| Overall Statistics |
|
Total Orders 10101 Average Win 0.42% Average Loss -0.39% Compounding Annual Return 20.793% Drawdown 51.900% Expectancy 0.076 Start Equity 100000 End Equity 369627.53 Net Profit 269.628% Sharpe Ratio 0.609 Sortino Ratio 0.637 Probabilistic Sharpe Ratio 13.689% Loss Rate 48% Win Rate 52% Profit-Loss Ratio 1.06 Alpha 0.089 Beta 0.702 Annual Standard Deviation 0.244 Annual Variance 0.06 Information Ratio 0.285 Tracking Error 0.222 Treynor Ratio 0.212 Total Fees $29605.75 Estimated Strategy Capacity $8000.00 Lowest Capacity Asset PNM R735QTJ8XC9X Portfolio Turnover 100.15% |
#region imports
from AlgorithmImports import *
#endregion
# https://quantpedia.com/strategies/trading-on-the-dividend-paydate/
#
# The investment universe consists of stocks from NYSE, AMEX and NASDAQ that offer company-sponsored DRIPs.
# Each day at close investors buy stocks which have dividend payday on the next working day and hold these stocks
# for one day. Stocks are weighted equally.
#
# QC implementation:
# Can scrape the below websites for info
# Source of drip tickers: http://www.dripdatabase.com/DRIP_Directory_AtoZ.aspx
# Source of dividend data: https://www.nasdaq.com/market-activity/dividends
from datetime import datetime
from pandas.tseries.offsets import BDay
class TradingDividendPaydate(QCAlgorithm):
def Initialize(self):
# self.SetStartDate(2010, 1, 1)
self.SetStartDate(2017, 10, 1)
# self.SetStartDate(2021, 12, 1)
# self.SetStartDate(2022, 9, 20)
# self.SetEndDate(2022, 8, 31) # Set End Date
self.SetEndDate(2024, 8, 31) # Set End Date
self.SetCash(100000)
self.symbol = self.AddEquity('SPY', Resolution.Minute).Symbol
# Store drip tickers.
# csv_string_file = self.Download('data.quantpedia.com/backtesting_data/economic/drip_tickers.csv')
csv_string_file = self.Download('https://www.dropbox.com/s/id5bvqweevq3cja/drip_tickers_1.csv?dl=1')
lines = csv_string_file.split('\r\n')
self.drip_tickers = [x for x in lines[1:]]
# dividend data
self.dividend_data = {} # dict of dicts indexed by paydate date
self.dividend_tickers = []
# csv_string_file = self.Download('data.quantpedia.com/backtesting_data/economic/dividend_dates.csv')
# csv_string_file = self.Download('https://www.dropbox.com/s/lgxdzccpsq3yvl8/nasdaq_dividend_dates_4.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/tlq1wty1a7twuhz/nasdaq_dividend_dates_5.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/5puro7dshiu5t2i/nasdaq_dividend_dates_5_short_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/thqtz9br5zhxfvu/nasdaq_dividend_dates_6_short_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/ydby6dber7i4347/dividend_dates_1.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/59frn1ixdzdbzaa/nasdaq_dividend_dates_7.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/8kq73i9qhuc0lvb/dividend_dates_8_short.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/5uuelwq20o46ruc/nasdaq_dividend_dates_9_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/p0y1n9p4cl4opww/nasdaq_dividend_dates_11_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/sdyfvyqymndud3w/nasdaq_dividend_dates_12_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/7urf9v9pc9al4ri/nasdaq_dividend_dates_13_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/dj68d3225os97ee/nasdaq_dividend_dates_14_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/42rlzc3ho76ypz9/nasdaq_dividend_dates_15_copy.csv?dl=1')
# csv_string_file = self.Download('https://www.dropbox.com/s/3u51mnzipod4e0x/nasdaq_dividend_dates_16_copy.csv?dl=1')
csv_string_file = self.Download('https://www.dropbox.com/s/svftrjdzxhukyw2/nasdaq_dividend_dates_18_copy.csv?dl=1')
lines = csv_string_file.split('\r\n')
# lines = csv_string_file.split('\n')
for line in lines[3:]: # skip first three comment lines
if line == '':
continue
line_split = line.split(';')
# self.Debug(str(line_split))
# self.Debug("Before datetime: " + str(line_split[0]))
ex_div_date = datetime.strptime(line_split[0], "%Y-%m-%d").date()
# ex_div_date = datetime.strptime(line_split[0], "%Y-%m-%d %H:%M").date()
# self.Debug("After datetime: " + str(ex_div_date))
# 1/0
# N stocks -> n*6 properties
for i in range(1, len(line_split), 6):
# parse dividend info
ticker = str(line_split[i])
# self.Debug("Before datetime: " + str(line_split[i+1]))
payday = datetime.strptime(line_split[i+1], "%m/%d/%Y").date() if line_split[i+1] != '' else None
# payday = datetime.strptime(line_split[i+1], "%m/%d/%Y").date() if line_split[i+1] != '' or line_split[i+1] != 'N/A' else None
# self.Debug(str(payday))
# self.Debug("After datetime: " + str(payday))
# 1/0
if payday not in self.dividend_data:
self.dividend_data[payday] = {}
# self.Debug("Before datetime: " + str(line_split[i+2]))
record_date = datetime.strptime(line_split[i+2], "%m/%d/%Y").date() if line_split[i+2] != '' else None
# record_date = datetime.strptime(line_split[i+2], "%m/%d/%Y").date() if line_split[i+2] != '' or line_split[i+2] != 'N/A' else None
# self.Debug(str(record_date))
# self.Debug("After datetime: " + str(record_date))
# self.Debug("Before datetime: " + str(line_split[i+3]))
dividend_value = float(line_split[i+3]) if line_split[i+3] != '' else None
