def test_for_best_parameter(dest_instrument, feed):
"""
多线程测试指定范围里的最佳参数
"""
def parameters_generator():
instrument = [dest_instrument]
SMA = range(5, 30)
return itertools.product(instrument, SMA, [10000])
local.run(MyStrategy, feed, parameters_generator())
def grid_search(code, start_date, end_date):
data = get_data(code, start_date, end_date)
if data is None: return
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame(code, data)
brk = gen_broker(feed)
local.run(KDJStrategy,
feed,
parameters_generator(code, brk, data),
workerCount=2)
def testLocal(self):
barFeed = yahoofeed.Feed()
instrument = "orcl"
barFeed.addBarsFromCSV(instrument, common.get_data_file_path("orcl-2000-yahoofinance.csv"))
res = local.run(sma_crossover.SMACrossOver, barFeed, parameters_generator(instrument, 5, 100))
self.assertEquals(round(res.getResult(), 2), 1295462.6)
self.assertEquals(res.getParameters()[1], 20)
def MyBackTest():
instrument = "mro"
mystrategy = rsi2.RSI2
feed = yahoofeed.Feed()
#add esv
for i in range(2000,2013):
year = str(i)
datafilename = MyFiles.dataPath+instrument+"-"+year+"-"+MyFiles.dataFlag+".csv"
if not os.path.exists(datafilename):
print "not exists:"+ datafilename
return
else:
feed.addBarsFromCSV(instrument,datafilename)
local.run(mystrategy, feed, parameters_generator())
def test(isOptimize=True, win=9):
if isOptimize: # 寻找最佳参数
results = local.run(SVMStrategy, feed, parameters_generator())
print 'Parameters:', results.getParameters(), 'Result:', results.getResult()
print results.getParameters()[0]
else: # 用最佳参数回测
testWithBestParameters(win=win)
def backtest(strategyClass, is_optimization, is_tick, start_date, end_date,
symbol, frequency, position_size, lb, i):
if is_tick:
feed = bitbf.CSVTradeFeed()
feed.addBarsFromCSV("../xbtusd_trades.csv",
'XBTUSD',
fromDateTime=start_date,
toDateTime=end_date)
else:
feed = GenericBarFeed(bar.Frequency.MINUTE, maxLen=10000)
feed.setColumnName('datetime', 'Date')
feed.setDateTimeFormat('%Y-%m-%dT%H:%M:%S.%fZ')
feed.setBarFilter(csvfeed.DateRangeFilter(start_date, end_date))
feed.addBarsFromCSV("XBTUSD", "../BITMEX_SPOT_BTC_USD_1MIN.csv")
if is_optimization:
strat = local.run(strategyClass,
feed,
parameters_generator(),
workerCount=None,
logLevel=10,
batchSize=1)
print(strat.getResult(), strat.getParameters())
else:
strat = strategyClass(feed, None, symbol, int(frequency),
position_size, lb, i)
strat.run()
def testFailingStrategy(self):
barFeed = yahoofeed.Feed()
instrument = "orcl"
barFeed.addBarsFromCSV(
instrument,
common.get_data_file_path("orcl-2000-yahoofinance.csv"))
res = local.run(FailingStrategy, barFeed,
parameters_generator(instrument, 5, 100))
self.assertIsNone(res)
def run_bband(code, from_year, end_year):
#instrument = '300104.SZ'
instrument = code + (".SS" if (code[0:3] == '600') else ".SZ")
feed = yahoofinance.build_feed([instrument], from_year, end_year, "./data")
#run = local.run(rsi2.RSI2, feed, parameters_generator())
print 'run in '
run = local.run(bbandsAll.BBandsAll, feed, parameters_generator_bBandsPeriod_list(instrument))
print 'run result:', run.getParameters(), run.getResult()
return run
def argument_for_multiple():
feed = yahoofeed.Feed()
for code in code_list:
feed.addBarsFromCSV(code, csv_path + "%s_hfq.csv" % code)
if strategy_name == "Ma_Rsi":
arg_set = "&"
ma_short = list(range(3, 10))
ma_long = list(range(10, 25))
rsi_in = list(range(3, 10))
rsi_out = list(range(10, 25))
generators = itertools.product(code_list, arg_set, ma_short, ma_long,
rsi_in, rsi_out)
local.run(MyStrategy.Ma_Rsi, feed, generators)
elif strategy_name == "Macd_Kdj":
arg_set = "&"
kdj_fast = list(range(3, 10))
kdj_slow = list(range(10, 25))
macd_fast = list(range(3, 10))
macd_slow = list(range(10, 30))
generators = itertools.product(code_list, arg_set, kdj_fast, kdj_slow,
macd_fast, macd_slow)
local.run(MyStrategy.Macd_Kdj, feed, generators)
elif strategy_name == "Ma_Macd":
arg_set = "&"
ma_short = list(range(3, 10))
ma_long = list(range(10, 25))
macd_fast = list(range(3, 10))
macd_slow = list(range(10, 30))
generators = itertools.product(code_list, arg_set, ma_short, ma_long,
macd_fast, macd_slow)
local.run(MyStrategy.Ma_Macd, feed, generators)
from pyalgotrade.broker import backtesting
from pyalgotrade.optimizer import local
from pytrade.algorithms.donchianchannels import DonchianTradingAlgorithm
from pytrade.base import TradingSystem
codes = ["ABEV3", "BBAS3", "BBDC3", "BBDC4", "BBSE3", "BRAP4", "BRFS3", "BRKM5", "BRML3", "BVMF3", "CCRO3", "CIEL3", "CMIG4", "CPFE3", "CPLE6", "CSAN3", "CSNA3", "CTIP3", "CYRE3", "ECOR3", "EGIE3", "EMBR3", "ENBR3", "EQTL3", "ESTC3", "FIBR3", "GGBR4", "GOAU4", "HYPE3", "ITSA4", "ITUB4", "JBSS3", "KLBN11", "KROT3", "LAME4", "LREN3", "MRFG3", "MRVE3", "MULT3", "NATU3", "PCAR4", "PETR3", "PETR4", "QUAL3", "RADL3", "RENT3", "RUMO3", "SANB11", "SBSP3", "SMLE3", "SUZB5", "TIMP3", "UGPA3", "USIM5", "VALE3", "VALE5", "VIVT4", "WEGE3"]
feed = googlefeed.Feed()
rowFilter = lambda row: row["Close"] == "-" or row["Open"] == "-" or row["High"] == "-" or row["Low"] == "-" or row["Volume"] == "-"
for code in codes:
datafile = './googlefinance/' + code + '-2015.csv'
print "Downloading %s ..." % (datafile)
# googlefinance.download_daily_bars(code, 2015, datafile)
feed.addBarsFromCSV(code, datafile, timezone=None, rowFilter=rowFilter)
class DonchianStrategyOptimizer(TradingSystem):
def __init__(self, feed, entrySize, exitSize, riskFactor):
broker = backtesting.Broker(10000, feed, backtesting.FixedPerTrade(10))
super(DonchianStrategyOptimizer, self).__init__(feed=feed, broker=broker, tradingAlgorithm=DonchianTradingAlgorithm(feed, broker, entrySize, exitSize, riskFactor), debugMode=False)
entrySize = range(10, 60)
exitSize = range(5, 30)
riskFactor = [float(x)/100 for x in range(1, 11)]
# entrySize = range(15, 40)
# exitSize = range(8, 25)
# riskFactor = [float(x)/100 for x in range(1, 11)]
result = local.run(DonchianStrategyOptimizer, feed, itertools.product(entrySize, exitSize, riskFactor), workerCount=6)
print "Best result is R$%s with parameters %s" %(result.getResult(), result.getParameters())
time_estimator(outparams)
return itertools.product(instruments, cash, shortlen, longlen, a1, a3)
# feed, instruments, cash, shortlen, longlen, sha1, sha2, sha3, la1, la2, la3)
def parameters_generator():
instrument = ["dia"]
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
instruments = ['JCI']
