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'])
class Calmar(KPI):
kpi_name = Ct.calmar_key()
def __init__(self, params=None):
super().__init__(params)
if not params:
self.params = {}
def calculate(self, df, params=None):
super().calculate(df, params)
self.result = Calmar.get_calmar(df, self.params)
return self.result
@staticmethod
def get_calmar(input_df, params=None):
"""function to calculate Calmar"""
if params is None:
params = {}
cagr = CAGR.get_cagr(input_df, params)
max_dd = MaxDrawdown.get_max_drawdown(input_df)
result_df = pd.DataFrame()
result_df[Calmar.kpi_name] = (cagr[Ct.cagr_key()] /
max_dd[Ct.max_drawdown_key()])
return result_df