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vol_adjsize.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Feb 27 10:46:08 2016
@author: Hidemi
"""
import time
import math
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
import sqlite3
import io
import traceback
import json
import imp
import urllib
import urllib2
import requests
import webbrowser
import re
import datetime
from datetime import datetime as dt
import calendar
import time
import inspect
import os
import os.path
import sys
import ssl
from copy import deepcopy
from suztoolz.transform import ATR2
import matplotlib.pyplot as plt
import seaborn as sns
from suztoolz.datatools.seasonalClass import seasonalClassifier
import logging
start_time = time.time()
version='v4'
systems = ['v4futures','v4mini','v4micro']
lookback=20
refresh=False
currencyFile = 'currenciesATR.csv'
systemFilename='system_v4futures.csv'
systemFilename2='system_v4mini.csv'
systemFilename3='system_v4micro.csv'
c2id_macro=110126294
c2id_mini=110125449
c2id_micro=110125347
c2key=c2key_micro=c2key_mini=c2key_macro='O9WoxVj7DNXkpifMY_blqHpFg5cp3Fjqc7Aiu4KQjb8mXQlEVx'
if len(sys.argv)==1:
debug=True
showPlots=False
#refreshSea tries to recreate the first run futuresATR file after new signals have been generated
refreshSea=False
dbPath='./data/futures.sqlite3'
dbPath2='./data/futures.sqlite3'
dbPathWeb = './web/tsdp/db.sqlite3'
dataPath='./data/csidata/v4futures2/'
savePath= './data/results/'
pngPath=savePath2 = './data/results/'
feedfile='.data/systems/system_ibfeed.csv'
#test last>old
#dataPath2=savePath2
#signalPath = 'C:/Users/Hidemi/Desktop/Python/SharedTSDP/data/signals/'
#test last=old
dataPath2='./data/'
signalPath ='./data/signals/'
signalSavePath = './data/signals/'
systemPath = './data/systems/'
logging.basicConfig(filename='/logs/vol_adjsize_error.log',level=logging.DEBUG)
else:
debug=False
showPlots=False
#if set to on its probably going to mess up the db.
refreshSea=False
dbPathWeb ='./web/tsdp/db.sqlite3'
dbPath=dbPath2='./data/futures.sqlite3'
dataPath='./data/csidata/v4futures2/'
dataPath2='./data/'
savePath='./data/'
signalPath = './data/signals/'
signalSavePath = './data/signals/'
savePath2 = './data/results/'
pngPath= './web/tsdp/betting/static/images/'
systemPath = './data/systems/'
feedfile='./data/systems/system_ibfeed.csv'
logging.basicConfig(filename='/logs/vol_adjsize_error.log',level=logging.DEBUG)
writeConn = sqlite3.connect(dbPath)
readConn = sqlite3.connect(dbPath2)
readWebConn = sqlite3.connect(dbPathWeb)
webready = False
if webready:
#request='http://www.globalsystemsmanagement.net/last_userselection/'
#selectionDF = pd.DataFrame(requests.get(request).json())
#selectionDict=eval(selectionDF.selection[0])
selectionDF=pd.read_sql('select * from betting_userselection where timestamp=\
(select max(timestamp) from betting_userselection as maxtimestamp)', con=readWebConn, index_col='userID')
selectionDict=eval(selectionDF.selection.values[0])
c2system_macro=c2system=selectionDict["v4futures"][0]
c2system_mini=selectionDict["v4mini"][0]
c2system_micro=selectionDict["v4micro"][0]
else:
c2system_macro=c2system='RiskOn'
c2system_mini='RiskOn'
c2system_micro='RiskOn'
c2safef=1
ComponentsDict ={
'Off':'None',
'Previous':'prevACT',
'Anti-Previous':'AntiPrevACT',
'RiskOn':'RiskOn',
'RiskOff':'RiskOff',
'Custom':'Custom',
'Anti-Custom':'AntiCustom',
'50/50':'0.75LastSIG',
'LowestEquity':'0.5LastSIG',
'HighestEquity':'1LastSIG',
'AntiHighestEquity':'Anti1LastSIG',
'Anti50/50':'Anti0.75LastSIG',
'AntiLowestEquity':'Anti0.5LastSIG',
'Seasonality':'LastSEA',
'Anti-Seasonality':'AntiSEA',
}
fxRates=pd.read_csv(dataPath2+currencyFile, index_col=0)
for i,col in enumerate(fxRates.columns):
if 'Last' in col:
fxRates = fxRates[fxRates.columns[i]]
break
fxDict={
'AUD':1/fxRates.ix['AUDUSD'],
'CAD':fxRates.ix['USDCAD'],
'CHF':fxRates.ix['USDCHF'],
'EUR':1/fxRates.ix['EURUSD'],
'GBP':1/fxRates.ix['GBPUSD'],
'HKD':fxRates.ix['USDHKD'],
'JPY':fxRates.ix['USDJPY'],
'NZD':1/fxRates.ix['NZDUSD'],
'SGD':fxRates.ix['USDSGD'],
'USD':1,
}
#csisym:[c2sym,usdFXrate,multiplier,riskON signal,Custom]
c2contractSpec = {
'AC':['@AC',fxDict['USD'],29000,'energy',1,1],
'AD':['@AD',fxDict['USD'],100000,'currency',1,1],
'AEX':['AEX',fxDict['EUR'],200,'index',1,1],
'BO':['@BO',fxDict['USD'],600,'grain',1,1],
'BP':['@BP',fxDict['USD'],62500,'currency',1,1],
'C':['@C',fxDict['USD'],50,'grain',1,1],
'CC':['@CC',fxDict['USD'],10,'soft',1,1],
'CD':['@CD',fxDict['USD'],100000,'currency',1,1],
'CGB':['CB',fxDict['CAD'],1000,'rates',-1,1],
'CL':['QCL',fxDict['USD'],1000,'energy',1,1],
'CT':['@CT',fxDict['USD'],500,'soft',1,1],
'CU':['@EU',fxDict['USD'],125000,'currency',1,1],
'DX':['@DX',fxDict['USD'],1000,'currency',-1,-1],
'EBL':['BD',fxDict['EUR'],1000,'rates',-1,1],
'EBM':['BL',fxDict['EUR'],1000,'rates',-1,1],
'EBS':['EZ',fxDict['EUR'],1000,'rates',-1,1],
'ED':['@ED',fxDict['USD'],2500,'rates',-1,1],
'EMD':['@EMD',fxDict['USD'],100,'index',1,1],
'ES':['@ES',fxDict['USD'],50,'index',1,1],
'FC':['@GF',fxDict['USD'],500,'meat',1,-1],
'FCH':['MT',fxDict['EUR'],10,'index',1,1],
'FDX':['DXM',fxDict['EUR'],5,'index',1,1],
'FEI':['IE',fxDict['EUR'],2500,'rates',-1,1],
