def __init__(self,
instrument,
startYear,
endYear,
strategy,
base="510300",
cash=1000000,
feeRate=0.0003):
self.__instrument = instrument
self.__startYear = startYear
self.__endYear = endYear
self.__strategy = strategy
self.__base = []
self.__base.append(base)
self.__cash = cash
self.__feeRate = feeRate
# 要创建的内部变量
self.__strategyTest = None
self.__feed = None
self.__strategyBase = None
self.__feedBase = None
self.__brk = None
self.__brkBase = None
self.__return = returns.Returns()
self.__returnBase = returns.Returns()
self.__sharpe = sharpe.SharpeRatio()
self.__drawdown = drawdown.DrawDown()
self.__trade = trades.Trades()
self.__result = pd.DataFrame()
self.__plter = None
# 使用pyalgotrade绘图要加上，不然报错
register_matplotlib_converters()
def run_strategy(cash):
instruments = ["000001"]
feeds = tools.build_feed(instruments, 2016, 2018, "histdata")
# 设置手续费
broker_commision = broker.backtesting.TradePercentage(0.0003)
brk = broker.backtesting.Broker(cash, feeds, broker_commision)
myStrategy = MyStrategy(feeds, instruments[0], brk)
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
# 以沪深300为基准#
instruments300 = ["510300"]
feeds300 = tools.build_feed(instruments300, 2016, 2018, "histdata300")
# 设置手续费
broker_commision = broker.backtesting.TradePercentage(0.0003)
brk = broker.backtesting.Broker(cash, feeds300, broker_commision)
myStrategy300 = MyStrategy(feeds300, instruments300[0], brk)
retAnalyzer300 = returns.Returns()
myStrategy300.attachAnalyzer(retAnalyzer300)
sharpeAnalyzer300 = sharpe.SharpeRatio()
myStrategy300.attachAnalyzer(sharpeAnalyzer300)
drawDownAnalyzer300 = drawdown.DrawDown()
myStrategy300.attachAnalyzer(drawDownAnalyzer300)
tradesAnalyzer300 = trades.Trades()
myStrategy300.attachAnalyzer(tradesAnalyzer300)
plter = plotter.StrategyPlotter(myStrategy)
plter.getOrCreateSubplot("return").addDataSeries("retuens",
retAnalyzer.getReturns())
plter.getOrCreateSubplot("CumReturn").addDataSeries(
"CumReturn", retAnalyzer.getCumulativeReturns())
plter.getOrCreateSubplot("return").addDataSeries(
"retuens300", retAnalyzer300.getReturns())
plter.getOrCreateSubplot("CumReturn").addDataSeries(
"CumReturn300", retAnalyzer300.getCumulativeReturns())
myStrategy.run()
myStrategy300.run()
plter.savePlot("testdata.png")
# print("交易次数:%d" % (tradesAnalyzer.getCount()))
print("策略回测指标")
analyzer((retAnalyzer, sharpeAnalyzer, drawDownAnalyzer, tradesAnalyzer))
print("基准回测指标")
analyzer((retAnalyzer300, sharpeAnalyzer300, drawDownAnalyzer300,
tradesAnalyzer300))
# 计算阿尔法 β 信息比例等值
calculater(retAnalyzer, retAnalyzer300)
return (myStrategy, retAnalyzer, sharpeAnalyzer, drawDownAnalyzer,
tradesAnalyzer)
def main(start_date,
end_date,
maxLen=DIVERGENCE_DETECT_DIF_LIMIT_BAR_NUM,
peried='D'):
'''
count: 采用过去count个bar内极值的最大值作为参考。
'''
all_df = get_stock_pool(start_date, end_date)
codes = list(set(all_df.code.tolist()))
strategys = dict()
for code in codes:
feed = get_feed(all_df, code, start_date, end_date, peried)
