上证指数

Posted 2020-07-11 佩雷尔曼的信徒

1.数据处理模块

• 保存为 sh.py，保存位置 …\\Lib\\site-packages，这样以后调用直接 import sh.py 即可

# -*- coding: utf-8 -*-
# sh.py
class sh:
    # 本地数据库连接信息初始化，用 pyodbc 连接只需三个信息
    def __init__(self, user=\'sa\', password=\'123456\', dsn=\'XiTongDSN\'):
        self.user = user
        self.password = password
        self.dsn = dsn
        import sqlalchemy
        # 建立数据库连接引擎，存取数据库需用到
        self.engine = sqlalchemy.create_engine(\'mssql+pyodbc://\'+self.user+\':\'+self.password+\'@\'+self.dsn)
    # 从 tushare.org 下载上证指数 sh 数据
    def get_url(self):
        import tushare
        import pandas
        dataframe = tushare.get_hist_data(\'sh\')
        # 由于 dataframe 存入 SQL Server 时 index 会报错，那就灵活处理下
        # 把 index 复制成列 date 插到最后一列
        index = list(dataframe[\'open\'].index)
        dataframe[\'date\'] = pandas.Series(index, index)
        pandas.DataFrame(dataframe, index)
        # 我只需列（开盘，最高，收盘，最低，成交量，日期），其他列统统删掉好了
        dataframe.drop([\'price_change\', \'p_change\', \'ma5\', \'ma10\', \'ma20\', \'v_ma5\', \'v_ma10\', \'v_ma20\'], axis=1, inplace=True)
        # 将处理完的 dataframe 存入到本地数据库
        dataframe.to_sql(\'sh\', self.engine, if_exists=\'replace\', index=False)
    # 调用本地数据库上证指数数据 sh
    def get_sql(self):
        import pandas
        connection = self.engine.connect()
        data = pandas.read_sql_table(\'sh\', connection)
        # 做处理时，由于 date 不连续且无什么意义，直接用计数当 date 用好了
        index = list(data[\'open\'].index)
        o = data[\'open\']
        h = data[\'high\']
        c = data[\'close\']
        l = data[\'low\']
        volume = data[\'volume\']
        return index, o, h, c, l, volume

• 调用 sh.py

# -*- coding: utf-8 -*-
# 清屏
import os
clearscreen = os.system(\'cls\')
# 调用 sh.py 模块
import sh
# 获取 sh 数据，先模块实例化
SH = sh.sh()
\'\'\'
# 下载数据到本地数据库
SH.get_url()
\'\'\'
# 调取数据库表 sh 数据
index, o, h, c, l, volume = SH.get_sql()

2.图形绘制模块

# -*- coding: utf-8 -*-
# 清屏
import os
clearscreen = os.system(\'cls\')
# 调用模块
import sh
# 获取 sh 数据，先模块实例化
SH = sh.sh()
\'\'\'
# 下载数据到本地数据库
SH.get_url()
\'\'\'
# 调取数据库表 sh 数据
index, o, h, c, l, volume = SH.get_sql()
# 这是暴力的分割线~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 载入绘图模块
import matplotlib.pyplot as plot
from matplotlib.finance import candlestick_ochl
# 根据需要按列合并成一个
quotes = []
for i in range(0, len(index)):
    quotes.append((index[i],
                   o[i],
                   c[i],
                   h[i],
                   l[i],
                   volume[i]))
# 获取图表实例
figure = plot.figure(\'Made by DengChaohai\')
# 上图
subplot1 = figure.add_subplot(211,
                           title=\'Index of Shanghai\',
                           xlabel=\'index of date\',
                           ylabel=\'index of Shanghai\',
                           xlim=[min(index), max(index)])
plot.grid(True,
          axis=\'both\')
# K 线图
candlestick_ochl(subplot1,
                 quotes,
                 colorup=\'r\',
                 colordown=\'g\')
# 成交量 bar 图，为了好看点，进行了缩放处理
b11 = subplot1.bar(left=index,
                height=[i*100/min(volume) for i in volume],
                bottom=0,
                width=1,
                color=\'c\',
                edgecolor=\'c\')

