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 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_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 get_prices_data(self, keys=None):
if keys is None or len(keys) == 0:
print("No keys has been specified. All keys were selected. ")
keys = {
Ct.high_key(): True,
Ct.low_key(): True,
Ct.open_key(): True,
Ct.close_key(): True,
Ct.adj_close_key(): True,
Ct.volume_key(): True
}
method_tag = "get_prices_data"
if self.data_source_historical is not None:
if self.data_source_historical.prices is None or self.data_source_historical.prices.empty is True:
raise ValueError(
"No historical data available, call method self.get_historical_data() first"
)
key_titles = self.get_key_titles(keys)
if len(key_titles) > 0:
prices = self.data_source_historical.get_prices(
self.tickers, key_titles)
else:
print("{} - There are no prices information, for ticker:{}".
format(method_tag, self.tickers))
raise ValueError
else:
print("There has been an error in {}".format(method_tag))
raise ValueError
# Validate Price dataframe
if prices.empty == True:
print("There has been an error in {}".format(method_tag))
raise ValueError
self.price_info = prices
return prices