def main(plot):
get_ticker_list()
smaPeriod = 163
for instrument in ticker_list[:10]:
# Download the bars
feed = csvfeed.GenericBarFeed(frequency=300)
feed.addBarsFromCSV(
instrument,
r"C:/Users/Rohit/Python_source_code/nse 500 stock data/Date Time Data/"
+ instrument + ".csv")
strat = SMACrossOver(feed, instrument, smaPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"sma", strat.getSMA())
strat.run()
print(instrument)
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def __init__(
self,
algo: GQAlgo,
symbols,
start_datetime,
end_datetime,
data_freq=FREQ_DAY,
initial_cash=10000,
):
self.algo = algo
self.symbols = symbols
self.start_datetime = start_datetime
self.end_datetime = end_datetime
self.data_freq = data_freq
self.datasource = algo.datasource
self.initial_cash = initial_cash
self.data = GQData()
freq_map = {
FREQ_DAY: Frequency.DAY,
FREQ_MINUTE: Frequency.MINUTE,
}
self.datafeed = csvfeed.GenericBarFeed(freq_map[data_freq])
self._load_datafeed()
self.my_strategy = MyStrategy(self.datafeed, self.symbols, self.algo,
self.initial_cash)
self.returnsAnalyzer = returns.Returns()
self.my_strategy.attachAnalyzer(self.returnsAnalyzer)
def feed(self, day=False): # can also use other file if exist
'''
通过本地数据库文件读出csv，创建创建数据源对象
遍历context的category给所有品种读入数据
:param day 如果为真，使用日线数据。
'''
conn = sqlite3.connect('./future_data.db')
Data = {}
res = csvfeed.GenericBarFeed(Frequency.MINUTE, maxLen=2000000)
if day:
res.setDateTimeFormat('%Y-%m-%d')
else:
res.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
for category in self.context.categorys:
file = pd.read_sql('SELECT * FROM [' +
self.context.categoryToFile[category] + '] ',
conn,
parse_dates=['Date Time'])
# file = file.loc[file['Date Time'] > '2018', :].reset_index(drop=True) # 测试
if day:
file = self._resample(file, 'D')
file.to_csv('temp.csv', index=False)
res.addBarsFromCSV(category, 'temp.csv')
Data[category] = file
os.remove('temp.csv')
conn.close()
return Data, res
def testResampleMtGoxHour(self):
# Resample.
feed = mtgoxfeed.CSVTradeFeed()
feed.addBarsFromCSV(
common.get_data_file_path("trades-mgtox-usd-2013-01-01.csv"))
resampledBarDS = resampled.ResampledBarDataSeries(
feed["BTC"], barfeed.Frequency.HOUR)
resampledFile = os.path.join(common.get_temp_path(),
"hour-mgtox-usd-2013-01-01.csv")
resample.resample_to_csv(feed, barfeed.Frequency.HOUR, resampledFile)
resampledBarDS.pushLast(
) # Need to manually push the last stot since time didn't change.
# Load the resampled file.
feed = csvfeed.GenericBarFeed(barfeed.Frequency.HOUR)
feed.addBarsFromCSV("BTC", resampledFile)
feed.loadAll()
self.assertEqual(len(feed["BTC"]), 24)
self.assertEqual(feed["BTC"][0].getDateTime(),
datetime.datetime(2013, 01, 01, 00, 59, 59))
self.assertEqual(feed["BTC"][-1].getDateTime(),
datetime.datetime(2013, 01, 01, 23, 59, 59))
self.assertEqual(len(resampledBarDS), len(feed["BTC"]))
self.assertEqual(resampledBarDS[0].getDateTime(),
dt.as_utc(feed["BTC"][0].getDateTime()))
self.assertEqual(resampledBarDS[-1].getDateTime(),
dt.as_utc(feed["BTC"][-1].getDateTime()))
def testResampleNinjaTraderHour(self):
with common.TmpDir() as tmp_path:
# Resample.
feed = ninjatraderfeed.Feed(ninjatraderfeed.Frequency.MINUTE)
feed.addBarsFromCSV("spy", common.get_data_file_path("nt-spy-minute-2011.csv"))
resampledBarDS = resampled_ds.ResampledBarDataSeries(feed["spy"], bar.Frequency.HOUR)
resampledFile = os.path.join(tmp_path, "hour-nt-spy-minute-2011.csv")
resample.resample_to_csv(feed, bar.Frequency.HOUR, resampledFile)
resampledBarDS.pushLast() # Need to manually push the last stot since time didn't change.
