def run_strategy():
# Load the yahoo feed from the CSV file
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("orcl", "2000.csv")
# commission
broker_commission = broker.backtesting.TradePercentage(0.002)
broker_brk = floatBroker(50000, feed, broker_commission)
# broker_brk = broker.backtesting.Broker(50000, feed)
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "orcl", broker_brk)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot("orcl").addDataSeries("SMA60", myStrategy.getSMA(60))
plt.getInstrumentSubplot("orcl").addDataSeries("SMA10", myStrategy.getSMA(10))
plt.getInstrumentSubplot("orcl").addDataSeries("SMA30", myStrategy.getSMA(30))
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
myStrategy.run()
print("Final portfolio value: $%.2f %.2f %.2f" %(myStrategy.getBroker().getEquity(), myStrategy.getBroker().getCash(), myStrategy.getBroker().getShares('orcl')))
# myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
# Plot the strategy.
plt.plot()
def run_strategy(smaPeriod, factor1):
import pandas as pd
# df_new = pd.DataFrame(columns=('shares_ID', 'total number of trades','Profit times number of trades','Final portfolio value'))
for (root, dirs, files) in os.walk("E:/SHZQ"):
for x in range(len(files)):
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("fd", "E:/SHZQ/" + files[x])
myStrategy = MyStrategy(feed, "fd", smaPeriod, factor1)
trade_situation = trades.Trades()
myStrategy.attachAnalyzer(trade_situation)
myStrategy.run()
print(files[x] + "Final portfolio value:$%.2f" %
myStrategy.getBroker().getEquity())
print("total number of trades", trade_situation.getCount())
print("Profit times number of trades ",
trade_situation.getProfitableCount())
print(
"百分比",
trade_situation.getProfitableCount() /
trade_situation.getCount())
print(trade_situation.getCount())
df1 = pd.DataFrame({
"shares_ID": [files[x]],
'total number of trades': [trade_situation.getCount()],
'Profit times number of trades':
trade_situation.getProfitableCount(),
'Final portfolio value': [myStrategy.getBroker().getEquity()]
})
def test():
# Load the yahoo feed from the CSV file
instruments = {
'XAUUSD': {"name":"XAUUSD", "dollar_per_point":100},
}
CAPITAL = 2000000
feed = GenericBarFeed(5 * Frequency.MINUTE)
indicators = {}
for i in instruments:
print i
csv_name = '%s.csv' % (i)
#convertTicketStoryCsv(csv_name)
csv_name_new = "new_" + csv_name
csv_name = os.path.join("data", csv_name)
csv_name_new = os.path.join("new", csv_name_new)
print csv_name_new
feed.addBarsFromCSV(i, csv_name_new)
indicators[i] = createIndicatorFromCsv(csv_name_new)
#pprint(indicators)
myStrategy = TurtleTrading(feed, instruments, CAPITAL, indicators)
myStrategy.run()
final = myStrategy.getBroker().getEquity() - CAPITAL * 99
profit = final - CAPITAL
print "Final portfolio value: $%.2f" % final
print "Profit = %.2f, %.2f " % (profit, 100 * profit / CAPITAL)
def main(plot):
symbol = sql_to_csv(True)
instruments = [symbol, symbol]
windowSize = 50
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(symbol, symbol + '.csv')
strat = StatArb(feed, instruments[0], instruments[1], windowSize)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("hedge").addDataSeries("Hedge Ratio",
strat.getHedgeRatioDS())
plt.getOrCreateSubplot("spread").addDataSeries("Spread",
strat.getSpreadDS())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main(plot):
symbol = sql_to_csv(True)
instrument = symbol
entrySMA = 200
exitSMA = 5
rsiPeriod = 2
overBoughtThreshold = 90
overSoldThreshold = 10
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(symbol, symbol + '.csv')
strat = RSI2(feed, instrument, entrySMA, exitSMA, rsiPeriod,
overBoughtThreshold, overSoldThreshold)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"Entry SMA", strat.getEntrySMA())
plt.getInstrumentSubplot(instrument).addDataSeries(
"Exit SMA", strat.getExitSMA())
plt.getOrCreateSubplot("rsi").addDataSeries("RSI", strat.getRSI())
plt.getOrCreateSubplot("rsi").addLine(
"Overbought", overBoughtThreshold)
plt.getOrCreateSubplot("rsi").addLine("Oversold", overSoldThreshold)
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main(plot):
"""
"""
# 初始化常量
instrument = 'BINA--BTC--USDT'
# 数据
# 1、从mysql导出csv数据
# 2、指定数据频率
# 3、读入csv给feed
sql_to_csv(True)
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(instrument, instrument + '.csv')
# 评价器
# 1、初始化一个sharpratio评价器
sharpeRatioAnalyzer = sharpe.SharpeRatio()
# 注入
# 1、实例化一个策略
# 2、注入评价器
strat = close_above_sma(feed, instrument)
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"sma", strat.getSma())
strat.run()
plt.plot()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
def main(plot):
symbol = sql_to_csv(True)
instrument = symbol
bBandsPeriod = 40
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(symbol, symbol + '.csv')
strat = BBands(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"upper", strat.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"middle", strat.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"lower", strat.getBollingerBands().getLowerBand())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
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 main(plot):
symbol = sql_to_csv(False)
instrument = symbol
vwapWindowSize = 5
threshold = 0.01
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(symbol, symbol + '.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(signal_period):
# Load the bar feed from the CSV file
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/btcusd_1h.csv')
feed = GenericBarFeed(Frequency.HOUR)
feed.addBarsFromCSV("BTC", data_path)
# Evaluate the strategy with the feed's bars.
strategy = OBVSignalCrossStrategy(feed, "BTC", signal_period=signal_period)
