├── style_factor_return.png ├── sw_l1.csv ├── Barra_Model.py ├── README.md ├── barra_function.py ├── 纯因子组合因子暴露矩阵.csv └── style_factors_cum.csv /style_factor_return.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/jackylwj/Barra_factor_model/HEAD/style_factor_return.png -------------------------------------------------------------------------------- /sw_l1.csv: -------------------------------------------------------------------------------- 1 | ,name,start_date 2 | 801740,国防军工I,2014/2/21 3 | 801020,采掘I,2004/2/10 4 | 801110,家用电器I,2004/2/10 5 | 801160,公用事业I,2004/2/10 6 | 801770,通信I,2014/2/21 7 | 801010,农林牧渔I,2004/2/9 8 | 801120,食品饮料I,2004/2/10 9 | 801750,计算机I,2014/2/21 10 | 801050,有色金属I,2004/2/10 11 | 801890,机械设备I,2014/2/21 12 | 801170,交通运输I,2004/2/10 13 | 801710,建筑材料I,2014/2/21 14 | 801780,银行I,2014/2/21 15 | 801040,钢铁I,2004/2/10 16 | 801130,纺织服装I,2004/2/10 17 | 801880,汽车I,2014/2/21 18 | 801180,房地产I,2004/2/10 19 | 801230,综合I,2004/2/10 20 | 801760,传媒I,2014/2/21 21 | 801200,商业贸易I,2004/2/10 22 | 801140,轻工制造I,2004/2/10 23 | 801720,建筑装饰I,2014/2/21 24 | 801080,电子I,2004/2/10 25 | 801790,非银金融I,2014/2/21 26 | 801030,化工I,2004/2/10 27 | 801210,休闲服务I,2004/2/10 28 | 801730,电气设备I,2014/2/21 29 | 801150,医药生物I,2004/2/10 30 | -------------------------------------------------------------------------------- /Barra_Model.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import os 4 | import sqlite3 5 | import matplotlib.pyplot as plt 6 | from jqdatasdk import * 7 | from tqdm import tqdm 8 | from barra_function import pure_factor_model 9 | 10 | 11 | def main(): 12 | cwd = os.path.abspath(os.path.curdir) 13 | db_path = cwd + '\\' + 'AllData_2019.sqlite' 14 | 15 | style_factors = ['size','beta','momentum','residual_volatility','non_linear_size','book_to_price_ratio','liquidity','earnings_yield','growth','leverage'] 16 | industry_factors = [str(code) for code in pd.read_csv('sw_l1.csv').iloc[:,0]] 17 | factor_list = ['cfactor'] + style_factors + industry_factors 18 | 19 | conn = sqlite3.connect(db_path) 20 | cur = conn.cursor() 21 | cur.execute("SELECT name FROM sqlite_master WHERE TYPE='table' ORDER BY name") 22 | tables_name = cur.fetchall() 23 | tables_name = [table[0] for table in tables_name] 24 | 25 | factor_return_dict = dict() 26 | tables_name = tqdm(tables_name) 27 | for date in tables_name: 28 | tables_name.set_description('Processing %s' %date) 29 | sql = "SELECT * FROM '{}'".format(date) 30 | table = pd.read_sql(sql,con=conn,index_col='index') 31 | table['cfactor'] = 1 32 | rt = table['cc'] 33 | factor_data = table[factor_list] 34 | _,factor_return,_ = pure_factor_model(rt,factor_data,style_factors,industry_factors,if_output=False) 35 | factor_return_dict[date] = factor_return 36 | tables_name.close() 37 | 38 | factor_return_df = pd.DataFrame(columns=factor_list) 39 | for date in factor_return_dict: 40 | factor_return_df.loc[date,] = factor_return_dict[date] 41 | 42 | #style_factor 43 | style_factors_return = factor_return_df[style_factors] 44 | style_factors_cum = (style_factors_return+1).cumprod(axis=0) 45 | style_factors_cum.to_csv('style_factors_cum.csv') 46 | ax = style_factors_cum.plot() 47 | fig = ax.get_figure() 48 | fig.savefig('style_factors_return.png') 49 | 50 | if __name__ =='__main__': 51 | main() 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Barra_factor_model 2 | This rep is a simple attempt of Application of Barra Risk Model on China A share Market. 