from AlgoLib import *
from typing import List, Dict, Tuple
import pandas as pd
class Month12CycleinCrossSectionofStocksReturns(XXX):
def Initialize(self) -> None:
self.SetStartDate(2000, 1, 1)
self.SetCash(100_000)
self.UniverseSettings.Leverage = 5
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.0
self.exchange_codes: List[str] = ['NYS', 'NAS', 'ASE']
self.fundamental_count: int = 1_000
self.fundamental_sorting_key = lambda x: x.DollarVolume
self.quantile: int = 5
self.year_period: int = 13
self.month_period: int = 30
# Monthly close data.
self.symbol_data: Dict[Symbol, SymbolData] = {}
self.portfolio_weights: Dict[Symbol, float] = {}
self.selection_flag: bool = False
symbol: Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
self.Schedule.On(self.DateRules.MonthEnd(symbol),
self.TimeRules.BeforeMarketClose(symbol),
self.Selection)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
if not self.selection_flag:
return Universe.Unchanged
# Update the rolling window every month.
for f in fundamental:
if f.Symbol in self.symbol_data:
self.symbol_data[f.Symbol].update(f.AdjustedPrice)
filtered: List[Fundamental] = [f for f in fundamental if f.HasFundamentalData
and f.SecurityReference.ExchangeId in self.exchange_codes
and not f.CompanyReference.IsREIT
and f.MarketCap != 0]
sorted_filter: List[Fundamental] = sorted(filtered,
key=self.fundamental_sorting_key,
reverse=True)[:self.fundamental_count]
# Warmup price rolling windows.
for f in sorted_filter:
if f.Symbol in self.symbol_data:
continue
self.symbol_data[f.Symbol] = SymbolData(self.year_period)
history: pd.DataFrame = self.History(f.Symbol, self.year_period * self.month_period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {f.Symbol} yet.")
continue
closes: pd.Series = history.loc[f.Symbol].close
# Find monthly closes.
for index, time_close in enumerate(closes.iteritems()):
# index out of bounds check.
if index + 1 < len(closes.keys()):
date_month: int = time_close[0].date().month
next_date_month: int = closes.keys()[index + 1].month
# Find last day of month.
if date_month != next_date_month:
self.symbol_data[f.Symbol].update(time_close[1])
ready_securities: List[Fundamental] = [x for x in sorted_filter if self.symbol_data[x.Symbol].is_ready()]
# Performance sorting. One month performance, one year ago.
performance: Dict[Fundamental, float] = {x: self.symbol_data[x.Symbol].performance() for x in ready_securities}
longs: List[Fundamental] = []
shorts: List[Fundamental] = []
if len(performance) >= self.quantile:
sorted_by_perf: List[Tuple[Fundamental, float]] = sorted(performance.items(), key=lambda x: x[1], reverse=True)
quantile: int = int(len(sorted_by_perf) / self.quantile)
longs = [x[0] for x in sorted_by_perf[:quantile]]
shorts = [x[0] for x in sorted_by_perf[-quantile:]]
# Market cap weighting.
for i, portfolio in enumerate([longs, shorts]):
mc_sum: float = sum(map(lambda x: x.MarketCap, portfolio))
for security in portfolio:
self.portfolio_weights[security.Symbol] = ((-1) ** i) * security.MarketCap / mc_sum
return list(self.portfolio_weights.keys())
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(CustomFeeModel())
def OnData(self, slice: Slice) -> None:
if not self.selection_flag:
return
self.selection_flag = False
# Trade execution.
portfolio: List[PortfolioTarget] = [PortfolioTarget(symbol, w)
for symbol, w in self.portfolio_weights.items()
if slice.ContainsKey(symbol) and slice[symbol] is not None]
self.SetHoldings(portfolio, True)
self.portfolio_weights.clear()
def Selection(self) -> None:
self.selection_flag = True
class SymbolData():
def __init__(self, period: int) -> None:
self.price: RollingWindow = RollingWindow[float](period)
def update(self, value: float) -> None:
self.price.Add(value)
def is_ready(self) -> bool:
return self.price.IsReady
# One month performance, one year ago.
def performance(self) -> float:
prices: List[float] = list(self.price)
return (prices[-2] / prices[-1] - 1)
# Custom fee model
class CustomFeeModel(FeeModel):
def GetOrderFee(self, parameters: OrderFeeParameters) -> OrderFee:
fee: float = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
