def main(plot):
# instruments and talbename in sql
instrument = "ibm"
tableName = "newyorkexchange.ibm_historicalquotes_newyork"
# create instance to connect Mysql
cnx = cnxStock()
# create instance to query Mysql data base
query = historicalPriceQuery(instrument, tableName)
df = query.pandasQuerySingle(cnx.connect())
#close connection
cnx.close_connection()
# feed data from PyAlgoTrade back testing
feed = MyLibrary.pandas_feed_mysql.DataFrameBarFeed(df, instrument, barfeed.Frequency.DAY)
smaPeriod = 163
strat = sma_crossover.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 getAllHistoricalQuotes(self,cnx, instruments):
tableNames = dict()
for instrument in instruments :
tableName = str("newyorkexchange.`%s_historicalquotes_newyork`" %instrument)
tableNames[instrument] = tableName
query = historicalPriceQuery(instruments, tableNames)
quotes = query.pandasQueryMulitple(cnx)
return quotes
def main(plot):
initialCash = 10000
instrumentsByClass = {
"US Stocks": ["ibm"],
"Foreign Stocks": ["ulbi"],
"US 10 Year Government Bonds": ["alex"],
"Real Estate": ["cx"],
"Commodities": ["tpvg"],
}
# Download the bars.
instruments = ["umc"]
for assetClass in instrumentsByClass:
instruments.extend(instrumentsByClass[assetClass])
tableNames = dict()
for instrument in instruments :
tableName = str("newyorkexchange.%s_historicalquotes_newyork" %instrument)
tableNames[instrument] = tableName
try :
# create instance to connect Mysql
cnx = cnxStock()
# create instance to query Mysql data base
query = historicalPriceQuery(instruments, tableNames)
dfs = query.pandasQueryMulitple(cnx.connect())
#close connection
finally:
cnx.close_connection()
feed = DataFrameMulitpleBarFeed(dfs,barfeed.Frequency.DAY)
feed.addMultipleBars()
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("umc", cumret.CumulativeReturn(feed["umc"].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(plot):
# trail
initialCash = 10000
instruments = ["aapl", "ibm"]
windowSize = 50
tableNames = dict()
for instrument in instruments :
tableName = str("newyorkexchange.%s_historicalquotes_newyork" %instrument)
tableNames[instrument] = tableName
try :
# create instance to connect Mysql
cnx = cnxStock()
# create instance to query Mysql data base
query = historicalPriceQuery(instruments, tableNames)
dfs = query.pandasQueryMulitple(cnx.connect())
#close connection
finally:
cnx.close_connection()
feed = DataFrameMulitpleBarFeed(dfs,barfeed.Frequency.DAY)
feed.addMultipleBars()
strat = StatArb(feed,dfs, windowSize, initialCash)
sharpeRatioAnalyzer = sharpe.SharpeRatio()
strat.attachAnalyzer(sharpeRatioAnalyzer)
if plot:
plt = plotter.StrategyPlotter(strat, False, False, True)
plt.getOrCreateSubplot("zScore").addDataSeries("zScore", strat.getZScoreDS())
plt.getOrCreateSubplot("spread").addDataSeries("Spread", strat.getSpreadDS())
strat.run()
print "Sharpe ratio: %.2f" % sharpeRatioAnalyzer.getSharpeRatio(0.05)
if plot:
plt.plot()