#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/strategies/low-volatility-factor-effect-in-stocks-long-only-version/
#
# The investment universe consists of global large-cap stocks (or US large-cap stocks). At the end of each month, the investor constructs
# equally weighted decile portfolios by ranking the stocks on the past three-year volatility of weekly returns. The investor goes long
# stocks in the top decile (stocks with the lowest volatility).
#
# QC implementation changes:
# - Top quartile (stocks with the lowest volatility) is selected instead of decile.
import numpy as np
class LowVolatilityFactorEffectStocks(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2000, 1, 1)
self.SetCash(100000)
self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.period = 12*21
self.coarse_count = 3000
self.last_coarse = []
self.data = {}
self.long = []
self.selection_flag = True
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(10)
def CoarseSelectionFunction(self, coarse):
# Update the rolling window every day.
for stock in coarse:
symbol = stock.Symbol
# Store daily price.
if symbol in self.data:
self.data[symbol].update(stock.AdjustedPrice)
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(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].update(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]
# market cap sorting
if len(fine) > self.coarse_count:
sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse=True)
fine = sorted_by_market_cap[:self.coarse_count]
weekly_vol = {x.Symbol : self.data[x.Symbol].volatility() for x in fine}
# volatility sorting
sorted_by_vol = sorted(weekly_vol.items(), key = lambda x: x[1], reverse = True)
quartile = int(len(sorted_by_vol) / 4)
self.long = [x[0] for x in sorted_by_vol[-quartile:]]
return self.long
def OnData(self, data):
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
stocks_invested = [x.Key for x in self.Portfolio if x.Value.Invested]
for symbol in stocks_invested:
if symbol not in self.long:
self.Liquidate(symbol)
for symbol in self.long:
if symbol in data and data[symbol]:
self.SetHoldings(symbol, 1 / len(self.long))
self.long.clear()
def Selection(self):
self.selection_flag = True
class SymbolData():
def __init__(self, period):
self.price = RollingWindow[float](period)
def update(self, value):
self.price.Add(value)
def is_ready(self) -> bool:
return self.price.IsReady
def volatility(self) -> float:
closes = [x for x in self.price]
# Weekly volatility calc.
separete_weeks = [closes[x:x+5] for x in range(0, len(closes), 5)]
weekly_returns = [(x[0] - x[-1]) / x[-1] for x in separete_weeks]
return np.std(weekly_returns)
# Custom fee model.
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))