回測框架pybacktest簡介（一）

時間 2019-12-13

標籤框架 pybacktest 簡介简体版

原文原文鏈接

pybacktest 教程

本教程讓你快速瞭解 pybacktest's 的功能。爲此，咱們回測精典交易策略移動平均線MA交叉。python

MA快線上穿慢線時，買進作多
MA快線下穿慢線時，賣出作空
進場規則，也是退場規則，交易策略相反相成

軟件包在此下載 https://github.com/ematvey/pybacktestgit

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import pybacktest
import pandas as pd

pybacktest 要求的 k 線數據格式爲 `pandas.DataFrame` ，以時間戳爲索引，各列字段名稱爲 `O`, `H`, `L`, `C。實際上，目前只檢查字段O的值是否爲空。`

從yahoo下載數據。github

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ohlc = pybacktest.load_from_yahoo('SPY')

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ohlc.tail()

	O	H	L	C	V	AC
Date
2013-04-22	155.78	156.54	154.75	156.17	106501600	156.17
2013-04-23	156.95	157.93	156.17	157.78	165950600	157.78
2013-04-24	157.83	158.30	157.54	157.88	96724000	157.88
2013-04-25	158.34	159.27	158.10	158.52	130916000	158.52
2013-04-26	158.33	158.60	157.73	158.24	95904500	158.24

定義交易策略。要建立以 True和False 表示交易信號的 Series ，和以浮點數表示交易價格的 Series。編程

夠簡單的吧？函數

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short_ma = 50
long_ma = 200
ms = pandas.rolling_mean(ohlc.C, short_ma)
ml = pandas.rolling_mean(ohlc.C, long_ma)
buy = cover = (ms > ml) & (ms.shift() < ml.shift()) # ma cross up
sell = short = (ms < ml) & (ms.shift() > ml.shift()) # ma cross down
print '> Short MA\n%s\n' % ms.tail()
print '> Long MA\n%s\n' % ml.tail()
print '> Buy/Cover signals\n%s\n' % buy.tail()
print '> Short/Sell signals\n%s\n' % sell.tail()

>  Short MA
Date
2013-04-22    154.5438
2013-04-23    154.6634
2013-04-24    154.7856
2013-04-25    154.9156
2013-04-26    155.0374

>  Long MA
Date
2013-04-22    145.50725
2013-04-23    145.60910
2013-04-24    145.71455
2013-04-25    145.82970
2013-04-26    145.94430

>  Buy/Cover signals
Date
2013-04-22    False
2013-04-23    False
2013-04-24    False
2013-04-25    False
2013-04-26    False

>  Short/Sell signals
Date
2013-04-22    False
2013-04-23    False
2013-04-24    False
2013-04-25    False
2013-04-26    False

開始回測吧。訪問類對象 Backtest 的第一個參數，是從字典式的對象中剝離出的交易信號、價格等。能夠是字典、pandas.DataFrame 或者其餘任何東西。ui

爲了簡化編程，把局部命名空間用函數 locals()傳遞過去。命名空間的內容，是至今你所建立的所有變量。spa

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bt = pybacktest.Backtest(locals(), 'ma_cross')

`Backtest` 工做懶惰，只有在你訪問它的屬性時，它纔會進行運算。它所運算的屬性包括：

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print filter(lambda x: not x.startswith('_'), dir(bt))
print '\n> bt.signals\n%s' % bt.signals.tail()
print '\n> bt.trades\n%s' % bt.trades.tail()
print '\n> bt.positions\n%s' % bt.positions.tail()
print '\n> bt.equity\n%s' % bt.equity.tail()
print '\n> bt.trade_price\n%s' % bt.trade_price.tail()

['dataobj', 'default_price', 'eqplot', 'equity', 'name', 'ohlc', 'plot_equity', 'plot_trades', 'positions', 'prices', 'report', 'run_time', 'signals', 'sigplot', 'summary', 'trade_price', 'trades', 'trdplot']

>  bt.signals
              Buy  Cover   Sell  Short
Date                                  
2013-04-22  False  False  False  False
2013-04-23  False  False  False  False
2013-04-24  False  False  False  False
2013-04-25  False  False  False  False
2013-04-26  False  False  False  False

>  bt.trades
            pos   price  vol
Date                        
2009-06-23    1   90.16    2
2010-07-06   -1  103.13   -2
2010-10-22    1  119.14    2
2011-08-12   -1  119.19   -2
2012-01-31    1  132.29    2

>  bt.positions
Date
2009-06-23    1
2010-07-06   -1
2010-10-22    1
2011-08-12   -1
2012-01-31    1

>  bt.equity
Date
2009-06-23    58.66
2010-07-06    12.97
2010-10-22   -16.01
2011-08-12     0.05
2012-01-31   -13.10

>  bt.trade_price
Date
2013-04-22    156.95
2013-04-23    157.83
2013-04-24    158.34
2013-04-25    158.33
2013-04-26       NaN
Name: O

調用 Backtest 的函數summary ，能夠得知經常使用的運算和運行數據

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bt.summary()

Backtest('ma_cross', 2013-28-04 23:14:15 MSK) performance summary

=================================================================
backtest:
  days: 6348
  from: '1994-09-14 00:00:00'
  to: '2012-01-31 00:00:00'
  trades: 17
exposure:
  holding periods:
    max: 1476 days, 0:00:00
    median: 354 days, 0:00:00
    min: 7 days, 0:00:00
  trades/month: 1.0625
performance:
  PF: 4.017
  RF: 6.1555
  averages:
    gain: 23.817
    loss: -8.47
    trade: 10.5224
  payoff: 2.8119
  profit: 178.88
  winrate: 0.5882
risk/return profile:
  UPI: 1.0656
  WCDD (monte-carlo 0.99 quantile): 52.09
  maxdd: 74.67
  sharpe: 0.4485
  sortino: 1.6792

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