'FFI':['LF',fxDict['GBP'],10,'index',1,1],
'FLG':['LG',fxDict['GBP'],1000,'rates',-1,1],
'FSS':['LL',fxDict['GBP'],1250,'rates',-1,-1],
'FV':['@FV',fxDict['USD'],1000,'rates',-1,1],
'GC':['QGC',fxDict['USD'],100,'metal',-1,1],
'HCM':['HHI',fxDict['HKD'],50,'index',1,1],
'HG':['QHG',fxDict['USD'],250,'metal',1,1],
'HIC':['HSI',fxDict['HKD'],50,'index',1,1],
'HO':['QHO',fxDict['USD'],42000,'energy',1,1],
'JY':['@JY',fxDict['USD'],125000,'currency',-1,1],
'KC':['@KC',fxDict['USD'],375,'soft',1,1],
'KW':['@KW',fxDict['USD'],50,'grain',1,1],
'LB':['@LB',fxDict['USD'],110,'soft',1,-1],
'LC':['@LE',fxDict['USD'],400,'meat',1,-1],
'LCO':['EB',fxDict['USD'],1000,'energy',1,1],
'LGO':['GAS',fxDict['USD'],100,'energy',1,1],
'LH':['@HE',fxDict['USD'],400,'meat',1,-1],
'LRC':['LRC',fxDict['USD'],10,'soft',1,1],
'LSU':['QW',fxDict['USD'],50,'soft',1,1],
'MEM':['@MME',fxDict['USD'],50,'index',1,1],
'MFX':['IB',fxDict['EUR'],10,'index',1,1],
'MP':['@PX',fxDict['USD'],500000,'currency',1,1],
'MW':['@MW',fxDict['USD'],50,'grain',1,1],
'NE':['@NE',fxDict['USD'],100000,'currency',1,1],
'NG':['QNG',fxDict['USD'],10000,'energy',1,1],
'NIY':['@NKD',fxDict['JPY'],500,'index',1,-1],
'NQ':['@NQ',fxDict['USD'],20,'index',1,1],
'O':['@O',fxDict['USD'],50,'grain',1,-1],
'OJ':['@OJ',fxDict['USD'],150,'soft',1,1],
'PA':['QPA',fxDict['USD'],100,'metal',1,1],
'PL':['QPL',fxDict['USD'],50,'metal',-1,1],
'RB':['QRB',fxDict['USD'],42000,'energy',1,1],
'RR':['@RR',fxDict['USD'],2000,'grain',1,-1],
'RS':['@RS',fxDict['CAD'],20,'grain',1,-1],
'S':['@S',fxDict['USD'],50,'grain',1,-1],
'SB':['@SB',fxDict['USD'],1120,'soft',1,1],
'SF':['@SF',fxDict['USD'],125000,'currency',1,1],
'SI':['QSI',fxDict['USD'],50,'metal',-1,1],
'SIN':['IN',fxDict['USD'],2,'index',1,1],
'SJB':['BB',fxDict['JPY'],100000,'rates',-1,1],
'SM':['@SM',fxDict['USD'],100,'grain',1,-1],
'SMI':['SW',fxDict['CHF'],10,'index',1,1],
'SSG':['SS',fxDict['SGD'],200,'index',1,-1],
'STW':['TW',fxDict['USD'],100,'index',1,1],
'SXE':['EX',fxDict['EUR'],10,'index',1,1],
'TF':['@TFS',fxDict['USD'],100,'index',1,1],
'TU':['@TU',fxDict['USD'],2000,'rates',-1,1],
'TY':['@TY',fxDict['USD'],1000,'rates',-1,1],
'US':['@US',fxDict['USD'],1000,'rates',-1,1],
'VX':['@VX',fxDict['USD'],1000,'index',-1,-1],
'W':['@W',fxDict['USD'],50,'grain',1,1],
'YA':['AP',fxDict['AUD'],25,'index',1,-1],
'YB':['HBS',fxDict['AUD'],2400,'rates',-1,1],
'YM':['@YM',fxDict['USD'],5,'index',1,1],
'YT2':['HTS',fxDict['AUD'],2800,'rates',-1,1],
'YT3':['HXS',fxDict['AUD'],8000,'rates',-1,1],
}
##ACCTINFO
acctinfofile='./web/tsdp/accountinfo_data.json'
with open(acctinfofile, 'r') as f:
acctinfo=json.load(f)
riskEquity=int(acctinfo['v4futures']['riskEquity'])
riskEquity_mini=int(acctinfo['v4mini']['riskEquity'])
riskEquity_micro=int(acctinfo['v4micro']['riskEquity'])
#offline =['AC','AEX','CC','CGB','CT','DX','EBL','EBM','EBS','ED','FCH','FDX','FEI','FFI','FLG','FSS','HCM','HIC','KC','KW','LB','LCO','LGO','LRC','LSU','MEM','MFX','MW','O','OJ','RR','RS','SB','SIN','SJB','SMI','SSG','STW','SXE','TF','VX','YA','YB','YT2','YT3',]
#offline_mini = ['AC','AD','AEX','BO','BP','CC','CD','CGB','CT','DX','EBL','EBM','EBS','ED','FC','FCH','FDX','FEI','FFI','FLG','FSS','FV','GC','HCM','HIC','HO','KC','KW','LB','LC','LCO','LGO','LH','LRC','LSU','MEM','MFX','MP','MW','NE','NIY','NQ','O','OJ','PA','PL','RB','RR','RS','S','SB','SF','SI','SIN','SJB','SMI','SSG','STW','SXE','TF','US','VX','YA','YB','YM','YT2','YT3',]
#offline_micro =['AC','AD','AEX','BP','C','CC','CD','CGB','CL','CT','CU','DX','EBL','EBM','EBS','ED','EMD','FC','FCH','FDX','FEI','FFI','FLG','FSS','FV','GC','HCM','HIC','HO','JY','KC','KW','LB','LC','LCO','LGO','LH','LRC','LSU','MEM','MFX','MP','MW','NE','NIY','NQ','O','OJ','PA','PL','RB','RR','RS','S','SB','SF','SI','SIN','SJB','SM','SMI','SSG','STW','SXE','TF','TU','US','VX','W','YA','YB','YM','YT2','YT3',]
offline=[sym for sym in c2contractSpec.keys() if sym not in eval(acctinfo['v4futures']['online'])]
offline_mini=[sym for sym in c2contractSpec.keys() if sym not in eval(acctinfo['v4mini']['online'])]
offline_micro=[sym for sym in c2contractSpec.keys() if sym not in eval(acctinfo['v4micro']['online'])]
'''
c2id_macro=acctinfo['v4futures']['c2id']
c2id_mini=acctinfo['v4mini']['c2id']
c2id_micro=acctinfo['v4micro']['c2id']
c2key_macro=acctinfo['v4futures']['c2key']
c2key_mini=acctinfo['v4mini']['c2key']
c2key_micro=acctinfo['v4micro']['c2key']
'''
accountInfo = pd.DataFrame(data=[[c2system, c2system_mini, c2system_micro]],columns=systems,index=['selection'])
accountInfo = accountInfo.append(pd.DataFrame(data=[[c2id_macro, c2id_mini, c2id_micro]],columns=systems,index=['c2id']))
accountInfo = accountInfo.append(pd.DataFrame(data=[[c2key_macro, c2key_mini, c2key_micro]],columns=systems,index=['c2key']))
accountInfo = accountInfo.append(pd.DataFrame(data=[[riskEquity, riskEquity_mini, riskEquity_micro]],columns=systems,index=['riskEquity']))
accountInfo = accountInfo.append(pd.DataFrame(data=[[str(offline), str(offline_mini), str(offline_micro)]],columns=systems,index=['offline']))
print 'loaded and updated account info from', acctinfofile
#range (-1 to 1) postive for counter-trend negative for trend i.e.