3 | ##1.Data preparation 4 | The buildbd.py can automatically download style factor data, including market cap, according to Barra CNE5 model from JoinQuant API and then build four databases. The first three databases are for style factors, market data and industry data respectively. The last database is a merged database of the above order by date. 5 | 6 | ## 1.Introduction to Barra Model 7 | According to CNE5, the factor excess returns over the period are obtained via a cross_sectional regression of asset excess returns on their associated factor exposures. Factor returns are estimated using weighted least-squares regression, assuming that the variance of specific returns is inversely proportional to the square root of total market capitalization. In order to avoid multicollinearity, Restriction is added that the industry cap-weighted sum of factor returns is zero. 8 | 9 | In order to faciliate the verification of the solution, I solved the weight matrix of pure factor portfolios instead of the factor returns themselves. The solution can be checked by examining the exposure of each factor portfolios where style factors portfolio should have unit exposure on the respective factor and no exposure on the others. 10 | 11 | ## 2.Results 12 | The factor_portfolio_exposure.csv is a single check of solution on a single day of 2019-12-25 on CSI500 constituents from which we can easily find the characteristics of pure factor portfolio exposure. The sw_l1.csv is actually Shenwan industry classification standard, emm... this file should be introduced in part2 but I forget, anyway. 13 | 14 | The style_factors_cum.png is the daily cummulative returns of the 10 style factors from 2019-01-01 to 2019-12-31 with style_factors_cum.csv as the original dataset. 15 | 16 | ## 3.Further exploration 17 | The value of Barra Factor Model lies on volatility forecast and portfolio risk attribution. There is a lot to talk such as risk adjustment of covariance of factor return. This rep is just a start and will be continually updated. 18 | 19 | ## 4.How to use this rep 20 | - Enter the database path and run builddb.py 21 | - run the Barra_Model.py 22 | 23 | -------------------------------------------------------------------------------- /barra_function.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import pandas as pd 3 | import os 4 | import sqlite3 5 | from jqdatasdk import * 6 | from tqdm import tqdm 7 | 8 | #style_factors = ['size','beta','momentum','residual_volatility','non_linear_size','book_to_price_ratio','liquidity','earnings_yield','growth','leverage'] 9 | #industry_factors = [str(code) for code in pd.read_csv('sw_l1.csv').iloc[:,0]] 10 | 11 | def pure_factor_model(rt,factor_data,style_factors,industry_factors,if_output=False): 12 | #函数文档： 13 | ''' 14 | :rt:收益率，Series 15 | :factor_data:因子暴露，DataFrame 16 | :style_factors:风格因子类型，list 17 | :industry_factors:行业因子类型，list 18 | :if_output:是否将结果输出为csv，Boolean 19 | :输出为(纯因子组合权重矩阵,因子收益率向量,纯因子组合因子暴露矩阵)(Omega,factors_return,Z_df) 20 | ''' 21 | cwd = os.path.abspath(os.path.curdir) 22 | Q = len(style_factors) 23 | P = len(industry_factors) 24 | factor_list = ['cfactor']+style_factors+industry_factors 25 | mkt_cap = factor_data['market_cap'] 26 | factor_data['cfactor'] = 1 27 | factor_data = factor_data[factor_list] 28 | factor_data = factor_data.dropna() 29 | mkt_cap = mkt_cap.loc[factor_data.index,] 30 | mkt_cap_weights = np.sqrt(mkt_cap)/np.sum(np.sqrt(mkt_cap)) 31 | rt = rt.loc[factor_data.index,] 32 | N = factor_data.shape[0] 33 | #1.