代码收藏家 Python – Quantstats量化投资策略绩效统计包 – 详解
使用Quantstats包做量化投资绩效统计的时候因为Pandas、Quantstats版本不匹配踩了一些坑；另外，Quantstats中的绩效统计指标非常全面，因此详细记录一下BUG修复方法、使用说明以及部分指标的内涵示意。
一、Quantstats安装及版本匹配问题
可以在cmd界面分别通过下面代码查询python/pandas/quantstats的版本。
python - version
pip show pandas
pip show quantstats我使用的是截止到文章发布时点的最新版本：
Python 3.12.8
Pandas 2.2.3
Quantstats 0.0.64
上述版本组合在Quantstats生成绩效统计页面时，因为Quantstats包没及时随着Pandas包的更新，会报两个错，需要修改Quantstats包。第一个是在Quantstats目录下_plotting文件夹下的core.py文件中294-297行要去掉sum函数的传参，因为新的2.2.3版本Pandas这里没有参数。
if resample:
returns = returns.resample(resample)
returns = returns.last() if compound is True else returns.sum(axis=0)
if isinstance(benchmark, _pd.Series):
benchmark = benchmark.resample(resample)
benchmark = benchmark.last() if compound is True else benchmark.sum(axis=0)第二个是把1015-1025行的inplace方法重写成以下形式，新版本Pandas不支持inplace。
port["Weekly"] = port["Daily"].resample("W-MON").apply(apply_fnc)
port["Weekly"] = port["Weekly"].ffill()
port["Monthly"] = port["Daily"].resample("ME").apply(apply_fnc)
port["Monthly"] = port["Monthly"].ffill()
port["Quarterly"] = port["Daily"].resample("QE").apply(apply_fnc)
port["Quarterly"] = port["Quarterly"].ffill()
port["Yearly"] = port["Daily"].resample("YE").apply(apply_fnc)
port["Yearly"] = port["Yearly"].ffill()上面修订提交了GITHUBGitHub – ranaroussi/quantstats: Portfolio analytics for quants, written in Python
二、Quantstats的使用
QuantStatus由3个主要模块组成：
quantstats.stats-用于计算各种绩效指标，如夏普比率、胜率、波动率等。
quantstats.plots-用于可视化绩效、滚动统计、月度回报等。
quantstats.reports-用于生成指标报告、批量绘图和创建可另存为HTML文件。
以持有长江电力600900为策略，以上证综指000001为基准，生成reports如下。EXCEL数据附后，没会员下不了附件的可以私我发。
import pandas as pd
import quantstats as qs
#read stock data： Seris格式，index为日期，列为return
stock = pd.read_excel('600900.XSHG.xlsx',index_col=0)[['close']].pct_change().dropna().rename({'close':'return'},axis=1)['return'].rename("600900")
#read benchmark data: Seris格式，index为日期，列为return
benchmark = pd.read_excel('000001.XSHG.xlsx',index_col=0)[['close']].pct_change().dropna().rename({'close':'return'},axis=1)['return'].rename("000001")
qs.reports.html(stock,benchmark,output='report.html')
三、指标详解
Quantstats有六个模块：
其中，extend_pandas的功能是可以实现通过Dataframe对象.方法()的方式调用QuantStatsd中的方法，例如：df.sharpe()，实现方式如下：
#!/usr/bin/env python
# -*- coding: UTF-8 -*-
#
# QuantStats: Portfolio analytics for quants
# https://github.com/ranaroussi/quantstats
#
# Copyright 2019-2024 Ran Aroussi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from . import version
__version__ = version.version
__author__ = "Ran Aroussi"
from . import stats, utils, plots, reports
__all__ = ["stats", "plots", "reports", "utils", "extend_pandas"]
# try automatic matplotlib inline
utils._in_notebook(matplotlib_inline=True)
def extend_pandas():
"""
Extends pandas by exposing methods to be used like:
df.sharpe(), df.best('day'), ...
