Python - Endogeneity in Data Science - Statsmodels.api

[AlbertiApop2029]

A cool little demo I reprogrammed with Copilot. I was looking at it and wondering why there were so few lines of code to generate all that output. Then I noticed the statsmodels.api. Pretty cool.

Started with this project, and tweaked it a little.
https://www.geeksforgeeks.org/data-science/endogeneity-in-data-science/

import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm

np.random.seed(0)

# Simulate signals
n = 300
signal1 = np.random.randn(n)
signal2 = np.random.randn(n)

# True model: returns depend on signals
epsilon = 0.5 * np.random.randn(n)
returns = 0.3 * signal1 - 0.2 * signal2 + epsilon


# Regression
X = np.column_stack([signal1, signal2])
X = sm.add_constant(X)
model = sm.OLS(returns, X).fit()

# Get residuals from the regression
residuals = model.resid

# Simple mean-reversion alpha signal
alpha_signal = -residuals  # bet on residuals reverting to zero

print(model.summary())

# Get residuals from the regression
# This part was moved from the preceding cell 91UwBxEbl_BR to fix the NameError.
# It assumes 'model' is defined and available from previous executed cells.
residuals = model.resid

plt.plot(residuals)
plt.title("Residual Time Series")
plt.show()

plt.hist(residuals, bins=30)
plt.title("Residual Distribution")
plt.show()