\n", + " uniPa=2000, uniPb=2010, Pmarg=2005, uniL=10*sqrt(2000))]\n", + "CC += [CPC.from_pk(pair=\"WETH/USDC\", cid=\"uv2\", fee=0, descr=\"\",\n", + " p=1950, k=5**2*2000)]\n", + "CC += [CPC.from_pk(pair=\"USDC/WETH\", cid=\"uv2r\", fee=0, descr=\"\",\n", + " p=1/1975, k=5**2*2000)]\n", + "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", + " tkny=\"USDC\", yint=1000, y=1000, pa=1850, pb=1750)]\n", + "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", + " tkny=\"WETH\", yint=1, y=0, pb=1/1850, pa=1/1750)]\n", + "CC += [CPC.from_carbon(pair=\"WETH/USDC\", cid=\"carb\", fee=0, descr=\"\",\n", + " tkny=\"USDC\", yint=1000, y=500, pa=1870, pb=1710)]\n", + "#CC.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "63150090-e540-4e8b-8195-628dc2e6bb48", + "metadata": {}, + "outputs": [], + "source": [ + "assert CC.price_estimate(tknq=T.WETH, tknb=T.USDC, result=CC.PE_PAIR) == f\"{T.USDC}/{T.WETH}\"\n", + "assert CC.price_estimate(pair=f\"{T.USDC}/{T.WETH}\", result=CC.PE_PAIR) == f\"{T.USDC}/{T.WETH}\"\n", + "assert raises(CC.price_estimate, tknq=\"a\", result=CC.PE_PAIR)\n", + "assert raises(CC.price_estimate, tknb=\"a\", result=CC.PE_PAIR)\n", + "assert raises(CC.price_estimate, tknq=\"a\", tknb=\"b\", pair=\"a/b\", result=CC.PE_PAIR)\n", + "assert raises(CC.price_estimate, pair=\"ab\", result=CC.PE_PAIR)\n", + "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, \n", + " unwrapsingle=False)[0][0] == f\"{T.USDC}/{T.WETH}\"\n", + "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, \n", + " unwrapsingle=True)[0] == f\"{T.USDC}/{T.WETH}\"\n", + "assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True)[0] == f\"{T.USDC}/{T.WETH}\"\n", + "r = CC.price_estimates(tknqs=list(\"ABC\"), tknbs=list(\"DEFG\"), pairs=True)\n", + "assert r.ndim == 2\n", + "assert r.shape == (3,4)\n", + "r = CC.price_estimates(tknqs=list(\"A\"), tknbs=list(\"DEFG\"), pairs=True)\n", + "assert r.ndim == 1\n", + "assert r.shape == (4,)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2e133f34-3769-4bfe-9a01-cb06cf574ed0", + "metadata": {}, + "outputs": [], + "source": [ + "assert CC[0].at_boundary == False\n", + "assert CC[1].at_boundary == False\n", + "assert CC[2].at_boundary == False\n", + "assert CC[3].at_boundary == True\n", + "assert CC[3].at_xmin == True\n", + "assert CC[3].at_ymin == False\n", + "assert CC[3].at_xmax == False\n", + "assert CC[3].at_ymax == True\n", + "assert CC[4].at_boundary == True\n", + "assert CC[4].at_ymin == True\n", + "assert CC[4].at_xmin == True\n", + "assert CC[4].at_ymax == True\n", + "assert CC[4].at_xmax == True\n", + "assert CC[5].at_boundary == True" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "bb1d6cbc-c555-4050-bfe8-a527b4993f0d", + "metadata": {}, + "outputs": [], + "source": [ + "r = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\", result=CC.PE_CURVES)\n", + "assert len(r)==3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b791befd-634c-4f42-8ec3-63bdc3310d8c", + "metadata": {}, + "outputs": [], + "source": [ + "p,w = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\", result=CC.PE_DATA)\n", + "assert len(p) == len(r)\n", + "assert len(w) == len(r)\n", + "assert iseq(sum(p), 5930)\n", + "assert iseq(sum(w), 894.4271909999159)\n", + "pe = CC.price_estimate(tknq=\"USDC\", tknb=\"WETH\")\n", + "assert pe == np.average(p, weights=w)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a5a39b7a-2588-432f-a01b-52a2ebed9952", + "metadata": {}, + "outputs": [], + "source": [ + "O = CPCArbOptimizer(CC)\n", + "Om = CPCArbOptimizer(CCmarket)\n", + "assert O.price_estimates(tknq=\"USDC\", tknbs=[\"WETH\"]) == CC.price_estimates(tknqs=[\"USDC\"], tknbs=[\"WETH\"])\n", + "CCmarket.fp(onein=\"USDC\")\n", + "r = Om.price_estimates(tknq=\"USDC\", tknbs=[\"WETH\", \"WBTC\"])\n", + "assert iseq(r[0], 1820.89875275)\n", + "assert iseq(r[1], 28351.08150121)" + ] + }, + { + "cell_type": "markdown", + "id": "c4ff4500-d5da-4b40-aa9d-bc4d7f9b6476", + "metadata": {}, + "source": [ + "## price estimates in optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "79f9312c-99c0-43be-91f1-c9b09faab243", + "metadata": {}, + "outputs": [], + "source": [ + "prices = {\"USDC\":1, \"LINK\": 5, \"AAVE\": 100, \"MKR\": 500, \"WETH\": 2000, \"WBTC\": 20000}\n", + "CCfm, ctr = CPCContainer(), 0\n", + "for tknb, pb in prices.items():\n", + " for tknq, pq in prices.items():\n", + " if pb>pq:\n", + " pair = f\"{tknb}/{tknq}\"\n", + " pp = pb/pq\n", + " k = (100000)**2/(pb*pq)\n", + " CCfm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f\"mkt-{ctr}\")\n", + " ctr += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "857cb1a7-fb00-442b-8380-30699c198668", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " WETH\n", + "tknb \n", + "WBTC 10.0000\n", + "USDC 0.0005\n", + "LINK 0.0025\n", + "MKR 0.2500\n", + "AAVE 0.0500" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "O = CPCArbOptimizer(CCfm)\n", + "assert O.MO_PSTART == O.MO_P\n", + "tknq = \"WETH\"\n", + "df = O.margp_optimizer(tknq, result=O.MO_PSTART)\n", + "rd = df[tknq].to_dict()\n", + "assert len(df) == len(prices)-1\n", + "assert df.columns[0] == tknq\n", + "assert df.index.name == \"tknb\"\n", + "assert rd == {k:v/prices[tknq] for k,v in prices.items() if k!=tknq}\n", + "df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=df))\n", + "assert np.all(df == df2)\n", + "df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=rd))\n", + "assert np.all(df == df2)\n", + "df" ] }, { @@ -45,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 13, "id": "62e862d3-c3a9-4be1-9417-4c0ba5a747a2", "metadata": {}, "outputs": [ @@ -53,14 +367,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "tknx = 10 [virtual: 10] ETH\n", - "tkny = 20000.0 [virtual: 20000.0] USDC\n", - "p = 2000.0 [min=None, max=None] USDC per ETH\n" + "None\n" ] } ], "source": [ - "c = CPC.from_px(p=2000,x=10, pair=\"eth/usdc\")\n", + "c = CPC.from_px(p=2000,x=10, pair=\"ETH/USDC\")\n", "assert c.pair == \"ETH/USDC\"\n", "assert c.tknb == c.pair.split(\"/\")[0]\n", "assert c.tknx == c.tknb\n", @@ -72,7 +384,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "id": "995f92a6-234b-4c3c-a19b-e08b81911e42", "metadata": {}, "outputs": [], @@ -88,186 +400,2649 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "c43fcf25-1ece-4781-9a74-6c33e5401663", + "execution_count": 15, + "id": "64f10130-a8db-4275-8221-5b137ad35e33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ConstantProductCurve(k=200, x=10, x_act=10, y_act=20.0, pair='TKNB/TKNQ', cid=None, fee=None, descr=None, constr='xy', params={})" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c43fcf25-1ece-4781-9a74-6c33e5401663", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", + "assert c.y_max*c.x_min == c.k\n", + "assert c.x_max*c.y_min == c.k\n", + "assert c.p_min == c.y_min / c.x_max\n", + "assert c.p_max == c.y_max / c.x_min\n", + "assert c.p_max >= c.p_min" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "98e31562-6fdc-4ab3-864e-215360b4793e", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", + "e = 1e-5\n", + "assert 95*c.yfromx_f(x=95) == c.k\n", + "assert 105*c.yfromx_f(x=105) == c.k\n", + "assert 190*c.xfromy_f(y=190) == c.k\n", + "assert 210*c.xfromy_f(y=210) == c.k\n", + "assert not c.yfromx_f(x=90) is None\n", + "assert c.yfromx_f(x=90-e) is None\n", + "assert not c.xfromy_f(y=180) is None\n", + "assert c.xfromy_f(y=180-e) is None\n", + "assert c.dyfromdx_f(dx=-5)\n", + "assert (c.y+c.dyfromdx_f(dx=-5))*(c.x-5) == c.k\n", + "assert (c.y+c.dyfromdx_f(dx=+5))*(c.x+5) == c.k\n", + "assert (c.x+c.dxfromdy_f(dy=-5))*(c.y-5) == c.k\n", + "assert (c.x+c.dxfromdy_f(dy=+5))*(c.y+5) == c.k" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "203a97ff-9590-4d4c-b2fe-fa6d32a50e74", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pkpp(p=100, k=100)\n", + "assert c.p_min == 100\n", + "assert c.p_max == 100\n", + "assert c.p == 100\n", + "assert c.k == 100" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1aef1862", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pkpp(p=100, k=100, p_min=80, p_max=120)\n", + "assert c.p_min == 80\n", + "assert iseq(c.p_max, 120)\n", + "assert c.p == 100\n", + "assert c.k == 100" + ] + }, + { + "cell_type": "markdown", + "id": "144c35ee-a90c-4e84-908f-80bb40f8646b", + "metadata": {}, + "source": [ + "## iseq" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "296f2f37-f1c9-4ecf-82d7-fb86d9871c94", + "metadata": {}, + "outputs": [], + "source": [ + "assert iseq(\"a\", \"a\", \"ab\") == False\n", + "assert iseq(\"a\", \"a\", \"a\")\n", + "assert iseq(1.0, 1, 1.0)\n", + "assert iseq(0,0)\n", + "assert iseq(0,1e-10)\n", + "assert iseq(0,1e-5) == False\n", + "assert iseq(1, 1.00001) == False\n", + "assert iseq(1, 1.000001)\n", + "assert iseq(1, 1.000001, eps=1e-7) == False\n", + "assert iseq(\"1\", 1) == False" + ] + }, + { + "cell_type": "markdown", + "id": "b7909e99-0634-4e44-ba98-58211e29d44a", + "metadata": {}, + "source": [ + "## CarbonOrderUI integration" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "35320166-5a3c-4acf-97ed-a1de4c5f7852", + "metadata": {}, + "outputs": [], + "source": [ + "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2500, 3000, 10, 10)\n", + "c = o.as_cpc\n", + "assert o.pair.slashpair == \"ETH/USDC\"\n", + "assert o.tkn == \"ETH\"\n", + "assert o.p_start == 2500\n", + "assert o.p_end == 3000\n", + "assert o.p_marg == 2500\n", + "assert o.y == 10\n", + "assert o.yint == 10\n", + "assert c.pair == o.pair.slashpair\n", + "assert c.tknb == o.pair.tknb\n", + "assert c.tknq == o.pair.tknq\n", + "assert c.x_act == o.y\n", + "assert c.y_act == 0\n", + "assert iseq(o.p_start, c.p, c.p_min)\n", + "assert iseq(o.p_end, c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "38296d00-a691-486a-a44c-62e49d478f40", + "metadata": {}, + "outputs": [], + "source": [ + "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"USDC\", 1500, 1000, 1000, 1000)\n", + "c = o.as_cpc\n", + "assert o.pair.slashpair == \"ETH/USDC\"\n", + "assert o.tkn == \"USDC\"\n", + "assert o.p_start == 1500\n", + "assert o.p_end == 1000\n", + "assert o.p_marg == 1500\n", + "assert o.y == 1000\n", + "assert o.yint == 1000\n", + "assert c.pair == o.pair.slashpair\n", + "assert c.tknb == o.pair.tknb\n", + "assert c.tknq == o.pair.tknq\n", + "assert c.x_act == 0\n", + "assert c.y_act == o.y\n", + "assert iseq(o.p_start, c.p, c.p_max)\n", + "assert iseq(o.p_end, c.p_min)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8a507163-2d5a-4eef-8614-9482c898fa48", + "metadata": {}, + "outputs": [], + "source": [ + "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2500, 3000, 10, 7)\n", + "c = o.as_cpc\n", + "assert o.y == 7\n", + "assert iseq(c.x_act, o.y)\n", + "assert iseq(c.y_act, 0)\n", + "assert iseq(o.p_marg, c.p, c.p_min)\n", + "assert iseq(o.p_end, c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "8c7098b7-a78d-4401-b2c3-2901ee481b24", + "metadata": {}, + "outputs": [], + "source": [ + "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"USDC\", 1500, 1000, 1000, 700)\n", + "c = o.as_cpc\n", + "assert o.y == 700\n", + "assert iseq(c.x_act, 0)\n", + "assert iseq(c.y_act, o.y)\n", + "assert iseq(o.p_marg, c.p, c.p_max)\n", + "assert iseq(o.p_end, c.p_min)" + ] + }, + { + "cell_type": "markdown", + "id": "d714ef31-80b1-4822-a004-cfe10c88f391", + "metadata": {}, + "source": [ + "## New CPC features in v2" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d740b68f-c9b1-48e4-9dd5-d5cce4cf6d29", + "metadata": {}, + "outputs": [], + "source": [ + "p = CPCContainer.Pair(\"ETH/USDC\")\n", + "assert str(p) == \"ETH/USDC\"\n", + "assert p.pair == str(p)\n", + "assert p.tknx == \"ETH\"\n", + "assert p.tkny == \"USDC\"\n", + "assert p.tknb == \"ETH\"\n", + "assert p.tknq == \"USDC\"\n", + "\n", + "pp = CPCContainer.Pair.wrap([\"ETH/USDC\", \"WBTC/ETH\"])\n", + "assert len(pp) == 2\n", + "assert pp[0].pair == \"ETH/USDC\"\n", + "assert pp[1].pair == \"WBTC/ETH\"\n", + "assert pp[0].unwrap(pp) == ('ETH/USDC', 'WBTC/ETH')" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "e53c1601-0a25-4d27-882a-ed39324937c9", + "metadata": {}, + "outputs": [], + "source": [ + "pairs = [\"A\", \"B\", \"C\"]\n", + "assert CPCContainer.pairset(\", \".join(pairs)) == set(pairs)\n", + "assert CPCContainer.pairset(pairs) == set(pairs)\n", + "assert CPCContainer.pairset(tuple(pairs)) == set(pairs)\n", + "assert CPCContainer.pairset(p for p in pairs) == set(pairs)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cc3ef889-d1fc-447c-b888-f26e2db3cdf0", + "metadata": {}, + "outputs": [], + "source": [ + "pairs = [f\"{a}/{b}\" for a in [\"ETH\", \"USDC\", \"DAI\"] for b in [\"DAI\", \"WBTC\", \"LINK\", \"ETH\"] if a!=b]\n", + "CC = CPCContainer()\n", + "fp = lambda **cond: CC.filter_pairs(pairs=pairs, **cond)\n", + "assert fp(bothin=\"ETH, USDC, DAI\") == {'DAI/ETH', 'ETH/DAI', 'USDC/DAI', 'USDC/ETH'}\n", + "assert fp(onein=\"WBTC\") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'}\n", + "assert fp(onein=\"ETH\") == fp(contains=\"ETH\")\n", + "assert fp(notin=\"WBTC, ETH, DAI\") == {'USDC/LINK'}\n", + "assert fp(tknbin=\"WBTC\") == set()\n", + "assert fp(tknqin=\"WBTC\") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'}\n", + "assert fp(tknbnotin=\"WBTC\") == set(pairs)\n", + "assert fp(tknbnotin=\"WBTC, ETH, DAI\") == {'USDC/DAI', 'USDC/ETH', 'USDC/LINK', 'USDC/WBTC'}\n", + "assert fp(notin_0=\"WBTC\", notin_1=\"DAI\") == fp(notin=\"WBTC, DAI\")\n", + "assert fp(onein = \"ETH\") == fp(anyall=CC.FP_ANY, tknbin=\"ETH\", tknqin=\"ETH\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "4712130e-aa86-4de2-9549-deadfd9e48a9", + "metadata": {}, + "outputs": [], + "source": [ + "P = CPCContainer.Pair\n", + "ETHUSDC = P(\"ETH/USDC\")\n", + "USDCETH = P(ETHUSDC.pairr)\n", + "assert ETHUSDC.pair == \"ETH/USDC\"\n", + "assert ETHUSDC.pairr == \"USDC/ETH\"\n", + "assert USDCETH.pairr == \"ETH/USDC\"\n", + "assert USDCETH.pair == \"USDC/ETH\"\n", + "assert ETHUSDC.isprimary\n", + "assert not USDCETH.isprimary\n", + "assert ETHUSDC.primary == ETHUSDC.pair\n", + "assert ETHUSDC.secondary == ETHUSDC.pairr\n", + "assert USDCETH.primary == USDCETH.pairr\n", + "assert USDCETH.secondary == USDCETH.pair\n", + "assert ETHUSDC.primary == USDCETH.primary\n", + "assert ETHUSDC.secondary == USDCETH.secondary" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c7a8a1e7-3437-4c08-a9d5-fc962413ef35", + "metadata": {}, + "outputs": [], + "source": [ + "assert P(\"BTC/ETH\").isprimary\n", + "assert P(\"WBTC/ETH\").isprimary\n", + "assert P(\"BTC/WETH\").isprimary\n", + "assert P(\"WBTC/ETH\").isprimary\n", + "assert P(\"BTC/USDC\").isprimary\n", + "assert P(\"XYZ/USDC\").isprimary\n", + "assert P(\"XYZ/USDT\").isprimary" + ] + }, + { + "cell_type": "markdown", + "id": "d124a181-1a00-4b7e-927b-a43798fdda01", + "metadata": {}, + "source": [ + "## Real data and retrieval of curves" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6c3217ab-ff79-45d4-9ea2-e314a782018a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num curves: 459\n", + "Num pairs: 326\n", + "Num tokens: 141\n" + ] + } + ], + "source": [ + "try:\n", + " df = pd.read_csv(\"NBTEST_063_Curves.csv.gz\")\n", + "except:\n", + " df = pd.read_csv(\"carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz\")\n", + "CC = CPCContainer.from_df(df)\n", + "assert len(CC) == 459\n", + "assert len(CC) == len(df)\n", + "assert len(CC.pairs()) == 326\n", + "assert len(CC.tokens()) == 141\n", + "assert CC.tokens_s\n", + "assert CC.tokens_s()[:60] == '1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARM'\n", + "print(\"Num curves:\", len(CC))\n", + "print(\"Num pairs:\", len(CC.pairs()))\n", + "print(\"Num tokens:\", len(CC.tokens()))\n", + "#print(CC.tokens_s())" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "847858b9-cd03-4c47-8cc7-6b03197361af", + "metadata": {}, + "outputs": [], + "source": [ + "assert CC.bypairs(CC.fp(onein=\"WETH, WBTC\")) == CC.bypairs(CC.fp(onein=\"WETH, WBTC\"), asgenerator=False)\n", + "assert len(CC.bypairs(CC.fp(onein=\"WETH, WBTC\"))) == 254\n", + "assert len(CC.bypairs(CC.fp(onein=\"WETH, WBTC\"), ascc=True)) == 254\n", + "CC1 = CC.bypairs(CC.fp(onein=\"WBTC\"), ascc=True)\n", + "assert len(CC1) == 29\n", + "cids = [c.cid for c in CC.bypairs(CC.fp(onein=\"WBTC\"))]\n", + "assert len(cids) == len(CC1)\n", + "assert CC.bycid(\"bla\") is None\n", + "assert not CC.bycid(191) is None\n", + "assert raises(CC.bycids, [\"bla\"])\n", + "assert len(CC.bycids(cids)) == len(cids)\n", + "assert len(CC.bytknx(\"WETH\")) == 46\n", + "assert len(CC.bytkny(\"WETH\")) == 181\n", + "assert len(CC.bytknys(\"WETH\")) == len(CC.bytkny(\"WETH\"))\n", + "assert len(CC.bytknxs(\"USDC, USDT\")) == 41\n", + "assert len(CC.bytknxs([\"USDC\", \"USDT\"])) == len(CC.bytknxs(\"USDC, USDT\"))\n", + "assert len(CC.bytknys([\"USDC\", \"USDT\"])) == len(CC.bytknys({\"USDC\", \"USDT\"}))\n", + "cs = CC.bytknx(\"WETH\", asgenerator=True)\n", + "assert raises(len, cs)\n", + "assert len(tuple(cs)) == 46\n", + "assert len(tuple(cs)) == 0 # generator empty" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "6f7ba5cb-b622-4c95-a28d-b14a94cd80dd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'USDC': TTE(x=[], y=[1, 2, 4, 5, 7]),\n", + " 'LINK': TTE(x=[2, 3, 5, 6], y=[]),\n", + " 'ETH': TTE(x=[], y=[0]),\n", + " 'DAI': TTE(x=[1, 4, 8], y=[3, 6]),\n", + " 'BNT': TTE(x=[0], y=[]),\n", + " 'AAVE': TTE(x=[7], y=[8])}" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "CC2 = CC.bypairs(CC.fp(bothin=\"USDC, DAI, BNT, SHIB, ETH, AAVE, LINK\"), ascc=True)\n", + "tt = CC2.tokentable()\n", + "assert tt[\"ETH\"].x == []\n", + "assert tt[\"ETH\"].y == [0]\n", + "assert tt[\"DAI\"].x == [1,4,8]\n", + "assert tt[\"DAI\"].y == [3,6]\n", + "tt" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "306765a9-831f-4c9a-a744-f77dde76319a", + "metadata": {}, + "outputs": [], + "source": [ + "assert CC2.tknxs() == {'AAVE', 'BNT', 'DAI', 'LINK'}\n", + "assert CC2.tknxl() == ['BNT', 'DAI', 'LINK', 'LINK', 'DAI', 'LINK', 'LINK', 'AAVE', 'DAI']\n", + "assert set(CC2.tknxl()) == CC2.tknxs() \n", + "assert set(CC2.tknyl()) == CC2.tknys() \n", + "assert len(CC2.tknxl()) == len(CC2.tknyl())\n", + "assert len(CC2.tknxl()) == len(CC2)" + ] + }, + { + "cell_type": "markdown", + "id": "0ab3291a-4cb6-4eec-9e49-9ed6f66af8fd", + "metadata": {}, + "source": [ + "## TokenScale tests" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "94cccc37-4ff3-48b8-8c93-35a1a7e54e4e", + "metadata": {}, + "outputs": [], + "source": [ + "TSB = ts.TokenScaleBase()\n", + "assert raises (TSB.scale,\"ETH\")\n", + "assert TSB.DEFAULT_SCALE == 1e-2" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "4ecbab8f-d3c7-4b87-b5d7-e9fd2c1696bb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "TokenScale(scale_dct={'USDC': 1.0, 'ETH': 1000.0, 'BTC': 10000.0})" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "TS = ts.TokenScale.from_tokenscales(USDC=1e0, ETH=1e3, BTC=1e4)\n", + "TS" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "4aca841e-1f12-4e03-a69c-4a4cd93a04b7", + "metadata": {}, + "outputs": [], + "source": [ + "assert TS(\"USDC\") == 1\n", + "assert TS(\"ETH\") == 1000\n", + "assert TS(\"BTC\") == 10000\n", + "assert TS(\"MEH\") == TS.DEFAULT_SCALE" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "2b3bb6c6-e6a2-4ee4-b7f8-e9a20b90db74", + "metadata": {}, + "outputs": [], + "source": [ + "TSD = ts.TokenScaleData" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "f114c5b4-4368-4aab-a989-7e7622c2e21d", + "metadata": {}, + "outputs": [], + "source": [ + "tknset = {'AAVE', 'BNT', 'BTC', 'ETH', 'LINK', 'USDC', 'USDT', 'WBTC', 'WETH'}\n", + "assert tknset - set(TSD.scale_dct.keys()) == set()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a2fe0e43-627c-4234-969b-8b0df4e39e27", + "metadata": {}, + "outputs": [], + "source": [ + "cc1 = CPC.from_xy(x=10, y=20000, pair=\"ETH/USDC\")\n", + "assert cc1.tokenscale is cc1.TOKENSCALE\n", + "assert cc1.tknx == \"ETH\"\n", + "assert cc1.tkny == \"USDC\"\n", + "assert cc1.scalex == 1\n", + "assert cc1.scaley == 1\n", + "cc2 = CPC.from_xy(x=10, y=20000, pair=\"BTC/MEH\")\n", + "assert cc2.tknx == \"BTC\"\n", + "assert cc2.tkny == \"MEH\"\n", + "assert cc2.scalex == 1\n", + "assert cc2.scaley == 1\n", + "assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "1af7425f-c11e-4184-b47a-ce166b871d67", + "metadata": {}, + "outputs": [], + "source": [ + "cc1 = CPC.from_xy(x=10, y=20000, pair=\"ETH/USDC\")\n", + "cc1.set_tokenscale(TSD)\n", + "assert cc1.tokenscale != cc1.TOKENSCALE\n", + "assert cc1.tknx == \"ETH\"\n", + "assert cc1.tkny == \"USDC\"\n", + "assert cc1.scalex == 1e3\n", + "assert cc1.scaley == 1e0\n", + "cc2 = CPC.from_xy(x=10, y=20000, pair=\"BTC/MEH\")\n", + "cc2.set_tokenscale(TSD)\n", + "assert cc2.tknx == \"BTC\"\n", + "assert cc2.tkny == \"MEH\"\n", + "assert cc2.scalex == 1e4\n", + "assert cc2.scaley == 1e-2\n", + "assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE" + ] + }, + { + "cell_type": "markdown", + "id": "a2f22c81-69d4-4955-bf18-2c1d31f51900", + "metadata": {}, + "source": [ + "## dx_min and dx_max etc" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "68b0a1b3-1778-4c78-9c1c-af044e36389c", + "metadata": {}, + "outputs": [], + "source": [ + "cc = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110)\n", + "assert iseq(cc.x_act, 4.653741075440777)\n", + "assert iseq(cc.y_act, 513.167019494862)\n", + "assert cc.dx_min == -cc.x_act\n", + "assert cc.dy_min == -cc.y_act\n", + "assert iseq( (cc.x + cc.dx_max)*(cc.y + cc.dy_min), cc.k)\n", + "assert iseq( (cc.y + cc.dy_max)*(cc.x + cc.dx_min), cc.k)" + ] + }, + { + "cell_type": "markdown", + "id": "10bae6ef-661e-481d-99b8-09b7db1d86c1", + "metadata": {}, + "source": [ + "## xyfromp_f and dxdyfromp_f" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "db6c4f98-ef82-4bb6-b826-780454d240be", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", + "\n", + "assert c.pair == 'WETH-6Cc2/USDC-eB48'\n", + "assert c.pairp == 'WETH/USDC'\n", + "assert c.p == 100\n", + "assert iseq(c.x_act, 4.653741075440777)\n", + "assert iseq(c.y_act, 513.167019494862)\n", + "assert c.tknx == T.ETH\n", + "assert c.tkny == T.USDC\n", + "assert c.tknxp == \"WETH\"\n", + "assert c.tknyp == \"USDC\"\n", + "assert c.xyfromp_f() == (c.x, c.y, c.p)\n", + "assert c.xyfromp_f(withunits=True) == (100.0, 10000.0, 100.0, 'WETH', 'USDC', 'WETH/USDC')\n", + "\n", + "x,y,p = c.xyfromp_f(p=85, ignorebounds=True)\n", + "assert p == 85\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,85)\n", + "\n", + "x,y,p = c.xyfromp_f(p=115, ignorebounds=True)\n", + "assert p == 115\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,115)\n", + "\n", + "x,y,p = c.xyfromp_f(p=95)\n", + "assert p == 95\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,p)\n", + "\n", + "x,y,p = c.xyfromp_f(p=105)\n", + "assert p == 105\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,p)\n", + "\n", + "x,y,p = c.xyfromp_f(p=85)\n", + "assert p == 85\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,90)\n", + "\n", + "x,y,p = c.xyfromp_f(p=115)\n", + "assert p == 115\n", + "assert iseq(x*y, c.k)\n", + "assert iseq(y/x,110)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "5ba5f10f-d8b2-4941-be14-befe1b758afc", + "metadata": {}, + "outputs": [], + "source": [ + "assert c.dxdyfromp_f(withunits=True) == (0.0, 0.0, 100.0, 'WETH', 'USDC', 'WETH/USDC')\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=85, ignorebounds=True)\n", + "assert p == 85\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx),p)\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=115, ignorebounds=True)\n", + "assert p == 115\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx),p)\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=95)\n", + "assert p == 95\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx),p)\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=105)\n", + "assert p == 105\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx),p)\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=85)\n", + "assert p == 85\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx), 90)\n", + "assert iseq(dy, -c.y_act)\n", + "\n", + "dx,dy,p = c.dxdyfromp_f(p=115)\n", + "assert p == 115\n", + "assert iseq((c.x+dx)*(c.y+dy), c.k)\n", + "assert iseq((c.y+dy)/(c.x+dx), 110)\n", + "assert iseq(dx, -c.x_act)\n", + "\n", + "assert iseq(c.x_min*c.y_max, c.k)\n", + "assert iseq(c.x_max*c.y_min, c.k)\n", + "assert iseq(c.y_max/c.x_min, c.p_max)\n", + "assert iseq(c.y_min/c.x_max, c.p_min)" + ] + }, + { + "cell_type": "markdown", + "id": "dbf81149-204c-45e8-8051-8ac8b6128773", + "metadata": {}, + "source": [ + "## CPCInverter" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "eab9ad99-c582-47a0-bc53-4d3ee60106f1", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_pkpp(p=2000, k=10*20000, p_min=1800, p_max=2200, pair=f\"{T.ETH}/{T.USDC}\")\n", + "c2 = CPC.from_pkpp(p=1/2000, k=10*20000, p_max=1/1800, p_min=1/2200, pair=f\"{T.USDC}/{T.ETH}\")\n", + "ci = CPCInverter(c)\n", + "c2i = CPCInverter(c2)\n", + "curves = CPCInverter.wrap([c,c2])\n", + "assert c.pairo == c2i.pairo\n", + "assert ci.pairo == c2.pairo" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "1e8a2542-586a-4e76-b3d1-14e0d9315e3c", + "metadata": {}, + "outputs": [], + "source": [ + "#print(\"x_act\", c.x_act, c2i.x_act)\n", + "assert iseq(c.x_act, c2i.x_act)\n", + "xact = c.x_act\n", + "dx = -0.1*xact\n", + "c_ex = c.execute(dx=dx)\n", + "assert isinstance(c_ex, CPC)\n", + "assert iseq(c_ex.x_act, xact+dx)\n", + "assert iseq(c_ex.x, c.x+dx)\n", + "c2i_ex = c2i.execute(dx=dx)\n", + "assert iseq(c2i_ex.x_act, xact+dx)\n", + "assert iseq(c2i_ex.x, c.x+dx)\n", + "assert isinstance(c2i_ex, CPCInverter)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "2ab158e0-adbc-40d0-a159-67839b1a1145", + "metadata": {}, + "outputs": [], + "source": [ + "assert len(curves) == 2\n", + "assert set(c.pair for c in curves) == {'WETH-6Cc2/USDC-eB48'}\n", + "assert len(set(c.pair for c in curves)) == 1\n", + "assert len(set(c.tknx for c in curves)) == 1\n", + "assert len(set(c.tkny for c in curves)) == 1" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "7689c9e2-92b7-4af3-a54d-dab909758eb0", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "assert c.tknx == ci.tkny\n", + "assert c.tkny == ci.tknx\n", + "assert c.tknxp == ci.tknyp\n", + "assert c.tknyp == ci.tknxp\n", + "assert c.tknb == ci.tknq\n", + "assert c.tknq == ci.tknb\n", + "assert c.tknbp == ci.tknqp\n", + "assert c.tknqp == ci.tknbp\n", + "assert f\"{c.tknq}/{c.tknb}\" == ci.pair\n", + "assert f\"{c.tknqp}/{c.tknbp}\" == ci.pairp\n", + "assert c.x == ci.y\n", + "assert c.y == ci.x\n", + "assert c.x_act == ci.y_act\n", + "assert c.y_act == ci.x_act\n", + "assert c.x_min == ci.y_min\n", + "assert c.x_max == ci.y_max\n", + "assert c.y_min == ci.x_min\n", + "assert c.y_max == ci.x_max\n", + "assert c.k == ci.k\n", + "assert iseq(c.p, 1/ci.p)\n", + "assert iseq(c.p_min, 1/ci.p_max)\n", + "assert iseq(c.p_max, 1/ci.p_min)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "0b353e51-60b0-4806-b842-1bc647aebd41", + "metadata": {}, + "outputs": [], + "source": [ + "assert c.pair == c2i.pair\n", + "assert c.tknx == c2i.tknx\n", + "assert c.tkny == c2i.tkny\n", + "assert c.tknxp == c2i.tknxp\n", + "assert c.tknyp == c2i.tknyp\n", + "assert c.tknb == c2i.tknb\n", + "assert c.tknq == c2i.tknq\n", + "assert c.tknbp == c2i.tknbp\n", + "assert c.tknqp == c2i.tknqp\n", + "assert iseq(c.p, c2i.p)\n", + "assert iseq(c.p_min, c2i.p_min)\n", + "assert iseq(c.p_max, c2i.p_max)\n", + "assert c.x == c2i.x\n", + "assert c.y == c2i.y\n", + "assert c.x_act == c2i.x_act\n", + "assert c.y_act == c2i.y_act\n", + "assert c.x_min == c2i.x_min\n", + "assert c.x_max == c2i.x_max\n", + "assert c.y_min == c2i.y_min\n", + "assert c.y_max == c2i.y_max\n", + "assert c.k == c2i.k" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "c19d81b1", + "metadata": {}, + "outputs": [], + "source": [ + "assert iseq(c.xfromy_f(c.y), c2i.xfromy_f(c2i.y))\n", + "assert iseq(c.yfromx_f(c.x), c2i.yfromx_f(c2i.x))\n", + "assert iseq(c.xfromy_f(c.y*1.05), c2i.xfromy_f(c2i.y*1.05))\n", + "assert iseq(c.yfromx_f(c.x*1.05), c2i.yfromx_f(c2i.x*1.05))\n", + "assert iseq(c.dxfromdy_f(1), c2i.dxfromdy_f(1))\n", + "assert iseq(c.dyfromdx_f(1), c2i.dyfromdx_f(1))" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "61076a28-62f0-492f-9800-5abfb326c25b", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "assert c.xyfromp_f() == c2i.xyfromp_f()\n", + "assert c.dxdyfromp_f() == c2i.dxdyfromp_f()\n", + "assert c.xyfromp_f(withunits=True) == c2i.xyfromp_f(withunits=True)\n", + "assert c.dxdyfromp_f(withunits=True) == c2i.dxdyfromp_f(withunits=True)\n", + "assert iseq(c.p, c2i.p)\n", + "x,y,p = c.xyfromp_f(c.p*1.05)\n", + "x2,y2,p2 = c2i.xyfromp_f(c2i.p*1.05)\n", + "assert iseq(x,x2)\n", + "assert iseq(y,y2)\n", + "assert iseq(p,p2)\n", + "dx,dy,p = c.dxdyfromp_f(c.p*1.05)\n", + "dx2,dy2,p2 = c2i.dxdyfromp_f(c2i.p*1.05)\n", + "assert iseq(dx,dx2)\n", + "assert iseq(dy,dy2)\n", + "assert iseq(p,p2)" + ] + }, + { + "cell_type": "markdown", + "id": "e044a237-9723-461e-8fd6-23e27c7666fd", + "metadata": {}, + "source": [ + "## simple_optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "d94a2af2-667b-4e04-ac2c-40ad91e94f77", + "metadata": {}, + "outputs": [], + "source": [ + "CC = CPCContainer(CPC.from_pk(p=2000+i*10, k=10*20000, pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", + "c0 = CC.curves[0]\n", + "c1 = CC.curves[-1]\n", + "CC0 = CPCContainer([c0])\n", + "assert len(CC) == 11\n", + "assert iseq([c.p for c in CC][-1], 2100)\n", + "assert len(CC0) == 1\n", + "assert iseq([c.p for c in CC0][-1], 2000)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "27902e19-bc90-4f42-a2a7-cdc40017e829", + "metadata": {}, + "outputs": [], + "source": [ + "O = CPCArbOptimizer(CC)\n", + "O0 = CPCArbOptimizer(CC0)\n", + "func = O.simple_optimizer(result=O.SO_DXDYVECFUNC)\n", + "func0 = O0.simple_optimizer(result=O.SO_DXDYVECFUNC)\n", + "funcs = O.simple_optimizer(result=O.SO_DXDYSUMFUNC)\n", + "funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC)\n", + "funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC)\n", + "x,y = func0(2100)[0]\n", + "xb, yb, _ = c0.dxdyfromp_f(2100)\n", + "assert x == xb\n", + "assert y == yb\n", + "x,y = func(2100)[-1]\n", + "xb, yb, _ = c1.dxdyfromp_f(2100)\n", + "assert x == xb\n", + "assert y == yb\n", + "assert np.all(sum(func(2100)) == funcs(2100))\n", + "\n", + "p = 2100\n", + "dx, dy = funcs(p)\n", + "assert iseq(dy + p*dx, funcvy(p))\n", + "assert iseq(dy/p + dx, funcvx(p))\n", + "\n", + "p = 1500\n", + "dx, dy = funcs(p)\n", + "assert iseq(dy + p*dx, funcvy(p))\n", + "assert iseq(dy/p + dx, funcvx(p))\n", + "\n", + "assert iseq(float(O0.simple_optimizer(result=O.SO_PMAX)), c0.p)\n", + "assert iseq(float(O.simple_optimizer(result=O.SO_PMAX)), 2049.6451720862074, eps=1e-3)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "f38807ad-9b98-44be-9c1d-dc099f44f60f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizerBase.SimpleResult(result=2049.881086733136, method='findminmax_nr', errormsg=None, context_dct=None)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "O.simple_optimizer(result=O.SO_PMAX)" + ] + }, + { + "cell_type": "markdown", + "id": "f3f978ea-f4d6-4ff3-b64b-becf7cb26f3f", + "metadata": {}, + "source": [ + "### global max" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "47b2d3b3-fd18-4518-b932-6477ad2a2713", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.simple_optimizer()\n", + "r_ = O.simple_optimizer(result=O.SO_GLOBALMAX)\n", + "assert raises(O.simple_optimizer, targettkn=T.WETH, result=O.SO_GLOBALMAX)\n", + "assert iseq(float(r), float(r_))\n", + "assert len(r.curves) == len(CC)\n", + "assert np.all(r.dxdy_sum == sum(r.dxdy_vec))\n", + "dx, dy = r.dxdy_vecs\n", + "assert tuple(tuple(_) for _ in r.dxdy_vec) == tuple(zip(dx,dy))\n", + "assert r.result == r.dxdy_valx\n", + "for dp in np.linspace(-500,500,100):\n", + " assert r.dxdyfromp_valx_f(p) < r.dxdy_valx\n", + " assert r.dxdyfromp_valy_f(p) < r.dxdy_valy" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "e7b74a3d-423b-40ba-9f03-b294b7eb0fef", + "metadata": {}, + "outputs": [], + "source": [ + "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", + "# CC.plot()\n", + "# CC_ex.plot()\n", + "prices = [c.p for c in CC]\n", + "prices_ex = [c.p for c in CC_ex]\n", + "assert iseq(np.std(prices), 31.622776601683707)\n", + "assert iseq(np.std(prices_ex), 4.547473508864641e-13)\n", + "#prices, prices_ex" + ] + }, + { + "cell_type": "markdown", + "id": "d1ae97e8-da5f-4c51-9104-b547ea519e0c", + "metadata": {}, + "source": [ + "### target token" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "1c613989-fedf-4bf6-816e-198a31f8377d", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.simple_optimizer(targettkn=T.WETH)\n", + "r_ = O.simple_optimizer(targettkn=T.WETH, result=O.SO_TARGETTKN)\n", + "assert raises(O.simple_optimizer,targettkn=T.DAI)\n", + "assert raises(O.simple_optimizer, result=O.SO_TARGETTKN)\n", + "assert iseq(float(r), float(r_))\n", + "assert abs(sum(r.dyvalues) < 1e-6)\n", + "assert sum(r.dxvalues) < 0\n", + "assert iseq(float(r),sum(r.dxvalues))" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "a4a7c75e-2115-4fb5-966f-d112a5d8f844", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.simple_optimizer(targettkn=T.USDC)\n", + "assert abs(sum(r.dxvalues) < 1e-6)\n", + "assert sum(r.dyvalues) < 0\n", + "assert iseq(float(r),sum(r.dyvalues))" + ] + }, + { + "cell_type": "markdown", + "id": "cbff1f21-4071-4aea-b8b7-70246ed788f8", + "metadata": {}, + "source": [ + "## optimizer plus inverted curves" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "5ec2a0d3-88a2-4bdc-ba9e-e79baf259127", + "metadata": {}, + "outputs": [], + "source": [ + "CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", + "CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f\"{T.USDC}/{T.ETH}\") for i in range(11))\n", + "CC = CCr.bycids()\n", + "assert len(CC) == len(CCr)\n", + "CC += CCi\n", + "assert len(CC) == len(CCr) + len(CCi)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "a45d6b01-16be-4530-a49f-7d1e768b68a3", + "metadata": {}, + "outputs": [], + "source": [ + "# CC.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c1f0f1c0-df0e-4ef1-a45b-07bfd83ca257", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arbitrage gains: 1.3195 WETH [time=0.0086s]\n" + ] + } + ], + "source": [ + "O = CPCArbOptimizer(CC)\n", + "r = O.simple_optimizer()\n", + "print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", + "assert iseq(r.result, -1.3194573866437527)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "a2a49469-0646-4c07-87e5-295228f26847", + "metadata": {}, + "outputs": [], + "source": [ + "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", + "# CC.plot()\n", + "# CC_ex.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "9361a460-3af5-48bb-af91-f389286a8d40", + "metadata": {}, + "outputs": [], + "source": [ + "prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", + "assert iseq(np.std(prices_ex), 5.130242014436283e-13)" + ] + }, + { + "cell_type": "markdown", + "id": "32c5ed4c-86c6-4a92-aa66-969d67528fb5", + "metadata": {}, + "source": [ + "## posx and negx" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "c1734f7b-7657-4c2b-8c5a-b8327b38823c", + "metadata": {}, + "outputs": [], + "source": [ + "O = CPCArbOptimizer\n", + "a = O.a" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "c270a822-fc80-4ebc-ad80-6be0bbd695ac", + "metadata": {}, + "outputs": [], + "source": [ + "assert O.posx([0,-1,2]) == (0, 0, 2)\n", + "assert O.posx((-1,-2, 3)) == (0, 0, 3)\n", + "assert O.negx([0,-1,2]) == (0, -1, 0)\n", + "assert O.negx((-1,-2, 3)) == (-1, -2, 0)\n", + "assert np.all(O.posx(a([0,-1,2])) == a((0, 0, 2)))\n", + "assert O.t(a((-1,-2))) == (-1,-2)" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "0bd88148-e19b-4120-8c37-001a0140f8bc", + "metadata": {}, + "outputs": [], + "source": [ + "for v in ((1,2,3), (1,-1,5-10,0), (-10.5,8,2.34,-17)):\n", + " assert np.all(O.posx(a(v))+O.negx(a(v)) == v)" + ] + }, + { + "cell_type": "markdown", + "id": "f81766fd-f3fe-4036-9325-1b6c8713403a", + "metadata": {}, + "source": [ + "## TradeInstructions" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "9d1a955f-4c56-4880-b810-a3fc39fbd8a1", + "metadata": {}, + "outputs": [], + "source": [ + "TI = CPCArbOptimizer.TradeInstruction" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c47c2351-0acf-49e2-8f1c-39c12fe16f4e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cid=1, out=-2000.0 USDC, , out=1.0 ETH\n" + ] + } + ], + "source": [ + "ti = TI.new(curve_or_cid=\"1\", tkn1=\"ETH\", amt1=1, tkn2=\"USDC\", amt2=-2000)\n", + "print(f\"cid={ti.cid}, out={ti.amtout} {ti.tknout}, , out={ti.amtin} {ti.tknin}\")\n", + "assert ti.tknin == \"ETH\"\n", + "assert ti.amtin > 0\n", + "assert ti.tknout == \"USDC\"\n", + "assert ti.amtout < 0\n", + "assert ti.price_outperin == 2000\n", + "assert ti.price_inperout == 1/2000\n", + "assert ti.prices == (2000, 1/2000)\n", + "assert ti.price_outperin == ti.p\n", + "assert ti.price_inperout == ti.pr\n", + "assert ti.prices == ti.pp" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "75bc1ef2-344c-4bb9-9f4e-2f69935c421e", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=-1)\n", + "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=1)\n", + "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=-1)\n", + "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=-2000, tknout=\"ETH\", amtout=1)\n", + "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=2000, tknout=\"ETH\", amtout=0)\n", + "assert raises(TI, cid=\"1\", tknin=\"USDC\", amtin=0, tknout=\"ETH\", amtout=-1)\n", + "assert not raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=-1)\n", + "assert not raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=1)\n", + "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=2000, tkn2=\"ETH\", amt2=1)\n", + "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=-1)\n", + "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=0, tkn2=\"ETH\", amt2=1)\n", + "assert raises(TI.new, curve_or_cid=\"1\", tkn1=\"USDC\", amt1=-2000, tkn2=\"ETH\", amt2=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "77572054-db53-4bd6-9252-5c126582ddb6", + "metadata": {}, + "outputs": [], + "source": [ + "til = [\n", + " TI.new(curve_or_cid=f\"{i+1}\", tkn1=\"ETH\", amt1=1*(1+i/100), tkn2=\"USDC\", amt2=-2000*(1+i/100)) \n", + " for i in range(10)\n", + "]\n", + "tild = TI.to_dicts(til)\n", + "tildf = TI.to_df(til)\n", + "assert len(tild) == 10\n", + "assert len(tildf) == 10\n", + "assert tild[0] == {'cid': '1', 'tknin': 'ETH', 'amtin': 1.0, 'tknout': 'USDC', 'amtout': -2000.0}\n", + "assert dict(tildf.iloc[0]) == {\n", + " 'pair': '',\n", + " 'pairp': '',\n", + " 'tknin': 'ETH',\n", + " 'tknout': 'USDC',\n", + " 'ETH': 1.0,\n", + " 'USDC': -2000.0\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "95de1850-624d-410a-98a5-46a9f6139040", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pairpairptknintknoutETHUSDC
cid
1ETHUSDC1.00-2000.0
2ETHUSDC1.01-2020.0
3ETHUSDC1.02-2040.0
4ETHUSDC1.03-2060.0
5ETHUSDC1.04-2080.0
6ETHUSDC1.05-2100.0
7ETHUSDC1.06-2120.0
8ETHUSDC1.07-2140.0
9ETHUSDC1.08-2160.0
10ETHUSDC1.09-2180.0
\n", + "
" + ], + "text/plain": [ + " pair pairp tknin tknout ETH USDC\n", + "cid \n", + "1 ETH USDC 1.00 -2000.0\n", + "2 ETH USDC 1.01 -2020.0\n", + "3 ETH USDC 1.02 -2040.0\n", + "4 ETH USDC 1.03 -2060.0\n", + "5 ETH USDC 1.04 -2080.0\n", + "6 ETH USDC 1.05 -2100.0\n", + "7 ETH USDC 1.06 -2120.0\n", + "8 ETH USDC 1.07 -2140.0\n", + "9 ETH USDC 1.08 -2160.0\n", + "10 ETH USDC 1.09 -2180.0" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tildf" + ] + }, + { + "cell_type": "markdown", + "id": "5b9301d1-99f3-405e-a042-f3e84b8cc853", + "metadata": {}, + "source": [ + "## margp_optimizer" + ] + }, + { + "cell_type": "markdown", + "id": "52d7c29c-cea6-4b3f-a635-cb5ec6e1ba1e", + "metadata": {}, + "source": [ + "### no arbitrage possible" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "e2d1a07c-6acf-42e3-b93d-a5fb66aa9363", + "metadata": {}, + "outputs": [], + "source": [ + "CCa = CPCContainer()\n", + "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", + "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", + "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.0, k=200000*200000, cid=\"c2\")\n", + "O = CPCArbOptimizer(CCa)" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "95d80905-b5a1-4157-9e95-f1989f35dd68", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.margp_optimizer(\"WETH\", result=O.MO_DEBUG)\n", + "assert isinstance(r, dict)\n", + "prices0 = r[\"price_estimates_t\"]\n", + "assert not prices0 is None, f\"prices0 must not be None [{prices0}]\"\n", + "r1 = O.arb(\"WETH\")\n", + "r2 = O.SelfFinancingConstraints.arb(\"WETH\")\n", + "assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints)\n", + "assert r1 == r2\n", + "assert r[\"sfc\"] == r1\n", + "assert r1.is_arbsfc()\n", + "assert r1.optimizationvar == \"WETH\"" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "c33d0c3b-7ed5-4776-8ffc-b921c74b1c7f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'price_estimates_t': array([0.0005, 0.0005]),\n", + " 'tokens_t': ('USDC', 'USDT'),\n", + " 'tokens_ix': {'USDC': 0, 'USDT': 1},\n", + " 'pairs': {'USDC/USDT', 'WETH/USDC', 'WETH/USDT'},\n", + " 'sfc': CPCArbOptimizer.SelfFinancingConstraints(data={'WETH': 'OptimizationVar'}, tokens={'WETH'}),\n", + " 'targettkn': 'WETH',\n", + " 'pairs_t': (('USDC', 'USDT'), ('WETH', 'USDT'), ('WETH', 'USDC')),\n", + " 'dtknfromp_f': .dtknfromp_f(p, *, islog10=True, asdct=False)>,\n", + " 'optimizer': }" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "0903b6b4-9987-4715-827f-8986806b30bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0005, 0.0005])" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "prices0" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "7a95208f-5322-4c25-b064-f58347810b51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[margp_optimizer] calculating price estimates\n" + ] + } + ], + "source": [ + "f = O.margp_optimizer(\"WETH\", result=O.MO_DTKNFROMPF, params=dict(verbose=True, debug=False))\n", + "r3 = f(prices0, islog10=False)\n", + "assert np.all(r3 == (0,0))\n", + "r4, r3b = f(prices0, asdct=True, islog10=False)\n", + "assert np.all(r3==r3b)\n", + "assert len(r4) == len(r3)+1\n", + "assert tuple(r4.values()) == (0,0,0)\n", + "assert set(r4) == {'USDC', 'USDT', 'WETH'}" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "43544b41-c57c-4e79-b819-4bb43cd3538c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[margp_optimizer] calculating price estimates\n", + "[margp_optimizer] pe [0.0005 0.0005]\n", + "[margp_optimizer] p 0.00, 0.00\n", + "[margp_optimizer] 1/p 2,000.00, 2,000.00\n", + "\n", + "[margp_optimizer] ========== cycle 0 =======>>>\n", + "log p0 [-3.3010299956639813, -3.3010299956639813]\n", + "log dp [3.1611697e-16 3.1611697e-16]\n", + "log p [-3.30103 -3.30103]\n", + "p (0.0005000000000000001, 0.0005000000000000001)\n", + "p 0.00, 0.00\n", + "1/p 2,000.00, 2,000.00\n", + "tokens_t ('USDC', 'USDT')\n", + "dtkn 0.000, 0.000\n", + "[criterium=4.47e-16, eps=1.0e-06, c/e=4e-10]\n", + "<<<========== cycle 0 ======= [margp_optimizer]\n" + ] + } + ], + "source": [ + "r = O.margp_optimizer(\"WETH\", result=O.MO_MINIMAL, params=dict(verbose=True))\n", + "rd = r.asdict\n", + "assert abs(float(r)) < 1e-10\n", + "assert r.result == float(r)\n", + "assert r.method == \"margp\"\n", + "assert r.curves is None\n", + "assert r.targettkn == \"WETH\"\n", + "assert r.dtokens is None\n", + "assert sum(abs(x) for x in r.dtokens_t) < 1e-10\n", + "assert r.p_optimal is None\n", + "assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1])\n", + "assert set(r.tokens_t) == {'USDC', 'USDT'}\n", + "assert r.errormsg is None\n", + "assert r.is_error == False\n", + "assert r.time > 0\n", + "assert r.time < 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "1bb345bd-dcaa-4915-8dd0-af7a7ee5945a", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.margp_optimizer(\"WETH\", result=O.MO_FULL)\n", + "rd = r.asdict()\n", + "r2 = O.margp_optimizer(\"WETH\")\n", + "r2d = r2.asdict()\n", + "for k in rd:\n", + " #print(k)\n", + " if not k in [\"time\", \"curves\"]:\n", + " assert rd[k] == r2d[k]\n", + "assert r2.curves == r.curves # the TokenScale object fails in the dict\n", + "\n", + "assert abs(float(r)) < 1e-10\n", + "assert r.result == float(r)\n", + "assert r.method == \"margp\"\n", + "assert len(r.curves) == 3\n", + "assert r.targettkn == \"WETH\"\n", + "assert set(r.dtokens.keys()) == set(['USDT', 'WETH', 'USDC'])\n", + "assert sum(abs(x) for x in r.dtokens.values()) < 1e-10\n", + "assert sum(abs(x) for x in r.dtokens_t) < 1e-10\n", + "assert iseq(0.0005, r.p_optimal[\"USDC\"], r.p_optimal[\"USDT\"])\n", + "assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1])\n", + "assert tuple(r.p_optimal.values()) == r.p_optimal_t\n", + "assert set(r.tokens_t) == set(('USDC', 'USDT'))\n", + "assert r.errormsg is None\n", + "assert r.is_error == False\n", + "assert r.time > 0\n", + "assert r.time < 0.1" + ] + }, + { + "cell_type": "markdown", + "id": "756e8ab6-a591-498a-a871-540acddff3df", + "metadata": {}, + "source": [ + "### arbitrage" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "b8452fe7-67b3-4789-899f-d203b2f9d259", + "metadata": {}, + "outputs": [], + "source": [ + "CCa = CPCContainer()\n", + "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", + "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", + "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.2, k=200000*200000, cid=\"c2\")\n", + "O = CPCArbOptimizer(CCa)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "28324e77-2d5d-4c42-b2b8-1b9654180af2", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.margp_optimizer(\"WETH\", result=O.MO_DEBUG)\n", + "assert isinstance(r, dict)\n", + "prices0 = r[\"price_estimates_t\"]\n", + "r1 = O.arb(\"WETH\")\n", + "r2 = O.SelfFinancingConstraints.arb(\"WETH\")\n", + "assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints)\n", + "assert r1 == r2\n", + "assert r[\"sfc\"] == r1\n", + "assert r1.is_arbsfc()\n", + "assert r1.optimizationvar == \"WETH\"" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "377bb0f5-2bcb-4379-89f9-d918112c9e80", + "metadata": {}, + "outputs": [], + "source": [ + "f = O.margp_optimizer(\"WETH\", result=O.MO_DTKNFROMPF)\n", + "r3 = f(prices0, islog10=False)\n", + "assert set(r3.astype(int)) == set((17425,-19089))\n", + "r4, r3b = f(prices0, asdct=True, islog10=False)\n", + "assert np.all(r3==r3b)\n", + "assert len(r4) == len(r3)+1\n", + "assert set(r4) == {'USDC', 'USDT', 'WETH'}" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "be30f072-d063-4aac-9e7f-b9a887bb9e4f", + "metadata": {}, + "outputs": [], + "source": [ + "r = O.margp_optimizer(\"WETH\", result=O.MO_FULL)\n", + "assert iseq(float(r), -0.03944401129301944)\n", + "assert r.result == float(r)\n", + "assert r.method == \"margp\"\n", + "assert len(r.curves) == 3\n", + "assert r.targettkn == \"WETH\"\n", + "assert abs(r.dtokens_t[0]) < 1e-6\n", + "assert abs(r.dtokens_t[1]) < 1e-6\n", + "assert r.dtokens[\"WETH\"] == float(r)\n", + "assert tuple(r.p_optimal.values()) == r.p_optimal_t\n", + "assert tuple(r.p_optimal) == r.tokens_t\n", + "assert iseq(r.p_optimal_t[0], 0.0005421803152482512) or iseq(r.p_optimal_t[0], 0.00045575394031021585)\n", + "assert iseq(r.p_optimal_t[1], 0.0005421803152482512) or iseq(r.p_optimal_t[1], 0.00045575394031021585)\n", + "assert tuple(r.p_optimal.values()) == r.p_optimal_t\n", + "assert set(r.tokens_t) == set(('USDC', 'USDT'))\n", + "assert r.errormsg is None\n", + "assert r.is_error == False\n", + "assert r.time > 0\n", + "assert r.time < 0.1" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "7a85271d-312c-4b95-8d33-0d235fb4e9f4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.9068465917371213e-07" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "abs(r.dtokens_t[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "66ff781d-f171-493e-b4c2-b1517b4f3307", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b60bdb92-5c5d-4f4f-956b-2eaac140a870", + "metadata": {}, + "source": [ + "## simple_optimizer demo [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "ea1c2d79-6e94-47e8-baf1-d52a0da888af", + "metadata": {}, + "outputs": [], + "source": [ + "CC = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+i*10000), pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", + "O = CPCArbOptimizer(CC)\n", + "c0 = CC.curves[0]\n", + "CC0 = CPCContainer([c0])\n", + "O = CPCArbOptimizer(CC)\n", + "O0 = CPCArbOptimizer(CC0)\n", + "funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC)\n", + "funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC)\n", + "funcvx0 = O0.simple_optimizer(result=O.SO_DXDYVALXFUNC)\n", + "funcvy0 = O0.simple_optimizer(result=O.SO_DXDYVALYFUNC)\n", + "#CC.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "f662f8d9-16bc-4742-b9b0-88b7b169f0ea", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xr = np.linspace(1500, 3000, 50)\n", + "plt.plot(xr, [funcvx(x)/len(CC) for x in xr], label=\"all curves [scaled]\")\n", + "plt.plot(xr, [funcvx0(x) for x in xr], label=\"curve 0 only\")\n", + "plt.xlabel(f\"price [{c0.pairp}]\")\n", + "plt.ylabel(f\"value [{c0.tknxp}]\")\n", + "plt.grid()\n", + "plt.show()\n", + "plt.plot(xr, [funcvy(x)/len(CC) for x in xr], label=\"all curves [scaled]\")\n", + "plt.plot(xr, [funcvy0(x) for x in xr], label=\"curve 0 only\")\n", + "plt.xlabel(f\"price [{c0.pairp}]\")\n", + "plt.ylabel(f\"value [{c0.tknyp}]\")\n", + "plt.grid()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "c94519fa-207e-45fd-80ea-1ec5f092b3ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arbitrage gains: 0.6731 WETH [time=0.0043s]\n" + ] + } + ], + "source": [ + "r = O.simple_optimizer()\n", + "print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "806bfb7f-dd6c-4b7b-959a-eaedcf9a8ea4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", + "CC.plot()\n", + "CC_ex.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "001a35b1-3f99-487e-b527-80eb93d720de", + "metadata": {}, + "source": [ + "## MargP Optimizer Demo [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "a045a304-f5f2-4eca-9394-2d1a86721e33", + "metadata": {}, + "outputs": [], + "source": [ + "CCa = CPCContainer()\n", + "CCa += CPC.from_pk(pair=\"WETH/USDC\", p=2000, k=10*20000, cid=\"c0\")\n", + "CCa += CPC.from_pk(pair=\"WETH/USDT\", p=2000, k=10*20000, cid=\"c1\")\n", + "CCa += CPC.from_pk(pair=\"USDC/USDT\", p=1.2, k=20000*20000, cid=\"c2\")\n", + "O = CPCArbOptimizer(CCa)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "ae37fa7b-356a-4de2-8b4d-ce264792f952", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = USDC/USDT\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDT\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CCa.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "ecc9f29a-9bd7-4b08-aba7-cc4cd58e2eb7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[margp_optimizer] calculating price estimates\n", + "[margp_optimizer] pe [0.0005 0.0005]\n", + "[margp_optimizer] p 0.00, 0.00\n", + "[margp_optimizer] 1/p 2,000.00, 2,000.00\n", + "\n", + "[margp_optimizer] ========== cycle 0 =======>>>\n", + "log p0 [-3.3010299956639813, -3.3010299956639813]\n", + "log dp [ 0.02281867 -0.03004231]\n", + "log p [-3.27821133 -3.3310723 ]\n", + "p (0.0005269733761120141, 0.0004665816971063286)\n", + "p 0.00, 0.00\n", + "1/p 1,897.63, 2,143.25\n", + "tokens_t ('USDC', 'USDT')\n", + "dtkn 1,742.581, -1,908.902\n", + "[criterium=3.77e-02, eps=1.0e-06, c/e=4e+04]\n", + "<<<========== cycle 0 ======= [margp_optimizer]\n", + "\n", + "[margp_optimizer] ========== cycle 1 =======>>>\n", + "log p0 [-3.2782113257736367, -3.331072301550902]\n", + "log dp [0.00197844 0.00203564]\n", + "log p [-3.27623289 -3.32903666]\n", + "p (0.0005293794916778223, 0.0004687738067091822)\n", + "p 0.00, 0.00\n", + "1/p 1,889.00, 2,133.22\n", + "tokens_t ('USDC', 'USDT')\n", + "dtkn 43.132, 49.919\n", + "[criterium=2.84e-03, eps=1.0e-06, c/e=3e+03]\n", + "<<<========== cycle 1 ======= [margp_optimizer]\n", + "\n", + "[margp_optimizer] ========== cycle 2 =======>>>\n", + "log p0 [-3.276232887408822, -3.329036663029794]\n", + "log dp [2.18800078e-06 2.23012250e-06]\n", + "log p [-3.2762307 -3.32903443]\n", + "p (0.0005293821587291089, 0.0004687762138908068)\n", + "p 0.00, 0.00\n", + "1/p 1,888.99, 2,133.21\n", + "tokens_t ('USDC', 'USDT')\n", + "dtkn 0.048, 0.054\n", + "[criterium=3.12e-06, eps=1.0e-06, c/e=3e+00]\n", + "<<<========== cycle 2 ======= [margp_optimizer]\n", + "\n", + "[margp_optimizer] ========== cycle 3 =======>>>\n", + "log p0 [-3.2762306994080452, -3.329034432907297]\n", + "log dp [-1.21938625e-10 -1.24095448e-10]\n", + "log p [-3.2762307 -3.32903443]\n", + "p (0.0005293821585804722, 0.0004687762137568585)\n", + "p 0.00, 0.00\n", + "1/p 1,888.99, 2,133.21\n", + "tokens_t ('USDC', 'USDT')\n", + "dtkn -0.000, -0.000\n", + "[criterium=1.74e-10, eps=1.0e-06, c/e=2e-04]\n", + "<<<========== cycle 3 ======= [margp_optimizer]\n" + ] + }, + { + "data": { + "text/plain": [ + "CPCArbOptimizer.MargpOptimizerResult(result=-0.027643519043587972, time=0.002852201461791992, method='margp', targettkn='WETH', p_optimal_t=(0.0005293821585804722, 0.0004687762137568585), dtokens_t=(1.4551915228366852e-10, 1.7826096154749393e-10), tokens_t=('USDC', 'USDT'), errormsg=None)" + ] + }, + "execution_count": 89, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r = O.margp_optimizer(\"WETH\", params=dict(verbose=True))\n", + "rd = r.asdict\n", + "r" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "2ff5b435-ac7e-4009-ba3a-cc3374999b4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 90, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rd" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "7aa2450d-33fa-4ad9-960f-753b42a3a3a7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = USDC/USDT\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDT\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CCa1 = O.adjust_curves(r.dxvalues)\n", + "CCa1.