def backtest(self, prices, ref_prices, params):
m = params["m"]
x = params["x"]
interval = params[Ct.interval_key()]
mon_ret_df = Financials.pct_change(prices)
p_return = IntradayResistanceBreakout.calculate(mon_ret_df, m, x)
cagr_params = {Ct.interval_key(): interval}
cagr = CAGR.get_cagr(p_return, cagr_params)
sharpe_params = {"rf": 0.025, Ct.interval_key(): interval}
sharpe = Sharpe.get_sharpe(p_return, sharpe_params)
max_dd = MaxDrawdown.get_max_drawdown(p_return)
# calculating overall strategy's KPIs
mon_ret_df = Financials.pct_change(ref_prices)
cagr_ref = CAGR.get_cagr(mon_ret_df, cagr_params)
sharpe_params = {"rf": 0.025, Ct.interval_key(): interval}
sharpe_ref = Sharpe.get_sharpe(mon_ret_df, sharpe_params)
max_dd_ref = MaxDrawdown.get_max_drawdown(mon_ret_df)
print("CAGR - Portfolio: {}".format(cagr[Ct.cagr_key()]["mon_ret"]))
print("Sharpe - Portfolio: {}".format(
sharpe[Ct.sharpe_key()]["mon_ret"]))
print("MAX-Drawdown - Portfolio: {}".format(
max_dd[Ct.max_drawdown_key()]["mon_ret"]))
print("CAGR - Ref: {}".format(cagr_ref[Ct.cagr_key()]["^DJI"]))
print("Sharpe - Ref: {}".format(sharpe_ref[Ct.sharpe_key()]["^DJI"]))
print("MAX-Drawdown - Ref: {}".format(
max_dd_ref[Ct.max_drawdown_key()]["^DJI"]))
IntradayResistanceBreakout.plot(p_return, mon_ret_df)
def test_KPI_calmar(self):
tickers = ["TSLA", "SPY"]
interval = Ct.INTERVAL.DAY
conf = {
Ct.tickers_key(): tickers,
Ct.historical_type_key(): DATASOURCETYPE.YFINANCE,
Ct.fundamentals_type_key(): None,
Ct.fundamentals_options_key(): [],
Ct.force_fundamentals_key(): False,
Ct.indicators_key(): [],
Ct.start_date_key(): dt.datetime(2021, 3, 7) - dt.timedelta(1825),
Ct.end_date_key(): dt.datetime(2021, 3, 7),
Ct.interval_key(): interval,
Ct.period_key(): None,
Ct.bulk_key(): True
}
stocks = \
StocksFactory.create_stocks(
conf=conf
)
prices_df = stocks[0].get_prices_data(keys={Ct.adj_close_key(): True})
df = Financials.pct_change(prices_df)
params = {Ct.interval_key(): interval}
calmar = Calmar()
result = calmar.calculate(df, params)
self.assertEqual(1.1700790532917946, result[Ct.calmar_key()]['TSLA'])
def test_KPI_max_drawdown(self):
tickers = ["TSLA", "SPY"]
interval = Ct.INTERVAL.DAY
conf = {
Ct.tickers_key(): tickers,
Ct.historical_type_key(): DATASOURCETYPE.YFINANCE,
Ct.fundamentals_type_key(): None,
Ct.fundamentals_options_key(): [],
Ct.force_fundamentals_key(): False,
Ct.indicators_key(): [],
Ct.start_date_key(): dt.datetime(2021, 3, 7) - dt.timedelta(1825),
Ct.end_date_key(): dt.datetime(2021, 3, 7),
Ct.interval_key(): interval,
Ct.period_key(): None,
Ct.bulk_key(): True
}
stocks = \
StocksFactory.create_stocks(
conf=conf
)
prices_df = stocks[0].get_prices_data(keys={Ct.adj_close_key(): True})
df = Financials.pct_change(prices_df)
md = MaxDrawdown()
result = md.calculate(df)
self.assertEqual(0.6062653645917145,
result[Ct.max_drawdown_key()]['TSLA'])
self.assertEqual(0.3371725544013077,
result[Ct.max_drawdown_key()]['SPY'])
def get_reference_days(params):
if Ct.interval_key() in params.keys():
period = params[Ct.interval_key()]
else:
raise ValueError(
"Please set the corresponding Interval parameter"
"{Ct.interval_key(): interval:Ct.INTERVAL.MONTH|Ct.INTERVAL.DAY}"
)
if period == Ct.INTERVAL.DAY:
# 252 trading days
reference_days = 252
else:
# 12 months
reference_days = 12
return reference_days
def test_portfolio_rebalance(self):
# DJI constituent stocks
tickers = ["MMM", "AXP", "T", "BA", "CAT", "CSCO", "KO", "XOM", "GE",
"GS", "HD", "IBM", "INTC", "JNJ", "JPM", "MCD", "MRK", "MSFT",
"NKE", "PFE", "PG", "TRV", "UNH", "VZ", "V", "WMT", "DIS"]
ref_tickers = ["^DJI"]
end_date = dt.datetime.now() # dt.datetime(2021, 3, 7)
interval = Ct.INTERVAL.MONTH
conf = {
Ct.tickers_key(): tickers,
Ct.historical_type_key(): DATASOURCETYPE.YFINANCE,
Ct.fundamentals_type_key(): None,
Ct.fundamentals_options_key(): [],
Ct.force_fundamentals_key(): False,
Ct.indicators_key(): [],
Ct.start_date_key(): end_date-dt.timedelta(3650),
Ct.end_date_key(): end_date,
Ct.interval_key(): interval,
Ct.period_key(): None,
Ct.bulk_key(): True
}
stocks = \
StocksFactory.create_stocks(
conf=conf
)
conf[Ct.tickers_key()] = ref_tickers
stocks_ref = \
StocksFactory.create_stocks(
conf=conf
)
prices_df = stocks[0].get_prices_data(keys={Ct.adj_close_key(): True})
ref_prices_df = stocks_ref[0].get_prices_data(keys={Ct.adj_close_key(): True})
pr = PortfolioRebalance()
params = {"m": 6, "x": 3, Ct.interval_key(): conf[Ct.interval_key()]}
pr.backtest(prices_df, ref_prices_df, params)
def create_stocks(conf):
tickers = conf[Ct.tickers_key()]
data_source_historical = None
data_source_fundamentals = None
data_sources = \
DataCollector.get_data_sources(
conf[Ct.historical_type_key()],
conf[Ct.fundamentals_type_key()]
)
if DataCollector.HISTORICAL_KEY in data_sources.keys():
data_source_historical = data_sources[DataCollector.HISTORICAL_KEY]
if data_source_historical is not None:
data_source_historical.extract_historical_data(
tickers=tickers,
start_date=conf[Ct.start_date_key()],
end_date=conf[Ct.end_date_key()],
period=conf[Ct.period_key()],
interval=conf[Ct.interval_key()])
if DataCollector.FUNDAMENTALS_KEY in data_sources.keys():
data_source_fundamentals = data_sources[
DataCollector.FUNDAMENTALS_KEY]
if data_source_fundamentals is not None:
data_source_fundamentals.extract_fundamentals(
tickers=tickers,
date=conf[Ct.start_date_key],
required_elements=conf[Ct.fundamentals_options_key()],
force_server_data=conf[Ct.force_fundamentals_key()])
stocks = StocksFactory.load_stocks(
tickers=tickers,
data_source_historical=data_source_historical,
data_source_fundamentals=data_source_fundamentals,
bulk=conf[Ct.bulk_key()],
indicators=conf[Ct.indicators_key()])
return stocks
