def RunPriceMomentum(tickerList:list, startDate:str='1/1/1982', durationInYears:int=36, stockCount:int=9, ReEvaluationInterval:int=20, filterOption:int=3, longHistory:int=365, shortHistory:int=90, minPercentGain=0.05, maxVolatility=.12, portfolioSize:int=30000, returndailyValues:bool=False, verbose:bool=False):
#Choose stockCount stocks with the greatest long term (longHistory days) price appreciation, using different filter options defined in the StockPicker class
#shortHistory is a shorter time frame (like 90 days) used differently by different filters
#ReEvaluationInterval is how often to re-evaluate our choices, ideally this should be very short and not matter, otherwise the date selection is biased.
startDate = ToDate(startDate)
endDate = AddDays(startDate, 365 * durationInYears)
picker = StockPicker(AddDays(startDate, -730), endDate) #Include earlier dates for statistics
for t in tickerList:
picker.AddTicker(t)
tm = TradingModel(modelName='PriceMomentumShort_longHistory_' + str(longHistory) +'_shortHistory_' + str(shortHistory) + '_reeval_' + str(ReEvaluationInterval) + '_stockcount_' + str(stockCount) + '_filter' + str(filterOption) + '_' + str(minPercentGain) + str(maxVolatility), startingTicker='^SPX', startDate=startDate, durationInYears=durationInYears, totalFunds=portfolioSize, tranchSize=portfolioSize/stockCount, verbose=verbose)
dayCounter = 0
if not tm.modelReady:
print('Unable to initialize price history for PriceMomentum date ' + str(startDate))
return 0
else:
while not tm.ModelCompleted():
currentDate = tm.currentDate
if dayCounter ==0:
print('\n')
print(currentDate)
c, a = tm.Value()
print(tm.modelName, int(c), int(a), int(c+a))
print('available/buy/sell/long',tm.PositionSummary())
candidates = picker.GetHighestPriceMomentum(currentDate, longHistoryDays=longHistory, shortHistoryDays=shortHistory, stocksToReturn=stockCount, filterOption=filterOption, minPercentGain=minPercentGain, maxVolatility=maxVolatility)
AlignPositions(tm=tm, targetPositions=candidates, stockCount=stockCount, allocateByPointValue=False)
tm.ProcessDay()
dayCounter+=1
if dayCounter >= ReEvaluationInterval: dayCounter=0
cv1 = tm.CloseModel(plotResults=False, saveHistoryToFile=((durationInYears>1) or verbose))
if returndailyValues:
return tm.GetDailyValue()
else:
return cv1
def RunModel(modelName: str,
modelFunction,
ticker: str,
startDate: str,
durationInYears: int,
portfolioSize: int,
saveHistoryToFile: bool = True,
returndailyValues: bool = False,
verbose: bool = False):
#Performs the logic of the given model over a period of time to evaluate the performance
modelName = modelName + '_' + ticker
print('Running model ' + modelName)
tm = TradingModel(modelName=modelName,
startingTicker=ticker,
startDate=startDate,
durationInYears=durationInYears,
totalFunds=portfolioSize,
tranchSize=round(portfolioSize / 10),
verbose=verbose)
if not tm.modelReady:
print('Unable to initialize price history for model for ' +
str(startDate))
if returndailyValues: return pd.DataFrame()
else: return portfolioSize
else:
while not tm.ModelCompleted():
modelFunction(tm, ticker)
tm.ProcessDay()
if tm.AccountingError():
print(
'Accounting error. The numbers do not add up correctly. Terminating model run.',
tm.currentDate)
tm.PositionSummary()
#tm.PrintPositions()
break
cash, asset = tm.Value()
#print('Ending Value: ', cash + asset, '(Cash', cash, ', Asset', asset, ')')