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3f780da0-eb2c-4564-b84e-c7791fab5e66", + "metadata": {}, + "source": [ + "## Optimizer plus inverted curves [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "fa769696-b65c-4500-a94f-3921ab7f2f23", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = USDC/WETH\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f\"{T.ETH}/{T.USDC}\") for i in range(11))\n", + "CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f\"{T.USDC}/{T.ETH}\") for i in range(11))\n", + "CC = CCr.bycids()\n", + "assert len(CC) == len(CCr)\n", + "CC += CCi\n", + "assert len(CC) == len(CCr) + len(CCi)\n", + "CC.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "3efeade6-48d5-4d1c-9ac2-09db20658b03", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arbitrage gains: 1.3195 WETH [time=0.0081s]\n", + "prices post arb: [2527.721669597842, 2527.7216695978414, 2527.721669597842, 2527.721669597842, 2527.7216695978423, 2527.7216695978423, 2527.721669597842, 2527.721669597842, 2527.7216695978423, 2527.7216695978423, 2527.7216695978414, 2527.721669597843, 2527.7216695978423, 2527.721669597842, 2527.721669597843, 2527.721669597842, 2527.7216695978423, 2527.721669597843, 2527.7216695978427, 2527.7216695978423, 2527.7216695978423, 2527.7216695978423]\n", + "stdev 5.130242014436283e-13\n", + "pair = USDC/WETH\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "O = CPCArbOptimizer(CC)\n", + "r = O.simple_optimizer()\n", + "print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", + "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", + "prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", + "print(\"prices post arb:\", prices_ex)\n", + "print(\"stdev\", np.std(prices_ex))\n", + "#CC.plot()\n", + "CC_ex.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "b8f26292-9900-4c39-af96-86c1060814a2", + "metadata": {}, + "source": [ + "## Operating on leverage ranges [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "8dde5e5d-ebdb-4bed-84c2-0ee3214bef16", + "metadata": {}, + "outputs": [], + "source": [ + "N = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "7ba3c796-4ac2-4090-a0ea-e5ddb7ad13bf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CCc, CCm, ctr = CPCContainer(), CPCContainer(), 0\n", + "U, U1 = CPCContainer.u, CPCContainer.u1\n", + "tknb, tknq = T.ETH, T.USDC\n", + "pb, pq = 2000, 1\n", + "pair = f\"{tknb}/{tknq}\"\n", + "pp = pb/pq\n", + "k = 100000**2/(pb*pq)\n", + "CCm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f\"mkt-{pair}\", params=dict(xc=\"market\"))\n", + "#print(\"\\n***PAIR:\", tknb, pb, tknq, pq, pair, pp)\n", + "for i in range(N):\n", + " p = pp * (1+0.2*U(-0.5, 0.5))\n", + " p_min, p_max = (p, U(1.001, 1.5)*p) if U1()>0.5 else (U(0.8, 0.999)*p, p)\n", + " amtusdc = U(10000, 200000)\n", + " k = amtusdc**2/(pb*pq)\n", + " #print(\"*curve\", int(amtusdc), p, p_min, p_max, int(k))\n", + " CCc += CPC.from_pkpp(p=p, k=k, p_min=p_min, p_max=p_max, \n", + " pair=pair, cid = f\"carb-{ctr}\", params=dict(xc=\"carbon\"))\n", + " ctr += 1\n", + " \n", + "CC = CCc.bycids().add(CCm)\n", + "CC.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "c9d09d0e-0767-41d4-a88e-a061b2f8c66d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Arbitrage gains: 0.3379 WETH [time=0.0057s]\n", + "prices post arb: [2015.1689532616774, 1945.3890287531108, 2015.168953261677, 2015.1689532616774, 2015.1689532616774, 1986.4240319729265, 2015.1689532616776, 2138.5118526374417, 1991.55356683476, 2113.998926253513, 2015.1689532616774]\n", + "stdev 52.50484372919725\n", + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "O = CPCArbOptimizer(CC)\n", + "r = O.simple_optimizer()\n", + "print(f\"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]\")\n", + "CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues))\n", + "prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex]\n", + "print(\"prices post arb:\", prices_ex)\n", + "print(\"stdev\", np.std(prices_ex))\n", + "#CC.plot()\n", + "CC_ex.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "63a18934-a79e-44b0-9001-0ce89b9d9598", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3.7613996799653933,\n", + " -0.3703508727444742,\n", + " 0.5279299885216133,\n", + " -0.9558584495888311,\n", + " -0.47335973170233103,\n", + " 0.0,\n", + " -2.791757344187303,\n", + " 0.0,\n", + " 0.0,\n", + " 0.2692713544428784,\n", + " -0.18854010753501882)" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r.dxvalues" + ] + }, + { + "cell_type": "markdown", + "id": "d0ed5167-4d92-4b24-8446-9c6b07c3861d", + "metadata": {}, + "source": [ + "## Arbitrage testing [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "id": "e4abb0a7-e3be-45cb-960b-ab1a9668fb15", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pair = WETH/USDC\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", - "assert c.y_max*c.x_min == c.k\n", - "assert c.x_max*c.y_min == c.k\n", - "assert c.p_min == c.y_min / c.x_max\n", - "assert c.p_max == c.y_max / c.x_min\n", - "assert c.p_max >= c.p_min" + "c1 = CPC.from_pkpp(p=95, k=100*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", + "c2 = CPC.from_pkpp(p=105, k=90*10000, p_min=90, p_max=110, pair=f\"{T.ETH}/{T.USDC}\")\n", + "CC = CPCContainer([c1,c2])\n", + "CC.plot()" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "98e31562-6fdc-4ab3-864e-215360b4793e", + "execution_count": 99, + "id": "77be5b24-8714-4170-a44a-dcb77cccc452", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "c = CPC.from_px(p=2, x=100, x_act=10, y_act=20)\n", - "e = 1e-5\n", - "assert 95*c.yfromx_f(x=95) == c.k\n", - "assert 105*c.yfromx_f(x=105) == c.k\n", - "assert 190*c.xfromy_f(y=190) == c.k\n", - "assert 210*c.xfromy_f(y=210) == c.k\n", - "assert not c.yfromx_f(x=90) is None\n", - "assert c.yfromx_f(x=90-e) is None\n", - "assert not c.xfromy_f(y=180) is None\n", - "assert c.xfromy_f(y=180-e) is None\n", - "assert c.dyfromdx_f(dx=-5)\n", - "assert (c.y+c.dyfromdx_f(dx=-5))*(c.x-5) == c.k\n", - "assert (c.y+c.dyfromdx_f(dx=+5))*(c.x+5) == c.k\n", - "assert (c.x+c.dxfromdy_f(dy=-5))*(c.y-5) == c.k\n", - "assert (c.x+c.dxfromdy_f(dy=+5))*(c.y+5) == c.k" + "a = lambda x: np.array(x)\n", + "pr = np.linspace(70,130,200)\n", + "dx1, dy1, p = zip(*(c1.dxdyfromp_f(p) for p in pr))\n", + "assert np.all(p == pr)\n", + "dx2, dy2, p = zip(*(c2.dxdyfromp_f(p) for p in pr))\n", + "assert np.all(p == pr)\n", + "v1 = a(dy1)+a(p)*a(dx1)\n", + "v2 = a(dy2)+a(p)*a(dx2)\n", + "plt.plot(p, v1, label=\"Value curve c1\")\n", + "plt.plot(p, v2, label=\"Value curve c2\")\n", + "plt.plot(p, v1+v2, label=\"Value combined curves\")\n", + "plt.legend()\n", + "plt.grid()" ] }, { "cell_type": "code", - "execution_count": 6, - "id": "203a97ff-9590-4d4c-b2fe-fa6d32a50e74", + "execution_count": 100, + "id": "9dd61773-3bb1-4e84-86cc-1dcebe6ef0b4", "metadata": {}, "outputs": [], "source": [ - "c = CPC.from_pkpp(p=100, k=100)\n", - "assert c.p_min == 100\n", - "assert c.p_max == 100\n", - "assert c.p == 100\n", - "assert c.k == 100" + "def vfunc(p):\n", + " \n", + " dx1, dy1, _ = c1.dxdyfromp_f(p)\n", + " dx2, dy2, _ = c2.dxdyfromp_f(p)\n", + " v1 = dy1 + p*dx1\n", + " v2 = dy2 + p*dx2\n", + " v = v1+v2\n", + " #print(f\"[v] v({p}) = {v}\")\n", + " return -v" ] }, { "cell_type": "code", - "execution_count": 7, - "id": "1aef1862", + "execution_count": 101, + "id": "3c44d75a-167c-40cc-8106-cd2b7a91989c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizerBase.SimpleResult(result=99.68104660486168, method='findminmax_nr', errormsg=None, context_dct=None)" + ] + }, + "execution_count": 101, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "c = CPC.from_pkpp(p=100, k=100, p_min=80, p_max=120)\n", - "assert c.p_min == 80\n", - "assert iseq(c.p_max, 120)\n", - "assert c.p == 100\n", - "assert c.k == 100" + "O = CPCArbOptimizer\n", + "O.findmin(vfunc, 100, N=100)" ] }, { - "cell_type": "markdown", - "id": "144c35ee-a90c-4e84-908f-80bb40f8646b", + "cell_type": "code", + "execution_count": 102, + "id": "ae1c2c25-271b-4c82-8dbe-56091de6c270", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizerBase.SimpleResult(result=2.0, method='findminmax_nr', errormsg=None, context_dct=None)" + ] + }, + "execution_count": 102, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "## iseq" + "func1 = lambda x: (x-2)**2\n", + "O.findmin(func1, 1)" ] }, { "cell_type": "code", - "execution_count": 8, - "id": "296f2f37-f1c9-4ecf-82d7-fb86d9871c94", + "execution_count": 103, + "id": "f4d5c834-f7ea-46e5-8e85-7c3615ebe122", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizerBase.SimpleResult(result=3.000000000003396, method='findminmax_nr', errormsg=None, context_dct=None)" + ] + }, + "execution_count": 103, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "assert iseq(\"a\", \"a\", \"ab\") == False\n", - "assert iseq(\"a\", \"a\", \"a\")\n", - "assert iseq(1.0, 1, 1.0)\n", - "assert iseq(0,0)\n", - "assert iseq(0,1e-10)\n", - "assert iseq(0,1e-5) == False\n", - "assert iseq(1, 1.00001) == False\n", - "assert iseq(1, 1.000001)\n", - "assert iseq(1, 1.000001, eps=1e-7) == False\n", - "assert iseq(\"1\", 1) == False" + "func2 = lambda x: 1-(x-3)**2\n", + "O.findmax(func2, 2.5)" ] }, { - "cell_type": "markdown", - "id": "b7909e99-0634-4e44-ba98-58211e29d44a", + "cell_type": "code", + "execution_count": 104, + "id": "23b1d421-a3f9-4b0d-9a44-53f4ec0006dd", "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "## CarbonOrderUI integration" + "val = tuple(float(O.findmin(func1, 100, N=n)) for n in range(100))\n", + "val = tuple(abs(v-val[-1]) for v in val)\n", + "val = tuple(v for v in val if v > 0)\n", + "plt.plot(val)\n", + "plt.yscale('log')\n", + "plt.grid()" ] }, { "cell_type": "code", - "execution_count": 16, - "id": "35320166-5a3c-4acf-97ed-a1de4c5f7852", + "execution_count": 105, + "id": "01cdc314-9b7e-4f47-8cd7-daa87cd5af4d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2500, 3000, 10, 10)\n", - "c = o.as_cpc\n", - "assert o.pair.slashpair == \"ETH/USDC\"\n", - "assert o.tkn == \"ETH\"\n", - "assert o.p_start == 2500\n", - "assert o.p_end == 3000\n", - "assert o.p_marg == 2500\n", - "assert o.y == 10\n", - "assert o.yint == 10\n", - "assert c.pair == o.pair.slashpair\n", - "assert c.tknb == o.pair.tknb\n", - "assert c.tknq == o.pair.tknq\n", - "assert c.x_act == o.y\n", - "assert c.y_act == 0\n", - "assert iseq(o.p_start, c.p, c.p_min)\n", - "assert iseq(o.p_end, c.p_max)" + "val = tuple(float(O.findmin(func2, 100, N=n)) for n in range(100))\n", + "val = tuple(abs(v-val[-1]) for v in val)\n", + "val = tuple(v for v in val if v > 0)\n", + "plt.plot(val)\n", + "plt.yscale('log')\n", + "plt.grid()" ] }, { "cell_type": "code", - "execution_count": 21, - "id": "38296d00-a691-486a-a44c-62e49d478f40", + "execution_count": 106, + "id": "d263dcc4-bfdd-4580-9584-b34b07fab178", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "99.68103950148166\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"USDC\", 1500, 1000, 1000, 1000)\n", - "c = o.as_cpc\n", - "assert o.pair.slashpair == \"ETH/USDC\"\n", - "assert o.tkn == \"USDC\"\n", - "assert o.p_start == 1500\n", - "assert o.p_end == 1000\n", - "assert o.p_marg == 1500\n", - "assert o.y == 1000\n", - "assert o.yint == 1000\n", - "assert c.pair == o.pair.slashpair\n", - "assert c.tknb == o.pair.tknb\n", - "assert c.tknq == o.pair.tknq\n", - "assert c.x_act == 0\n", - "assert c.y_act == o.y\n", - "assert iseq(o.p_start, c.p, c.p_max)\n", - "assert iseq(o.p_end, c.p_min)" + "val0 = tuple(float(O.findmin(vfunc, 99, N=n)) for n in range(100))\n", + "val = tuple(abs(v-val0[-1]) for v in val0)\n", + "val = tuple(v for v in val if v > 0)\n", + "print(val0[-1])\n", + "plt.plot(val)\n", + "plt.yscale('log')\n", + "plt.grid()" ] }, { "cell_type": "code", - "execution_count": 30, - "id": "8a507163-2d5a-4eef-8614-9482c898fa48", + "execution_count": 107, + "id": "11418d75-986d-4b10-a2fa-37cb442027cb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "99.68102109480606\n" + ] + }, + { + "data": { + "image/png": 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A0JexEMN4NptN4/rHalz/WJ083awXy6uVX+LXovV79ItX92va8CTl+bwanvLxf9UCAAB6LxZi4B/Ehrt0a06a5o9NVUnlKeWXBvTCzmrllwQ0LNmt3GyPbhieLHcobx0AAPoKPlQHI1xM3nVnWvRSRUdrvKemQWFOu6YMS1Kuz6tsb5RsNh4V3RPxHjcLeZuFvM3Ch+qAHiAqzKmbxqRq3mivdlbVq6DEr/UVNVqzo0qDEiKU5/Nq+vBkxYS7rB4VAAB0Ag0xjBDsvBuaWrS+okYFpQHtDNQpxGHTdZlJyvV5NDYthta4B+A9bhbyNgt5m4WGGOihIkOcyvN5lefzald1R2v8Ynm1XiyvVkZcuHJ9Xs0ckay4iBCrRwUAAJ+ChhhG6I68Tze3asOujta4pPKUnHabrh6SqFyfRxPSY2WnNe5WvMfNQt5mIW+z0BADvUi4y6FZozyaNcqjfccaVFAa0As7q7Rhd43SYsJ0Y3bH1xIjaY0BAOhJaIhhBKvyPtvSplf21KigJKBtR96Xw27T5EHxyvV5dUlGnBx2WuOuwnvcLORtFvI2Cw0x0MuFOu2aPjxF04en6ODxRq0uDej5HVV6bW+tPFGhmp3t0exRHqVEhVo9KgAAxqIhhhF6Ut7NrW16fW+t8kv8eufQSdlt0mUD45Wb7dXlg+LlpDUOip6UOboeeZuFvM1CQwz0QS6HXdcPS9L1w5J05ORpFZYFVFhWpTf371CSO0SzRqboxmyvUmPCrB4VAAAj0BDDCD0975bWNr25/7gKSgN668BxSdKkjDjl+Ty6cnCCnA67xRP2Pj09cwQXeZuFvM1CQwwYwumw6+qhibp6aKICp85oTVmVCkr9+u6acsVHuPSZkR7lZnvUPy7c6lEBAOhzaIhhhN6Yd2tbu95+74TyS/x6c3+tWtul8emxysv26OohiQpx0hp/kt6YOTqPvM1C3mahIQYM5rDbdPmgeF0+KF419We1pqxKq0v9+t7aCsWEOTVzZIrysr0akBBh9agAAPRqLMRAL5DkDtXnL0nXXZP6652DJ1RQGtCK7ZV6eutRjU2LVq7Pq2uHJirM5bB6VAAAeh0WYqAXsdtsumRAvC4ZEK/ahiat3dFx1viHL+7So6/s04wRycrN9mpIUqTVowIA0GuwEAO9VEJkiO6c2F8LJvTT1sPvq6DUr1Ulfq3YXqlR3ijlZns0ZViyIkJojQEA+CQsxEAvZ7PZND49VuPTY3WysVkvlFepoCSgRev36L9e269pWcnK9Xk0POXjP0wAAIDJWIiBPiQ2wqXbxvXTrTlpKqk8pfwSv9burNKqEr+ykt3K9Xk0LStZ7lDe+gAA/B23XYMRTM677kyLXiyvVkGpX3tqGhTmtGtqVpLyfF6N9ETJZuubj4o2OXMTkbdZyNss3HYNwEWLCnPq5rGpummMVzsDdcovDWh9RbUKy6o0JDFSudkeTR+RrOgwl9WjAgBgCRpiGIG8/1n92Rat31WjghK/yqvqFeq067rMROVlezU6LbpPtMZkbhbyNgt5m4WGGECXcIc6Ncfn1RyfVxVVdSooDeil8mq9sLNaA+LDlZvt1cwRKYqNoDUGAPR9NMQwAnl/utPNrfpLRY0KSv0q9dfJ5bDpmiGJyvV5NK5/rOy9rDUmc7OQt1nI2yw0xAC6TbjLodnZHs3O9mhvTYMKSv16sbxa63fVqF9smHKzvfrMyBQlRIZYPSoAAEFFQwwjkHfnnGlu1St7jqmgNKDtR96Xw27TVYMTlOvzaFJGXI9ujcncLORtFvI2Cw0xAEuFuRyaMSJFM0ak6L3aRhWUBrR2Z5Ve2XNM3uhQ3Zjt0ayRHiVHhVo9KgAAnUZDDCOQd/A0tbTptb0drfG7h07KbpOuGJSg3GyPLhsYL4e9Z7TGZG4W8jYLeZuFhhhAjxPitGtqVrKmZiXryMnTKigNaE1ZQBv31SrZHaLZozy6MdsjT3SY1aMCAHBeaIhhBPLuWi2tbdq4/7gKSvx6+70TkqRLB8YpN9uryYPi5XTYu30mMjcLeZuFvM1CQwygV3A67Lp2aKKuHZoo/6kzWv231nhh4U4lRIZo1sgU3ZjtUb/YcKtHBQDgQ2iIYQTy7n4tbe1660BHa7zpwHG1tUsT02OV6/Pq6iEJcnVxa0zmZiFvs5C3WWiIAfRaTrtNVw5O0JWDE1RVd1ZrygJaXRrQ/c+XKy7cpZkjU5Sb7VFGfITVowIADEdDDCOQd8/Q2tauLQdPqKC040N4rW3tyukXo1yfR9cOTVKoM3itMZmbhbzNQt5moSEG0Kc47DZdNjBelw2M17GGJj1fFlBBaUAPvLBLPw/bp+kjOlrjwYmRVo8KADAICzEASyRGhuiuSem6c2J//fXQSeWXBPRsUaWWbzsqX2q08nweXZ+ZpDCXw+pRAQB9HAsxAEvZbTZNzIjTxIw4nWhs0tqd1cov8es/X9qtn7+6TzdkJSvP51VmstvqUQEAfRQLMYAeIy4iRHeM76fbx6Vp+9H3lV8SUGFZQM8W+zXCE6W8bI+mZiUrIoTWGAAQPHyoDkYg797r/dPNerG8ozXeX9uoCJdD04YnKc/n1fCUj/+ABJmbhbzNQt5m6fUfqtu8ebPy8/N15swZ/fu//7uysrK68nIA+qCYcJfm56TplrGpKqk8pYLSgF7YWa38koCGJbuV5/NoWlay3KH8hRcAoHO69E+Q06dP6yc/+YnKy8v15ptvshAD6DSbzabRaTEanRajb109WC9VdLTGizfs1S9f26+pWUnKzfZqlDdKNpvN6nEBAL1IUBfipUuXavPmzZKkMWPG6O6771ZjY6OWLVume++9N5iXAmCwqDCnbhqTqnmjvdoZqFN+aUDrK6pVWFalwYkRysv2av6lA6weEwDQS3TpGeITJ07o0Ucf1de+9jV5vd6P/B7OEKM7kHff19DUonUVNSoo8au8ql6hTruuy0xUXrZXo9OiaY37ON7jZiFvs3THGeLzXoiLi4v16KOPatmyZWpra9ODDz6oXbt2KSQkRIsWLVJGRsaHfmbhwoU6fvy4YmNjdf311+uGG2740PewEKM7kLdZdlXV64XdNVpdVKmGplYNjI9Qrs+jGSNSFBvusno8dAHe42Yhb7P0mA/VLVmyRIWFhQoPD5ckbdiwQU1NTVqxYoWKioq0ePFiPf744x/6uZ/+9KedHBkAOm9YiluThiXrK5ek6y+7Olrj/3ptv379xgFdOzRRudlejesfQ2sMAJB0ngtxenq6HnvsMS1cuFCStHXrVk2ePFlSx1nhsrKyTg/gdofK6ez+e4o6HHbFxkZ0+3VhDfI2j8NhlzcpSncmRenOKwZpV6BOK7Ye1uqiSq2rqNGAhAjdNK6f5o5NU4I71OpxcZF4j5uFvM3SHXmf10I8bdo0HTly5Nzr+vp6ud0fPDXK4XCopaVFTueFf0avvv7sBf9MMPDXLWYhb/P8a+YpYQ594/IB+reJ/fXKnmMqKPHrZ+t367827NFVQxKUl+3VhIxY2WmNeyXe42Yhb7P0mCMT/8rtdquhoeHc67a2tk4twwDQ3cJcDs0YkaIZI1J0oLZRBaV+rd1RpZd3H1NqTJhysz2aNTJFibTGAGAMe2d+KCcnRxs3bpQkFRUVKTMzM6hDAUB3GJgQof+4erBe+PIl+vHMLKVGh+p/33xPn/ntFn1n9Q5t2n9crW2WPswTANANOlXrTpkyRZs2bdL8+fPV3t6uhx9+ONhzAUC3CXHaNTUrWVOzknXoxGmtLvVrTVmVXttbq5SoUN04yqNZo1LkiQ6zelQAQBfo0vsQnw9uu4buQN7mudjMm1vbtHFfrQpKAnr74AnZbdJlA+OVm+3V5YPi5bRz1rgn4T1uFvI2S489QwwAfZ3LYdd1mUm6LjNJR06eVmFZQIVlVXpz/w4luUM0a5RHN47yKDWG1hgAejsaYhiBvM3TFZm3tLbpzf3HVVAa0FsHjkuSLhkQpzyfV5MHxcvp6NTHMhAEvMfNQt5moSEGgB7E6bDr6qGJunpoovynzqiwNKDCsoAWFu5UQmSIZo1M0Y3ZHvWLDbd6VADABaAhhhHI2zzdlXlLW7s2Hziu/BK/Nh04rrZ2aVJGrHKzvbpqSIJctMbdgve4WcjbLDTEANDDOe02TR6coMmDE1RVd7bjrHFpQP/v+XLFhbs0a1SKbsz2Kj2O1hgAeioaYhiBvM1jZeatbe16++AJFZT49ca+WrW2S+P7xyjP59XVQxIV4qQ1Djbe42Yhb7PQEANAL+Sw23T5wHhdPjBex+rPas2OKhWUBvS9tRWKCXPqMyM9yvV5NCA+wupRAQBiIQaALpXoDtXnJqXrsxP7692DJ5Vf6tfy7Uf1p61HNLZfjHKzPbp2aKLCXA6rRwUAY7EQA0A3sNtsmjQgTpMGxKm2oUlrd1Qpv9SvH764Sz9/dZ9mjEhRbrZHgxMjrR4VAIzDQgwA3SwhMkR3TuyvOyb009bDJ1VQEtCzRZVavu2oRqdGK8/n1XWZtMYA0F1YiAHAInabTRPS4zQhPU4nGpu0dme18kv8evClXXr01b2aMTxFeT6vhiTRGgNAV2IhBoAeIC4iRHeM76fbx6Vp+9H3lV8SUEGpXyuLKpXtjVJutldTspIUTmsMAEHHbddgBPI2T1/I/OTpZr2ws0oFJQEdON6oyBCHbhierLxsr4aluK0er0fpC3nj/JG3WbjtGgAYLDbcpdvG9dOtOWkqPnpKBaV+Pb+jSs8V+zXCE6W8bI+mZiUrIoTWGAAuBg0xjEDe5umrmZ8606wXdlaroNSvfccaFeFyaNrwJOX5vBqe8vHtR1/XV/PGRyNvs9AQAwD+SXSYS/Nz0nTL2FSV+uuUX+LXCzurlV8SUFayW3m+jtbYHcq/3gHgfNEQwwjkbR6TMq8706KXKjruULGnpkHhLrumZiUrz+fViBS3bDab1SN2OZPyBnmbhoYYAPCposKcumlMquaN9mpnoE75JQGtK6/W6tKAMpMilevzavpwWmMA+Dg0xDACeZvH9Mzrz7ZoXUW1VhX7tbumQWFOu6ZmdZw1HumJ6nOtsel5m4a8zUJDDADoFHeoU3NHp2qOz6udVfUqKPFrXUW1CsuqNDQpUrnZHa1xVBh/DAAADTGMQN7mIfMPa2hq0bqKGuUX+1VRXa9Qp11ThnW0xtne3t0ak7dZyNssNMQAgKCJDHFqjs+rOT6vyqs67lCxrrxGz++o0uDECOVlezV9RLKiw1xWjwoA3YqGGEYgb/OQ+flpaGrR+ooa5Zf4VV7V0Rpfn5moPJ9XvtToXtMak7dZyNssNMQAgC4VGeJUns+rPJ9XFVV1KigN6KXyaq3dWa1BCRHK83k1g9YYQB9HQwwjkLd5yLzzGptatb6iWgWlAe0I1CnUadd1mYnKy/ZqdFrPbI3J2yzkbRYaYgBAt4sIcSjX51Wuz6td1R13qHixvFov7KzWwL+3xsOTFRNOawygb6AhhhHI2zxkHlynm1v1l10dZ43L/HUKcdh0XWbHHSrG9IDWmLzNQt5moSEGAPQI4S6HZo/yaPYoj3ZX1yv/b63xi+XVGhgfoVyfRzNHpNAaA+iVaIhhBPI2D5l3vZ7UGpO3WcjbLDTEAIAei9YYQF9BQwwjkLd5yNwaH9caz/F17R0qyNss5G0WGmIAQK/yia0xd6gA0EPREMMI5G0eMu85Tje36i8VNcov7brWmLzNQt5moSEGAPR64S6HZmd7NDvbo11/a41f+pfWeCZPwwNgIRpiGIG8zUPmPdvp5o6n4eWXfPA0vOszE5Xn88qXeuGtMXmbhbzNQkMMAOiTwl0O3Zjt1Y3Z3n9qjdfurNbgxAjlZXs1ndYYQDehIYYRyNs8ZN77NDa16i+7qrWqJKCdf2+Nh3WcNc72Rn1ia0zeZiFvs9AQAwCMERHyQWtcUVWn/JJAR2u8o0pDEiM77lAxIlnuUP7oAhBcNMQwAnmbh8z7hoamFq2rqFF+sV8V1fUKc9o1NaujNR7h+aA1Jm+zkLdZaIgBAEaLDHFqjs+rOT6vdgbqtKrEr/UV1Sosq1JmUqTmjPbqhuHJirV6UAC9Gg0xjEDe5iHzvqv+bIteKq/WqhK/9tQ0KNxl1+zRqZqZlaThKR/fAKHv4P1tFhpiAAD+hTvUqXljUjV3tFc7AnVaVezX6uJKrfjrEQ1PcWuOz6upWcmKCHFYPSqAXoKGGEYgb/OQuVnsoS4tf/s9PVfs1/7aRkWGOHTD8GTN8XmVmey2ejwEGe9vs/T6hrisrEy/+93v5HQ69Z3vfEeJiYldeTkAgKGiw126eWyabhqTqpLKU1pV4teasoCeK/Yr2xulOaO9uj4zSWEuWmMAH9alDfHWrVs1dOhQvfnmmwoJCdH111//oe+hIUZ3IG/zkLlZPirv9083a+3OKq0q9uvgidOKCnVqxohkzRnt1aCESIsmRTDw/jZLr2uIly5dqs2bN0uSxowZo7vvvlvbtm3TU089pV/96lfBvBQAAJ8oJtyl28b10605adp25H2tKvbruWK/Vmyv1Ni0aM0ZnaprhyYqxGm3elQAFuvShrikpERZWVlqaGjQb3/7W333u9/90PfQEKM7kLd5yNws55v38cYmPV9WpfxSv46cPKPYcJdmjUxRns+r/nHh3TApgoH3t1l6VENcXFysRx99VMuWLVNbW5sefPBB7dq1SyEhIVq0aJEyMjI+9DMNDQ26//771d7ergULFnRuegAAgiQ+IkR3TuyvOyb007sHT+q5Er+e3npEy/56RBPTYzVntFdXDU6Q00FrDJjkvBbiJUuWqLCwUOHhHf/1vGHDBjU1NWnFihUqKirS4sWL9fjjj3/o5y699FJdeumlwZ0YAICLZLfZNGlAnCYNiFNN/VkVlgWUXxLQfWvKlRAZohuzPcrL9sgTHWb1qAC6wXkdmVi3bp2GDRumhQsXauXKlXrkkUfk8/k0c+ZMSdLkyZP1xhtvdGqA06eb5HR2/6d+HQ67Wlvbuv26sAZ5m4fMzRKMvFvb2vX6nho9885hvb6nRjZJV2Um6dYJ/XXl0CQ57LbgDIuLxvvbLMHK2/UJd5k5r4Z42rRpOnLkyLnX9fX1crs/uK+jw+FQS0uLnM4L/4xeff3ZC/6ZYOD8kVnI2zxkbpZg5Z2T4lbOrOHynxqogtKAVpcG9OquGnmiQpXn82p2tkeJkSFBmBgXg/e3WXrUGeJ/5Ha71dDQcO51W1tbp5ZhAAB6Im90mO6+fIC+dEm6Nu6r1bPFfj2+6T39dvNBXT0kQXN8Xo1Pj5XdRmsM9AWd2mJzcnL06quvasaMGSoqKlJmZmaw5wIAwHJOh13XZibp2swkHTpxWvl/e+DHy7uPKT0uXHk+rz4zMkWx4S6rRwVwETq1EE+ZMkWbNm3S/Pnz1d7erocffjjYcwEA0KOkx4Xrm1cN0lcuH6CXd9doVbFfv3p9vx5/84CuH5akuaNTle2Nko3WGOh1uvQ+xOeD+xCjO5C3ecjcLFblvbemQc8VV+rF8mo1NLVqaFKk5o726obhyYoM4ShhV+H9bZbuOEPMQgwjkLd5yNwsVufd2NSqlyqq9VxRpXbXNCjC5dD0EcmaO9qroUnuT/8FuCBW543u1WM/VAcAAD4QEeLQHJ9Xedke7QjU6dliv57fUaXniv3ypUZr7mivrstMUiiPiQZ6JBpiGIG8zUPmZumJeb9/ulnP76jSqhK/Dp04rZgwp2aP8mjOaK/6xfKY6IvRE/NG16EhBgCgl4oJd+n28f1067g0vXvopJ4r/uAx0ZcMiNO80am6YlA8D/wAegAWYgAAupDdZtOkjDhNyohTdd1ZrS4NKL/Ur3tX71BKVKjm8MAPwHIcmYARyNs8ZG6W3pZ3S2ubNu4/rueKKvXOoZNy2G26Zkii5o3xKqdfDLdu+xS9LW9cHI5MAADQBzkddl07NFHXDk3UweONWlXi15qyKm3YXaOBCRGaNzpVM0Ykyx3KH9NAd6AhhhHI2zxkbpa+kPeZ5lat31WjZ4sqVV5Vr3CXXdOHp2jeGG7d9q/6Qt44fzTEAAAYIszl0OxRHs0e9bdbtxVVau3OjrtUjE6N1k1jUnVtZqJcDm7dBgQbDTGMQN7mIXOz9NW83z/drDU7qrSquFKHT55RfIRLudke5fm88kSHWT2eZfpq3vhoNMQAABgsJtylO8b3023j0rTl4Ak9W+TX0ncOa+k7hzV5UIJuGpOqCRmxsvMhPOCisBADANDD2W02XTogXpcOiJf/1BmtKvZrdWlAr++rVXpcuOaO9mrWSI+iwvhjHegMjkzACORtHjI3i4l5N7W06eU9Nfrzdr9K/acU5rTrhuHJumlMqjKT+/aH8EzM22QcmQAAAB8pxNlxF4rpw1O0q6pefy6q1Ivl1SooDfAhPOAC0RDDCORtHjI3C3l3OHWmWWvKqvTcP3wIL8/n1RyfV8lRoVaPFzTkbRYaYgAAcN6iw1y6fXw/3TouTW+/d0J/LqrUU28f0tIth3T10ETdNCaVJ+EBH4GFGACAPsZus+mygfG6bGC8jpw8reeK/SosC+jl3cc0ODFCN49J1fQRKQp3OaweFegRODIBI5C3ecjcLOT96c40t2pdRbVWbq/U7poGuUMdmjXSo3ljUpUeF271eBeEvM3CkQkAABAUYS6Hbsz2avYoj0oqT+nPRZVaWVSpZ7Yd1aUD4nTz2FRdNjCeexrDSCzEAAAYxGazaXRajEanxeieq84qvzSgVcV+/Uf+DvWPDdO8MamaPcojdygrAszBkQkYgbzNQ+ZmIe+L09zaplf3HNOK7ZUqqTylcJddM0ek6OaxaRqYEGH1eB9C3mbhyAQAAOhyLoddU7OSNTUrWeVVdVqxvVKFZQE9W+zXxPRY3ZKTpssHxsth5zgF+iYaYhiBvM1D5mYh7+A70dikgtKAni2qVHV9k1JjwnTz345TWP2IaPI2S3c0xCzEMAJ5m4fMzULeXaeltU2v7a3Vyu1Htf1oxyOiZ4xI0S05qRqUEGnJTORtFo5MAAAASzkddl0/LEnXD0vSrup6rdx+VGt3VmlVScdxivk5abp8EHenQO9GQwwjkLd5yNws5N29TjY2K7/Uf+44Rf/YMN08Nk2zRqUoMqTruzbyNgtHJroQbyazkLd5yNws5G2NltY2vbLnmJZvq1Sp/5QiQxyaPcqjm8emql9s1z3sg7zNwpEJAADQYzn/4e4UOwJ1Wr7tqFYWVWr5tqO6akiC5uekKadfjGwcp0APx0IMAAAu2khPlB6akaVvXDlQzxZV6rliv17bW6vMpEjdOi5NU4clK8Rpt3pM4CNxZAJGIG/zkLlZyLvnOdPcqpfKq/XMtqPaX9uo+AiX5o1O1dwxXsVHhFzU7yZvs3BkAgAA9EphLodyfV7dmO3RO4dO6umtR/TbzQe19J1Dmj48RbeOS9PgRGtu2wb8KxZiAADQZWw2myZlxGlSRpwO1DZq+baO27atLgvokow43TY+TZdkxHHOGJbiyASMQN7mIXOzkHfvcrKxWatK/PpzUaWONTRpUEKEbh/XTzcMP79zxuRtlu44MsHpdgAA0K1iI1z6/CXpKvzSRD14wzA57DY9tH63Zi3ZoiffPqiTjc1WjwjDcGQCAABYwuWwa+bIFM0YkXzunPFvNh3U77Yc1mdGpujWnDRlxEdYPSYMwEIMAAAs9Y/njPcda9AzW4+qsCygVcV+XTUkQXeM76fRaTFWj4k+jIUYAAD0GIMTI/X9aZm6+4oBWllUqWeLKvXa3lr5UqN1x/h+unJwgtUjog/iQ3UwAnmbh8zNQt591+nmVhWWBvT01iOqPHVW6XHh+uLkgbpmQJzCXA6rx0M34D7EAADAaOEuh27JSdPcMal6dc8xLXv3sB4o3Kn4CJduGZumeWO8ig5zWT0mejkWYgAA0OM57TZNGZak6zMTtfvkGf3PK3v1+Kb3tPSdQ8rzeXVrTpo80WFWj4leioUYAAD0GjabTZMGJmjY3HDtqanXsnePaMW2o1qxvVLTspL02Yn9NSiBJ+DhwrAQAwCAXmlokls/mpGlu68YoKe3HlVBiV8v7KzW1UMSdNfE/hrpjbZ6RPQSLMQAAKBX80aH6dvXDNYXJqVrxfajWvm3O1NMSI/VXRP7a0J6LI+Gxifq8ifV1dbWas6cOV19GQAAYLjYCJe+fPkAFX5por5x5UAdqG3UV58t1eeeLtLre4+pzdoba6EH69KGuL29Xf/3f/+ntLS0rrwMAADAOZEhTi2Y0F83j03T2h0B/f7dI7p39U4NTozQ5yam6/phSXLYaYzxgaAuxEuXLtXmzZslSWPGjFFMTIxmzZqlp556KpiXAQAA+FShTrvmjE7V7Gyv1ldUa+k7h/X9Fyr0xFvv6c4J/TVzZIpcji7/y3L0Al36YI6vfe1rio+P15YtW3TPPfdo+vTpH/oeHsyB7kDe5iFzs5C3WTqbd1t7u17bW6vfvX1IFdX1SnaH6LMT++vGbK9CnSzGPVV3PJjjvNMvLi7WggULJEltbW164IEHdMstt2jBggU6ePDgR/7Mr3/9a/3oRz9Sdnb2Ry7DAAAA3cVus+naoYn6wx1j9d9zRyk1Jkw/e2Wf8p58R8u3HdWZ5larR4RFzqshXrJkiQoLCxUeHq6VK1dq/fr1euWVV7R48WIVFRXpiSee0OOPP96pAU6fbpLT2f2PXnQ47Gptbev268Ia5G0eMjcLeZslWHm3t7fr7QPH9dire/XueyeUHBWqL00eqPnj+/NY6B4kWHm7PiHT8zpDnJ6erscee0wLFy6UJG3dulWTJ0+W1HFWuKysrNPD1def7fTPXgz+es0s5G0eMjcLeZslmHkPjw/X/87N1tbDJ7Vk80H9+IUK/eb1/bpzQj/N8XlZjHuAHnNkYtq0aXI6P9id6+vr5Xa7z712OBxqaWm5iBEBAACsM65/rH5z82j95mafBsaH679e2685T72rldsr1dTC3z70dZ26y4Tb7VZDQ8O5121tbf+0MAMAAPRG4/rHalz/WG09fFK/2fSefvbKXv3h3cP6/CXpmj0yRU7uStEndSrVnJwcbdy4UZJUVFSkzMzMoA4FAABgpXH9Y/XbW0br13OzleQO0SN/2aO5v/ur1pQF1NLGAz76mk7VulOmTNGmTZs0f/58tbe36+GHHw72XAAAAJay2WyaNCBOEzNi9daBE3rirff0o3W79Yd3D+srlw/QtUMTeSR0H9Gl9yE+H9yHGN2BvM1D5mYhb7NYlXf73+5j/Pim93SgtlHDU9y6+4oBuiQjjsW4C3XHh+o4+AsAAHAebDabrhmaqCsHJ+il8mr99q339I3nyjS2X4y+esUAjU6LsXpEdBInwwEAAC6Aw27TzJEpevbzE/Sda4fo4PFGfXF5sb5dsEP7axs+/Regx2EhBgAA6ASXw66bx6aq4IsT9e9XDNDWwyd16++3atG63aqqs+Y5C+gcjkwAAABchHCXQ5+blK68bK+e2nJIfy6q1EsV1Zqfk6bPTuivqDDWrZ6OhhgAACAIYiNc+tY1g/Xs58frmqGJ+v07h5X35Dtavu2oWni0eI/GQgwAABBEaTHhemhGlv54R46GJbv181f3af7vt+qNfbWy+OZe+BgsxAAAAF1gWIpbv56XrV/kjlS7pG8V7NDXnyvV3mN88K6nYSEGAADoIjabTZMHJ2jFZ8fp29cMVnlVvW7/w1Y98pc9OtHYZPV4+BsWYgAAgC7mdNg1PydNqz4/QTeNSdXqsoDmPPVux/liHgVtORZiAACAbhIT7tK91w7RM3eO00hPlH7+6j4tWLZNWw+ftHo0o7EQAwAAdLOBCRF6bG62fjp7hBqaWvSVlSW6//ly7l9sEW6MBwAAYIG/Pwr60gFx+sO7h/WHd4/ojX21+sIl6bpjfD85HfSW3YX/pwEAACwU5nLo3y4boJV3jdclA+L0P2++pzv+uE0llaesHs0YLMQAAAA9QGpMmH5240g9euNI1Z1p0RefKdLiDXtUf7bF6tH6PBZiAACAHuSqIQla+bnxuiUnTfklft30u7/q5d01PNSjC7EQAwAA9DCRIU59+5rB+t1tY5UQGaL71pTr2wU7VFPPh+66AgsxAABADzXCE6Wlt4/VPVcN0juHTmr+77fqxfIq2uIgYyEGAADowZx2m24f309/WpCjjLgIPfDCLi0s3KnjPOkuaFiIAQAAeoGM+AgtmT9aX588UJsOHNctS7fqld01Vo/VJ7AQAwAA9BIOu013TuyvZXfkyBsdqu+uKdf315br1Jlmq0fr1ViIAQAAepnBiZF66tYx+vJlGdqw+5hu/8M2FR993+qxei0WYgAAgF7I6bDri5dm6Mn5o+Ww2/RvK4r1f5sPqrWND9xdKBZiAACAXmykN1p/XJCjqVnJeuKtg7r7zyUKnDpj9Vi9CgsxAABAL+cOdeqhGVn6z+nDtKuqXrcv26ZX9hyzeqxeg4UYAACgj5gxIkV/XJCjtJgwfbdwp37x6j61cITiU7EQAwAA9CH948L15K1jdMvYVD2z7ai+/lypTjZyF4pPwkIMAADQx7gcdt177RA9MC1TJUff12f/tE27q+utHqvHYiEGAADoo2aN8ui3t4xWS1u7Pv9MkdZXVFs9Uo/EQgwAANCHjfRG6/d35Cgr2a3vra3QYxsPcGu2f8FCDAAA0MclRobo8Zt9mjvaqz+8e1gLC3fqTHOr1WP1GCzEAAAABnA57Lrv+qH6zrWD9ca+Wn1zVZnqz7ZYPVaPwEIMAABgkJvHpumhGVkqrjylr6ws0fHGJqtHshwLMQAAgGGmDU/Wz3NH6r3jjfrS8mL5DX+yHQsxAACAgS4fGK//mZet441N+uIzRdpf22D1SJZhIQYAADDU6LSYc7dl+7flxdrhP2X1SJZgIQYAADDY0CS3nrx1jCJDnfrac6XaZeADPFiIAQAADNcvNlxP3OxThMuhbzxXqkMnTls9UrdiIQYAAIA80WH6n3k+tbVLX3u2RFV1Z60eqduwEAMAAECSNCAhQv89d5ROnWnR158t1cnGZqtH6hYsxAAAADhneEqUfp47UkffP61vrCpVQ1Pff3gHCzEAAAD+ybj+sXpk1gjtrq7XvQU7dLalzeqRuhQLMQAAAD7kysEJeuCGYfrr4ff1/bXlamtvt3qkLsNCDAAAgI80Y0SKvnXNYL22t1ZLtxy2epwu06ULcXl5uW6//Xbdd999evvtt7vyUgAAAOgC88emalpWkp546z399dBJq8fpEl26EJeUlCgxMVF2u11Dhw7tyksBAACgC9hsNt0/JVP9Y8P1vbXlOtbQZPVIQecM5i9bunSpNm/eLEkaM2aMpkyZohkzZujYsWN68skntXDhwmBeDgAAAN0gIsShxbNG6K6nt+sHa8v163k+Oew2q8cKmqAuxHfddZfuuuuuc6/XrFmjpKQkxcTEqLW1NZiXAgAAQDcakhSp7143RD9at1tLNh/UVy4fYPVIQXPeRyaKi4u1YMECSVJbW5seeOAB3XLLLVqwYIEOHjz4kT+Tlpamhx56SD/72c/O/SwAAAB6p1mjPPrMyBQ99fYhbX7vuNXjBI2tvf3T76GxZMkSFRYWKjw8XCtXrtT69ev1yiuvaPHixSoqKtITTzyhxx9/vFMDnD7dJKfT0amfvRgOh12trX37nnr4AHmbh8zNQt5mIW9rnW5q1bwnNqum/qxW//vl8saEden1gpW3y/Xx++Z5HZlIT0/XY489du4M8NatWzV58mRJHWeFy8rKOj1cfb01z8mOjY3QyZONllwb3Y+8zUPmZiFvs5C39X48I0t3/mmbvv70Nv3mZp+cjq67T0Ow8k5KivrYr53X9NOmTZPT+cHuXF9fL7fbfe61w+FQS0vff6wfAAAApAEJEfp/U4aquPKUVpX4rR7nonVqnXe73WpoaDj3uq2t7Z8WZgAAAPRtN2Qla3z/GP32rYOqO9O7i9FOLcQ5OTnauHGjJKmoqEiZmZlBHQoAAAA9m81m0z1XDdapMy16asshq8e5KJ2qdadMmaJNmzZp/vz5am9v18MPPxzsuQAAANDDDUtxa+bIFK3YflTzxniVFhNu9Uidcl53mehKNTV1llyXA/lmIW/zkLlZyNss5N2zVNed1dyn3tUVgxL0yKzhQf/9PeZDdQAAAMBHSY4K1R3j+2nD7hqVVJ6yepxOYSEGAADARVkwob8SI0P0y9f2yeLDB53CQgwAAICLEhHi0N2XD1Cpv05/2VVj9TgXjIUYAAAAF23myBQNTYrU/7xxQGdbeteTBFmIAQAAcNEcdpu+edUgVZ46qxXbjlo9zgVhIQYAAEBQTMqI0xWD4vXUlkM60dhk9TjnjYUYAAAAQfONKwepsalVzxb1nkc6sxADAAAgaAYmRCinf4zW76ruNXecYCEGAABAUE0dlqT3jp/WnpoGq0c5LyzEAAAACKprhybJYZPW95JbsLEQAwAAIKhiI1yamBGnv1T0jmMTLMQAAAAIuqlZSao8dVY7AnVWj/KpWIgBAAAQdFcPSZTLYdP6ip5/bIKFGAAAAEHnDnXq8oHx+suuGrW29exjEyzEAAAA6BJThiXpWEOTio6+b/Uon4iFGAAAAF1i8uAEhbvs+ksPv9sECzEAAAC6RLjLoSsHJ+jl3cfU0tpm9Tgfi4UYAAAAXWbKsGSdPN2sdw6dtHqUj8VCDAAAgC5z6YA4uUMdPfohHSzEAAAA6DIhTruuGZKo1/Yc09mWnnlsgoUYAAAAXWpqVpIamlq1+cBxq0f5SCzEAAAA6FLj0+MUG+7qsccmWIgBAADQpZx2m67LTNQb+2p1urnV6nE+hIUYAAAAXW5qVpLOtLTpjX21Vo/yISzEAAAA6HJj0mLULzZMu6obrB7lQ5xWDwAAAIC+z26zaeltYxUR4rB6lA9hIQYAAEC3iAl3WT3CR+LIBAAAAIzGQgwAAACjsRADAADAaCzEAAAAMBoLMQAAAIzGQgwAAACjsRADAADAaCzEAAAAMBoLMQAAAIzGQgwAAACjsRADAADAaCzEAAAAMJqtvb293eohAAAAAKvQEAMAAMBoLMQAAAAwGgsxAAAAjOa0eoDu1tbWpgcffFC7du1SSEiIFi1apIyMDKvHQhA1Nzfr/vvv19GjR9XU1KS7775bQ4YM0X333SebzaahQ4fqhz/8oex2/nuwL6mtrdWcOXP01FNPyel0kncf98QTT+iVV15Rc3Ozbr31Vk2cOJHM+6jm5mbdd999Onr0qOx2ux566CHe431UcXGxHn30US1btkwHDx78yIxXrlyp5cuXy+l06u6779Y111wTlGsb90/Phg0b1NTUpBUrVujb3/62Fi9ebPVICLLCwkLFxsbq6aef1pIlS/TQQw/pkUce0T333KOnn35a7e3tevnll60eE0HU3NysBx54QGFhYZJE3n3cli1btH37dj3zzDNatmyZAoEAmfdhr7/+ulpaWrR8+XJ99atf1S9/+Uvy7oOWLFmi73//+zp79qykj/73eE1NjZYtW6bly5frySef1C9+8Qs1NTUF5frGLcRbt27V5MmTJUljxoxRWVmZxRMh2G644QZ985vfPPfa4XBox44dmjhxoiTpyiuv1FtvvWXVeOgCP/nJTzR//nwlJydLEnn3cW+++aYyMzP11a9+VV/5yld09dVXk3kfNnDgQLW2tqqtrU319fVyOp3k3Qelp6frscceO/f6ozIuKSnR2LFjFRISoqioKKWnp6uioiIo1zduIa6vr5fb7T732uFwqKWlxcKJEGyRkZFyu92qr6/XN77xDd1zzz1qb2+XzWY79/W6ujqLp0SwrFq1SvHx8ef+Q1cSefdxJ06cUFlZmX71q1/pP//zP3XvvfeSeR8WERGho0ePavr06frBD36gBQsWkHcfNG3aNDmdH5zk/aiM6+vrFRUVde57IiMjVV9fH5TrG3eG2O12q6Gh4dzrtra2fwoAfYPf79dXv/pV3XbbbZo1a5Z+9rOfnftaQ0ODoqOjLZwOwfTcc8/JZrNp8+bNKi8v13e/+10dP3783NfJu++JjY3VoEGDFBISokGDBik0NFSBQODc18m8b1m6dKmuuOIKffvb35bf79dnP/tZNTc3n/s6efdN/3gm/O8Z/+sO19DQ8E8L8kVdLyi/pRfJycnRxo0bJUlFRUXKzMy0eCIE27Fjx/T5z39e3/nOdzRv3jxJ0ogRI7RlyxZJ0saNGzV+/HgrR0QQ/elPf9If//hHLVu2TMOHD9dPfvITXXnlleTdh40bN05vvPGG2tvbVVVVpdOnT+vSSy8l8z4qOjr63NITExOjlpYW/p1ugI/K2OfzaevWrTp79qzq6uq0b9++oO1xxj2p7u93mdi9e7fa29v18MMPa/DgwVaPhSBatGiRXnzxRQ0aNOjc//a9731PixYtUnNzswYNGqRFixbJ4XBYOCW6woIFC/Tggw/KbrfrBz/4AXn3YT/96U+1ZcsWtbe36z/+4z/Ur18/Mu+jGhoadP/996umpkbNzc268847NWrUKPLug44cOaJvfetbWrlypQ4cOPCRGa9cuVIrVqxQe3u7vvzlL2vatGlBubZxCzEAAADwj4w7MgEAAAD8IxZiAAAAGI2FGAAAAEZjIQYAAIDRWIgBAABgNBZiAAAAGI2FGAAAAEZjIQYAAIDR/j+aoTPxGyjojgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"ETH\", 