竟然可以用python炒股？

由于笔者并无深厚的数学功底也无深厚的金融知识, 所以不会在本文中引用各种高深的投资模型或数学模型，参考书籍主要是《海龟交易法则》《以交易为生》。

交易系统

在交易之前，我们应该首先有一个交易系统用于指导我们自己交易，不一定有什么规范，但是可以作为一个交易的依据，至于这个依据可不可行，科不科学那就见仁见智了。

当然了,这里的交易系统不一定是程序，只是指你自己的交易原则或者遵守的一些技巧或者方法，你可以手动执行也可以借助编程语言，编程语言不就是一套用来使用的工具么.

这里参考海龟交易法则里面的交易体系(这里只是参考大方向).

建立一个完善的交易体系，我们至少应该思考一下六个方面。

1、市场----买卖什么
2、头寸规模----买卖多少
3、入市----何时买入
4、止损----何时退出亏损的头寸
5、止盈----何时退出盈利的头寸
6、离市----何时离市

简单的示例

买卖A股
全仓
当日涨幅超过3%买入。
当持有头寸亏损超过3%，平仓
当日跌幅大于3%或者三个连续阴线

分析: 这个交易策略其实只有在行情以波浪形状向上的行情时候才能获利，如果是盘整的情况下，怕是会亏的很惨。这里之所以写的这么简单粗暴是为了后面策略测试撸代码简单。

数据获取及处理

因为这里说的是用python炒股，所以应该采用程序的方式去获取数据，如果人工炒股，下载任何股票行情软件都是可以的，但是人工的执行是需要花费比较多的精力的。

而python语言中用于获取股票行情数据的库，最有名莫过于tushare了。

这里以上证乐视的股票为例吧。

python环境安装

安装Anaconda(python2版本)

下载地址:https://www.anaconda.com/download/
注:如果没安装过这个环境的经验，就百度或者谷歌一下吧,如果不是安装anaconda则需要艰难的自行解决依赖。

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安装tushare

pip install tushare

参考: http://tushare.org/

获取行情数据

import pandas as pd
import tushare as ts# 通过股票代码获取股票数据,这里没有指定开始及结束日期
df = ts.get_k_data("300104")# 查看前十条数据
df.head()# 查看后十条数据
df.tail()# 将数据的index转换成date字段对应的日期
df.index = pd.to_datetime(df.date)# 将多余的date字段删除
df.drop("date", inplace=True, axis=1)

注:关于股票数据的相关处理需要由pandas,matplotlib的知识，参考:http://pandas.pydata.org/pandas-docs/version/0.20/10min.html

计算常用指标

# 计算5,15,50日的移动平均线, MA5, MA15, MA50
days = [5, 15, 50]
for ma in days:column_name = "MA{}".format(ma)df[column_name] = pd.rolling_mean(df.close, ma)# 计算浮动比例
df["pchange"] = df.close.pct_change()
# 计算浮动点数
df["change"] = df.close.diff()

最终处理完成后的结果如下:

df.head()
Out[13]: open  close   high    low    volume    code     MA5  MA15  MA50  \
date                                                                           
2013-11-29  9.396  9.741  9.870  9.389  146587.0  300104     NaN   NaN   NaN   
2013-12-02  9.298  8.768  9.344  8.768  177127.0  300104     NaN   NaN   NaN   
2013-12-03  8.142  8.414  8.546  7.890  176305.0  300104     NaN   NaN   NaN   
2013-12-04  8.391  8.072  8.607  8.053  120115.0  300104     NaN   NaN   NaN   
2013-12-05  7.983  7.366  8.108  7.280  253764.0  300104  8.4722   NaN   NaN   pchange  change  
date                          
2013-11-29       NaN     NaN  
2013-12-02 -0.099887  -0.973  
2013-12-03 -0.040374  -0.354  
2013-12-04 -0.040647  -0.342

可视化

走势图

所谓一图胜前言，将数据可视化可以非常直观的感受到股票的走势。
个人觉得，如果用程序炒股还是应该一切都量化的，不应该有过多的主观观点，如果过于依赖直觉或者当时心情，那么实在没必要用程序分析了。

df[["close", "MA5", "MA15", "MA50"]].plot(figsiz=(10,18))

效果如下:

k线图

import matplotplib.pyplot as plt
from matplotlib.daet import DateFormatter
from matplotlib.finance import date2num, candlestick_ohlcdef candlePlot(data, title=""):data["date"] = [date2num(pd.to_datetime(x)) for x in data.index]dataList = [tuple(x) for x in data[["date", "open", "high", "low", "close"]].values]ax = plt.subplot()ax.set_title(title)ax.xaxis.set_major_formatter(DateFormatter("%y-%m-%d"))candlestick_ohlc(ax, dataList, width=0.7, colorup="r", colordown="g")plt.setp(plt.gca().get_xticklabels(), rotation=50,horizontalalignment="center")fig = plt.gcf()fig.set_size_inches(20, 15)plt.grid(True)candlePlot(df)