"""
from pandas.core.base import PandasObject as _po
_po.compsum = stats.compsum
_po.comp = stats.comp
_po.expected_return = stats.expected_return
_po.geometric_mean = stats.geometric_mean
_po.ghpr = stats.ghpr
_po.outliers = stats.outliers
_po.remove_outliers = stats.remove_outliers
_po.best = stats.best
_po.worst = stats.worst
_po.consecutive_wins = stats.consecutive_wins
_po.consecutive_losses = stats.consecutive_losses
_po.exposure = stats.exposure
_po.win_rate = stats.win_rate
_po.avg_return = stats.avg_return
_po.avg_win = stats.avg_win
_po.avg_loss = stats.avg_loss
_po.volatility = stats.volatility
_po.rolling_volatility = stats.rolling_volatility
_po.implied_volatility = stats.implied_volatility
_po.sharpe = stats.sharpe
_po.smart_sharpe = stats.smart_sharpe
_po.rolling_sharpe = stats.rolling_sharpe
_po.sortino = stats.sortino
_po.smart_sortino = stats.smart_sortino
_po.adjusted_sortino = stats.adjusted_sortino
_po.rolling_sortino = stats.rolling_sortino
_po.omega = stats.omega
_po.cagr = stats.cagr
_po.rar = stats.rar
_po.skew = stats.skew
_po.kurtosis = stats.kurtosis
_po.calmar = stats.calmar
_po.ulcer_index = stats.ulcer_index
_po.ulcer_performance_index = stats.ulcer_performance_index
_po.upi = stats.upi
_po.serenity_index = stats.serenity_index
_po.risk_of_ruin = stats.risk_of_ruin
_po.ror = stats.ror
_po.value_at_risk = stats.value_at_risk
_po.var = stats.var
_po.conditional_value_at_risk = stats.conditional_value_at_risk
_po.cvar = stats.cvar
_po.expected_shortfall = stats.expected_shortfall
_po.tail_ratio = stats.tail_ratio
_po.payoff_ratio = stats.payoff_ratio
_po.win_loss_ratio = stats.win_loss_ratio
_po.profit_ratio = stats.profit_ratio
_po.profit_factor = stats.profit_factor
_po.gain_to_pain_ratio = stats.gain_to_pain_ratio
_po.cpc_index = stats.cpc_index
_po.common_sense_ratio = stats.common_sense_ratio
_po.outlier_win_ratio = stats.outlier_win_ratio
_po.outlier_loss_ratio = stats.outlier_loss_ratio
_po.recovery_factor = stats.recovery_factor
_po.risk_return_ratio = stats.risk_return_ratio
_po.max_drawdown = stats.max_drawdown
_po.to_drawdown_series = stats.to_drawdown_series
_po.kelly_criterion = stats.kelly_criterion
_po.monthly_returns = stats.monthly_returns
_po.pct_rank = stats.pct_rank
_po.treynor_ratio = stats.treynor_ratio
_po.probabilistic_sharpe_ratio = stats.probabilistic_sharpe_ratio
_po.probabilistic_sortino_ratio = stats.probabilistic_sortino_ratio
_po.probabilistic_adjusted_sortino_ratio = (
stats.probabilistic_adjusted_sortino_ratio
)
# methods from utils
_po.to_returns = utils.to_returns
_po.to_prices = utils.to_prices
_po.to_log_returns = utils.to_log_returns
_po.log_returns = utils.log_returns
_po.exponential_stdev = utils.exponential_stdev
_po.rebase = utils.rebase
_po.aggregate_returns = utils.aggregate_returns
_po.to_excess_returns = utils.to_excess_returns
_po.multi_shift = utils.multi_shift
_po.curr_month = utils._pandas_current_month
_po.date = utils._pandas_date
_po.mtd = utils._mtd
_po.qtd = utils._qtd
_po.ytd = utils._ytd
# methods that requires benchmark stats
_po.r_squared = stats.r_squared
_po.r2 = stats.r2
_po.information_ratio = stats.information_ratio
_po.greeks = stats.greeks
_po.rolling_greeks = stats.rolling_greeks
_po.compare = stats.compare
# plotting methods
_po.plot_snapshot = plots.snapshot
_po.plot_earnings = plots.earnings
_po.plot_daily_returns = plots.daily_returns
_po.plot_distribution = plots.distribution
_po.plot_drawdown = plots.drawdown
_po.plot_drawdowns_periods = plots.drawdowns_periods
_po.plot_histogram = plots.histogram
_po.plot_log_returns = plots.log_returns
_po.plot_returns = plots.returns
_po.plot_rolling_beta = plots.rolling_beta
_po.plot_rolling_sharpe = plots.rolling_sharpe
_po.plot_rolling_sortino = plots.rolling_sortino
_po.plot_rolling_volatility = plots.rolling_volatility
_po.plot_yearly_returns = plots.yearly_returns
_po.plot_monthly_heatmap = plots.monthly_heatmap
_po.metrics = reports.metrics
# extend_pandas()
…正在更新
作者：Maple丶峰