2500, 3000, 10, 7)\n", - "c = o.as_cpc\n", - "assert o.y == 7\n", - "assert iseq(c.x_act, o.y)\n", - "assert iseq(c.y_act, 0)\n", - "assert iseq(o.p_marg, c.p, c.p_min)\n", - "assert iseq(o.p_end, c.p_max)" + "val0 = tuple(float(O.findmin_gd(vfunc, 99, N=n)) for n in range(100))\n", + "val = tuple(abs(v-val0[-1]) for v in val0)\n", + "val = tuple(v for v in val if v > 0)\n", + "print(val0[-1])\n", + "plt.plot(val)\n", + "plt.yscale('log')\n", + "plt.grid()" ] }, { "cell_type": "code", - "execution_count": 33, - "id": "8c7098b7-a78d-4401-b2c3-2901ee481b24", + "execution_count": 108, + "id": "39c3cc3a-98cd-473d-9de0-0aa8a78d4e92", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "OptimizerBase.SimpleResult(result=99.65287573579084, method='findminmax_nr', errormsg=None, context_dct=None)" + ] + }, + "execution_count": 108, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "o = CarbonOrderUI.from_prices(\"ETH/USDC\", \"USDC\", 1500, 1000, 1000, 700)\n", - "c = o.as_cpc\n", - "assert o.y == 700\n", - "assert iseq(c.x_act, 0)\n", - "assert iseq(c.y_act, o.y)\n", - "assert iseq(o.p_marg, c.p, c.p_max)\n", - "assert iseq(o.p_end, c.p_min)" + "O.findmin(vfunc, 99, N=700)" ] }, { @@ -288,7 +3063,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "85ccbd93-8821-40e6-94e3-85391676861a", "metadata": {}, "outputs": [], @@ -298,10 +3073,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "d3179497-4340-41ff-859b-ff11936a081b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2)\n", "curves = [\n", @@ -319,10 +3105,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "5b1ed2c5-6bb7-44b0-a07e-f4b6a124cdd1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, x_act=10)\n", "curves = [\n", @@ -340,10 +3137,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "21513447-aacf-4fcc-8d71-94924ae44845", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, y_act=20)\n", "curves = [\n", @@ -361,12 +3169,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "8a5df413-de9f-485a-951a-bb046fd9687c", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, x_act=10, y_act=20)\n", "curves = [\n", @@ -394,7 +3213,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "id": "f7127e10-e463-4ae2-ba78-2c10483cdae0", "metadata": {}, "outputs": [], @@ -405,10 +3224,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "id": "d1051e52-d073-4656-b43e-d6c7404fe2e6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2)\n", "curves = [\n", @@ -426,10 +3256,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "f6ae5188-ded5-49c4-b106-88cb2d7eddbe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, x_act=10)\n", "curves = [\n", @@ -447,10 +3288,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "id": "f1650f4a-56c7-4aec-a5e6-196f5a5e77aa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, y_act=20)\n", "curves = [\n", @@ -468,10 +3320,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "id": "b0cfdea8-ae01-406a-9f7e-5871d9e5d140", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, x_act=10, y_act=20)\n", "curves = [\n", @@ -489,12 +3352,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "id": "669bcaca-0d61-44be-bda1-8a271719064d", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "defaults = dict(p=2, x_act=10, y_act=20)\n", "curves = [\n", @@ -513,10 +3387,24 @@ { "cell_type": "code", "execution_count": null, - "id": "61076a28-62f0-492f-9800-5abfb326c25b", - "metadata": { - "lines_to_next_cell": 2 - }, + "id": "59b18c4c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5d91773", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5c0af1b", + "metadata": {}, "outputs": [], "source": [] } diff --git a/resources/NBTest/NBTest_063_CPC.py b/resources/NBTest/NBTest_063_CPC.py index c147f58e..a8a36c9b 100644 --- a/resources/NBTest/NBTest_063_CPC.py +++ b/resources/NBTest/NBTest_063_CPC.py @@ -15,18 +15,150 @@ # --- from carbon.helpers.stdimports import * -from carbon import ConstantProductCurve as CPC, CarbonOrderUI +from carbon import CarbonOrderUI +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer, T, CPCInverter +from carbon.tools.optimizer import CPCArbOptimizer, F +import carbon.tools.tokenscale as ts plt.style.use('seaborn-dark') plt.rcParams['figure.figsize'] = [12,6] print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonOrderUI)) -print_version(require="2.3.3") +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ts.TokenScaleBase)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) +print_version(require="2.4.2") -# # Constant product curve [NBTest063] +# # Constant product curve and Optimizer [NBTest063] + +try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") +except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") +CCmarket = CPCContainer.from_df(df) + +# ## P + +c = CPC.from_pk(pair="USDC/WETH", p=1, k=100, params=dict(exchange="univ3", a=dict(b=1, c=2))) +assert c.P("exchange") == "univ3" +assert c.P("a") == {'b': 1, 'c': 2} +assert c.P("a:b") == 1 +assert c.P("a:c") == 2 +assert c.P("a:d") is None +assert c.P("b") is None +assert c.P("b", "meh") == "meh" + +# ## TVL + +c = CPC.from_pk(pair="WETH/USDC", p=2000, k=1*2000) +assert c.tvl(incltkn=True) == (4000.0, 'USDC', 1) +assert c.tvl("USDC", incltkn=True) == (4000.0, 'USDC', 1) +assert c.tvl("WETH", incltkn=True) == (2.0, 'WETH', 1) +assert c.tvl("USDC", incltkn=True, mult=2) == (8000.0, 'USDC', 2) +assert c.tvl("WETH", incltkn=True, mult=2) == (4.0, 'WETH', 2) +assert c.tvl("WETH", incltkn=False) == 2.0 +assert c.tvl("WETH") == 2.0 +assert c.tvl() == 4000 +assert c.tvl("WETH", mult=2000) == 4000 + +# ## estimate prices + +CC = CPCContainer() +CC += [CPC.from_univ3(pair="WETH/USDC", cid="uv3", fee=0, descr="", + uniPa=2000, uniPb=2010, Pmarg=2005, uniL=10*sqrt(2000))] +CC += [CPC.from_pk(pair="WETH/USDC", cid="uv2", fee=0, descr="", + p=1950, k=5**2*2000)] +CC += [CPC.from_pk(pair="USDC/WETH", cid="uv2r", fee=0, descr="", + p=1/1975, k=5**2*2000)] +CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="USDC", yint=1000, y=1000, pa=1850, pb=1750)] +CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="WETH", yint=1, y=0, pb=1/1850, pa=1/1750)] +CC += [CPC.from_carbon(pair="WETH/USDC", cid="carb", fee=0, descr="", + tkny="USDC", yint=1000, y=500, pa=1870, pb=1710)] +#CC.plot() + +assert CC.price_estimate(tknq=T.WETH, tknb=T.USDC, result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" +assert CC.price_estimate(pair=f"{T.USDC}/{T.WETH}", result=CC.PE_PAIR) == f"{T.USDC}/{T.WETH}" +assert raises(CC.price_estimate, tknq="a", result=CC.PE_PAIR) +assert raises(CC.price_estimate, tknb="a", result=CC.PE_PAIR) +assert raises(CC.price_estimate, tknq="a", tknb="b", pair="a/b", result=CC.PE_PAIR) +assert raises(CC.price_estimate, pair="ab", result=CC.PE_PAIR) +assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, + unwrapsingle=False)[0][0] == f"{T.USDC}/{T.WETH}" +assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True, + unwrapsingle=True)[0] == f"{T.USDC}/{T.WETH}" +assert CC.price_estimates(tknqs=[T.WETH], tknbs=[T.USDC], pairs=True)[0] == f"{T.USDC}/{T.WETH}" +r = CC.price_estimates(tknqs=list("ABC"), tknbs=list("DEFG"), pairs=True) +assert r.ndim == 2 +assert r.shape == (3,4) +r = CC.price_estimates(tknqs=list("A"), tknbs=list("DEFG"), pairs=True) +assert r.ndim == 1 +assert r.shape == (4,) + +assert CC[0].at_boundary == False +assert CC[1].at_boundary == False +assert CC[2].at_boundary == False +assert CC[3].at_boundary == True +assert CC[3].at_xmin == True +assert CC[3].at_ymin == False +assert CC[3].at_xmax == False +assert CC[3].at_ymax == True +assert CC[4].at_boundary == True +assert CC[4].at_ymin == True +assert CC[4].at_xmin == True +assert CC[4].at_ymax == True +assert CC[4].at_xmax == True +assert CC[5].at_boundary == True + +r = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_CURVES) +assert len(r)==3 + +p,w = CC.price_estimate(tknq="USDC", tknb="WETH", result=CC.PE_DATA) +assert len(p) == len(r) +assert len(w) == len(r) +assert iseq(sum(p), 5930) +assert iseq(sum(w), 894.4271909999159) +pe = CC.price_estimate(tknq="USDC", tknb="WETH") +assert pe == np.average(p, weights=w) + +O = CPCArbOptimizer(CC) +Om = CPCArbOptimizer(CCmarket) +assert O.price_estimates(tknq="USDC", tknbs=["WETH"]) == CC.price_estimates(tknqs=["USDC"], tknbs=["WETH"]) +CCmarket.fp(onein="USDC") +r = Om.price_estimates(tknq="USDC", tknbs=["WETH", "WBTC"]) +assert iseq(r[0], 1820.89875275) +assert iseq(r[1], 28351.08150121) + +# ## price estimates in optimizer + +prices = {"USDC":1, "LINK": 5, "AAVE": 100, "MKR": 500, "WETH": 2000, "WBTC": 20000} +CCfm, ctr = CPCContainer(), 0 +for tknb, pb in prices.items(): + for tknq, pq in prices.items(): + if pb>pq: + pair = f"{tknb}/{tknq}" + pp = pb/pq + k = (100000)**2/(pb*pq) + CCfm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{ctr}") + ctr += 1 + +O = CPCArbOptimizer(CCfm) +assert O.MO_PSTART == O.MO_P +tknq = "WETH" +df = O.margp_optimizer(tknq, result=O.MO_PSTART) +rd = df[tknq].to_dict() +assert len(df) == len(prices)-1 +assert df.columns[0] == tknq +assert df.index.name == "tknb" +assert rd == {k:v/prices[tknq] for k,v in prices.items() if k!=tknq} +df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=df)) +assert np.all(df == df2) +df2 = O.margp_optimizer(tknq, result=O.MO_PSTART, params=dict(pstart=rd)) +assert np.all(df == df2) +df # ## Assertions and testing -c = CPC.from_px(p=2000,x=10, pair="eth/usdc") +c = CPC.from_px(p=2000,x=10, pair="ETH/USDC") assert c.pair == "ETH/USDC" assert c.tknb == c.pair.split("/")[0] assert c.tknx == c.tknb @@ -43,6 +175,8 @@ assert c == CPC.from_px(c.p, c.x) assert c == CPC.from_py(c.p, c.y) +c + c = CPC.from_px(p=2, x=100, x_act=10, y_act=20) assert c.y_max*c.x_min == c.k assert c.x_max*c.y_min == c.k @@ -143,6 +277,862 @@ assert iseq(o.p_marg, c.p, c.p_max) assert iseq(o.p_end, c.p_min) +# ## New CPC features in v2 + +# + +p = CPCContainer.Pair("ETH/USDC") +assert str(p) == "ETH/USDC" +assert p.pair == str(p) +assert p.tknx == "ETH" +assert p.tkny == "USDC" +assert p.tknb == "ETH" +assert p.tknq == "USDC" + +pp = CPCContainer.Pair.wrap(["ETH/USDC", "WBTC/ETH"]) +assert len(pp) == 2 +assert pp[0].pair == "ETH/USDC" +assert pp[1].pair == "WBTC/ETH" +assert pp[0].unwrap(pp) == ('ETH/USDC', 'WBTC/ETH') +# - + +pairs = ["A", "B", "C"] +assert CPCContainer.pairset(", ".join(pairs)) == set(pairs) +assert CPCContainer.pairset(pairs) == set(pairs) +assert CPCContainer.pairset(tuple(pairs)) == set(pairs) +assert CPCContainer.pairset(p for p in pairs) == set(pairs) + +pairs = [f"{a}/{b}" for a in ["ETH", "USDC", "DAI"] for b in ["DAI", "WBTC", "LINK", "ETH"] if a!=b] +CC = CPCContainer() +fp = lambda **cond: CC.filter_pairs(pairs=pairs, **cond) +assert fp(bothin="ETH, USDC, DAI") == {'DAI/ETH', 'ETH/DAI', 'USDC/DAI', 'USDC/ETH'} +assert fp(onein="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} +assert fp(onein="ETH") == fp(contains="ETH") +assert fp(notin="WBTC, ETH, DAI") == {'USDC/LINK'} +assert fp(tknbin="WBTC") == set() +assert fp(tknqin="WBTC") == {'DAI/WBTC', 'ETH/WBTC', 'USDC/WBTC'} +assert fp(tknbnotin="WBTC") == set(pairs) +assert fp(tknbnotin="WBTC, ETH, DAI") == {'USDC/DAI', 'USDC/ETH', 'USDC/LINK', 'USDC/WBTC'} +assert fp(notin_0="WBTC", notin_1="DAI") == fp(notin="WBTC, DAI") +assert fp(onein = "ETH") == fp(anyall=CC.FP_ANY, tknbin="ETH", tknqin="ETH") + +P = CPCContainer.Pair +ETHUSDC = P("ETH/USDC") +USDCETH = P(ETHUSDC.pairr) +assert ETHUSDC.pair == "ETH/USDC" +assert ETHUSDC.pairr == "USDC/ETH" +assert USDCETH.pairr == "ETH/USDC" +assert USDCETH.pair == "USDC/ETH" +assert ETHUSDC.isprimary +assert not USDCETH.isprimary +assert ETHUSDC.primary == ETHUSDC.pair +assert ETHUSDC.secondary == ETHUSDC.pairr +assert USDCETH.primary == USDCETH.pairr +assert USDCETH.secondary == USDCETH.pair +assert ETHUSDC.primary == USDCETH.primary +assert ETHUSDC.secondary == USDCETH.secondary + +assert P("BTC/ETH").isprimary +assert P("WBTC/ETH").isprimary +assert P("BTC/WETH").isprimary +assert P("WBTC/ETH").isprimary +assert P("BTC/USDC").isprimary +assert P("XYZ/USDC").isprimary +assert P("XYZ/USDT").isprimary + +# ## Real data and retrieval of curves + +try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") +except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") +CC = CPCContainer.from_df(df) +assert len(CC) == 459 +assert len(CC) == len(df) +assert len(CC.pairs()) == 326 +assert len(CC.tokens()) == 141 +assert CC.tokens_s +assert CC.tokens_s()[:60] == '1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARM' +print("Num curves:", len(CC)) +print("Num pairs:", len(CC.pairs())) +print("Num tokens:", len(CC.tokens())) +#print(CC.tokens_s()) + +assert CC.bypairs(CC.fp(onein="WETH, WBTC")) == CC.bypairs(CC.fp(onein="WETH, WBTC"), asgenerator=False) +assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"))) == 254 +assert len(CC.bypairs(CC.fp(onein="WETH, WBTC"), ascc=True)) == 254 +CC1 = CC.bypairs(CC.fp(onein="WBTC"), ascc=True) +assert len(CC1) == 29 +cids = [c.cid for c in CC.bypairs(CC.fp(onein="WBTC"))] +assert len(cids) == len(CC1) +assert CC.bycid("bla") is None +assert not CC.bycid(191) is None +assert raises(CC.bycids, ["bla"]) +assert len(CC.bycids(cids)) == len(cids) +assert len(CC.bytknx("WETH")) == 46 +assert len(CC.bytkny("WETH")) == 181 +assert len(CC.bytknys("WETH")) == len(CC.bytkny("WETH")) +assert len(CC.bytknxs("USDC, USDT")) == 41 +assert len(CC.bytknxs(["USDC", "USDT"])) == len(CC.bytknxs("USDC, USDT")) +assert len(CC.bytknys(["USDC", "USDT"])) == len(CC.bytknys({"USDC", "USDT"})) +cs = CC.bytknx("WETH", asgenerator=True) +assert raises(len, cs) +assert len(tuple(cs)) == 46 +assert len(tuple(cs)) == 0 # generator empty + +CC2 = CC.bypairs(CC.fp(bothin="USDC, DAI, BNT, SHIB, ETH, AAVE, LINK"), ascc=True) +tt = CC2.tokentable() +assert tt["ETH"].x == [] +assert tt["ETH"].y == [0] +assert tt["DAI"].x == [1,4,8] +assert tt["DAI"].y == [3,6] +tt + +assert CC2.tknxs() == {'AAVE', 'BNT', 'DAI', 'LINK'} +assert CC2.tknxl() == ['BNT', 'DAI', 'LINK', 'LINK', 'DAI', 'LINK', 'LINK', 'AAVE', 'DAI'] +assert set(CC2.tknxl()) == CC2.tknxs() +assert set(CC2.tknyl()) == CC2.tknys() +assert len(CC2.tknxl()) == len(CC2.tknyl()) +assert len(CC2.tknxl()) == len(CC2) + +# ## TokenScale tests + +TSB = ts.TokenScaleBase() +assert raises (TSB.scale,"ETH") +assert TSB.DEFAULT_SCALE == 1e-2 + +TS = ts.TokenScale.from_tokenscales(USDC=1e0, ETH=1e3, BTC=1e4) +TS + +assert TS("USDC") == 1 +assert TS("ETH") == 1000 +assert TS("BTC") == 10000 +assert TS("MEH") == TS.DEFAULT_SCALE + +TSD = ts.TokenScaleData + +tknset = {'AAVE', 'BNT', 'BTC', 'ETH', 'LINK', 'USDC', 'USDT', 'WBTC', 'WETH'} +assert tknset - set(TSD.scale_dct.keys()) == set() + +cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") +assert cc1.tokenscale is cc1.TOKENSCALE +assert cc1.tknx == "ETH" +assert cc1.tkny == "USDC" +assert cc1.scalex == 1 +assert cc1.scaley == 1 +cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") +assert cc2.tknx == "BTC" +assert cc2.tkny == "MEH" +assert cc2.scalex == 1 +assert cc2.scaley == 1 +assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE + +cc1 = CPC.from_xy(x=10, y=20000, pair="ETH/USDC") +cc1.set_tokenscale(TSD) +assert cc1.tokenscale != cc1.TOKENSCALE +assert cc1.tknx == "ETH" +assert cc1.tkny == "USDC" +assert cc1.scalex == 1e3 +assert cc1.scaley == 1e0 +cc2 = CPC.from_xy(x=10, y=20000, pair="BTC/MEH") +cc2.set_tokenscale(TSD) +assert cc2.tknx == "BTC" +assert cc2.tkny == "MEH" +assert cc2.scalex == 1e4 +assert cc2.scaley == 1e-2 +assert cc2.scaley == cc2.tokenscale.DEFAULT_SCALE + +# ## dx_min and dx_max etc + +cc = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110) +assert iseq(cc.x_act, 4.653741075440777) +assert iseq(cc.y_act, 513.167019494862) +assert cc.dx_min == -cc.x_act +assert cc.dy_min == -cc.y_act +assert iseq( (cc.x + cc.dx_max)*(cc.y + cc.dy_min), cc.k) +assert iseq( (cc.y + cc.dy_max)*(cc.x + cc.dx_min), cc.k) + +# ## xyfromp_f and dxdyfromp_f + +# + +c = CPC.from_pkpp(p=100, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") + +assert c.pair == 'WETH-6Cc2/USDC-eB48' +assert c.pairp == 'WETH/USDC' +assert c.p == 100 +assert iseq(c.x_act, 4.653741075440777) +assert iseq(c.y_act, 513.167019494862) +assert c.tknx == T.ETH +assert c.tkny == T.USDC +assert c.tknxp == "WETH" +assert c.tknyp == "USDC" +assert c.xyfromp_f() == (c.x, c.y, c.p) +assert c.xyfromp_f(withunits=True) == (100.0, 10000.0, 100.0, 'WETH', 'USDC', 'WETH/USDC') + +x,y,p = c.xyfromp_f(p=85, ignorebounds=True) +assert p == 85 +assert iseq(x*y, c.k) +assert iseq(y/x,85) + +x,y,p = c.xyfromp_f(p=115, ignorebounds=True) +assert p == 115 +assert iseq(x*y, c.k) +assert iseq(y/x,115) + +x,y,p = c.xyfromp_f(p=95) +assert p == 95 +assert iseq(x*y, c.k) +assert iseq(y/x,p) + +x,y,p = c.xyfromp_f(p=105) +assert p == 105 +assert iseq(x*y, c.k) +assert iseq(y/x,p) + +x,y,p = c.xyfromp_f(p=85) +assert p == 85 +assert iseq(x*y, c.k) +assert iseq(y/x,90) + +x,y,p = c.xyfromp_f(p=115) +assert p == 115 +assert iseq(x*y, c.k) +assert iseq(y/x,110) + +# + +assert c.dxdyfromp_f(withunits=True) == (0.0, 0.0, 100.0, 'WETH', 'USDC', 'WETH/USDC') + +dx,dy,p = c.dxdyfromp_f(p=85, ignorebounds=True) +assert p == 85 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx),p) + +dx,dy,p = c.dxdyfromp_f(p=115, ignorebounds=True) +assert p == 115 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx),p) + +dx,dy,p = c.dxdyfromp_f(p=95) +assert p == 95 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx),p) + +dx,dy,p = c.dxdyfromp_f(p=105) +assert p == 105 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx),p) + +dx,dy,p = c.dxdyfromp_f(p=85) +assert p == 85 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx), 90) +assert iseq(dy, -c.y_act) + +dx,dy,p = c.dxdyfromp_f(p=115) +assert p == 115 +assert iseq((c.x+dx)*(c.y+dy), c.k) +assert iseq((c.y+dy)/(c.x+dx), 110) +assert iseq(dx, -c.x_act) + +assert iseq(c.x_min*c.y_max, c.k) +assert iseq(c.x_max*c.y_min, c.k) +assert iseq(c.y_max/c.x_min, c.p_max) +assert iseq(c.y_min/c.x_max, c.p_min) +# - + +# ## CPCInverter + +c = CPC.from_pkpp(p=2000, k=10*20000, p_min=1800, p_max=2200, pair=f"{T.ETH}/{T.USDC}") +c2 = CPC.from_pkpp(p=1/2000, k=10*20000, p_max=1/1800, p_min=1/2200, pair=f"{T.USDC}/{T.ETH}") +ci = CPCInverter(c) +c2i = CPCInverter(c2) +curves = CPCInverter.wrap([c,c2]) +assert c.pairo == c2i.pairo +assert ci.pairo == c2.pairo + +#print("x_act", c.x_act, c2i.x_act) +assert iseq(c.x_act, c2i.x_act) +xact = c.x_act +dx = -0.1*xact +c_ex = c.execute(dx=dx) +assert isinstance(c_ex, CPC) +assert iseq(c_ex.x_act, xact+dx) +assert iseq(c_ex.x, c.x+dx) +c2i_ex = c2i.execute(dx=dx) +assert iseq(c2i_ex.x_act, xact+dx) +assert iseq(c2i_ex.x, c.x+dx) +assert isinstance(c2i_ex, CPCInverter) + +assert len(curves) == 2 +assert set(c.pair for c in curves) == {'WETH-6Cc2/USDC-eB48'} +assert len(set(c.pair for c in curves)) == 1 +assert len(set(c.tknx for c in curves)) == 1 +assert len(set(c.tkny for c in curves)) == 1 + +assert c.tknx == ci.tkny +assert c.tkny == ci.tknx +assert c.tknxp == ci.tknyp +assert c.tknyp == ci.tknxp +assert c.tknb == ci.tknq +assert c.tknq == ci.tknb +assert c.tknbp == ci.tknqp +assert c.tknqp == ci.tknbp +assert f"{c.tknq}/{c.tknb}" == ci.pair +assert f"{c.tknqp}/{c.tknbp}" == ci.pairp +assert c.x == ci.y +assert c.y == ci.x +assert c.x_act == ci.y_act +assert c.y_act == ci.x_act +assert c.x_min == ci.y_min +assert c.x_max == ci.y_max +assert c.y_min == ci.x_min +assert c.y_max == ci.x_max +assert c.k == ci.k +assert iseq(c.p, 1/ci.p) +assert iseq(c.p_min, 1/ci.p_max) +assert iseq(c.p_max, 1/ci.p_min) + + +assert c.pair == c2i.pair +assert c.tknx == c2i.tknx +assert c.tkny == c2i.tkny +assert c.tknxp == c2i.tknxp +assert c.tknyp == c2i.tknyp +assert c.tknb == c2i.tknb +assert c.tknq == c2i.tknq +assert c.tknbp == c2i.tknbp +assert c.tknqp == c2i.tknqp +assert iseq(c.p, c2i.p) +assert iseq(c.p_min, c2i.p_min) +assert iseq(c.p_max, c2i.p_max) +assert c.x == c2i.x +assert c.y == c2i.y +assert c.x_act == c2i.x_act +assert c.y_act == c2i.y_act +assert c.x_min == c2i.x_min +assert c.x_max == c2i.x_max +assert c.y_min == c2i.y_min +assert c.y_max == c2i.y_max +assert c.k == c2i.k + +assert iseq(c.xfromy_f(c.y), c2i.xfromy_f(c2i.y)) +assert iseq(c.yfromx_f(c.x), c2i.yfromx_f(c2i.x)) +assert iseq(c.xfromy_f(c.y*1.05), c2i.xfromy_f(c2i.y*1.05)) +assert iseq(c.yfromx_f(c.x*1.05), c2i.yfromx_f(c2i.x*1.05)) +assert iseq(c.dxfromdy_f(1), c2i.dxfromdy_f(1)) +assert iseq(c.dyfromdx_f(1), c2i.dyfromdx_f(1)) + +assert c.xyfromp_f() == c2i.xyfromp_f() +assert c.dxdyfromp_f() == c2i.dxdyfromp_f() +assert c.xyfromp_f(withunits=True) == c2i.xyfromp_f(withunits=True) +assert c.dxdyfromp_f(withunits=True) == c2i.dxdyfromp_f(withunits=True) +assert iseq(c.p, c2i.p) +x,y,p = c.xyfromp_f(c.p*1.05) +x2,y2,p2 = c2i.xyfromp_f(c2i.p*1.05) +assert iseq(x,x2) +assert iseq(y,y2) +assert iseq(p,p2) +dx,dy,p = c.dxdyfromp_f(c.p*1.05) +dx2,dy2,p2 = c2i.dxdyfromp_f(c2i.p*1.05) +assert iseq(dx,dx2) +assert iseq(dy,dy2) +assert iseq(p,p2) + + +# ## simple_optimizer + +CC = CPCContainer(CPC.from_pk(p=2000+i*10, k=10*20000, pair=f"{T.ETH}/{T.USDC}") for i in range(11)) +c0 = CC.curves[0] +c1 = CC.curves[-1] +CC0 = CPCContainer([c0]) +assert len(CC) == 11 +assert iseq([c.p for c in CC][-1], 2100) +assert len(CC0) == 1 +assert iseq([c.p for c in CC0][-1], 2000) + +# + +O = CPCArbOptimizer(CC) +O0 = CPCArbOptimizer(CC0) +func = O.simple_optimizer(result=O.SO_DXDYVECFUNC) +func0 = O0.simple_optimizer(result=O.SO_DXDYVECFUNC) +funcs = O.simple_optimizer(result=O.SO_DXDYSUMFUNC) +funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC) +funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC) +x,y = func0(2100)[0] +xb, yb, _ = c0.dxdyfromp_f(2100) +assert x == xb +assert y == yb +x,y = func(2100)[-1] +xb, yb, _ = c1.dxdyfromp_f(2100) +assert x == xb +assert y == yb +assert np.all(sum(func(2100)) == funcs(2100)) + +p = 2100 +dx, dy = funcs(p) +assert iseq(dy + p*dx, funcvy(p)) +assert iseq(dy/p + dx, funcvx(p)) + +p = 1500 +dx, dy = funcs(p) +assert iseq(dy + p*dx, funcvy(p)) +assert iseq(dy/p + dx, funcvx(p)) + +assert iseq(float(O0.simple_optimizer(result=O.SO_PMAX)), c0.p) +assert iseq(float(O.simple_optimizer(result=O.SO_PMAX)), 2049.6451720862074, eps=1e-3) +# - + +O.simple_optimizer(result=O.SO_PMAX) + +# ### global max + +r = O.simple_optimizer() +r_ = O.simple_optimizer(result=O.SO_GLOBALMAX) +assert raises(O.simple_optimizer, targettkn=T.WETH, result=O.SO_GLOBALMAX) +assert iseq(float(r), float(r_)) +assert len(r.curves) == len(CC) +assert np.all(r.dxdy_sum == sum(r.dxdy_vec)) +dx, dy = r.dxdy_vecs +assert tuple(tuple(_) for _ in r.dxdy_vec) == tuple(zip(dx,dy)) +assert r.result == r.dxdy_valx +for dp in np.linspace(-500,500,100): + assert r.dxdyfromp_valx_f(p) < r.dxdy_valx + assert r.dxdyfromp_valy_f(p) < r.dxdy_valy + +CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) +# CC.plot() +# CC_ex.plot() +prices = [c.p for c in CC] +prices_ex = [c.p for c in CC_ex] +assert iseq(np.std(prices), 31.622776601683707) +assert iseq(np.std(prices_ex), 4.547473508864641e-13) +#prices, prices_ex + +# ### target token + +r = O.simple_optimizer(targettkn=T.WETH) +r_ = O.simple_optimizer(targettkn=T.WETH, result=O.SO_TARGETTKN) +assert raises(O.simple_optimizer,targettkn=T.DAI) +assert raises(O.simple_optimizer, result=O.SO_TARGETTKN) +assert iseq(float(r), float(r_)) +assert abs(sum(r.dyvalues) < 1e-6) +assert sum(r.dxvalues) < 0 +assert iseq(float(r),sum(r.dxvalues)) + +r = O.simple_optimizer(targettkn=T.USDC) +assert abs(sum(r.dxvalues) < 1e-6) +assert sum(r.dyvalues) < 0 +assert iseq(float(r),sum(r.dyvalues)) + +# ## optimizer plus inverted curves + +CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) +CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"{T.USDC}/{T.ETH}") for i in range(11)) +CC = CCr.bycids() +assert len(CC) == len(CCr) +CC += CCi +assert len(CC) == len(CCr) + len(CCi) + +# + +# CC.plot() +# - + +O = CPCArbOptimizer(CC) +r = O.simple_optimizer() +print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") +assert iseq(r.result, -1.3194573866437527) + +CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) +# CC.plot() +# CC_ex.plot() + +prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] +assert iseq(np.std(prices_ex), 5.130242014436283e-13) + +# ## posx and negx + +O = CPCArbOptimizer +a = O.a + +assert O.posx([0,-1,2]) == (0, 0, 2) +assert O.posx((-1,-2, 3)) == (0, 0, 3) +assert O.negx([0,-1,2]) == (0, -1, 0) +assert O.negx((-1,-2, 3)) == (-1, -2, 0) +assert np.all(O.posx(a([0,-1,2])) == a((0, 0, 2))) +assert O.t(a((-1,-2))) == (-1,-2) + +for v in ((1,2,3), (1,-1,5-10,0), (-10.5,8,2.34,-17)): + assert np.all(O.posx(a(v))+O.negx(a(v)) == v) + +# ## TradeInstructions + +TI = CPCArbOptimizer.TradeInstruction + +ti = TI.new(curve_or_cid="1", tkn1="ETH", amt1=1, tkn2="USDC", amt2=-2000) +print(f"cid={ti.cid}, out={ti.amtout} {ti.tknout}, , out={ti.amtin} {ti.tknin}") +assert ti.tknin == "ETH" +assert ti.amtin > 0 +assert ti.tknout == "USDC" +assert ti.amtout < 0 +assert ti.price_outperin == 2000 +assert ti.price_inperout == 1/2000 +assert ti.prices == (2000, 1/2000) +assert ti.price_outperin == ti.p +assert ti.price_inperout == ti.pr +assert ti.prices == ti.pp + +assert not raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=-1) +assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=1) +assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=-1) +assert raises(TI, cid="1", tknin="USDC", amtin=-2000, tknout="ETH", amtout=1) +assert raises(TI, cid="1", tknin="USDC", amtin=2000, tknout="ETH", amtout=0) +assert