>>>> from Stock import Stock
>>>> aapl = Stock('AAPL')
>>>> aapl.financial_data
             AccountsPayable  ...  WorkingCapital
ReleaseDate                   ...                
2020-01-28      4.511100e+10  ...    6.107000e+10
2020-04-30      3.242100e+10  ...    4.765900e+10
2020-07-30      3.532500e+10  ...    4.474700e+10
2020-10-29      4.229600e+10  ...    3.832100e+10
2021-01-27      6.384600e+10  ...    2.159900e+10

[5 rows x 129 columns]

>>>> aapl['PriceClose']
date
2018-02-15     41.725037
2018-02-16     41.589962
2018-02-20     41.450069
2018-02-21     41.261936
2018-02-22     41.606850
   
2021-02-08    136.910004
2021-02-09    136.009995
2021-02-10    135.389999
2021-02-11    135.130005
2021-02-12    135.369995
Name: PriceClose, Length: 754, dtype: float6

>>>> aapl['PriceClose'] / aapl['TotalRevenue']
date
2018-02-15             NaN
2018-02-16             NaN
2018-02-20             NaN
2018-02-21             NaN
2018-02-22             NaN
    
2021-02-08    1.228565e-09
2021-02-09    1.220488e-09
2021-02-10    1.214925e-09
2021-02-11    1.212592e-09
2021-02-12    1.214745e-09
Length: 754, dtype: float64

>>>> from Signals import Quick_Ratio_Signal
>>>> ford = Stock('F')
>>>> Quick_Ratio_Signal(ford)
date
2018-02-15         NaN
2018-02-16         NaN
2018-02-20         NaN
2018-02-21         NaN
2018-02-22         NaN
  
2021-02-08    1.089966
2021-02-09    1.089966
2021-02-10    1.089966
2021-02-11    1.089966
2021-02-12    1.089966
Length: 754, dtype: float64

>>>> from SignalTransformers import Z_Score
>>>> Z_Score(Quick_Ratio_Signal(ford), window = 21) # Computes the rolling 21 day Z-score
date
2018-02-15         NaN
2018-02-16         NaN
2018-02-20         NaN
2018-02-21         NaN
2018-02-22         NaN
  
2021-02-08    4.248529
2021-02-09    2.924038
2021-02-10    2.320201
2021-02-11    1.949359
2021-02-12    1.688194
Length: 754, dtype: float64

>>>> from Stock import Stock
>>>> from Signals import Quick_Ratio_Signal
>>>> from Portfolio import PairTradingPortfolio
>>>> ford = Stock('F')
>>>> gm = Stock('GM')
>>>> ptp = PairTradingPortfolio(stock_obj1=ford, stock_obj2=gm, signal_func=Quick_Ratio_Signal)
>>>> ptp.relative_differencing() # The weights are set based on the rolling z-score of the difference of the signals for the 2 stocks
>>>> ptp.get_returns()
date
2018-02-15         NaN
2018-02-16         NaN
2018-02-20         NaN
2018-02-21         NaN
2018-02-22         NaN
  
2021-02-08   -0.033217
2021-02-09    0.037791
2021-02-10    0.005568
2021-02-11   -0.001001
2021-02-12   -0.001700
Length: 754, dtype: float64
>>>> ptp.stock_obj1_wght_ts # Get the weight of Stock 1 ( Weight of stock 2 is just -1 times weight of stock 1)
Out[9]: 
date
2018-02-15         NaN
2018-02-16         NaN
2018-02-20         NaN
2018-02-21         NaN
2018-02-22         NaN
  
2021-02-08    0.814045
2021-02-09    0.818967
2021-02-10    0.823901
2021-02-11    0.909396
2021-02-12    0.910393
Length: 754, dtype: float64

>>>> from Stock import Stock
>>>> from Signals import Price_to_Sales_Signal
>>>> from Portfolio import SingleSignalPortfolio
>>>> from SignalTransformers import Z_Score
>>>> fb = Stock('FB')
>>>> msft = Stock('MSFT')
>>>> aapl = Stock('AAPL')
>>>> signal_func = lambda stock_obj : Z_Score(Price_to_Sales_Signal(stock_obj), window=42) # Use a rolling Z score over 42 days rather than the raw ratio
>>>> ssp.relative_ranking() # Rank the stock from -1 to +1, in this case we have 3 stocks it will be {-1, 0, 1}, if we have 4 sotck it would be {-1, -0.33, 0.33, 1}
>>>> ssp.weight_df
             FB  MSFT  AAPL
date                       
2018-02-15  0.0   0.0   0.0
2018-02-16  0.0   0.0   0.0
2018-02-20  0.0   0.0   0.0
2018-02-21  0.0   0.0   0.0
2018-02-22  0.0   0.0   0.0
        ...   ...   ...
2021-02-08  0.0   1.0  -1.0
2021-02-09  0.0   1.0  -1.0
2021-02-10  0.0   1.0  -1.0
2021-02-11  0.0   1.0  -1.0
2021-02-12  0.0   1.0  -1.0

