Hi

I was looking do diagnose the quality of the prediction.

In a first step, I'm comparing the prediction (y_pred) vs. y_test with a ratio.

Here is a list of estimators:

```
# 1. Mean Squared Error (MSE)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error (MSE): { mse:.3}")
# Root Mean Squared Error (RMSE)
rmse = mean_squared_error(y_test, y_pred, squared=False)
print(f"Root Mean Squared Error (RMSE): { rmse:.3}")
# 2. Mean Absolute Error (MAE)
mae = mean_absolute_error(y_test, y_pred)
print(f"Mean Absolute Error (MAE): { mae:.3}")
# 3. R-squared (R^2)
r2 = r2_score(y_test, y_pred)
print(f"R-squared (R^2): {r2:.3}")
# 4. Pearson Correlation Coefficient
pearson_corr, _ = pearsonr(y_test, y_pred)
print(f"Pearson Correlation Coefficient: { pearson_corr:.3} ")
# 5. Spearman Rank Correlation
spearman_corr, _ = spearmanr(y_test, y_pred)
print(f"Spearman Rank Correlation: { spearman_corr:.3} ")
#spearman_corr = 0
# 6. Hit Ratio or Accuracy
def calculate_hit_ratio(y_test, y_pred, n_top_stocks=100):
# Assuming y_test and y_pred have a multi-level index with 'ticker' and 'date'
hit_ratios = []
# Iterate over each unique date
for date in y_test.index.get_level_values('date').unique():
# Get the top N stocks based on predicted returns for the current date
top_predicted_stocks = y_pred.loc[(slice(None), date)].nlargest(n_top_stocks).index.get_level_values('ticker').tolist()
# Get the top N stocks based on actual returns for the current date
top_actual_stocks = y_test.loc[(slice(None), date)].nlargest(n_top_stocks).index.get_level_values('ticker').tolist()
# Calculate the hit ratio for the current date
hits = len(set(top_predicted_stocks) & set(top_actual_stocks))
hit_ratio = hits / n_top_stocks
#print(hit_ratio)
hit_ratios.append(hit_ratio)
# Return the average hit ratio across all dates
return sum(hit_ratios) / len(hit_ratios)
hit_ratio = calculate_hit_ratio(y_test, y_pred, n_top_stocks=100)
print(f"Hit Ratio: { hit_ratio:.3}")
# 7. Information Coefficient (IC)
def calculate_ic(y_test, y_pred):
# Calculate the IC for each date
ic_scores = y_test.groupby(level='date').apply(lambda x: pearsonr(x, y_pred.loc[x.index])[0])
#ic_scores = y_test.groupby(level='date').apply(lambda x: spearmanr(x, y_pred.loc[x.index])[0])
# Return the mean IC score
return ic_scores.mean()
ic = calculate_ic(y_test, y_pred)
print(f"Information Coefficient (IC): { ic:.3} ")
```

The most promising and intuitive one was Hit Ratio:

Basically I'm checking every day, which stocks I should have selected as the top stocks from my predictor (in this case largest returns) and compare it with the top stocks from y_test.

It looks like the Hit Ratio scales nicely with the Pearson and Spearman Rank Correlation, and as well with IC - at least most of the time (70%)

Just for information, I'm hanging around with a hit ratio of 7% using 100 stocks.

Does someone have so other suggestion how to access the quality of the prediction? (Just looking at return, I believe does not help.)