Overall Statistics
Total Trades
31025
Average Win
0.37%
Average Loss
-0.35%
Compounding Annual Return
24.726%
Drawdown
46.500%
Expectancy
0.058
Net Profit
1607.853%
Sharpe Ratio
0.825
Probabilistic Sharpe Ratio
12.479%
Loss Rate
49%
Win Rate
51%
Profit-Loss Ratio
1.06
Alpha
0.186
Beta
0.116
Annual Standard Deviation
0.239
Annual Variance
0.057
Information Ratio
0.378
Tracking Error
0.271
Treynor Ratio
1.705
Total Fees
$46488.88
Estimated Strategy Capacity
$4000.00
Lowest Capacity Asset
WSOB R735QTJ8XC9X
from AlgorithmImports import *

import numpy as np
import statsmodels.api as sm

# Custom fee model
class CustomFeeModel(FeeModel):
    def GetOrderFee(self, parameters):
        fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
        return OrderFee(CashAmount(fee, "USD"))

# NOTE: Manager for new trades. It's represented by certain count of equally weighted brackets for long and short positions.
# If there's a place for new trade, it will be managed for time of holding period.
class TradeManager():
    def __init__(self, algorithm, long_size, short_size, holding_period):
        self.algorithm = algorithm  # algorithm to execute orders in.
        
        self.long_size = long_size
        self.short_size = short_size
        
        self.long_len = 0
        self.short_len = 0
    
        # Arrays of ManagedSymbols
        self.symbols = []
        
        self.holding_period = holding_period    # Days of holding.
    
    # Add stock symbol object
    def Add(self, symbol, long_flag):
        # Open new long trade.
        managed_symbol = ManagedSymbol(symbol, self.holding_period, long_flag)
        
        if long_flag:
            # If there's a place for it.
            if self.long_len < self.long_size:
                self.symbols.append(managed_symbol)
                self.algorithm.SetHoldings(symbol, 1 / self.long_size)
                self.long_len += 1
            else:
                self.algorithm.Log("There's not place for additional trade.")

        # Open new short trade.
        else:
            # If there's a place for it.
            if self.short_len < self.short_size:
                self.symbols.append(managed_symbol)
                self.algorithm.SetHoldings(symbol, - 1 / self.short_size)
                self.short_len += 1
            else:
                self.algorithm.Log("There's not place for additional trade.")
   
    # Decrement holding period and liquidate symbols.
    def TryLiquidate(self):
        symbols_to_delete = []
        for managed_symbol in self.symbols:
            managed_symbol.days_to_liquidate -= 1
            
            # Liquidate.
            if managed_symbol.days_to_liquidate == 0:
                symbols_to_delete.append(managed_symbol)
                self.algorithm.Liquidate(managed_symbol.symbol)
                
                if managed_symbol.long_flag: self.long_len -= 1
                else: self.short_len -= 1

        # Remove symbols from management.
        for managed_symbol in symbols_to_delete:
            self.symbols.remove(managed_symbol)
    
    def LiquidateTicker(self, ticker):
        symbol_to_delete = None
        for managed_symbol in self.symbols:
            if managed_symbol.symbol.Value == ticker:
                self.algorithm.Liquidate(managed_symbol.symbol)
                symbol_to_delete = managed_symbol
                if managed_symbol.long_flag: self.long_len -= 1
                else: self.short_len -= 1
                
                break
        
        if symbol_to_delete: self.symbols.remove(symbol_to_delete)
        else: self.algorithm.Debug("Ticker is not held in portfolio!")
    
class ManagedSymbol():
    def __init__(self, symbol, days_to_liquidate, long_flag):
        self.symbol = symbol
        self.days_to_liquidate = days_to_liquidate
        self.long_flag = long_flag
# https://quantpedia.com/strategies/reversal-during-earnings-announcements/
#
# The investment universe consists of stocks listed at NYSE, AMEX, and NASDAQ, whose daily price data are available at the CRSP database. 
# Earnings-announcement dates are collected from Compustat. Firstly, the investor sorts stocks into quintiles based on firm size. Then he
# further sorts the stocks in the top quintile (the biggest) into quintiles based on their average returns in the 3-day window between 
# t-4 and t-2, where t is the day of the earnings announcement. The investor goes long on the bottom quintile (past losers) and short on
# the top quintile (past winners) and holds the stocks during the 3-day window between t-1, t, and t+1. Stocks in the portfolios are 
# weighted equally.
#
# QC Impelmentation:
#   - Universe consits of stocks, which have earnings dates in Quantpedia data.
#   - Maximum of 20 long and 20 short stock are held at the same time.