# Load the resampled file.
feed = csvfeed.GenericBarFeed(bar.Frequency.HOUR, marketsession.USEquities.getTimezone())
feed.addBarsFromCSV("spy", resampledFile)
feed.loadAll()
self.assertEqual(len(feed["spy"]), 340)
self.assertEqual(feed["spy"][0].getDateTime(), dt.localize(datetime.datetime(2011, 1, 3, 9), marketsession.USEquities.getTimezone()))
self.assertEqual(feed["spy"][-1].getDateTime(), dt.localize(datetime.datetime(2011, 2, 1, 1), marketsession.USEquities.getTimezone()))
self.assertEqual(feed["spy"][0].getOpen(), 126.35)
self.assertEqual(feed["spy"][0].getHigh(), 126.45)
self.assertEqual(feed["spy"][0].getLow(), 126.3)
self.assertEqual(feed["spy"][0].getClose(), 126.4)
self.assertEqual(feed["spy"][0].getVolume(), 3397.0)
self.assertEqual(feed["spy"][0].getAdjClose(), None)
self.assertEqual(len(resampledBarDS), len(feed["spy"]))
self.assertEqual(resampledBarDS[0].getDateTime(), dt.as_utc(datetime.datetime(2011, 1, 3, 9)))
self.assertEqual(resampledBarDS[-1].getDateTime(), dt.as_utc(datetime.datetime(2011, 2, 1, 1)))
def testResampleNinjaTraderDay(self):
with common.TmpDir() as tmp_path:
# Resample.
feed = ninjatraderfeed.Feed(ninjatraderfeed.Frequency.MINUTE)
feed.addBarsFromCSV(
"spy", common.get_data_file_path("nt-spy-minute-2011.csv"))
resampledBarDS = resampled_ds.ResampledBarDataSeries(
feed["spy"], bar.Frequency.DAY)
resampledFile = os.path.join(tmp_path,
"day-nt-spy-minute-2011.csv")
resample.resample_to_csv(feed, bar.Frequency.DAY, resampledFile)
resampledBarDS.pushLast(
) # Need to manually push the last stot since time didn't change.
# Load the resampled file.
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV("spy", resampledFile,
marketsession.USEquities.getTimezone())
feed.loadAll()
self.assertEqual(len(feed["spy"]), 25)
self.assertEqual(
feed["spy"][0].getDateTime(),
dt.localize(datetime.datetime(2011, 1, 3),
marketsession.USEquities.getTimezone()))
self.assertEqual(
feed["spy"][-1].getDateTime(),
dt.localize(datetime.datetime(2011, 2, 1),
marketsession.USEquities.getTimezone()))
self.assertEqual(len(resampledBarDS), len(feed["spy"]))
self.assertEqual(resampledBarDS[0].getDateTime(),
dt.as_utc(datetime.datetime(2011, 1, 3)))
self.assertEqual(resampledBarDS[-1].getDateTime(),
dt.as_utc(datetime.datetime(2011, 2, 1)))
def main():
feed = csvfeed.GenericBarFeed(frequency=Frequency.MINUTE)
feed.addBarsFromCSV("ETH", "sampledata.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = Accumulator(feed, "ETH", buy_offset=0.0017, buy_percent=0.49)
# myStrategy.run()
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot("ETH").addDataSeries("SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
# Plot the strategy.
plt.plot()
print("Final portfolio value: $%.2f" % myStrategy.getResult())
def main(plot):
instrument = "aapl"
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
#feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
#feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
#feed = yahoofeed.Feed()
feed = csvfeed.GenericBarFeed(1)
#feed.addBarsFromCSV("aapl", "BTC_NXT_CSV_NOTIME")
feed.addBarsFromCSV("aapl", "a-2016-yahoofinance.csv")
strat = VWAPMomentum(feed, instrument, vwapWindowSize, threshold)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"vwap", strat.getVWAP())
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def run_strategy(fastSma, slowSma, tp, stop):
# Load the bar feed from the CSV file
feed = csvfeed.GenericBarFeed(frequency=60 * 5)
feed.setDateTimeFormat("%Y-%m-%dT%H:%M:%S.%fZ")
feed.addBarsFromCSV("btc", "btc-5m-apr.csv")
# Create our broker defining the comission(0,01%) and the initial balance($15000)
commission = backtesting.TradePercentage(0.0001)
broker = backtesting.Broker(15000, feed, commission)
# Evaluate the strategy with the feed
myStrategy = MACrossoverStrategy(feed, "btc", broker, fastSma, slowSma, tp,
stop)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(myStrategy)
# Include the MA in the instrument's subplot to get it displayed along with the closing prices
plt.getOrCreateSubplot("MA").addDataSeries("50 MA",
myStrategy.getFastSMA())
plt.getOrCreateSubplot("MA").addDataSeries("200 MA",
myStrategy.getSlowSMA())
# Run the strategy and show the final portfolio value
myStrategy.run()
myStrategy.info(f'Final portfolio value: ${myStrategy.getResult()}')
# Plot the strategy
plt.plot()
def testResampleMtGoxMinute(self):