# Attach a returns analyzers to the strategy.
returns_analyzer = returns.Returns()
strategy.attachAnalyzer(returns_analyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(strategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returns_analyzer.getReturns())
# Plot indicators
plt.getOrCreateSubplot("obv").addDataSeries("OBV", strategy.get_obv())
plt.getOrCreateSubplot("obv").addDataSeries("Signal", strategy.get_signal())
strategy.run()
print("Final portfolio value: $%.2f" % strategy.getBroker().getEquity())
plt.plot()
def main(plot):
bBandsPeriod = 60 * 24 * 2
# download and load the historic trade data from mtgox
currency = 'USD'
instrument = 'BTC'
startdate = datetime.date(2013, 1, 1)
enddate = datetime.date.today() - relativedelta(days=1)
barFrequency = pyalgotrade.barfeed.Frequency.MINUTE
print '(down-)loading bars'
feed = GenericBarFeed(barFrequency)
month = startdate
while month <= enddate:
fnamOrig = 'trades-mtgox-%s-%d-%d.csv' % (currency, month.year,
month.month)
if barFrequency == pyalgotrade.barfeed.Frequency.MINUTE:
fnamResa = 'resamp1m-mtgox-%s-%d-%d.csv' % (currency, month.year,
month.month)
else:
fnamResa = 'resamp1h-mtgox-%s-%d-%d.csv' % (currency, month.year,
month.month)
if not os.path.exists(fnamOrig):
print 'downloading ', fnamOrig
tools.download_trades_by_month(currency, month.year, month.month,
fnamOrig)
if not os.path.exists(fnamResa):
print 'resampling ', fnamResa
feedLocal = barfeed.CSVTradeFeed()
feedLocal.addBarsFromCSV(fnamOrig)
resample_to_csv(feedLocal, barFrequency, fnamResa)
print 'loading ', fnamResa
feed.addBarsFromCSV(instrument, fnamResa)
month += relativedelta(months=1)
print 'set up strategy and plot'
myStrategy = MyStrategy(feed, instrument, bBandsPeriod)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"upper",
myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"middle",
myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"lower",
myStrategy.getBollingerBands().getLowerBand())
print 'run the strategy'
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def mainPlot():
instrument = 'BINA--BTC--USDT'
if mysqlToCsv(label=True, database='vic_5mk', table='5mk', instrument=instrument):
feed = GenericBarFeed(Frequency.MINUTE * 5, None, None)
feed.addBarsFromCSV(instrument, instrument + '.csv')
strat = myStrategy(feed, instrument)
strat.run()
plt.plot(strat.ncp5)
plt.plot(strat.ncp55)
plt.show()
print(strat.counter)
def __init__(self):
super().__init__()
self.title = 'PyQt5 image - pythonspot.com'
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("fd", "8-11.csv")
# 3.实例化策略
myStrategy = MyStrategy(feed, "fd")
plot = plotter.StrategyPlotter(myStrategy)
myStrategy.run()
plot.plot()
self.initUI()
def __init__(self):
super().__init__()
self.title = 'PyQt5 image - pythonspot.com'
feed = GenericBarFeed(Frequency.DAY, None, None)
path = "C:\\Users\\user\\Desktop\\lastest(8.14)"
if os.path.exists(path+"\\8-11.csv"):
feed.addBarsFromCSV("fd", "8-11.csv")
# 3.实例化策略
myStrategy = MyStrategy(feed, "fd")
plot = plotter.StrategyPlotter(myStrategy)
myStrategy.run()
plot.plot()
self.initUI()
def main(plot):
instrument = ["price_diff", 'SHFE_AU', 'COMEX_AU']
bBandsPeriod = 20
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV('price_diff', 'price_oneyear_diff.csv')
feed.addBarsFromCSV('SHFE_AU', 'AU_oneyear_samedate.csv')
feed.addBarsFromCSV('COMEX_AU', 'GC_oneyear_samedate.csv')
# 3. broker setting
broker_commission = broker.backtesting.FixedPerTrade(390) # 2230
fill_stra = broker.fillstrategy.DefaultStrategy(volumeLimit=1)
sli_stra = broker.slippage.VolumeShareSlippage(priceImpact=0.2)
fill_stra.setSlippageModel(sli_stra)
brk = broker.backtesting.Broker(10000000, feed, broker_commission)
brk.setFillStrategy(fill_stra)
strat = BBands(feed, instrument[0], instrument[1], instrument[2],
bBandsPeriod, brk)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
trade_situation = trades.Trades()
strat.attachAnalyzer(trade_situation)
draw_down = drawdown.DrawDown()
strat.attachAnalyzer(draw_down)
returning = returns.Returns()
strat.attachAnalyzer(returning)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument[0]).addDataSeries(
"upper",
strat.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument[0]).addDataSeries(
"middle",
strat.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument[0]).addDataSeries(
"lower",
strat.getBollingerBands().getLowerBand())
strat.run()
strat.info('最终资产值: ¥ %.2f' % strat.getResult()) #info表示记录每一次的交易
print '夏普率: ', sharpeRatioAnalyzer.getSharpeRatio(0.05,
False) #print表示只打印一次
print '累计收益率：', (returning.getCumulativeReturns()[-1])
print '最大回撤：', draw_down.getMaxDrawDown()
print '总交易次数：', trade_situation.getCount()
print '每次交易的手续费：', trade_situation.getCommissionsForAllTrades()
print '赚钱的交易次数：', trade_situation.getProfitableCount()
print '亏损的交易次数：', trade_situation.getUnprofitableCount()
print '不赚不亏的交易次数：', trade_situation.getEvenCount()
print '每次交易的盈亏：', trade_situation.getAll()
if plot:
plt.plot()
def main(plot):
instrument = "mro"
fastSMA = 5
slowSMA = 10
fastEMA = 12
slowEMA = 26
signalEMA = 9
frequency = bar.Frequency.MINUTE
if frequency == bar.Frequency.DAY:
path = MyFiles.dataPath+"mro-2013-yahoofinance.csv"
elif frequency == bar.Frequency.MINUTE:
path = MyFiles.dataIntradayPath+"MRO.csv"
filepath = path
#############################################don't change ############################33
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
feed = GenericBarFeed(frequency)
feed.setDateTimeFormat('%Y/%m/%d %H:%M')
feed.addBarsFromCSV(instrument, filepath)
#feed = yahoofeed.Feed()
#feed.addBarsFromCSV(instrument,MyFiles.dataPath+"mro-2013-yahoofinance.csv")
#feed.addBarsFromCSV(instrument,MyFiles.dataIntradayPath+"MRO.csv")