tradeCount = len(tm.tradeHistory)
RecordPerformance(ModelName=modelName,
StartDate=startDate,
EndDate=tm.currentDate,
StartValue=portfolioSize,
EndValue=(cash + asset),
TradeCount=tradeCount)
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetDailyValue(
) #return daily value for model comparisons
else:
return tm.CloseModel(
verbose, saveHistoryToFile
) #return simple closing value to view net effect
def RunBuyHold(ticker: str, startDate:str, durationInYears:int, ReEvaluationInterval:int=20, portfolioSize:int=30000, verbose:bool=False):
#Baseline model to compare against. Buy on day one, hold for the duration and then sell
modelName = 'BuyHold_' + (ticker) + '_' + startDate[-4:]
tm = TradingModel(modelName=modelName, startingTicker=ticker, startDate=startDate, durationInYears=durationInYears, totalFunds=portfolioSize, tranchSize=portfolioSize/10, verbose=verbose)
if not tm.modelReady:
print('Unable to initialize price history for model BuyHold date ' + str(startDate))
return 0
else:
dayCounter =0
while not tm.ModelCompleted():
if dayCounter ==0:
i=0
while tm.TranchesAvailable() and i < 100:
tm.PlaceBuy(ticker=ticker, price=1, marketOrder=True, expireAfterDays=10, verbose=verbose)
i +=1
dayCounter+=1
if dayCounter >= ReEvaluationInterval: dayCounter=0
tm.ProcessDay()
cash, asset = tm.Value()
print('Ending Value: ', cash + asset, '(Cash', cash, ', Asset', asset, ')')
return tm.CloseModel(plotResults=False, saveHistoryToFile=verbose)
def RunPriceMomentum(tickerList: list,
startDate: str = '1/1/1982',
durationInYears: int = 36,
stockCount: int = 9,
ReEvaluationInterval: int = 20,
filterOption: int = 3,
longHistory: int = 365,
shortHistory: int = 90,
minPercentGain=0.05,
maxVolatility=.12,
portfolioSize: int = 30000,
returndailyValues: bool = False,
verbose: bool = False):
#Choose the stock with the greatest long term (longHistory days) price appreciation
#shortHistory is a shorter time frame (like 90 days) used differently by different filters
#ReEvaluationInterval is how often to re-evaluate our choices, ideally this should be very short and not matter, otherwise the date selection is biased.
startDate = datetime.datetime.strptime(startDate, '%m/%d/%Y')
endDate = startDate + datetime.timedelta(days=365 * durationInYears)
picker = StockPicker(startDate, endDate)
for t in tickerList:
picker.AddTicker(t)
tm = TradingModel(modelName='PriceMomentumShort_longHistory_' +
str(longHistory) + '_shortHistory_' + str(shortHistory) +
'_reeval_' + str(ReEvaluationInterval) + '_stockcount_' +
str(stockCount) + '_filter' + str(filterOption) + '_' +
str(minPercentGain) + str(maxVolatility),
startingTicker='^SPX',
startDate=startDate,
durationInYears=durationInYears,
totalFunds=portfolioSize,
tranchSize=portfolioSize / stockCount,
verbose=verbose)
dayCounter = 0
if not tm.modelReady:
print('Unable to initialize price history for PriceMomentum date ' +
str(startDate))
return 0
else:
while not tm.ModelCompleted():
currentDate = tm.currentDate
if dayCounter == 0:
print('\n')
print(currentDate)
c, a = tm.Value()
print(tm.modelName, int(c), int(a), int(c + a))
print('available/buy/sell/long', tm.PositionSummary())
tm.SellAllPositions(currentDate)
tm.ProcessDay()
dayCounter += 1
shortList = picker.GetHighestPriceMomentum(
currentDate,
longHistoryDays=longHistory,
shortHistoryDays=shortHistory,
stocksToReturn=stockCount,
filterOption=filterOption,
