利用pyfolio工具评价回测资金曲线
Author: 雨幕（youquant）, Created: 2020-03-11 15:34:16, Updated: 2024-06-13 14:24:56
1、前言
前些天发现优宽策略回测结果输出的盈亏曲线结果比较简单，故想着是否获取收益结果数据后自己再进行处理，得到更详细的资金曲线评估报告，并且用图形可视化展示出来。着手把想法写出来的时候发现并不是那么容易，故想是否有人也有相同的想法，并且已经做出相应的工具呢？故上网搜罗了一圈，发现确实有这类工具，在GitHub上看了好几个项目，最终选择了pyfolio这个工具。
2、pyfolio是什么
pyfolio是一个由quantinc .开发的用于金融投资组合的性能和风险分析的Python库。它可以很好地与Zipline开源回溯测试库一起工作。quant还为专业人士提供全面管理的服务，包括Zipline、Alphalens、Pyfolio、FactSet数据等。 pyfolio的核心是所谓的“ so-called tear sheet ”，它由各种各样的独立图组成，这些图提供了交易算法表现的综合图像。 GitHub地址:https://github.com/quantopian/pyfolio
3、学习使用pyfolio
由于该工具网上的学习资料比较少，自己摸索了好些时间才会简单的使用。 PyFolio API参考:https://www.quantopian.com/docs/api-reference/pyfolio-api-reference#pyfolio-api-reference 这里比较详细介绍了pyfolio的API，该平台可以做美股回测，回测结果可以直接通过pyfolio展示，我只粗略地学习了一下，貌似其他功能都挺强大的。
4、安装pyfolio
pyfolio的安装比较简单，按照GitHub上的说明安装即可。
5、优宽回测结果通过pyfolio展示
好了介绍就到这了，开始进入正题。首先在优宽获取回测资金曲线数据。
在回测结果的浮动盈亏图表中点击上图中全屏旁边的按钮，然后选择‘Download CSV’。
获取到的CSV数据，格式如下图(文件名根据自己需求更改)：
如果想要分析结果有比较的基准的话，则还需要准备一份交易标的的K线日线数据，当然如果没有K线数据只有收益数据也是可以分析的，只是有基准数据分析结果会多几个指标，例如：阿尔法（Alpha)、贝塔（Beta)等，下文按提供基准K线数据来写。
K线数据我们可以通过优宽研究环境直接从平台获取:
#使用优宽研究环境提供的API获取与收益数据等长的K线数据
dfh = get_bars('bitfinex.btc_usd', '1d', start=str(startd), end=str(endd))
数据准备完毕后，就可以写代码了。我们需要将获取到的数据进行处理，以使其符合pyfolio所需要的数据结构，然后调用pyfolio的create_returns_tear_sheet接口计算并输出结果。我们主要需要传入(returns,benchmark_rets=None,live_start_date=None)三个参数。 returns参数为必须的收益数据； benchmark_rets为基准数据，不是必须的； live_start_datelive_start_date，不是必须的，这个参数的意思就是，你的returns啥时候是开始实盘的？比如我们上面这一串returns，假设我们在2019-12-01之后是实盘，而前面是模拟盘或者是回测的结果，那么我们就可以这样设置：live_start_date = ‘2019-12-01’。通过设置该参数获得的结果，理论上我们可以分析一下我们的策略有没有过拟合。如果样本内外的差异很大，那么大概率这就是过拟合了。 我们可以在优宽研究环境下实现该分析功能，也可以在自己本地实现，下面以在优宽研究环境实现为例：
首先在自己本地新建“csv转成py代码.py”python文件，并将如下代码复制进去，以生成包含从FMZ下载的资金曲线CSV文件的py代码，本地运行新建的py文件会生成“chart_hex.py”文件。
#!/usr/bin/python
# -*- coding: UTF-8 -*-
import binascii
#文件名可根据需要自定义,本示例使用默认文件名
filename = 'chart.csv'
with open(filename, 'rb') as f:
content = f.read()
#csv转py
wFile = open(filename.split('.')[0] + '_hex.py', "w")
wFile.write("hexstr = bytearray.fromhex('" +
bytes.decode(binascii.hexlify(content))
+ "').decode()\nwFile = open('" + filename + "', 'w')\nwFile.write(hexstr)\nwFile.close()")
wFile.close()打开上面生成的“chart_hex.py”文件，将里面的内容复制出来全部替换下面的代码块，然后逐一运行下面代码块，即可获取chart.csv文件
hexstr = bytearray.fromhex('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').decode()
wFile = open('chart.csv', 'w')
wFile.write(hexstr)
wFile.close()!ls -la
cat chart.csv
# 在研究环境下安装pyfolio库
#在研究环境中安装pyfolio !pip3 install --user pyfolio
import pandas as pd
import sys
sys.path.append('/home/quant/.local/lib/python3.6/site-packages')
import pyfolio as pf
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from fmz import * # 导入所有FMZ函数
#读取资金曲线数据，FMZ平台下载，累计收益数据
