def RunTradingModelBuyHold(tm: TradingModel, ticker: str):
currentPrices = tm.GetPriceSnapshot()
if tm.verbose:
print(currentPrices.snapShotDate, currentPrices.nextDayTarget)
if not currentPrices == None:
if tm.TranchesAvailable() > 0 and tm.FundsAvailable(
) > currentPrices.high:
tm.PlaceBuy(ticker, currentPrices.low, True)
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 RunTradingModelBuyHold(tm: TradingModel, ticker: str):
#Baseline model, buy and hold
currentPrices = tm.GetPriceSnapshot()
if tm.verbose:
print(currentPrices.snapShotDate, currentPrices.nextDayTarget)
if not currentPrices == None:
for i in range(tm._tranchCount):
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
if tm.TranchesAvailable() > 0 and tm.FundsAvailable(
) > currentPrices.high:
tm.PlaceBuy(ticker, currentPrices.low, True)
if available == 0: break
def AlignPositions(tm:TradingModel, targetPositions:pd.DataFrame, stockCount:int, allocateByPointValue:bool= False, verbose:bool = False):
#Helper function. Performs necessary Buy/Sells to get from current positions to target positions
print(targetPositions)
TotalTranches = tm._tranchCount
targetPositions=pd.DataFrame(targetPositions.groupby(['Ticker','pointValue']).size()).reset_index()
targetPositions.set_index(['Ticker'], inplace=True)
targetPositions.rename(columns={0:'TargetHoldings'}, inplace=True)
targetPositions.sort_values(by=['TargetHoldings', 'pointValue'], axis=0, ascending=False, inplace=True, kind='quicksort', na_position='last')
targetPositions = targetPositions[:stockCount]
print(targetPositions)
if allocateByPointValue:
TotalPoints = targetPositions['pointValue'].sum()
scale = TotalTranches/TotalPoints
print('scale', TotalPoints, TotalTranches, scale)
targetPositions.loc[:, 'TargetHoldings'] = round(targetPositions.loc[:, 'TargetHoldings'] * targetPositions.loc[:, 'pointValue'] * scale)
else:
TotalTargets = targetPositions['TargetHoldings'].sum()
scale = TotalTranches/TotalTargets
print('scale', TotalTargets, TotalTranches, scale)
targetPositions.loc[:, 'TargetHoldings'] = round(targetPositions.loc[:, 'TargetHoldings'] * scale)
print(targetPositions)
currentPositions = tm.GetPositions(asDataFrame=True)
if len(currentPositions) > 0: #evaluate the difference between current holdings and target, act accordingly
targetPositions = targetPositions.join(currentPositions, how='outer')
targetPositions.fillna(value=0, inplace=True)
targetPositions['Difference'] = targetPositions['TargetHoldings'] - targetPositions['CurrentHoldings']
print(targetPositions)
for i in range(len(targetPositions)):
sells = int(targetPositions.iloc[i]['Difference'])
if sells < 0:
t = targetPositions.index.values[i]
print('Sell ' + str(abs(sells)) + ' ' + t)
for _ in range(abs(sells)):
tm.PlaceSell(ticker=t, price=1, marketOrder=True, expireAfterDays=10, verbose=verbose)
tm.ProcessDay(withIncrement=False)
for i in range(len(targetPositions)):
buys = int(targetPositions.iloc[i]['Difference'])
if buys > 0:
t = targetPositions.index.values[i]
print('Buy ' + str(buys) + ' ' + t)
for _ in range(buys):
tm.PlaceBuy(ticker=t, price=1, marketOrder=True, expireAfterDays=10, verbose=verbose)
tm.ProcessDay(withIncrement=False)
elif len(targetPositions) > 0:
for i in range(len(targetPositions)):
buys = int(targetPositions.iloc[i]['TargetHoldings'])
if buys > 0:
t = targetPositions.index.values[i]
print(t)
print(buys)
print('Buy ' + str(buys) + ' ' + t)
for _ in range(buys):
tm.PlaceBuy(ticker=t, price=1, marketOrder=True, expireAfterDays=10, verbose=verbose)
tm.ProcessDay(withIncrement=False)
print(tm.GetPositions(asDataFrame=True))
print(tm.PositionSummary())
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,
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 RunTradingModelQLearn(ticker: str,
startDate: str,
durationInYears: int,
totalFunds: int,
verbose: bool = False,
saveHistoryToFile: bool = True,
returndailyValues: bool = False):
Actions = [
'Hold', 'BuyMarket', 'BuyAgressiveLeve10', 'BuyAgressiveLeve11',
'BuyAgressiveLeve12', 'SellMarket', 'SellAgressiveLeve10',
'SellAgressiveLeve11', 'SellAgressiveLeve12'
]
modelName = 'QLearn' + '_' + 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:
pass
#do stuff
if returndailyValues:
tm.CloseModel(verbose, saveHistoryToFile)
return tm.GetdailyValue() #return daily value
else:
return tm.CloseModel(verbose,
saveHistoryToFile) #return closing value
def RunTradingModelSeasonal(tm: TradingModel, ticker: str):
SellMonth = 4 #April
BuyMonth = 10 #October
currentPrices = tm.GetPriceSnapshot()
if not currentPrices == None:
low = currentPrices.low
high = currentPrices.high
m = tm.currentDate.month
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
if m >= SellMonth and m <= BuyMonth:
if longPositions > 0: tm.PlaceSell(ticker, high, True)
else:
if available > 0 and tm.FundsAvailable() > high:
tm.PlaceBuy(ticker, low, True)
def RunTradingModelSeasonal(tm: TradingModel, ticker: str):
#Buy in November, sell in May
SellMonth = 5