# Load the feed from the CSV files.
feed = daq.build_stock_feed(instruments, (2006, 2011))
# local.run(strategies.EMACrossover, feed,
# EMACrossoverParams(instruments), workerCount=None)
local.run(strategies.SLTRIXCrossover,
feed,
SLTRIXCrossoverParams(instruments),
workerCount=None)
# Source: http://gbeced.github.io/pyalgotrade/docs/v0.17/html/tutorial.html#optimizing
# Accessed: 06/10/16 0520Z
import itertools
from pyalgotrade.optimizer import local
from pyalgotrade.barfeed import yahoofeed
import rsi2
def parameters_generator():
instrument = ["dia"]
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Load the feed from the CSV files.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("dia", "dia-2009.csv")
feed.addBarsFromCSV("dia", "dia-2010.csv")
feed.addBarsFromCSV("dia", "dia-2011.csv")
local.run(rsi2.RSI2, feed, parameters_generator())
def main():
feed = csvfeed.GenericBarFeed(frequency=Frequency.MINUTE)
feed.addBarsFromCSV("BTC", "sampledata.csv")
# Plot the strategy.
local.run(Accumulator, feed, parameters_generator())
All rights reserved.
*******************************************************************************
"""
from pyalgotrade.optimizer import local
from pyalgotrade.barfeed import sina_feed as sf
import itertools
import os
from samples.statstical import ArimaGarch
import logging
def param_generator():
instrument = ["FG0"]
# window = range(250, 888)
window = range(500, 888)
# window = [300, 400, 500, 600, 700, 800, 888]
return itertools.product(instrument, window)
if __name__ == '__main__':
instrument = "FG0"
feed = sf.Feed()
csv_path = os.path.abspath(
'../histdata/commodity') + '/' + instrument + '.csv'
# feed = sf.Feed(frequency=bar.Frequency.MINUTE)
feed = sf.Feed()
feed.addBarsFromCSV(instrument, csv_path)
a = param_generator()
local.run(ArimaGarch, feed, param_generator())
tradingAlgorithm=DonchianTradingAlgorithm(
feed, broker, entrySize, exitSize,
riskFactor),
debugMode=False)
entrySize = range(10, 60)
exitSize = range(5, 30)
riskFactor = [float(x) / 100 for x in range(1, 11)]
# entrySize = range(15, 40)
# exitSize = range(8, 25)
# riskFactor = [float(x)/100 for x in range(1, 11)]
result = local.run(DonchianStrategyOptimizer,
feed,
itertools.product(entrySize, exitSize, riskFactor),
workerCount=6)
print "Best result is R$%s with parameters %s" % (result.getResult(),
result.getParameters())
##########################################################
#######SMA
##########################################################
class SMAStrategyOptimizer(TradingSystem):
def __init__(self, feed, longsize, shortsize, riskfactor):
broker = backtesting.Broker(10000, feed, backtesting.FixedPerTrade(10))
super(SMAStrategyOptimizer,
self).__init__(feed=feed,
broker=broker,
tradingAlgorithm=SMATradingAlgorithm(
import itertools
import utility.windutility as wu
from utility import dataframefeed
from pyalgotrade import bar
from pyalgotrade.optimizer import local
from strategy import singleMA
instruments = ['000001.SH']
fromDate = '20000101'
toDate = '20160314'
frequency = bar.Frequency.DAY
feed = dataframefeed.Feed()
maPeriod = xrange(5,120)
for instrument in instruments:
data = wu.wsd(instrument, 'open, high, low, close, volume, adjfactor', fromDate, toDate)
data['adjclose'] = data['close'] * data['adjfactor'] / data['adjfactor'][-1]
feed.addBarsFromDataFrame(instrument, data)
def parameters_generator():
return itertools.product(instruments, maPeriod)
if __name__ == '__main__':
local.run(singleMA.SingleMA, feed, parameters_generator())
import RSIpietro
def parameters_generator():
instrument = ["orcl"]
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Load the feed from the CSV files.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2000.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2001.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2002.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2003.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2004.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2005.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2006.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2007.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2008.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2009.csv")