#-1 would 0 safef ==1 and double safef==2
#1 would 0 safef ==2 and double safef==1
safefAdjustment=0
filename='./web/tsdp/custom_signals_data.json'
if isfile(filename):
with open(filename, 'r') as f:
custom_signals_data = json.load(f)
custom_signals_data=custom_signals_data['customsignals']
custom_signals={sym:custom_signals_data[sym]['signals'] for sym in custom_signals_data.keys()}
#print custom_signals
for sym in custom_signals:
#print sym, custom_signals[sym]
if custom_signals[sym] is not None:
c2contractSpec[sym][5]=int(custom_signals[sym])
print 'loaded and updated custom signals from', filename
months = {
1:'F',
2:'G',
3:'H',
4:'J',
5:'K',
6:'M',
7:'N',
8:'Q',
9:'U',
10:'V',
11:'X',
12:'Z'
}
files = [ f for f in listdir(dataPath) if isfile(join(dataPath,f)) ]
marketList = [x.split('_')[0] for x in files]
#futuresDF_old=pd.read_csv(dataPath2+'futuresATR_Signals.csv', index_col=0)
futuresDF=pd.DataFrame()
corrDF=pd.DataFrame()
try:
for i,contract in enumerate(marketList):
data = pd.read_csv(dataPath+contract+'_B.csv', index_col=0, header=None)[-lookback-1:]
data.index = pd.to_datetime(data.index,format='%Y%m%d')
data.columns = ['Open','High','Low','Close','Volume','OI','R','S']
data.index.name = 'Dates'
data.R = data.R.astype(int)
atr=ATR2(data.High.values,data.Low.values,data.Close.values,lookback)
pc=data.Close.pct_change()
#no zeros in act because ACT used in prevACT.
act=np.where(pc<0,-1,1)
if i==0:
lastDate = data.index[-1]
else:
if data.index[-1]> lastDate:
lastDate=data.index[-1]
#print pc
#print sym, atr,data.tail()
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
contractYear=str(data.R[-1])[3]
contractMonth=str(data.R[-1])[-2:]
contractName=c2contractSpec[sym][0]+months[int(contractMonth)]+contractYear
#print sym, atr[-1], c2contractSpec[sym][2], c2contractSpec[sym][1]
usdATR = atr[-1]*c2contractSpec[sym][2]/c2contractSpec[sym][1]
cValue = data.Close[-1]*c2contractSpec[sym][2]/c2contractSpec[sym][1]
var=riskEquity/usdATR
qty = int(math.floor(var) if var>1 else math.ceil(var))
var=riskEquity_mini/usdATR
qty_mini = int(math.floor(var) if var>1 else math.ceil(var))
var=riskEquity_micro/usdATR
qty_micro = int(math.floor(var) if var>1 else math.ceil(var))
#print sym, data.R[-1], contractName
#signalFilename='v4_'+sym+'.csv'
corrDF[sym]=pc
futuresDF.set_value(sym,'Contract',contractName)
futuresDF.set_value(sym,'LastClose',data.Close[-1])
futuresDF.set_value(sym,'ATR'+str(lookback),atr[-1])
futuresDF.set_value(sym,'LastPctChg',pc[-1])
futuresDF.set_value(sym,'ACT',act[-1])
#futuresDF.set_value(sym,'prevACT',act[-2])
futuresDF.set_value(sym,'usdATR',usdATR)
futuresDF.set_value(sym,'QTY',qty)
futuresDF.set_value(sym,'QTY_MINI',qty_mini)
futuresDF.set_value(sym,'QTY_MICRO',qty_micro)
futuresDF.set_value(sym,'contractValue',cValue)
futuresDF.set_value(sym,'FX',c2contractSpec[sym][1])
futuresDF.set_value(sym,'PC'+str(data.index[-1]),pc[-1])
futuresDF.set_value(sym,'Close'+str(data.index[-1]),data.Close[-1])
futuresDF.set_value(sym,'group',c2contractSpec[sym][3])
futuresDF.set_value(sym,'RiskOn',c2contractSpec[sym][4])
futuresDF.set_value(sym,'Custom',c2contractSpec[sym][5])
futuresDF.index.name = lastDate
#feeddata=pd.read_csv(feedfile,index_col='CSIsym')
#feeddata['Date']=int(lastDate.strftime('%Y%m%d'))
#feeddata['timestamp']=int(calendar.timegm(dt.utcnow().utctimetuple()))
#feeddata.to_sql(name='feeddata', con=writeConn, index=True, if_exists='append', index_label='CSIsym')
#print 'Saved', feedfile, 'to', dbPath
if refreshSea:
print 'refreshSea is on.. debug mode'
#for sig
idx=-2
prevUpdateDate=pd.read_sql(\
'select DISTINCT Date from futuresATRhist where Date < %s order by Date ASC' \
% lastDate.strftime('%Y%m%d') , con=readConn).iloc[-1]
prevUpdateDate = dt.strptime(str(prevUpdateDate.values.flatten()[-1]),'%Y%m%d')
futuresDF_old=pd.read_sql( 'select * from futuresDF_all where timestamp=\
(select max(timestamp) from futuresDF_all where Date=%s)' %prevUpdateDate.strftime('%Y%m%d'),\
con=readConn, index_col='CSIsym')
else:
idx=-1
futuresDF_old=pd.read_csv(dataPath2+'futuresATR.csv', index_col=0)
prevUpdateDate=dt.strptime(futuresDF_old.index.name,"%Y-%m-%d %H:%M:%S")
#save last seasonal signal for pnl processing
#update correl charts
if lastDate >prevUpdateDate:
#first time run needs to update pivot dates for runsystems.
