Pre-Earnings Buyback Announcement Momentum Strategy

1from AlgoLib import *
2import numpy as np
3#endregion
4
5class EarningsAnnouncementsCombinedWithStockRepurchases(XXX):
6
7def Initialize(self):
8self.SetStartDate(2011, 1, 1) # Buyback data strats at 2011
9self.SetCash(100000) 
10
11self.fine = {}
12self.price = {}
13self.managed_symbols = []
14self.earnings_universe = []
15
16self.earnings = {}
17self.buybacks = {}
18
19self.max_traded_stocks = 40 # maximum number of trading stocks
20self.quantile = 4
21
22self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
23
24# load earnings dates
25csv_data = self.Download('data.quantpedia.com/backtesting_data/economic/earning_dates.csv')
26lines = csv_data.split('\r\n')
27
28for line in lines:
29    line_split = line.split(';')
30    date = line_split[0]
31    
32    if date == '' :
33        continue
34    
35    date = datetime.strptime(date, "%Y-%m-%d").date()
36    self.earnings[date] = []
37    
38    for ticker in line_split[1:]: # skip date in current line
39        self.earnings[date].append(ticker)
40        
41        if ticker not in self.earnings_universe:
42            self.earnings_universe.append(ticker)
43
44# load buyback dates
45csv_data = self.Download('data.quantpedia.com/backtesting_data/equity/BUY_BACKS.csv')
46lines = csv_data.split('\r\n')
47
48for line in lines[1:]: # skip header
49    line_split = line.split(';')
50    date = line_split[0]
51    
52    if date == '' :
53        continue
54    
55    date = datetime.strptime(date, "%d.%m.%Y").date()
56    self.buybacks[date] = []
57    
58    for ticker in line_split[1:]: # skip date in current line
59        self.buybacks[date].append(ticker)
60
61self.months_counter = 0
62self.selection_flag = False
63self.UniverseSettings.Resolution = Resolution.Daily
64self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
65self.Schedule.On(self.DateRules.MonthStart(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
66
67def OnSecuritiesChanged(self, changes):
68for security in changes.AddedSecurities:
69    security.SetFeeModel(CustomFeeModel())
70    security.SetLeverage(5)
71    
72def CoarseSelectionFunction(self, coarse):
73# update stocks last prices
74for stock in coarse:
75    ticker = stock.Symbol.Value
76    
77    if ticker in self.earnings_universe:
78        # store stock's last price
79        self.price[ticker] = stock.AdjustedPrice
80
81# rebalance quarterly
82if not self.selection_flag:
83    return Universe.Unchanged
84self.selection_flag = False
85
86# select stocks, which had spin off
87selected = [x.Symbol for x in coarse if x.Symbol.Value in self.earnings_universe]
88
89return selected
90
91def FineSelectionFunction(self, fine):
92fine = [x for x in fine if x.MarketCap != 0 and 
93                            ((x.SecurityReference.ExchangeId == "NYS") or
94                            (x.SecurityReference.ExchangeId == "NAS") or 
95                            (x.SecurityReference.ExchangeId == "ASE"))]
96
97if len(fine) < self.quantile:
98    return Universe.Unchanged
99
100# exclude 25% stocks with lowest market capitalization
101quantile = int(len(fine) / self.quantile)
102sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap)
103selected = sorted_by_market_cap[quantile:]
104self.fine = {x.Symbol.Value : x.Symbol for x in selected}
105
106return list(self.fine.values())
107
108def OnData(self, data:Slice) -> None:
109remove_managed_symbols = []
110# maybe there should be BDay(15)
111liquidate_date = self.Time.date() - timedelta(15)
112
113# check if bought stocks have 15 days after earnings annoucemnet
114for managed_symbol in self.managed_symbols:
115    if managed_symbol.earnings_date >= liquidate_date:
116        remove_managed_symbols.append(managed_symbol)
117        
118        # liquidate stock by selling it's quantity
119        self.MarketOrder(managed_symbol.symbol, -managed_symbol.quantity)
120        
121# remove liquidated stocks from self.managed_symbols
122for managed_symbol in remove_managed_symbols:
123    self.managed_symbols.remove(managed_symbol)
124
125# maybe there should be BDay(10)
126after_current = self.Time.date() + timedelta(10)
127
128if after_current in self.earnings:
129    # this stocks has earnings annoucement after 10 days
130    stocks_with_earnings = self.earnings[after_current]
131    
132    # 30 days before earnings annoucement
133    buyback_start = self.Time.date() - timedelta(20)
134    # 15 days before earnings annoucement
135    buyback_end = self.Time.date() - timedelta(5)
136    
137    stocks_with_buyback = [] # storing stocks with buyback in period -30 to -15 days before earnings annoucement
138    
139    for buyback_date, tickers in self.buybacks.items():
140        # check if buyback date is in period before earnings annoucement
141        if buyback_date >= buyback_start and buyback_date <= buyback_end:
142            # iterate through each stock ticker for buyback date
143            for ticker in tickers:
144                # add stock ticker if it isn't already added, it has earnings annoucement after 10 days and was selected in fine
145                if (ticker not in stocks_with_buyback) and (ticker in stocks_with_earnings) and (ticker in self.fine):
146                    stocks_with_buyback.append(self.fine[ticker])
147                    
148    # buying stocks buyback in period -30 to -15 days before earnings annoucement
149    # and stocks, which have earnings date -10 days before current date
150    for symbol in stocks_with_buyback:
151        # check if there is a place in Portfolio for trading current stock
152        if not len(self.managed_symbols) < self.max_traded_stocks:
153            continue
154        
155        # calculate stock quantity
156        weight = self.Portfolio.TotalPortfolioValue / self.max_traded_stocks
157        quantity = np.floor(weight / self.price[symbol.Value])
158        
159        # go long stock
160        self.MarketOrder(symbol, quantity)
161        
162        # store stock's ticker, earnings date and traded quantity
163        if symbol in data and data[symbol]:
164            self.managed_symbols.append(ManagedSymbol(symbol, after_current, quantity))
165
166def Selection(self):
167# quarterly selection
168if self.months_counter % 3 == 0:
169    self.selection_flag = True
170self.months_counter += 1
171
172class ManagedSymbol():
173def __init__(self, symbol, earnings_date, quantity):
174self.symbol = symbol
175self.earnings_date = earnings_date
176self.quantity = quantity
177
178# custom fee model
179class CustomFeeModel(FeeModel):
180def GetOrderFee(self, parameters):
181fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
182return OrderFee(CashAmount(fee, "USD"))