# dataframe for a historical instrument feature (basis in this case). The index is the timestamps
# atmost upto lookback data points. The columns of this dataframe are the stocks/instrumentIds.
lookbackInstrumentValue = lookbackInstrumentFeatures.getFeatureDf('F5')
# The last row of the previous dataframe gives the last calculated value for that feature (basis in this case)
# This returns a series with stocks/instrumentIds as the index.
currentValue = lookbackInstrumentValue.iloc[-1]
if param1Value == 'value1':
return currentValue * 0.1
else:
return currentValue * 0.5
if __name__ == "__main__":
if updateCheck():
print(
'Your version of the auquan toolbox package is old. Please update by running the following command:'
)
print('pip install -U auquan_toolbox')
else:
tf = MyTradingFunctions()
tsParams = MyTradingParams(tf)
tradingSystem = TradingSystem(tsParams)
# Set onlyAnalyze to True to quickly generate csv files with all the features
# Set onlyAnalyze to False to run a full backtest
# Set makeInstrumentCsvs to False to not make instrument specific csvs in runLogs. This improves the performance BY A LOT
tradingSystem.startTrading(onlyAnalyze=False,
shouldPlot=True,
makeInstrumentCsvs=True)
param1Value = featureParams['param1']
# A holder for the all the instrument features
lookbackInstrumentFeatures = instrumentManager.getLookbackInstrumentFeatures()
# dataframe for a historical instrument feature (basis in this case). The index is the timestamps
# atmost upto lookback data points. The columns of this dataframe are the stocks/instrumentIds.
lookbackInstrumentValue = lookbackInstrumentFeatures.getFeatureDf('Adj Close')
# The last row of the previous dataframe gives the last calculated value for that feature (basis in this case)
# This returns a series with stocks/instrumentIds as the index.
currentValue = lookbackInstrumentValue.iloc[-1]
if param1Value == 'value1':
return currentValue * 0.1
else:
return currentValue * 0.5
if __name__ == "__main__":
if updateCheck():
print('Your version of the auquan toolbox package is old. Please update by running the following command:')
print('pip install -U auquan_toolbox')
else:
tsParams = MyTradingParams()
tradingSystem = TradingSystem(tsParams)
# Set onlyAnalyze to True to quickly generate csv files with all the features
# Set onlyAnalyze to False to run a full backtest
# Set makeInstrumentCsvs to False to not make instrument specific csvs in runLogs. This improves the performance BY A LOT
tradingSystem.startTrading(onlyAnalyze=False, shouldPlot=True, makeInstrumentCsvs=True, createResultDict=False)
def run_plain_momentum():
tsParams = PlainMomParams()
tradingSystem = TradingSystem(tsParams)
results = tradingSystem.startTrading()
return results
currentMarketFeatures['ma_r%s_90' % i]
) / currentMarketFeatures['sdev_r%s_90' % i]
else:
z_score[i] = 0
instrument = instrumentManager.getInstrument(instrumentIds[0])
#z_score = z_score + instrument.getDataDf()['position']/20000
if currentMarketFeatures['correl_r%s_90' % i] < 0.5:
z_score[i] = 0
if z_score[i] > 1:
prediction[PAIRIDS[i][0]] = 0
prediction[PAIRIDS[i][1]] = 1
elif z_score[i] < -1:
prediction[PAIRIDS[i][0]] = 1
prediction[PAIRIDS[i][1]] = 0
elif (z_score[i] > 0.5) or (z_score[i] < -0.5):
prediction[PAIRIDS[i][0]] = 0.75
prediction[PAIRIDS[i][1]] = 0.25
else:
prediction[PAIRIDS[i][0]] = 0.5
prediction[PAIRIDS[i][1]] = 0.5
return prediction
if __name__ == "__main__":
tsParams = MyTradingParams()
tradingSystem = TradingSystem(tsParams)
tradingSystem.startTrading(onlyAnalyze=False)
ma90 = ma90Data.iloc[-1]
# Calculate Hurst Exponent
hurst = ma90Data.apply(hurst_f, axis=0)
# Go long if Hurst > 0.5 and both long term and short term momentum are positive
predictions[(hurst > 0.5) & (mom30 > 0) & (mom10 > 0)] = 1
# Go short if Hurst > 0.5 and both long term and short term momentum are negative
predictions[(hurst > 0.5) & (mom30 <= 0) & (mom10 <= 0)] = 0
# Get out of position if Hurst > 0.5 and long term momentum is positive while short term is negative
predictions[(hurst > 0.5) & (mom30 > 0) & (mom10 <= 0)] = 0.5
# Get out of position if Hurst > 0.5 and long term momentum is negative while short term is positive
predictions[(hurst > 0.5) & (mom30 <= 0) & (mom10 > 0)] = 0.5
# Get out of position if Hurst < 0.5
predictions[hurst <= 0.5] = 0.5
else:
# If no sufficient data then don't take any positions
predictions.values[:] = 0.5
return predictions
def updateCount(self):
self.count = self.count + 1
tf = MyTradingFunctions()
tsParams = MomentumTradingParams(tf)
tradingSystem = TradingSystem(tsParams)
results = tradingSystem.startTrading()