df=pd.read_csv(filepath_or_buffer='chart.csv')
#转换为日期格式
df['Date'] = pd.to_datetime(df['DateTime'],format='%Y-%m-%d %H:%M:%S')
#获取开始与结束时间
startd = df.at[0,'Date']
endd = df.at[df.shape[0]-1,'Date']
#读取标的资产日K线数据，用作分析中的基准收益数据
#使用FMZ研究环境提供的API获取与收益数据等长的K线数据
dfh = get_bars('bitfinex.btc_usd', '1d', start=str(startd), end=str(endd))
dfh=dfh[['close']]
#以k线数据的收盘价计算日涨跌幅
dfh['close_shift'] = dfh['close'].shift(1)
dfh = dfh.fillna(method='bfill') # 向下寻找最近的一个非空值，以该值来填充确实的位置，全称backward fill
dfh['changeval']=dfh['close']-dfh['close_shift']
dfh['change']=dfh['changeval']/dfh['close_shift']
#涨跌幅保留6位小数
dfh = dfh.round({'change': 6})
#收益数据处理，FMZ平台获取的是累计收益，将其转换为日收益变化率
df['return_shift'] = df['浮动盈亏'].shift(1)
df['dayly']=df['浮动盈亏']-df['return_shift']
chushizichan = 3 #FMZ回测中的初始资产值
df['returns'] = df['dayly']/(df['return_shift']+chushizichan)
df=df[['Date','浮动盈亏','return_shift','dayly','returns']]
df = df.fillna(value=0.0)
df = df.round({'dayly': 3}) #保留三位小数
df = df.round({'returns': 6})
#将pd.DataFrame,转换为pyfolio收益所需的pd.Series
df['Date'] = pd.to_datetime(df['Date'])
df=df[['Date','returns']]
df.set_index('Date', inplace=True)
#处理好的收益数据
returns = df['returns'].tz_localize('UTC')
#将pd.DataFrame,转换为pyfolio基准收益所需的pd.Series
dfh=dfh[['change']]
dfh = pd.Series(dfh['change'].values, index=dfh.index)
#处理好的基准数据
benchmark_rets = dfh
#策略的回测期后开始进行实时交易的时间点。
live_start_date = '2020-02-01'
#调用pyfolio的API计算并输出资金曲线分析结果图
#returns参数是必须的，其余参数可以不输入
pf.create_returns_tear_sheet(returns,benchmark_rets=benchmark_rets,live_start_date=live_start_date)输出的分析结果：
6、结果解读
输出的结果数据比较多，我们需要静下心来学习一下这些指标都是啥意思，输出结果都是英文显示的，英文比较少我们对照着翻译来看还是比较容易理解的，下面我介绍几个我找到的对相关指标的介绍，明白了指标的意思，我们才能解读我们的策略状态。
- 年化收益（Annual return） 年化收益率是指把当前的收益率（日收益率、周收益率、月收益率等）换算成年收益率来计算的，是一种理论收益，并不是真正已经取得的收益率。年化收益率需要和年收益率区分开，年收益率是指策略执行一年的收益率，是实际的收益。
- 累计收益（Cumulative returns） 最容易理解的一个概念，策略收益也就是策略开始到结束，总资产的变化率。 年化波动率(Annual volatility) 年化波动率用来衡量投资标的的波动风险。
- 夏普率（Sharpe ratio） 描述的是策略在单位总风险下所能获得的超额收益。
- 最大回撤（Max Drawdown） 描述的策略最大的亏损情况。最大回撤通常越小越好。
- 欧米伽比率（Omega ratio） 另一种风险回报绩效指标。它相对于Sharpe比率的最大优势是-通过构造-它考虑了所有统计时刻，而Sharpe比率仅考虑前两个时刻。
- 索提诺比率（Sortino） 描述的是策略在单位下行风险下所能获得的超额收益。
- 每日风险价值（daily Value-at-Risk ） 每日风险价值-另一个非常流行的风险指标。在这种情况下，这表明在95％的情况下，将头寸（投资组合）再保留1天，损失不会超过1.8％。 参考:https://towardsdatascience.com/the-easiest-way-to-evaluate-the-performance-of-trading-strategies-in-python-4959fd798bb3
- Tail ratio 对daily return的分布选取95分位和5分位，然后相除取绝对值。本质的含义就是赚取的return比亏钱的大多少倍。
- 稳定性(stability) 也就是所谓的稳定性。其实很简单，就是时间增量对累计净值的解释力度是多少，也就是回归的r平方。这样说有点抽象，我们简单解释一下。 参考：https://blog.csdn.net/qtlyx/article/details/88724236
7、小小的建议
希望优宽能够也能够增加丰富资金曲线的评价功能，并且增加历史回测结果的储存功能，这样能够更方便和更专业地展示回测结果，帮助大家创造出更优秀的策略。
8、感谢优宽平台
让我在这里学到了很多量化知识以及各种技能，感谢小小梦老师、张超大神悉心指导！希望优宽平台越来越好！ 本人菜鸟一枚，初次作文，还望大佬们多多指教。!
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