Python量化交易——爬取股票日K线画图检验股票策略
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2022-07-13 17:15:00
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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()
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