raises(TI, cid="1", tknin="USDC", amtin=0, tknout="ETH", amtout=-1) +assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=-1) +assert not raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=1) +assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=2000, tkn2="ETH", amt2=1) +assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=-1) +assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=0, tkn2="ETH", amt2=1) +assert raises(TI.new, curve_or_cid="1", tkn1="USDC", amt1=-2000, tkn2="ETH", amt2=0) + +til = [ + TI.new(curve_or_cid=f"{i+1}", tkn1="ETH", amt1=1*(1+i/100), tkn2="USDC", amt2=-2000*(1+i/100)) + for i in range(10) +] +tild = TI.to_dicts(til) +tildf = TI.to_df(til) +assert len(tild) == 10 +assert len(tildf) == 10 +assert tild[0] == {'cid': '1', 'tknin': 'ETH', 'amtin': 1.0, 'tknout': 'USDC', 'amtout': -2000.0} +assert dict(tildf.iloc[0]) == { + 'pair': '', + 'pairp': '', + 'tknin': 'ETH', + 'tknout': 'USDC', + 'ETH': 1.0, + 'USDC': -2000.0 +} + +tildf + +# ## margp_optimizer + +# ### no arbitrage possible + +CCa = CPCContainer() +CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") +CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") +CCa += CPC.from_pk(pair="USDC/USDT", p=1.0, k=200000*200000, cid="c2") +O = CPCArbOptimizer(CCa) + +r = O.margp_optimizer("WETH", result=O.MO_DEBUG) +assert isinstance(r, dict) +prices0 = r["price_estimates_t"] +assert not prices0 is None, f"prices0 must not be None [{prices0}]" +r1 = O.arb("WETH") +r2 = O.SelfFinancingConstraints.arb("WETH") +assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) +assert r1 == r2 +assert r["sfc"] == r1 +assert r1.is_arbsfc() +assert r1.optimizationvar == "WETH" + +r + +prices0 + +f = O.margp_optimizer("WETH", result=O.MO_DTKNFROMPF, params=dict(verbose=True, debug=False)) +r3 = f(prices0, islog10=False) +assert np.all(r3 == (0,0)) +r4, r3b = f(prices0, asdct=True, islog10=False) +assert np.all(r3==r3b) +assert len(r4) == len(r3)+1 +assert tuple(r4.values()) == (0,0,0) +assert set(r4) == {'USDC', 'USDT', 'WETH'} + +r = O.margp_optimizer("WETH", result=O.MO_MINIMAL, params=dict(verbose=True)) +rd = r.asdict +assert abs(float(r)) < 1e-10 +assert r.result == float(r) +assert r.method == "margp" +assert r.curves is None +assert r.targettkn == "WETH" +assert r.dtokens is None +assert sum(abs(x) for x in r.dtokens_t) < 1e-10 +assert r.p_optimal is None +assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) +assert set(r.tokens_t) == {'USDC', 'USDT'} +assert r.errormsg is None +assert r.is_error == False +assert r.time > 0 +assert r.time < 0.1 + +# + +r = O.margp_optimizer("WETH", result=O.MO_FULL) +rd = r.asdict() +r2 = O.margp_optimizer("WETH") +r2d = r2.asdict() +for k in rd: + #print(k) + if not k in ["time", "curves"]: + assert rd[k] == r2d[k] +assert r2.curves == r.curves # the TokenScale object fails in the dict + +assert abs(float(r)) < 1e-10 +assert r.result == float(r) +assert r.method == "margp" +assert len(r.curves) == 3 +assert r.targettkn == "WETH" +assert set(r.dtokens.keys()) == set(['USDT', 'WETH', 'USDC']) +assert sum(abs(x) for x in r.dtokens.values()) < 1e-10 +assert sum(abs(x) for x in r.dtokens_t) < 1e-10 +assert iseq(0.0005, r.p_optimal["USDC"], r.p_optimal["USDT"]) +assert iseq(0.0005, r.p_optimal_t[0], r.p_optimal_t[1]) +assert tuple(r.p_optimal.values()) == r.p_optimal_t +assert set(r.tokens_t) == set(('USDC', 'USDT')) +assert r.errormsg is None +assert r.is_error == False +assert r.time > 0 +assert r.time < 0.1 +# - + +# ### arbitrage + +CCa = CPCContainer() +CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") +CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") +CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=200000*200000, cid="c2") +O = CPCArbOptimizer(CCa) + +r = O.margp_optimizer("WETH", result=O.MO_DEBUG) +assert isinstance(r, dict) +prices0 = r["price_estimates_t"] +r1 = O.arb("WETH") +r2 = O.SelfFinancingConstraints.arb("WETH") +assert isinstance(r1, CPCArbOptimizer.SelfFinancingConstraints) +assert r1 == r2 +assert r["sfc"] == r1 +assert r1.is_arbsfc() +assert r1.optimizationvar == "WETH" + +f = O.margp_optimizer("WETH", result=O.MO_DTKNFROMPF) +r3 = f(prices0, islog10=False) +assert set(r3.astype(int)) == set((17425,-19089)) +r4, r3b = f(prices0, asdct=True, islog10=False) +assert np.all(r3==r3b) +assert len(r4) == len(r3)+1 +assert set(r4) == {'USDC', 'USDT', 'WETH'} + +r = O.margp_optimizer("WETH", result=O.MO_FULL) +assert iseq(float(r), -0.03944401129301944) +assert r.result == float(r) +assert r.method == "margp" +assert len(r.curves) == 3 +assert r.targettkn == "WETH" +assert abs(r.dtokens_t[0]) < 1e-6 +assert abs(r.dtokens_t[1]) < 1e-6 +assert r.dtokens["WETH"] == float(r) +assert tuple(r.p_optimal.values()) == r.p_optimal_t +assert tuple(r.p_optimal) == r.tokens_t +assert iseq(r.p_optimal_t[0], 0.0005421803152482512) or iseq(r.p_optimal_t[0], 0.00045575394031021585) +assert iseq(r.p_optimal_t[1], 0.0005421803152482512) or iseq(r.p_optimal_t[1], 0.00045575394031021585) +assert tuple(r.p_optimal.values()) == r.p_optimal_t +assert set(r.tokens_t) == set(('USDC', 'USDT')) +assert r.errormsg is None +assert r.is_error == False +assert r.time > 0 +assert r.time < 0.1 + +abs(r.dtokens_t[0]) + + + +# ## simple_optimizer demo [NOTEST] + +CC = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+i*10000), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) +O = CPCArbOptimizer(CC) +c0 = CC.curves[0] +CC0 = CPCContainer([c0]) +O = CPCArbOptimizer(CC) +O0 = CPCArbOptimizer(CC0) +funcvx = O.simple_optimizer(result=O.SO_DXDYVALXFUNC) +funcvy = O.simple_optimizer(result=O.SO_DXDYVALYFUNC) +funcvx0 = O0.simple_optimizer(result=O.SO_DXDYVALXFUNC) +funcvy0 = O0.simple_optimizer(result=O.SO_DXDYVALYFUNC) +#CC.plot() + +xr = np.linspace(1500, 3000, 50) +plt.plot(xr, [funcvx(x)/len(CC) for x in xr], label="all curves [scaled]") +plt.plot(xr, [funcvx0(x) for x in xr], label="curve 0 only") +plt.xlabel(f"price [{c0.pairp}]") +plt.ylabel(f"value [{c0.tknxp}]") +plt.grid() +plt.show() +plt.plot(xr, [funcvy(x)/len(CC) for x in xr], label="all curves [scaled]") +plt.plot(xr, [funcvy0(x) for x in xr], label="curve 0 only") +plt.xlabel(f"price [{c0.pairp}]") +plt.ylabel(f"value [{c0.tknyp}]") +plt.grid() +plt.show() + +r = O.simple_optimizer() +print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") + +CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) +CC.plot() +CC_ex.plot() + +# ## MargP Optimizer Demo [NOTEST] + +CCa = CPCContainer() +CCa += CPC.from_pk(pair="WETH/USDC", p=2000, k=10*20000, cid="c0") +CCa += CPC.from_pk(pair="WETH/USDT", p=2000, k=10*20000, cid="c1") +CCa += CPC.from_pk(pair="USDC/USDT", p=1.2, k=20000*20000, cid="c2") +O = CPCArbOptimizer(CCa) + +CCa.plot() + +r = O.margp_optimizer("WETH", params=dict(verbose=True)) +rd = r.asdict +r + +rd + +CCa1 = O.adjust_curves(r.dxvalues) +CCa1.plot() + +# ## Optimizer plus inverted curves [NOTEST] + +CCr = CPCContainer(CPC.from_pk(p=2000+i*100, k=10*(20000+10000*i), pair=f"{T.ETH}/{T.USDC}") for i in range(11)) +CCi = CPCContainer(CPC.from_pk(p=1/(2050+i*100), k=10*(20000+10000*i), pair=f"{T.USDC}/{T.ETH}") for i in range(11)) +CC = CCr.bycids() +assert len(CC) == len(CCr) +CC += CCi +assert len(CC) == len(CCr) + len(CCi) +CC.plot() + +O = CPCArbOptimizer(CC) +r = O.simple_optimizer() +print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") +CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) +prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] +print("prices post arb:", prices_ex) +print("stdev", np.std(prices_ex)) +#CC.plot() +CC_ex.plot() + +# ## Operating on leverage ranges [NOTEST] + +N = 10 + +# + +CCc, CCm, ctr = CPCContainer(), CPCContainer(), 0 +U, U1 = CPCContainer.u, CPCContainer.u1 +tknb, tknq = T.ETH, T.USDC +pb, pq = 2000, 1 +pair = f"{tknb}/{tknq}" +pp = pb/pq +k = 100000**2/(pb*pq) +CCm += CPC.from_pk(p=pp, k=k, pair=pair, cid = f"mkt-{pair}", params=dict(xc="market")) +#print("\n***PAIR:", tknb, pb, tknq, pq, pair, pp) +for i in range(N): + p = pp * (1+0.2*U(-0.5, 0.5)) + p_min, p_max = (p, U(1.001, 1.5)*p) if U1()>0.5 else (U(0.8, 0.999)*p, p) + amtusdc = U(10000, 200000) + k = amtusdc**2/(pb*pq) + #print("*curve", int(amtusdc), p, p_min, p_max, int(k)) + CCc += CPC.from_pkpp(p=p, k=k, p_min=p_min, p_max=p_max, + pair=pair, cid = f"carb-{ctr}", params=dict(xc="carbon")) + ctr += 1 + +CC = CCc.bycids().add(CCm) +CC.plot() +# - + +O = CPCArbOptimizer(CC) +r = O.simple_optimizer() +print(f"Arbitrage gains: {-r.valx:.4f} {r.tknxp} [time={r.time:.4f}s]") +CC_ex = CPCContainer(c.execute(dx=dx) for c, dx in zip(r.curves, r.dxvalues)) +prices_ex = [c.pairo.primary_price(c.p) for c in CC_ex] +print("prices post arb:", prices_ex) +print("stdev", np.std(prices_ex)) +#CC.plot() +CC_ex.plot() + +r.dxvalues + +# ## Arbitrage testing [NOTEST] + +c1 = CPC.from_pkpp(p=95, k=100*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") +c2 = CPC.from_pkpp(p=105, k=90*10000, p_min=90, p_max=110, pair=f"{T.ETH}/{T.USDC}") +CC = CPCContainer([c1,c2]) +CC.plot() + +a = lambda x: np.array(x) +pr = np.linspace(70,130,200) +dx1, dy1, p = zip(*(c1.dxdyfromp_f(p) for p in pr)) +assert np.all(p == pr) +dx2, dy2, p = zip(*(c2.dxdyfromp_f(p) for p in pr)) +assert np.all(p == pr) +v1 = a(dy1)+a(p)*a(dx1) +v2 = a(dy2)+a(p)*a(dx2) +plt.plot(p, v1, label="Value curve c1") +plt.plot(p, v2, label="Value curve c2") +plt.plot(p, v1+v2, label="Value combined curves") +plt.legend() +plt.grid() + + +def vfunc(p): + + dx1, dy1, _ = c1.dxdyfromp_f(p) + dx2, dy2, _ = c2.dxdyfromp_f(p) + v1 = dy1 + p*dx1 + v2 = dy2 + p*dx2 + v = v1+v2 + #print(f"[v] v({p}) = {v}") + return -v + + +O = CPCArbOptimizer +O.findmin(vfunc, 100, N=100) + +func1 = lambda x: (x-2)**2 +O.findmin(func1, 1) + +func2 = lambda x: 1-(x-3)**2 +O.findmax(func2, 2.5) + +val = tuple(float(O.findmin(func1, 100, N=n)) for n in range(100)) +val = tuple(abs(v-val[-1]) for v in val) +val = tuple(v for v in val if v > 0) +plt.plot(val) +plt.yscale('log') +plt.grid() + +val = tuple(float(O.findmin(func2, 100, N=n)) for n in range(100)) +val = tuple(abs(v-val[-1]) for v in val) +val = tuple(v for v in val if v > 0) +plt.plot(val) +plt.yscale('log') +plt.grid() + +val0 = tuple(float(O.findmin(vfunc, 99, N=n)) for n in range(100)) +val = tuple(abs(v-val0[-1]) for v in val0) +val = tuple(v for v in val if v > 0) +print(val0[-1]) +plt.plot(val) +plt.yscale('log') +plt.grid() + +val0 = tuple(float(O.findmin_gd(vfunc, 99, N=n)) for n in range(100)) +val = tuple(abs(v-val0[-1]) for v in val0) +val = tuple(v for v in val if v > 0) +print(val0[-1]) +plt.plot(val) +plt.yscale('log') +plt.grid() + +O.findmin(vfunc, 99, N=700) + # ## Charts [NOTEST] # ### Chars (x,y) @@ -283,3 +1273,6 @@ # - + + + diff --git a/resources/NBTest/NBTest_064_Serialization.ipynb b/resources/NBTest/NBTest_064_Serialization.ipynb new file mode 100644 index 00000000..fdc9cd0c --- /dev/null +++ b/resources/NBTest/NBTest_064_Serialization.ipynb @@ -0,0 +1,1042 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c19d0663-ac37-4095-b6a3-18afcee2493c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[stdimports] imported np, pd, plt, os, sqrt, exp, log\n", + "CPCContainer v2.5 (15/Apr/2023)\n", + "CPCArbOptimizer v3.2 (16/Apr/2023)\n", + "Carbon v2.4.2-BETA2 (09/Apr/2023)\n" + ] + } + ], + "source": [ + "from carbon.helpers.stdimports import *\n", + "from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer\n", + "from carbon.tools.optimizer import CPCArbOptimizer, cp, time\n", + "\n", + "import json\n", + "import time\n", + "import pandas as pd\n", + "import numpy as np\n", + "from math import sqrt\n", + "from matplotlib import pyplot as plt\n", + "plt.style.use('seaborn-dark')\n", + "plt.rcParams['figure.figsize'] = [12,6]\n", + "\n", + "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCContainer))\n", + "print(\"{0.__name__} v{0.__VERSION__} ({0.__DATE__})\".format(CPCArbOptimizer))\n", + "print_version(require=\"2.4.2\")" + ] + }, + { + "cell_type": "markdown", + "id": "feaede6f-89cb-48d2-b929-cd523e56b1bb", + "metadata": {}, + "source": [ + "# Serialization [NBTest030]" + ] + }, + { + "cell_type": "markdown", + "id": "b1e8566e-2b6d-4564-8c3d-534d968f3bf1", + "metadata": {}, + "source": [ + "## Optimizer pickling [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8cb4f9bc-2f31-4eae-b77f-533aa188e49b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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kxx_acty_actpairfeedescrconstrparams
cid
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22400112400.0ETH/USDCNoneNonexy{}
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22400112400.0ETH/USDCNoneNonexy{}
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02000112000.0ETH/USDCNoneNonexy{}
12200112200.0ETH/USDCNoneNonexy{}
22400112400.0ETH/USDCNoneNonexy{}
\n", + "
" + ], + "text/plain": [ + " k x x_act y_act pair fee descr constr params\n", + "cid \n", + "0 2000 1 1 2000.0 ETH/USDC None None xy {}\n", + "1 2200 1 1 2200.0 ETH/USDC None None xy {}\n", + "2 2400 1 1 2400.0 ETH/USDC None None xy {}\n", + "0 2000 1 1 2000.0 ETH/USDC None None xy {}\n", + "1 2200 1 1 2200.0 ETH/USDC None None xy {}\n", + "2 2400 1 1 2400.0 ETH/USDC None None xy {}\n", + "0 2000 1 1 2000.0 ETH/USDC None None xy {}\n", + "1 2200 1 1 2200.0 ETH/USDC None None xy {}\n", + "2 2400 1 1 2400.0 ETH/USDC None None xy {}\n", + "0 2000 1 1 2000.0 ETH/USDC None None xy {}\n", + "1 2200 1 1 2200.0 ETH/USDC None None xy {}\n", + "2 2400 1 1 2400.0 ETH/USDC None None xy {}\n", + "0 2000 1 1 2000.0 ETH/USDC None None xy {}\n", + "1 2200 1 1 2200.0 ETH/USDC None None xy {}\n", + "2 2400 1 1 2400.0 ETH/USDC None None xy {}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "N=5\n", + "curves = [\n", + " CPC.from_xy(x=1, y=2000, pair=\"ETH/USDC\"),\n", + " CPC.from_xy(x=1, y=2200, pair=\"ETH/USDC\"),\n", + " CPC.from_xy(x=1, y=2400, pair=\"ETH/USDC\"),\n", + "]\n", + "# note: the below is a bit icky as the same curve objects are added multiple times\n", + "CC = CPCContainer(curves*N)\n", + "O = CPCArbOptimizer(CC)\n", + "O.CC.asdf()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "a5ed0075-5ee5-4592-a192-e06d2b5af454", + "metadata": {}, + "outputs": [], + "source": [ + "O.pickle(\"delme\")\n", + "O.pickle(\"delme\", addts=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1bf13d91-2bc0-4819-96b9-2712ef89b6f1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "delme.1681640767.025454.pickle delme.168164096776.optimizer.pickle\n", + "delme.168164080590.pickle delme.optimizer.pickle\n", + "delme.168164091197.optimizer.pickle delme.pickle.1681640749.019922.pickle\n", + "delme.168164094930.optimizer.pickle\n" + ] + } + ], + "source": [ + "!ls *.pickle" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ce05c578-5060-498e-b4eb-f55617d10cdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "O.unpickle(\"delme\")" + ] + }, + { + "cell_type": "markdown", + "id": "cf1c3ec2-0956-4698-8c0c-5781edfe457f", + "metadata": {}, + "source": [ + "## Creating curves\n", + "\n", + "Note: for those constructor, the parameters `cid` and `descr` as well as `fee` are mandatory. Typically `cid` would be a field uniquely identifying this curve in the database, and `descr` description of the pool. The description should neither include the pair nor the fee level. We recommend using `UniV3`, `UniV3`, `Sushi`, `Carbon` etc. The `fee` is quoted as decimal, ie 0.01 is 1%. If there is no fee, the number `0` must be provided, not `None`." + ] + }, + { + "cell_type": "markdown", + "id": "8d326169-f9e2-4bba-9572-9b83989812b7", + "metadata": {}, + "source": [ + "### Uniswap v2\n", + "\n", + "In the Uniswap v2 constructor, $x$ is the base token of the pair `TKNB`, and $y$ is the quote token `TKNQ`.\n", + "\n", + "By construction, Uniswap v2 curves map directly to CPC curves with the following parameter choices\n", + "\n", + "- $x,y,k$ are the same as in the $ky=k$ formula defining the AMM (provide any 2)\n", + "- $x_a = x$ and $y_a = y$ because there is no leverage on the curves.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41a5cdfe-fb7b-4c8b-a270-1a52f0765e94", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_univ2(x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", + "c2 = CPC.from_univ2(x_tknb=100, k=10000, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", + "c3 = CPC.from_univ2(y_tknq=100, k=10000, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV2\")\n", + "assert c.k == 10000\n", + "assert c.x == 100\n", + "assert c.y == 100\n", + "assert c.x_act == 100\n", + "assert c.y_act == 100\n", + "assert c == c2\n", + "assert c == c3\n", + "assert c.fee == 0\n", + "assert c.cid == \"1\"\n", + "assert c.descr == \"UniV2\"\n", + "c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ea3cdfbc-8edd-41f1-9703-0ae0d72fdb9a", + "metadata": {}, + "outputs": [], + "source": [ + "c.asdict()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "595de023-5c66-40fc-928f-eca5fe6a50c9", + "metadata": {}, + "outputs": [], + "source": [ + "assert c.asdict() == {\n", + " 'k': 10000,\n", + " 'x': 100,\n", + " 'x_act': 100,\n", + " 'y_act': 100,\n", + " 'pair': 'TKNB/TKNQ',\n", + " 'cid': \"1\",\n", + " 'fee': 0,\n", + " 'descr': 'UniV2',\n", + " 'constr': 'uv2',\n", + " 'params': {}\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "215b5105-08d9-4077-a51a-7658cafcffa9", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, k=10, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, k=10, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, fee=0, cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", cid=1, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, descr=\"UniV2\")\n", + "assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair=\"TKNB/TKNQ\", fee=0, cid=1)" + ] + }, + { + "cell_type": "markdown", + "id": "23a41a55-a500-4d74-9998-f0f20fedeaa0", + "metadata": {}, + "source": [ + "### Uniswap v3\n", + "\n", + "Uniswap V3 uses an implicit virtual token model. The most important relationship here is that $L^2=k$, ie the square of the Uniswap pool constant is the constant product parameter $k$. Alternatively we find that $L=\\bar k$ if we use the alternative pool invariant $\\sqrt{xy}=\\bar k$ for the constant product pool. The conventions are as in the Uniswap v2 case, ie $x$ is the base token `TKNB` and $y$ is the quote token `TKNQ`. The parameters are\n", + "\n", + "- $L$ is the so-called _liquidity_ parameter, indicating the size of the pool at this particular tick (see above)\n", + "- $P_a, P_b$ are the lower and upper end of the _current_ tick range*\n", + "- $P_{marg}$ is the current (marginal) price of the range; we have $P_a \\leq P_{marg} \\leq P_b$\n", + "\n", + "*note that for Uniswap v3 curves we _only_ usually model the current tick range as crossing a tick boundary is relatively expensive and most arb bots do not do that; in principle however nothing prevents us from also adding inactive tick ranges, in which case every tick range corresponds to a single, out of the money curve." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0963034a-b36c-4cfb-84da-ccb3c88c4389", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_univ3(Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=\"1\", descr=\"UniV3\")\n", + "assert c.x == 1000\n", + "assert c.y == 1000\n", + "assert c.k == 1000*1000\n", + "assert iseq(c.p_max, 1.1)\n", + "assert iseq(c.p_min, 0.9)\n", + "assert c.fee == 0\n", + "assert c.cid == \"1\"\n", + "assert c.descr == \"UniV3\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb5dd380-dd90-4a3b-b88a-5a697bdbc3a0", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", + "assert raises(CPC.from_univ3, Pmarg=2, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", + "assert raises(CPC.from_univ3, Pmarg=0.5, uniL=1000, uniPa=0.9, uniPb=1.1, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")\n", + "assert raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=1.1, uniPb=0.9, pair=\"TKNB/TKNQ\", fee=0, cid=1, descr=\"UniV3\")" + ] + }, + { + "cell_type": "markdown", + "id": "172acba9-47e6-45db-9cf8-03cb8bfa0b9d", + "metadata": {}, + "source": [ + "### Carbon\n", + "\n", + "First a bried reminder that the Carbon curves here correspond to Carbon Orders, ie half a Carbon strategy. Those order trade unidirectional only, and as we here are only looking at a single trade we do not care about collateral moving from an order to another one. We provide slightly more flexibility here in terms of tokens and quotes: $y$ corresponds to `tkny` which must be part of `pair` but which can be quote or base token.\n", + "\n", + "- $y, y_{int}$ are the current amounts of token y and the y-intercept respectively, in units of `tkny`\n", + "\n", + "- $P_a, P_b$ are the prices determining the range, either quoted as $dy/dx$ is `isdydx` is True (default), or in the natural direction of the pair*\n", + "\n", + "- $A, B$ are alternative price parameters, with $B=\\sqrt{P_b}$ and $A=\\sqrt{P_a}-\\sqrt{P_b}\\geq 0$; those must _always_ be quoted in $dy/dx$*\n", + "\n", + "*The ranges must _either_ be specificed with `pa, pb, isdydx` or with `A, B` and in the second case `isdydx` must be True. There is no mix and match between those two parameter sets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "624b80f1-c811-483b-ba24-b76c72fe3e0c", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert c.y_act == 1\n", + "assert c.x_act == 0\n", + "assert iseq(1/c.p_min, 2200)\n", + "assert iseq(1/c.p_max, 1800)\n", + "assert iseq(1/c.p, 1/c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "34d52402-18d6-4485-8e5c-6cb4f8af2ab2", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_carbon(yint=1, y=1, A=1/256, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert c.y_act == 1\n", + "assert c.x_act == 0\n", + "assert iseq(1/c.p_min, 2000)\n", + "print(\"pa\", 1/c.p_max, 1/(1/256+sqrt(c.p_min))**2)\n", + "assert iseq(1/c.p_max, 1/(1/256+sqrt(c.p_min))**2)\n", + "assert iseq(1/c.p, 1/c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "85175836-0fa9-4f64-a42f-b5b787e622f0", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_carbon(yint=3000, y=3000, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert c.y_act == 3000\n", + "assert c.x_act == 0\n", + "assert iseq(c.p_min, 2900)\n", + "assert iseq(c.p_max, 3100)\n", + "assert iseq(c.p, c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9753798a-b154-4865-a845-a1f5f1eb8e4b", + "metadata": {}, + "outputs": [], + "source": [ + "c = CPC.from_carbon(yint=2000, y=2000, A=10, B=sqrt(3000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert c.y_act == 2000\n", + "assert c.x_act == 0\n", + "assert iseq(c.p_min, 3000)\n", + "print(\"pa\", c.p_max, (10+sqrt(c.p_min))**2)\n", + "assert iseq(c.p_max, (10+sqrt(c.p_min))**2)\n", + "assert iseq(1/c.p, 1/c.p_max)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5f683913-1799-4f3a-9473-a663d803448a", + "metadata": {}, + "outputs": [], + "source": [ + "CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "CPC.from_carbon(yint=1, y=1, A=1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "CPC.from_carbon(yint=1, y=1, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"3\", descr=\"Carbon\", isdydx=True)\n", + "CPC.from_carbon(yint=1, y=1, A=10, B=sqrt(3000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"4\", descr=\"Carbon\", isdydx=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cffdcaa4-f221-4bd7-bf2d-5418a33e3592", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, descr=\"Carbon\", isdydx=False)\n", + "#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"LINK\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, B=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, B=100, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pb=1800, pa=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"1\", descr=\"Carbon\", isdydx=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f66fc490-97e0-4c5e-958d-1e9014934d5c", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=False)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pa=1000, A=1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pb=1000, A=1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, A=-1/10, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "465ff937-2382-4215-8e11-ec8096e1ea3d", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises(CPC.from_carbon, yint=1, y=1, pa=3100, pb=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)\n", + "assert raises(CPC.from_carbon, yint=1, y=1, pb=3100, pa=2900, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"2\", descr=\"Carbon\", isdydx=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b933b5ac-090d-452b-9b11-6ae1a3595356", + "metadata": {}, + "source": [ + "## Charts [NOTEST]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5c8d6c3-0d15-4c3d-8852-b2870a7b4caa", + "metadata": {}, + "outputs": [], + "source": [ + "curves_uni =[\n", + " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=\"U2/1\", descr=\"UniV2\"),\n", + " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=\"U2/2\", descr=\"UniV2\"),\n", + " CPC.from_univ3(Pmarg=2000, uniL=100, uniPa=1800, uniPb=2200, pair=\"ETH/USDC\", fee=0, cid=\"U3/1\", descr=\"UniV3\"),\n", + " CPC.from_univ3(Pmarg=2010, uniL=75, uniPa=1800, uniPb=2200, pair=\"ETH/USDC\", fee=0, cid=\"U3/1\", descr=\"UniV3\"),\n", + "]\n", + "CC = CPCContainer(curves_uni)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8296d087-d5a5-4b77-825a-dd53ed60d4bd", + "metadata": {}, + "outputs": [], + "source": [ + "curves_carbon = [\n", + " CPC.from_carbon(yint=3000, y=3000, pa=3500, pb=2500, pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C1\", descr=\"Carbon\", isdydx=True),\n", + " CPC.from_carbon(yint=3000, y=3000, A=20, B=sqrt(2500), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C2\", descr=\"Carbon\", isdydx=True),\n", + " CPC.from_carbon(yint=3000, y=3000, A=40, B=sqrt(2500), pair=\"ETH/USDC\", tkny=\"USDC\", fee=0, cid=\"C3\", descr=\"Carbon\", isdydx=True),\n", + " CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C4\", descr=\"Carbon\", isdydx=False),\n", + " CPC.from_carbon(yint=1, y=1, pa=1/1800, pb=1/2000, pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C5\", descr=\"Carbon\", isdydx=True),\n", + " CPC.from_carbon(yint=1, y=1, A=1/500, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C6\", descr=\"Carbon\", isdydx=True),\n", + " CPC.from_carbon(yint=1, y=1, A=1/1000, B=sqrt(1/2000), pair=\"ETH/USDC\", tkny=\"ETH\", fee=0, cid=\"C7\", descr=\"Carbon\", isdydx=True),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e72d0162-dd59-489c-8efb-dbb8327ff553", + "metadata": {}, + "outputs": [], + "source": [ + "curves = curves_uni + curves_carbon\n", + "CC = CPCContainer(curves)\n", + "CC.plot(params=CC.Params())" + ] + }, + { + "cell_type": "markdown", + "id": "48de3a65-a36c-4ea0-aaf3-fc2d3cf415d1", + "metadata": {}, + "source": [ + "## Serializing curves\n", + "\n", + "The `CPCContainer` and `ConstantProductCurve` objects do not strictly have methods that would allow for serialization. However, they allow conversion from an to datatypes that are easily serialized. \n", + "\n", + "- on the `ConstantProductCurve` level there is `asdict()` and `from_dicts(.)