feed.addBarsFromCSV("orcl", "C:\\Users\\Spe\\PycharmProjects\\AlgoPietro\\algoPackage\\data\\orcl-2010.csv")
local.run(RSIpietro.RSIpietro, feed, parameters_generator())
self.info("Sell shares at $%.2f" % (tradeInfo.getPrice()))
self.position = None
def onBars(self, bars):
if self.slowsma[-1] is None:
return
if self.position is None:
if self.fastsma[-1] > self.slowsma[-1]:
self.position = self.enterLong(self.instrument, 10, True)
elif self.fastsma[-1] < self.slowsma[
-1] and not self.position.exitActive():
self.position.exitMarket()
# Create Parameter Combinations using itertools
def parameters():
nfast = (5, 10, 15, 20, 25, 30)
nslow = (20, 25, 30, 40, 50, 100)
return itertools.product(nfast, nslow)
# Use optimizer from pyalgotrade
# If__name__ == '__main__': only needed for Windows
if __name__ == '__main__':
instruments = ["NIFTY"]
# Data Reading
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//NIFTY.csv")
# Strategy Optimization
local.run(MAStrategy, feed, parameters())
#############################################don't change ############################33
from pyalgotrade.cn import csvfeed
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.tools import yahoofinance
barfeed = csvfeed.Feed(frequency)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
# feed = csvfeed.Feed(frequency)
#feed = yahoofeed.Feed()
#feed.addBarsFromCSV("dia",'gdx-2009-yahoofinance.csv')
barfeed.loadBars(instrument, market, fromDate, toDate, filepath)
if __name__ == "__main__":
local.run(thrSMA.thrSMA, barfeed, parameters_generator())
"""
#############################################path set ############################33
import os
if frequency == bar.Frequency.MINUTE:
path = os.path.join('..', 'histdata', 'minute')
elif frequency == bar.Frequency.DAY:
path = os.path.join('..', 'histdata', 'day')
filepath = os.path.join(path, instrument + market + ".csv")
#############################################don't change ############################33
from pyalgotrade.cn.csvfeed import Feed
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Config params
config = {
'user': '******',
'password': '******',
'host': '127.0.0.1',
'database': 'ibex35',
'raise_on_warnings': True,
}
logger.log_format = "[%(levelname)s] %(message)s"
startDate = datetime.date(2001, 05, 24)
endDate = datetime.date(2015, 12, 21)
instrument = 'GAS.MC'
feed = None
if DBFEED:
feed = dbfeed.DbFeed(config, [], 100, startDate, endDate)
feed.registerInstrument(instrument)
else:
feed = yahoofeed.Feed()
feed.sanitizeBars(True)
feed.addBarsFromCSV(instrument, instrument + ".csv")
local.run(MyTaLibStrategy, feed, parameters_generator())
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Config params
config = {
'user': '******',
'password': '******',
'host': '127.0.0.1',
'database': 'ibex35',
'raise_on_warnings': True,
}
logger.log_format = "[%(levelname)s] %(message)s"
startDate = datetime.date(2001, 05, 24)
endDate = datetime.date(2015, 12, 21)
instrument = 'GAS.MC'
feed = None
if DBFEED:
feed = dbfeed.DbFeed(config, [], 100, startDate, endDate)
feed.registerInstrument(instrument)
else:
feed = yahoofeed.Feed()
feed.sanitizeBars(True)
feed.addBarsFromCSV(instrument, instrument + ".csv")
local.run(MyBasicStrategy, feed, parameters_generator())
# f. Exit wjen moving average crosses back (regardless of RSI value)
#Important Note: maximum value for fastperiod must remain less than the #minimum value for the slowperiod
#we want to predict the S&P500 (^GSPC index)
instrument = ['AAPL']
fpath = 'C:/Users/Eric/Documents/PyBlockchainProj/'
ftype = '.csv'
fname = fpath + instrument[0] + ftype
#Important Note: maximum value for fastperiod must remain less than the
#minimum value for the slowperiod
slowPeriod = (50, 60, 80, 100, 150, 170, 200)
fastPeriod = (5, 10, 20, 30, 40)
rsiPeriod = (2, 3, 5, 7, 10, 14)