if feed is None: return False
brk = gen_broker(feed)
strategys[code] = MACDStrategy(code, feed, brk, SHORT, LONG, MID,
maxLen)
# Attach a returns analyzers to the strategy
returnsAnalyzer = returns.Returns()
strategys[code].attachAnalyzer(returnsAnalyzer)
# Attach a sharpe ratio analyzers to the strategy
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strategys[code].attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(strategys[code], True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
plt.getOrCreateSubplot("Macd").addDataSeries("dif",
strategys[code].getDif())
plt.getOrCreateSubplot("Macd").addDataSeries("dea",
strategys[code].getDea())
# Run Strategy
strategys[code].run()
strategys[code].info("Final portfolio value: $%.2f" %
strategys[code].getResult())
plt.plot()
def testTwoBarReturns_CloseClose(self):
initialCash = 15.90
barFeed = yahoofeed.Feed()
barFeed.setBarFilter(
csvfeed.DateRangeFilter(
strategy_test.datetime_from_date(2001, 12, 06),
strategy_test.datetime_from_date(2001, 12, 07)))
barFeed.addBarsFromCSV(
ReturnsTestCase.TestInstrument,
common.get_data_file_path("orcl-2001-yahoofinance.csv"))
strat = strategy_test.TestStrategy(barFeed, initialCash)
# 2001-12-06,15.61,16.03,15.50,15.90,66944900,15.55
# 2001-12-07,15.74,15.95,15.55,15.91,42463200,15.56
# Manually place the entry order, to get it filled on the first bar.
order = strat.getBroker().createMarketOrder(
broker.Order.Action.BUY, ReturnsTestCase.TestInstrument, 1,
True) # Close: 15.90
strat.getBroker().placeOrder(order)
strat.addOrder(strategy_test.datetime_from_date(2001, 12, 06),
strat.getBroker().createMarketOrder,
broker.Order.Action.SELL,
ReturnsTestCase.TestInstrument, 1, True) # Close: 15.91
stratAnalyzer = returns.Returns()
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertTrue(strat.getBroker().getCash() == initialCash +
(15.91 - 15.90))
# First day returns: 0
self.assertTrue(stratAnalyzer.getReturns()[0] == 0)
# Second day returns: Open vs Prev. day's close
self.assertTrue(stratAnalyzer.getReturns()[1] == (15.91 - 15.90) /
15.90)
def main():
# Load the orders file.
ordersFile = OrdersFile("./OrderFiles/orders.csv")
print "First date", ordersFile.getFirstDate()
print "Last date", ordersFile.getLastDate()
print "Symbols", ordersFile.getInstruments()
qsPath = "/Users/LunaFire/Anaconda/lib/python2.7/site-packages/QSTK"
feed = yahoofeed.Feed()
feed.setBarFilter(csvfeed.DateRangeFilter(ordersFile.getFirstDate(), ordersFile.getLastDate()))
feed.setDailyBarTime(datetime.time(0, 0, 0)) # This is to match the dates loaded with the ones in the orders file.
for symbol in ordersFile.getInstruments():
feed.addBarsFromCSV(symbol, os.path.join(qsPath, "QSData", "Yahoo", symbol + ".csv"))
# Run the strategy.
cash = 1000000
useAdjustedClose = True
myStrategy = MyStrategy(feed, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
myStrategy.run()