# Resample.
feed = mtgoxfeed.CSVTradeFeed()
feed.addBarsFromCSV(
common.get_data_file_path("trades-mgtox-usd-2013-01-01.csv"))
resampledBarDS = resampled.ResampledBarDataSeries(
feed["BTC"], barfeed.Frequency.MINUTE)
resample.resample_to_csv(feed, barfeed.Frequency.MINUTE,
"minute-mgtox-usd-2013-01-01.csv")
resampledBarDS.pushLast(
) # Need to manually push the last stot since time didn't change.
# Load the resampled file.
feed = csvfeed.GenericBarFeed(barfeed.Frequency.MINUTE)
feed.addBarsFromCSV("BTC", "minute-mgtox-usd-2013-01-01.csv")
feed.loadAll()
self.assertEqual(len(feed["BTC"]), 537)
self.assertEqual(feed["BTC"][0].getDateTime(),
datetime.datetime(2013, 01, 01, 00, 04, 59))
self.assertEqual(feed["BTC"][-1].getDateTime(),
datetime.datetime(2013, 01, 01, 23, 58, 59))
self.assertEqual(len(resampledBarDS), len(feed["BTC"]))
self.assertEqual(resampledBarDS[0].getDateTime(),
dt.as_utc(feed["BTC"][0].getDateTime()))
self.assertEqual(resampledBarDS[-1].getDateTime(),
dt.as_utc(feed["BTC"][-1].getDateTime()))
def RSIBacktest(nper):
# Create Instruments object with stock tickers
instruments = ["BANKNIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//bnf.csv")
# create object to run strategy
rsi_strat = RSIStrategy(feed, instruments[0], nper)
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(rsi_strat,
plotAllInstruments=True,
plotBuySell=True,
plotPortfolio=False)
plt.getOrCreateSubplot("RSI").addDataSeries("RSI", rsi_strat.getRSI())
plt.getOrCreateSubplot("RSI").addLine("Upper Threshold", 70)
plt.getOrCreateSubplot("RSI").addLine("Lower Threshold", 40)
# plt.getOrCreateSubplot("RSI").addLine("Center Line", 40)
retAnalyzer = ret.Returns()
rsi_strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
rsi_strat.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
rsi_strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
rsi_strat.attachAnalyzer(tradesAnalyzer)
# Run Strategy
rsi_strat.run()
# Print Strategy Trading Statistics
print
print "------------------------------"
print "1. Portfolio statistics"
print
print "Portfolio initial equity: $100000.00"
print "Portfolio final equity: $%.2f" % rsi_strat.getBroker().getEquity()
print "Portfolio net trading p&l: $%.2f" % tradesAnalyzer.getAll().sum()
print "Portfolio maximum drawdown: %.2f %%" % (
drawDownAnalyzer.getMaxDrawDown() * 100)
print "Portfolio annualized return: %.2f %%" % (
retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Portfolio annualized Sharpe ratio (Rf = 0%%): %.2f" % (
sharpeRatioAnalyzer.getSharpeRatio(0.0))
print "------------------------------"
print "2. Total trades statistics"
print "Total trades: %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
tradesProfits = tradesAnalyzer.getAll()
print "Total trades average p&l: $%2.f" % (tradesProfits.mean())
print "Largest Winning Trade: $%2.f" % (tradesProfits.max())
print "Largest Losing Trade: $%2.f" % (tradesProfits.min())
tradesReturns = tradesAnalyzer.getAllReturns()
print "Average return: %2.f %%" % (tradesReturns.mean() * 100)
print "Largest Winning Trade Return: %2.f %%" % (tradesReturns.max() *
