from AlgoLib import *
class MomentumEffectinStocksinSmallPortfolios(XXX):
def Initialize(self):
self.SetStartDate(2010, 1, 1)
self.SetCash(100000)
self.coarse_count = 500
self.long = []
self.short = []
# Daily data.
self.data = {}
self.period = 12 * 21
self.quantile = 10
self.leverage = 5
self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.selection_flag = True
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.month = 11
self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(self.leverage)
def CoarseSelectionFunction(self, coarse):
# Update the rolling window every day.
for stock in coarse:
symbol = stock.Symbol
if symbol in self.data:
# Store daily price.
self.data[symbol].update(stock.AdjustedPrice)
# Selection once a month.
if not self.selection_flag:
return Universe.Unchanged
# selected = [x.Symbol for x in coarse if x.HasFundamentalData and x.Market == 'usa']
selected = [x.Symbol
for x in sorted([x for x in coarse if x.HasFundamentalData and x.Market == 'usa'],
key = lambda x: x.DollarVolume, reverse = True)[:self.coarse_count]]
# Warmup price rolling windows.
for symbol in selected:
if symbol in self.data:
continue
self.data[symbol] = SymbolData(symbol, self.period)
history = self.History(symbol, self.period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {symbol} yet")
continue
closes = history.loc[symbol].close
for time, close in closes.iteritems():
self.data[symbol].Price.Add(close)
return [x for x in selected if self.data[x].is_ready()]
def FineSelectionFunction(self, fine):
fine = [x for x in fine if x.MarketCap != 0]
# if len(fine) > self.coarse_count:
# sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True)
# top_by_market_cap = sorted_by_market_cap[:self.coarse_count]
# else:
# top_by_market_cap = fine
# Performance sorting.
performance = {x.Symbol : self.data[x.Symbol].performance() for x in fine}
if len(performance) >= self.quantile:
decile = int(len(performance) / self.quantile)
sorted_by_perf = sorted(performance.items(), key = lambda x: x[1], reverse = True)
self.long = [x[0] for x in sorted_by_perf[:decile]]
self.short = [x[0] for x in sorted_by_perf[-decile:]]
return self.long + self.short
def OnData(self, data):
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
long_count = len(self.long)
short_count = len(self.short)
invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in invested:
if symbol not in self.long + self.short:
self.Liquidate(symbol)
for symbol in self.long:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1 / long_count)
for symbol in self.short:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, -1 / short_count)
def Selection(self):
# Rebalance every 12 months.
if self.month == 12:
self.selection_flag = True
self.month += 1
if self.month > 12:
self.month = 1
class SymbolData():
def __init__(self, symbol, period):
self.Symbol = symbol
self.Price = RollingWindow[float](period)
def update(self, value):
self.Price.Add(value)
def is_ready(self):
return self.Price.IsReady
def performance(self):
closes = [x for x in self.Price]
return (closes[0] / closes[-1] - 1)
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