# Print the results.
print "Final portfolio value: $%.2f" % myStrategy.getResult()
print "Anual 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))
def ENE(type='old', code='600281'):
dat = pd.read_csv("../../api/stock/csv/%sSH_5min.csv" % code, encoding='gbk')
dat = dat.fillna(method='pad')
dat['Adj Close'] = dat['Close']
dat = dat.rename(columns={'Open': 'open', 'High': 'high', 'Volume': 'volume', 'Close': 'close', 'Low': 'low'})
dat.index = dat['Date Time']
dat.index.name = 'date'
# dat = dat.ix[17000:18000,:]
feed = dataFramefeed.Feed(frequency=bar.Frequency.MINUTE)
feed.addBarsFromDataFrame("orcl", dat)
if type == 'macd':
myStrategy = mdd.ENE_backtest(feed, 'orcl')
elif type == 'old':
myStrategy = mdd.ENE_backtest(feed, 'orcl')
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
plt.plot()
print('最大回撤：\t%f\t 交易笔数：\t%d\t 盈利笔数：\t%d\n' % (ds.getMaxDrawDown(), ds.getCount(), ds.getProfitableCount()))
print('累计收益：\t%f\t 夏普比率：\t%f\t' % (returnsAnalyzer.getCumulativeReturns()[-1], ds.getSharpeRatio()))
def testFirstBar(self):
initialCash = 1000
barFeed = yahoofeed.Feed()
barFeed.addBarsFromCSV(
AnalyzerTestCase.TestInstrument,
common.get_data_file_path("orcl-2001-yahoofinance.csv"))
strat = strategy_test.TestStrategy(barFeed, initialCash)
strat.addOrder(datetime.datetime(2001, 0o1, 0o2),
strat.getBroker().createMarketOrder,
broker.Order.Action.BUY,
AnalyzerTestCase.TestInstrument, 1,
False) # 2001-01-03 Open: 25.25 Close: 32.00
stratAnalyzer = returns.Returns()
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertEqual(stratAnalyzer.getReturns()[0], 0)
self.assertEqual(stratAnalyzer.getReturns()[1], (32.00 - 25.25) / 1000)
# Check date times.
datetimes = barFeed[AnalyzerTestCase.TestInstrument].getDateTimes()
for i in [0, -1]:
self.assertEqual(stratAnalyzer.getReturns().getDateTimes()[i],
datetimes[i])
self.assertEqual(
stratAnalyzer.getCumulativeReturns().getDateTimes()[i],
datetimes[i])
def turtle_test(dataString='pyalg'):
"""
:param dataString:
:return:
"""
feed = yahoofeed.Feed()
feed.addBarsFromCSV(
"orcl", "../../api/stock/csv/orcl-2000.csv") #注意查看该csv的格式，Open为大写
myStrategy = pdr.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the trategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() # 获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0],
rs["cumulativeReturns"][:, 1]) # 简单作图示例
plt.plot()
def main():
instrument = "yhoo"
bBandsPeriod = 40
# Download the bars.
# feed = yahoofinance.build_feed([instrument], 2011, 2012, ".")
feed = yahoofeed.Feed()
feed.addBarsFromCSV(instrument, "dat\\yhoo-201x.csv")
strat = BBands(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
retAnalyzer = returns.Returns()
strat.attachAnalyzer(retAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
strat.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
strat.attachAnalyzer(tradesAnalyzer)
# ------
mpl.style.use('seaborn-whitegrid');
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()
plt.plot()
# ==============================
print("最终资产价值 Final portfolio value: $%.2f" % strat.getResult())
print("累计回报率 Cumulative returns: %.2f %%" % (retAnalyzer.getCumulativeReturns()[-1] * 100))
print("夏普比率 Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05)))
print("最大回撤率 Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100))
print("最长回撤时间 Longest drawdown duration: %s" % (drawDownAnalyzer.getLongestDrawDownDuration()))
def run_strategy(smaPeriod):
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("zggf", "E:/PythonData/CSV/000938.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "zggf", smaPeriod)
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plt = plotter.StrategyPlotter(myStrategy)
sharpe_ratio = sharpe.SharpeRatio()