BuyMonth = 11
currentPrices = tm.GetPriceSnapshot()
if not currentPrices == None:
low = currentPrices.low
high = currentPrices.high
m = tm.currentDate.month
for i in range(tm._tranchCount):
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
if m >= SellMonth and m <= BuyMonth:
if longPositions > 0:
tm.PlaceSell(ticker, high, True)
else:
break
else:
if available > 0 and tm.FundsAvailable() > high:
tm.PlaceBuy(ticker, low, True)
else:
break
def RunTradingModelFirstHalfOfMonth(tm: TradingModel, ticker: str):
#From Robert Ariel's observations, most gains are in the first half of the month
BuyDay = 25 #Buy at the end of the month, after the 25th
SellDay = 15 #Sell mid month, after the 15th
currentPrices = tm.GetPriceSnapshot()
if not currentPrices == None:
low = currentPrices.low
high = currentPrices.high
d = tm.currentDate.day
for i in range(tm._tranchCount):
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
if d >= BuyDay or d < 3:
if available > 0 and tm.FundsAvailable() > high:
tm.PlaceBuy(ticker, low, True)
else:
break
elif d >= SellDay:
if longPositions > 0:
tm.PlaceSell(ticker, high, True)
else:
break
else:
break
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 RunBuyHold(ticker: str,
startDate: datetime,
durationInYears: int,
ReEvaluationInterval: int = 20,
portfolioSize: int = 30000,
verbose: bool = False):
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:
tm.SellAllPositions(tm.currentDate)
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 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 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
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(tm: TradingModel, ticker: str):
#Combines trending model with targeted "swing" buys, attempting to gain better deals by anticipating daily price fluctuations
minActionableSlope = 0.002
prevTrendState, trendDuration = tm.GetCustomValues()
if prevTrendState == None: prevTrendState = ''
if trendDuration == None: trendDuration = 0
p = tm.GetPriceSnapshot()
if not p == None:
for i in range(tm._tranchCount):
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
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
tm.SetCustomValues(prevTrendState, trendDuration)
def RunTradingModelTrending(tm: TradingModel, ticker: str):
#This compares the slope of short term (6 day) and long term (18 day) exponential moving averages to determine buying opportunities. Positive, negative, or flat slopes
#trend states: ++,--,+-,-+,Flat
minActionableSlope = 0.002
prevTrendState, trendDuration = tm.GetCustomValues()
if prevTrendState == None: prevTrendState = ''
if trendDuration == None: trendDuration = 0
p = tm.GetPriceSnapshot()
if not p == None:
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
maxPositions = available + buyPending + sellPending + longPositions
targetBuy = p.nextDayTarget * (1 + p.fiveDayDeviation / 2)
targetSell = p.nextDayTarget * (1 - p.fiveDayDeviation / 2)
for i in range(tm._tranchCount):
if p.longEMASlope >= minActionableSlope and p.shortEMASlope >= minActionableSlope: #++ Positive trend, 100% long
trendState = '++'
if p.low > p.channelHigh: #Over Bought
pass
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, True)
if buyPending < 5 and longPositions < maxPositions:
tm.PlaceBuy(ticker, targetBuy, False, 3)
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 * .98, 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, get out
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, possible early up
pass
else:
if trendDuration > 2:
if sellPending < 5 and longPositions > 5:
tm.PlaceSell(ticker, targetSell, True)
if sellPending < 5 and longPositions > 0:
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
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
if buyPending < 3 and longPositions < 6:
tm.PlaceBuy(ticker, targetBuy * .95, False, 2)
else:
pass
else: #flat, aim for 70% long
trendState = 'Flat'
if p.low > p.channelHigh: #Over Bought
pass
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 buyPending < 3 and longPositions < maxPositions:
tm.PlaceBuy(ticker, targetBuy, False, 5)
tm.SetCustomValues(trendState, trendDuration)
if trendState == prevTrendState:
trendDuration = trendDuration + 1
else:
trendDuration = 0
tm.SetCustomValues(prevTrendState, trendDuration)
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
def RunTradingModelSwingTrend(tm: TradingModel, ticker: str):
#Give it a date range, some money, and a stock, it will execute a strategy and return the results
#minDeviationToTrade = .025
minActionableSlope = 0.002
prevTrendState, trendDuration = tm.GetCustomValues()
if prevTrendState == None: prevTrendState = ''
if trendDuration == None: trendDuration = 0
p = tm.GetPriceSnapshot()
if not p == None:
available, buyPending, sellPending, longPositions = tm.PositionSummary(
)
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
tm.SetCustomValues(prevTrendState, trendDuration)
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