strat = MyStrategy1(feed, instrument, fastSMA, slowSMA, fastEMA,slowEMA,signalEMA)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getOrCreateSubplot("macd").addDataSeries("MACD Value", strat.getMACD())
plt.getOrCreateSubplot("macd").addDataSeries("MACD SignalValue", strat.getSignal())
# plt.getOrCreateSubplot("macd").addDataSeries("MACD Histogram", strat.getHistogram())
plt.getInstrumentSubplot(instrument).addDataSeries("Fast SMA", strat.getSMAF())
plt.getInstrumentSubplot(instrument).addDataSeries("Slow SMA", strat.getSMAS())
#plt.getOrCreateSubplot("rsi").addLine("Overbought", overBoughtThreshold)
#plt.getOrCreateSubplot("rsi").addLine("Oversold", overSoldThreshold)
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def main(plot):
bBandsPeriod = 60 * 24 * 2
# download and load the historic trade data from mtgox
currency = 'USD'
instrument = 'BTC'
startdate = datetime.date(2013, 1, 1)
enddate = datetime.date.today() - relativedelta(days=1)
barFrequency = pyalgotrade.barfeed.Frequency.MINUTE
print '(down-)loading bars'
feed = GenericBarFeed(barFrequency)
month = startdate
while month <= enddate:
fnamOrig = 'trades-mtgox-%s-%d-%d.csv' % (currency, month.year, month.month)
if barFrequency == pyalgotrade.barfeed.Frequency.MINUTE:
fnamResa = 'resamp1m-mtgox-%s-%d-%d.csv' % (currency, month.year, month.month)
else:
fnamResa = 'resamp1h-mtgox-%s-%d-%d.csv' % (currency, month.year, month.month)
if not os.path.exists(fnamOrig):
print 'downloading ', fnamOrig
tools.download_trades_by_month(currency, month.year, month.month, fnamOrig)
if not os.path.exists(fnamResa):
print 'resampling ', fnamResa
feedLocal = barfeed.CSVTradeFeed()
feedLocal.addBarsFromCSV(fnamOrig)
resample_to_csv(feedLocal, barFrequency, fnamResa)
print 'loading ', fnamResa
feed.addBarsFromCSV(instrument, fnamResa)
month += relativedelta(months=1)
print 'set up strategy and plot'
myStrategy = MyStrategy(feed, instrument, bBandsPeriod)
if plot:
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries("upper", myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries("middle", myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries("lower", myStrategy.getBollingerBands().getLowerBand())
print 'run the strategy'
myStrategy.run()
print "Result: %.2f" % myStrategy.getResult()
if plot:
plt.plot()
def table_sitting(self):
feed = GenericBarFeed(Frequency.DAY, None, None)
path = "C:\\Users\\user\\Desktop\\lastest(8.14)"
if os.path.exists(path+"\\8-11.csv"):
feed.addBarsFromCSV("fd", "8-11.csv")
# 3.实例化策略
myStrategy = MyStrategy(feed, "fd")
# 4.设置指标和绘图
sharpe_ratio = sharpe.SharpeRatio()
mytrade = trades.Trades()
myStrategy.attachAnalyzer(sharpe_ratio)
myStrategy.attachAnalyzer(mytrade)
#plt = plotter.StrategyPlotter(myStrategy)
# 5.运行策略
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
self.tableWidget.setHorizontalHeaderLabels(["第一行", "第二行"])
self.tableWidget.setVerticalHeaderLabels(["淨利", "毛利","毛損","報酬率","總交易次數","勝率","獲利因子","夏普比率"])
#设置表头
lb1 = QLabel(str(int(myStrategy.getResult()-1000000)))
self.tableWidget.setCellWidget(0,0,lb1)
lb2 = QLabel("test")
self.tableWidget.setCellWidget(1,0,lb2)
a=str(((myStrategy.getResult()-1000000)/1000000)*100)
last_a=a[:6]+"(%)"
lb3 = QLabel(last_a)
self.tableWidget.setCellWidget(3,0,lb3)
if mytrade.getCount()==0:
last_a=" N/A"
else:
last_a=str(mytrade.getCount())
lb4 = QLabel(last_a)
self.tableWidget.setCellWidget(4,0,lb4)
try:
a=str(mytrade.getProfitableCount()/mytrade.getCount()*100)
last_a=a[:6]+"(%)"
except ZeroDivisionError:
last_a=" N/A"
lb5 = QLabel(last_a)
self.tableWidget.setCellWidget(5,0,lb5)
lb7 = QLabel(str(sharpe_ratio.getSharpeRatio(0)))
self.tableWidget.setCellWidget(7,0,lb7)
def run_strategy(smaPeriod):
# Load the yahoo feed from the CSV file
# feed = yahoofeed.Feed()
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("zggf", "E:\Stock\Data_Day/000831.SZ.csv")
global myStrategy
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "zggf", smaPeriod)
plt = plotter.StrategyPlotter(myStrategy)
sharpe_ratio = sharpe.SharpeRatio()
trade_situation = trades.Trades()
myStrategy.attachAnalyzer(sharpe_ratio)
myStrategy.attachAnalyzer(trade_situation)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
print("sharpe_ratio", sharpe_ratio.getSharpeRatio(0))
print("total number of trades", trade_situation.getCount())
print("Profit times number of trades ",
trade_situation.getProfitableCount())
print(myStrategy.Sma_price)
plt.plot()
def run_strategy():
# Load the yahoo feed from the CSV file
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(coin, get_file_path())
# commission
broker_commission = broker.backtesting.TradePercentage(0.002)
broker_brk = floatBroker(10000, feed, broker_commission)
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, coin, broker_brk)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
myStrategy.run()
print(
"Final portfolio value: $%.2f %.2f %.2f" %
(myStrategy.getBroker().getEquity(), myStrategy.getBroker().getCash(),
myStrategy.getBroker().getShares(coin)))
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
def table_sitting(self):
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("fd", "8-11.csv")
# 3.实例化策略
myStrategy = MyStrategy(feed, "fd")
# 4.设置指标和绘图
sharpe_ratio = sharpe.SharpeRatio()
mytrade = trades.Trades()
myStrategy.attachAnalyzer(sharpe_ratio)
myStrategy.attachAnalyzer(mytrade)
plt = plotter.StrategyPlotter(myStrategy)
# 5.运行策略
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" %
myStrategy.getResult())
self.tableWidget.setHorizontalHeaderLabels(["第一行", "第二行"])
self.tableWidget.setVerticalHeaderLabels(
["淨利", "毛利", "毛損", "報酬率", "總交易次數", "勝率", "獲利因子", "夏普比率"])
#设置表头
lb1 = QLabel(str(myStrategy.getResult() - 1000000))
