def job():
for func in [ema, sma, macd, rsi, bb]:
buy_list = []
sell_list = []
for symbol in sp.get_sp500():
# TODO: needs a refresh
func.generate_signals(symbol,
start_date=utils.add_business_days(
datetime.date.today(), -200),
end_date=datetime.date.today())
df = pd.read_csv(utils.get_file_path(config.ta_data_path,
func.table_filename,
symbol=symbol),
index_col="Date",
parse_dates=["Date"])
if df.index[-1].date() != datetime.date.today():
print(
"Last date in {} file does not equal todays date. Is today a weekend or a holiday?"
.format(symbol))
if df[func.get_signal_name()][-1] == ta.buy_signal:
buy_list.append(symbol)
if df[func.get_signal_name()][-1] == ta.sell_signal:
sell_list.append(symbol)
df = pd.DataFrame({
"Buy": pd.Series(buy_list),
"Sell": pd.Series(sell_list)
})
df.to_csv(utils.get_file_path(config.ta_data_path,
func.get_signal_name() + table_filename,
dated=True,
start_date="",
end_date=datetime.date.today()),
index=False)
def sell(self, symbol, date, sell_size=0):
"""Simulates selling a stock
Parameters:
symbol : str
date : datetime
sell_size : float, optional
How much to sell. If 0, sell all
partial_shares : bool
Whether partial shares are supported. If True, the amount sold will always equal amount, even if that number isn't reachable in a number of whole shares
short_sell : bool
"""
if symbol in self.portfolio:
if self.portfolio[symbol] > 0:
# sell at close price, or next day's open price?
if sell_size == 0: # sell all
self.cash += self.portfolio[symbol] * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")
self.cash -= self.calculate_commission(self.portfolio[symbol])
self.log.loc[date][actions_column_name] = self.log.loc[date][actions_column_name] + "{} {} {} Shares at {} totalling {:.2f} ".format(ta.sell_signal, symbol, self.portfolio[symbol], self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), self.portfolio[symbol] * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"))
self.portfolio.pop(symbol)
else:
shares = (sell_size // self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")) if not self.partial_shares else (sell_size / self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"))
shares = shares if shares < self.portfolio[symbol] else self.portfolio[symbol]
if shares < 0:
print("Amount {} Price {} Shares {}".format(sell_size, self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), shares), flush=True)
raise Exception
if shares != 0:
self.cash += shares * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")
self.cash -= self.calculate_commission(shares)
self.log.loc[date][actions_column_name] = self.log.loc[date][actions_column_name] + "{} {} {} Shares at {} totalling {:.2f} ".format(ta.sell_signal, symbol, shares, self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), shares * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"))
self.portfolio[symbol] -= shares
if self.portfolio[symbol] < 0:
print("Amount {} Price {} Shares {} Shares in portfolio {}".format(sell_size, self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), shares, self.portfolio[symbol]), flush=True)
raise Exception
if self.portfolio[symbol] == 0:
self.portfolio.pop(symbol)
self.set_stop_loss(symbol, date, Operation.Sell)
self.update_winners_losers(symbol, date, Operation.Sell)
else:
# short sell more
pass
else:
if self.short_sell:
pass
def update_winners_losers(self, symbol, date, func):
"""Updates the cost basis when buying and updates winners/losers when selling
Parameters:
symbol : str
date : datetime
func : str
"""
if func == Operation.Buy:
self.cost_basis[symbol] = self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")
if func == Operation.Sell:
if self.cost_basis[symbol] < self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"):
self.winners_losers["Winners"] += 1
else:
self.winners_losers["Losers"] += 1
try:
self.cost_basis.pop(symbol) # not actually nessecary since next time we buy the stock it will overwrite the cost basis
except KeyError:
print(symbol)
print(self.cost_basis)
raise
def set_stop_loss(self, symbol, date, func):
"""Sets initial values in the stop loss dictionary when buying and removes entries when selling
Parameters:
symbol : str
date : datetime
func : str
"""
if func == Operation.Buy:
self.stop_loss[symbol] = self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")
if func == Operation.Sell:
self.stop_loss.pop(symbol)
def buy(self, symbol, date, purchase_size):
"""Simulates buying a stock
Parameters:
symbol : str
date : datetime
purchase_size : float, optional
How much to buy. If 0, buy the default amount
partial_shares : bool
Whether partial shares are supported. If True, the amount bought will always equal amount, even if that number isn't reachable in a number of whole shares
"""
if purchase_size == 0:
purchase_size = self.purchase_size
if self.cash < purchase_size:
self.log.loc[date][actions_column_name] = self.log.loc[date][actions_column_name] + "Unable to buy {} ".format(symbol)
# purchase_size = self.cash
elif symbol not in self.portfolio:
# buy at close price, or next day's open price?
shares = (purchase_size // self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")) if not self.partial_shares else (purchase_size / self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"))
if shares < 0:
# Sometimes shares is -1. When variables are printed, math does not add up to -1??
print("Symbol {} self.purchase_size {} Purchase size {} Price {} Shares {}".format(symbol, self.purchase_size, purchase_size, self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), shares), flush=True)
raise Exception
if shares != 0:
self.portfolio[symbol] = shares
self.cash -= shares * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open")
self.cash -= self.calculate_commission(shares)
self.log.loc[date][actions_column_name] = self.log.loc[date][actions_column_name] + "{} {} {} Shares at {} totaling {:.2f} ".format(ta.buy_signal, symbol, shares, self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"), shares * self.get_price_on_date(symbol, utils.add_business_days(date, self.slippage), time="Close" if self.slippage == 0 else "Open"))
self.set_stop_loss(symbol, date, Operation.Buy)
self.update_winners_losers(symbol, date, Operation.Buy)
else:
if self.short_sell and self.portfolio[symbol] < 0:
pass
elif self.portfolio[symbol] > 0:
# buy more
pass
def get_price_on_date(self, symbol, date, time="Close"):
"""Gets the price of the given symbol on the given date
Parameters:
symbol : str
date : datetime
time : str
Which column to use to determine price. Valid times are "Open" and "Close"
Returns:
float
The price of the given symbol on the given date
"""
start_time = timer()
if symbol in self.price_files:
df = self.price_files[symbol]
else:
if utils.refresh(utils.get_file_path(config.prices_data_path, prices.price_table_filename, symbol=symbol), refresh=False):
prices.download_data_from_yahoo(symbol, start_date=self.start_date, end_date=self.end_date)
df = pd.read_csv(utils.get_file_path(config.prices_data_path, prices.price_table_filename, symbol=symbol), index_col="Date", parse_dates=["Date"])[self.start_date:self.end_date]
self.price_files[symbol] = df
price = df.loc[date][time] if date in df.index else self.get_price_on_date(symbol, utils.add_business_days(date, -1), time=time)
self.times[get_price_time] = self.times[get_price_time] + timer() - start_time
return price
"RMO", "VINC", "PRCH", "ARKO", "OPEN", "AVCT", "PAE", "VRT", "RPAY", "WTRH"
]
# list of spacs with missing data
data_issues_list = ["AGLY", "CLVR", "HUNTF", "KLDI", "LAZY", "SONG"]
spac_data = pd.read_csv(utils.get_file_path(config.spac_data_path,
"SPAC.csv",
symbol=""),
index_col=False,
parse_dates=["Completion Date"])
pre_and_post_merger_prices = []
failed_count = 0
failed_symbols = []
for index, row in spac_data.iterrows():
symbol = row["Post-SPAC Ticker Symbol"]
start_date = utils.add_business_days(row["Completion Date"],
days_before_merger)
end_date = utils.add_business_days(row["Completion Date"],
days_after_merger)
if symbol not in data_issues_list:
# Never refresh for spacs, Yahoo consistently missing data around mergers
df = pd.read_csv(utils.get_file_path(config.spac_data_path,
prices.price_table_filename,
symbol=symbol),
index_col="Date",
parse_dates=["Date"])
try:
# Preferably we would use the below line and also filter dfs for [start_date:end_date]
# but there are issues where the dates are not as expected due to holdidays. Also OPEN is missing a day randomly
pre_and_post_merger_prices.append(
(df.loc[start_date]["Close"], df.loc[end_date]["Close"]))