import data_tools
from AlgorithmImports import *
import numpy as np
from collections import deque

class ReversalDuringEarningsAnnouncements(QCAlgorithm):
    
    def Initialize(self):
        self.SetStartDate(2010, 1, 1) # earnings dates start in 2010
        self.SetCash(100000)

        self.ear_period = 4
        self.symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
        
        # Daily price data.
        self.data = {}
        
        # Import earnigns data.
        self.earnings_data = {}
        
        # Available symbols from earning_dates dataset.
        self.tickers:Set(str) = set()
        
        self.first_date:datetime.date|None = None

        earnings_data:str = self.Download('data.quantpedia.com/backtesting_data/economic/earnings_dates_eps.json')
        earnings_data_json:list[dict] = json.loads(earnings_data)
        
        for obj in earnings_data_json:
            date:datetime.date = datetime.strptime(obj['date'], "%Y-%m-%d").date()
            self.earnings_data[date] = []
            
            if not self.first_date: self.first_date = date

            for stock_data in obj['stocks']:
                ticker:str = stock_data['ticker']

                self.earnings_data[date].append(ticker)
                self.tickers.add(ticker)

        # EAR history for previous quarter used for statistics. 
        self.ear_previous_quarter = []
        self.ear_actual_quarter = []
        
        # 5 equally weighted brackets for traded symbols. - 20 symbols long , 20 for short, 3 days of holding.
        self.trade_manager = data_tools.TradeManager(self, 20, 20, 3)
        
        self.month:int = 0
        self.selection_flag = False
        self.rebalance_flag = False
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction)
        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(data_tools.CustomFeeModel())
            security.SetLeverage(5)

    def CoarseSelectionFunction(self, coarse):
        # update daily prices
        for stock in coarse:
            symbol = stock.Symbol
            
            if symbol in self.data:
                self.data[symbol].Add(stock.AdjustedPrice)
        
        if not self.selection_flag:
            return Universe.Unchanged
        self.selection_flag = False
    
        selected = [x.Symbol for x in coarse if x.Symbol.Value in self.tickers]
        
        for symbol in selected:
            if symbol in self.data:
                continue
            
            self.data[symbol] = RollingWindow[float](self.ear_period)
            history = self.History(symbol, self.ear_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].Add(close)
        
        return selected

    def OnData(self, data):
        date_to_lookup = (self.Time + timedelta(days=1)).date()
        
        # Liquidate opened symbols after three days.
        self.trade_manager.TryLiquidate()
        
        ret_t4_t2 = {}
        
        for symbol in self.data:
            # Data is ready.
            if self.data[symbol].IsReady:
                # Earnings is in next two day for the symbol.
                if date_to_lookup in self.earnings_data and symbol.Value in self.earnings_data[date_to_lookup]:
                    closes = [x for x in self.data[symbol]]
                    # Calculate t-4 to t-2 return.
                    ret = (closes[0] - closes[-1]) / closes[-1]
                    ret_t4_t2[symbol] = ret
                    
                    # Store return in this month's history.
                    self.ear_actual_quarter.append(ret)
            
        # Wait until we have history data for previous three months.
        if len(self.ear_previous_quarter) != 0:
            # Sort by EAR.
            ear_values = self.ear_previous_quarter
            top_ear_quintile = np.percentile(ear_values, 80)
            bottom_ear_quintile = np.percentile(ear_values, 20)
            
            # Store symbol to set.
            long = [x[0] for x in ret_t4_t2.items() if x[1] <= bottom_ear_quintile and x[0] in data and data[x[0]]]
            short = [x[0] for x in ret_t4_t2.items() if x[1] >= top_ear_quintile and x[0] in data and data[x[0]]]
            
            # Open new trades.
            for symbol in long:
                self.trade_manager.Add(symbol, True)
            for symbol in short:
                self.trade_manager.Add(symbol, False)

    def Selection(self):
        # There is no earnings data yet.
        if self.Time.date() < self.first_date:
            return

        self.selection_flag = True
        
        # Every three months.    
        if self.month % 3 == 0:
            # Save quarter history.
            self.ear_previous_quarter = [x for x in self.ear_actual_quarter]
            self.ear_actual_quarter.clear()

        self.month += 1