self.tableWidget.setCellWidget(0, 0, lb1)
lb2 = QLabel("test")
self.tableWidget.setCellWidget(1, 0, lb2)
a = str(((myStrategy.getResult() - 1000000) / 1000000) * 100)
last_a = a[:6] + "(%)"
lb3 = QLabel(last_a)
self.tableWidget.setCellWidget(3, 0, lb3)
lb4 = QLabel(str(mytrade.getCount()))
self.tableWidget.setCellWidget(4, 0, lb4)
a = str(mytrade.getProfitableCount() / mytrade.getCount() * 100)
last_a = a[:6] + "(%)"
lb5 = QLabel(last_a)
self.tableWidget.setCellWidget(5, 0, lb5)
lb7 = QLabel(str(sharpe_ratio.getSharpeRatio(0)))
self.tableWidget.setCellWidget(7, 0, lb7)
def backtest(stockcode,date,preclose,prehigh,prelow,p1,p2,p3,p4):
stockcode=stockcode
#date=['2016-02-29','2016-03-02','2016-03-11']
path = "../histdata/tick/bak/"
strat =rbreaker
paras=[preclose,prehigh,prelow,p1,p2,p3,p4]
plot = True
# barfeed = tickcsvfeed.TickBarFeed(bar.Frequency.SECOND)
barfeed=GenericBarFeed(pyalgotrade.bar.Frequency.SECOND)
dstr=''
for d in date:
filepath = path +'stock_'+ stockcode + "_"+d+".csv"
barfeed.addBarsFromCSV(stockcode, filepath)
dstr=dstr+'_'+d
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
strat = strat(barfeed, stockcode, *paras)
strat.run()
cash=strat.getBroker()._Broker__cash
#tradeTimes=strat.getTradeTimes()
ticker=open(path +'stock_'+ stockcode+'_' + dstr+".csv", 'a')
ticker.write(str(p1))
ticker.write(',')
ticker.write(str(p2))
ticker.write(',')
ticker.write(str(p3))
ticker.write(',')
ticker.write(str(p4))
ticker.write(',')
ticker.write(str(cash))
ticker.write(',')
#ticker.write(str(tradeTimes))
ticker.write('\n')
ticker.close()
del strat
#return ( stockcode,date,p1,p2,p3,p4,cash,tradeTimes)
return ( stockcode,date,p1,p2,p3,p4,cash)
def main(plot):
symbol = sql_to_csv(True)
instrument = symbol
smaPeriod = 10
# Download the bars.
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV(symbol, symbol + '.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("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def run_strategy(smaPeriod):
df_new = pd.DataFrame(columns=('shares_ID', 'total number of trades',
'Profit times number of trades',
'Profit Percentage',
'Final portfolio value'))
for (root, dirs, files) in os.walk("E:/SHZQ"):
for x in range(len(files)):
feed = GenericBarFeed(Frequency.WEEK, None, None)
feed.addBarsFromCSV("fd", "E:/SHZQ/" + files[x])
myStrategy = MyStrategy(feed, "fd", smaPeriod)
trade_situation = trades.Trades()
myStrategy.attachAnalyzer(trade_situation)
myStrategy.run()
print(files[x] + "Final portfolio value:$%.2f" %
myStrategy.getBroker().getEquity())
print("total number of trades", trade_situation.getCount())
print("Profit times number of trades ",
trade_situation.getProfitableCount())
if trade_situation.getCount() > 0:
Percentage = trade_situation.getProfitableCount(
) / trade_situation.getCount()
print("百分比", Percentage)
else:
Percentage = 0
print("百分比", 0)
df1 = pd.DataFrame({
"shares_ID": [files[x]],
'total number of trades': [trade_situation.getCount()],
'Profit times number of trades':
trade_situation.getProfitableCount(),
'Profit Percentage':
Percentage,
'Final portfolio value': [myStrategy.getBroker().getEquity()]
})
df_new = pd.concat([df1, df_new], ignore_index=True)
df_new.to_csv("E:/Stock/html_png_Total//Total.csv", index=False)
def main(plot):
"""
"""
# 初始化常量
instrument = 'BINA--BTC--USDT'
# 数据
# 1、从mysql导出csv数据
# 2、指定数据频率
# 3、读入csv给feed
sql_to_csv(False)
feed = GenericBarFeed(Frequency.MINUTE * 5, None, None)
feed.addBarsFromCSV(instrument, instrument + '.csv')
# 评价器
# 1、初始化一个sharpratio评价器
sharpeRatioAnalyzer = sharpe.SharpeRatio()
# 注入
# 1、实例化一个策略
# 2、注入评价器
strat = DualThrust(feed, instrument)
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
# plt.getInstrumentSubplot(instrument).addDataSeries(
# "High", strat.get_high())
# plt.getInstrumentSubplot(instrument).addDataSeries(
# "Low", strat.get_low())
# plt.getInstrumentSubplot(instrument).addDataSeries(
# "BuyPosition", strat.get_buy_position())
# plt.getInstrumentSubplot(instrument).addDataSeries(
# "SellPosition", strat.get_sell_position())
strat.run()
plt.plot()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
def main(selected_features, predict_n_days, model_form, testing_datetime,
testing_end_datetime, n_retrain_times):
feed = GenericBarFeed(frequency='DAY')
instruments = {
'hk_equity_fund': 'dataset/hk_equity_fund_data.csv',
'growth_fund': 'dataset/growth_fund_data.csv',
'balanced_fund': 'dataset/balanced_fund_data.csv',
'conservative_fund': 'dataset/conservative_fund_data.csv',
'hkdollar_bond_fund': 'dataset/hkdollar_bond_fund_data.csv',
'stable_fund': 'dataset/stable_fund_data.csv',
'HSI': 'dataset/HSI_investing_com.csv',
'IXIC': 'dataset/IXIC_investing_com.csv',
'us2yrby': 'dataset/us2yrbondyield_investing_com.csv',
'us10yrby': 'dataset/us10yrbondyield_investing_com.csv'
}
for instu, fname in instruments.iteritems():
print('Importing data %s from file path %s' % (instu, fname))
feed.addBarsFromCSV(instu, fname)
capital = 1000000.0
tree_reg_strategy = WalkForwardBacktester(
feed=feed,
instrument=instruments.keys(),
capital=capital,
selected_features=selected_features,
predict_n_days=predict_n_days,
model_form=model_form,
testing_datetime=testing_datetime,
testing_end_datetime=testing_end_datetime,
n_retrain_times=n_retrain_times)
retAnalyzer = rets.Returns()
tree_reg_strategy.attachAnalyzer(retAnalyzer)
tree_reg_strategy.run()
equity_curve = list()
for ret in retAnalyzer.getCumulativeReturns():
equity_curve.append(capital * (1 + ret))
return equity_curve
def run_strategy(sma_period):
# Load the bar feed from the CSV file
data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '../data/btcusd_1h.csv')
feed = GenericBarFeed(Frequency.HOUR)
feed.addBarsFromCSV("BTC", data_path)