100)
print "Largest Losing Trade Return: %2.f %%" % (tradesReturns.min() *
100)
print "------------------------------"
# Plot Strategy
plt.plot()
def run_strategy(smaPeriod):
# Load the bar feed from the CSV file
# feed = quandlfeed.Feed()
# feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")
feed = csvfeed.GenericBarFeed(Frequency.MINUTE)
feed.addBarsFromCSV(
"orcl",
"../../data/input/EURUSD_Candlestick_15_M_ASK_01.04.2018-21.09.2018_after.csv"
)
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "orcl", smaPeriod)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
def algo_test(target_name,
target_csv,
model_name,
model_csv,
model_prediction_std=0.0):
# Load the bar feed from the CSV file
feed = csvfeed.GenericBarFeed(pyalgotrade.bar.Frequency.DAY)
feed.addBarsFromCSV(target_name, target_csv)
feed.addBarsFromCSV(model_name, model_csv)
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed, target_name, model_prediction_std)
# myStrategy = MyBasicStrategy(feed, target_name)
# Attach analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
sortinoRatioAnalyzer = sortino.SortinoRatio()
myStrategy.attachAnalyzer(sortinoRatioAnalyzer)
# Run the strategy
myStrategy.run()
# Print the results.
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Total return: %.2f %%" %
(retAnalyzer.getCumulativeReturns()[-1] * 100))
print("Average daily return: %.2f %%" %
(stats.mean(retAnalyzer.getReturns()) * 100))
print("Std. dev. daily return: %.4f" %
(stats.stddev(retAnalyzer.getReturns())))
print("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0)))
print("Sortino ratio: %.2f" % (sortinoRatioAnalyzer.getSortinoRatio(0)))
returnsPerDay = stats.mean(retAnalyzer.getReturns())
# print("shares list : ")
# print(myStrategy.getShares())
output = {
'sharpe': sharpeRatioAnalyzer.getSharpeRatio(0),
'sortino': sortinoRatioAnalyzer.getSortinoRatio(0),
'returnAnalyzer': retAnalyzer,
'annualReturns': returnsPerDay * 252
}
return output
def ZScorePlot(nper, lowerThreshold, upperThreshold):
# Create Instruments object with stock tickers
instruments = ["NIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//NIFTY.csv")
# create object to run strategy
zscore_plot = ZScoreStrategy(feed, instruments[0], nper, lowerThreshold, upperThreshold)
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(zscore_plot, plotAllInstruments=True, plotBuySell=True, plotPortfolio=False)
plt.getOrCreateSubplot("ZScore").addDataSeries("ZScore", zscore_plot.getZScore())
# plt.getOrCreateSubplot("RSI").addLine("Center Line", 40)
# Run Strategy
zscore_plot.run()
# Plot Strategy
plt.plot()
def testResampleMtGoxDay(self):
# Resample.
feed = mtgoxfeed.CSVTradeFeed()
feed.addBarsFromCSV(
common.get_data_file_path("trades-mgtox-usd-2013-01-01.csv"))
resampledBarDS = resampled.ResampledBarDataSeries(
feed["BTC"], barfeed.Frequency.DAY)
resampledFile = os.path.join(common.get_temp_path(),
"day-mgtox-usd-2013-01-01.csv")
resample.resample_to_csv(feed, barfeed.Frequency.DAY, resampledFile)
resampledBarDS.pushLast(
) # Need to manually push the last stot since time didn't change.
