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 testDownloadAndParseDaily_UseAdjClose(self):
with common.TmpDir() as tmpPath:
symbol = "ORCL"
instrument = "%s/%s" % (symbol, PRICE_CURRENCY)
path = os.path.join(tmpPath, "quandl-daily-orcl-2010.csv")
quandl.download_daily_bars("WIKI",
symbol,
2010,
path,
authToken=QUANDL_API_KEY)
bf = quandlfeed.Feed()
bf.addBarsFromCSV(instrument, path)
# Need to setUseAdjustedValues(True) after loading the file because we
# can't tell in advance if adjusted values are there or not.
bf.setUseAdjustedValues(True)
bf.loadAll()
ds = bf.getDataSeries(instrument)
self.assertEqual(ds[-1].getDateTime(),
datetime.datetime(2010, 12, 31))
self.assertEqual(ds[-1].getOpen(), 31.22)
self.assertEqual(ds[-1].getHigh(), 31.33)
self.assertEqual(ds[-1].getLow(), 30.93)
self.assertEqual(ds[-1].getClose(), 31.3)
self.assertEqual(ds[-1].getVolume(), 11716300)
self.assertEqual(ds[-1].getPrice(), ds[-1].getAdjClose())
# Not checking against a specific value since this is going to change
# as time passes by.
self.assertNotEqual(ds[-1].getAdjClose(), None)
def testDownloadAndParseWeekly(self):
with common.TmpDir() as tmpPath:
instrument = "AAPL"
path = os.path.join(tmpPath, "quandl-aapl-weekly-2010.csv")
quandl.download_weekly_bars("WIKI", instrument, 2010, path,
auth_token)
bf = quandlfeed.Feed(frequency=bar.Frequency.WEEK)
bf.addBarsFromCSV(instrument, path)
bf.loadAll()
# Quandl used to report 2010-1-3 as the first week of 2010.
self.assertTrue(bf[instrument][0].getDateTime() in [
datetime.datetime(2010, 1, 3),
datetime.datetime(2010, 1, 10)
])
self.assertEquals(bf[instrument][-1].getDateTime(),
datetime.datetime(2010, 12, 26))
self.assertEquals(bf[instrument][-1].getOpen(), 325.0)
self.assertEquals(bf[instrument][-1].getHigh(), 325.15)
self.assertEquals(bf[instrument][-1].getLow(), 323.17)
self.assertEquals(bf[instrument][-1].getClose(), 323.6)
self.assertEquals(bf[instrument][-1].getVolume(), 7969900)
self.assertEquals(bf[instrument][-1].getPrice(), 323.6)
# Not checking against a specific value since this is going to change
# as time passes by.
self.assertNotEquals(bf[instrument][-1].getAdjClose(), None)
def testCommandLineDailyCreatingDir(self):
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
try:
instrument = "ORCL"
subprocess.call([
"python", "-m", "pyalgotrade.tools.quandl",
"--source-code=WIKI",
"--table-code=%s" % instrument, "--from-year=2010",
"--to-year=2010",
"--storage=%s" % tmpPath,
"--auth-token=%s" % QUANDL_API_KEY
])
bf = quandlfeed.Feed()
bf.addBarsFromCSV(
instrument, os.path.join(tmpPath, "WIKI-ORCL-2010-quandl.csv"))
bf.loadAll()
self.assertEqual(bf[instrument][-1].getDateTime(),
datetime.datetime(2010, 12, 31))
self.assertEqual(bf[instrument][-1].getOpen(), 31.22)
self.assertEqual(bf[instrument][-1].getHigh(), 31.33)
self.assertEqual(bf[instrument][-1].getLow(), 30.93)
self.assertEqual(bf[instrument][-1].getClose(), 31.3)
self.assertEqual(bf[instrument][-1].getVolume(), 11716300)
self.assertEqual(bf[instrument][-1].getPrice(), 31.3)
finally:
shutil.rmtree(tmpPath)
def main(plot):
instrument = "orcl"
