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advance version & add new notebook
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Signed-off-by: Amichay Oren <[email protected]>
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@amor71
amor71 committed Dec 22, 2020
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389 changes: 389 additions & 0 deletions analysis/notebooks/strategy-p-value.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How good was my strategy?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"environment = \"PAPER\" # PAPER / PROD / BACKTEST\n",
"date = \"2020-12-17\"\n",
"strategy ='short_trap_buster'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from datetime import datetime, timedelta\n",
"import numpy as np\n",
"from pytz import timezone\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import HTML, display, Markdown\n",
"from liualgotrader.models.gain_loss import TradeAnalysis\n",
"from liualgotrader.models.trending_tickers import TrendingTickers\n",
"from liualgotrader.analytics.analysis import strategy_return_for_qty_one\n",
"import alpaca_trade_api as tradeapi"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### connect to data-source"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"api = tradeapi.REST(base_url=\"https://api.alpaca.markets\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How to calculate strategy relevance, using p-value"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* Pick $e𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡$ , $𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦$ , and a $𝑑𝑎𝑡𝑒$ when strategy was used,\n",
"* Let $window$ be the length in minutes of trading windows, during which $strategy$ may buy a stock,\n",
"* Let $C$ be the list of all scanned stocks during $date$,\n",
"* For $c \\in C$, Let $T_{0}(c)$=the time $c$ was added to $C$, \n",
"* For $c \\in C$, Let $Duration(c)$=$window-T_{0}(c)$, \n",
"* For $c \\in C$, Let $Hold(c)$=sum of time $stategy$ had position in $c$, \n",
"* Let $buy-actions$/$sell-actions$ represent all buy(/sell) decisions made by $strategy$,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### $Prob(BUY) = \\frac{|buy-actions|}{\\sum \\limits _{c}^{C} Duration(c) - Hold(c)}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### $Prob(SELL) = \\frac{|sell-actions|}{\\sum \\limits _{c}^{C} Hold(c)}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### pseudo-code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<code>gain-vs-strat = 0 \n",
" for i in range(10000):\n",
" gain = 0 \n",
" for c in C:\n",
" for t in range($T_{0}(c)$,$windows$):\n",
" gain += calculate gain based on Prob(BUY) and Prob(SELL)\n",
" gain-vs-strat += 1 if stategy_gain > gain else 0</code> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## $1 - p_{value} = \\frac{gain-vs-strat}{10,000}$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NOTE: below 0.95 strategy is bad. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## The real stuff"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### calculate gains from my strategy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"my_gains = await strategy_return_for_qty_one(\n",
" strategy=strategy, env=environment, start_date=datetime.strptime(date, \"%Y-%m-%d\")\n",
")\n",
"my_gains"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scanned = await TrendingTickers.load_by_date_and_env(env=environment, start_date=datetime.strptime(date, \"%Y-%m-%d\"))\n",
"scanned['hold'] = timedelta(0)\n",
"print(f\"loaded {len(scanned)} entries\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = await TradeAnalysis.load(environment, datetime.strptime(date, \"%Y-%m-%d\"))\n",
"df = df.loc[df.algo_name == strategy]\n",
"print(f\"loaded {len(df)} entries\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for i, row in scanned.iterrows():\n",
" hold_time = df.loc[\n",
" (df.batch_id == row.batch_id) & (df.symbol == row.symbol)\n",