`\n", + "- on the `CPCContainer` level there is also `asdf()` and `from_df(.)`, allowing conversion from and to pandas dataframes\n", + "\n", + "Recommended serialization is either dict to json via the `json` library, or any of the serialization methods inherent in dataframes, notably also pickling (Excel formates are not recommended as they are slow and heavy).\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2d5dc97-05e8-4eca-abc7-66eee6e7d706", + "metadata": {}, + "outputs": [], + "source": [ + "curves = [\n", + " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=\"1\", descr=\"UniV2\", params={\"meh\":1}),\n", + " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=\"2\", descr=\"UniV2\"),\n", + " CPC.from_univ2(x_tknb=1, y_tknq=1970, pair=\"ETH/USDC\", fee=0.001, cid=\"3\", descr=\"UniV2\"),\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f467a32-370b-4634-bec8-3c28be84a0a0", + "metadata": {}, + "outputs": [], + "source": [ + "c0 = curves[0]\n", + "assert c0.params.__class__.__name__ == \"AttrDict\"\n", + "assert c0.params == {'meh': 1}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7563934-5381-476d-b9cb-99b909691049", + "metadata": {}, + "outputs": [], + "source": [ + "CC = CPCContainer(curves)\n", + "assert raises(CPCContainer, [1,2,3])\n", + "assert len(CC.curves) == len(curves)\n", + "assert len(CC.asdicts()) == len(CC.curves)\n", + "assert CPCContainer.from_dicts(CC.asdicts()) == CC\n", + "ccjson = json.dumps(CC.asdicts())\n", + "assert CPCContainer.from_dicts(json.loads(ccjson)) == CC\n", + "CC" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "131928b8-f927-4799-97c6-ec50631c7959", + "metadata": {}, + "outputs": [], + "source": [ + "df = CC.asdf()\n", + "assert len(df) == 3\n", + "assert tuple(df.reset_index().columns) == ('cid', 'k', 'x', 'x_act', 'y_act', \n", + " 'pair', 'fee', 'descr', 'constr', 'params')\n", + "assert tuple(df[\"k\"]) == (2000, 8040, 1970)\n", + "assert CPCContainer.from_df(df) == CC\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "b36575fb-cd50-4415-a885-7c2b5ac689ba", + "metadata": {}, + "source": [ + "## Saving curves [NOTEST]\n", + "\n", + "Most serialization methods we use go via the a pandas DataFram object. To create a dataframe we use the `asdf()` method, and to instantiate curve container from a dataframe we use `CPCContainer.from_df(df)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6cd062ae-c465-4102-a57c-587874023de5", + "metadata": {}, + "outputs": [], + "source": [ + "N=5000\n", + "curves = [\n", + " CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0.001, cid=1, descr=\"UniV2\"),\n", + " CPC.from_univ2(x_tknb=2, y_tknq=4020, pair=\"ETH/USDC\", fee=0.001, cid=2, descr=\"UniV2\"),\n", + " CPC.from_univ2(x_tknb=1, y_tknq=1970, pair=\"ETH/USDC\", fee=0.001, cid=3, descr=\"UniV2\"),\n", + "]\n", + "CC = CPCContainer(curves*N)\n", + "df = CC.asdf()\n", + "#CC" + ] + }, + { + "cell_type": "markdown", + "id": "a4908c7d-d363-4fe5-978a-a038ea3416fd", + "metadata": {}, + "source": [ + "### Formats\n", + "#### json\n", + "\n", + "Using `json.dumps(.)` the list of dicts returned by `asdicts()` can be converted to json, and then saved as a textfile. When loaded back, the text can be expanded into json using `json.loads(.)` and the new object can be instantiated using `CPCContainer.from_dicts(dicts)`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c046e70-ef8a-4de8-bd17-726afb617ea1", + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "cc_json = json.dumps(CC.asdicts())\n", + "print(\"len\", len(cc_json))\n", + "CC2 = CPCContainer.from_dicts(json.loads(cc_json))\n", + "assert CC == CC2\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", + "#CC2" + ] + }, + { + "cell_type": "markdown", + "id": "dc67cf95-3872-4292-b13b-d742c4d55b66", + "metadata": {}, + "source": [ + "#### csv\n", + "\n", + "`to_csv` converts a dataframe to a csv file; this file can also be zipped; this format is ideal for maximum interoperability as pretty much every software allows dealing with csvs; it is very fast, and the zipped files are much smaller than everything else" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e892dc06-329d-477f-adcb-40a87eb7a009", + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "df.to_csv(\".curves.csv\")\n", + "df_csv = pd.read_csv(\".curves.csv\")\n", + "assert CPCContainer.from_df(df_csv) == CC\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", + "df_csv[:3]" + ] + }, + { + "cell_type": "markdown", + "id": "41370f26-e16e-4f67-a801-f8d62f9b9e04", + "metadata": {}, + "source": [ + "#### tsv\n", + "\n", + "`to_csv` can be used with `sep=\"\\t\"` to create a tab separated file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2976017-2a84-4fba-885d-7680d9f61c3a", + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "df.to_csv(\".curves.tsv\", sep=\"\\t\")\n", + "df_tsv = pd.read_csv(\".curves.tsv\", sep=\"\\t\")\n", + "assert CPCContainer.from_df(df_tsv) == CC\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")" + ] + }, + { + "cell_type": "markdown", + "id": "ef6b415f-9e97-477e-8488-7a1348094730", + "metadata": {}, + "source": [ + "#### compressed csv\n", + "\n", + "`to_csv` can be used with `compression = \"gzip\"` to create a compressed file. This is by far the smallest output available, and takes little more time compared to uncompressed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ed5aaa2c-2f5a-4863-87cf-a77240826a85", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "df.to_csv(\".curves.csv.gz\", compression = \"gzip\")\n", + "df_csv = pd.read_csv(\".curves.csv.gz\")\n", + "assert CPCContainer.from_df(df_csv) == CC\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")" + ] + }, + { + "cell_type": "markdown", + "id": "c0eca8e2-8017-4989-88c2-beafe97d7c3a", + "metadata": {}, + "source": [ + "#### Excel\n", + "\n", + "`to_excel` converts the dataframe to an xlsx file; older versions of pandas may allow to also save in the old xls format, but this is deprecated; note that Excel files can be rather big, and saving them is very slow, 10-15x(!) longer than csv." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f1507cc7-96ba-4342-bf1e-955b248bd8b4", + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "df.to_excel(\".curves.xlsx\")\n", + "df_xlsx = pd.read_excel(\".curves.xlsx\")\n", + "assert CPCContainer.from_df(df_xlsx) == CC\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", + "df_xlsx[:3]" + ] + }, + { + "cell_type": "markdown", + "id": "705f0e47-d154-4dba-9d26-c4c809f55788", + "metadata": {}, + "source": [ + "#### pickle\n", + "\n", + "`to_pickle` pickles the dataframe; this format is rather big, but it is the fastest to process, albeit not at a significant margin" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1c75dfe-ce14-4840-9c62-39a8d5cfc3ad", + "metadata": {}, + "outputs": [], + "source": [ + "start_time = time.time()\n", + "df.to_pickle(\".curves.pkl\")\n", + "df_pickle = pd.read_pickle(\".curves.pkl\")\n", + "assert CPCContainer.from_df(df_pickle) == CC\n", + "print(f\"elapsed time: {time.time()-start_time:.2f}s\")\n", + "df_pickle[:3]" + ] + }, + { + "cell_type": "markdown", + "id": "3cfc2ff5-bf9d-4684-9b8c-2aff57937a46", + "metadata": {}, + "source": [ + "### Benchmarking\n", + "\n", + "below a comparison of the different methods in terms of size and speed; the benchmark run used **300,000 curves**\n", + "\n", + " 33000000 .curves.json -- 5.2s (without read/write)\n", + " 11100035 .curves.csv -- 3.4s\n", + " 37817 .curves.csv.gz -- 3.4s\n", + " 15602482 .curves.pkl -- 2.6s\n", + " 11100035 .curves.tsv -- 3.2s\n", + " 8031279 .curves.xlsx -- 45.0s (!)\n", + " \n", + "Below are the figures for the current run (timing figures inline above)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c43b9431-603d-49af-b5fd-1975e9f59e2f", + "metadata": {}, + "outputs": [], + "source": [ + "print(f\"{len(df_xlsx)} curves\")\n", + "print(f\" {len(cc_json)} .curves.json\", )\n", + "!ls -l .curves*" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3fc27e4d-6d5e-4da5-8ab6-e073b6d5ace3", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:light" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/resources/NBTest/NBTest_064_Serialization.py b/resources/NBTest/NBTest_064_Serialization.py new file mode 100644 index 00000000..7c5e3654 --- /dev/null +++ b/resources/NBTest/NBTest_064_Serialization.py @@ -0,0 +1,370 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:light +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.13.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# + +from carbon.helpers.stdimports import * +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer +from carbon.tools.optimizer import CPCArbOptimizer, cp, time + +import json +import time +import pandas as pd +import numpy as np +from math import sqrt +from matplotlib import pyplot as plt +plt.style.use('seaborn-dark') +plt.rcParams['figure.figsize'] = [12,6] + +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCContainer)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPCArbOptimizer)) +print_version(require="2.4.2") +# - + +# # Serialization [NBTest030] + +# ## Optimizer pickling [NOTEST] + +N=5 +curves = [ + CPC.from_xy(x=1, y=2000, pair="ETH/USDC"), + CPC.from_xy(x=1, y=2200, pair="ETH/USDC"), + CPC.from_xy(x=1, y=2400, pair="ETH/USDC"), +] +# note: the below is a bit icky as the same curve objects are added multiple times +CC = CPCContainer(curves*N) +O = CPCArbOptimizer(CC) +O.CC.asdf() + +O.pickle("delme") +O.pickle("delme", addts=False) + +# !ls *.pickle + +O.unpickle("delme") + +# ## Creating curves +# +# Note: for those constructor, the parameters `cid` and `descr` as well as `fee` are mandatory. Typically `cid` would be a field uniquely identifying this curve in the database, and `descr` description of the pool. The description should neither include the pair nor the fee level. We recommend using `UniV3`, `UniV3`, `Sushi`, `Carbon` etc. The `fee` is quoted as decimal, ie 0.01 is 1%. If there is no fee, the number `0` must be provided, not `None`. + +# ### Uniswap v2 +# +# In the Uniswap v2 constructor, $x$ is the base token of the pair `TKNB`, and $y$ is the quote token `TKNQ`. +# +# By construction, Uniswap v2 curves map directly to CPC curves with the following parameter choices +# +# - $x,y,k$ are the same as in the $ky=k$ formula defining the AMM (provide any 2) +# - $x_a = x$ and $y_a = y$ because there is no leverage on the curves. +# + +c = CPC.from_univ2(x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") +c2 = CPC.from_univ2(x_tknb=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") +c3 = CPC.from_univ2(y_tknq=100, k=10000, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV2") +assert c.k == 10000 +assert c.x == 100 +assert c.y == 100 +assert c.x_act == 100 +assert c.y_act == 100 +assert c == c2 +assert c == c3 +assert c.fee == 0 +assert c.cid == "1" +assert c.descr == "UniV2" +c + +c.asdict() + +assert c.asdict() == { + 'k': 10000, + 'x': 100, + 'x_act': 100, + 'y_act': 100, + 'pair': 'TKNB/TKNQ', + 'cid': "1", + 'fee': 0, + 'descr': 'UniV2', + 'constr': 'uv2', + 'params': {} +} + +assert not raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, k=10, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, fee=0, cid=1, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", cid=1, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, descr="UniV2") +assert raises(CPC.from_univ2, x_tknb=100, y_tknq=100, pair="TKNB/TKNQ", fee=0, cid=1) + +# ### Uniswap v3 +# +# Uniswap V3 uses an implicit virtual token model. The most important relationship here is that $L^2=k$, ie the square of the Uniswap pool constant is the constant product parameter $k$. Alternatively we find that $L=\bar k$ if we use the alternative pool invariant $\sqrt{xy}=\bar k$ for the constant product pool. The conventions are as in the Uniswap v2 case, ie $x$ is the base token `TKNB` and $y$ is the quote token `TKNQ`. The parameters are +# +# - $L$ is the so-called _liquidity_ parameter, indicating the size of the pool at this particular tick (see above) +# - $P_a, P_b$ are the lower and upper end of the _current_ tick range* +# - $P_{marg}$ is the current (marginal) price of the range; we have $P_a \leq P_{marg} \leq P_b$ +# +# *note that for Uniswap v3 curves we _only_ usually model the current tick range as crossing a tick boundary is relatively expensive and most arb bots do not do that; in principle however nothing prevents us from also adding inactive tick ranges, in which case every tick range corresponds to a single, out of the money curve. + +c = CPC.from_univ3(Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid="1", descr="UniV3") +assert c.x == 1000 +assert c.y == 1000 +assert c.k == 1000*1000 +assert iseq(c.p_max, 1.1) +assert iseq(c.p_min, 0.9) +assert c.fee == 0 +assert c.cid == "1" +assert c.descr == "UniV3" + +assert not raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") +assert raises(CPC.from_univ3, Pmarg=2, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") +assert raises(CPC.from_univ3, Pmarg=0.5, uniL=1000, uniPa=0.9, uniPb=1.1, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") +assert raises(CPC.from_univ3, Pmarg=1, uniL=1000, uniPa=1.1, uniPb=0.9, pair="TKNB/TKNQ", fee=0, cid=1, descr="UniV3") + +# ### Carbon +# +# First a bried reminder that the Carbon curves here correspond to Carbon Orders, ie half a Carbon strategy. Those order trade unidirectional only, and as we here are only looking at a single trade we do not care about collateral moving from an order to another one. We provide slightly more flexibility here in terms of tokens and quotes: $y$ corresponds to `tkny` which must be part of `pair` but which can be quote or base token. +# +# - $y, y_{int}$ are the current amounts of token y and the y-intercept respectively, in units of `tkny` +# +# - $P_a, P_b$ are the prices determining the range, either quoted as $dy/dx$ is `isdydx` is True (default), or in the natural direction of the pair* +# +# - $A, B$ are alternative price parameters, with $B=\sqrt{P_b}$ and $A=\sqrt{P_a}-\sqrt{P_b}\geq 0$; those must _always_ be quoted in $dy/dx$* +# +# *The ranges must _either_ be specificed with `pa, pb, isdydx` or with `A, B` and in the second case `isdydx` must be True. There is no mix and match between those two parameter sets. + +c = CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert c.y_act == 1 +assert c.x_act == 0 +assert iseq(1/c.p_min, 2200) +assert iseq(1/c.p_max, 1800) +assert iseq(1/c.p, 1/c.p_max) + +c = CPC.from_carbon(yint=1, y=1, A=1/256, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) +assert c.y_act == 1 +assert c.x_act == 0 +assert iseq(1/c.p_min, 2000) +print("pa", 1/c.p_max, 1/(1/256+sqrt(c.p_min))**2) +assert iseq(1/c.p_max, 1/(1/256+sqrt(c.p_min))**2) +assert iseq(1/c.p, 1/c.p_max) + +c = CPC.from_carbon(yint=3000, y=3000, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert c.y_act == 3000 +assert c.x_act == 0 +assert iseq(c.p_min, 2900) +assert iseq(c.p_max, 3100) +assert iseq(c.p, c.p_max) + +c = CPC.from_carbon(yint=2000, y=2000, A=10, B=sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert c.y_act == 2000 +assert c.x_act == 0 +assert iseq(c.p_min, 3000) +print("pa", c.p_max, (10+sqrt(c.p_min))**2) +assert iseq(c.p_max, (10+sqrt(c.p_min))**2) +assert iseq(1/c.p, 1/c.p_max) + +CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +CPC.from_carbon(yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="2", descr="Carbon", isdydx=True) +CPC.from_carbon(yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="3", descr="Carbon", isdydx=True) +CPC.from_carbon(yint=1, y=1, A=10, B=sqrt(3000), pair="ETH/USDC", tkny="USDC", fee=0, cid="4", descr="Carbon", isdydx=True) + +assert not raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", fee=0, cid="1", descr="Carbon", isdydx=False) +#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", cid="1", descr="Carbon", isdydx=False) +#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, descr="Carbon", isdydx=False) +#assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="LINK", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1800, pb=2200, A=100, B=100, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pb=1800, pa=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="1", descr="Carbon", isdydx=False) + +assert not raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert raises(CPC.from_carbon, yint=1, y=1, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=False) +assert raises(CPC.from_carbon, yint=1, y=1, pa=1000, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert raises(CPC.from_carbon, yint=1, y=1, pb=1000, A=1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert raises(CPC.from_carbon, yint=1, y=1, A=-1/10, B=sqrt(1/2000), pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + +assert not raises(CPC.from_carbon, yint=1, y=1, pa=3100, pb=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) +assert raises(CPC.from_carbon, yint=1, y=1, pb=3100, pa=2900, pair="ETH/USDC", tkny="USDC", fee=0, cid="2", descr="Carbon", isdydx=True) + +# ## Charts [NOTEST] + +curves_uni =[ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="U2/1", descr="UniV2"), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="U2/2", descr="UniV2"), + CPC.from_univ3(Pmarg=2000, uniL=100, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), + CPC.from_univ3(Pmarg=2010, uniL=75, uniPa=1800, uniPb=2200, pair="ETH/USDC", fee=0, cid="U3/1", descr="UniV3"), +] +CC = CPCContainer(curves_uni) + +curves_carbon = [ + CPC.from_carbon(yint=3000, y=3000, pa=3500, pb=2500, pair="ETH/USDC", tkny="USDC", fee=0, cid="C1", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=3000, y=3000, A=20, B=sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C2", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=3000, y=3000, A=40, B=sqrt(2500), pair="ETH/USDC", tkny="USDC", fee=0, cid="C3", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, pa=1800, pb=2200, pair="ETH/USDC", tkny="ETH", fee=0, cid="C4", descr="Carbon", isdydx=False), + CPC.from_carbon(yint=1, y=1, pa=1/1800, pb=1/2000, pair="ETH/USDC", tkny="ETH", fee=0, cid="C5", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, A=1/500, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C6", descr="Carbon", isdydx=True), + CPC.from_carbon(yint=1, y=1, A=1/1000, B=sqrt(1/2000), pair="ETH/USDC", tkny="ETH", fee=0, cid="C7", descr="Carbon", isdydx=True), +] + +curves = curves_uni + curves_carbon +CC = CPCContainer(curves) +CC.plot(params=CC.Params()) + +# ## Serializing curves +# +# The `CPCContainer` and `ConstantProductCurve` objects do not strictly have methods that would allow for serialization. However, they allow conversion from an to datatypes that are easily serialized. +# +# - on the `ConstantProductCurve` level there is `asdict()` and `from_dicts(.)` +# - on the `CPCContainer` level there is also `asdf()` and `from_df(.)`, allowing conversion from and to pandas dataframes +# +# Recommended serialization is either dict to json via the `json` library, or any of the serialization methods inherent in dataframes, notably also pickling (Excel formates are not recommended as they are slow and heavy). +# +# +# + +curves = [ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid="1", descr="UniV2", params={"meh":1}), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid="2", descr="UniV2"), + CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid="3", descr="UniV2"), +] + +c0 = curves[0] +assert c0.params.__class__.__name__ == "AttrDict" +assert c0.params == {'meh': 1} + +CC = CPCContainer(curves) +assert raises(CPCContainer, [1,2,3]) +assert len(CC.curves) == len(curves) +assert len(CC.asdicts()) == len(CC.curves) +assert CPCContainer.from_dicts(CC.asdicts()) == CC +ccjson = json.dumps(CC.asdicts()) +assert CPCContainer.from_dicts(json.loads(ccjson)) == CC +CC + +df = CC.asdf() +assert len(df) == 3 +assert tuple(df.reset_index().columns) == ('cid', 'k', 'x', 'x_act', 'y_act', + 'pair', 'fee', 'descr', 'constr', 'params') +assert tuple(df["k"]) == (2000, 8040, 1970) +assert CPCContainer.from_df(df) == CC +df + +# ## Saving curves [NOTEST] +# +# Most serialization methods we use go via the a pandas DataFram object. To create a dataframe we use the `asdf()` method, and to instantiate curve container from a dataframe we use `CPCContainer.from_df(df)`. + +N=5000 +curves = [ + CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0.001, cid=1, descr="UniV2"), + CPC.from_univ2(x_tknb=2, y_tknq=4020, pair="ETH/USDC", fee=0.001, cid=2, descr="UniV2"), + CPC.from_univ2(x_tknb=1, y_tknq=1970, pair="ETH/USDC", fee=0.001, cid=3, descr="UniV2"), +] +CC = CPCContainer(curves*N) +df = CC.asdf() +#CC + +# ### Formats +# #### json +# +# Using `json.dumps(.)` the list of dicts returned by `asdicts()` can be converted to json, and then saved as a textfile. When loaded back, the text can be expanded into json using `json.loads(.)` and the new object can be instantiated using `CPCContainer.from_dicts(dicts)`. + +start_time = time.time() +cc_json = json.dumps(CC.asdicts()) +print("len", len(cc_json)) +CC2 = CPCContainer.from_dicts(json.loads(cc_json)) +assert CC == CC2 +print(f"elapsed time: {time.time()-start_time:.2f}s") +#CC2 + +# #### csv +# +# `to_csv` converts a dataframe to a csv file; this file can also be zipped; this format is ideal for maximum interoperability as pretty much every software allows dealing with csvs; it is very fast, and the zipped files are much smaller than everything else + +start_time = time.time() +df.to_csv(".curves.csv") +df_csv = pd.read_csv(".curves.csv") +assert CPCContainer.from_df(df_csv) == CC +print(f"elapsed time: {time.time()-start_time:.2f}s") +df_csv[:3] + +# #### tsv +# +# `to_csv` can be used with `sep="\t"` to create a tab separated file + +start_time = time.time() +df.to_csv(".curves.tsv", sep="\t") +df_tsv = pd.read_csv(".curves.tsv", sep="\t") +assert CPCContainer.from_df(df_tsv) == CC +print(f"elapsed time: {time.time()-start_time:.2f}s") + +# #### compressed csv +# +# `to_csv` can be used with `compression = "gzip"` to create a compressed file. This is by far the smallest output available, and takes little more time compared to uncompressed. + +start_time = time.time() +df.to_csv(".curves.csv.gz", compression = "gzip") +df_csv = pd.read_csv(".curves.csv.gz") +assert CPCContainer.from_df(df_csv) == CC +print(f"elapsed time: {time.time()-start_time:.2f}s") + + +# #### Excel +# +# `to_excel` converts the dataframe to an xlsx file; older versions of pandas may allow to also save in the old xls format, but this is deprecated; note that Excel files can be rather big, and saving them is very slow, 10-15x(!) longer than csv. + +start_time = time.time() +df.to_excel(".curves.xlsx") +df_xlsx = pd.read_excel(".curves.xlsx") +assert CPCContainer.from_df(df_xlsx) == CC +print(f"elapsed time: {time.time()-start_time:.2f}s") +df_xlsx[:3] + +# #### pickle +# +# `to_pickle` pickles the dataframe; this format is rather big, but it is the fastest to process, albeit not at a significant margin + +start_time = time.time() +df.to_pickle(".curves.pkl") +df_pickle = pd.read_pickle(".curves.pkl") +assert CPCContainer.from_df(df_pickle) == CC +print(f"elapsed time: {time.time()-start_time:.2f}s") +df_pickle[:3] + +# ### Benchmarking +# +# below a comparison of the different methods in terms of size and speed; the benchmark run used **300,000 curves** +# +# 33000000 .curves.json -- 5.2s (without read/write) +# 11100035 .curves.csv -- 3.4s +# 37817 .curves.csv.gz -- 3.4s +# 15602482 .curves.pkl -- 2.6s +# 11100035 .curves.tsv -- 3.2s +# 8031279 .curves.xlsx -- 45.0s (!) +# +# Below are the figures for the current run (timing figures inline above) + +print(f"{len(df_xlsx)} curves") +print(f" {len(cc_json)} .curves.json", ) +# !ls -l .curves* + + diff --git a/resources/NBTest/NBTest_065-GraphCode.ipynb b/resources/NBTest/NBTest_065-GraphCode.ipynb new file mode 100644 index 00000000..50e8576b --- /dev/null +++ b/resources/NBTest/NBTest_065-GraphCode.ipynb @@ -0,0 +1,3035 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c19d0663-ac37-4095-b6a3-18afcee2493c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[stdimports] imported np, pd, plt, os, sqrt, exp, log\n", + "ArbGraph v2.1 (16/Apr/2023)\n", + "ConstantProductCurve v2.6.1 (18/Apr/2023)\n", + "Carbon v2.4.2-BETA2 (09/Apr/2023)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1189/4074401983.py:8: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. 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USDCLINKAAVEWETHBTC
tknb
USDC1.00.20.010.00050.0001
LINK5.01.00.050.00250.0005
AAVE100.020.01.000.05000.0100
WETH2000.0400.020.001.00000.2000
BTC10000.02000.0100.005.00001.0000