print "First Run.. running seasonalClassifier"
nextColOrder = ['0.75LastSIG','0.5LastSIG','1LastSIG','prevSEA','prevSRUN','prevvSTART']
for i,contract in enumerate(marketList):
print i+1,
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
#seasonality
seaBias, currRun, date,vStart = seasonalClassifier(sym, dataPath, savePath=pngPath+version+'_'+sym+'_MODE2',\
debug=showPlots)
futuresDF.set_value(sym,'vSTART',vStart)
futuresDF.set_value(sym,'LastSEA',seaBias)
futuresDF.set_value(sym,'SEA'+str(date),seaBias)
futuresDF.set_value(sym,'LastSRUN',currRun)
futuresDF.set_value(sym,'SRUN'+str(date),currRun)
futuresDF['prevACT']=futuresDF_old['prevACT']
futuresDF['prevSEA']=futuresDF_old.LastSEA
futuresDF['prevSRUN']=futuresDF_old.LastSRUN
futuresDF['prevvSTART']=futuresDF_old.vSTART
#corrDF.to_csv(savePath+'futuresPCcsv')
#corrDF.corr().to_csv(savePath+'futuresCorr.csv')
corrDF=corrDF.corr()
fig,ax = plt.subplots(figsize=(15,13))
ax.set_title('Correlation '+str(data.index[0])+' to '+str(data.index[-1]))
sns.heatmap(ax=ax,data=corrDF)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
corrDF.to_html(savePath2+'futures_3.html')
if pngPath != None:
print 'Saving '+pngPath+'futures_3.png'
fig.savefig(pngPath+'futures_3.png', bbox_inches='tight')
if len(sys.argv)==1 and showPlots:
#print data.index[0],'to',data.index[-1]
plt.show()
plt.close()
for i,col in enumerate(corrDF):
plt.figure(figsize=(8,10))
corrDF[col].sort_values().plot.barh(color='r')
plt.axvline(0, color='k')
plt.title(col+' '+str(lookback)+' Day Correlation '+str(data.index[0])+' to '+str(data.index[-1]))
plt.xlim(-1,1)
plt.xticks(np.arange(-1,1.25,.25))
plt.grid(True)
filename=version+'_'+col+'_CORREL'+'.png'
if pngPath != None:
print i+1,'Saving '+pngPath+filename
plt.savefig(pngPath+filename, bbox_inches='tight')
if len(sys.argv)==1 and showPlots:
#print data.index[0],'to',data.index[-1]
plt.show()
plt.close()
else:
print "Second Run.. skipping seasonalClassifier"
#second time load "old" file for seasonality signals.
nextColOrder = ['0.75LastSIG','0.5LastSIG','1LastSIG','LastSEA','LastSRUN','vSTART']
futuresDF['vSTART']=futuresDF_old.vSTART
futuresDF['LastSEA']=futuresDF_old.LastSEA
futuresDF['LastSRUN']=futuresDF_old.LastSRUN
for col in futuresDF_old.columns:
if col.startswith('SEA'):
futuresDF[col]=futuresDF_old[col]
for col in futuresDF_old.columns:
if col.startswith('SRUN'):
futuresDF[col]=futuresDF_old[col]
for i2,contract in enumerate(marketList):
#print i,
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
signalFilename=version+'_'+sym+'.csv'
#print signalFilename
data = pd.read_csv(signalPath+signalFilename, index_col=0)
if sym in offline:
adjQty=0
else:
if data.safef.iloc[idx] ==1:
adjQty = int(round(futuresDF.ix[sym].QTY*(1+safefAdjustment)))
else:
adjQty = int(round(futuresDF.ix[sym].QTY*(1-safefAdjustment)))
futuresDF.set_value(sym,'LastSIG',data.signals.iloc[idx])
futuresDF.set_value(sym,'LastSAFEf',data.safef.iloc[idx])
futuresDF.set_value(sym,'finalQTY',adjQty)
futuresDF.set_value(sym,'SIG'+str(data.index[idx]),data.signals.iloc[idx])
for i2,contract in enumerate(marketList):
#print i,
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
signalFilename='0.5_'+sym+'_1D.csv'
#print signalFilename
data = pd.read_csv(signalPath+signalFilename, index_col=0)
data.index = pd.to_datetime(data.index,format='%Y-%m-%d')
if i2==0:
sigDate = data.index[idx]
else:
if data.index[idx]> sigDate:
sigDate=data.index[idx]
if sym in offline:
adjQty=0
else:
if data.dpsSafef.iloc[idx] ==1:
adjQty = int(round(futuresDF.ix[sym].QTY*(1+safefAdjustment)))
else:
adjQty = int(round(futuresDF.ix[sym].QTY*(1-safefAdjustment)))
futuresDF.set_value(sym,'0.5LastSIG',data.signals.iloc[idx])
futuresDF.set_value(sym,'0.5LastSAFEf',data.dpsSafef.iloc[idx])
futuresDF.set_value(sym,'0.5finalQTY',adjQty)
futuresDF.set_value(sym,'0.5SIG'+str(data.index[idx]),data.signals.iloc[idx])
for i2,contract in enumerate(marketList):
#print i,
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
signalFilename='0.75_'+sym+'_1D.csv'
#print signalFilename
data = pd.read_csv(signalPath+signalFilename, index_col=0)
if sym in offline:
adjQty=0
else:
if data.dpsSafef.iloc[idx] ==1:
adjQty = int(round(futuresDF.ix[sym].QTY*(1+safefAdjustment)))
else:
adjQty = int(round(futuresDF.ix[sym].QTY*(1-safefAdjustment)))
futuresDF.set_value(sym,'0.75LastSIG',data.signals.iloc[idx])