trade_situation = trades.Trades()
myStrategy.attachAnalyzer(sharpe_ratio)
myStrategy.attachAnalyzer(trade_situation)
plt.getInstrumentSubplot("zggf").addDataSeries("SMA", myStrategy.getSMA())
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getCumulativeReturns())
print(stats.mean(returnsAnalyzer.getReturns()))
print(returnsAnalyzer.getCumulativeReturns())
print(myStrategy.getResult())
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())
plt.plot()
print(returnsAnalyzer.getReturns())
print(stats.mean(returnsAnalyzer.getReturns()))
print(stats.mean(returnsAnalyzer.getCumulativeReturns()))
def main(plot):
instrument = "n225"
# Load the bar feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV(instrument, r".\N225.csv")
hurstPeriod = 100
strat = StrategyModule.HurstBasedStrategy(feed, "n225", hurstPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getOrCreateSubplot("hurst").addDataSeries("Hurst", strat.getHurst())
plt.getOrCreateSubplot("hurst").addLine("random", 0.5)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
strat.run()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
print("Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05))
if plot:
plt.plot()
def main(plot):
bBandsPeriod = 1
instrument = '600519'
feed = dataFramefeed.Feed()
dat = ts.get_hist_data("hs300", start='2017-01-01', end='2017-12-27')
dat['Adj Close'] = dat['close']
#feed.addBarsFromDataFrame("hs300", dat.ix[:,(0,1,2,3,4,13)])
dat = ts.get_hist_data("600519", start='2017-01-01', end='2017-12-27')
dat['Adj Close'] = dat['close']
feed.addBarsFromDataFrame("600519", dat.ix[:, (0, 1, 2, 3, 4, 14)])
strat = BBands(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
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():
# Load the orders file.
ordersFile = OrdersFile("orders.csv")
print "First date", ordersFile.getFirstDate()
print "Last date", ordersFile.getLastDate()
print "Symbols", ordersFile.getInstruments()
# Load the data from QSTK storage. QS environment variable has to be defined.
if os.getenv("QS") == None:
raise Exception("QS environment variable not defined")
feed = yahoofeed.Feed()
feed.setBarFilter(csvfeed.DateRangeFilter(ordersFile.getFirstDate(), ordersFile.getLastDate()))
feed.setDailyBarTime(datetime.time(0, 0, 0)) # This is to match the dates loaded with the ones in the orders file.
for symbol in ordersFile.getInstruments():
feed.addBarsFromCSV(symbol, os.path.join(os.getenv("QS"), "QSData", "Yahoo", symbol + ".csv"))
# Run the strategy.
cash = 1000000
useAdjustedClose = True
myStrategy = MyStrategy(feed, cash, ordersFile, useAdjustedClose)
# Attach returns and sharpe ratio analyzers.
retAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(retAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
myStrategy.run()
# Print the results.
print "Final portfolio value: $%.2f" % myStrategy.getResult()
print "Anual 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, 252))
def runStrategy():
# 下载数据
jdf = ts.get_k_data("000725")
# 新建Adj Close字段
jdf["Adj Close"] = jdf.close
# 将tushare下的数据的字段保存为pyalgotrade所要求的数据格式
jdf.columns = [
"Date", "Open", "Close", "High", "Low", "Volume", "code", "Adj Close"
]
# 将数据保存成本地csv文件
jdf.to_csv("jdf.csv", index=False)
feed = yahoofeed.Feed()
feed.addBarsFromCSV("jdf", "jdf.csv")
myStrategy = MyStrategy(feed, "jdf")
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
plt = plotter.StrategyPlotter(myStrategy)
plt.getInstrumentSubplot("jdf")
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getResult())
plt.plot()
def analyze_and_draw(feed, instrument, period, cash=10000):
my_strategy = MyStrategy(feed, instrument, period, cash)
# 在执行前添加不同的分析器
ret_analyzer = returns.Returns()