bBandsPeriod = 40
# Download the bars.
feed = quandlfeed.Feed()
feed.addBarsFromCSV("orcl", "WIKI-ORCL-2015-quandl.csv")
strat = BBands(feed, instrument, bBandsPeriod)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
start = strat.getBroker().getEquity()
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))
final = strat.getBroker().getEquity()
print("Net: " + str(final - start))
if plot:
plt.plot()
def testDownloadAndParseDailyNoAdjClose(self):
with common.TmpDir() as tmpPath:
instrument = "IWG"
year = 2017
path = os.path.join(tmpPath,
"quandl-daily-%s-%s.csv" % (instrument, year))
quandl.download_daily_bars("LSE",
instrument,
year,
path,
authToken=QUANDL_API_KEY)
bf = quandlfeed.Feed()
bf.setNoAdjClose()
bf.setColumnName("open", "Price")
bf.setColumnName("close", "Price")
bf.addBarsFromCSV(instrument, path, skipMalformedBars=True)
bf.loadAll()
self.assertEqual(bf[instrument][0].getDateTime(),
datetime.datetime(year, 1, 3))
self.assertEqual(bf[instrument][0].getOpen(), 237.80)
self.assertEqual(bf[instrument][0].getHigh(), 247.00)
self.assertEqual(bf[instrument][0].getLow(), 236.30)
self.assertEqual(bf[instrument][0].getClose(), 237.80)
self.assertEqual(bf[instrument][0].getVolume(), 3494173)
self.assertEqual(bf[instrument][0].getAdjClose(), None)
self.assertEqual(bf[instrument][0].getPrice(), 237.80)
def testCommandLineWeeklyCreatingDir(self):
tmpPath = tempfile.mkdtemp()
shutil.rmtree(tmpPath)
try:
symbol = "AAPL"
instrument = "%s/%s" % (symbol, PRICE_CURRENCY)
subprocess.call([
"python", "-m", "pyalgotrade.tools.quandl",
"--source-code=WIKI",
"--table-code=%s" % symbol, "--from-year=2010",
"--to-year=2010",
"--storage=%s" % tmpPath, "--frequency=weekly",
"--auth-token=%s" % QUANDL_API_KEY
])
bf = quandlfeed.Feed()
bf.addBarsFromCSV(
instrument, os.path.join(tmpPath, "WIKI-AAPL-2010-quandl.csv"))
bf.loadAll()
ds = bf.getDataSeries(instrument)
self.assertEqual(ds[-1].getDateTime(),
datetime.datetime(2010, 12, 26))
self.assertEqual(ds[-1].getOpen(), 325.0)
self.assertEqual(ds[-1].getHigh(), 325.15)
self.assertEqual(ds[-1].getLow(), 323.17)
self.assertEqual(ds[-1].getClose(), 323.6)
self.assertEqual(ds[-1].getVolume(), 7969900)
self.assertEqual(ds[-1].getPrice(), 323.6)
finally:
shutil.rmtree(tmpPath)
def testDownloadAndParseDailyNoAdjClose(self):
with common.TmpDir() as tmpPath:
symbol = "ADYEN"
instrument = "%s/%s" % (symbol, PRICE_CURRENCY)
year = 2018
path = os.path.join(tmpPath,
"quandl-daily-%s-%s.csv" % (symbol, year))
quandl.download_daily_bars("EURONEXT",
symbol,
year,
path,
authToken=QUANDL_API_KEY)
bf = quandlfeed.Feed()
bf.setNoAdjClose()
bf.setColumnName("open", "Open")
bf.setColumnName("high", "High")
bf.setColumnName("low", "Low")
bf.setColumnName("close", "Last")
bf.setColumnName("volume", "Volume")
bf.addBarsFromCSV(instrument, path, skipMalformedBars=True)
bf.loadAll()
ds = bf.getDataSeries(instrument)
self.assertEqual(ds[0].getDateTime(),
datetime.datetime(year, 6, 13))
self.assertEqual(ds[0].getOpen(), 400.00)
self.assertEqual(ds[0].getHigh(), 503.90)
self.assertEqual(ds[0].getLow(), 400.00)
self.assertEqual(ds[0].getClose(), 455.00)
self.assertEqual(ds[0].getVolume(), 1529232)
self.assertEqual(ds[0].getAdjClose(), None)
self.assertEqual(ds[0].getPrice(), 455.00)
def run_strategy(smaPeriod):
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("btc", "BTCUSD_bitstamp_daily.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = MyStrategy(feed, "btc", smaPeriod)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
def main():
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("nflx", "WIKI-NFLX-2017-quandl.csv")
