Scikit Learn 股票投资：p10

▌前言

本期的教学视频比较短，主要的教学内容是“标记”我们的数据。Machie Learning(机器学习)主要步骤都离不开数据获取、整理数据、数据标准化。这些步骤通常都是最浪费时间的。

▌视频

▌视频出处

视频系列：Scikit-learn Machine Learning with Python and SKlearn

本视频出处：Scikit Learn Machine Learning for investing Tutorial with Python p. 10

哔哩哔哩：Scikit Learn Machine Learning for investing Tutorial with Python p. 10

▌数据下载

下载地址：数据

百 度 云： 地址 密码: yyq8

▌内容

首先我们添加新的列Status用于判断股票是否优于大盘。

df = pd.DataFrame(
  columns = [
    'Date',
    'Unix',
    'Ticker',
    'DE Ratio',
    'Price',
    'stock_p_change',
    'SP500',
    'sp500_p_change',
    'Difference',
    'Status'
  ]
)

并在df = df.append()添加status的对应字典。 作者将df = df.append()中的 DIfference数据单独抽取了出来，并加入了IF判断，由此获得相应股票是否outperform或者underperform：

  stock_p_change = ((stock_price - starting_stock_value) / starting_stock_value) * 100
  sp500_p_change = ((sp500_value - starting_sp500_value) / starting_sp500_value) * 100
  difference = stock_p_change - sp500_p_change

  if difference > 0:
      status = "outperform"
  else:
      status = "underperform"

  #将数据叠加并保存在字典里面
  df = df.append({'Date':date_stamp,
                  'Unix':unix_time,
                  'Ticker':ticker,
                  'DE Ratio':value,
                  'Price':stock_price,
                  'stock_p_change':stock_p_change,
                  'SP500':sp500_value,
                  'sp500_p_change':sp500_p_change,
                  'Difference': difference,
                  'Status': status},
                  ignore_index = True
  )
except Exception as e:
  pass
  #print(str(e))

最后在画图部分添加:

#将每只股票与指数的差用画图显示出来，会发现有些数据缺失了，然后修复
for each_ticker in ticker_list:
  try:
    plot_df = df[(df['Ticker'] == each_ticker)]
    plot_df = plot_df.set_index(['Date'])

    if plot_df['Status'][-1] =="underperform":
        color = 'r'
    else:
        color = 'g'

    plot_df['Difference'].plot(label=each_ticker, color=color)
    plt.legend()
  except:
    pass

▌输出

▌源代码

import pandas as pd
import os
import time
from datetime import datetime
import re
from time import mktime
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import style
style.use("dark_background")


#获取数据的具体路径
path = "../intraQuarter"

#定义一个function，默认值为Total Debt/Equity (mrq)，以后可以更改为其他
def Key_Stats(gather="Total Debt/Equity (mrq)"):
  statspath = path+'/_KeyStats'

  #os模块提供的walk方法很强大，能够把给定的目录下的所有目录和文件遍历出来。
  #方法：os.walk(path),遍历path，返回一个对象，他的每个部分都是一个三元组,('目录x'，[目录x下的目录list]，目录x下面的文件)
  stock_list = [x[0] for x in os.walk(statspath)]
  df = pd.DataFrame(
    columns = [
      'Date',
      'Unix',
      'Ticker',
      'DE Ratio',
      'Price',
      'stock_p_change',
      'SP500',
      'sp500_p_change',
      'Difference',
      'Status'
    ]
  )

  #读取SP500指数数据
  sp500_df = pd.DataFrame.from_csv("SPY.csv")

  ticker_list = []

  #stock_list[1:] -- 主要作用是跳过根目录intraQuarter
  for each_dir in stock_list[1:25]:
    #os.listdir(each_dir)：列出each_dir下的目录和文件
    each_file = os.listdir(each_dir)

    # ticker = each_dir.split("\\")[1] # Windows only
    # ticker = each_dir.split("/")[1] # this didn't work so do this:
    ticker = os.path.basename(os.path.normpath(each_dir))
    # print(ticker) # 用作验证是否成功获取股票代码
    ticker_list.append(ticker)

    starting_stock_value = False
    starting_sp500_value = False

    if len(each_file) > 0:
      for file in each_file:
        #将文件名转换为时间序列
        date_stamp = datetime.strptime(file, '%Y%m%d%H%M%S.html')
        #转换为unix_time
        unix_time = time.mktime(date_stamp.timetuple())
        full_file_path = each_dir+'/'+file
        #读取html文件
        source = open(full_file_path,'r').read()
        try:
          try:
            #读取html后并以gather + ':</td><td class="yfnc_tabledata1">作为split，提取第一位元素，然后将后面</td>作为结束  
            value = float(source.split(gather+':</td><td class="yfnc_tabledata1">')[1].split('</td>')[0])
          except Exception as e:

            try:
              #因为yahoo中的数据经过调整，获取页面数值的位置已经更改，会出现读取错误，新的数值会多了一行，所以要添加\n跳过                 
              value = float(source.split(gather+':</td>\n<td class="yfnc_tabledata1">')[1].split('</td>')[0])
              #print(str(e),ticker, file)
            except Exception as e:
              pass
              #print(str(e),ticker, file)
              #time.sleep(15)
              #value = float(source.split(gather+':</td>\n<td class="yfnc_tabledata1">')[1].split('</td>')[0])

          try:
            sp500_date = datetime.fromtimestamp(unix_time).strftime('%Y-%m-%d')
            row = sp500_df[(sp500_df.index == sp500_date)]
            sp500_value = float(row["Adj Close"])
          except:
            sp500_date = datetime.fromtimestamp(unix_time-259200).strftime('%Y-%m-%d')
            row = sp500_df[(sp500_df.index == sp500_date)]
            sp500_value = float(row["Adj Close"])

          try:
             #将文件内容以':</td><td class="yfnc_tabledata1">'开始进行分割，[1]表示分割后获取后面的第一个内容D/E的值
            stock_price = float(source.split('</small><big><b>')[1].split('</b></big>')[0])
          except Exception as e:
            #    <span id="yfs_l10_afl">43.27</span>
            try:
              #因为yahoo中的数据经过调整，获取页面数值的位置已经更改，所以要利用正则表达式通过re.search查找出数值
              #r'(\d{1,8}\.\d{1,8})'中的\d表示数字，{1,8}表示查找1-8数字，中间的\.表示跳过数值中的"."
              stock_price = (source.split('</small><big><b>')[1].split('</b></big>')[0])
              stock_price = re.search(r'(\d{1,8}\.\d{1,8})',stock_price)
              stock_price = float(stock_price.group(1))
              #print(stock_price)
            except Exception as e:
              try:
                #原因同上
                stock_price = (source.split('<span class="time_rtq_ticker">')[1].split('</span>')[0])
                stock_price = re.search(r'(\d{1,8}\.\d{1,8})',stock_price)
                stock_price = float(stock_price.group(1))
              except Exception as e:
                print(str(e),'a;lsdkfh',file,ticker)

              #print('Latest:',stock_price)
              #print('stock price',str(e),ticker,file)
              #time.sleep(15)

          #print("stock_price:",stock_price,"ticker:", ticker)

          if not starting_stock_value:
            starting_stock_value = stock_price
          if not starting_sp500_value:
            starting_sp500_value = sp500_value

          stock_p_change = ((stock_price - starting_stock_value) / starting_stock_value) * 100
          sp500_p_change = ((sp500_value - starting_sp500_value) / starting_sp500_value) * 100
          difference = stock_p_change - sp500_p_change

          if difference > 0:
              status = "outperform"
          else:
              status = "underperform"

          #将数据叠加并保存在字典里面
          df = df.append({'Date':date_stamp,
                          'Unix':unix_time,
                          'Ticker':ticker,
                          'DE Ratio':value,
                          'Price':stock_price,
                          'stock_p_change':stock_p_change,
                          'SP500':sp500_value,
                          'sp500_p_change':sp500_p_change,
                          'Difference': difference,
                          'Status': status},
                          ignore_index = True
          )
        except Exception as e:
          pass
          #print(str(e))

  #将每只股票与指数的差用画图显示出来，会发现有些数据缺失了，然后修复
  for each_ticker in ticker_list:
    try:
      plot_df = df[(df['Ticker'] == each_ticker)]
      plot_df = plot_df.set_index(['Date'])

      if plot_df['Status'][-1] =="underperform":
          color = 'r'
      else:
          color = 'g'

      plot_df['Difference'].plot(label=each_ticker, color=color)
      plt.legend()
    except:
      pass

  plt.show()

  #将gather="Total Debt/Equity (mrq)" 中的多余符号去掉，作为文件名
  save = gather.replace(' ','').replace(')','').replace('(','').replace('/','')+('.csv')
  print(save)
  df.to_csv(save)

Key_Stats()

▌最后

虽然分c君_BingWong只是作为一名搬运工，连码农都称不上。 但制作代码中的注释、翻译和搬运都花了很多时间，请各位大侠高抬贵手，在转载时请注明出处。