futuresDF.set_value(sym,'0.75LastSAFEf',data.dpsSafef.iloc[idx])
futuresDF.set_value(sym,'0.75finalQTY',adjQty)
futuresDF.set_value(sym,'0.75SIG'+str(data.index[idx]),data.signals.iloc[idx])
for i2,contract in enumerate(marketList):
#print i,
if 'YT' not in contract:
sym = ''.join([i for i in contract.split('_')[0] if not i.isdigit()])
else:
sym=contract
signalFilename='1_'+sym+'_1D.csv'
#print signalFilename
data = pd.read_csv(signalPath+signalFilename, index_col=0)
if sym in offline:
adjQty=0
else:
if data.dpsSafef.iloc[idx] ==1:
adjQty = int(round(futuresDF.ix[sym].QTY*(1+safefAdjustment)))
else:
adjQty = int(round(futuresDF.ix[sym].QTY*(1-safefAdjustment)))
futuresDF.set_value(sym,'1LastSIG',data.signals.iloc[idx])
futuresDF.set_value(sym,'1LastSAFEf',data.dpsSafef.iloc[idx])
futuresDF.set_value(sym,'1finalQTY',adjQty)
futuresDF.set_value(sym,'1SIG'+str(data.index[idx]),data.signals.iloc[idx])
futuresDF=futuresDF.sort_index()
columns = futuresDF.columns.tolist()
futuresDF.LastPctChg=futuresDF[sorted([x for x in columns if 'PC' in x])[-1]].values
start_idx =columns.index('ACT')+1
new_order = columns[:start_idx]+nextColOrder
new_order = new_order+[x for x in columns if x not in new_order]
futuresDF = futuresDF[new_order]
print futuresDF.iloc[:,:4]
#system file update
system = pd.read_csv(systemPath+systemFilename)
system_mini = pd.read_csv(systemPath+systemFilename2)
system_micro = pd.read_csv(systemPath+systemFilename3)
#macro
for sys in system.System:
sym=sys.split('_')[1]
idx=system[system.System==sys].index[0]
print 'MACRO', sys, sym, system.ix[idx].c2qty,
system.set_value(idx,'c2qty',int(futuresDF.ix[sym]['finalQTY']))
print system.ix[idx].c2qty, system.ix[idx].c2sym,
system.set_value(idx,'c2sym',futuresDF.ix[sym]['Contract'])
print system.ix[idx].c2sym
system.Name=systemFilename.split('_')[1][:-4]
system.c2id=c2id_macro
system.c2api=c2key_macro
#mini
for sys in system_mini.System:
sym=sys.split('_')[1]
idx=system_mini[system_mini.System==sys].index[0]
print 'MINI', sys, sym, system_mini.ix[idx].c2qty,
if sym in offline_mini:
system_mini.set_value(idx,'c2qty',0)
else:
system_mini.set_value(idx,'c2qty',int(futuresDF.ix[sym]['QTY_MINI']))
print system_mini.ix[idx].c2qty, system_mini.ix[idx].c2sym,
system_mini.set_value(idx,'c2sym',futuresDF.ix[sym]['Contract'])
print system_mini.ix[idx].c2sym
system_mini.Name=systemFilename2.split('_')[1][:-4]
system_mini.c2id=c2id_mini
system_mini.c2api=c2key_mini
#micro
for sys in system_micro.System:
sym=sys.split('_')[1]
idx=system_micro[system_micro.System==sys].index[0]
print 'MICRO', sys, sym, system_micro.ix[idx].c2qty,
if sym in offline_micro:
system_micro.set_value(idx,'c2qty',0)
else:
system_micro.set_value(idx,'c2qty',int(futuresDF.ix[sym]['QTY_MICRO']))
print system_micro.ix[idx].c2qty, system_micro.ix[idx].c2sym,
system_micro.set_value(idx,'c2sym',futuresDF.ix[sym]['Contract'])
print system_micro.ix[idx].c2sym
system_micro.Name=systemFilename3.split('_')[1][:-4]
system_micro.c2id=c2id_micro
system_micro.c2api=c2key_micro
#signalDF=signalDF.sort_index()
#print signalDF
#signalDF.to_csv(savePath+'futuresSignals.csv')
#for signal files
#for system files
#use LastSEA for seasonality in c2
if lastDate > sigDate:
AdjSEACols= ['RiskOn','AntiPrevACT','prevSEA']
votingCols = ['Anti1LastSIG','prevACT','prevSEA']
voting2Cols = ['0.5LastSIG','AntiPrevACT','AdjSEA']
voting3Cols = ['Anti0.75LastSIG','AntiPrevACT','AntiSEA']
voting4Cols=['Voting','Voting2','Voting3','Voting8']
#voting5Cols=['prevACT','Anti1LastSIG','AntiAdjSEA']
voting5Cols=['Voting','Voting2','Voting3','Voting14']
#voting6Cols = ['0.5LastSIG','prevACT','AntiSEA']
voting6Cols =['Voting','Voting2','Voting3','Voting15']
voting7Cols = ['RiskOn','0.5LastSIG','AntiSEA']
voting8Cols = ['RiskOn','0.5LastSIG','AntiPrevACT']
#voting9Cols = ['RiskOn','0.5LastSIG','AntiSEA']
voting9Cols=['RiskOn','AntiPrevACT','AntiSEA']
voting10Cols = ['RiskOn','0.5LastSIG','AntiSEA','AntiPrevACT']
voting11Cols = ['RiskOn','Anti0.75LastSIG','AntiSEA','AntiPrevACT']
voting12Cols = ['1LastSIG','AntiPrevACT','AdjSEA']
voting13Cols = ['RiskOn','0.75LastSIG','AntiSEA','prevACT']
voting14Cols = ['RiskOn','Anti1LastSIG','prevSEA','prevACT']
voting15Cols = ['RiskOff','Anti0.75LastSIG','AntiSEA']
#1bi. Run v4size(to update vlookback)
#calc the previous day's results.