\n", + "" + ], + "text/plain": [ + " USDC LINK AAVE WETH BTC\n", + "tknb \n", + "USDC 1.0 0.2 0.01 0.0005 0.0001\n", + "LINK 5.0 1.0 0.05 0.0025 0.0005\n", + "AAVE 100.0 20.0 1.00 0.0500 0.0100\n", + "WETH 2000.0 400.0 20.00 1.0000 0.2000\n", + "BTC 10000.0 2000.0 100.00 5.0000 1.0000" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.pricetable()" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "b999442c-7119-4dc6-8d91-de8acaed33f7", + "metadata": {}, + "outputs": [], + "source": [ + "pt = AG.pricetable(asdf=False)\n", + "assert pt[\"labels\"] == ['USDC', 'LINK', 'AAVE', 'WETH', 'BTC']\n", + "assert len(pt[\"data\"]) == len(pt[\"labels\"])\n", + "assert pt[\"data\"][0] == [1, 0.2, 0.01, 0.0005, 0.0001]" + ] + }, + { + "cell_type": "markdown", + "id": "4f383864-957d-4ae5-92c9-0fae2343b6de", + "metadata": { + "tags": [] + }, + "source": [ + "## Arbraph connection only edges test" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "b3353635-f182-4e79-824e-d4c2f2228a03", + "metadata": {}, + "outputs": [], + "source": [ + "nodes = lambda: ag.create_node_list(\"ETH, USDC\")\n", + "ETH, USDC = nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "4ee65b5f-7045-4a08-8d01-1f33d56a6d99", + "metadata": {}, + "outputs": [], + "source": [ + "e = e1 = ag.Edge.connection_edge(node_in=ETH, node_out=USDC, price=3000)\n", + "e = e2 = ag.Edge.connection_edge(node_in=ETH, node_out=USDC, price=2000)\n", + "assert e.convention() == 'USDC per ETH'\n", + "assert e.convention_outperin() == 'USDC per ETH'\n", + "assert e.price() == 2000\n", + "assert e.price_outperin == 2000\n", + "assert e.edgetype == e.EDGE_CONNECTION\n", + "assert e.is_amounttype == False\n", + "assert not raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", + "assert raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", + "assert e1.label == '3000.0 [None]'\n", + "assert e2.label == '2000.0 [None]'\n", + "assert (e1+e2).price() == 2500\n", + "assert (e1+3*e2).price() == 2250\n", + "assert raises(lambda: e1*0)\n", + "assert raises(lambda: e1*(-10))\n", + "assert raises(lambda: 0*e1)\n", + "assert raises(lambda: -10*e1)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "ced876f3-17ce-42fb-b928-be4267d453f5", + "metadata": {}, + "outputs": [], + "source": [ + "e = e3 = ag.Edge.connection_edge(node_out=ETH, node_in=USDC, price=2000, inverse=True)\n", + "assert e.convention() == 'USDC per ETH'\n", + "assert e.convention_outperin() == 'ETH per USDC'\n", + "assert e.price() == 2000\n", + "assert e.price_outperin == 1/2000\n", + "assert e.edgetype == e.EDGE_CONNECTION\n", + "assert e.is_amounttype == False\n", + "assert not raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", + "assert raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", + "assert e3.label == '0.0005 [None]'" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5aa80d7f-c1b8-4025-8b69-8307035d086a", + "metadata": {}, + "outputs": [], + "source": [ + "e= e4 = ag.Edge(node_in=ETH, node_out=USDC, amount_in=1, amount_out=2000, inverse=True)\n", + "assert e.edgetype == e.EDGE_AMOUNT\n", + "assert e.is_amounttype\n", + "assert not raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", + "assert raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", + "e = e5 = 2*e4\n", + "assert e.edgetype == e.EDGE_AMOUNT\n", + "assert e.is_amounttype\n", + "assert not raises(e.assert_edgetype, e.EDGE_AMOUNT)\n", + "assert raises(e.assert_edgetype, e.EDGE_CONNECTION)\n", + "e = e6 = ag.Edge(node_in=ETH, node_out=USDC, amount_in=1, amount_out=3000)\n", + "assert e.price() == e1.price()\n", + "assert e.price_outperin == e1.price_outperin\n", + "assert e4.label == '1 ETH(0) --> 2000 USDC(1)'" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a4f3d14f-2c36-48c9-b9b3-169a69bc66c9", + "metadata": {}, + "outputs": [], + "source": [ + "assert raises (lambda: e1+e3)\n", + "assert raises (lambda: -2*e1)\n", + "assert raises (lambda: e3*(-2))\n", + "try:\n", + " e1 += e3\n", + " raise\n", + "except ValueError as e:\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "7b6ad261-5eb6-4560-878f-bfb74cda33a9", + "metadata": {}, + "outputs": [], + "source": [ + "assert not raises (lambda: e4+e5)\n", + "assert not raises (lambda: 2*e4)\n", + "assert not raises (lambda: e4*2)\n", + "e4 += e5" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "a85d1b67-ea52-4e09-be6c-e01a5b8b42f2", + "metadata": {}, + "outputs": [], + "source": [ + "assert e6.amount_in == 1\n", + "assert e1.transport() == e6.transport()\n", + "assert e1.transport(amount_in=1e6) == 1e6*e1.transport()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "3cdc6998-cdd3-4f40-9723-4cadb0796110", + "metadata": {}, + "outputs": [], + "source": [ + "AG = ag.ArbGraph(nodes = [ETH, USDC])\n", + "assert AG.edgetype is None\n", + "AG.add_edge_obj(e1)\n", + "assert AG.edgetype == AG.EDGE_CONNECTION\n", + "assert AG.edgetype == e1.EDGE_CONNECTION\n", + "AG.add_edge_obj(e2)\n", + "assert raises(AG.add_edge_obj, e4)\n", + "assert AG.edgetype == e1.EDGE_CONNECTION" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "7d8a7329-62a0-4a44-a8e2-ad4dd45c8994", + "metadata": {}, + "outputs": [], + "source": [ + "AG = ag.ArbGraph(nodes = [ETH, USDC])\n", + "assert AG.edgetype is None\n", + "AG.add_edge_obj(e4)\n", + "assert AG.edgetype == AG.EDGE_AMOUNT\n", + "assert AG.edgetype == e1.EDGE_AMOUNT\n", + "AG.add_edge_obj(e5)\n", + "assert raises(AG.add_edge_obj, e1)\n", + "assert AG.edgetype == e1.EDGE_AMOUNT" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "ae4b80ec-153c-457e-bcd1-94cd0658897f", + "metadata": {}, + "outputs": [], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000)\n", + "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"BTC\", price=1/5)\n", + "AG.add_edge_connectiontype(tkn_in=\"BTC\", tkn_out=\"USDC\", price=10000)\n", + "assert AG.edgetype == AG.EDGE_CONNECTION\n", + "assert len(AG) == 6\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "718b0faf-3f94-41b7-8b8b-4878ea413559", + "metadata": {}, + "outputs": [], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000, symmetric=False)\n", + "AG.add_edge_connectiontype(tkn_in=\"ETH\", tkn_out=\"BTC\", price=1/5, symmetric=False)\n", + "AG.add_edge_connectiontype(tkn_in=\"BTC\", tkn_out=\"USDC\", price=10000, symmetric=False)\n", + "assert AG.edgetype == AG.EDGE_CONNECTION\n", + "assert len(AG) == 3\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "54d2538b-4a24-462a-95cc-a9369570c9b4", + "metadata": {}, + "outputs": [], + "source": [ + "AG = ag.ArbGraph()\n", + "assert raises (AG.add_edge_connectiontype, tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000, price_outperin=2000)\n", + "assert raises (AG.add_edge_connectiontype, tkn_in=\"ETH\", tkn_out=\"USDC\", inverse = True, price_outperin=2000)\n", + "assert AG.add_edge_connectiontype == AG.add_edge_ct" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "2c853729-60ce-4597-9ab0-5404ffbff61f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 1)\t1\n", + " (0, 2)\t1\n", + " (1, 0)\t1\n", + " (1, 2)\t1\n", + " (2, 0)\t1\n", + " (2, 1)\t1\n" + ] + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "for i in range(5):\n", + " mul = 1+i/50\n", + " AG.add_edge_ct(tkn_in=\"ETH\", tkn_out=\"USDC\", price=2000*mul)\n", + " AG.add_edge_ct(tkn_in=\"WBTC\", tkn_out=\"USDC\", price=10000*mul)\n", + " AG.add_edge_ct(tkn_in=\"ETH\", tkn_out=\"WBTC\", price=0.2/mul)\n", + "assert AG.len() == (2*3*5, 3)\n", + "assert len(AG.cycles()) == 5\n", + "assert np.array_equal(AG.A.toarray(), np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]]))\n", + "print(AG.A)\n", + "AG2 = AG.duplicate()\n", + "assert AG2.len() == (6,3)\n", + "edges = AG.filter_edges(\"ETH\", \"USDC\")\n", + "assert len(edges) == 5\n", + "edges2 = AG2.filter_edges(\"ETH\", \"USDC\")\n", + "assert len(edges2) == 1\n", + "assert [e.p_outperin for e in edges] == [2000.0, 2040.0, 2080.0, 2120.0, 2160.0]\n", + "assert edges2[0].p_outperin == np.mean([e.p_outperin for e in edges])" + ] + }, + { + "cell_type": "markdown", + "id": "4db52bf5-bad1-4e91-8ecd-19dfbdc39435", + "metadata": {}, + "source": [ + "## Interaction with CPC" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "94d3d623-0868-4987-8832-6ff39f2bac27", + "metadata": {}, + "outputs": [], + "source": [ + "c1 = CPC.from_univ2(x_tknb=1, y_tknq=2000, pair=\"ETH/USDC\", fee=0, cid=\"1\", descr=\"UniV2\")\n", + "c2 = CPC.from_univ2(x_tknb=1, y_tknq=10000, pair=\"WBTC/USDC\", fee=0, cid=\"2\", descr=\"UniV2\")\n", + "c3 = CPC.from_univ2(x_tknb=1, y_tknq=5, pair=\"WBTC/ETH\", fee=0, cid=\"3\", descr=\"UniV2\")\n", + "assert c1.p == 2000\n", + "assert c2.p == 10000\n", + "assert c3.p == 5" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "9c0f82b7-fc39-48be-9465-09198266060a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edges_cpc(c1)\n", + "AG.add_edges_cpc(c2)\n", + "AG.add_edges_cpc(c3)\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "44580cb8-1a34-4fc8-9cfe-e95948771995", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edges_cpc([c1, c2, c3])\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "e8c79e3e-b41e-4920-ada2-3ca5e67126d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edges_cpc(c for c in [c1, c2, c3])\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "8529263f-1472-4332-a684-8eef6a562a95", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ArbGraph(nodes=[ETH(0), USDC(1), WBTC(2)], edges=[Edge(node_in=ETH(0), amount_in=-1, node_out=USDC(1), amount_out=-2000.0, ix=0, inverse=False, uid='1'), Edge(node_in=USDC(1), amount_in=-1, node_out=ETH(0), amount_out=-0.0005, ix=1, inverse=True, uid='1-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=USDC(1), amount_out=-10000.0, ix=2, inverse=False, uid='2'), Edge(node_in=USDC(1), amount_in=-1, node_out=WBTC(2), amount_out=-0.0001, ix=3, inverse=True, uid='2-r'), Edge(node_in=WBTC(2), amount_in=-1, node_out=ETH(0), amount_out=-5.0, ix=4, inverse=False, uid='3'), Edge(node_in=ETH(0), amount_in=-1, node_out=WBTC(2), amount_out=-0.2, ix=5, inverse=True, uid='3-r')])" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "CC = CPCContainer([c1,c2,c3])\n", + "AG.add_edges_cpc(CC)\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "f032a193-a6f9-4f13-b311-86ddbeaa43d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " (0, 1)\t1\n", + " (0, 2)\t1\n", + " (1, 0)\t1\n", + " (1, 2)\t1\n", + " (2, 0)\t1\n", + " (2, 1)\t1\n" + ] + } + ], + "source": [ + "print(AG.A)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "f1e0e122-72dc-417d-8628-6379edb36a40", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Cycle(data=[ETH(0), USDC(1)], uid=0),\n", + " Cycle(data=[ETH(0), USDC(1), WBTC(2)], uid=1),\n", + " Cycle(data=[ETH(0), WBTC(2), USDC(1)], uid=2),\n", + " Cycle(data=[ETH(0), WBTC(2)], uid=3),\n", + " Cycle(data=[USDC(1), WBTC(2)], uid=4))" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.cycles()" + ] + }, + { + "cell_type": "markdown", + "id": "b18b27a6-3d38-4e03-a8be-30d35424c1b5", + "metadata": {}, + "source": [ + "## With real data from CPC" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "6e98f201-62f2-4f1c-9f58-f787e1a7267b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Num curves: 459\n", + "Num pairs: 326\n", + "Num tokens: 141\n", + "1INCH,1ONE,AAVE,ALCX,ALEPH,ALPHA,AMP,ANKR,ANT,APW,ARCONA,ARMOR,AST,AUC,BAL,BAT,BBADGER,BDIGG,BMI,BNB,BNT,BOBA,BOND,BOR,BORING,BZRX,CEL,CHZ,COMP,COT,CRO,CRV,CTSI,DAI,DAO,DATA,DDX,DEXE,DIP,DRC,DUSK,DXD,DYDX,EDEN,ELF,ENJ,ENS,ERSDL,ETH,EWTB,FARM,FODL,FOX,FRM,FTX TOKEN,FXS,GNO,GRT,GTC,GUSD,HEGIC,HOT,HY,ICHI,IDLE,INDEX,INST,KNC,KTN,LINK,LPL,LQTY,LRC,LYRA,MANA,MASK,MATIC,MFG,MFI,MKR,MLN,MONA,MPH,MTA,NDX,NEXO,NMR,NOIA,OCEAN,OMG,OPIUM,PATH,PERP,PHTR,PLR,POOL,POOLZ,POWR,PSP,QNT,QUICK,RAIL,RARI,REN,RENBTC,RENZEC,REQ,RETH,RLC,RNB,ROOK,RUNE,SATA,SFI,SHEESHA,SHIBGF,SMARTCREDIT,SNX,STAKE,SUSHI,TOMOE,TRAC,TRU,UMA,UNI,UOS,USDC,USDT,VBNT,VISION,VLX,WBTC,WETH,WNXM,WOO,WSTETH,WXT,XSUSHI,YFI,ZCN,ZRX\n" + ] + } + ], + "source": [ + "try:\n", + " df = pd.read_csv(\"NBTEST_063_Curves.csv.gz\")\n", + "except:\n", + " df = pd.read_csv(\"carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz\")\n", + "CC0 = CPCContainer.from_df(df)\n", + "print(\"Num curves:\", len(CC0))\n", + "print(\"Num pairs:\", len(CC0.pairs()))\n", + "print(\"Num tokens:\", len(CC0.tokens()))\n", + "print(CC0.tokens_s())" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "1de1050e-ecbc-4377-a540-c08bd9a48432", + "metadata": {}, + "outputs": [], + "source": [ + "AG0 = ag.ArbGraph().add_edges_cpc(CC0)\n", + "#AG0.plot()\n", + "assert AG0.len() == (918, 141)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "4aa3bb42-9842-43c0-9fe2-70dace48bb63", + "metadata": {}, + "outputs": [], + "source": [ + "assert str(AG0.A)[:60] ==' (0, 1)\\t1\\n (1, 0)\\t1\\n (2, 3)\\t1\\n (2, 4)\\t1\\n (2, 5)\\t1\\n (2,'" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "6903c18c-e046-4031-b3d6-04d0fb7b528e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pairs = CC0.filter_pairs(bothin=\"WETH, USDC, UNI, AAVE, LINK\")\n", + "CC = CC0.bypairs(pairs, ascc=True)\n", + "AG = ag.ArbGraph().add_edges_cpc(CC)\n", + "#AG.plot()\n", + "AG.len() == (24, 5)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "f3e69f0b-99f0-4196-89af-bc5bf11f5e41", + "metadata": {}, + "outputs": [], + "source": [ + "assert np.all(AG.A.toarray() == np.array(\n", + " [[0, 1, 1, 0, 0],\n", + " [1, 0, 1, 1, 1],\n", + " [1, 1, 0, 1, 1],\n", + " [0, 1, 1, 0, 0],\n", + " [0, 1, 1, 0, 0]]))" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "61f65aad-7493-4aaf-9819-dd19950e032f", + "metadata": {}, + "outputs": [], + "source": [ + "assert raises(AG.edge_statistics,\"WETH\", \"USDC\")" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "8ba71383-94a7-4224-aa74-e9601839a68a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pairtkn_intkn_outnis_reverseprice_outinprice
0LINK/WETHLINKWETH1False0.0041530.004153
1LINK/WETHWETHLINK1True240.7650160.004153
2LINK/USDCLINKUSDC1False6.1005216.100521
3LINK/USDCUSDCLINK1True0.1639206.100521
4AAVE/WETHAAVEWETH1False0.0408050.040805
5AAVE/WETHWETHAAVE1True24.5068920.040805
6UNI/WETHUNIWETH1False0.0033270.003327
7UNI/WETHWETHUNI1True300.6130150.003327
8WETH/USDCUSDCWETH1True0.0005491822.819584
9WETH/USDCWETHUSDC1False1822.8195841822.819584
10LINK/WETHLINKWETH1False0.0041440.004144
11LINK/WETHWETHLINK1True241.2888110.004144
12LINK/USDCLINKUSDC1False7.3008817.300881
13LINK/USDCUSDCLINK1True0.1369707.300881
14AAVE/WETHAAVEWETH1False0.0405490.040549
15AAVE/WETHWETHAAVE1True24.6612930.040549
16AAVE/USDCAAVEUSDC1False80.82639380.826393
17AAVE/USDCUSDCAAVE1True0.01237280.826393
18UNI/WETHUNIWETH1False0.0033300.003330
19UNI/WETHWETHUNI1True300.2552450.003330
20UNI/USDCUNIUSDC1False6.0986346.098634
21UNI/USDCUSDCUNI1True0.1639716.098634
22WETH/USDCUSDCWETH1True0.0005491819.922154
23WETH/USDCWETHUSDC1False1819.9221541819.922154
\n", + "
" + ], + "text/plain": [ + " pair tkn_in tkn_out n is_reverse price_outin price\n", + "0 LINK/WETH LINK WETH 1 False 0.004153 0.004153\n", + "1 LINK/WETH WETH LINK 1 True 240.765016 0.004153\n", + "2 LINK/USDC LINK USDC 1 False 6.100521 6.100521\n", + "3 LINK/USDC USDC LINK 1 True 0.163920 6.100521\n", + "4 AAVE/WETH AAVE WETH 1 False 0.040805 0.040805\n", + "5 AAVE/WETH WETH AAVE 1 True 24.506892 0.040805\n", + "6 UNI/WETH UNI WETH 1 False 0.003327 0.003327\n", + "7 UNI/WETH WETH UNI 1 True 300.613015 0.003327\n", + "8 WETH/USDC USDC WETH 1 True 0.000549 1822.819584\n", + "9 WETH/USDC WETH USDC 1 False 1822.819584 1822.819584\n", + "10 LINK/WETH LINK WETH 1 False 0.004144 0.004144\n", + "11 LINK/WETH WETH LINK 1 True 241.288811 0.004144\n", + "12 LINK/USDC LINK USDC 1 False 7.300881 7.300881\n", + "13 LINK/USDC USDC LINK 1 True 0.136970 7.300881\n", + "14 AAVE/WETH AAVE WETH 1 False 0.040549 0.040549\n", + "15 AAVE/WETH WETH AAVE 1 True 24.661293 0.040549\n", + "16 AAVE/USDC AAVE USDC 1 False 80.826393 80.826393\n", + "17 AAVE/USDC USDC AAVE 1 True 0.012372 80.826393\n", + "18 UNI/WETH UNI WETH 1 False 0.003330 0.003330\n", + "19 UNI/WETH WETH UNI 1 True 300.255245 0.003330\n", + "20 UNI/USDC UNI USDC 1 False 6.098634 6.098634\n", + "21 UNI/USDC USDC UNI 1 True 0.163971 6.098634\n", + "22 WETH/USDC USDC WETH 1 True 0.000549 1819.922154\n", + "23 WETH/USDC WETH USDC 1 False 1819.922154 1819.922154" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.edgedf(consolidated=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "adb634cb-a53e-4a8b-8bf3-3e99602d1d6a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
nn_revprice
pair
AAVE/USDC1180.826393
AAVE/WETH220.040677
LINK/USDC226.700701
LINK/WETH220.004149
UNI/USDC116.098634
UNI/WETH220.003329
WETH/USDC221821.370869
\n", + "
" + ], + "text/plain": [ + " n n_rev price\n", + "pair \n", + "AAVE/USDC 1 1 80.826393\n", + "AAVE/WETH 2 2 0.040677\n", + "LINK/USDC 2 2 6.700701\n", + "LINK/WETH 2 2 0.004149\n", + "UNI/USDC 1 1 6.098634\n", + "UNI/WETH 2 2 0.003329\n", + "WETH/USDC 2 2 1821.370869" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = AG.edgedf(consolidated=True)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "74fa4d4f-e077-4f54-8719-d91ce21bff3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "71.22 LINK -0.3 WETH 170\n", + "-0.28 LINK 1.99 USDC 171\n", + "3.4 AAVE -0.14 WETH 180\n", + "-10.82 UNI 0.04 WETH 305\n", + "755278.31 USDC -393.48 WETH 309\n", + "-65.01 LINK 0.27 WETH 337\n", + "-5.93 LINK 46.42 USDC 339\n", + "-3.38 AAVE 0.13 WETH 349\n", + "-0.02 AAVE 1.41 USDC 351\n", + "60.27 UNI -0.2 WETH 599\n", + "-49.45 UNI 316.84 USDC 601\n", + "1507698.66 USDC -786.1 WETH 606\n" + ] + } + ], + "source": [ + "dx,dy = ((71.22, -0.28, 3.4, -10.82, 755278.31, -65.01, -5.93, -3.38, -0.02, 60.27, -49.45, 1507698.66, -2263343.63), \n", + " (-0.3, 1.99, -0.14, 0.04, -393.48, 0.27, 46.42, 0.13, 1.41, -0.2, 316.84, -786.1, 833.78))\n", + "AG2 = ag.ArbGraph()\n", + "for cpc, dx_, dy_ in zip(CC, dx, dy):\n", + " print(dx_, cpc.tknx, dy_, cpc.tkny, cpc.cid)\n", + " AG2.add_edge_dxdy(cpc.tknx, dx_, cpc.tkny, dy_, uid=cpc.cid)\n", + " #print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "519e81fb-180b-4003-9104-09df28bda6a4", + "metadata": {}, + "outputs": [], + "source": [ + "#_=AG2.plot()\n", + "assert AG2.len() == (12,5)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "7657cc5e-b0fc-459c-8a10-bbe1f9960ecb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 0 0 0]\n", + " [1 0 0 1 1]\n", + " [1 1 0 1 1]\n", + " [0 1 0 0 0]\n", + " [0 1 0 0 0]]\n" + ] + } + ], + "source": [ + "assert np.all(AG2.A.toarray() == np.array(\n", + " [[0, 1, 0, 0, 0],\n", + " [1, 0, 0, 1, 1],\n", + " [1, 1, 0, 1, 1],\n", + " [0, 1, 0, 0, 0],\n", + " [0, 1, 0, 0, 0]]))\n", + "print(AG2.A.toarray())" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "5fe9565c-71a6-4efd-a690-44341813c423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'len': 2,\n", + " 'edges': ({'node_in': {'tkn': 'USDC', 'ix': 2},\n", + " 'amount_in': 755278.31,\n", + " 'node_out': {'tkn': 'WETH', 'ix': 1},\n", + " 'amount_out': 393.48,\n", + " 'ix': 4,\n", + " 'inverse': False,\n", + " 'uid': 309},\n", + " {'node_in': {'tkn': 'USDC', 'ix': 2},\n", + " 'amount_in': 1507698.66,\n", + " 'node_out': {'tkn': 'WETH', 'ix': 1},\n", + " 'amount_out': 786.1,\n", + " 'ix': 11,\n", + " 'inverse': False,\n", + " 'uid': 606}),\n", + " 'amount_in': {'amount': 2262976.9699999997, 'node': {'tkn': 'USDC', 'ix': 2}},\n", + " 'amount_in_remaining': {'amount': 2262976.9699999997,\n", + " 'node': {'tkn': 'USDC', 'ix': 2}},\n", + " 'amount_out': {'amount': 1179.58, 'node': {'tkn': 'WETH', 'ix': 1}},\n", + " 'price': 0.0005212514381001412,\n", + " 'utilization': 0.0,\n", + " 'amounts_in': (755278.31, 1507698.66),\n", + " 'amounts_in_remaining': (755278.31, 1507698.66),\n", + " 'amounts_out': (393.48, 786.1),\n", + " 'prices': (0.0005209735203437789, 0.0005213906603856769),\n", + " 'utilizations': (0.0, 0.0)}" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assert AG2.edge_statistics(\"WETH\", \"USDC\", bothways=False) is None\n", + "assert len(AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)) == 2\n", + "assert AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)[1].asdict()[\"amounts_in_remaining\"] == (755278.31, 1507698.66)\n", + "AG2.edge_statistics(\"WETH\", \"USDC\", bothways=True)[1].asdict()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "80e653d3-8c77-4085-b8f7-c74ec83de173", + "metadata": {}, + "outputs": [], + "source": [ + "assert AG2.filter_edges(\"WETH\", \"USDC\") == []\n", + "assert AG2.filter_edges(\"WETH\", \"USDC\", bothways=True)[0].amount_in == 755278.31\n", + "assert AG2.filter_edges(\"WETH\", \"USDC\", bothways=True) == AG2.filter_edges(\"USDC\", \"WETH\")\n", + "assert AG2.filter_edges(pair=\"WETH/USDC\", bothways=False) == []\n", + "assert AG2.filter_edges(pair=\"WETH/USDC\") == AG2.filter_edges(\"WETH\", \"USDC\", bothways=True)\n", + "assert AG2.filter_edges == AG2.fe\n", + "assert AG2.fep(\"WETH/USDC\") == AG2.filter_edges(pair=\"WETH/USDC\")\n", + "assert AG2.fep(\"WETH/USDC\", bothways=False) == AG2.filter_edges(pair=\"WETH/USDC\", bothways=False)\n", + "assert tuple(AG2.edgedf(consolidated=True, resetindex=False).iloc[0]) == (1.41, 0.02)\n", + "assert len(AG2.edgedf(consolidated=False)) == len(AG2)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "18c718aa-6539-4e32-b2ac-cce270a48356", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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pairtkn_intkn_outamount_inamount_out
uid
170LINK/WETHLINKWETH71.220.30
171LINK/USDCUSDCLINK1.990.28
180AAVE/WETHAAVEWETH3.400.14
305UNI/WETHWETHUNI0.0410.82
309WETH/USDCUSDCWETH755278.31393.48
337LINK/WETHWETHLINK0.2765.01
339LINK/USDCUSDCLINK46.425.93
349AAVE/WETHWETHAAVE0.133.38
351AAVE/USDCUSDCAAVE1.410.02
599UNI/WETHUNIWETH60.270.20
601UNI/USDCUSDCUNI316.8449.45
606WETH/USDCUSDCWETH1507698.66786.10
\n", + "
" + ], + "text/plain": [ + " pair tkn_in tkn_out amount_in amount_out\n", + "uid \n", + "170 LINK/WETH LINK WETH 71.22 0.30\n", + "171 LINK/USDC USDC LINK 1.99 0.28\n", + "180 AAVE/WETH AAVE WETH 3.40 0.14\n", + "305 UNI/WETH WETH UNI 0.04 10.82\n", + "309 WETH/USDC USDC WETH 755278.31 393.48\n", + "337 LINK/WETH WETH LINK 0.27 65.01\n", + "339 LINK/USDC USDC LINK 46.42 5.93\n", + "349 AAVE/WETH WETH AAVE 0.13 3.38\n", + "351 AAVE/USDC USDC AAVE 1.41 0.02\n", + "599 UNI/WETH UNI WETH 60.27 0.20\n", + "601 UNI/USDC USDC UNI 316.84 49.45\n", + "606 WETH/USDC USDC WETH 1507698.66 786.10" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assert len(AG2.edgedf(consolidated=False)) == 12\n", + "AG2.edgedf(consolidated=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "1f40c9ae-767e-4b68-9cf1-c5cd32fc7d35", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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amount_inamount_out
pairtkn_intkn_out
AAVE/USDCUSDCAAVE1.410.02
AAVE/WETHAAVEWETH3.400.14
WETHAAVE0.133.38
LINK/USDCUSDCLINK48.416.21
LINK/WETHLINKWETH71.220.30
WETHLINK0.2765.01
UNI/USDCUSDCUNI316.8449.45
UNI/WETHUNIWETH60.270.20
WETHUNI0.0410.82
WETH/USDCUSDCWETH2262976.971179.58
\n", + "
" + ], + "text/plain": [ + " amount_in amount_out\n", + "pair tkn_in tkn_out \n", + "AAVE/USDC USDC AAVE 1.41 0.02\n", + "AAVE/WETH AAVE WETH 3.40 0.14\n", + " WETH AAVE 0.13 3.38\n", + "LINK/USDC USDC LINK 48.41 6.21\n", + "LINK/WETH LINK WETH 71.22 0.30\n", + " WETH LINK 0.27 65.01\n", + "UNI/USDC USDC UNI 316.84 49.45\n", + "UNI/WETH UNI WETH 60.27 0.20\n", + " WETH UNI 0.04 10.82\n", + "WETH/USDC USDC WETH 2262976.97 1179.58" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assert len(AG2.edgedf(consolidated=True, resetindex=False)) == 10\n", + "AG2.edgedf(consolidated=True, resetindex=False)" + ] + }, + { + "cell_type": "markdown", + "id": "b73a9fe5-f486-4cae-b86d-c59a8139451f", + "metadata": {}, + "source": [ + "## Amount algebra" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "818d1633-8b04-459d-9c1b-9756c9b1b0b3", + "metadata": {}, + "outputs": [], + "source": [ + "A = ag.Amount\n", + "nodes = lambda: ag.create_node_list(\"ETH, USDC\")\n", + "ETH, USDC = nodes()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "e1666418-0dd0-4b22-8470-ca881bb2a291", + "metadata": {}, + "outputs": [], + "source": [ + "ae1, ae2, au1 = A(1, ETH), A(2, ETH), A(1, USDC)" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "de56707f-35c3-402e-9b29-b651475380d3", + "metadata": {}, + "outputs": [], + "source": [ + "assert ae1 + ae2 == 3*ae1\n", + "assert ae2 - ae1 == ae1\n", + "assert -ae1 + ae2 == ae1\n", + "assert 2*ae1 == ae2\n", + "assert ae1*2 == ae2\n", + "assert ae1/2 +ae1/2 == ae1\n", + "assert round(ae1/9,2) == round(1/9,2)*ae1\n", + "assert round(ae1/9,4) == round(1/9,4)*ae1\n", + "assert math.floor(ae1/9) == math.floor(1/9)*ae1\n", + "assert math.ceil(ae1/9) == math.ceil(1/9)*ae1\n", + "assert (ae1 + 2*ae1)/ae1 == 3" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "274aea35-d311-4995-8878-fc8cf447452d", + "metadata": {}, + "outputs": [], + "source": [ + "assert raises (lambda: ae1 + 1)\n", + "assert raises (lambda: ae1 - 1)\n", + "assert raises (lambda: 1 + ae1)\n", + "assert raises (lambda: 1 - ae1)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "b325f79e-f43a-49d5-b74f-7fbaf4cac6ca", + "metadata": {}, + "outputs": [], + "source": [ + "assert 2*ae1 > ae1\n", + "assert 2*ae1 >= ae1\n", + "assert .2*ae1 < ae1\n", + "assert .2*ae1 <= ae1\n", + "assert ae1 <= ae1\n", + "assert ae1 >= ae1\n", + "assert not ae1 < ae1\n", + "assert not ae1 > ae1" + ] + }, + { + "cell_type": "markdown", + "id": "6a863003-9227-4fa8-953f-d338a18605c8", + "metadata": {}, + "source": [ + "## Specific Arb examples" + ] + }, + { + "cell_type": "markdown", + "id": "0f849ba9-36bf-4f17-9559-7b1a38f48e2d", + "metadata": {}, + "source": [ + "### USDC/ETH" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "91c93306-b019-468a-ba5a-c345490f362c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Cycle(data=[ETH(0), USDC(1)], uid=0),)\n" + ] + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", + "AG.add_edge(\"USDC\", 1800, \"ETH\", 1, inverse=True)\n", + "G = AG.as_graph()\n", + "print(AG.cycles())\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "0b259f89-6537-4b6e-bc37-853f84c6fafd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===cycle [0]: ETH->USDC->...===\n", + "(ETH(0), USDC(1))\n", + "(USDC(1), ETH(0))\n" + ] + } + ], + "source": [ + "for C in AG.cycles():\n", + " print(f\"==={C}===\")\n", + " for c in C.pairs(start_val=AG.n(\"ETH\")): \n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "0decf9b2-28cb-4327-9821-ff8b6b08db33", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((USDC(1), ETH(0)),\n", + " [Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=1, inverse=True, uid=None)])" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c, AG.filter_edges(*c)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "ac6983c1-c55c-4666-aed5-0acd26d0819e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1],\n", + " [1, 0]])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.A.toarray()" + ] + }, + { + "cell_type": "markdown", + "id": "926914d2-d515-412c-ab15-be5cc577ce7d", + "metadata": {}, + "source": [ + "### USDC/LINK to ETH (oneway)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "f0743e1d-8709-41f9-8ae7-dd13cdfe40a0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Cycle(data=[USDC(0), LINK(2)], uid=0),)\n" + ] + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge(\"USDC\", 100, \"ETH\", 100/2000)\n", + "AG.add_edge(\"LINK\", 100, \"USDC\", 1000)\n", + "AG.add_edge(\"USDC\", 900, \"LINK\", 100, inverse=True)\n", + "G = AG.as_graph()\n", + "print(AG.cycles())\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3e8b2ed6", + "metadata": {}, + "source": [ + "_=AG.duplicate().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "69797a28-a7e7-43aa-8442-c164c8bedfed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===cycle [0]: USDC->LINK->...===\n", + "(USDC(0), LINK(2))\n", + "(LINK(2), USDC(0))\n" + ] + } + ], + "source": [ + "for C in AG.cycles():\n", + " print(f\"==={C}===\")\n", + " for c in C.pairs(start_val=AG.n(\"USDC\")): \n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "id": "5958e342-d8d5-4a19-8692-4cf8f3c90ca1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((LINK(2), USDC(0)),\n", + " [Edge(node_in=LINK(2), amount_in=100, node_out=USDC(0), amount_out=1000, ix=1, inverse=False, uid=None)])" + ] + }, + "execution_count": 83, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c, AG.filter_edges(*c)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "id": "5aa7ed65-ce02-4680-9508-cc793ec287bf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 1],\n", + " [0, 0, 0],\n", + " [1, 0, 0]])" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.A.toarray()" + ] + }, + { + "cell_type": "markdown", + "id": "d118509e-94d0-4f1a-9b19-772a80b60966", + "metadata": {}, + "source": [ + "### USDD, LINK, ETH cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "2f5230ec-578a-4c93-bf17-daaa9468d9e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Cycle(data=[ETH(0), USDC(1), LINK(2)], uid=0),)\n" + ] + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", + "AG.add_edge(\"USDC\", 1500, \"LINK\", 200, inverse=True)\n", + "AG.add_edge(\"LINK\", 200, \"ETH\", 1, inverse=True)\n", + "G = AG.as_graph()\n", + "print(AG.cycles())\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "12706bbd-0e07-4e2f-a54e-51bf60956311", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===cycle [0]: ETH->USDC->LINK->...