# Load the resampled file.
feed = csvfeed.GenericBarFeed(barfeed.Frequency.DAY)
feed.addBarsFromCSV("BTC", resampledFile)
feed.loadAll()
self.assertEqual(len(feed["BTC"]), 1)
self.assertEqual(feed["BTC"][0].getDateTime(),
datetime.datetime(2013, 01, 01, 23, 59, 59))
self.assertEqual(feed["BTC"][0].getOpen(), 13.51001)
self.assertEqual(feed["BTC"][0].getHigh(), 13.56)
self.assertEqual(feed["BTC"][0].getLow(), 13.16123)
self.assertEqual(feed["BTC"][0].getClose(), 13.30413)
self.assertEqual(feed["BTC"][0].getVolume(), 28168.9114596)
self.assertEqual(feed["BTC"][0].getAdjClose(), 13.30413)
self.assertEqual(len(resampledBarDS), len(feed["BTC"]))
self.assertEqual(resampledBarDS[0].getDateTime(),
dt.as_utc(feed["BTC"][0].getDateTime()))
self.assertEqual(resampledBarDS[-1].getDateTime(),
dt.as_utc(feed["BTC"][-1].getDateTime()))
self.assertEqual(resampledBarDS[0].getOpen(), feed["BTC"][0].getOpen())
self.assertEqual(resampledBarDS[0].getHigh(), feed["BTC"][0].getHigh())
self.assertEqual(resampledBarDS[0].getLow(), feed["BTC"][0].getLow())
self.assertEqual(resampledBarDS[0].getClose(),
feed["BTC"][0].getClose())
self.assertEqual(round(resampledBarDS[0].getVolume(), 5),
round(feed["BTC"][0].getVolume(), 5))
self.assertEqual(resampledBarDS[0].getAdjClose(),
feed["BTC"][0].getAdjClose())
def main(plot):
instrument = "BTC"
initialCash = 1000
vwapWindowSize = 100
buyThreshold = 0.02
sellThreshold = 0.01
barFeed = csvfeed.GenericBarFeed(bar.Frequency.MINUTE*30)
barFeed.addBarsFromCSV(instrument, "30min-bitstampUSD.csv")
brk = broker.BacktestingBroker(initialCash, barFeed)
strat = VWAPMomentum(barFeed, brk, instrument, vwapWindowSize, buyThreshold, sellThreshold)
if plot:
plt = plotter.StrategyPlotter(strat)
plt.getInstrumentSubplot(instrument).addDataSeries("VWAP", strat.getVWAP())
strat.run()
if plot:
plt.plot()
def MACrossSignals(nfast, nslow):
# Create Instruments object with stock tickers
instruments = ["NIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//NIFTY.csv")
# create object to run strategy
ma_signals = MAStrategy(feed, instruments[0], nfast, nslow)
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(ma_signals,
plotAllInstruments=True,
plotBuySell=True,
plotPortfolio=False)
plt.getInstrumentSubplot("NIFTY").addDataSeries("Fast SMA",
ma_signals.getfastSMA())
plt.getInstrumentSubplot("NIFTY").addDataSeries("Slow SMA",
ma_signals.getslowSMA())
# Run Strategy
ma_signals.run()
# Plot Strategy
plt.plot()
def main(plot):
# merge_csv("./hoge.csv", ["./USDJPY_M5_2001.txt","./USDJPY_M5_2002.txt","./USDJPY_M5_2003.txt","./USDJPY_M5_2004.txt",\
# "./USDJPY_M5_2005.txt","./USDJPY_M5_2006.txt","./USDJPY_M5_2007.txt","./USDJPY_M5_2008.txt"])
instrument = "USDJPY"
feed = csvfeed.GenericBarFeed(Frequency.MINUTE, pytz.utc)
feed.addBarsFromCSV(instrument, "./hoge.csv")
strat = MATrade(feed, instrument)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"MA20", strat.getSMA())
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def main():
# Initiate strategy with provided data
feed = csvfeed.GenericBarFeed(Frequency.DAY)
feed.addBarsFromCSV(INSTRUMENT, BACKTESTING_DATA)
momentum_strategy = CryptoMomentumStrategy(feed, INSTRUMENT, MA_LOOKBACK, EMA_LOOKBACK, ATR_LOOKBACK)
# Attach returns analyzer
returns_analyzer = returns.Returns()
momentum_strategy.attachAnalyzer(returns_analyzer)
# Attach trades analyzer
trades_analyzer = trades.Trades()
momentum_strategy.attachAnalyzer(trades_analyzer)
# Configure plotter
plt = plotter.StrategyPlotter(momentum_strategy)
plt.getInstrumentSubplot(INSTRUMENT).addDataSeries('MA', momentum_strategy.get_plot_ma())
plt.getInstrumentSubplot(INSTRUMENT).addDataSeries('EMA', momentum_strategy.get_plot_ema())
plt.getOrCreateSubplot('returns').addDataSeries('Simple Returns', returns_analyzer.getReturns())
# Run strategy
momentum_strategy.run()
momentum_strategy.info('Final portfolio value: ${:.2f}'.format(momentum_strategy.getResult()))