my_strategy.attachAnalyzer(ret_analyzer)
sharpe_ratio_analyzer = sharpe.SharpeRatio()
my_strategy.attachAnalyzer(sharpe_ratio_analyzer)
draw_down_analyzer = drawdown.DrawDown()
my_strategy.attachAnalyzer(draw_down_analyzer)
trades_analyzer = trades.Trades()
my_strategy.attachAnalyzer(trades_analyzer)
# 设置中文等线字体以显示中文
plts.rcParams['font.sans-serif'] = ['DengXian']
plt = plotter.StrategyPlotter(my_strategy, True, False, True)
plt.getInstrumentSubplot(instrument).addDataSeries("SMA",
my_strategy.getSMA())
# 开始执行策略
my_strategy.run()
plt.plot()
# 现金+有价证券
print("最终资产价值 Final portfolio value: ￥%.2f" %
my_strategy.getBroker().getEquity())
print("最终资产价值 Final portfolio value: ￥%.2f" % my_strategy.getResult())
print("累计回报率 Cumulative returns: %.2f %%" %
(ret_analyzer.getCumulativeReturns()[-1] * 100))
print("夏普比率 Sharpe ratio: %.2f" %
(sharpe_ratio_analyzer.getSharpeRatio(0.05)))
print("最大回撤率 Max. drawdown: %.2f %%" %
(draw_down_analyzer.getMaxDrawDown() * 100))
print("最长回撤时间 Longest drawdown duration: %s" %
(draw_down_analyzer.getLongestDrawDownDuration()))
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 testTwoBarReturns_CloseOpen(self):
initialCash = 15.9
barFeed = yahoofeed.Feed()
barFeed.setBarFilter(csvfeed.DateRangeFilter(datetime.datetime(2001, 12, 6), datetime.datetime(2001, 12, 7)))
barFeed.addBarsFromCSV(
INSTRUMENT, common.get_data_file_path("orcl-2001-yahoofinance.csv")
)
strat = strategy_test.TestStrategy(barFeed, {PRICE_CURRENCY: initialCash})
# 2001-12-06,15.61,16.03,15.50,15.90,66944900,15.55
# 2001-12-07,15.74,15.95,15.55,15.91,42463200,15.56
# Manually place the entry order, to get it filled on the first bar.
order = strat.getBroker().createMarketOrder(
broker.Order.Action.BUY, INSTRUMENT, 1, True
) # Close: 15.90
strat.getBroker().submitOrder(order)
strat.addOrder(
datetime.datetime(2001, 12, 6), strat.getBroker().createMarketOrder, broker.Order.Action.SELL,
INSTRUMENT, 1, False
) # Open: 15.74
stratAnalyzer = returns.Returns(PRICE_CURRENCY)
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertTrue(strat.getBroker().getBalance(PRICE_CURRENCY) == initialCash + (15.74 - 15.90))
# First day returns: 0
self.assertTrue(stratAnalyzer.getReturns()[0] == 0)
# Second day returns: Open vs Prev. day's close
self.assertTrue(stratAnalyzer.getReturns()[1] == (15.74 - 15.90) / 15.90)
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 main(start_date,
end_date,
maxLen=DIVERGENCE_DETECT_DIF_LIMIT_BAR_NUM,
peried='D'):
all_df = get_stock_pool(start_date, end_date)
codes = list(set(all_df.code.tolist()))
feed = get_feed(all_df, codes, start_date, end_date, peried)
if feed is None: return False
#每只股票可投资的金额
cash = 100000
stockNum = 10
totalRisk = 0.1
duaration = 10 #调仓周期
macdStrategy = MACDStrategy(totalRisk, codes, feed, cash, SHORT, LONG, MID,
maxLen, stockNum, duaration)
# Attach a returns analyzers to the strategy
returnsAnalyzer = returns.Returns()
macdStrategy.attachAnalyzer(returnsAnalyzer)
# Attach a sharpe ratio analyzers to the strategy
sharpeRatioAnalyzer = sharpe.SharpeRatio()
macdStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(macdStrategy, False, True, True)
plt.getOrCreateSubplot("returns").addDataSeries(
"returns", returnsAnalyzer.getReturns())
plt.getOrCreateSubplot("sharpRatio").addDataSeries(
"sharpRatio", sharpeRatioAnalyzer.getReturns())
#plt.getOrCreateSubplot("Macd").addDataSeries("dif", strategys[code].getDif())
#plt.getOrCreateSubplot("Macd").addDataSeries("dea", strategys[code].getDea())
# Run Strategy
macdStrategy.run()
macdStrategy.info("Final portfolio value: $%.2f" %
macdStrategy.getResult())
plt.plot()
def main(plot):
instrument = "000300.ss"
feed = yahoofinance.build_feed([instrument], 2013, 2015, ".")