nrows=futuresDF.shape[0]
totalsDF = pd.DataFrame()
futuresDF['None']=0
futuresDF['RiskOff']=np.where(futuresDF.RiskOn<0,1,-1)
futuresDF['AntiCustom']=np.where(futuresDF.Custom<0,1,-1)
futuresDF['Anti1LastSIG'] = np.where(futuresDF['1LastSIG']==1,-1,1)
futuresDF['Anti0.75LastSIG'] = np.where(futuresDF['0.75LastSIG']==1,-1,1)
futuresDF['Anti0.5LastSIG'] = np.where(futuresDF['0.5LastSIG']==1,-1,1)
futuresDF['AntiSEA'] = np.where(futuresDF.prevSEA==1,-1,1)
futuresDF['AntiPrevACT'] = np.where(futuresDF.prevACT==1,-1,1)
#futuresDF['AdjSEA'] = np.where(futuresDF.prevSRUN <0, futuresDF.prevSEA*-1, futuresDF.prevSEA)
futuresDF['AdjSEA']=np.where(futuresDF[AdjSEACols].sum(axis=1)<0,-1,1)
futuresDF['AntiAdjSEA'] = np.where(futuresDF.AdjSEA==1,-1,1)
futuresDF['Voting']=np.where(futuresDF[votingCols].sum(axis=1)<0,-1,1)
futuresDF['Voting2']=np.where(futuresDF[voting2Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting3']=np.where(futuresDF[voting3Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting5']=np.where(futuresDF[voting5Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting6']=np.where(futuresDF[voting6Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting7']=np.where(futuresDF[voting7Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting8']=np.where(futuresDF[voting8Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting9']=np.where(futuresDF[voting9Cols].sum(axis=1)<0,-1,1)
v9=futuresDF[voting9Cols].sum(axis=1)
v9[v9<0]=-1
v9[v9>0]=1
futuresDF['Voting9']=v9.values
#futuresDF['Voting10']=np.where(futuresDF[voting10Cols].sum(axis=1)<0,-1,1)
v10=futuresDF[voting10Cols].sum(axis=1)
v10[v10<0]=-1
v10[v10>0]=1
futuresDF['Voting10']=v10.values
#futuresDF['Voting11']=np.where(futuresDF[voting11Cols].sum(axis=1)<0,-1,1)
v11=futuresDF[voting11Cols].sum(axis=1)
v11[v11<0]=-1
v11[v11>0]=1
futuresDF['Voting11']=v11.values
#futuresDF['Voting12']=np.where(futuresDF[voting12Cols].sum(axis=1)<0,-1,1)
v12=futuresDF[voting12Cols].sum(axis=1)
v12[v12<0]=-1
v12[v12>0]=1
futuresDF['Voting12']=v12.values
#futuresDF['Voting13']=np.where(futuresDF[voting13Cols].sum(axis=1)<0,-1,1)
v13=futuresDF[voting13Cols].sum(axis=1)
v13[v13<0]=-1
v13[v13>0]=1
futuresDF['Voting13']=v13.values
#futuresDF['Voting14']=np.where(futuresDF[voting14Cols].sum(axis=1)<0,-1,1)
v14=futuresDF[voting14Cols].sum(axis=1)
v14[v14<0]=-1
v14[v14>0]=1
futuresDF['Voting14']=v14.values
futuresDF['Voting15']=np.where(futuresDF[voting15Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting4']=np.where(futuresDF[voting4Cols].sum(axis=1)<0,-1,1)
#Voting of Voting
v4=futuresDF[voting4Cols].sum(axis=1)
v4[v4<0]=-1
v4[v4>0]=1
futuresDF['Voting4']=v4.values
v5=futuresDF[voting5Cols].sum(axis=1)
v5[v5<0]=-1
v5[v5>0]=1
futuresDF['Voting5']=v5.values
v6=futuresDF[voting6Cols].sum(axis=1)
v6[v6<0]=-1
v6[v6>0]=1
futuresDF['Voting6']=v6.values
pctChgCol = sorted([x for x in columns if 'PC' in x])[-1]
futuresDF['chgValue'] = futuresDF[pctChgCol]* futuresDF.contractValue*futuresDF.finalQTY
cv_online = futuresDF['chgValue'].drop(offline,axis=0)
signals = ['ACT','prevACT','AntiPrevACT','RiskOn','RiskOff','Custom','AntiCustom',\
'LastSIG', '0.75LastSIG','0.5LastSIG','1LastSIG','Anti1LastSIG','Anti0.75LastSIG','Anti0.5LastSIG',\
'prevSEA','LastSEA','AntiSEA','AdjSEA','AntiAdjSEA',\
'Voting','Voting2','Voting3','Voting4','Voting5','Voting6','Voting7','Voting8','Voting9',\
'Voting10','Voting11','Voting12','Voting13','Voting14','Voting15']
for sig in signals:
futuresDF['PNL_'+sig]=futuresDF['chgValue']*futuresDF[sig]
totalsDF.set_value(lastDate, 'ACC_'+sig, sum(futuresDF[sig]==futuresDF.ACT)/float(nrows))
totalsDF.set_value(lastDate, 'L%_'+sig, sum(futuresDF[sig]==1)/float(nrows))
totals =futuresDF[[x for x in futuresDF if 'PNL' in x]].sum()
for i,value in enumerate(totals):
totalsDF.set_value(lastDate, totals.index[i], value)
bygroup = pd.concat([abs(futuresDF['chgValue']), futuresDF['group']],axis=1).drop(offline, axis=0).groupby(['group'])
volByGroupByContract = bygroup.sum()/bygroup.count()
bygroup2 = pd.concat([futuresDF['chgValue'], futuresDF['group']],axis=1).drop(offline, axis=0).groupby(['group'])
chgByGroupByContract = bygroup2.sum()/bygroup2.count()
bygroup3 = pd.concat([futuresDF['ACT']==1, futuresDF['group']],axis=1).drop(offline, axis=0).groupby(['group'])
longPerByGroup = bygroup3.sum()/bygroup3.count()
if cv_online.count() >0:
totalsDF.set_value(lastDate, 'Vol_All', abs(cv_online).sum()/cv_online.count())
totalsDF.set_value(lastDate, 'Chg_All', cv_online.sum()/cv_online.count())
totalsDF.set_value(lastDate, 'L%_All', sum(futuresDF.ACT.drop(offline, axis=0)==1)/float(cv_online.count()))
else:
totalsDF.set_value(lastDate, 'Vol_All', np.nan)
totalsDF.set_value(lastDate, 'Chg_All', np.nan)
totalsDF.set_value(lastDate, 'L%_All', np.nan)
for i,value in enumerate(volByGroupByContract['chgValue']):
totalsDF.set_value(lastDate, 'Vol_'+volByGroupByContract.index[i], value)
