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254 lines (218 loc) · 7.1 KB
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import backtrader as bt
import numpy as np
import pandas as pd
pd.set_option("display.max_columns", None)
class KeyIndicatorAnalyzer(bt.Analyzer):
""" """
def __init__(self):
super(KeyIndicatorAnalyzer, self).__init__()
# period
self.year_period = 252
# period
self.month_period = 21
# period
self.week_period = 5
#
self.daily_details = []
#
self.commission = 0
#
self.win_list = []
#
self.loss_list = []
#
self.key_indicators_df = pd.DataFrame(
columns=["", "", "", "", "", "", "", "7", "30", "", ""]
)
# ，，{：DataFrame, ：DataFrame}，，
self.daily_chart_dict = dict()
def get_analysis_data(self, benchmark_df, benchmark_name):
"""
，，。
@param benchmark_df:
@param benchmark_name:
"""
self._calculate_benchmark_indicators(benchmark_df, benchmark_name)
return self.key_indicators_df, self.daily_chart_dict
def _calculate_benchmark_indicators(self, benchmark_df, benchmark_name):
""" """
series = benchmark_df["close"]
total_return = self.total_return(series)
annual_return = self.annual_return(series)
period = self.week_period
recent_7_days_return = self.recent_period_return(series, period)
period = self.month_period
recent_30_days_return = self.recent_period_return(series, period)
max_drawdown = self.max_drawdown(series)
sharp_ratio = self.sharp_ratio(series)
self.key_indicators_df.loc[len(self.key_indicators_df)] = [
benchmark_name,
total_return,
annual_return,
max_drawdown,
None,
sharp_ratio,
None,
recent_7_days_return,
recent_30_days_return,
None,
None,
]
#
df = pd.DataFrame(index=benchmark_df.index)
s = self.yield_curve(series)
#
df.insert(0, "", s)
df.index.name = ""
self.daily_chart_dict[benchmark_name] = df
def next(self):
super(KeyIndicatorAnalyzer, self).next()
#
current_date = self.strategy.data.datetime.date(0)
#
total_value = self.strategy.broker.getvalue()
#
cash = self.strategy.broker.getcash()
self.daily_details.append({"": current_date, "": total_value, "": cash})
def notify_trade(self, trade):
#
if trade.isclosed:
#
self.commission += trade.commission
#
if trade.pnlcomm >= 0:
# ， 0
self.win_list.append(trade.pnlcomm)
else:
#
self.loss_list.append(trade.pnlcomm)
def stop(self):
#
if self._win_times() + self._loss_times() == 0:
win_rate = 0
else:
win_percent = self._win_times() / (self._win_times() + self._loss_times())
win_rate = f"{round(win_percent * 100, 2)}%"
df = pd.DataFrame(self.daily_details)
#
total_return = self.total_return(df[""])
#
annual_return = self.annual_return(df[""])
# 7
period = self.week_period
recent_7_days_return = self.recent_period_return(df[""], period)
# 30
period = self.month_period
recent_30_days_return = self.recent_period_return(df[""], period)
#
max_drawdown = self.max_drawdown(df[""])
#
sharp_ratio = self.sharp_ratio(df[""])
#
kelly_percent = self.kelly_percent()
#
commission_percent = self.commission_percent(df[""])
#
trade_times = self._win_times() + self._loss_times()
#
self.key_indicators_df.loc[len(self.key_indicators_df)] = [
"",
total_return,
annual_return,
max_drawdown,
win_rate,
sharp_ratio,
kelly_percent,
recent_7_days_return,
recent_30_days_return,
commission_percent,
trade_times,
]
#
df[""] = self.yield_curve(df[""])
df.set_index("", inplace=True)
#
self.daily_chart_dict[""] = df
def commission_percent(self, series) -> str:
""" """
percent = self.commission / series.iloc[0]
return f"{round(percent * 100, 2)}%"
def yield_curve(self, series) -> pd.Series:
""" """
percent = (series - series.iloc[0]) / series.iloc[0]
return round(percent * 100, 2)
def total_return(self, series) -> str:
""" """
percent = (series.iloc[-1] - series.iloc[0]) / series.iloc[0]
return f"{round(percent * 100, 2)}%"
def annual_return(self, series) -> str:
""" """
percent = (
(series.iloc[-1] - series.iloc[0])
/ series.iloc[0]
/ len(series)
* self.year_period
)
return f"{round(percent * 100, 2)}%"
def recent_period_return(self, series, period) -> str:
""" """
percent = (series.iloc[-1] - series.iloc[-period]) / series.iloc[-period]
return f"{round(percent * 100, 2)}%"
def max_drawdown(self, series) -> str:
""" """
s = (series - series.expanding().max()) / series.expanding().max()
percent = s.min()
return f"{round(percent * 100, 2)}%"
def sharp_ratio(self, series) -> float:
"""
：，（）
，，。
，，。
，，。
，，。
，，1.0。
：(Rp-Rf)/σp
，Rp，Rf，σp。
3%
：sharpe = ( - ) /
"""
ret_s = series.pct_change().fillna(0)
avg_ret_s = ret_s.mean()
avg_risk_free = 0.03 / self.year_period
sd_ret_s = ret_s.std()
sharp = (avg_ret_s - avg_risk_free) / sd_ret_s
sharp_year = round(np.sqrt(self.year_period) * sharp, 3)
return sharp_year
def kelly_percent(self) -> str:
"""
：，，
，。
：K = W - [(1 - W) / R]
，K，W，R，。
：，，；，
kelly_percent = 0.2，20%。
，
"""
win_times = self._win_times()
loss_times = self._loss_times()
if win_times > 0 and loss_times > 0:
avg_win = np.average(self.win_list) #
avg_loss = abs(np.average(self.loss_list)) # ，
win_loss_ratio = avg_win / avg_loss #
if win_loss_ratio == 0:
kelly_percent = None
else:
sum_trades = win_times + loss_times
win_percent = win_times / sum_trades #
#
#
kelly_percent = win_percent - ((1 - win_percent) / win_loss_ratio)
else:
kelly_percent = None #
return f"{round(kelly_percent * 100, 2)}%" if kelly_percent else None
def _win_times(self):
""" """
return len(self.win_list)
def _loss_times(self):
""" """
return len(self.loss_list)