效果如下:

注: 这里只是一个示例，说明matplotlib的强大以及小小的演示，如果遇到什么奇怪的问题就查api或者google吧。

策略测试

手动撸代码

这里用最近买过的一只股票吧，京东方A(000725)。

# 导入相关模块
import tushare as ts
import pandas as pd# 获取数据
df = ts.get_k_data("000725")# 处理数据
df.index = pd.to_datetime(df.date)
df.drop("date", axis=1, inplace=True)# 计算浮动比例
df["pchange"] = df.close.pct_change()
# 计算浮动点数
df["change"] = df.close.diff()# 查看当前数据数据前五行open  close   high    low      volume    code   pchange  change
date                                                                        
2015-07-20  4.264  4.234  4.342  4.165  13036186.0  000725       NaN     NaN
2015-07-21  4.136  4.195  4.274  4.096   8776773.0  000725 -0.009211  -0.039
2015-07-22  4.175  4.146  4.214  4.067   9083703.0  000725 -0.011681  -0.049
2015-07-23  4.136  4.254  4.283  4.096  12792734.0  000725  0.026049   0.108
2015-07-24  4.224  4.136  4.254  4.106  13009620.0  000725 -0.027739  -0.118# 设定回撤值
withdraw = 0.03
# 设定突破值
breakthrough = 0.03
# 设定账户资金
account = 10000
# 持有仓位手数
position = 0def buy(bar):global account, positionprint("{}: buy {}".format(bar.date, bar.close))# 一手价格one = bar.close * 100position = account // oneaccount = account - (position * one)def sell(bar):global account, position# 一手价格print("{}: sell {}".format(bar.date, bar.close))one = bar.close * 100account += position * oneposition = 0print("开始时间投资时间: ", df.iloc[0].date)
for date in df.index:bar = df.loc[date]if bar.pchange and bar.pchange > breakthrough and position == 0:buy(bar)elif bar.pchange and bar.pchange < withdraw and position > 0:sell(bar)print("最终可有现金: ", account)
print("最终持有市值: ", position * df.iloc[-1].close * 100)

输出如下:

开始时间投资时间:  2015-07-20
2015-07-29: buy 3.83
2015-07-30: sell 3.653
2015-08-04: buy 3.752
......
2018-02-27: sell 5.71
2018-03-06: buy 5.79
最终可有现金:  333.3
最终持有市值:  7527.0

结论: 通过上面的测试发现资亏了两千多...

借助测试框架

借助测试框架才是正确的回撤姿势,因为框架包含了更多的功能。这里使用pyalgotrade。

简单使用

from pyalgotrade import strategy
from pyalgotrade import technical
from pyalgotrade.barfeed import yahoofeed# 自定义事件窗口类
class DiffEventWindow(technical.EventWindow):def __init__(self, period):assert(period > 0)super(DiffEventWindow, self).__init__(period)self.__value = Nonedef onNewValue(self, dateTime, value):super(DiffEventWindow, self).onNewValue(dateTime, value)if self.windowFull():lastValue = self.getValues()[0]nowValue = self.getValues()[1]self.__value = (nowValue - lastValue) / lastValuedef getValue(self):return self.__value# 自定义指标
class Diff(technical.EventBasedFilter):def __init__(self, dataSeries, period, maxLen=None):super(Diff, self).__init__(dataSeries, DiffEventWindow(period), maxLen)# 定义自己的策略
class MyStrategy(strategy.BacktestingStrategy):def __init__(self, feed, instrument, diffPeriod=2):# 传入feed及初始账户资金super(MyStrategy, self).__init__(feed, 10000)self.__instrument = instrumentself.__position = Noneself.setUseAdjustedValues(True)self.__prices = feed[instrument].getPriceDataSeries()self.__diff = Diff(self.__prices, diffPeriod)self.__break = 0.03self.__withdown = -0.03def getDiff(self):return self.__diffdef onEnterCanceled(self, position):self.__position = Nonedef onEnterOk(self, position):execInfo = position.getEntryOrder().getExecutionInfo()self.info("BUY at $%.2f" % (execInfo.getPrice()))def onExitOk(self, position):execInfo = position.getExitOrder().getExecutionInfo()self.info("SELL at $%.2f" % (execInfo.getPrice()))self.__position = Nonedef onExitCanceled(self, position):# If the exit was canceled, re-submit it.self.__position.exitMarket()def onBars(self, bars):account = self.getBroker().getCash()bar = bars[self.__instrument]if self.__position is None:one = bar.getPrice() * 100oneUnit = account // oneif oneUnit > 0 and self.__diff[-1] > self.__break:self.__position = self.enterLong(self.__instrument, oneUnit * 100, True)elif self.__diff[-1] < self.__withdown and not self.__position.exitActive():self.__position.exitMarket()def runStrategy():# 下载数据jdf = ts.get_k_data("000725")# 新建Adj Close字段jdf["Adj Close"] =jdf.close# 将tushare下的数据的字段保存为pyalgotrade所要求的数据格式jdf.columns = ["Date", "Open", "Close", "High", "Low", "Volume", "code", "Adj Close"]# 将数据保存成本地csv文件jdf.to_csv("jdf.csv", index=False)feed = yahoofeed.Feed()feed.addBarsFromCSV("jdf", "jdf.csv")myStrategy = MyStrategy(feed, "jdf")myStrategy.run()print("Final portfolio value: $%.2f" % myStrategy.getResult())runStrategy()