for i,value in enumerate(chgByGroupByContract['chgValue']):
totalsDF.set_value(lastDate, 'Chg_'+chgByGroupByContract.index[i], value)
for i,value in enumerate(longPerByGroup['ACT']):
totalsDF.set_value(lastDate, 'L%_'+longPerByGroup.index[i], value)
print totalsDF.sort_index().transpose()
filename='futuresResults_'+lastDate.strftime("%Y%m%d%H%M")+'.csv'
print 'Saving', savePath2+filename
totalsDF.sort_index().transpose().to_csv(savePath2+filename)
filename='futuresResults_Last.csv'
print 'Saving', savePath+filename
totalsDF.sort_index().transpose().to_csv(savePath+filename)
totalsDF['Date']=int(lastDate.strftime('%Y%m%d'))
totalsDF['timestamp']=int(calendar.timegm(dt.utcnow().utctimetuple()))
totalsDF.to_sql(name='totalsDF',con=writeConn, index=False, if_exists='append')
print 'Saved totalsDF to', dbPath
files = [ f for f in listdir(savePath) if isfile(join(savePath,f)) ]
filename = 'futuresResultsHistory.csv'
if filename not in files:
print 'Saving', savePath+filename
totalsDF.to_csv(savePath+filename)
else:
print 'Saving', savePath+filename
pd.read_csv(savePath+filename, index_col=0).append(totalsDF).to_csv(savePath+filename)
filename='futuresATR_'+lastDate.strftime("%Y%m%d%H%M")+'.csv'
print 'Saving', savePath2+filename
futuresDF.to_csv(savePath2+filename)
print 'Saving', savePath+'futuresATR_Results.csv'
futuresDF.to_csv(savePath+'futuresATR_Results.csv')
futuresDF['Date']=int(lastDate.strftime('%Y%m%d'))
futuresDF['timestamp']=int(calendar.timegm(dt.utcnow().utctimetuple()))
futuresDF.drop([col for col in futuresDF.columns if '00:00:00' in col], axis=1).to_sql(name='futuresDF_results',\
con=writeConn, index=True, if_exists='append', index_label='CSIsym')
print 'Saved futuresDF_results to', dbPath
#futuresDF_live = pd.read_sql('select * from futuresATRhist where timestamp=\
# (select max(timestamp) from futuresATRhist where Date=%s)' %prevUpdateDate.strftime('%Y%m%d'),\
# con=readConn, index_col='CSIsym')
futuresDF_live = pd.read_sql('select * from (select * from futuresATRhist where Date=%s\
order by timestamp ASC) group by CSIsym' %prevUpdateDate.strftime('%Y%m%d'),\
con=readConn, index_col='CSIsym')
futuresDF_toexcel=pd.concat([futuresDF_live, futuresDF.drop(futuresDF_live.index,axis=0)],axis=0).sort_index()
futuresDF_toexcel.columns=[x if x !='Date' else 'signalDate' for x in futuresDF_toexcel.columns]
#update old act and pct change to csi values
futuresDF_toexcel['ACT_IB']=futuresDF.ACT
futuresDF_toexcel['LastPctChg_IB']=futuresDF.LastPctChg
#overwrite CSI's values with ones from IB.
ACT_IB=pd.read_sql('select CSIsym, ACT from futuresATR', con=readConn, index_col='CSIsym')
for sym in ACT_IB.index:
futuresDF_toexcel.set_value(sym, 'ACT_IB', ACT_IB.ix[sym][0])
LastPctChg_IB=pd.read_sql('select CSIsym, LastPctChg from futuresATR', con=readConn, index_col='CSIsym')
for sym in LastPctChg_IB.index:
futuresDF_toexcel.set_value(sym, 'LastPctChg_IB', LastPctChg_IB.ix[sym][0])
futuresDF_toexcel['pcDate']=pd.read_sql('select CSIsym, Date from futuresATR', con=readConn, index_col='CSIsym')
futuresDF_toexcel['ACT']=futuresDF.ACT
futuresDF_toexcel['LastPctChg']=futuresDF.LastPctChg
pc_cols=[x for x in futuresDF.columns if 'PC' in x]
futuresDF_toexcel[pc_cols]=futuresDF[pc_cols]
#seasonality data same as before
futuresDF_toexcel['prevSEA']=futuresDF.LastSEA
futuresDF_toexcel['prevSRUN']=futuresDF.LastSRUN
futuresDF_toexcel['prevvSTART']=futuresDF.prevvSTART
cols =[x for x in futuresDF.columns if x in futuresDF_toexcel.columns]+['LastPctChg_IB','ACT_IB','pcDate','signalDate','timestamp']
futuresDF_toexcel=futuresDF_toexcel[cols]
#recreated new price change data with previous signals from csi (offline) ib(online)
futuresDF_toexcel.to_csv(savePath+'futuresATR_Excel.csv')
print 'Saved', savePath+'futuresATR_Excel.csv'
filename='futuresL_History.csv'
#cols=['L%_currency',
# 'L%_energy',
# 'L%_grain',
# 'L%_index',
# 'L%_meat',
# 'L%_metal',
# 'L%_ACT',
# 'L%_rates',
# 'L%_soft']
cols = [x for x in totalsDF if 'L%' in x]
if filename not in files:
print 'Saving', savePath+filename
totalsDF[cols].to_csv(savePath+filename)
else:
print 'Saving', savePath+filename
pd.read_csv(savePath+filename, index_col=0).append(totalsDF[cols]).to_csv(savePath+filename)
else:
AdjSEACols= ['RiskOn','AntiPrevACT','LastSEA']
votingCols =['Anti1LastSIG','prevACT','LastSEA']
voting2Cols = ['0.5LastSIG','AntiPrevACT','AdjSEA']
voting3Cols = ['Anti0.75LastSIG','AntiPrevACT','AntiSEA']
voting4Cols=['Voting','Voting2','Voting3','Voting8']
#voting5Cols=['prevACT','Anti1LastSIG','AntiAdjSEA']
voting5Cols=['Voting','Voting2','Voting3','Voting14']
#voting6Cols = ['0.5LastSIG','prevACT','AntiSEA']
voting6Cols =['Voting','Voting2','Voting3','Voting15']
voting7Cols = ['RiskOn','0.5LastSIG','AntiSEA']
voting8Cols = ['RiskOn','0.5LastSIG','AntiPrevACT']
#voting9Cols = ['RiskOn','0.5LastSIG','AntiSEA']
voting9Cols= ['RiskOn','AntiPrevACT','AntiSEA']