period = 100
strat = HurstBasedStrategy(feed, instrument, period)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, True, False, True)
plt.getOrCreateSubplot("hurst").addDataSeries("Hurst",
strat.getHurst())
plt.getOrCreateSubplot("hurst").addLine("random", 0.5)
# Plot the simple returns on each bar.
# plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
strat.run()
strat.info("Final portfolio value: $%.2f" % strat.getResult())
# print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()
def testCumulativeReturn(self):
initialCash = 33.06
barFeed = yahoofeed.Feed()
barFeed.addBarsFromCSV(
AnalyzerTestCase.TestInstrument,
common.get_data_file_path("orcl-2001-yahoofinance.csv"))
strat = position_test.TestStrategy(barFeed,
AnalyzerTestCase.TestInstrument,
initialCash)
strat.addPosEntry(datetime.datetime(2001, 1, 12), strat.enterLong,
AnalyzerTestCase.TestInstrument, 1) # 33.06
strat.addPosExitMarket(datetime.datetime(2001, 11, 27)) # 14.32
stratAnalyzer = returns.Returns(maxLen=10)
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertTrue(
round(strat.getBroker().getCash(), 2) == round(
initialCash + (14.32 - 33.06), 2))
self.assertTrue(
round(33.06 *
(1 + stratAnalyzer.getCumulativeReturns()[-1]), 2) == 14.32)
self.assertEqual(len(stratAnalyzer.getCumulativeReturns()), 10)
self.assertEqual(len(stratAnalyzer.getReturns()), 10)
def __init__(self, feed, brk, instrument):
super(LiveStrategy, self).__init__(feed, brk)
brk.addBitmexFeed(feed)
feed.getNewValuesEvent().subscribe(self.__onBarsImpl)
self.initial_capital = self.getResult()
brk.setStrategy(self)
self.positions = {}
for symbol in instrument:
self.positions[symbol] = brk.get_synced_position(self, symbol)
retAnalyzer = returns.Returns()
self.attachAnalyzerEx(retAnalyzer, 'retAnalyzer')
sharpeRatioAnalyzer = sharpe.SharpeRatio()
self.attachAnalyzerEx(sharpeRatioAnalyzer, 'sharpeRatioAnalyzer')
drawDownAnalyzer = drawdown.DrawDown()
self.attachAnalyzerEx(drawDownAnalyzer, 'drawDownAnalyzer')
tradesAnalyzer = trades.Trades()
self.attachAnalyzerEx(tradesAnalyzer, 'tradesAnalyzer')
self.plt = plotter.StrategyPlotter(self)
self.plt.getOrCreateSubplot("returns").addDataSeries(
"Net return", retAnalyzer.getReturns())
self.plt.getOrCreateSubplot("returns").addDataSeries(
"Cum. return", retAnalyzer.getCumulativeReturns())
self.strategy_id = brk.getStrategyId()
if not os.path.exists(self.strategy_id + '.csv'):
with open(self.strategy_id + '.csv', 'w') as f:
f.write('Date,Type,Signal,Symbol,Price,Shares,PnL,Cash\n')
if not os.path.exists(self.strategy_id + '_orders.csv'):
with open(self.strategy_id + '_orders.csv', 'w') as f:
f.write(
'Date,OrderDate,OrderId,Type,Signal,Symbol,Price,Size,Filled,Timedelta,StopHit\n'
)
def __init__(self, strategy, index_code=None):
"""
:param strategy: 策略
:param index_code: index基准利率代码应该同tushare保持一致
sh 上证指数 sz 深圳成指 hs300 沪深300指数 sz50 上证50指数 zxb 中小板指数 cyb 创业板指数
:return:
"""
self.__baseRet_code = index_code
self.__baseRet_data = None
self.__currBaseRet = []
self.__last_baseRet = 0.0
self.__baseRet = dataseries.SequenceDataSeries()
self.__betaRet = dataseries.SequenceDataSeries()
self.__alphaRet = dataseries.SequenceDataSeries()
self.__sharpeRet = dataseries.SequenceDataSeries()
self.__LongestDrawDown = dataseries.SequenceDataSeries()
self.__MaxDrawDown = dataseries.SequenceDataSeries()
