import data_tools
from AlgoLib import *
import numpy as np
from typing import Dict, List
from collections import deque
class ReversalDuringEarningsAnnouncements(XXX):
def Initialize(self):
self.SetStartDate(2010, 1, 1) # earnings dates start in 2010
self.SetCash(100000)
self.long_count:int = 20
self.short_count:int = 20
self.holding_period:int = 3
self.leverage:int = 5
self.ear_period:int = 4
self.symbol:Symbol = self.AddEquity('SPY', Resolution.Daily).Symbol
# Daily price data.
self.data:Dict[Symbol, RollingWindow] = {}
# Import earnigns data.
self.earnings_data:Dict[datetime, List[str]] = {}
# Available symbols from earning_dates dataset.
self.tickers:Set(str) = set()
self.first_date:Union[None, datetime.date] = 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:List[float] = []
self.ear_actual_quarter:List[float] = []
# 5 equally weighted brackets for traded symbols. - 20 symbols long , 20 for short, 3 days of holding.
self.trade_manager = data_tools.TradeManager(self, self.long_count, self.short_count, self.holding_period)
self.month:int = 0
self.fundamental_sorting_key = lambda x: x.DollarVolume
self.fundamental_count:int = 1000
self.selection_flag = False
self.rebalance_flag = False
self.Settings.MinimumOrderMarginPortfolioPercentage = 0.
self.UniverseSettings.Resolution = Resolution.Daily
self.AddUniverse(self.FundamentalSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
def OnSecuritiesChanged(self, changes: SecurityChanges) -> None:
for security in changes.AddedSecurities:
security.SetFeeModel(data_tools.CustomFeeModel())
security.SetLeverage(self.leverage)
def FundamentalSelectionFunction(self, fundamental: List[Fundamental]) -> List[Symbol]:
# update daily prices
for stock in fundamental:
symbol: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:List[Symbol] = [x for x in fundamental if x.HasFundamentalData and x.Market == 'usa' and x.Symbol.Value in self.tickers]
if len(selected) > self.fundamental_count:
selected = [x for x in sorted(selected, key=self.fundamental_sorting_key, reverse=True)[:self.fundamental_count]]
for stock in selected:
symbol:Symbol = stock.Symbol
if symbol in self.data:
continue
self.data[symbol] = RollingWindow[float](self.ear_period)
history:DataFrame = self.History(symbol, self.ear_period, Resolution.Daily)
if history.empty:
self.Log(f"Not enough data for {symbol} yet")
continue
closes:Series = history.loc[symbol].close
for time, close in closes.items():
self.data[symbol].Add(close)
return list(map(lambda x: x.Symbol, selected))
def OnData(self, data: Slice) -> None:
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]]
if closes[-1] != 0:
# 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) -> None:
# 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
