def ticker_to_eq(ticker):
ohlc = pybacktest.load_from_yahoo(ticker, '2008')
normalize(ohlc)
orig_eq(ohlc)
cci(ohlc)
#trade_cci(ohlc)
#sig_to_eq(ohlc)
return ohlc
def ticker_to_eq(ticker):
ohlc = pybacktest.load_from_yahoo(ticker, '2008')
normalize(ohlc)
orig_eq(ohlc)
cci(ohlc)
#trade_cci(ohlc)
#sig_to_eq(ohlc)
return ohlc
def handle(self, *args, **options):
#models.Run.objects.all().delete()
for stock in models.Stock.objects.all():
ohlc = pb.load_from_yahoo(stock.symbol + '.ns',
adjust_close=True,
start='2005')
for name, strategy in models.strategies.items():
params = {}
for param, value in strategy['default_params'].items():
params[param] = (math.floor(0.75*value), math.ceil(1.25*value), 1)
optimizer = pb.Optimizer(strategy['strategy'],
ohlc,
params,
metrics=['profit',
'pf',
'average',
'average_gain',
'average_loss',
'winrate',
'payoff',
'pf',
'maxdd',
'rf',
'trades'],
processes=1)
for run in optimizer.results.to_dict('records'):
models.Run.objects.create(
stock = stock,
strategy = name,
profit = run.pop('profit'),
profit_factor = run.pop('pf'),
average = run.pop('average'),
average_gain = run.pop('average_gain'),
average_loss = run.pop('average_loss'),
winrate = run.pop('winrate'),
payoff = run.pop('payoff'),
max_drawdown = run.pop('maxdd'),
risk_factor = run.pop('rf'),
trades = run.pop('trades'),
params = run,
default = run == strategy['default_params'])
#headers==========================
import pandas as pd
import pybacktest as pt
import macd
import ema
import rsi
import json
import matplotlib.pyplot as plt
# script = 'WOCKPHARMA.NS'
# script = 'SIEMENS.NS'
script = '^nsei'
# stg = "MACD"
# stg = "RSI"
stg = "_MACD_EMA_"
ohlc = pt.load_from_yahoo(script, adjust_close=True)
#=================================
#strategy=========================
def macd_sma_combination(price, macd_small, macd_long, signal, sma_period):
value_macd = macd.macd(price, macd_small, macd_long)
value_signal = pd.rolling_mean(value_macd, signal)
value_sma = pd.rolling_mean(price, sma_period)
buy = cover = (value_macd > value_signal) & (
value_macd.shift() < value_signal.shift()) & (price > value_sma)
sell = short = (value_macd < value_signal) & (
value_macd.shift() > value_signal.shift()) & (price < value_sma)
del cover
del short
return value_macd, value_signal, value_sma, buy, sell
import sys import pandas import pybacktest #script = sys.argv[1] script = 'siemens.ns' ohlc = pybacktest.load_from_yahoo(script, adjust_close=True) lips_sma_period = 5 teeth_sma_period = 8 jaw_sma_period = 13 hl_diff = (ohlc.H+ ohlc.L)/ 2 ml1 = pandas.rolling_mean(hl_diff, lips_sma_period) mt1 = pandas.rolling_mean(hl_diff, teeth_sma_period) mj1 = pandas.rolling_mean(hl_diff, jaw_sma_period) ml = ml1.shift(2) mt = mt1.shift(4) mj = mj1.shift(7) buy = cover = (ml > mt) & (mt > mj) & ((ml.shift() < mt.shift()) | (mt.shift() < mj.shift())) sell = short = (ml < mt) & (mt < mj) & ((ml.shift() > mt.shift()) | (mt.shift() > mj.shift())) del cover del short bt = pybacktest.Backtest(locals(), 'alligator') # df = pandas.DataFrame()
import pybacktest
import pandas as pd
ohlc = pybacktest.load_from_yahoo('SPY')
ohlc.tail()
short_ma = 50
long_ma = 200
ms = pd.rolling_mean(ohlc.C, short_ma)
ml = pd.rolling_mean(ohlc.C, long_ma)
buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
bt = pybacktest.Backtest(locals(), 'ma_cross')
import pylab
bt.plot_trades()
pd.rolling_mean(ohlc.C, short_ma).plot(c='green')
pd.rolling_mean(ohlc.C, long_ma).plot(c='blue')
pylab.legend(loc='upper left')
pylab.show()
def datas(symbols):
return {symbol: pt.load_from_yahoo(symbol + '.ns') for symbol in symbols}
import pandas as pd
import datetime as dt
import time
import pybacktest as pt
nifty_data = pt.load_from_yahoo('^nsei', start='2014', adjust_close=True)
nifty_all = pd.read_csv('/home/sigmasoft6/Desktop/indicator/nifty_all.csv')
data = dict(zip(nifty_all['Symbol'], nifty_all['Company']))
def datas(symbols):
return {symbol: pt.load_from_yahoo(symbol + '.ns') for symbol in symbols}
def strategy_sma(script, period_small=5, period_long=10):
ms = pd.rolling_mean(script.C, period_small)
ml = pd.rolling_mean(script.C, period_long)
buy = (ms > ml) & (ms.shift() < ml.shift())
sell = (ms < ml) & (ms.shift() > ml.shift())
return buy, sell
def stgy2(script, period_small=50, period_long=100):
ms = pd.rolling_mean(script.C, period_small)
ml = pd.rolling_mean(script.C, period_long)
buy = (ms > ml) & (ms.shift() < ml.shift())
sell = (ms < ml) & (ms.shift() > ml.shift())
return buy, sell
def trades(stock_stgy, nifty):
from numpy import inf
from scipy.optimize import brute
from pybacktest import Backtest, load_from_yahoo
from ta import *
def cost(params, ohlc=None, fn=None):
signals = fn(ohlc, *params)
bt = Backtest(signals)
try:
sharpe = bt.report['risk/return profile']['sharpe']
cost = 1.0 / sharpe
print params, sharpe, cost
return cost
except Exception, e:
cost = inf
print params, None, cost
return cost
if __name__ == "__main__":
ohlc = load_from_yahoo('AAPL', start='2000')
# TODO: multiprocessing?
fn, params = mav_cross_double, [slice(5, 35, 5), slice(20, 110, 10)]
res = brute(cost, params, args=(ohlc, fn), finish=None)
print 'Solution (fast period, slow period): %s\n' % res
print Backtest(fn(ohlc, *res)).summary()