# Evaluate the strategy with the feed's bars.
myStrategy = VWMAStrategy(feed, "nflx", 10000, 70, 50)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
def run_strategy(smaPeriod):
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("orcl", "pyalgotrade/WIKI-ORCL-2000-quandl.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "orcl", smaPeriod)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
def run_strategy(smaPeriod):
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV("xom", "D:\WIKI-XOM-2018-quandl.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, "xom", smaPeriod)
myStrategy.run()
print("Final portfolio value: $%.2f" % myStrategy.getBroker().getEquity())
return myStrategy.getBroker().getEquity()
def build_feed(sourceCode, tableCodes, fromYear, toYear, storage, frequency=bar.Frequency.DAY, timezone=None, skipErrors=False, noAdjClose=False, authToken=None):
"""Build and load a :class:`pyalgotrade.barfeed.quandlfeed.Feed` using CSV files downloaded from Quandl.
CSV files are downloaded if they haven't been downloaded before.
:param sourceCode: The dataset source code.
:type sourceCode: string.
:param tableCodes: The dataset table codes.
:type tableCodes: list.
:param fromYear: The first year.
:type fromYear: int.
:param toYear: The last year.
:type toYear: int.
:param storage: The path were the files will be loaded from, or downloaded to.
:type storage: string.
:param frequency: The frequency of the bars. Only **pyalgotrade.bar.Frequency.DAY** or **pyalgotrade.bar.Frequency.WEEK**
are supported.
:param timezone: The default timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
:type timezone: A pytz timezone.
:param skipErrors: True to keep on loading/downloading files in case of errors.
:type skipErrors: boolean.
:param noAdjClose: True if the instruments don't have adjusted close values.
:type noAdjClose: boolean.
:param authToken: Optional. An authentication token needed if you're doing more than 50 calls per day.
:type authToken: string.
:rtype: :class:`pyalgotrade.barfeed.quandlfeed.Feed`.
"""
logger = pyalgotrade.logger.getLogger("quandl")
ret = quandlfeed.Feed(frequency, timezone)
if noAdjClose:
ret.setNoAdjClose()
if not os.path.exists(storage):
logger.info("Creating %s directory" % (storage))
os.mkdir(storage)
for year in range(fromYear, toYear+1):
for tableCode in tableCodes:
fileName = os.path.join(storage, "%s-%s-%d-quandl.csv" % (sourceCode, tableCode, year))
if not os.path.exists(fileName):
logger.info("Downloading %s %d to %s" % (tableCode, year, fileName))
try:
if frequency == bar.Frequency.DAY:
download_daily_bars(sourceCode, tableCode, year, fileName, authToken)
elif frequency == bar.Frequency.WEEK:
download_weekly_bars(sourceCode, tableCode, year, fileName, authToken)
else:
raise Exception("Invalid frequency")
except Exception, e:
if skipErrors:
logger.error(str(e))
continue
else:
raise e
ret.addBarsFromCSV(tableCode, fileName)
def run_strategy(smaPeriod):
instrument = "ORCL/USD"
# Load the bar feed from the CSV file
feed = quandlfeed.Feed()
feed.addBarsFromCSV(instrument, "WIKI-ORCL-2000-quandl.csv")