minPercentGain=minPercentGain,
maxVolatility=maxVolatility)
shortList = shortList[:stockCount]
print(shortList)
if len(shortList) > 0:
i = 0
ii = 0
while tm.TranchesAvailable(
) and i < 100: #Over long periods 100 will not be enough, 1000 would be better, but 100 also limits the impact of early gains, after a bit you are trading $300K
tm.PlaceBuy(ticker=shortList.index[ii],
price=1,
marketOrder=True,
expireAfterDays=10,
verbose=verbose)
i += 1
ii += 1
if ii >= len(shortList): ii = 0
tm.ProcessDay()
dayCounter += 1
if dayCounter >= ReEvaluationInterval: dayCounter = 0
cv1 = tm.CloseModel(plotResults=False,
saveHistoryToFile=((durationInYears > 1)
or verbose))
if returndailyValues:
return tm.GetDailyValue()
else:
return cv1
def RunPriceMomentumBlended(tickerList: list,
startDate: str = '1/1/1980',
durationInYears: int = 29,
stockCount: int = 9,
ReEvaluationInterval: int = 20,
longHistory: int = 365,
shortHistory: int = 90,
portfolioSize: int = 30000,
returndailyValues: bool = False,
verbose: bool = False):
#Uses blended option for selecting stocks using three different filters
startDate = datetime.datetime.strptime(startDate, '%m/%d/%Y')
endDate = startDate + datetime.timedelta(days=365 * durationInYears)
picker = StockPicker(startDate, endDate)
for t in tickerList:
picker.AddTicker(t)
tm = TradingModel(modelName='PriceMomentum_Blended_longHistory_' +
str(longHistory) + '_shortHistory_' + str(shortHistory) +
'_reeval_' + str(ReEvaluationInterval) + '_stockcount_' +
str(stockCount) + '_filterBlended_134',
startingTicker='^SPX',
startDate=startDate,
durationInYears=durationInYears,
totalFunds=portfolioSize,
tranchSize=portfolioSize / stockCount,
verbose=verbose)
dayCounter = 0
if not tm.modelReady:
print('Unable to initialize price history for PriceMomentum date ' +
str(startDate))
return 0
else:
while not tm.ModelCompleted():
currentDate = tm.currentDate
if dayCounter == 0:
print('\n')
print(currentDate)
c, a = tm.Value()
print(tm.modelName, int(c), int(a), int(c + a))
print('available/buy/sell/long', tm.PositionSummary())
tm.SellAllPositions(currentDate)
tm.ProcessDay()
dayCounter += 1
shortList1 = picker.GetHighestPriceMomentum(
currentDate,
longHistoryDays=longHistory,
shortHistoryDays=shortHistory,
stocksToReturn=int(stockCount / 3),
filterOption=1)
shortList2 = picker.GetHighestPriceMomentum(
currentDate,
longHistoryDays=longHistory,
shortHistoryDays=shortHistory,
stocksToReturn=int(stockCount / 3),
filterOption=2)
shortList3 = picker.GetHighestPriceMomentum(
currentDate,
longHistoryDays=longHistory,
shortHistoryDays=shortHistory,
stocksToReturn=int(stockCount / 3),
filterOption=4)
#shortList3 = picker.GetHighestPriceMomentum(currentDate, longHistoryDays=longHistory, shortHistoryDays=shortHistory, stocksToReturn=int(stockCount/3), filterOption=6, maxVolatility=.12)
shortList = pd.concat([shortList1, shortList2, shortList3])
shortList
print(shortList)
if len(shortList) > 0:
i = 0
ii = 0
while tm.TranchesAvailable() and i < 100:
tm.PlaceBuy(ticker=shortList.index[ii],
price=1,
marketOrder=True,
expireAfterDays=10,
verbose=verbose)
i += 1
ii += 1
if ii >= len(shortList): ii = 0
tm.ProcessDay()
dayCounter += 1
if dayCounter >= ReEvaluationInterval: dayCounter = 0
cv1 = tm.CloseModel(plotResults=False,
saveHistoryToFile=((durationInYears > 1)
or verbose))
if returndailyValues:
return tm.GetDailyValue()
else:
return cv1
