def __compute_final_stats(self):
self._final_stats["MeanProfit"] = np.mean(
self._trade_detail["EverytimeProfit"])
self._final_stats["MaxLoss"] = np.min(
self._trade_detail["EverytimeProfit"])
self._final_stats["FinalProfit"] = self._trade_detail[
"EverytimeProfit"].values[-1]
self._final_stats["MeanProfitPer"] = round(
self._final_stats["MeanProfit"] / self.trader_fund * 100, 2)
self._final_stats["FinalProfitPer"] = round(
self._final_stats["FinalProfit"] / self.trader_fund * 100, 2)
self._final_stats["MaxLossPer"] = round(
self._final_stats["MaxLoss"] / self.trader_fund * 100, 2)
self._final_stats["AnnualReturnPer"] = round(
period_return2annual_return(
self._final_stats["FinalProfitPer"] / 100,
self._trade_period_years,
) * 100,
2,
)
time_step_returns = (
self._trade_detail["EverytimeProfit"] -
self._trade_detail["EverytimeProfit"].shift(1)
) / (self._trade_detail["EverytimeProfit"].shift(1) + self.trader_fund)
strategy_return = np.mean(time_step_returns)
strategy_std = np.std(time_step_returns)
self._final_stats["AnnualSharpRatio"] = calculate_sharp_ratio(
strategy_return, strategy_std)
def __compute_compare_market(self):
self._compare_market_detail = self._trade_detail[[
"date", "EverytimeTotalProfit"
]].copy()
self._compare_market_detail["CumDailyReturn"] = (
np.log(self._compare_market_detail["EverytimeTotalProfit"]) -
np.log(
self._compare_market_detail["EverytimeTotalProfit"].shift(1))
).fillna(0)
self._compare_market_detail["CumDailyReturn"] = round(
self._compare_market_detail["CumDailyReturn"].cumsum(), 5)
tai_ex = self.data_loader.get_data(
dataset="TaiwanStockPrice",
data_id="TAIEX",
start_date=self.start_date,
end_date=self.end_date,
)[["date", "close"]]
tai_ex["CumTaiExDailyReturn"] = (
np.log(tai_ex["close"]) -
np.log(tai_ex["close"].shift(1))).fillna(0)
tai_ex["CumTaiExDailyReturn"] = round(
tai_ex["CumTaiExDailyReturn"].cumsum(), 5)
self._compare_market_detail = pd.merge(
self._compare_market_detail,
tai_ex[["date", "CumTaiExDailyReturn"]],
on=["date"],
how="left",
)
self._compare_market_detail = self._compare_market_detail.dropna()
self._compare_market_stats = pd.Series()
self._compare_market_stats["AnnualTaiexReturnPer"] = (
period_return2annual_return(
self._compare_market_detail["CumTaiExDailyReturn"].values[-1],
self._trade_period_years,
) * 100)
self._compare_market_stats["AnnualReturnPer"] = self._final_stats[
"AnnualReturnPer"]
def test_return2annual(period_return, period_years, expected):
resp = period_return2annual_return(period_return, period_years)
assert resp == expected