fama-french三因子模型代码

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关键词：FRENCH 三因子模型 FAMA Fre Ama

1. import numpy as np
   
2. import pandas as pd
   
3. import scipy
   
4. import math
   
5. import statsmodels

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1. dp = get_fundamentals(query(fundamentals.eod_derivative_indicator.market_cap,fundamentals.financial_indicator.book_value_per_share),'2010-11-01', '60m')
   
2. market = all_instruments()
   
3. price_data = get_price(list(market['order_book_id']),start_date='2005-11-01', end_date='2010-10-30',frequency='1d',fields='ClosingPx')

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1. price_data = get_price(list(market['order_book_id']),start_date='2005-11-01', end_date='2010-11-05',frequency='1d',fields='ClosingPx')

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1. index_date = dp['book_value_per_share'].index
   
2. stocks = dp['book_value_per_share'].columns

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1. Index(['2010-11-01', '2010-09-30', '2010-09-01', '2010-07-30', '2010-07-01',
   
2.        '2010-06-01', '2010-04-30', '2010-04-01', '2010-03-01', '2010-02-01',
   
3.        '2009-12-31', '2009-12-01', '2009-10-30', '2009-09-30', '2009-09-01',
   
4.        '2009-07-31', '2009-07-01', '2009-06-01', '2009-04-30', '2009-04-01',
   
5.        '2009-02-27', '2009-01-23', '2008-12-31', '2008-12-01', '2008-10-31',
   
6.        '2008-09-26', '2008-09-01', '2008-08-01', '2008-07-01', '2008-05-30',
   
7.        '2008-04-30', '2008-04-01', '2008-02-29', '2008-02-01', '2007-12-28',
   
8.        '2007-11-30', '2007-11-01', '2007-09-28', '2007-08-31', '2007-08-01',
   
9.        '2007-06-29', '2007-06-01', '2007-04-30', '2007-03-30', '2007-03-01',
   
10.        '2007-02-01', '2006-12-29', '2006-12-01', '2006-11-01', '2006-09-29',
    
11.        '2006-09-01', '2006-08-01', '2006-06-30', '2006-06-01', '2006-04-28',
    
12.        '2006-03-31', '2006-03-01', '2006-01-25', '2005-12-30', '2005-12-01',
    
13.        '2005-11-01'],
    
14.       dtype='object')

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1. #调整nan值为零
   
2. 
   
3. where_are_NaNs = np.isnan(dp['book_value_per_share'])
   
4. dp['book_value_per_share'][where_are_NaNs] = 0
   
5. where_are_NaNs = np.isnan(dp['market_cap'])
   
6. dp['market_cap'][where_are_NaNs] = 0
   
7. where_are_NaNs = np.isnan(price_data)
   
8. price_data[where_are_NaNs] = 0

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1. ##btm stores the index book_to_market ratio
   
2. btm = pd.DataFrame(index=index_date,columns=stocks)
   
3. for stk in stocks:
   
4.     for date in index_date:
   
5.         if price_data[stk][date]!=0:
   
6.             btm[stk][date] = dp['book_value_per_share'][stk][date]/price_data[stk][date]
   
7.         else:
   
8.             btm[stk][date] = 0

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