# dividend_value = float(line_split[i+3]) if line_split[i+3] != '' or line_split[i+3] != 'N/A' else None
# self.Debug(str(dividend_value))
# self.Debug("After datetime: " + str(dividend_value))
# self.Debug("Before datetime: " + str(line_split[i+4]))
ann_dividend_value = float(line_split[i+4]) if line_split[i+4] != '' else None
# ann_dividend_value = float(line_split[i+4]) if line_split[i+4] != '' or line_split[i+4] != 'N/A' else None
# self.Debug(str(ann_dividend_value))
# self.Debug("After datetime: " + str(ann_dividend_value))
# self.Debug("Before datetime: " + str(line_split[i+5]))
try:
announcement_date = datetime.strptime(line_split[i+5], "%m/%d/%Y").date() if line_split[i+5] != '' else None
except:
"05/02/2022"
# announcement_date = datetime.strptime(line_split[i+5], "%m/%d/%Y").date() if line_split[i+5] != '' or line_split[i+5] != 'N/A' else None
# self.Debug(str(announcement_date))
# self.Debug("After datetime: " + str(announcement_date))
# 1/0
# store ticker dividend info to current ex-div date
self.dividend_data[payday][ticker] = DividendInfo(ticker, ex_div_date, payday, record_date, dividend_value, ann_dividend_value, announcement_date)
# self.Debug("A")
# store dividend info ticker universe
self.dividend_tickers.append(ticker)
# self.Debug("Dividend Tickers: " + str(self.dividend_tickers))
self.opened_positions = {}
self.active_universe = [] # selected stock universe
self.selection_flag = False
self.UniverseSettings.Resolution = Resolution.Minute
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.Schedule.On(self.DateRules.MonthEnd(self.symbol), self.TimeRules.AfterMarketOpen(self.symbol), self.Selection)
self.Schedule.On(self.DateRules.EveryDay(self.symbol), self.TimeRules.BeforeMarketClose(self.symbol, 16), self.Rebalance)
def OnSecuritiesChanged(self, changes):
for security in changes.AddedSecurities:
# security.SetFeeModel(CustomFeeModel(self))
security.SetFeeModel(CustomFeeModel())
security.SetLeverage(10)
def CoarseSelectionFunction(self, coarse):
if not self.selection_flag:
return Universe.Unchanged
self.selection_flag = False
# For Scalable Version
# sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
# filtered = [ x.Symbol for x in sortedByDollarVolume if x.Price > 10 and x.DollarVolume > 10000000 ]
# selected = [x.Symbol for x in sortedByDollarVolume if x.Symbol in self.drip_tickers #x.HasFundamentalData
# and x.Market == 'usa' and x.Price > 5
# and x.DollarVolume > 10000000 # My addition to help with scale
# and x.Symbol in self.drip_tickers
# and x.Symbol in self.dividend_tickers]
# selected = selected[:2000]
# selected = [x.Symbol for x in sortedByDollarVolume if x.Price > 5
# and x.DollarVolume > 10000000
# and x.Symbol.Value in self.drip_tickers
# and x.Symbol.Value in self.dividend_tickers]
# Original Version
selected = [x.Symbol for x in coarse if x.Symbol.Value in self.drip_tickers and x.Symbol.Value in self.dividend_tickers]
return selected
def FineSelectionFunction(self, fine):
fine = [x for x in fine if x.MarketCap != 0 and \
((x.SecurityReference.ExchangeId == "NYS") or (x.SecurityReference.ExchangeId == "NAS") or (x.SecurityReference.ExchangeId == "ASE"))]
# sorting by market cap
sorted_by_market_cap = sorted(fine, key = lambda x: x.MarketCap, reverse = True)
half = int(len(sorted_by_market_cap) / 2)
# pick lower half
self.active_universe = [x.Symbol for x in sorted_by_market_cap[-half:]]
# pick upper half
# self.active_universe = [x.Symbol for x in sorted_by_market_cap[:half]]
return self.active_universe
def Rebalance(self):
# close opened positions
if len(self.opened_positions) != 0:
for symbol, q in self.opened_positions.items():
self.MarketOnCloseOrder(symbol, -q)
self.opened_positions.clear()
day_to_check = (self.Time.date() + BDay(1)).date()
# there are stocks with payday next business day
if day_to_check in self.dividend_data:
payday_tickers = list(self.dividend_data[day_to_check].keys())
long = []
for symbol in self.active_universe:
if symbol.Value in payday_tickers:
long.append(symbol)
if len(long) != 0:
portfolio_value = self.Portfolio.TotalPortfolioValue / len(long)
for symbol in long:
price = self.Securities[symbol].Price
if price != 0:
q = portfolio_value / price
self.MarketOnCloseOrder(symbol, q)
self.opened_positions[symbol] = q
def Selection(self):
if self.Time.month % 3 == 0:
self.selection_flag = True
# custom fee model
# class CustomFeeModel(FeeModel):
class CustomFeeModel:
def GetOrderFee(self, parameters):
fee = parameters.Security.Price * parameters.Order.AbsoluteQuantity * 0.00005
return OrderFee(CashAmount(fee, "USD"))
class DividendInfo():
def __init__(
self,
ticker:str,
ex_div_date:datetime,
payday:datetime,
record_date:datetime,
dividend_value:float,
ann_dividend_value:float,
announcement_date:datetime
):
self.ticker:str = ticker
self.ex_div_date:datetime = ex_div_date
self.payday:datetime = payday
self.record_date:datetime = record_date
self.dividend_value:float = dividend_value
self.ann_dividend_value:float = ann_dividend_value
self.announcement_date:datetime = announcement_date