def Process(s, game, version=3):
pre_process(s, game)
gather_info(s)
strategy(s)
controls(s)
feedback(s)
return output(s, version)
def Process(s, game, version=3):
# t0 = time.time()
pre_process(s, game)
gather_info(s)
strategy(s)
controls(s)
feedback(s)
# if not s.counter % 50:
# print(1 / 60 - (time.time() - t0))
return output(s, version)
#Pull and structure AMAT data
AMATChain = pd.read_csv('./Options/AMAT.csv', header=0,
parse_dates=True, sep=',', dayfirst=True)
AMATChain['Days To Expiry'] = 5
AMATChain = AMATChain.head(n=4)
AMATPredictions = pd.DataFrame({"Upper Bound": 51, "Lower Bound": 48.7,
"Mean": 50, "Spot": 49.37}, index=[0,])
AMATPredictions['Ticker'] = 'AMAT'
#Pull and structure AAPL data
AAPLChain = pd.read_csv('./Options/AAPL.csv', header=0,
parse_dates=True, sep=',', dayfirst=True)
AAPLChain['Days To Expiry'] = 5
AAPLChain = AAPLChain.head(n=4)
AAPLPredictions = pd.DataFrame({"Upper Bound": 140, "Lower Bound": 134.7,
"Mean": 138, "Spot": 136.86}, index=[0,])
AAPLPredictions['Ticker'] = 'AAPL'
#Create one chain
optionChain = AMATChain
optionChain = AMATChain.append(AAPLChain, ignore_index=True)
#Create one prediction dataframe
predictions = AMATPredictions
predictions = predictions.append(AAPLPredictions, ignore_index=True)
print("Predictions")
print("="*25)
print(predictions)
test = strategy(optionChain, predictions)
opt = optimizer(test)