# Evaluate the strategy with the feed.
myStrategy = MyStrategy(feed, instrument, {"USD": 1000}, smaPeriod)
myStrategy.run()
print("Final portfolio value: $%.2f" %
myStrategy.getBroker().getEquity("USD"))
def backtest(nsim=N_SIM, history=HISTORY):
"""
Backtest RL Trader on whole bitstamp dataset
:param nsim: Number of simulation to run
:return: New entry to DB with info about simulations
"""
# prepare CSV to read bars from
bars_df = db_to_pandas(db_name="trader",
table_name="bitstamp_ohlcv",
index_name="Datetime")
bars_df.dropna(axis=0, inplace=True)
bars_df.reset_index(inplace=True)
bars_df.columns = [
"Date", "Open", "High", "Low", "Close", "Volume", "to_drop",
"Adj Close"
]
bars_df.drop(axis=1, columns=["to_drop"], inplace=True)
bars_df['Date'] = pd.to_datetime(bars_df['Date'], format='%Y-%m-%d')
final_portfolios = list()
for i in range(nsim):
# read bars
bars = quandlfeed.Feed(bar.Frequency.DAY)
bars_df.to_csv("data/btc_bars.csv", index=False)
bars.addBarsFromCSV('BTC', "data/btc_bars.csv")
actions = ['Buy', 'Sell', 'Hold']
budget = INITIAL_CASH # initial cash on hand
ncoins = 0.0
params = [actions, budget, ncoins, history, i]
RLstrat = RLTraderBacktesting(bars, *params)
RLstrat.run()
final_portfolios.append(RLstrat.getResult())
# print "Average return out of {} backtesting simulations: {}".format(len(final_portfolios), np.mean(final_portfolios))
# print "Standard deviation of return: {}".format(np.std(final_portfolios))
# print "Best model with saved Q values: {}".format(np.argmax(final_portfolios))
simulation_df = pd.DataFrame(
{
"Number of simulations": [len(final_portfolios)],
"Average return": [np.mean(final_portfolios)],
"Std. deviation of return": [np.std(final_portfolios)],
"Best model index": [np.argmax(final_portfolios)],
"Best model return":
[final_portfolios[np.argmax(final_portfolios)]]
},
index=[datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')])
pandas_to_db(simulation_df, db_name="trader", table_name="rl_backtesting")
def testDownloadAndParseDailyNoAdjClose(self):
with common.TmpDir() as tmpPath:
instrument = "ORCL"
path = os.path.join(tmpPath, "quandl-daily-orcl-2013.csv")
quandl.download_daily_bars("GOOG", "NASDAQ_ORCL", 2013, path, auth_token)
bf = quandlfeed.Feed()
bf.setNoAdjClose()
bf.addBarsFromCSV(instrument, path)
bf.loadAll()
self.assertEquals(bf[instrument][-1].getDateTime(), datetime.datetime(2013, 12, 31))
self.assertEquals(bf[instrument][-1].getOpen(), 37.94)
self.assertEquals(bf[instrument][-1].getHigh(), 38.34)
self.assertEquals(bf[instrument][-1].getLow(), 37.88)
self.assertEquals(bf[instrument][-1].getClose(), 38.26)
self.assertEquals(bf[instrument][-1].getVolume(), 11747517)
self.assertEquals(bf[instrument][-1].getAdjClose(), None)
self.assertEquals(bf[instrument][-1].getPrice(), 38.26)
def testDownloadAndParseDaily(self):
with common.TmpDir() as tmpPath:
instrument = "ORCL"
path = os.path.join(tmpPath, "quandl-daily-orcl-2010.csv")
quandl.download_daily_bars("WIKI", instrument, 2010, path, auth_token)
bf = quandlfeed.Feed()
bf.addBarsFromCSV(instrument, path)
bf.loadAll()
self.assertEquals(bf[instrument][-1].getDateTime(), datetime.datetime(2010, 12, 31))
self.assertEquals(bf[instrument][-1].getOpen(), 31.22)
self.assertEquals(bf[instrument][-1].getHigh(), 31.33)
self.assertEquals(bf[instrument][-1].getLow(), 30.93)
self.assertEquals(bf[instrument][-1].getClose(), 31.3)
self.assertEquals(bf[instrument][-1].getVolume(), 11716300)
self.assertEquals(bf[instrument][-1].getPrice(), 31.3)
# Not checking against a specific value since this is going to change
# as time passes by.
self.assertNotEquals(bf[instrument][-1].getAdjClose(), None)
def testDownloadAndParseDaily(self):
with common.TmpDir() as tmpPath:
instrument = "ORCL"
path = os.path.join(tmpPath, "quandl-daily-orcl-2010.csv")
quandl.download_daily_bars("WIKI", instrument, 2010, path,
auth_token)
bf = quandlfeed.Feed()
bf.addBarsFromCSV(instrument, path)
bf.loadAll()
self.assertEquals(bf[instrument][-1].getDateTime(),
datetime.datetime(2010, 12, 31))
self.assertEquals(bf[instrument][-1].getOpen(), 31.22)
self.assertEquals(bf[instrument][-1].getHigh(), 31.33)
self.assertEquals(bf[instrument][-1].getLow(), 30.93)
self.assertEquals(bf[instrument][-1].getClose(), 31.3)
self.assertEquals(bf[instrument][-1].getVolume(), 11716300)
self.assertEquals(bf[instrument][-1].getAdjClose(),
30.23179912467581)
self.assertEquals(bf[instrument][-1].getPrice(), 31.3)
def main(plot):
instrument = "AMD" #"orcl"