self.__ret_analyzer = returns.Returns()
self.__sharpe_ratio = sharpe.SharpeRatio(False)
self.__draw_down = drawdown.DrawDown()
self.__beta = Beta()
strategy.attachAnalyzer(self.__ret_analyzer)
strategy.attachAnalyzer(self.__sharpe_ratio)
strategy.attachAnalyzer(self.__draw_down)
strategy.attachAnalyzer(self.__beta)
strategy.attachAnalyzer(self)
def __init__(self, feed, instrument):
super(SimpleBacktestStrategy, self).__init__(feed)
self.initial_capital = self.getResult()
self.positions = {}
for symbol in instrument:
self.positions[symbol] = {}
retAnalyzer = returns.Returns()
self.attachAnalyzerEx(retAnalyzer, 'retAnalyzer')
sharpeRatioAnalyzer = sharpe.SharpeRatio()
self.attachAnalyzerEx(sharpeRatioAnalyzer, 'sharpeRatioAnalyzer')
drawDownAnalyzer = drawdown.DrawDown()
self.attachAnalyzerEx(drawDownAnalyzer, 'drawDownAnalyzer')
tradesAnalyzer = trades.Trades()
self.attachAnalyzerEx(tradesAnalyzer, 'tradesAnalyzer')
self.plt = plotter.StrategyPlotter(self)
self.plt.getOrCreateSubplot("returns").addDataSeries(
"Net return", retAnalyzer.getReturns())
self.plt.getOrCreateSubplot("returns").addDataSeries(
"Cum. return", retAnalyzer.getCumulativeReturns())
self.strategy_id = 'backtest'
if not os.path.exists(self.strategy_id + '.csv'):
with open(self.strategy_id + '.csv', 'w') as f:
f.write('Date,Type,Signal,Symbol,Price,Shares,PnL,Cash\n')
def run_strategy(smaPeriod):
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV(
"orcl",
"D:/Develop/project/python/PycharmProjects/python3/pyalgotrade/samples/data/WIKI-ORCL-2011-quandl.csv"
)
# Evaluate the strategy with the feed's bars
myStratege = MyStrategy(feed, "orcl", smaPeriod)
# Attach a returns analyzer to to strategy
returnsAnalyzer = returns.Returns()
myStratege.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy
plt = plotter.StrategyPlotter(myStratege)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices
plt.getInstrumentSubplot("orcl").addDataSeries("SMA",
myStratege.getSMA())
# Plot the simple returns on each bar
#plt.getOrCreateSubplot("returns").addDataSeries("Simple Returns", returnsAnalyzer.getReturns())
myStratege.run()
myStratege.info("Final portfolio value: $%.2f" %
myStratege.getBroker().getEquity())
# Plot the strategy
plt.plot()
def turtle_test(loadtype='pgs', dataString='pyalg'):
"""
:param dataString:
:return:
"""
# 从postgres 中加载
if loadtype == 'pgs':
from api.stock.histmd import to_postgres_md as tpd
dat = tpd.get_h_data('600848')
else:
from api.stock.histmd.to_mongodb_md import k_data_dao as tmd
w = tmd.KdataDbCache()
dat = w.get_k_data(index=False,start='2015-02-05', end='2015-02-19',code='300426')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
myStrategy = pdr.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) # 抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() # 获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0], rs["cumulativeReturns"][:, 1]) # 简单作图示例
plt.plot()
def testOneBarReturn(self):
initialCash = 1000
barFeed = yahoofeed.Feed()
barFeed.setBarFilter(
csvfeed.DateRangeFilter(
strategy_test.datetime_from_date(2001, 12, 07),
strategy_test.datetime_from_date(2001, 12, 07)))
barFeed.addBarsFromCSV(
ReturnsTestCase.TestInstrument,
common.get_data_file_path("orcl-2001-yahoofinance.csv"))
strat = strategy_test.TestStrategy(barFeed, initialCash)