# 下图
subplot2 = figure.add_subplot(212,
                           title=\'Singal of Buy or Sell\',
                           xlabel=\'index of date\',
                           ylabel=\'index of Shanghai\',
                           xlim=[min(index), max(index)])
plot.grid(True,
          axis=\'both\')
# open 线图
subplot2.plot(o,
              \'g\')
# 自定义加权价 weighting price
wp = []
for i in range(0, len(index)):
    wp.append(o[i]*2/7+c[i]*3/7+h[i]/7+l[i]/7)
# weighting price 线图
subplot2.plot(wp,
              \'r\')
# 差价 bar 图，为好看同意进行了缩放
subplot2.bar(left=index,
             height=[(o[i]-wp[i])*10 for i in range(0, len(index))],
             bottom=0,
             width=1,
             color=\'c\',
             edgecolor=\'c\')

3.买卖信号模块

• 保存为 bs.py，保存位置 …\\Lib\\site-packages，这样以后调用直接 import bs.py 即可

# -*- coding: utf-8 -*-
class bs:
    # 数据初始化
    def __init__(self, index, o, wp):
        self.index = index
        self.o = o
        self.wp = wp
    # 买点发生器
    def buy(self):
        bindex = []
        bo = []
        bkey = [0]
        for i in range(1, len(self.index)):
            # 买点条件
            if (self.o[i] > self.wp[i]) and (self.o[i-1] < self.wp[i-1]):
                bindex.append(self.index[i])
                bo.append(self.o[i])
                bkey.append(1)
            else:
                bkey.append(0)
        # 返回买点的位置，用于画图，bkey 则是插入回数据库 sh 用，为后续交易铺垫
        return bindex, bo, bkey
    def sell(self):
        sindex = []
        so = []
        skey = [0]
        for i in range(1, len(self.index)):
            if (self.o[i] < self.wp[i]) and (self.o[i-1] > self.wp[i-1]):
                sindex.append(self.index[i])
                so.append(self.o[i])
                skey.append(1)
            else:
                skey.append(0)
        return sindex, so, skey

• 调用效果

# -*- coding: utf-8 -*-
# 清屏
import os
clearscreen = os.system(\'cls\')
# 调用模块
import sh
# 获取 sh 数据，先模块实例化
SH = sh.sh()
\'\'\'
# 下载数据到本地数据库
SH.get_url()
\'\'\'
# 调取数据库表 sh 数据
index, o, h, c, l, volume = SH.get_sql()
# 这是暴力的分割线~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 载入绘图模块
import matplotlib.pyplot as plot
from matplotlib.finance import candlestick_ochl
# 根据需要按列合并成一个
quotes = []
for i in range(0, len(index)):
    quotes.append((index[i],
                   o[i],
                   c[i],
                   h[i],
                   l[i],
                   volume[i]))
# 获取图表实例
figure = plot.figure(\'Made by DengChaohai\')
# 上图
subplot1 = figure.add_subplot(211,
                           title=\'Index of Shanghai\',
                           xlabel=\'index of date\',
                           ylabel=\'index of Shanghai\',
                           xlim=[min(index), max(index)])
plot.grid(True,
          axis=\'both\')
# K 线图
candlestick_ochl(subplot1,
                 quotes,
                 colorup=\'r\',
                 colordown=\'g\')
# 成交量 bar 图，为了好看点，进行了缩放处理
b11 = subplot1.bar(left=index,
                height=[i*100/min(volume) for i in volume],
                bottom=0,
                width=1,
                color=\'c\',
                edgecolor=\'c\')

# 下图
subplot2 = figure.add_subplot(212,
                           title=\'Singal of Buy or Sell\',
                           xlabel=\'index of date\',
                           ylabel=\'index of Shanghai\',
                           xlim=[min(index), max(index)])
plot.grid(True,
          axis=\'both\')
# open 线图
subplot2.plot(o,
              \'g\')
# 自定义加权价 weighting price
wp = []
for i in range(0, len(index)):
    wp.append(o[i]*2/7+c[i]*3/7+h[i]/7+l[i]/7)
# weighting price 线图
subplot2.plot(wp,
              \'r\')
# 差价 bar 图，为好看同意进行了缩放
subplot2.bar(left=index,
             height=[(o[i]-wp[i])*10 for i in range(0, len(index))],
             bottom=0,
             width=1,
             color=\'c\',
             edgecolor=\'c\')
# 这是暴力的分割线~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
import bs
BS = bs.bs(index, o, wp)
bindex, bo, bkey = BS.buy()
subplot2.plot(bindex,
              bo,
              \'ro\')
sindex, so, skey = BS.sell()
subplot2.plot(sindex,
              so,
              \'go\')