bBandsPeriod = 40
overBoughtThreshold = 70
overSoldThreshold = 30
# Download the bars.
feed = quandlfeed.Feed()
feed.addBarsFromCSV("AMD", "WIKI-AMD-2016-quandl.csv")
strat = BBands(feed, instrument, bBandsPeriod, overBoughtThreshold,
overSoldThreshold)
strat.getBroker().setCash(1000)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
start = strat.getBroker().getEquity()
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())
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))
final = strat.getBroker().getEquity()
print("Net: " + str(final - start))
if plot:
plt.plot()
def testDownloadAndParseDaily_UseAdjClose(self):
with common.TmpDir() as tmpPath:
instrument = "ORCL"
path = os.path.join(tmpPath, "quandl-daily-orcl-2010.csv")
quandl.download_daily_bars("WIKI", instrument, 2010, path,
auth_token)
bf = quandlfeed.Feed()
bf.addBarsFromCSV(instrument, path)
# Need to setUseAdjustedValues(True) after loading the file because we
# can't tell in advance if adjusted values are there or not.
bf.setUseAdjustedValues(True)
bf.loadAll()
self.assertEquals(bf[instrument][-1].getDateTime(),
datetime.datetime(2010, 12, 31))
self.assertEquals(bf[instrument][-1].getOpen(), 31.22)
self.assertEquals(bf[instrument][-1].getHigh(), 31.33)
self.assertEquals(bf[instrument][-1].getLow(), 30.93)
self.assertEquals(bf[instrument][-1].getClose(), 31.3)
self.assertEquals(bf[instrument][-1].getVolume(), 11716300)
self.assertEquals(bf[instrument][-1].getAdjClose(),
30.23179912467581)
self.assertEquals(bf[instrument][-1].getPrice(), 30.23179912467581)
from pyalgotrade import plotter from pyalgotrade.barfeed import quandlfeed from pyalgotrade.stratanalyzer import returns from pyalgotrade.stratanalyzer import sharpe from pyalgotrade.stratanalyzer import drawdown from pyalgotrade.stratanalyzer import trades import sma_crossover # 运行前先调整 r instrument feed | adjust params(r,instrument,feed) before running # 无风险利率 risk free rate r = 0.04 # Load the bar feed from the CSV file instrument = "moutai" feed = quandlfeed.Feed() # 调整需要的CSV路径 Adjust CSV file feed.addBarsFromCSV(instrument, 'E:\\backtest\\csv\\' + instrument + 'csv.csv') # 修改长短期均线 Adjust fast and slow MA fastMA = 5 slowMA = 20 ##line 55 调整存储图片路径 Change the root of saving image #======================================================================================== # Evaluate the strategy with the feed's bars. myStrategy = sma_crossover.SMACrossOver(feed, instrument, fastMA, slowMA) # Attach a returns analyzers to the strategy. returnsAnalyzer = returns.Returns() sharpeRatioAnalyzer = sharpe.SharpeRatio() drawdown = drawdown.DrawDown() trades = trades.Trades() myStrategy.attachAnalyzer(returnsAnalyzer)
def testInvalidFrequency(self):
with self.assertRaisesRegexp(Exception, "Invalid frequency.*"):
quandlfeed.Feed(frequency=bar.Frequency.MINUTE)
def run_strategy():
bBandsPeriod = 21
instrument = "399300"
# 下载股票数据
MyDownload.download_csv(instrument, "2017-01-01", "2020-01-01",
instrument + ".csv")
# 从CSV文件加载bar feed
feed = quandlfeed.Feed()
feed.addBarsFromCSV(instrument, instrument + ".csv")
# 创建MyStrategy实例
myStrategy = MyStrategy(feed, instrument, bBandsPeriod)
plt = plotter.StrategyPlotter(myStrategy, True, True, True)
# 图例添加BOLL
plt.getInstrumentSubplot(instrument).addDataSeries(
"upper",
myStrategy.getBollingerBands().getUpperBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"middle",
myStrategy.getBollingerBands().getMiddleBand())
plt.getInstrumentSubplot(instrument).addDataSeries(
"lower",
myStrategy.getBollingerBands().getLowerBand())
# test = myStrategy.getBollingerBands().getUpperBand() - myStrategy.getBollingerBands().getLowerBand()
# plt.getOrCreateSubplot("test").addDataSeries("test", test)
# 图例添加MACD
plt.getOrCreateSubplot("macd").addDataSeries("DIF", myStrategy.getMACD())
