def RunTradingModelBuyHold(ticker: str,
startDate: str,
durationInYears: int,
totalFunds: int,
verbose: bool = False,
saveHistoryToFile: bool = True,
returndailyValues: bool = False):
modelName = 'BuyHold' + '_' + ticker
tm = TradingModel(modelName, ticker, startDate, durationInYears,
totalFunds, verbose)
if not tm.modelReady:
print('Unable to initialize price history for model for ' +
str(startDate))
if returndailyValues: return pandas.DataFrame()
else: return totalFunds
else:
while not tm.ModelCompleted():
tm.ProcessDay()
currentPrices = tm.GetPriceSnapshot()
if not currentPrices == None:
if tm.TraunchesAvailable(
) and tm.FundsAvailable() > currentPrices.high:
tm.PlaceBuy(ticker, currentPrices.low, True)
if tm.AccountingError(): break
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetdailyValue() #return daily value
else:
return tm.CloseModel(verbose,
saveHistoryToFile) #return closing value
def RunModel(modelName: str,
modelFunction,
ticker: str,
startDate: str,
durationInYears: int,
totalFunds: int,
saveHistoryToFile: bool = True,
returndailyValues: bool = False,
verbose: bool = False):
modelName = modelName + '_' + ticker
tm = TradingModel(modelName=modelName,
startingTicker=ticker,
startDate=startDate,
durationInYears=durationInYears,
totalFunds=totalFunds,
verbose=verbose)
if not tm.modelReady:
print('Unable to initialize price history for model for ' +
str(startDate))
if returndailyValues: return pandas.DataFrame()
else: return totalFunds
else:
while not tm.ModelCompleted():
tm.ProcessDay()
modelFunction(tm, ticker)
if tm.AccountingError():
print(
'Accounting error. The numbers do not add up correctly.')
break
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetDailyValue() #return daily value
else:
return tm.CloseModel(verbose,
saveHistoryToFile) #return closing value
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 RunModel(modelName: str,
modelFunction,
ticker: str,
startDate: str,
durationInYears: int,
portfolioSize: int,
saveHistoryToFile: bool = True,
returndailyValues: bool = False,
verbose: bool = False):
modelName = modelName + '_' + ticker
tm = TradingModel(modelName=modelName,
startingTicker=ticker,
startDate=startDate,
durationInYears=durationInYears,
totalFunds=portfolioSize,
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():
tm.ProcessDay()
modelFunction(tm, ticker)
if tm.AccountingError():
print(
'Accounting error. The numbers do not add up correctly. Terminating model run.'
)
tm.PositionSummary()
#tm.PrintPositions()
break
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 RunTradingModelSeasonal(ticker: str,
startDate: str,
durationInYears: int,
totalFunds: int,
verbose: bool = False,
saveHistoryToFile: bool = True,
returndailyValues: bool = False):
modelName = 'Seasonal' + '_' + ticker
tm = TradingModel(modelName, ticker, startDate, durationInYears,
totalFunds, verbose)
if not tm.modelReady:
print('Unable to initialize price history for model for ' +
str(startDate))
if returndailyValues: return pandas.DataFrame()
else: return totalFunds
else:
while not tm.ModelCompleted():
tm.ProcessDay()
currentPrices = tm.GetPriceSnapshot()
if not currentPrices == None:
low = currentPrices.low
high = currentPrices.high
m = tm.currentDate.month
available, buyPending, sellPending, longPositions = tm.GetPositionSummary(
)
if m >= 11 or m <= 4: #Buy if Nov through April, else sell
if available > 0 and tm.FundsAvailable() > high:
tm.PlaceBuy(ticker, low, True)
else:
if longPositions > 0: tm.PlaceSell(ticker, high, True)
if tm.AccountingError(): break
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetdailyValue() #return daily value
else:
return tm.CloseModel(verbose,
saveHistoryToFile) #return closing value
def RunTradingModelSwingTrend(ticker: str,
startDate: str,
durationInYears: int,
totalFunds: int,
verbose: bool = False,
