第一阶段、一个简单策略入门量化投资
1.1股票数据预处理练习
无论我们要对股票市场进行何种探索，在开始前，研究如何获取数据，并进行对应的预处理都是必要的。
本节以美股为例，进行股票数据预处理的练习。正文如下：
利用Yahoo财经提供的接口，获取一家公司的股票是相当容易的。下面这段代码可以获取苹果公司16年至今的股数
据。
import pandas as pd
import pandas_datareader.data as web
import datetime
start = datetime.datetime(2010,1,1)
end = datetime.date.today()
apple = web.DataReader("AAPL", "yahoo", start, end)
print(apple.head())
得到的数据如下所示：
Open High Low Close Adj Close Volume
Date
2015-12-31 107.010002 107.029999 104.820000 105.260002 101.703697 40912300
2016-01-04 102.610001 105.370003 102.000000 105.349998 101.790649 67649400
2016-01-05 105.750000 105.849998 102.410004 102.709999 99.239845 55791000
2016-01-06 100.559998 102.370003 99.870003 100.699997 97.297760 68457400
2016-01-07 98.680000 100.129997 96.430000 96.449997 93.191338 81094400
你也许已经发现，受网络的影响，上面这段代码不一定能够运行成功，可能出现连接远程服务器失败的情况，那么把获取到的数据存到本地，需要时再读取就是件很自然的工作了。下面这段代码，模拟了将苹果公司股票数据保存成.csv文件，并在需要时读取的过程。
##### save data
apple.to_csv(path_or_buf='data_AAPL.csv')
##### read the data from .csv when need
apple=pd.read_csv(filepath_or_buffer='data_AAPL.csv')
print(apple.head())
请仔细对比从.csv文件中重新读取的数据发生的变化
Date Open High Low Close Adj Close Volume
0 2009-12-31 30.447144 30.478571 30.080000 30.104286 27.083506 88102700
1 2010-01-04 30.490000 30.642857 30.340000 30.572857 27.505054 123432400
2 2010-01-05 30.657143 30.798571 30.464285 30.625713 27.552608 150476200
3 2010-01-06 30.625713 30.747143 30.107143 30.138571 27.114347 138040000
4 2010-01-07 30.250000 30.285715 29.864286 30.082857 27.064222 119282800
可以看到，重新读取的数据的索引列发生了变化，这并不希望被看到，因为使用时间作为数据的索引列将会使后续的数据处理更加方便，也更加合理。
因此，使用如下代码来修改从.csv中读取的数据，使其恢复最初的样子。
date_list = []
for i in range(len(apple)):
date_str = apple['Date'][i]
t = time.strptime(date_str, "%Y-%m-%d")
temp_date = datetime.datetime(t[0], t[1], t[2])
date_list.append(temp_date)
apple['DateTime'] = pd.Series(date_list,apple.index)
del apple['Date']
apple = apple.set_index('DateTime')
还有一点需要注意，从Yahoo获取的数据中，只有收盘价提供了调整后的收盘价。但使用调整后收盘价与收盘价的比例，可以很容易的将开盘价，最低价，最高价的调整后价格计算出来。于是实现如下函数：
def ohlc_adjust(dat):
return pd.DataFrame({"Open": dat["Open"] * dat["Adj Close"] / dat["Close"],
"High": dat["High"] * dat["Adj Close"] / dat["Close"],
"Low": dat["Low"] * dat["Adj Close"] / dat["Close"],
"Close": dat["Adj Close"]})
最后，我们将上面的内容进行整合，使得程序能够批量的获取、保存、读取、修改不同公司的股票数据。这分别通过实现stockdata_preProcess.py中的三个函数实现（代码在文末）：
downloadAndSaveData()
repairAndGetData()
ohlc_adjust()
此时，我们只需提供一个所关心公司的股票代码列表即可完成预处理工作，例如：
listed_company_list = ["AAPL", "MSFT", "GOOG", "FB", "TWTR", "NFLX", "AMZN", "SNY", "NTDOY", "IBM", "HPQ"]
调用函数downloadAndSaveData(listed_company_list,start,end)，可以自动获取所提供列表中的公司，从start到end时间段内的股票数据。由于网络可能出现问题，代码中还加入了失败重连的机制。测试效果如下，所需的数据已经都保存到相应的.csv文件中了：
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完整代码：
import pandas as pd
import pandas_datareader.data as web
import datetime
import time
import os
# download the stock data
# parameter explanation:
# start & end : the time interval of the data we want to get(from start to end)
# e.g : start = datetime.datetime(2010, 1, 1)
# end = datetime.date.today()
# listed_company_list : the list of listed companies that we are concerned about
# e.g : listed_company_list = ["AAPL", "MSFT", "GOOG", "FB", "TWTR", "NFLX", "AMZN", "YHOO", "SNY", "NTDOY", "IBM", "HPQ"]
def downloadAndSaveData(listed_company_list, start, end):
downloadResult_list=[] # use downloadResult_list to denote whether the data has download successfully
for index in range(len(listed_company_list)):
downloadResult_list.append(False)
