预期效果

根据输入的数据爬取一段时期内每天的股价信息(以上证指数为例)，根据15日均价制定简易的股票交易策略，并对结果作图展示。

代码实现

import json

import requests

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

def run():

''' 主程序, 用来调度各个重要流程 '''

kline = load_sse()

df = init_df(kline)

df = strategy(df)

df = backtest(df)

draw(df,days)

df.to_csv('result.csv', index = False)

def load_sse():

''' 获取上交所的上证指数K线, 最近N个交易日数据 '''

response = requests.get(

# 'http://yunhq.sse.com.cn:32041/v1/sh1/dayk/000001?callback=jQuery111205234775875526079_1542185571865&select=date%2Copen%2Chigh%2Clow%2Cclose%2Cvolume&begin=-2000&end=-1&_=1542185571881',

'http://yunhq.sse.com.cn:32041/v1/sh1/dayk/000001?callback=jQuery111205234775875526079_1542185571865&select=date%2Copen%2Chigh%2Clow%2Cclose%2Cvolume&begin=-'+ begin +'&end=-'+ end +'&_=1542185571881',

headers={'Referer': 'http://www.sse.com.cn/market/price/trends/'}

)

# 针对结果进行格式处理

json_str = response.text[42:-1]

data = json.loads(json_str)

return data['kline']

def init_df(kline):

''' 根据K线数据，创建含有日期与收盘价的矩阵 '''

df = pd.DataFrame({})

df['date'] = [x[0] for x in kline]

#kline中包含日期、开盘价、最高价、最低价、收盘价等信息

df['close'] = [x[1] for x in kline]

return df

def strategy(df):

# 连续15天数据，计算平均值，作为当天的平均价格指标

window_size = 15

df['avg'] = df['close'].rolling(window_size).apply(lambda x: sum(x) / len(x))

def avg_buy(x):

''' 做多策略 '''

min_percent = 0.995

max_percent = 1.005

# 追涨，当我们的价格超过了均线一定程度时

if (x[1] / x[0]) < min_percent:

return 'open buy'

# 杀跌，当我们的价格低于均线一定程度时

if (x[1] / x[0]) > max_percent:

return 'close buy'

# 其他情况不操作

return 'wait'

# df['action'] = avg_buy([df['close'], df['avg']])

df['action'] = df[['close', 'avg']].apply(avg_buy, axis=1)

return df

def backtest(df):

''' 回归测试 '''

global shares, cash

amount = 1000000

shares = 0

cash = amount

def run_strategy(row):

''' 把每天的数据执行策略 '''

global shares, cash

action = row['action']

close = row['close']

# 资产 = 现金 + 股票价值

liquidate = cash + shares * close

message = 'nothing'

# 策略要求开仓做多，而且当前空仓时，做多

if action == 'open buy' and shares == 0:

shares = int(cash / close)

cash -= shares * close

message = 'open buy ' + str(shares)

# 策略要求平仓，而且当前有仓时，平掉

if action == 'close buy' and shares > 0:

message = 'close buy ' + str(shares)

cash += shares * close

shares = 0

return [message, shares, cash, liquidate]

rows = df[['close', 'action']].apply(run_strategy, axis=1)

df['message'], df['shares'], df['cash'], df['liquidate'] = zip(*rows)

return df

def draw(df,days):

''' 画图 '''

# 创建画板

fig = plt.figure(figsize=(10, 5))

# 准备横坐标

count = df.count()['close']

index = np.arange(count)

df['index'] = index

# 设置横坐标的刻度与显示标签

limit = days

plt.xticks(index[::limit], df['date'][::limit])

# 收盘价与资产的两套坐标系

ax_close = plt.gca()

ax_liquidate = ax_close.twinx()

# 画收盘价曲线

ax_close.set(xlabel='Date', ylabel='close')

l_close, = ax_close.plot(index, df['close'], 'black', label='close')

l_avg, = ax_close.plot(index, df['avg'], 'pink', label='avg')

# 画资产曲线

ax_liquidate.set(ylabel = 'liquidate')

l_liquidate, = ax_liquidate.plot(index, df['liquidate'], 'blue', label='liquidate')

def drawAction(row):

if row['message'] == 'nothing':

return

color = ''

marker = 'o'

size = 12

if row['action'] == 'open buy':

color='r'

if row['action'] == 'close buy':

color='g'

ax_close.scatter(row['index'], row['close'], s=size, color=color, zorder=2, marker=marker)

df[['index', 'action', 'message', 'close']].apply(drawAction, axis=1)

# 给两条线都提供一个图例说明

plt.legend(handles=[l_close, l_avg, l_liquidate])

plt.show()

if __name__ == '__main__':

begin=input('从前多少天：')

end=input('到最近几天：')

days=input('横坐标日期间隔天数：')

days=int(days)

run()