plt.getOrCreateSubplot("macd").addDataSeries(
"DEA",
myStrategy.getMACD().getSignal())
plt.getOrCreateSubplot("macd").addDataSeries(
"MACD",
myStrategy.getMACD().getHistogram())
# 图例添加KD
plt.getOrCreateSubplot("stoch").addDataSeries("K", myStrategy.getStoch())
plt.getOrCreateSubplot("stoch").addDataSeries("D",
myStrategy.getStoch().getD())
# 图例添加RSI
plt.getOrCreateSubplot("rsi").addDataSeries("RSI7", myStrategy.getRSI7())
plt.getOrCreateSubplot("rsi").addDataSeries("RSI14", myStrategy.getRSI14())
plt.getOrCreateSubplot("rsi").addLine("Overbought", 70)
plt.getOrCreateSubplot("rsi").addLine("Oversold", 30)
# 添加回测分析
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# 添加夏普比率分析
sharpeRatioAnalyzer = sharpe.SharpeRatio()
myStrategy.attachAnalyzer(sharpeRatioAnalyzer)
# 运行策略
myStrategy.run()
# 输出投资组合的最终资产总值
print("最终资产总值: $%.2f" % myStrategy.getBroker().getEquity())
# 输出年度收益
print("年度收益: %.2f %%" % (returnsAnalyzer.getCumulativeReturns()[-1] * 100))
# 输出夏普比率
print("夏普比率: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0))
# 展示折线图
plt.plot()
def build_feed(sourceCode,
tableCodes,
fromYear,
toYear,
storage,
frequency=bar.Frequency.DAY,
timezone=None,
skipErrors=False,
authToken=None,
columnNames={},
forceDownload=False,
skipMalformedBars=False):
"""Build and load a :class:`pyalgotrade.barfeed.quandlfeed.Feed` using CSV files downloaded from Quandl.
CSV files are downloaded if they haven't been downloaded before.
:param sourceCode: The dataset source code.
:type sourceCode: string.
:param tableCodes: The dataset table codes.
:type tableCodes: list.
:param fromYear: The first year.
:type fromYear: int.
:param toYear: The last year.
:type toYear: int.
:param storage: The path were the files will be loaded from, or downloaded to.
:type storage: string.
:param frequency: The frequency of the bars. Only **pyalgotrade.bar.Frequency.DAY** or **pyalgotrade.bar.Frequency.WEEK**
are supported.
:param timezone: The default timezone to use to localize bars. Check :mod:`pyalgotrade.marketsession`.
:type timezone: A pytz timezone.
:param skipErrors: True to keep on loading/downloading files in case of errors.
:type skipErrors: boolean.
:param authToken: Optional. An authentication token needed if you're doing more than 50 calls per day.
:type authToken: string.
:param columnNames: Optional. A dictionary to map column names. Valid key values are:
* datetime
* open
* high
* low
* close
* volume
* adj_close
:type columnNames: dict.
:param skipMalformedBars: True to skip errors while parsing bars.
:type skipMalformedBars: boolean.
:rtype: :class:`pyalgotrade.barfeed.quandlfeed.Feed`.
"""
logger = pyalgotrade.logger.getLogger("quandl")
ret = quandlfeed.Feed(frequency, timezone)
# Additional column names.
for col, name in six.iteritems(columnNames):
ret.setColumnName(col, name)
if not os.path.exists(storage):
logger.info("Creating %s directory" % (storage))
os.mkdir(storage)
for year in range(fromYear, toYear + 1):
for tableCode in tableCodes:
fileName = os.path.join(
storage, "%s-%s-%d-quandl.csv" % (sourceCode, tableCode, year))
if not os.path.exists(fileName) or forceDownload:
logger.info("Downloading %s %d to %s" %
(tableCode, year, fileName))
try:
if frequency == bar.Frequency.DAY:
download_daily_bars(sourceCode, tableCode, year,
fileName, authToken)
else:
assert frequency == bar.Frequency.WEEK, "Invalid frequency"
download_weekly_bars(sourceCode, tableCode, year,
fileName, authToken)
except Exception as e:
if skipErrors:
logger.error(str(e))
continue
else:
raise e
ret.addBarsFromCSV(tableCode,
fileName,
skipMalformedBars=skipMalformedBars)
return ret