# Evaluate the strategy with the feed's bars.
strategy = SmaCrossStrategy(feed, "BTC", sma_period=sma_period)
# Attach a returns analyzers to the strategy.
returns_analyzer = returns.Returns()
strategy.attachAnalyzer(returns_analyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(strategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
plt.getInstrumentSubplot("BTC").addDataSeries("SMA", strategy.get_sma())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returns_analyzer.getReturns())
strategy.run()
print("Final portfolio value: $%.2f" % strategy.getBroker().getEquity())
plt.plot()
def buildFeed(code, filename):
feed = GenericBarFeed(Frequency.MINUTE)
feed.setDateTimeFormat("%Y-%m-%d %H:%M")
feed.addBarsFromCSV(code, filename)
#feed = csvfeed.Feed("Date Time", "%Y-%m-%d %H:%M")
# feed.addValuesFromCSV(filename)
#for item in feed:
# print(item[0], len(item), item[1])
# print(item.getDateTime())
return feed
def __getFeedFromGenericCsv(self,instrument):
'''mid
使用系统自定义的与数据提供者的数据格式相互独立的csv格式储存的文件
可以作为用户管理各种数据源的统一格式使用
'''
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
from pyalgotrade import bar
frequency = bar.Frequency.DAY
barfeed = GenericBarFeed(frequency)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
dataRoot = os.path.abspath(os.path.join(os.path.dirname(__file__),'data','generic','csv'))
filename = dataRoot+os.sep+'day'+os.sep+('%s.csv'%instrument)
barfeed.addBarsFromCSV(instrument, filename)
return barfeed
def main(plot):
from pyalgotrade import bar
from pyalgotrade import plotter
instrument = "002665"
bBandsPeriod = 40
strat = BBands
frequency = bar.Frequency.DAY
plot = True
# Download the bars.
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
barfeed = GenericBarFeed(frequency)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
barfeed.addBarsFromCSV("002665", "F:/shuju/002665-barfeed.csv")
strat = strat(barfeed, instrument, bBandsPeriod)
strat = BBands(barfeed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
plt.getInstrumentSubplot(instrument).addDataSeries(
"upper",
strat.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"middle",
strat.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"lower",
strat.getBollingerBands().getLowerBand())
strat.run()
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main():
filedir = "testresults"
iteration = 0
resultsdict = {}
for instrument in instrumentListGetter():
if len(instrument.split()) > 1:
instrument, instrumentSource = instrument.split()
print "instrument: %s, source: %s" % (instrument, instrumentSource)
else:
instrumentSource = ""
#feed = yahoofeed.Feed()
#feed.addBarsFromCSV("hm", DATAPATH + "/hm_table.csv")
# Load the feed from the CSV files.
#for params in parameters_generator():
faileddataforinstrument = False
for params in teststrategy.parameters_generator():
# Do not move to outer loop or severe performance problems arise # take the data we have and use
yiter = 0
if "quandl" in instrumentSource: # https://www.quandl.com/data/
'''/home/carl/Projects/algotrader/algotrader/lib64/python2.7/site-packages/pyalgotrade/barfeed/csvfeed.py:171 needs try hack '''
try:
feed = GenericBarFeed(frequency=bar.Frequency.DAY)
feed.setDateTimeFormat("%Y-%m-%d")
feed.setColumnName("datetime", "Date")
feed.setColumnName("adj_close", "Adjusted Close")
#feed.addBarsFromCSV(instrument, DATAPATH)
feed.addBarsFromCSV(instrument,
DATAPATH + "/" + instrument + ".csv")
except:
print sys.exc_info()
faileddataforinstrument = True
else:
for year in range(YEARSOFDATA, 0, -1):
startyear = localtime().tm_year - (YEARSOFDATA - yiter)
try:
feed = build_feed([instrument],
startyear,
2016,
DATAPATH,
frequency=86400,
timezone=None,
skipErrors=False)
except:
print "\n\nFailed downloading %s for year %d yiter %d \n\n" % (
instrument, startyear, yiter)
yiter += 1
if year == (YEARSOFDATA - 1):
faileddataforinstrument = True
if faileddataforinstrument:
break
strat = teststrategy.TestStrat(feed, instrument, *params)
iteration += 1
paramnames = teststrategy.parameters_generator(paramnames=True)
paramsarray = zip(paramnames, [str(p) for p in params])
paramsstring = str([
":".join(t) for t in zip(paramnames, [str(p) for p in params])
])
print "\n\nIteration %d / %d; Instrument %s ; Params %s" % (
iteration, iterations, instrument, paramsstring)
#retAnalyzer = returns.Returns()
#strat.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
#drawDownAnalyzer = drawdown.DrawDown()
#strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
# with instrument
#csvdict["Params"] = [":".join(t) for t in zip(("instrument", "entrySMA", "exitDays", "rsiPeriod", "overBoughtThreshold", "overSoldThreshold"), [str(p) for p in params] )]
# without instrument
print paramsstring
strat.run()
tradetotal = 0
tradetotal = sum(
[t * 100.00 for t in tradesAnalyzer.getAllReturns()])
nr_of_trades = tradesAnalyzer.getCount()
try:
profitable_tradeprcnt = "%.2f" % (
(float(tradesAnalyzer.getProfitableCount()) /
float(tradesAnalyzer.getCount())) * 100)
trade_avg_result_prcnt = "%.2f" % (
tradetotal / float(tradesAnalyzer.getCount()))
except ZeroDivisionError:
profitable_tradeprcnt = "0.0"
trade_avg_result_prcnt = "0.0"
sharpe_ratio = "%.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05))
#print "Cumulative returns: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100)
print "Trade stats"
print "Nr of trades:\t\t\t%d" % (nr_of_trades)