# 2001-12-07,15.74,15.95,15.55,15.91,42463200,15.56
# Manually place the orders to get them filled on the first (and only) bar.
order = strat.getBroker().createMarketOrder(
broker.Order.Action.BUY, ReturnsTestCase.TestInstrument, 1,
False) # Open: 15.74
strat.getBroker().placeOrder(order)
order = strat.getBroker().createMarketOrder(
broker.Order.Action.SELL, ReturnsTestCase.TestInstrument, 1,
True) # Close: 15.91
strat.getBroker().placeOrder(order)
stratAnalyzer = returns.Returns()
strat.attachAnalyzer(stratAnalyzer)
strat.run()
self.assertTrue(strat.getBroker().getCash() == initialCash +
(15.91 - 15.74))
finalValue = 1000 - 15.74 + 15.91
rets = (finalValue - initialCash) / float(initialCash)
self.assertEqual(stratAnalyzer.getReturns()[-1], rets)
def run_strategy(smaPeriod):
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("zggf", "E:/PythonData/CSV/000938.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "zggf", smaPeriod)
returnsAnalyzer=returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
drawDownAnalyzer = drawdown.DrawDown()
myStrategy.attachAnalyzer(drawDownAnalyzer)
tradesAnalyzer = trades.Trades()
myStrategy.attachAnalyzer(tradesAnalyzer)
myStrategy.run()
print(myStrategy.getResult())
print("Final portfolio value: $%.2f" % myStrategy.getResult())
print("Cumulative returns: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100))
print(returnsAnalyzer.getCumulativeReturns())
print("Sharpe ratio: %.2f" % (sharpeRatioAnalyzer.getSharpeRatio(0.05)))
print("Max. drawdown: %.2f %%" % (drawDownAnalyzer.getMaxDrawDown() * 100))
print("Longest drawdown duration: %s" % (drawDownAnalyzer.getLongestDrawDownDuration()))
print()
print("Total trades: %d" % (tradesAnalyzer.getCount()))
def main(plot):
initialCash = 10000
instrumentsByClass = {
"US Stocks": ["VTI"],
"Foreign Stocks": ["VEU"],
"US 10 Year Government Bonds": ["IEF"],
"Real Estate": ["VNQ"],
"Commodities": ["DBC"],
}
# Download the bars.
instruments = ["SPY"]
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
feed = yahoofinance.build_feed(instruments, 2007, 2013, "data", skipErrors=True)
strat = MarketTiming(feed, instrumentsByClass, initialCash)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
returnsAnalyzer = returns.Returns()
strat.attachAnalyzer(returnsAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("cash").addCallback("Cash", lambda x: strat.getBroker().getCash())
# Plot strategy vs. SPY cumulative returns.
plt.getOrCreateSubplot("returns").addDataSeries("SPY", cumret.CumulativeReturn(feed["SPY"].getPriceDataSeries()))
plt.getOrCreateSubplot("returns").addDataSeries("Strategy", returnsAnalyzer.getCumulativeReturns())
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
print "Returns: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100)
if plot:
plt.plot()
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())