oversoldThreshold = (10, 20, 30)
overboughtThreshold = (70, 80, 90)
#===========================================================================#
#we can generate all the permutations
all = itertools.product(instrument, fastPeriod, slowPeriod, rsiPeriod,
overboughtThreshold, oversoldThreshold)
#the data is in the CSV file (one row per day)
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instrument[0], fname)
#we can run the optimization parallel
local.run(RSIMovingAverageStrategy, feed, all)
stddev=list(range(20,35))
return itertools.product(instrument,longLine,shortLine,bollingerLenth,stddev)
if __name__=='__main__':
instrument='002099'
stockcode='002099'
date=['2016-02-29','2016-03-02','2016-03-11']
path = "../histdata/tick/bak/"
# barfeed = tickcsvfeed.TickBarFeed(bar.Frequency.SECOND)
barfeed=GenericBarFeed(bar.Frequency.SECOND)
for d in date:
filepath = path +'stock_'+ stockcode + "_"+d+".csv"
barfeed.addBarsFromCSV(instrument, filepath)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
local.run(bollinger_band,barfeed,parameters_generator())
def testFailingStrategy(self):
barFeed = yahoofeed.Feed()
instrument = "orcl"
barFeed.addBarsFromCSV(instrument, common.get_data_file_path("orcl-2000-yahoofinance.csv"))
res = local.run(FailingStrategy, barFeed, parameters_generator(instrument, 5, 100), logLevel=logging.DEBUG)
self.assertIsNone(res)
import itertools
from pyalgotrade.optimizer import local
from pyalgotrade.barfeed import yahoofeed
import rsi2
def parameters_generator():
instrument = ["dia"]
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Load the feed from the CSV files.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("dia", "dia-2009.csv")
feed.addBarsFromCSV("dia", "dia-2010.csv")
feed.addBarsFromCSV("dia", "dia-2011.csv")
local.run(rsi2.RSI2, feed, parameters_generator())
import itertools
from pyalgotrade.optimizer import local
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade.tools import yahoofinance
from my_sma_crossover import *
def parameters_generator():
instrument = ["ivv"]
entrySMA = range(5, 20)
exitSMA = range(5, 20)
return itertools.product(instrument, entrySMA, exitSMA)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Download the bars.
feed = yahoofinance.build_feed(["ivv"], 2008, 2009, ".")
local.run(SMACrossOver2, feed, parameters_generator())
def enterLongSignal(self, bar):
return bar.getClose() > self.__entrySMA[-1] and self.__rsi[-1] <= self.__overSoldThreshold
def exitLongSignal(self, bar):
return bar.getClose() > self.__exitSMA[-1]
def enterShortSignal(self, bar):
return bar.getClose() < self.__entrySMA[-1] and self.__rsi[-1] >= self.__overBoughtThreshold
def exitShortSignal(self, bar):
return bar.getClose() < self.__exitSMA[-1]
def parameters_generator():
entrySMA = range(150, 251)
exitSMA = range(5, 16)
rsiPeriod = range(2, 11)
overBoughtThreshold = range(75, 96)
overSoldThreshold = range(5, 26)
return itertools.product(entrySMA, exitSMA, rsiPeriod, overBoughtThreshold, overSoldThreshold)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
# Load the feed from the CSV files.
feed = yahoofeed.Feed()
feed.addBarsFromCSV("dia", "dia-2009.csv")
feed.addBarsFromCSV("dia", "dia-2010.csv")
feed.addBarsFromCSV("dia", "dia-2011.csv")
local.run(MyStrategy, feed, parameters_generator())
import itertools
import utility.windutility as wu
from utility import dataframefeed
from pyalgotrade import bar
from pyalgotrade.optimizer import local
from strategy import singleMA
instruments = ['000001.SH']
fromDate = '20000101'
toDate = '20160314'
frequency = bar.Frequency.DAY
feed = dataframefeed.Feed()
maPeriod = xrange(5, 120)
for instrument in instruments:
data = wu.wsd(instrument, 'open, high, low, close, volume, adjfactor',
fromDate, toDate)
data[
'adjclose'] = data['close'] * data['adjfactor'] / data['adjfactor'][-1]
feed.addBarsFromDataFrame(instrument, data)
def parameters_generator():
return itertools.product(instruments, maPeriod)
if __name__ == '__main__':
local.run(singleMA.SingleMA, feed, parameters_generator())