saveHistoryToFile: bool = True,
returndailyValues: bool = False):
#Give it a date range, some money, and a stock, it will execute a strategy and return the results
#minDeviationToTrade = .025
minActionableSlope = 0.002
trendState = 'Flat' #++,--,+-,-+,Flat
prevTrendState = ''
trendDuration = 0
modelName = 'SwingTrend' + '_' + ticker
tm = TradingModel(modelName, ticker, startDate, durationInYears,
totalFunds, verbose)
if not tm.modelReady:
print('Unable to initialize price history for model for ' +
str(startDate))
if returndailyValues: return pandas.DataFrame()
else: return totalFunds
else:
while not tm.ModelCompleted():
tm.ProcessDay()
p = tm.GetPriceSnapshot()
if not p == None:
available, buyPending, sellPending, longPositions = tm.GetPositionSummary(
)
maxPositions = available + buyPending + sellPending + longPositions
targetBuy = p.nextDayTarget * (1 + p.fiveDayDeviation / 2)
targetSell = p.nextDayTarget * (1 - p.fiveDayDeviation / 2)
if p.longEMASlope >= minActionableSlope and p.shortEMASlope >= minActionableSlope: #++ Positive trend, 70% long
trendState = '++'
if p.low > p.channelHigh: #Over Bought
if sellPending < 3 and longPositions > 7:
tm.PlaceSell(ticker, targetSell * (1.03), False,
10)
elif p.low < p.channelLow: #Still early
if buyPending < 3 and longPositions < 6:
tm.PlaceBuy(ticker, targetBuy, True)
if trendDuration > 1 and buyPending < 3:
tm.PlaceBuy(ticker, targetBuy, True)
else:
if buyPending < 3 and longPositions < 6:
tm.PlaceBuy(ticker, targetBuy, False)
if buyPending < 5 and longPositions < maxPositions:
tm.PlaceBuy(ticker, targetBuy, False)
elif p.longEMASlope >= minActionableSlope and p.shortEMASlope < minActionableSlope: #+- Correction or early downturn
trendState = '+-'
if p.low > p.channelHigh: #Over Bought, try to get out
if sellPending < 3 and longPositions > 7:
tm.PlaceSell(ticker, targetSell, False, 3)
elif p.low < p.channelLow and p.high > p.channelLow: #Deep correction
if sellPending < 3 and longPositions > 7:
tm.PlaceSell(ticker, targetSell, False, 3)
else:
pass
elif p.longEMASlope < -minActionableSlope and p.shortEMASlope < -minActionableSlope: #-- Negative trend, aim for < 30% long
trendState = '--'
if p.high < p.channelLow: #Over sold
if buyPending < 3 and longPositions < 6:
tm.PlaceBuy(ticker, targetBuy * .95, False, 2)
elif p.low < p.channelLow and p.high > p.channelLow: #Straddle Low, early down or up
pass
else:
if sellPending < 5 and longPositions > 3:
tm.PlaceSell(ticker, targetSell, True)
if trendDuration > 1:
tm.PlaceSell(ticker, targetSell, True)
if sellPending < 5 and longPositions > 3:
tm.PlaceSell(ticker, targetSell, False, 2)
tm.PlaceSell(ticker, targetSell, False, 2)
elif p.longEMASlope < (
-1 * minActionableSlope) and p.shortEMASlope < (
-1 *
minActionableSlope): #-+ Bounce or early recovery
trendState = '-+'
if p.high < p.channelLow: #Over sold
pass
elif p.low < p.channelLow and p.high > p.channelLow: #Straddle Low
if sellPending < 3 and longPositions > 3:
tm.PlaceSell(ticker, targetSell, False, 3)
else:
pass
else: #flat, aim for 70% long
trendState = 'Flat'
if p.low > p.channelHigh: #Over Bought
if sellPending < 3 and longPositions > 7:
tm.PlaceSell(ticker, targetSell * (1.03), False,
10)
elif p.high < p.channelLow: #Over sold
if buyPending < 3 and longPositions < 8:
tm.PlaceBuy(ticker, targetBuy, False, 5)
if buyPending < 4:
tm.PlaceBuy(ticker, targetBuy, False, 5)
else:
pass
if sellPending < 3 and longPositions > 7:
tm.PlaceSell(ticker, targetSell, False, 5)
if buyPending < 3 and longPositions < maxPositions:
tm.PlaceBuy(ticker, targetBuy, False, 5)
if trendState == prevTrendState:
trendDuration = trendDuration + 1
else:
trendDuration = 0
if tm.AccountingError(): break
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetdailyValue() #return daily value
else:
return tm.CloseModel(verbose,
saveHistoryToFile) #return closing value
