Top 30% Market Cap Stocks Momentum Long-Short Strategy

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"))