print "Trade avg:\t\t\t%s%%" % (trade_avg_result_prcnt)
print "Profitable trades:\t\t%s%%" % (profitable_tradeprcnt)
print "Sharpe ratio:\t\t\t", sharpe_ratio
print
returns = tradesAnalyzer.getAllReturns()
if tradesAnalyzer.getProfitableCount() > 0:
trade_max = "%.2f" % (returns.max() * 100)
trade_min = "%.2f" % (returns.min() * 100)
trade_stddev = "%.2f" % (returns.std() * 100)
else:
trade_max = "%.2f" % 0
trade_min = "%.2f" % 0
trade_stddev = "%.2f" % 0
print "Returns std. dev.: %s %%\t\t\t" % trade_stddev
print "Max. return: %s %%\t\t\t" % trade_max
print "Min. return: %s %%\t\t\t" % trade_min
#print "Trade total: %.2f" % ( tradetotal )
#print "Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100)
#if tradesAnalyzer.getProfitableCount() > 0:
#profits = tradesAnalyzer.getProfits()
#losses = tradesAnalyzer.getLosses()
#print "Avg. profit: $%2.f" % (profits.mean())
#print "Avg. loss: $%2.f" % (losses.mean())
#returns = tradesAnalyzer.getPositiveReturns()
#returns = tradesAnalyzer.getAllReturns()
#print "Avg. return: %2.f %%" % (returns.mean() * 100) # too much rounding
#print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
#print "Max. return: %2.f %%" % (returns.max() * 100)
#print "Min. return: %2.f %%" % (returns.min() * 100)
#print "Trades std. dev.: $%2.f" % (profits.std())
#print "Max. profit: $%2.f" % (profits.max())
#print "Min. profit: $%2.f" % (profits.min())
if paramsstring in resultsdict.keys():
try:
resultsdict[paramsstring]['nr_of_trades'] = int(
resultsdict[paramsstring]['nr_of_trades']) + int(
nr_of_trades)
resultsdict[paramsstring][
'profitable_tradeprcnt'] = "%.2f" % (
(float(resultsdict[paramsstring]
['profitable_tradeprcnt']) +
float(profitable_tradeprcnt)) / 2.00)
resultsdict[paramsstring][
'trade_avg_result_prcnt'] = "%.2f" % (
(float(resultsdict[paramsstring]
['trade_avg_result_prcnt']) +
float(trade_avg_result_prcnt)) / 2.00)
resultsdict[paramsstring]['sharpe_ratio'] = "%.2f" % (
(float(resultsdict[paramsstring]['sharpe_ratio']) +
float(sharpe_ratio)) / 2.00)
resultsdict[paramsstring]['trade_max'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_max']) +
float(trade_max) / 2.00)
resultsdict[paramsstring]['trade_min'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_min']) +
float(trade_min) / 2.00)
resultsdict[paramsstring]['trade_stddev'] = "%.2f" % (
float(resultsdict[paramsstring]['trade_stddev']) +
float(trade_stddev) / 2.00)
except ZeroDivisionError:
print "\nError (ZeroDivisionError) trying averaging with: %s\n" % paramsstring
else: # First time with params
resultsdict[paramsstring] = dict(paramsarray)
resultsdict[paramsstring]['params'] = paramsstring
resultsdict[paramsstring]['nr_of_trades'] = nr_of_trades
resultsdict[paramsstring][
'profitable_tradeprcnt'] = profitable_tradeprcnt
resultsdict[paramsstring][
'trade_avg_result_prcnt'] = trade_avg_result_prcnt
resultsdict[paramsstring]['sharpe_ratio'] = sharpe_ratio
resultsdict[paramsstring]['trade_max'] = trade_max
resultsdict[paramsstring]['trade_min'] = trade_min
resultsdict[paramsstring]['trade_stddev'] = trade_stddev
feed.reset() # feed must be reset
del sharpeRatioAnalyzer
del tradesAnalyzer
with open(filedir + "/" + strat.STRATNAME + '.csv', 'wb') as csvfile:
fieldnames = ['params']
fieldnames.extend(paramnames)
fieldnames.extend(
('nr_of_trades', 'profitable_tradeprcnt', 'trade_avg_result_prcnt',
'sharpe_ratio', 'trade_max', 'trade_min', 'trade_stddev'))
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
#csvheader = {'params':'Params: ' + strat.STRATNAME, paramnames, 'nr_of_trades':'Nr of trades',\
# 'profitable_tradeprcnt':'Profitable trades%', 'trade_avg_result_prcnt':'Trade avg %', 'sharpe_ratio': 'Sharpe Ratio'}
csvheader = {'params': 'Params: ' + strat.STRATNAME}
for n in paramnames:
csvheader[n] = n
csvheader['nr_of_trades'] = 'Nr of trades'
csvheader['profitable_tradeprcnt'] = 'Profitable trades%'
csvheader['trade_avg_result_prcnt'] = 'Trade avg %'
csvheader['sharpe_ratio'] = 'Sharpe Ratio'
csvheader['trade_max'] = 'Trade Max'
csvheader['trade_min'] = 'Trade Min'
csvheader['trade_stddev'] = 'Trade Stddev'
writer.writerow(csvheader)
for result in resultsdict.keys():
writer.writerow(resultsdict[result])
#############################################path set ############################33
if frequency == bar.Frequency.MINUTE:
path = "..\\histdata\\min\\"
elif frequency == bar.Frequency.DAY:
path = "..\\histdata\\day\\"
elif frequency == bar.Frequency.SECOND:
path = "..\\histdata\\tick\\"
filepath = path +'stock_'+ instrument + "_"+date+".csv"
#############################################don't change ############################33
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
barfeed = GenericBarFeed(frequency)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
barfeed.addBarsFromCSV(instrument, filepath)
strat = strat(barfeed, instrument, *paras)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
mall , mals , masl , mass = strat.getSMA()
# # print type(ma1)
plt.getInstrumentSubplot('indicator').addDataSeries("small", mall)
plt.getInstrumentSubplot('indicator').addDataSeries("smals", mals)
# plt.getInstrumentSubplot('indicator').addDataSeries("smasl", masl)
# plt.getInstrumentSubplot('indicator').addDataSeries("smass", mass)
position = strat.getTest()
plt.getOrCreateSubplot("position").addDataSeries("position", position)