# dataframe for a historical instrument feature (basis in this case). The index is the timestamps
# atmost upto lookback data points. The columns of this dataframe are the symbols/instrumentIds.
lookbackInstrumentValue = lookbackInstrumentFeatures.getFeatureDf('symbolVWAP')
# The last row of the previous dataframe gives the last calculated value for that feature (basis in this case)
# This returns a series with symbols/instrumentIds as the index.
currentValue = lookbackInstrumentValue.iloc[-1]
if param1Value == 'value1':
return currentValue * 0.1
else:
return currentValue * 0.5
if __name__ == "__main__":
if updateCheck():
print('Your version of the auquan toolbox package is old. Please update by running the following command:')
print('pip install -U auquan_toolbox')
else:
print('Loading your config dicts and prediction function')
tf = MyTradingFunctions()
print('Loaded config dicts and prediction function, Loading Problem Params')
tsParams1 = MyTradingParams(tf, 'Revenue(Y)')
tradingSystem = TradingSystem(tsParams1)
# s=MyTradingFunctions()
# s.getInstrumentIds()
results1 = tradingSystem.startTrading(onlyAnalyze=False, shouldPlot=False, makeInstrumentCsvs=False)
# tsParams2 = MyTradingParams(tf, 'Income(Y)')
# tradingSystem = TradingSystem(tsParams2)
# results2 = tradingSystem.startTrading(onlyAnalyze=False, shouldPlot=False, makeInstrumentCsvs=False)
print('Score: %0.3f'%((results1['score'])))#+results2['score'])/2))
instrument: Instrument for which this feature is being calculated
instrumentManager: A holder for all the instruments
'''
@classmethod
def computeForInstrument(cls, featureParams, featureKey, currentFeatures, instrument, instrumentManager):
# Current Book Data (dictionary) for the Instrument. This is the last update to the instrument
bookData = instrument.getCurrentBookData()
# Custom parameter which can be used as input to computation of this feature
param1Value = featureParams['param1']
# dataframe for historical instrument features. The last row of this data frame
# would contain the features which are being calculated in this update cycle or for this time.
# The second to last row (if exists) would have the features for the previous
# time update. Columns will be featureKeys for different features
lookbackInstrumentFeaturesDf = instrument.getDataDf()
return 0
if __name__ == "__main__":
if updateCheck():
print 'Your version of the auquan toolbox package is old. Please update by running the following command:'
print 'pip install -U auquan_toolbox'
problem1Solver = Problem1Solver()
tsParams = FairValueTradingParams(problem1Solver)
tradingSystem = TradingSystem(tsParams)
# Set onlyAnalyze to True to quickly generate csv files with all the features
# Set onlyAnalyze to False to run a full backtest
tradingSystem.startTrading(onlyAnalyze=False, shouldPlot=False)
def run_hurst_momentum():
tsParams = HurstMomParams()
tradingSystem = TradingSystem(tsParams)
results = tradingSystem.startTrading()
return results
def run_hurst_pairs():
tsParams = HurstPairParams()
tradingSystem = TradingSystem(tsParams)
result = tradingSystem.startTrading()
return result
def run_plain_pairs():
tsParams = PlainPairParams()
tradingSystem = TradingSystem(tsParams)
result = tradingSystem.startTrading()
return result