构建权重矩阵V，残差波动率反比于股票市值平方根 34 | V = np.zeros((N,N)) 35 | for i in range(N): 36 | V[i,i]= mkt_cap_weights[i] 37 | #2.构建限制矩阵，由行业市值加权行业因子收益率和为0 38 | industry_matrix = np.matrix(factor_data[industry_factors]) 39 | mkt_cap_weights = np.matrix(mkt_cap_weights) 40 | industry_weights = np.matmul(mkt_cap_weights,industry_matrix) 41 | R1 = np.identity(Q+P) 42 | R2 = np.zeros((1,Q+P)) 43 | R2[0,(1+Q):] = -industry_weights[0,:-1]/industry_weights[0,-1] 44 | R = np.vstack((R1,R2)) 45 | # print(R.shape) 46 | #3.因子暴露矩阵 47 | X = np.matrix(factor_data) 48 | # print(X.shape) 49 | #4.计算纯因子投资组合权重矩阵OMEGA 50 | Omega1 = np.linalg.inv(np.matmul(R.T,np.matmul(X.T,np.matmul(V,np.matmul(X,R))))) 51 | Omega = np.matmul(R,np.matmul(Omega1,np.matmul(R.T,np.matmul(X.T,V)))) 52 | #5.通过纯因子投资组合权重矩阵和资产收益率计算因子收益率 53 | rt = np.matrix(rt,dtype=float).T 54 | factors_return = np.matmul(Omega,rt) 55 | factors_return = pd.DataFrame(factors_return) 56 | factors_return = pd.Series(factors_return.iloc[:,0]) 57 | factors_return.index = factor_list 58 | factors_return.columns = ['factors_return'] 59 | Z = np.matmul(Omega,X) 60 | Z = pd.DataFrame(Z) 61 | Z_columns = factor_list 62 | Z.columns = Z_columns 63 | Z_df = Z.set_index([Z_columns],drop=False) 64 | Omega = pd.DataFrame(Omega) 65 | Omega.index = factor_list 66 | if if_output: 67 | factors_return.to_csv(cwd+'\\'+'factors_return.csv') 68 | Omega.to_csv(cwd+'\\'+'OMEGA_Mat.csv') 69 | Z_df.to_csv(cwd+'\\'+'factors_exposer.csv') 70 | return(Omega,factors_return,Z_df) 71 | 72 | 73 | ''' 74 | def main(): 75 | factor_data = pd.read_csv('factor_df20191225.csv',index_col=1) 76 | factor_data = factor_data.iloc[:,1:] 77 | market_data = pd.read_csv('Market_Price.csv',index_col=0) 78 | rt = market_data['rt1'] 79 | Omega,factors_return,_ = pure_factor_model(rt,factor_data,style_factors,industry_factors,if_output=False) 80 | ''' 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | -------------------------------------------------------------------------------- /纯因子组合因子暴露矩阵.csv: -------------------------------------------------------------------------------- 1 | ,cfactor,size,beta,momentum,residual_volatility,non_linear_size,book_to_price_ratio,liquidity,earnings_yield,growth,leverage,801740,801020,801110,801160,801770,801010,801120,801750,801050,801890,801170,801710,801780,801040,801130,801880,801180,801230,801760,801200,801140,801720,801080,801790,801030,801210,801730,801150 2 | cfactor,1.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.02463 ,0.02299 ,0.01066 ,0.04723 ,0.02646 ,0.01581 ,0.02608 ,0.05026 ,0.03988 ,0.04493 ,0.05098 ,0.01650 ,0.01322 ,0.03517 ,0.00652 ,0.03886 ,0.04849 ,0.00748 ,0.06023 ,0.03426 ,0.01825 ,0.02215 ,0.07752 ,0.03491 ,0.08172 ,0.00537 ,0.04436 ,0.09506 3 | size,0.00000 ,1.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 ,0.00000 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