Quickstart

Installation

pip install ravix

To upgrade:

pip install --upgrade ravix

Import

import ravix

Load a dataset

df = ravix.get_data("Betas.csv")
df.head()

   Unnamed: 0      AAPL       CAT  ...      MSFT       XOM       SPY
0  2018-09-01 -0.004825  0.098236  ...  0.022079  0.071435  0.001412
1  2018-10-01 -0.030477 -0.204407  ... -0.066101 -0.062809 -0.064891
2  2018-11-01 -0.184045  0.125409  ...  0.038199 -0.002259  0.018549
3  2018-12-01 -0.113616 -0.063389  ... -0.080090 -0.133569 -0.093343
4  2019-01-01  0.055154  0.047927  ...  0.028158  0.074644  0.086373

[5 rows x 9 columns]

Tip: Call ravix.get_data() with no arguments to list all available datasets.

Fit an OLS model

model = ravix.ols("AAPL ~ SPY", df)

View the summary

model.summary()

Summary of OLS Regression Analysis:
======================================================

Coefficients:
------------------------------------------------------
              Estimate  Std. Error  t-value    p-value
Intercept     0.014223    0.011989    1.186     0.2437
SPY             1.2266     0.21591    5.681   2.24e-06 ***

Model Statistics:
------------------------------------------------------
Residual Std. Error: 0.069147
R-squared:      0.4870          AIC: -88.2432
Adj. R-squared: 0.4719          BIC: -85.0761
F-statistic: 32.2745 on 1 and 34 DF, p-value: 2.24e-06
======================================================

Scatter plot with fitted line

ravix.plot("AAPL ~ SPY", df)

Diagnostics

ravix.bp(model)

Breusch-Pagan Test for Heteroscedasticity
=======================================================
Test Statistic      : 0.0309
p-value             : 0.8604
Result              : Homoscedastic (p >= 0.05)
=======================================================

ravix.shapiro(model)

Shapiro-Wilk Test for Normality
=======================================================
Test Statistic      : 0.9465
p-value             : 0.0811
Result              : Normal (p >= 0.05)
=======================================================

Next steps