voting10Cols = ['RiskOn','0.5LastSIG','AntiSEA','AntiPrevACT']
voting11Cols = ['RiskOn','Anti0.75LastSIG','AntiSEA','AntiPrevACT']
voting12Cols = ['1LastSIG','AntiPrevACT','AdjSEA']
voting13Cols = ['RiskOn','0.75LastSIG','AntiSEA','prevACT']
voting14Cols = ['RiskOn','Anti1LastSIG','LastSEA','prevACT']
voting15Cols = ['RiskOff','Anti0.75LastSIG','AntiSEA']
#voting9Cols=['Anti1LastSIG','AntiSEA']
#voting4Cols= votingCols+voting2Cols+voting3Cols
futuresDF['None']=0
futuresDF['RiskOff']=np.where(futuresDF.RiskOn<0,1,-1)
futuresDF['AntiCustom']=np.where(futuresDF.Custom<0,1,-1)
futuresDF['Anti1LastSIG'] = np.where(futuresDF['1LastSIG']==1,-1,1)
futuresDF['Anti0.75LastSIG'] = np.where(futuresDF['0.75LastSIG']==1,-1,1)
futuresDF['Anti0.5LastSIG'] = np.where(futuresDF['0.5LastSIG']==1,-1,1)
futuresDF['AntiSEA'] = np.where(futuresDF.LastSEA==1,-1,1)
futuresDF['prevACT'] = futuresDF.ACT
futuresDF['AntiPrevACT'] = np.where(futuresDF.ACT==1,-1,1)
#futuresDF['AdjSEA'] = np.where(futuresDF.LastSRUN <0, futuresDF.LastSEA*-1, futuresDF.LastSEA)
futuresDF['AdjSEA']=np.where(futuresDF[AdjSEACols].sum(axis=1)<0,-1,1)
futuresDF['AntiAdjSEA'] = np.where(futuresDF.AdjSEA==1,-1,1)
futuresDF['Voting']=np.where(futuresDF[votingCols].sum(axis=1)<0,-1,1)
futuresDF['Voting2']=np.where(futuresDF[voting2Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting3']=np.where(futuresDF[voting3Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting4']=np.where(futuresDF[voting4Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting5']=np.where(futuresDF[voting5Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting6']=np.where(futuresDF[voting6Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting7']=np.where(futuresDF[voting7Cols].sum(axis=1)<0,-1,1)
futuresDF['Voting8']=np.where(futuresDF[voting8Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting9']=np.where(futuresDF[voting9Cols].sum(axis=1)<0,-1,1)
v9=futuresDF[voting9Cols].sum(axis=1)
v9[v9<0]=-1
v9[v9>0]=1
futuresDF['Voting9']=v9.values
#futuresDF['Voting10']=np.where(futuresDF[voting10Cols].sum(axis=1)<0,-1,1)
v10=futuresDF[voting10Cols].sum(axis=1)
v10[v10<0]=-1
v10[v10>0]=1
futuresDF['Voting10']=v10.values
#futuresDF['Voting11']=np.where(futuresDF[voting11Cols].sum(axis=1)<0,-1,1)
v11=futuresDF[voting11Cols].sum(axis=1)
v11[v11<0]=-1
v11[v11>0]=1
futuresDF['Voting11']=v11.values
#futuresDF['Voting12']=np.where(futuresDF[voting12Cols].sum(axis=1)<0,-1,1)
v12=futuresDF[voting12Cols].sum(axis=1)
v12[v12<0]=-1
v12[v12>0]=1
futuresDF['Voting12']=v12.values
#futuresDF['Voting13']=np.where(futuresDF[voting13Cols].sum(axis=1)<0,-1,1)
v13=futuresDF[voting13Cols].sum(axis=1)
v13[v13<0]=-1
v13[v13>0]=1
futuresDF['Voting13']=v13.values
#futuresDF['Voting14']=np.where(futuresDF[voting14Cols].sum(axis=1)<0,-1,1)
v14=futuresDF[voting14Cols].sum(axis=1)
v14[v14<0]=-1
v14[v14>0]=1
futuresDF['Voting14']=v14.values
futuresDF['Voting15']=np.where(futuresDF[voting15Cols].sum(axis=1)<0,-1,1)
#futuresDF['Voting4']=np.where(futuresDF[voting4Cols].sum(axis=1)<0,-1,1)
#Voting of Voting
v4=futuresDF[voting4Cols].sum(axis=1)
v4[v4<0]=-1
v4[v4>0]=1
futuresDF['Voting4']=v4.values
v5=futuresDF[voting5Cols].sum(axis=1)
v5[v5<0]=-1
v5[v5>0]=1
futuresDF['Voting5']=v5.values
v6=futuresDF[voting6Cols].sum(axis=1)
v6[v6<0]=-1
v6[v6>0]=1
futuresDF['Voting6']=v6.values
for group in futuresDF.groupby(by='group').Custom:
#print group
colors=np.array(['r']*group[1].shape[0])
mask = group[1]>0
colors[mask.values]='g'
Lper = round(sum(mask.values)/float(group[1].shape[0])*100,1)
title = 'Custom '+group[0] + ' L% '+ str(Lper)
group[1].plot(kind='barh', title =title, color=colors)
plt.xlim(-1,1)
filename='custom_'+group[0]+'.png'
plt.savefig(pngPath+filename, bbogroup_inches='tight')
print 'Saved '+pngPath+filename
if debug:
plt.show()
plt.close()
print 'Saving signals from', c2system
#1biv. Run v4size (signals and size) and check system.csv for qty,contracts with futuresATR
#save signals to v4_ signal files for order processing
nsig=0
for ticker in futuresDF.index:
nsig+=1
signalFile=pd.read_csv(signalSavePath+ version+'_'+ ticker+ '.csv', index_col=['dates'])
#addLine = signalFile.iloc[-1]
#addLine = addLine.append(pd.Series(data=timenow.strftime("%Y%m%d %H:%M:%S %Z"), index=['timestamp']))
#addLine = addLine.append(pd.Series(data=cycleTime, index=['cycleTime']))
#addLine.name = sst.iloc[-1].name
addLine = pd.Series(name=lastDate)
addLine['signals']=futuresDF.ix[ticker][c2system]
addLine['safef']=c2safef
addLine['timestamp']=dt.now().strftime("%Y%m%d %H:%M:%S %Z")
signalFile = signalFile.append(addLine)
filename=signalSavePath + version+'_'+ ticker+ '.csv'
print 'Saving...', addLine['signals'], addLine['safef'], filename
signalFile.to_csv(filename, index=True)
print nsig, 'files updated'
print 'Saving', savePath+'futuresATR_Signals.csv'
futuresDF.to_csv(savePath+'futuresATR_Signals.csv')
futuresDF['Date']=int(lastDate.strftime('%Y%m%d'))