[754 rows x 3 columns]
>>>> ssp.get_returns() # Initial values are 0 since signal is not available at the start for any of the stocks
date
2018-02-15    0.000000
2018-02-16    0.000000
2018-02-20    0.000000
2018-02-21    0.000000
2018-02-22    0.000000
  
2021-02-08    0.000018
2021-02-09    0.011935
2021-02-10    0.000661
2021-02-11    0.008798
2021-02-12    0.000269
Length: 754, dtype: float64

>>>> from datetime import datetime
>>>> from Stock import Stock
>>>> from Signals import Quick_Ratio_Signal, Price_to_Sales_Signal
>>>> from Portfolio import SimpleBlackLitterman
>>>> from SignalTransformers import Z_Score
>>>> ford = Stock('F')
>>>> gm = Stock('GM')
>>>> toyota = Stock('TM')
>>>> signal_func1 = lambda stock_obj: Quick_Ratio_Signal(stock_obj) # Use the Raw quick Ratio
>>>> signal_func2 = lambda stock_obj: Z_Score(-1*Price_to_Sales_Signal(stock_obj), window=63) # Use the moving 63 Z score for Price to Sales. -1 to Flip the signal
>>>> signal_view_ret_arr = [0.02, 0.01] # Expected returns from each signal. Typically denoted as Q
>>>> sbl = SimpleBlackLitterman(stock_arr=[ford, gm, toyota], signal_func_arr=[signal_func1, signal_func2], signal_view_ret_arr=signal_view_ret_arr)
>>>> dt = datetime(2021, 2, 12).date()
>>>> sbl.weights_df # Weights based on MarketCap
                   F        GM        TM
date                                    
2020-02-07  0.059205  0.085642  0.855153
2020-02-10  0.059145  0.087673  0.853182
2020-02-11  0.059010  0.088974  0.852016
2020-02-12  0.059782  0.089820  0.850399
2020-02-13  0.060360  0.090068  0.849572
             ...       ...       ...
2021-02-08  0.075640  0.127209  0.797151
2021-02-09  0.077582  0.124607  0.797810
2021-02-10  0.073859  0.117810  0.808331
2021-02-11  0.073232  0.116954  0.809814
2021-02-12  0.072642  0.116230  0.811128

[257 rows x 3 columns]
>>>> sbl.var_covar_ts[dt] # Variance Covariance Martix computed based on rolling 126 days of returns, var_covar_ts is a dict of dataframes. Typically denoted as Sigma
           F        GM        TM
F   0.140825  0.085604  0.021408
GM  0.085604  0.197158  0.020909
TM  0.021408  0.020909  0.044832
>>>> sbl.implied_returns_df # Implied Returns for each day. This is often denoted as Pi
                   F        GM        TM
2020-02-10  0.012125  0.016345  0.014762
2020-02-11  0.012131  0.016199  0.014818
2020-02-12  0.011994  0.016279  0.014773
2020-02-13  0.012199  0.016374  0.014645
2020-02-14  0.011042  0.014466  0.013649
             ...       ...       ...
2021-02-08  0.038776  0.047958  0.037335
2021-02-09  0.039060  0.049541  0.037451
2021-02-10  0.038827  0.048351  0.037453
2021-02-11  0.036424  0.045034  0.040050
2021-02-12  0.037661  0.046260  0.040319

[256 rows x 3 columns]
>>>> sbl.link_mat_ts[dt] # The link matrix on a given day. link_mat_ts is a dict of dataframes. Typically denoted as Sigma
            F   GM   TM
signal_0  1.0 -1.0  0.0
signal_1 -1.0  0.0  1.0
>>>> sbl.view_var_covar_ts[dt] # The View variance covariance matrix on a given day. view_var_covar_ts is a dict of dataframes. Typically denoted as Omega
          signal_0  signal_1
signal_0  0.166775 -0.043777
signal_1 -0.043777  0.142840
>>>> sbl.black_litterman_weights_df # The Black litterman weights over time, based on the changing views
                   F        GM        TM
2020-05-07  0.077305  0.127350  0.795345
2020-05-08  0.077354  0.130177  0.792469
2020-05-11  0.077862  0.132684  0.789454
2020-05-12  0.065264  0.071459  0.863277
2020-05-13  0.114959  0.074012  0.811028
             ...       ...       ...
2021-02-08  0.116730  0.123745  0.759526
2021-02-09  0.116043  0.127209  0.756747
2021-02-10  0.149960  0.232889  0.617152
2021-02-11  0.109802 -0.032208  0.922406
2021-02-12  0.107529 -0.033443  0.925915