输出如下

2015-07-30 00:00:00 strategy [INFO] BUY at $3.78
2015-07-31 00:00:00 strategy [INFO] SELL at $3.57
2015-08-05 00:00:00 strategy [INFO] BUY at $3.73
2015-08-06 00:00:00 strategy [INFO] SELL at $3.56
...
2018-02-13 00:00:00 strategy [INFO] BUY at $5.45
Final portfolio value: $7877.30

猛地一看会发现，用框架似乎写了更多的代码,但是框架内置了更多分析工具。
下面简单介绍。

策略可视化

from pyalgotrade import strategy
from pyalgotrade import technical
from pyalgotrade.barfeed import yahoofeed
from pyalgotrade import plotter
from pyalgotrade.stratanalyzer import returnsclass DiffEventWindow(technical.EventWindow):def __init__(self, period):assert(period > 0)super(DiffEventWindow, self).__init__(period)self.__value = Nonedef onNewValue(self, dateTime, value):super(DiffEventWindow, self).onNewValue(dateTime, value)if self.windowFull():lastValue = self.getValues()[0]nowValue = self.getValues()[1]self.__value = (nowValue - lastValue) / lastValuedef getValue(self):return self.__valueclass Diff(technical.EventBasedFilter):def __init__(self, dataSeries, period, maxLen=None):super(Diff, self).__init__(dataSeries, DiffEventWindow(period), maxLen)class MyStrategy(strategy.BacktestingStrategy):def __init__(self, feed, instrument, diffPeriod=2):super(MyStrategy, self).__init__(feed, 10000)self.__instrument = instrumentself.__position = Noneself.setUseAdjustedValues(True)self.__prices = feed[instrument].getPriceDataSeries()self.__diff = Diff(self.__prices, diffPeriod)self.__break = 0.03self.__withdown = -0.03def getDiff(self):return self.__diffdef onEnterCanceled(self, position):self.__position = Nonedef onEnterOk(self, position):execInfo = position.getEntryOrder().getExecutionInfo()self.info("BUY at $%.2f" % (execInfo.getPrice()))def onExitOk(self, position):execInfo = position.getExitOrder().getExecutionInfo()self.info("SELL at $%.2f" % (execInfo.getPrice()))self.__position = Nonedef onExitCanceled(self, position):# If the exit was canceled, re-submit it.self.__position.exitMarket()def onBars(self, bars):account = self.getBroker().getCash()bar = bars[self.__instrument]if self.__position is None:one = bar.getPrice() * 100oneUnit = account // oneif oneUnit > 0 and self.__diff[-1] > self.__break:self.__position = self.enterLong(self.__instrument, oneUnit * 100, True)elif self.__diff[-1] < self.__withdown and not self.__position.exitActive():self.__position.exitMarket()def runStrategy():# 下载数据jdf = ts.get_k_data("000725")# 新建Adj Close字段jdf["Adj Close"] =jdf.close# 将tushare下的数据的字段保存为pyalgotrade所要求的数据格式jdf.columns = ["Date", "Open", "Close", "High", "Low", "Volume", "code", "Adj Close"]# 将数据保存成本地csv文件jdf.to_csv("jdf.csv", index=False)feed = yahoofeed.Feed()feed.addBarsFromCSV("jdf", "jdf.csv")myStrategy = MyStrategy(feed, "jdf")returnsAnalyzer = returns.Returns()myStrategy.attachAnalyzer(returnsAnalyzer)plt = plotter.StrategyPlotter(myStrategy)plt.getInstrumentSubplot("jdf")plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())myStrategy.run()print("Final portfolio value: $%.2f" % myStrategy.getResult())plt.plot()runStrategy()
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