金融量化分析【day112】：均值回归策略

一、均值回归策略

1、什么是回归策略

二、归一标准化

import numpy as np

a = np.random.uniform(100,5000,1000)

b = np.random.uniform(0.1,3.0,1000)

(a.min(),a.max())

　　输出

预处理

(a - a.min())/(a.max()-a.min())

　　输出

预处理

aa = (a - a.min())/(a.max()-a.min())

bb = (b - b.min())/(b.max()-b.min())

(aa.min(),aa.max())

　　输出

画图

aaa = (a - a.mean())/a.std()

import matplotlib.pyplot as plt

%matplotlib

plt.hist(aaa)

输出

二、均值回归策略代码

# 导入函数库

import jqdata

import math

import numpy as np

import pandas as pd

def initialize(context):

    set_benchmark('000002.XSHG')

    set_option('use_real_price', True)

    set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')

    g.security = get_index_stocks('000002.XSHG')

    g.ma_days = 30

    g.stock_num = 10

    run_monthly(handle, 1)

def handle(context):

    sr = pd.Series(index=g.security)

    for stack in sr.index:

        ma = attribute_history(stack,g.stock_days)['close'].mean

        p = get_current_data()[stack].day_open

        ratio = (ma-p)/ma

        sr[stock] = ratio

    tohold = sr.nlarges(g.stock_num).index.values

    for stock in context.portfolio/positions:

        if stock not in tohold:

             order_target_value(stock, 0)

    tobuy = [stock for stock in tohold if stock not in context.portfolio.positions]

    if len(tobuy)>0:

        cash = context.portfolio.available_cash

        cash_every_stock = cash / len(tobuy)

        for stock in tobuy:

            order_value(stock,cash_every_stock)