" ].hold.sum()\n",
" scanned.loc[scanned.index == i, \"hold\"] = (\n",
" hold_time if not pd.isnull(hold_time) else timedelta(0)\n",
" )\n",
" duration = timedelta(minutes=120) - (\n",
" row.create_tstamp.to_pydatetime()\n",
" - row.create_tstamp.to_pydatetime().replace(\n",
" hour=14, minute=30, second=0, microsecond=0\n",
" )\n",
" )\n",
" scanned.loc[scanned.index == i, \"duration\"] = (\n",
" duration if duration >= timedelta(days=0) else pd.NaT\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scanned=scanned.dropna(how='any',axis=0) \n",
"scanned['delta'] = scanned.duration-scanned.hold"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prob_buy = 1.0 * len(df) / (scanned.delta.sum().total_seconds() // 60)\n",
"prob_buy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"prob_sell = 1.0 * len(df) / (scanned.hold.sum().total_seconds() // 60)\n",
"prob_sell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def gen_rand(prob:float) -> int:\n",
" return np.random.choice([1,0],1,p=[prob,1-prob])[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"utc = timezone(\"UTC\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"minute_history = {}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def load_data(symbol: str, start: datetime, end: datetime) -> pd.DataFrame:\n",
" print(\"load\", symbol, start)\n",
" retry = 5\n",
" while retry > 0:\n",
" try:\n",
" payload = api.polygon.historic_agg_v2(\n",
" symbol,\n",
" 1,\n",
" \"minute\",\n",
" _from=str(start),\n",
" to=str(end),\n",
" ).df\n",
" break\n",
" except Exception:\n",
" retry -= 1\n",
" continue\n",
" \n",
" return payload\n",
"\n",
"\n",
"def calculate_random_strategy():\n",
" gain = 0\n",
" for i, row in scanned.iterrows():\n",
" b = row.create_tstamp.replace(second=0, microsecond=0)\n",
" end_buy = b.replace(hour=16, minute=30, second=0, microsecond=0)\n",
" end_sell = b.replace(hour=21, minute=0, second=0, microsecond=0)\n",
" while b < end_buy:\n",
" to_buy = False\n",
" if gen_rand(prob_buy):\n",
" # print(f\"found buy {row.symbol} {b}\")\n",
" to_buy = True\n",
"\n",
" b += timedelta(minutes=1)\n",
" if to_buy:\n",
" s = b\n",
" while s < end_sell:\n",
" if gen_rand(prob_sell):\n",
" break\n",
" s += timedelta(minutes=1)\n",
" \n",
" if row.symbol not in minute_history:\n",
" minute_history[row.symbol] = load_data(row.symbol, b, s)\n",
" else:\n",
" try:\n",
" _ = minute_history[row.symbol].index.get_loc(\n",
" str(pd.Timestamp(b, tz=utc).tz_convert(\"US/Eastern\")),\n",
" method=\"pad\",\n",
" tolerance=None,\n",
" )\n",
" except Exception as e:\n",
" new_data = load_data(row.symbol, b, s)\n",
"\n",
" minute_history[row.symbol] = (\n",
" minute_history[row.symbol].append(new_data)\n",
" if minute_history[row.symbol].index[0] < new_data.index[0]\n",
" else new_data.append(minute_history[row.symbol])\n",
" )\n",
"\n",
" b_index = minute_history[row.symbol].index.get_loc(\n",
" str(pd.Timestamp(b, tz=utc).tz_convert(\"US/Eastern\")),\n",
" method=\"pad\",\n",
" tolerance=None,\n",
" )\n",
" s_index = minute_history[row.symbol].index.get_loc(\n",
" str(pd.Timestamp(s, tz=utc).tz_convert(\"US/Eastern\")),\n",
" method=\"pad\",\n",
" tolerance=None,\n",
" )\n",
" gain += (\n",
" minute_history[row.symbol].iloc[s_index].close\n",
" - minute_history[row.symbol].iloc[b_index].close\n",
" )\n",
"\n",
" b = s\n",
" return gain"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"i_win = 0\n",
"for i in range (10000):\n",
" random_gains = calculate_random_strategy()\n",
" if my_gains > random_gains:\n",
" i_win += 1\n",
" \n",
" print(i+1, i_win/(i+1), random_gains)"
]
}
],
"metadata": {
"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": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
2 changes: 1 addition & 1 deletion liualgotrader/__init__.py
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@@ -1 +1 @@
__version__ = "0.0.81"
__version__ = "0.0.82"

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