# Plot strategy
plt.plot()
def MACDPlot(nfastEMA, nslowEMA, nsignalEMA):
# Create Instruments object with stock tickers
instruments = ["NIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//NIFTY.csv")
# create object to run strategy
macd_plot = MACDStrategy(feed, instruments[0], nfastEMA, nslowEMA,
nsignalEMA)
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(macd_plot,
plotAllInstruments=True,
plotBuySell=True,
plotPortfolio=False)
plt.getOrCreateSubplot("MACD").addDataSeries("MACD", macd_plot.getMACD())
plt.getOrCreateSubplot("MACD").addDataSeries(
"Signal",
macd_plot.getMACD().getSignal())
plt.getOrCreateSubplot("MACD").addLine("CenterLine", 0)
# Run Strategy
macd_plot.run()
# Plot Strategy
plt.plot()
def BbandPlot(nper, nstdev):
# Create Instruments object with stock tickers
instruments = ["NIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//NIFTY.csv")
# create object to run strategy
bb_plot = BBStrategy(feed, instruments[0], nper, nstdev)
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(bb_plot,
plotAllInstruments=True,
plotBuySell=True,
plotPortfolio=False)
plt.getInstrumentSubplot("NIFTY").addDataSeries(
"Lower band",
bb_plot.getBBands().getLowerBand())
# plt.getInstrumentSubplot("NIFTY").addDataSeries("Middle band", bb_plot.getBBands().getMiddleBand())
plt.getInstrumentSubplot("NIFTY").addDataSeries(
"Upper band",
bb_plot.getBBands().getUpperBand())
# Run Strategy
bb_plot.run()
# Plot Strategy
plt.plot()
fastlist.append(tmp_fast)
#backtest fastperiod/slowperiod pair
net_profit_list = []
for i in range(len(fastvec)):
fastPeriod = fastvec[i]
tmp_profit_list = []
for slowPeriod in slowlist[i]:
print([fastPeriod, slowPeriod])
INITIAL_BUDGET = 15000
NSHARES = 20
#the data is in the CSV file
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instrument, fname)
# define the time for the moving average models
movingAverageStrategy = MovingAverageStrategy(
feed, instrument, fastPeriod, slowPeriod)
#define the cost of trading
movingAverageStrategy.getBroker().setCommission(
backtesting.FixedPerTrade(cost_per_trade))
#analyze the returns during the backtest
returnAnalyzer = ret.Returns()
movingAverageStrategy.attachAnalyzer(returnAnalyzer)
#analyze the Sharpe ratio during backtest
def get_generic_feed(instrument, relative_filepath, frequency):
# feed.setBarFilter(csvfeed.DateRangeFilter(firstDate,endDate))
feed = csvfeed.GenericBarFeed(frequency)
feed.addBarsFromCSV(instrument, get_filepath(relative_filepath))
return feed
def main():
feed = csvfeed.GenericBarFeed(frequency=Frequency.MINUTE)
feed.addBarsFromCSV("BTC", "sampledata.csv")
# Plot the strategy.
local.run(Accumulator, feed, parameters_generator())
from pyalgotrade.stratanalyzer import returns
from pyalgotrade.stratanalyzer import sharpe
from pyalgotrade.stratanalyzer import drawdown
from pyalgotrade.stratanalyzer import trades
from pyalgotrade import plotter
from pyalgotrade.bar import Frequency
from pyalgotrade_itf_db_feed import ITFDatabaseFeed
import pyalgotrade_sample_strat
# Load the feed from ITF database
feed = ITFDatabaseFeed(frequency=Frequency.HOUR)
feed.addBarsFromDB("BTC")
# Load the feed from a CSV file
feed = csvfeed.GenericBarFeed(frequency=Frequency.HOUR)
feed.addBarsFromCSV("BTC", "sample_data.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = pyalgotrade_sample_strat.SMACrossOver(feed, "BTC", 20)
# Attach different analyzers to a strategy before executing it.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
from pyalgotrade.barfeed import csvfeed
from pyalgotrade import bar
def parameters_generator():
instrument = ["btc"]
entrySMA = range(120, 155)
exitSMA = range(10, 15)
rsiPeriod = range(2, 4)
overBoughtThreshold = range(70, 90)
overSoldThreshold = range(19, 35)
initialAmount = [5000]
return itertools.product(instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold,
initialAmount)