• 承上，把 buy sell 信号存回表 sh

# 这是暴力的分割线~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 把 bkey skey 当作列存回 sh 数据表
# 先合并新的 dataframe
import pandas
# 提取旧表
oldsh = pandas.read_sql(\'sh\', SH.engine.connect())
oldsh[\'buy\'] = pandas.Series(bkey, list(oldsh[\'open\'].index))
oldsh[\'sell\'] = pandas.Series(skey, list(oldsh[\'open\'].index))

pandas.DataFrame(oldsh, bkey)
pandas.DataFrame(oldsh, skey)
# 存回新表
oldsh.to_sql(\'sh\', SH.engine, if_exists=\'replace\', index=False)

4.交易测试模块

# -*- coding: utf-8 -*-
########################################################################
class trade:
    """交易模块"""
    #----------------------------------------------------------------------
    def __init__(self, money = 1000000, rate = 0.01):
        """初始函数"""
        \'\'\'本金\'\'\'
        self.money = money
        \'\'\'比例，即每次买股票的钱占本金的比例\'\'\'
        self.rate = rate
        \'\'\'每次买股票的钱\'\'\'
        self.moneybuy = money * rate
        \'\'\'持有股票，初始为 0 \'\'\'
        self.stockhold = 0
        \'\'\'计数，用于往前一级级跳数\'\'\'
        self.k = 0
        \'\'\'用于绘制资金曲线\'\'\'
        self.amount = [0]
    #----------------------------------------------------------------------
    def trade(self, index = [1, 2, 3, 4, 5], buy = [1, 0, 0, 1, 0], sell = [0, 1, 1, 0, 1], price = [0.1, 0.2, 0.3, 0.4, 0.5]):
        """交易函数"""
        \'\'\'遍历买点\'\'\'
        for i in range(self.k, len(buy) - 1):
            \'\'\'如果出现买点，则\'\'\'
            if buy[i] == 1:
                \'\'\'钱减少\'\'\'
                self.money = self.money - self.moneybuy
                \'\'\'股票增加\'\'\'
                self.stockhold = self.moneybuy / price[i]
                \'\'\'如果股票持有数不空，遍历卖点\'\'\'
                while self.stockhold:
                    \'\'\'k + 1 因为买入后第二天才能卖\'\'\'
                    for i in range(self.k + 1, len(sell) - 1):
                        \'\'\'如果碰到卖点，则\'\'\'
                        if sell[i] == 1:
                            \'\'\'钱增加\'\'\'
                            self.money = self.money + self.stockhold * price[i]
                            \'\'\'股票清空\'\'\'
                            self.stockhold = 0
                            \'\'\'计数加一，从第二天起重新遍历买点\'\'\'
                            self.k = self.k + 1
                            \'\'\'用于绘制资金曲线\'\'\'
                            self.amount.append(self.money)
                            \'\'\'跳出 for 循环\'\'\'
                            break
        \'\'\'返回钱数\'\'\'
        return self.money, self.amount

• 调用

# -*- coding: utf-8 -*-
\'\'\'添加文件路径\'\'\'
import sys
sys.path.append(\'D:\\360data\\重要数据\\桌面\\sh\')
"""调用 sh.py 模块"""
import sh
Sh = sh.sh()
Index, Open, High,  Close, Low, Volume = Sh.get_sql()
"""调用 bs.py 模块"""
import bs
Bs = bs.bs(Index, Open, Close)
a, b, c = Bs.buy()
d, e, f = Bs.sell()
"""调用 trade.py 模块"""
import trade
Trade = trade.trade(money=1000000, rate=0.05)
Money, Amount = Trade.trade(index = Index, buy = c, sell = f, price = Open)
print(\'total money = \', Money)
\'\'\'绘制资金曲线\'\'\'
import matplotlib.pyplot as plot
plot.plot(Amount, \'g-\')