===\n", + "(USDC(1), LINK(2))\n", + "(LINK(2), ETH(0))\n", + "(ETH(0), USDC(1))\n" + ] + } + ], + "source": [ + "for C in AG.cycles():\n", + " print(f\"==={C}===\")\n", + " for c in C.pairs(start_val=AG.n(\"USDC\")): \n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "af355336-48d0-481b-bcef-d49692a5e275", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((ETH(0), USDC(1)),\n", + " [Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None)])" + ] + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c, AG.filter_edges(*c)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "id": "0ad02c8f-c4b1-4eb8-a84e-3071e3e40434", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0],\n", + " [0, 0, 1],\n", + " [1, 0, 0]])" + ] + }, + "execution_count": 88, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.A.toarray()" + ] + }, + { + "cell_type": "markdown", + "id": "22382914-2714-4c8c-a234-739c0b2c88da", + "metadata": {}, + "source": [ + "### USDD, LINK, ETH cycle plus ETH/USDC" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "id": "3aa752af-03db-4d32-816e-199fe861e1d2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(Cycle(data=[ETH(0), USDC(1), LINK(2)], uid=0), Cycle(data=[ETH(0), USDC(1)], uid=1))\n" + ] + } + ], + "source": [ + "AG = ag.ArbGraph()\n", + "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", + "AG.add_edge(\"ETH\", 1, \"USDC\", 2000)\n", + "AG.add_edge(\"USDC\", 1500, \"LINK\", 200, inverse=True)\n", + "AG.add_edge(\"LINK\", 200, \"ETH\", 1, inverse=True)\n", + "AG.add_edge(\"USDC\", 1800, \"ETH\", 1, inverse=True)\n", + "G = AG.as_graph()\n", + "print(AG.cycles())\n", + "#_=AG.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "id": "b8008e76-42c0-4bea-ab27-c5f76622837f", + "metadata": {}, + "outputs": [], + "source": [ + "#_=AG.duplicate().plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "d788ef90-4537-41f7-beec-a3c8edb63589", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=0, inverse=False, uid=None),\n", + " Edge(node_in=ETH(0), amount_in=1, node_out=USDC(1), amount_out=2000, ix=1, inverse=False, uid=None),\n", + " Edge(node_in=USDC(1), amount_in=1500, node_out=LINK(2), amount_out=200, ix=2, inverse=True, uid=None),\n", + " Edge(node_in=LINK(2), amount_in=200, node_out=ETH(0), amount_out=1, ix=3, inverse=True, uid=None),\n", + " Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=4, inverse=True, uid=None)]" + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.edges" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "150bc2d2-91cb-40de-bb5c-c99aca5750c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(Edge(node_in=ETH(0), amount_in=2, node_out=USDC(1), amount_out=4000, ix=0, inverse=False, uid=None),\n", + " Edge(node_in=USDC(1), amount_in=1800, node_out=ETH(0), amount_out=1, ix=1, inverse=True, uid=None),\n", + " Edge(node_in=USDC(1), amount_in=1500, node_out=LINK(2), amount_out=200, ix=2, inverse=True, uid=None),\n", + " Edge(node_in=LINK(2), amount_in=200, node_out=ETH(0), amount_out=1, ix=3, inverse=True, uid=None))" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.duplicate().edges" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "id": "b739e7f3-2fb7-4def-901b-37aea603632d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 0],\n", + " [1, 0, 1],\n", + " [1, 0, 0]])" + ] + }, + "execution_count": 93, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "AG.A.toarray()" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "75a82201-5489-489e-aadd-49d5b2f002a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "===cycle [0]: ETH->USDC->LINK->...===\n", + "(ETH(0), USDC(1))\n", + "(USDC(1), LINK(2))\n", + "(LINK(2), ETH(0))\n", + "===cycle [1]: ETH->USDC->...===\n", + "(ETH(0), USDC(1))\n", + "(USDC(1), ETH(0))\n" + ] + } + ], + "source": [ + "for C in AG.cycles():\n", + " print(f\"==={C}===\")\n", + " for c in C.pairs(start_val=AG.n(\"ETH\")): \n", + " print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "06b66d5c-a52d-40ee-ad3f-d0facbd60d3d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Cycle(data=[ETH(0), USDC(1)], uid=1)" + ] + }, + "execution_count": 95, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cycle = AG.cycles()[1]\n", + "cycle" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "548a9736-819c-4adc-9d6c-966462b6bcec", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(ETH(0), USDC(1)): 2 edges, capacity 2 ETH -> 4000 USDC, actual 2 -> 4000.0 [1.0x]\n", + "(USDC(1), LINK(2)): 1 edges, capacity 1500 USDC -> 200 LINK, actual 1500 -> 200.0 [0.375x]\n", + "(LINK(2), ETH(0)): 1 edges, capacity 200 LINK -> 1 ETH, actual 200.0 -> 1.0 [0.375x]\n", + "Profit: 0.25 ETH [in: 0.75; out: 1.0]\n", + "RACResult(profit: 0.2 [ETH], in: 0.8, rpcs: 8.3%, ppcs: 0.1, len: 3, uid: 0)\n", + "---\n", + "(ETH(0), USDC(1)): 2 edges, capacity 2 ETH -> 4000 USDC, actual 2 -> 4000.0 [1.0x]\n", + "(USDC(1), ETH(0)): 1 edges, capacity 1800 USDC -> 1 ETH, actual 1800 -> 1.0 [0.45x]\n", + "Profit: 0.09999999999999998 ETH [in: 0.9; out: 1.0]\n", + "RACResult(profit: 0.1 [ETH], in: 0.9, rpcs: 5.0%, ppcs: 0.0, len: 2, uid: 1)\n", + "---\n" + ] + } + ], + "source": [ + "for cycle in AG.cycles():\n", + " result = AG.run_arbitrage_cycle(cycle=cycle, verbose=True)\n", + " print(result)\n", + " print(\"---\")" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "id": "6b182784-b8eb-433f-867a-4e38d4bc5839", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'cannot get price on amount-type graphs'" + ] + }, + "execution_count": 97, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "assert raises(AG.price, AG.nodes[0], AG.nodes[1])\n", + "raises(AG.price, AG.nodes[0], AG.nodes[1])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bc5a98c8-5750-4a9c-9afd-38a0d36ff213", + "metadata": { + "lines_to_next_cell": 2 + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "jupytext": { + "encoding": "# -*- coding: utf-8 -*-", + "formats": "ipynb,py:light" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/resources/NBTest/NBTest_065-GraphCode.py b/resources/NBTest/NBTest_065-GraphCode.py new file mode 100644 index 00000000..8e489adb --- /dev/null +++ b/resources/NBTest/NBTest_065-GraphCode.py @@ -0,0 +1,792 @@ +# -*- coding: utf-8 -*- +# --- +# jupyter: +# jupytext: +# formats: ipynb,py:light +# text_representation: +# extension: .py +# format_name: light +# format_version: '1.5' +# jupytext_version: 1.13.1 +# kernelspec: +# display_name: Python 3 +# language: python +# name: python3 +# --- + +# + +from carbon.helpers.stdimports import * +#from carbon import CarbonOrderUI +import carbon.tools.arbgraphs as ag +from carbon.tools.arbgraphs import np, pd, plt # convenience imports +from carbon.tools.cpc import ConstantProductCurve as CPC, CPCContainer +import math + +plt.style.use('seaborn-dark') +plt.rcParams['figure.figsize'] = [12,6] +#print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CarbonOrderUI)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(ag.ArbGraph)) +print("{0.__name__} v{0.__VERSION__} ({0.__DATE__})".format(CPC)) +print_version(require="2.4.2") +# - + +# # Graph Code [NBTest065] + +# ## ArbGraphs test and demo + +nodes = lambda: ag.create_node_list("ETH, USDC, WBTC, BNT") +assert [str(n) for n in nodes()] == ['ETH(0)', 'USDC(1)', 'WBTC(2)', 'BNT(3)'] +nodes() + +AG = ag.ArbGraph(nodes=nodes()) +N = AG.node_by_tkn +assert str(N("ETH")) == "ETH(0)" +assert str(N("BNT")) == "BNT(3)" +assert str(AG.node_by_ix(1)) == "USDC(1)" +assert str(AG.node_by_tkn("USDC")) == "USDC(1)" +AG + +assert str(N("ETH")) == "ETH(0)" + +edge = ag.Edge(N("ETH"), 1, N("USDC"), 2000) +edge1 = ag.Edge(N("ETH"), 1, N("USDC"), 2000, inverse=True, ix=10) +assert (edge.pair(), edge.price(), edge.convention()) == ('ETH/USDC', 2000.0, 'USDC per ETH') +assert (edge1.pair(), edge1.price(), edge1.convention()) == ('USDC/ETH', 0.0005, 'ETH per USDC') +edge, str(edge), str(edge1) + +assert (edge+0).asdict() == edge.asdict() +assert (edge+0) != edge # == means objects are the same +assert not edge+0 is edge +assert (2*edge).asdict() == (edge*2).asdict() +assert (edge + 2*edge).asdict() == (3*edge).asdict() +assert sum([edge,edge,edge]).asdict() == (3*edge).asdict() + +(edge+0).asdict() + +# ## Paths and cycles + +C = ag.Cycle([1,2,3,4,5]) +assert len(C) == 5 +assert [x for x in C.items()] == [1, 2, 3, 4, 5, 1] +assert [x for x in C.items(start_ix=3)] == [4, 5, 1, 2, 3, 4] +assert [x for x in C.items(start_val=3)] == [3, 4, 5, 1, 2, 3] +assert [p for p in C.pairs()] == [(1, 2), (2, 3), (3, 4), (4, 5), (5, 1)] + +c1 = ag.Cycle([1,2,3,4,5,6], "c1") +assert ag.Cycle([8,9]).is_subcycle_of(c1) == False +assert ag.Cycle([1,5,6]).is_subcycle_of(c1) == True +assert ag.Cycle([1,6,5]).is_subcycle_of(c1) == False +assert c1.filter_subcycles([ag.Cycle([8,9]), ag.Cycle([1,5,6]), ag.Cycle([1,6,5])]) == (ag.Cycle([1, 5, 6]),) +assert c1.filter_subcycles(ag.Cycle([1,5,6])) == (ag.Cycle([1, 5, 6]),) +assert str(c1) == 'cycle [c1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6 ->...' + +assert c1.asdict() == {'data': [1, 2, 3, 4, 5, 6], 'uid': 'c1', 'graph': None} +assert c1.astuple() == ([1, 2, 3, 4, 5, 6], 'c1', None) +assert (c1.asdf().set_index("uid")["data"] == c1.asdf(index="uid")["data"]).iloc[0] +assert list(c1.asdf(exclude=["data"]).columns) == ['uid', 'graph'] +assert list(c1.asdf(include=["data", "graph"], exclude=["graph"]).columns) == ['data'] + +import types +nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") +c2 = ag.Cycle(nodes, "c2") +assert c2.uid == "c2" +assert str(c2) == 'cycle [c2]: ETH->USDC->WBTC->BNT->...' +print(nodes) +print(c2) +gc2 = (c for c in c2.items()) +assert isinstance(gc2, types.GeneratorType) +tc2 = tuple(gc2) +assert str(tc2) == "(ETH(0), USDC(1), WBTC(2), BNT(3), ETH(0))" +assert tuple(gc2) == tuple() # generator spent +pc2 = (p for p in c2.pairs()) +assert isinstance(pc2, types.GeneratorType) +tpc2 = tuple(pc2) +assert len(tpc2) == 4 +assert str(tpc2[0]) == '(ETH(0), USDC(1))' +assert str(tpc2[-1]) == '(BNT(3), ETH(0))' +assert c2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT', 'BNT/ETH'] + +p1 = ag.Path([1,2,3,4,5,6], "p1") +assert p1.uid == "p1" +assert (str(p1)).strip() == 'path [p1]: 1 -> 2 -> 3 -> 4 -> 5 -> 6' +gp1 = (p for p in p1.items()) +assert isinstance(gp1, types.GeneratorType) +tp1 = tuple(gp1) +assert tp1 == (1, 2, 3, 4, 5, 6) + +nodes = ag.create_node_list("ETH, USDC, WBTC, BNT") +p2 = ag.Path(nodes, "p2") +assert p2.uid == "p2" +assert str(p2) == 'path [p2]: ETH->USDC->WBTC->BNT' +gp2 = (c for c in p2.items()) +assert isinstance(gp2, types.GeneratorType) +tp2 = tuple(gp2) +assert str(tp2) == "(ETH(0), USDC(1), WBTC(2), BNT(3))" +assert tuple(gp2) == tuple() # generator spent +pp2 = (p for p in p2.pairs()) +assert isinstance(pp2, types.GeneratorType) +tpp2 = tuple(pp2) +assert len(tpp2) == 3 +assert str(tpp2[0]) == '(ETH(0), USDC(1))' +assert str(tpp2[-1]) == '(WBTC(2), BNT(3))' +assert p2.pairs_s() == ['ETH/USDC', 'USDC/WBTC', 'WBTC/BNT'] + +# ## Arbgraph transport test and demo + +n = ag.Node("ETH") +assert isinstance(n.state, n.State) +assert n.state == n.State(amount = 0) + +try: + ag.Edge("ETH", 1, "USDC", 2000) + raise +except: + pass + +ETH = ag.Node("ETH") +USDC = ag.Node("USDC") +assert ETH != n # nodes are only equal if they are the same object! +assert ETH.asdict() == n.asdict() +edge = ag.Edge(ETH, 1, USDC, 2000) +edge2 = ag.Edge(ETH, 1, USDC, 2000) +edge3 = ag.Edge(ETH, 2, USDC, 3500) +assert (edge == edge2) == False +assert edge != ag.Edge(ETH, 1, USDC, 2000) +assert edge.asdict() == ag.Edge(ETH, 1, USDC, 2000).asdict() +assert edge.node_in == ETH +assert edge.node_out == USDC +assert edge.amount_in == 1 +assert edge.amount_out == 2000 +assert edge.state == ag.Edge.State(amount_in_remaining=1) + +ETH.reset_state() +USDC.reset_state() +edge.reset_state() +ETH.state.amount_.set(1) +assert ETH.state.amount == 1 +edge.transport(1, record=True) +assert ETH.state.amount == 0 +assert USDC.state.amount == 2000 +assert edge.state.amount_in_remaining == 0 + +ETH.reset_state() +USDC.reset_state() +edge.reset_state() +ETH.state.amount_.set(1) +edge.transport(0.25, record=True) +assert ETH.state.amount == 0.75 +assert USDC.state.amount == 500 +assert edge.state.amount_in_remaining == 0.75 +edge.transport(0.25, record=True) +assert ETH.state.amount == 0.5 +assert USDC.state.amount == 1000 +assert edge.state.amount_in_remaining == 0.50 + +ETH.reset_state() +USDC.reset_state() +edge.reset_state() +ETH.state.amount = 1 +try: + edge.transport(2, record=True) +except Exception as e: + print(e) + +ETH.reset_state() +USDC.reset_state() +edge.reset_state() +ETH.state.amount = 0.5 +try: + edge.transport(1, record=True) +except Exception as e: + print(e) + +ETH.reset_state() +USDC.reset_state() +edge.reset_state() +ETH.state.amount = 2 +edge.transport(0.5, record=True) +try: + edge.transport(1, record=True) +except Exception as e: + print(e) + +ETH.state.amount = 10 +edge.state.amount_in_remaining = 10 +AG = ag.ArbGraph(nodes=[ETH, USDC], edges=[edge, edge2, edge3]) +assert AG.nodes == [ETH, USDC] +assert AG.edges == [edge, edge2, edge3] +assert AG.nodes[0].state.amount == 10 +assert AG.edges[0].state.amount_in_remaining == 10 +AG.reset_state() +assert AG.nodes[0].state.amount == 0 +assert AG.edges[0].state.amount_in_remaining == 1 +assert AG.state.nodes[0] == ETH.state +assert AG.state.edges[0] == edge.state + +assert AG.node_by_tkn("ETH") is ETH +assert AG.node_by_tkn(ETH) is ETH +try: + AG.node_by_tkn(ag.Node("ETH")) + raise +except Exception as e: + print(e) + +AG.reset_state() +ETH.state.amount = 4 +r = AG.transport(2, "ETH", "USDC", record=True) +assert ETH.state.amount == 2 +assert r.amount_in.amount == 2 +assert r.amount_in.tkn == "ETH" +capacity_in = sum([e_.amount_in for e_ in r.edges]) +assert capacity_in == 4 +capacity_out = sum([e_.amount_out for e_ in r.edges]) +assert capacity_out == 7500 +assert r.amount_out.amount == r.amount_in.amount * capacity_out / capacity_in +assert sum(r.amounts_in) == r.amount_in.amount +assert sum(r.amounts_out) == r.amount_out.amount +assert AG.has_capacity("ETH", "USDC") +assert AG.has_capacity() +AG.transport(2, "ETH", "USDC", record=True) +assert AG.has_capacity() == False +r + +rs = AG.edge_statistics(edges=r.edges) +assert rs.len == 3 +assert rs.edges is r.edges +assert rs.amounts_in == (1, 1, 2) +assert rs.amounts_in_remaining == (0.0, 0.0, 0.0) +assert rs.amounts_out == (2000, 2000, 3500) +assert rs.prices == (2000.0, 2000.0, 1750.0) +assert rs.utilizations == (1.0, 1.0, 1.0) +assert rs.amount_in.amount == 4 +assert rs.amount_in_remaining.amount == 0.0 +assert rs.amount_out.amount == 7500 +assert rs.amount_in.tkn == "ETH" +assert rs.amount_in_remaining.tkn == "ETH" +assert rs.amount_out.tkn == "USDC" +assert rs.utilization == 1.0 +assert rs.price == 1875.0 +rs + +rns = AG.node_statistics("ETH") +assert len(rns.edges_out) == 3 +assert len(rns.edges_in) == 0 +assert rns.amount_in.amount == 0 +assert rns.amount_out.amount == 4 +assert rns.amount_out_remaining.amount == 0 +assert rns.nodes_in==set() +assert rns.nodes_out=={"USDC"} +rns + +rns2 = AG.node_statistics("USDC") +assert len(rns2.edges_out) == 0 +assert len(rns2.edges_in) == 3 +assert rns2.amount_in.amount == 7500 +assert rns2.amount_out.amount == 0 +assert rns2.amount_out_remaining.amount == 0 +assert rns2.nodes_in==set(["ETH",]) +assert rns2.nodes_out==set() +rns2 + + +# ## Arbgraph transport test and demo 2 + +@ag.dataclass +class MyState(): + myval_: ag.TrackedStateFloat = ag.field(default_factory=ag.TrackedStateFloat, init=False) + myval: ag.InitVar=None + + def __post_init__(self, myval): + self.myval = myval + + @property + def myval(self): + return self.myval_.value + + @myval.setter + def myval(self, value): + self.myval_.set(value) + + +mystate = MyState(0) +mystate.myval_.set(10) +assert mystate.myval == 10 +mystate.myval += 5 +assert mystate.myval == 15 +mystate.myval -= 4 +assert mystate.myval == 11 +assert mystate.myval_.history == [0, 0, 10, 15, 11] + +mystate = MyState(10) +assert mystate.myval == 10 +assert mystate.myval_.history == [0,10] +mystate.myval = 20 +assert mystate.myval == 20 +assert mystate.myval_.history == [0,10,20] +mystate.myval += 5 +assert mystate.myval == 25 +mystate.myval -= 4 +assert mystate.myval == 21 +assert mystate.myval_.history == [0,10,20,25,21] +assert mystate.myval_.reset(42) +assert mystate.myval == 42 +assert mystate.myval_.history == [42] + +n = ag.Node("MEH") +n.state.amount = 10 +n.state.amount += 5 +n.state.amount -= 4 +assert n.state.amount == 11 +assert n.state.amount_.history == [0, 10, 15, 11] +n.reset_state() +assert n.state.amount_.history == [0] + +nodes = ag.Node.create_node_list("USDC, LINK, ETH, WBTC") +assert len(nodes)==4 +assert nodes[0].tkn == "USDC" +AG = ag.ArbGraph(nodes) +AG.add_edge("USDC", 10000, "ETH", 5) +AG.add_edge_obj(AG.edges[-1].R()) +AG.add_edge("USDC", 10000, "WBTC", 1) +AG.add_edge_obj(AG.edges[-1].R()) +AG.add_edge("USDC", 10000, "LINK", 1000) +AG.add_edge_obj(AG.edges[-1].R()) +AG.add_edge("LINK", 1000, "ETH", 5) +AG.add_edge_obj(AG.edges[-1].R()) +AG.add_edge("ETH", 5, "WBTC", 1) +AG.add_edge_obj(AG.edges[-1].R()) +assert len(AG.edges)==10 +assert len(AG.cycles())==11 +ns = AG.node_statistics("USDC") +assert ns.amount_in.amount == 30000 +assert ns.amount_out.amount == 30000 +assert ns.amount_out_remaining == ns.amount_out +assert ns.nodes_out==set(['WBTC', 'ETH', 'LINK']) +assert ns.nodes_in==set(['WBTC', 'ETH', 'LINK']) +#_=AG.plot() + +# ## Transport 3 and prices + +AG = ag.ArbGraph() +prices = dict(USDC=1, LINK=5, AAVE=100, WETH=2000, BTC=10000) +for t1,p1 in prices.items(): + for t2,p2 in prices.items(): + if t1 2000 USDC(1)' + +assert raises (lambda: e1+e3) +assert raises (lambda: -2*e1) +assert raises (lambda: e3*(-2)) +try: + e1 += e3 + raise +except ValueError as e: + pass + +assert not raises (lambda: e4+e5) +assert not raises (lambda: 2*e4) +assert not raises (lambda: e4*2) +e4 += e5 + +assert e6.amount_in == 1 +assert e1.transport() == e6.transport() +assert e1.transport(amount_in=1e6) == 1e6*e1.transport() + +AG = ag.ArbGraph(nodes = [ETH, USDC]) +assert AG.edgetype is None +AG.add_edge_obj(e1) +assert AG.edgetype == AG.EDGE_CONNECTION +assert AG.edgetype == e1.EDGE_CONNECTION +AG.add_edge_obj(e2) +assert raises(AG.add_edge_obj, e4) +assert AG.edgetype == e1.EDGE_CONNECTION + +AG = ag.ArbGraph(nodes = [ETH, USDC]) +assert AG.edgetype is None +AG.add_edge_obj(e4) +assert AG.edgetype == AG.EDGE_AMOUNT +assert AG.edgetype == e1.EDGE_AMOUNT +AG.add_edge_obj(e5) +assert raises(AG.add_edge_obj, e1) +assert AG.edgetype == e1.EDGE_AMOUNT + +AG = ag.ArbGraph() +AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000) +AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5) +AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000) +assert AG.edgetype == AG.EDGE_CONNECTION +assert len(AG) == 6 +#_=AG.plot() + +AG = ag.ArbGraph() +AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="USDC", price=2000, symmetric=False) +AG.add_edge_connectiontype(tkn_in="ETH", tkn_out="BTC", price=1/5, symmetric=False) +AG.add_edge_connectiontype(tkn_in="BTC", tkn_out="USDC", price=10000, symmetric=False) +assert AG.edgetype == AG.EDGE_CONNECTION +assert len(AG) == 3 +#_=AG.plot() + +AG = ag.ArbGraph() +assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", price=2000, price_outperin=2000) +assert raises (AG.add_edge_connectiontype, tkn_in="ETH", tkn_out="USDC", inverse = True, price_outperin=2000) +assert AG.add_edge_connectiontype == AG.add_edge_ct + +AG = ag.ArbGraph() +for i in range(5): + mul = 1+i/50 + AG.add_edge_ct(tkn_in="ETH", tkn_out="USDC", price=2000*mul) + AG.add_edge_ct(tkn_in="WBTC", tkn_out="USDC", price=10000*mul) + AG.add_edge_ct(tkn_in="ETH", tkn_out="WBTC", price=0.2/mul) +assert AG.len() == (2*3*5, 3) +assert len(AG.cycles()) == 5 +assert np.array_equal(AG.A.toarray(), np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])) +print(AG.A) +AG2 = AG.duplicate() +assert AG2.len() == (6,3) +edges = AG.filter_edges("ETH", "USDC") +assert len(edges) == 5 +edges2 = AG2.filter_edges("ETH", "USDC") +assert len(edges2) == 1 +assert [e.p_outperin for e in edges] == [2000.0, 2040.0, 2080.0, 2120.0, 2160.0] +assert edges2[0].p_outperin == np.mean([e.p_outperin for e in edges]) + +# ## Interaction with CPC + +c1 = CPC.from_univ2(x_tknb=1, y_tknq=2000, pair="ETH/USDC", fee=0, cid="1", descr="UniV2") +c2 = CPC.from_univ2(x_tknb=1, y_tknq=10000, pair="WBTC/USDC", fee=0, cid="2", descr="UniV2") +c3 = CPC.from_univ2(x_tknb=1, y_tknq=5, pair="WBTC/ETH", fee=0, cid="3", descr="UniV2") +assert c1.p == 2000 +assert c2.p == 10000 +assert c3.p == 5 + +AG = ag.ArbGraph() +AG.add_edges_cpc(c1) +AG.add_edges_cpc(c2) +AG.add_edges_cpc(c3) +#_=AG.plot() + +AG = ag.ArbGraph() +AG.add_edges_cpc([c1, c2, c3]) +#_=AG.plot() + +AG = ag.ArbGraph() +AG.add_edges_cpc(c for c in [c1, c2, c3]) +#_=AG.plot() + +AG = ag.ArbGraph() +CC = CPCContainer([c1,c2,c3]) +AG.add_edges_cpc(CC) +#_=AG.plot() + +print(AG.A) + +AG.cycles() + +# ## With real data from CPC + +try: + df = pd.read_csv("NBTEST_063_Curves.csv.gz") +except: + df = pd.read_csv("carbon/tests/nbtest_data/NBTEST_063_Curves.csv.gz") +CC0 = CPCContainer.from_df(df) +print("Num curves:", len(CC0)) +print("Num pairs:", len(CC0.pairs())) +print("Num tokens:", len(CC0.tokens())) +print(CC0.tokens_s()) + +AG0 = ag.ArbGraph().add_edges_cpc(CC0) +#AG0.plot() +assert AG0.len() == (918, 141) + +assert str(AG0.A)[:60] ==' (0, 1)\t1\n (1, 0)\t1\n (2, 3)\t1\n (2, 4)\t1\n (2, 5)\t1\n (2,' + +pairs = CC0.filter_pairs(bothin="WETH, USDC, UNI, AAVE, LINK") +CC = CC0.bypairs(pairs, ascc=True) +AG = ag.ArbGraph().add_edges_cpc(CC) +#AG.plot() +AG.len() == (24, 5) + +assert np.all(AG.A.toarray() == np.array( + [[0, 1, 1, 0, 0], + [1, 0, 1, 1, 1], + [1, 1, 0, 1, 1], + [0, 1, 1, 0, 0], + [0, 1, 1, 0, 0]])) + +assert raises(AG.edge_statistics,"WETH", "USDC") + +AG.edgedf(consolidated=False) + +df = AG.edgedf(consolidated=True) +df + +dx,dy = ((71.22, -0.28, 3.4, -10.82, 755278.31, -65.01, -5.93, -3.38, -0.02, 60.27, -49.45, 1507698.66, -2263343.63), + (-0.3, 1.99, -0.14, 0.04, -393.48, 0.27, 46.42, 0.13, 1.41, -0.2, 316.84, -786.1, 833.78)) +AG2 = ag.ArbGraph() +for cpc, dx_, dy_ in zip(CC, dx, dy): + print(dx_, cpc.tknx, dy_, cpc.tkny, cpc.cid) + AG2.add_edge_dxdy(cpc.tknx, dx_, cpc.tkny, dy_, uid=cpc.cid) + #print("---") + +#_=AG2.plot() +assert AG2.len() == (12,5) + +assert np.all(AG2.A.toarray() == np.array( + [[0, 1, 0, 0, 0], + [1, 0, 0, 1, 1], + [1, 1, 0, 1, 1], + [0, 1, 0, 0, 0], + [0, 1, 0, 0, 0]])) +print(AG2.A.toarray()) + +assert AG2.edge_statistics("WETH", "USDC", bothways=False) is None +assert len(AG2.edge_statistics("WETH", "USDC", bothways=True)) == 2 +assert AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict()["amounts_in_remaining"] == (755278.31, 1507698.66) +AG2.edge_statistics("WETH", "USDC", bothways=True)[1].asdict() + +assert AG2.filter_edges("WETH", "USDC") == [] +assert AG2.filter_edges("WETH", "USDC", bothways=True)[0].amount_in == 755278.31 +assert AG2.filter_edges("WETH", "USDC", bothways=True) == AG2.filter_edges("USDC", "WETH") +assert AG2.filter_edges(pair="WETH/USDC", bothways=False) == [] +assert AG2.filter_edges(pair="WETH/USDC") == AG2.filter_edges("WETH", "USDC", bothways=True) +assert AG2.filter_edges == AG2.fe +assert AG2.fep("WETH/USDC") == AG2.filter_edges(pair="WETH/USDC") +assert AG2.fep("WETH/USDC", bothways=False) == AG2.filter_edges(pair="WETH/USDC", bothways=False) +assert tuple(AG2.edgedf(consolidated=True, resetindex=False).iloc[0]) == (1.41, 0.02) +assert len(AG2.edgedf(consolidated=False)) == len(AG2) + +assert len(AG2.edgedf(consolidated=False)) == 12 +AG2.edgedf(consolidated=False) + +assert len(AG2.edgedf(consolidated=True, resetindex=False)) == 10 +AG2.edgedf(consolidated=True, resetindex=False) + +# ## Amount algebra + +A = ag.Amount +nodes = lambda: ag.create_node_list("ETH, USDC") +ETH, USDC = nodes() + +ae1, ae2, au1 = A(1, ETH), A(2, ETH), A(1, USDC) + +assert ae1 + ae2 == 3*ae1 +assert ae2 - ae1 == ae1 +assert -ae1 + ae2 == ae1 +assert 2*ae1 == ae2 +assert ae1*2 == ae2 +assert ae1/2 +ae1/2 == ae1 +assert round(ae1/9,2) == round(1/9,2)*ae1 +assert round(ae1/9,4) == round(1/9,4)*ae1 +assert math.floor(ae1/9) == math.floor(1/9)*ae1 +assert math.ceil(ae1/9) == math.ceil(1/9)*ae1 +assert (ae1 + 2*ae1)/ae1 == 3 + +assert raises (lambda: ae1 + 1) +assert raises (lambda: ae1 - 1) +assert raises (lambda: 1 + ae1) +assert raises (lambda: 1 - ae1) + +assert 2*ae1 > ae1 +assert 2*ae1 >= ae1 +assert .2*ae1 < ae1 +assert .2*ae1 <= ae1 +assert ae1 <= ae1 +assert ae1 >= ae1 +assert not ae1 < ae1 +assert not ae1 > ae1 + +# ## Specific Arb examples + +# ### USDC/ETH + +AG = ag.ArbGraph() +AG.add_edge("ETH", 1, "USDC", 2000) +AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) +G = AG.as_graph() +print(AG.cycles()) +#_=AG.plot() + +for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("ETH")): + print(c) + +c, AG.filter_edges(*c) + +AG.A.toarray() + +# ### USDC/LINK to ETH (oneway) + +AG = ag.ArbGraph() +AG.add_edge("USDC", 100, "ETH", 100/2000) +AG.add_edge("LINK", 100, "USDC", 1000) +AG.add_edge("USDC", 900, "LINK", 100, inverse=True) +G = AG.as_graph() +print(AG.cycles()) +#_=AG.plot() + +# _=AG.duplicate().plot() + +for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("USDC")): + print(c) + +c, AG.filter_edges(*c) + +AG.A.toarray() + +# ### USDD, LINK, ETH cycle + +AG = ag.ArbGraph() +AG.add_edge("ETH", 1, "USDC", 2000) +AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) +AG.add_edge("LINK", 200, "ETH", 1, inverse=True) +G = AG.as_graph() +print(AG.cycles()) +#_=AG.plot() + +for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("USDC")): + print(c) + +c, AG.filter_edges(*c) + +AG.A.toarray() + +# ### USDD, LINK, ETH cycle plus ETH/USDC + +AG = ag.ArbGraph() +AG.add_edge("ETH", 1, "USDC", 2000) +AG.add_edge("ETH", 1, "USDC", 2000) +AG.add_edge("USDC", 1500, "LINK", 200, inverse=True) +AG.add_edge("LINK", 200, "ETH", 1, inverse=True) +AG.add_edge("USDC", 1800, "ETH", 1, inverse=True) +G = AG.as_graph() +print(AG.cycles()) +#_=AG.plot() + +# + +#_=AG.duplicate().plot() +# - + +AG.edges + +AG.duplicate().edges + +AG.A.toarray() + +for C in AG.cycles(): + print(f"==={C}===") + for c in C.pairs(start_val=AG.n("ETH")): + print(c) + +cycle = AG.cycles()[1] +cycle + +for cycle in AG.cycles(): + result = AG.run_arbitrage_cycle(cycle=cycle, verbose=True) + print(result) + print("---") + +assert raises(AG.price, AG.nodes[0], AG.nodes[1]) +raises(AG.price, AG.nodes[0], AG.nodes[1]) + + + diff --git a/resources/NBTest/NBTest_066_Uniswap.ipynb b/resources/NBTest/NBTest_066_Uniswap.ipynb new file mode 100644 index 00000000..61396c33 --- /dev/null +++ b/resources/NBTest/NBTest_066_Uniswap.ipynb @@ -0,0 +1,463 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cc40bc23-abde-4094-abec-419f0a7fa81e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[stdimports] imported np, pd, plt, os, sqrt, exp, log\n", + "ConstantProductCurve v2.6.1 (18/Apr/2023)\n", + "Univ3Calculator v1.1 (19/Apr/2023)\n", + "Carbon v2.4.2-BETA2 (09/Apr/2023)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_1172/2876167754.py:8: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-