# The if __name__ == '__main__' part is necessary if running on Windows.
if __name__ == '__main__':
instrument = "btc"
year = "2015"
# Load the feed from the CSV files.
barFeed = csvfeed.GenericBarFeed(bar.Frequency.MINUTE * 30)
barFeed.addBarsFromCSV(instrument, "30min-%s-%s.csv" % (instrument, year))
#feed = yahoofeed.Feed()
# feed.addBarsFromCSV("btc", "btc_all_daily.csv")
# Run the server.
server.serve(barFeed, parameters_generator(), "0.0.0.0", 5000)
def BbandBacktest(nper, nstdev):
# Create Instruments object with stock tickers
instruments = ["BANKNIFTY"]
# create feed object, add bars to the data feed
feed = csvfeed.GenericBarFeed(bar.Frequency.DAY)
feed.addBarsFromCSV(instruments[0], "Data//bnf.csv")
# create object to run strategy
bb_strat = BBStrategy(feed, instruments[0], nper, nstdev)
# add trading costs using TradePercentage()
bb_strat.getBroker().setCommission(
broker.backtesting.TradePercentage(0.01))
# Attach Strategy Plotter
plt = plotter.StrategyPlotter(bb_strat,
plotAllInstruments=True,
plotBuySell=True,
plotPortfolio=False)
plt.getInstrumentSubplot("BANKNIFTY").addDataSeries(
"Lower band",
bb_strat.getBBands().getLowerBand())
# plt.getInstrumentSubplot("NIFTY").addDataSeries("Middle band", bb_strat.getBBands().getMiddleBand())
plt.getInstrumentSubplot("BANKNIFTY").addDataSeries(
"Upper band",
bb_strat.getBBands().getUpperBand())
retAnalyzer = ret.Returns()
bb_strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
bb_strat.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
bb_strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
bb_strat.attachAnalyzer(tradesAnalyzer)
# Run Strategy
bb_strat.run()
# Print Strategy Trading Statistics
print
print "------------------------------"
print "1. Portfolio statistics"
print
print "Portfolio initial equity: $100000.00"
print "Portfolio final equity: $%.2f" % bb_strat.getBroker().getEquity()
print "Portfolio net trading p&l: $%.2f" % tradesAnalyzer.getAll().sum()
print "Portfolio maximum drawdown: %.2f %%" % (
drawDownAnalyzer.getMaxDrawDown() * 100)
print "Portfolio annualized return: %.2f %%" % (
retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Portfolio annualized Sharpe ratio (Rf = 0%%): %.2f" % (
sharpeRatioAnalyzer.getSharpeRatio(0.0))
print "------------------------------"
print "2. Total trades statistics"
print "Total trades: %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
tradesProfits = tradesAnalyzer.getAll()
print "Total trades average p&l: $%2.f" % (tradesProfits.mean())
print "Largest Winning Trade: $%2.f" % (tradesProfits.max())
print "Largest Losing Trade: $%2.f" % (tradesProfits.min())
tradesReturns = tradesAnalyzer.getAllReturns()
print "Average return: %2.f %%" % (tradesReturns.mean() * 100)
print "Largest Winning Trade Return: %2.f %%" % (tradesReturns.max() *
100)
print "Largest Losing Trade Return: %2.f %%" % (tradesReturns.min() *
100)
print "------------------------------"
# Plot Strategy
plt.plot()
from pyalgotrade import strategy
from pyalgotrade.barfeed import quandlfeed
from pyalgotrade.barfeed import csvfeed
from pyalgotrade.bar import Frequency
class MyStrategy(strategy.BacktestingStrategy):
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__instrument = instrument
def onBars(self, bars):
bar = bars[self.__instrument]
self.info(bar.getClose())
# Load the bar feed from the CSV file
feed = csvfeed.GenericBarFeed(Frequency.MINUTE)
# feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")
# feed.addBarsFromCSV("orcl", "../../data/input/EURUSD-2018_04_01-2018_09_24.csv")
feed.addBarsFromCSV(
"orcl",
"../../data/input/EURUSD_Candlestick_15_M_ASK_01.04.2018-21.09.2018_after.csv"
)
# feed.addBarsFromCSV("orcl", "../../data/input/EURUSD-2018_09_23-2018_09_24.csv")
# feed.addBarsFromCSV("orcl", "../../data/input/EURUSD_Candlestick_15_M_BID_01.04.2018-21.09.2018.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed, "orcl")
myStrategy.run()