# feed = quandlfeed.Feed()
# feed.addBarsFromCSV("orcl", "WIKI-ORCL-2000-quandl.csv")
# Evaluate the strategy with the feed's bars.
#import through quandl
# instrument = "AAPL"
# feed = quandl.build_feed("WIKI", [instrument], 2017, 2018, ".")
#import from google
# feed = googlefeed.Feed()
# instrument = "spy"
# feed.addBarsFromCSV(instrument, "spy_2017.csv")
#import through local csv
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("output", "output.csv")
instrument = "output"
myStrategy = MyStrategy(feed, instrument)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plot = True
if plot:
plt = plotter.StrategyPlotter(myStrategy)
plt.getInstrumentSubplot(instrument).addDataSeries("sma20", myStrategy.getSMA4())
plt.getInstrumentSubplot(instrument).addDataSeries("sma40", myStrategy.getSMA40())
myStrategy.run()
def run():
# Load the yahoo feed from the CSV file
instruments = {
#'orcl'
#,'aapl'
'XAGUSD': {"name":"XAGUSD", "dollar_per_point":1},
#'fxcm',
}
CAPITAL = 20000000
feed = GenericBarFeed(5 * Frequency.MINUTE)
datas = {}
for i in instruments:
datas[i] = []
csv_name = '%s.csv' % (i)
csv_name_new = "new_" + csv_name
csv_name = os.path.join("data", csv_name)
csv_name_new = os.path.join("new", csv_name_new)
with open(csv_name, "rb") as fr:
reader = csv.DictReader(fr)
with open(csv_name_new, "wb")as fw:
w = csv.DictWriter(fw, ["Date Time", "Open", "High", "Low", "Close", "Volume", "Adj Close"])
w.writeheader()
for row in reader:
new = {}
d = row["Date"]
new["Date Time"] = d[:4] + "-" + d[4:6] + "-" + d[6:] + " " + row["Timestamp"]
new["Open"] = row["Open"]
new["High"] = row["High"]
new["Low"] = row["Low"]
new["Close"] = row["Close"]
new["Volume"] = row["Volume"]
#w.writerow(new)
new["datetime"] = datetime.strptime(new["Date Time"], "%Y-%m-%d %H:%M:%S")
datas[i].append(new)
feed.addBarsFromCSV(i, csv_name_new)
# print datas
indicator = OrderedDict()
datelist = []
for i in datas:
for d in datas[i]:
key = d["datetime"].strftime("%Y-%m-%d")
if key not in indicator:
indicator[key] = {
"Date":key,
"Open": float(d["Open"]),
"High": float(d["High"]),
"Low": float(d["Low"]),
"Close": float(d["Close"]),
"High55":"",
"Low55":"",
"High20":"",
"Low20":"",
"ATR20":"",
"TR":""
}
datelist.append(indicator[key])
else:
indicator[key]["High"] = max(indicator[key]["High"], float(d["High"]))
indicator[key]["Low"] = min(indicator[key]["Low"], float(d["Low"]))
indicator[key]["Close"] = float(d["Close"])
for i in range(0, len(datelist)):
if i > 20:
datelist[i]["High20"] = max([d["High"] for d in datelist[i-20:i]])
datelist[i]["Low20"] = min([d["Low"] for d in datelist[i-20:i]])
if i > 55:
datelist[i]["High55"] = max([d["High"] for d in datelist[i-55:i]])
datelist[i]["Low55"] = min([d["Low"] for d in datelist[i-55:i]])
if i > 1:
lastday = datelist[i-1]
#lastlastday = datelist[i-2]
tr = max([datelist[i]["High"] - datelist[i]["Low"], datelist[i]["High"] - lastday["Close"], lastday["Close"] - datelist[i]["Low"]])
datelist[i]["TR"] = tr
if i > 20:
if lastday["ATR20"] == "":
datelist[i]["ATR20"] = sum(d["TR"] for d in datelist[i-20:i]) / 20
else:
datelist[i]["ATR20"] = (19*lastday["ATR20"] + tr)/20
from pprint import pprint
for d in indicator.values():
pprint( d)
from pyalgotrade import strategy
from pyalgotrade.bar import Frequency
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
# 1.构建一个策略
class MyStrategy(strategy.BacktestingStrategy):
def __init__(self, feed, instrument):
super(MyStrategy, self).__init__(feed)
self.__instrument = instrument
def onBars(self, bars): # 每一个数据都会抵达这里，就像becktest中的next
bar = bars[self.__instrument]
self.info(bar.getClose()) # 我们打印输出收盘价
# 2.获得回测数据，官网来源于yahoo，由于墙的关系，我们用本地数据
feed = GenericBarFeed(Frequency.DAY, None, None)
feed.addBarsFromCSV("fd", "fd.csv")
# 3.把策略跑起来
myStrategy = MyStrategy(feed, "fd")
instrument = '000001'
market = 'SZ'
fromDate = '20140101'
toDate = '20160101'
frequency = bar.Frequency.DAY
paras = [2, 20, 60, 10]
plot = True
#############################################path set ############################33
if frequency == bar.Frequency.MINUTE:
path = "..\\histdata\\min\\"
elif frequency == bar.Frequency.DAY:
path = "..\\histdata\\day\\"
filepath = path + instrument + market + ".csv"
#############################################don't change ############################33
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
barfeed = GenericBarFeed(frequency)
barfeed.setDateTimeFormat('%Y-%m-%d %H:%M:%S')
barfeed.addBarsFromCSV(instrument, filepath)
strat = strat(barfeed, instrument, *paras)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
strat.run()
if plot:
plt.plot()
def main(selected_features, predict_n_days, model_form, testing_datetime,
testing_end_datetime, n_retrain_times):
feed = GenericBarFeed(frequency='DAY')
instruments = {