# start downloading data...
for index in range(len(listed_company_list)):
companyStr = listed_company_list[index]
filename = "data_" + companyStr + ".csv"
if os.path.exists(filename): # if the file has existed, we don't need to download again
print(companyStr+"'s data has already exists ^_^")
downloadResult_list[index]=True
continue
tryNumbers = 0
max_tryNumbers = 10
while tryNumbers<max_tryNumbers :
try:
print(companyStr + " data connecting start...")
# try to get data, this may throw exception
data = web.DataReader(companyStr, "yahoo", start, end)
# save data in .csv
data.to_csv(path_or_buf=filename)
print(companyStr + "'s data has successfully saved in " + filename + " ^_^")
downloadResult_list[index]=True
time.sleep(10)
break
except Exception as e:
print("error:",e)
print("connecting failed, waiting to reconnect...")
tryNumbers += 1
time.sleep(5*tryNumbers)
if tryNumbers == max_tryNumbers:
print("give up to get "+companyStr+"'s data -_-|")
print("the result shows below:")
for index in range(len(listed_company_list)):
print(listed_company_list[index] +" : " + str(downloadResult_list[index]))
return downloadResult_list
# get the data we save in .csv file (download and save by function downloadAndSaveData)
# and then return the repaired data to the user
# why repair?
# note that some format(data type) of data we read from .csv has changed
# for example the attribute 'Date' should be the index of the dataframe, and the date type changed from datetime to string
# this changes would made some methods got trouble. So we need to repair the data before return
def repairAndGetData(listed_company_list):
companyNumber = len(listed_company_list)
DataSetList = []
# traverse all the listed companies
for c in range(companyNumber):
cur_companyStr = listed_company_list[c]
cur_fileName = "data_" + cur_companyStr + ".csv"
cur_companyData = pd.read_csv(filepath_or_buffer=cur_fileName)
# repair current company's data
# change the data type of attribute "Date" from string to datetime, and let it become the index of the dataframe
date_list = []
for i in range(len(cur_companyData)):
date_str = cur_companyData['Date'][i]
t = time.strptime(date_str, "%Y-%m-%d")
temp_date = datetime.datetime(t[0], t[1], t[2])
date_list.append(temp_date)
cur_companyData['DateTime'] = pd.Series(date_list, cur_companyData.index)
del cur_companyData['Date']
cur_companyData = cur_companyData.set_index('DateTime')
# save the repaired data
DataSetList.append(cur_companyData)
# return all the repaired data in the original order
return DataSetList
# adjust the price of ohlc("Open","High","Low","Close")
# normally only interface only provides the adjust price of 'Close'
# but it is easy to adjust the price by the proportion of 'Adj Close' and 'Close'
def ohlc_adjust(dat):
return pd.DataFrame({"Open": dat["Open"] * dat["Adj Close"] / dat["Close"],
"High": dat["High"] * dat["Adj Close"] / dat["Close"],
"Low": dat["Low"] * dat["Adj Close"] / dat["Close"],
"Close": dat["Adj Close"]})