Ikaros
Ikaros is a free financial library built in pure python that can be used to get information for single stocks, generate signals and build portfolios
How to use
Stock
The Stock object is a representation of all information what is available for a given security. For example for AAPL we scrape information from -
- https://finviz.com/quote.ashx?t=AAPL
- https://www.zacks.com/stock/research/AAPL/earnings-announcements
We also use the Yahoo Finance Library: yahooquery (GitHub link - https://github.com/dpguthrie/yahooquery ) to get fundamental data and price data.
>>>> 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
Mix and match market data with fundamental data directly. Ikaros uses the earnings calendar from Zacks to get an accurate Point in time, timeseries from fundamental data.
>>>> 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
Ikaros also caches the data webscraped into readable csv files. If you want to save the data in a custom location, ensure that the enviornment variable IKAROSDATA is set on your operating system.
Signal
The Signal Library is repository of functions that provide useful insights into stocks. We have a limited number of signals so far but stay tuned! for more
>>>> 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
Portfolio
Finally, use the signals and stock objects to construct Portfolios yourself. Currently we have
- Pair Trading Portfolio for 2 Stocks and a Signal
- Single Signal Portfolio for multiple Sotcks given a Signal
- A basic implementation of the Black Litterman Model
For a PairTradingPortfolio, lets look at GM and Ford and compare the two based on the Quick Ratio
>>>> 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
For a SingleSignalPortfolio, lets look at FaceBook, Microsfot and Apple and compare them based on the Price to Sales Ratio.
>>>> 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
For a SimpleBlackLitterman, we can provide multiple stocks and multiple signals. Let us try to look at Ford, GM and Toyota based on the Price to Sales and Quick Ratio
>>>> 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
12 Jun 15, 2022
3 Dec 12, 2021
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5 Jan 12, 2022
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