'hk_equity_fund': 'dataset/hk_equity_fund_data.csv',
'growth_fund': 'dataset/growth_fund_data.csv',
'balanced_fund': 'dataset/balanced_fund_data.csv',
'conservative_fund': 'dataset/conservative_fund_data.csv',
'hkdollar_bond_fund': 'dataset/hkdollar_bond_fund_data.csv',
'stable_fund': 'dataset/stable_fund_data.csv',
'HSI': 'dataset/HSI_investing_com.csv',
'IXIC': 'dataset/IXIC_investing_com.csv',
'us2yrby': 'dataset/us2yrbondyield_investing_com.csv',
'us10yrby': 'dataset/us10yrbondyield_investing_com.csv'
}
for instu, fname in instruments.iteritems():
print('Importing data %s from file path %s' % (instu, fname))
feed.addBarsFromCSV(instu, fname)
# selected_features = [
# 'hk_equity_fund', 'growth_fund', 'balanced_fund', 'conservative_fund', 'hkdollar_bond_fund', 'stable_fund'
# ]
# selected_features = list()
# indicators = [
# 'hk_equity_fund_rsi', 'growth_fund_rsi', 'balanced_fund_rsi', 'conservative_fund_rsi',
# 'hkdollar_bond_fund_rsi', 'stable_fund_rsi'
# ]
# selected_features += indicators
# predict_n_days = 30
tree_reg_strategy = WalkForwardBacktester(
feed=feed,
instrument=instruments.keys(),
capital=1000000.0,
selected_features=selected_features,
predict_n_days=predict_n_days,
model_form=model_form,
testing_datetime=testing_datetime,
testing_end_datetime=testing_end_datetime,
n_retrain_times=n_retrain_times)
retAnalyzer = rets.Returns()
tree_reg_strategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
tree_reg_strategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
tree_reg_strategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
tree_reg_strategy.attachAnalyzer(tradesAnalyzer)
tree_reg_strategy.run()
print('************************')
print('** Backtesting Report **')
print('************************')
portfo_val = tree_reg_strategy.getResult()
print "Final portfolio value: $%.2f" % portfo_val
cumulative_returns = retAnalyzer.getCumulativeReturns()[-1]
print "Cumulative returns: %.2f %%" % (cumulative_returns * 100)
sharpe_ratio = sharpeRatioAnalyzer.getSharpeRatio(0.03)
print "Sharpe ratio: %.2f" % (sharpe_ratio)
mdd = drawDownAnalyzer.getMaxDrawDown()
print "Max. drawdown: %.2f %%" % (mdd * 100)
print "Longest drawdown duration: %s" % (
drawDownAnalyzer.getLongestDrawDownDuration())
if mdd > 0:
calmar_ratio = retAnalyzer.getCumulativeReturns()[-1] / mdd
print "Calmar ratio: %.4f" % (calmar_ratio)
else:
calmar_ratio = None
print
print "Total trades: %d" % (tradesAnalyzer.getCount())
if tradesAnalyzer.getCount() > 0:
profits = tradesAnalyzer.getAll()
print "Avg. profit: $%2.f" % (profits.mean())
print "Profits std. dev.: $%2.f" % (profits.std())
print "Max. profit: $%2.f" % (profits.max())
print "Min. profit: $%2.f" % (profits.min())
returns = tradesAnalyzer.getAllReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Max. return: %2.f %%" % (returns.max() * 100)
print "Min. return: %2.f %%" % (returns.min() * 100)
print
print "Profitable trades: %d" % (tradesAnalyzer.getProfitableCount())
if tradesAnalyzer.getProfitableCount() > 0:
profits = tradesAnalyzer.getProfits()
print "Avg. profit: $%2.f" % (profits.mean())
print "Profits std. dev.: $%2.f" % (profits.std())
print "Max. profit: $%2.f" % (profits.max())
print "Min. profit: $%2.f" % (profits.min())
returns = tradesAnalyzer.getPositiveReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Max. return: %2.f %%" % (returns.max() * 100)
print "Min. return: %2.f %%" % (returns.min() * 100)
print
print "Unprofitable trades: %d" % (tradesAnalyzer.getUnprofitableCount())
if tradesAnalyzer.getUnprofitableCount() > 0:
losses = tradesAnalyzer.getLosses()
print "Avg. loss: $%2.f" % (losses.mean())
print "Losses std. dev.: $%2.f" % (losses.std())
print "Max. loss: $%2.f" % (losses.min())
print "Min. loss: $%2.f" % (losses.max())
returns = tradesAnalyzer.getNegativeReturns()
print "Avg. return: %2.f %%" % (returns.mean() * 100)
print "Returns std. dev.: %2.f %%" % (returns.std() * 100)
print "Max. return: %2.f %%" % (returns.max() * 100)
print "Min. return: %2.f %%" % (returns.min() * 100)
return portfo_val, cumulative_returns, sharpe_ratio, mdd, calmar_ratio
plot = True
#############################################path set ############################33
if frequency == bar.Frequency.MINUTE:
path = "..\\histdata\\min\\"
elif frequency == bar.Frequency.DAY:
path = "..\\histdata\\day\\"
filepath = path + instrument + market + ".csv"
#############################################don't change ############################33
from pyalgotrade.bar import Frequency
from pyalgotrade.barfeed.csvfeed import GenericBarFeed
barfeed = GenericBarFeed(Frequency.DAY)
barfeed.addBarsFromCSV(instrument, filepath)
strat = strat(barfeed, instrument, *paras)
if plot:
plt = plotter.StrategyPlotter(strat, True, True, True)
strat.run()
if plot:
plt.plot()
