Hands-On Machine Learning for Algorithmic Trading

Test results – information coefficient and RMSE

We have captured the test predictions from the 250 folds and can compute both the overall and a 21-day rolling average:

fig, axes = plt.subplots(nrows=2)rolling_result = test_result.rolling(21).mean()rolling_result[['ic', 'pval']].plot(ax=axes[0], title='Information Coefficient')axes[0].axhline(test_result.ic.mean(), lw=1, ls='--', color='k')rolling_result[['rmse']].plot(ax=axes[1], title='Root Mean Squared Error')axes[1].axhline(test_result.rmse.mean(), lw=1, ls='--', color='k')

We obtain the following chart that highlights the negative correlation of IC and RMSE and their respective values:

Chart highlighting the negative correlation of IC and RMSE

For the entire period, we see that the Information ...