Simon/OHLCVPredictor
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best prediction so far

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This commit is contained in:
Simon Moisy 2025-06-03 16:46:01 +08:00
parent 0bbb0e52af
commit 120c366576
1 changed files with 9 additions and 9 deletions
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18
main.py
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@ -205,13 +205,13 @@ if __name__ == '__main__':
test_timestamps = df['Timestamp'].values[split_idx:] test_timestamps = df['Timestamp'].values[split_idx:]
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test) model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
booster = model.train( booster = model.train(eval_metric='rmse')
colsample_bytree=1.0, # colsample_bytree=1.0,
learning_rate=0.05, # learning_rate=0.05,
max_depth=7, # max_depth=7,
n_estimators=200, # n_estimators=200,
subsample=0.8 # subsample=0.8
) # )
model.save_model(f'../data/xgboost_model_all_features.json') model.save_model(f'../data/xgboost_model_all_features.json')
test_preds = model.predict(X_test) test_preds = model.predict(X_test)
@ -232,7 +232,7 @@ if __name__ == '__main__':
predicted_prices.append(predicted_prices[-1] * np.exp(r_)) predicted_prices.append(predicted_prices[-1] * np.exp(r_))
predicted_prices = np.array(predicted_prices[1:]) predicted_prices = np.array(predicted_prices[1:])
mae = mean_absolute_error(actual_prices, predicted_prices) # mae = mean_absolute_error(actual_prices, predicted_prices)
r2 = r2_score(actual_prices, predicted_prices) r2 = r2_score(actual_prices, predicted_prices)
direction_actual = np.sign(np.diff(actual_prices)) direction_actual = np.sign(np.diff(actual_prices))
direction_pred = np.sign(np.diff(predicted_prices)) direction_pred = np.sign(np.diff(predicted_prices))
@ -247,7 +247,7 @@ if __name__ == '__main__':
importance = feature_importance_dict.get(feature, 0.0) importance = feature_importance_dict.get(feature, 0.0)
fi_str = format(importance, ".6f") fi_str = format(importance, ".6f")
row = [feature] row = [feature]
for val in [rmse, mae, r2, mape, directional_accuracy]: for val in [rmse, mape, r2, directional_accuracy]:
if isinstance(val, float): if isinstance(val, float):
row.append(format(val, '.10f')) row.append(format(val, '.10f'))
else: else: