model updated

xgboost/custom_xgboost.py

@@ -31,3 +31,9 @@ class CustomXGBoostGPU:
             raise ValueError('Model not trained yet.')
         dmatrix = xgb.DMatrix(X.astype(np.float32))
         return self.model.predict(dmatrix)
+
+    def save_model(self, file_path):
+        """Save the trained XGBoost model to the specified file path."""
+        if self.model is None:
+            raise ValueError('Model not trained yet.')
+        self.model.save_model(file_path)

xgboost/main.py

@@ -6,7 +6,7 @@ import numpy as np
 from sklearn.model_selection import train_test_split
 from custom_xgboost import CustomXGBoostGPU
 from sklearn.metrics import mean_squared_error
-from plot_results import display_actual_vs_predicted, plot_target_distribution, plot_predicted_vs_actual_log_returns
+from plot_results import display_actual_vs_predicted, plot_target_distribution, plot_predicted_vs_actual_log_returns, plot_predicted_vs_actual_prices
 import ta
 from cycles.supertrend import Supertrends
 from ta.trend import SMAIndicator, DPOIndicator, IchimokuIndicator, PSARIndicator
@@ -14,7 +14,6 @@ from ta.momentum import ROCIndicator, KAMAIndicator, UltimateOscillator, Stochas
 from ta.volatility import KeltnerChannel, DonchianChannel
 from ta.others import DailyReturnIndicator
 import time
-import concurrent.futures
 from numba import njit
 
 def run_indicator(func, *args):
@@ -101,13 +100,9 @@ def calc_momentum(close):
     return ('momentum_10', close - close.shift(10))
 
 def calc_psar(high, low, close):
-    from ta.trend import PSARIndicator
-    psar = PSARIndicator(high, low, close)
-    return [
-        ('psar', psar.psar()),
-        ('psar_up', psar.psar_up()),
-        ('psar_down', psar.psar_down())
-    ]
+    # Use the Numba-accelerated fast_psar function for speed
+    psar_values = fast_psar(np.array(high), np.array(low), np.array(close))
+    return [('psar', pd.Series(psar_values, index=close.index))]
 
 def calc_donchian(high, low, close):
     from ta.volatility import DonchianChannel
@@ -220,17 +215,18 @@ if __name__ == '__main__':
     csv_path = './data/btcusd_1-min_data.csv'
     csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
 
+    print('Reading CSV and filtering data...')
     df = pd.read_csv(csv_path)
     df = df[df['Volume'] != 0]
 
     min_date = '2017-06-01'
+    print('Converting Timestamp and filtering by date...')
     df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
     df = df[df['Timestamp'] >= min_date]
 
     lags = 3
 
     print('Calculating log returns as the new target...')
-
     df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
 
     ohlcv_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
@@ -243,55 +239,282 @@ if __name__ == '__main__':
 
     # --- Technical Indicator Features: Calculate or Load from Cache ---
     print('Calculating or loading technical indicator features...')
-    indicator_jobs = [
-        ('rsi', calc_rsi, [df['Close']]),
-        ('macd', calc_macd, [df['Close']]),
-        ('atr', calc_atr, [df['High'], df['Low'], df['Close']]),
-        ('cci', calc_cci, [df['High'], df['Low'], df['Close']]),
-        ('williams_r', calc_williamsr, [df['High'], df['Low'], df['Close']]),
-        ('ema_14', calc_ema, [df['Close']]),
-        ('obv', calc_obv, [df['Close'], df['Volume']]),
-        ('cmf', calc_cmf, [df['High'], df['Low'], df['Close'], df['Volume']]),
-        ('roc_10', calc_roc, [df['Close']]),
-        ('dpo_20', calc_dpo, [df['Close']]),
-        ('ultimate_osc', calc_ultimate, [df['High'], df['Low'], df['Close']]),
-        ('daily_return', calc_daily_return, [df['Close']]),
-    ]
+    # RSI
+    feature_file = f'./data/{csv_prefix}_rsi.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['rsi'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: rsi')
+        _, values = calc_rsi(df['Close'])
+        features_dict['rsi'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # MACD
+    feature_file = f'./data/{csv_prefix}_macd.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['macd'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: macd')
+        _, values = calc_macd(df['Close'])
+        features_dict['macd'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # ATR
+    feature_file = f'./data/{csv_prefix}_atr.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['atr'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: atr')
+        _, values = calc_atr(df['High'], df['Low'], df['Close'])
+        features_dict['atr'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # CCI
+    feature_file = f'./data/{csv_prefix}_cci.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['cci'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: cci')
+        _, values = calc_cci(df['High'], df['Low'], df['Close'])
+        features_dict['cci'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Williams %R
+    feature_file = f'./data/{csv_prefix}_williams_r.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['williams_r'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: williams_r')
+        _, values = calc_williamsr(df['High'], df['Low'], df['Close'])
+        features_dict['williams_r'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # EMA 14
+    feature_file = f'./data/{csv_prefix}_ema_14.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['ema_14'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: ema_14')
+        _, values = calc_ema(df['Close'])
+        features_dict['ema_14'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # OBV
+    feature_file = f'./data/{csv_prefix}_obv.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['obv'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: obv')
+        _, values = calc_obv(df['Close'], df['Volume'])
+        features_dict['obv'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # CMF
+    feature_file = f'./data/{csv_prefix}_cmf.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['cmf'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: cmf')
+        _, values = calc_cmf(df['High'], df['Low'], df['Close'], df['Volume'])
+        features_dict['cmf'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # ROC 10
+    feature_file = f'./data/{csv_prefix}_roc_10.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['roc_10'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: roc_10')
+        _, values = calc_roc(df['Close'])
+        features_dict['roc_10'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # DPO 20
+    feature_file = f'./data/{csv_prefix}_dpo_20.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['dpo_20'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: dpo_20')
+        _, values = calc_dpo(df['Close'])
+        features_dict['dpo_20'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Ultimate Oscillator
+    feature_file = f'./data/{csv_prefix}_ultimate_osc.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['ultimate_osc'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: ultimate_osc')
+        _, values = calc_ultimate(df['High'], df['Low'], df['Close'])
+        features_dict['ultimate_osc'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
+    # Daily Return
+    feature_file = f'./data/{csv_prefix}_daily_return.npy'
+    if os.path.exists(feature_file):
+        print(f'A Loading cached feature: {feature_file}')
+        arr = np.load(feature_file)
+        features_dict['daily_return'] = pd.Series(arr, index=df.index)
+    else:
+        print('Calculating feature: daily_return')
+        _, values = calc_daily_return(df['Close'])
+        features_dict['daily_return'] = values
+        np.save(feature_file, values.values)
+        print(f'Saved feature: {feature_file}')
+
     # Multi-column indicators
-    multi_indicator_jobs = [
-        ('bollinger', calc_bollinger, [df['Close']]),
-        ('stochastic', calc_stochastic, [df['High'], df['Low'], df['Close']]),
-        ('sma', calc_sma, [df['Close']]),
-        ('psar', calc_psar, [df['High'], df['Low'], df['Close']]),
-        ('donchian', calc_donchian, [df['High'], df['Low'], df['Close']]),
-        ('keltner', calc_keltner, [df['High'], df['Low'], df['Close']]),
-        ('ichimoku', calc_ichimoku, [df['High'], df['Low']]),
-        ('elder_ray', calc_elder_ray, [df['Close'], df['Low'], df['High']]),
-    ]
-    for feature_name, func, args in indicator_jobs:
-        feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
-        if os.path.exists(feature_file):
-            print(f'Loading cached feature: {feature_file}')
-            features_dict[feature_name] = np.load(feature_file)
+    # Bollinger Bands
+    print('Calculating multi-column indicator: bollinger')
+    result = calc_bollinger(df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
         else:
-            result = func(*args)
-            if isinstance(result, tuple):
-                _, values = result
-                features_dict[feature_name] = values
-                np.save(feature_file, values.values)
-            else:
-                raise ValueError(f"Unexpected result for {feature_name}")
-    for feature_name, func, args in multi_indicator_jobs:
-        # These return a list of (name, values)
-        result = func(*args)
-        for subname, values in result:
-            sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
-            if os.path.exists(sub_feature_file):
-                print(f'Loading cached feature: {sub_feature_file}')
-                features_dict[subname] = np.load(sub_feature_file)
-            else:
-                features_dict[subname] = values
-                np.save(sub_feature_file, values.values)
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Stochastic Oscillator
+    print('Calculating multi-column indicator: stochastic')
+    result = calc_stochastic(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # SMA
+    print('Calculating multi-column indicator: sma')
+    result = calc_sma(df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # PSAR
+    print('Calculating multi-column indicator: psar')
+    result = calc_psar(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Donchian Channel
+    print('Calculating multi-column indicator: donchian')
+    result = calc_donchian(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Keltner Channel
+    print('Calculating multi-column indicator: keltner')
+    result = calc_keltner(df['High'], df['Low'], df['Close'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Ichimoku
+    print('Calculating multi-column indicator: ichimoku')
+    result = calc_ichimoku(df['High'], df['Low'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
+
+    # Elder Ray
+    print('Calculating multi-column indicator: elder_ray')
+    result = calc_elder_ray(df['Close'], df['Low'], df['High'])
+    for subname, values in result:
+        print(f"Adding subfeature: {subname}")
+        sub_feature_file = f'./data/{csv_prefix}_{subname}.npy'
+        if os.path.exists(sub_feature_file):
+            print(f'B Loading cached feature: {sub_feature_file}')
+            arr = np.load(sub_feature_file)
+            features_dict[subname] = pd.Series(arr, index=df.index)
+        else:
+            features_dict[subname] = values
+            np.save(sub_feature_file, values.values)
+            print(f'Saved feature: {sub_feature_file}')
 
     # Prepare jobs for lags, rolling stats, log returns, and volatility
     feature_jobs = []
@@ -301,9 +524,10 @@ if __name__ == '__main__':
             feature_name = f'{col}_lag{lag}'
             feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
             if os.path.exists(feature_file):
-                print(f'Loading cached feature: {feature_file}')
+                print(f'C Loading cached feature: {feature_file}')
                 features_dict[feature_name] = np.load(feature_file)
             else:
+                print(f'Adding lag feature job: {feature_name}')
                 feature_jobs.append((feature_name, compute_lag, col, lag))
     # Rolling statistics
     for col in ohlcv_cols:
@@ -320,48 +544,56 @@ if __name__ == '__main__':
                 feature_name = f'{col}_roll_{stat}_{window}'
                 feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
                 if os.path.exists(feature_file):
-                    print(f'Loading cached feature: {feature_file}')
+                    print(f'D Loading cached feature: {feature_file}')
                     features_dict[feature_name] = np.load(feature_file)
                 else:
+                    print(f'Adding rolling stat feature job: {feature_name}')
                     feature_jobs.append((feature_name, compute_rolling, col, stat, window))
     # Log returns for different horizons
     for horizon in [5, 15, 30]:
         feature_name = f'log_return_{horizon}'
         feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
         if os.path.exists(feature_file):
-            print(f'Loading cached feature: {feature_file}')
+            print(f'E Loading cached feature: {feature_file}')
             features_dict[feature_name] = np.load(feature_file)
         else:
+            print(f'Adding log return feature job: {feature_name}')
             feature_jobs.append((feature_name, compute_log_return, horizon))
     # Volatility
     for window in window_sizes:
         feature_name = f'volatility_{window}'
         feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
         if os.path.exists(feature_file):
-            print(f'Loading cached feature: {feature_file}')
+            print(f'F Loading cached feature: {feature_file}')
             features_dict[feature_name] = np.load(feature_file)
         else:
+            print(f'Adding volatility feature job: {feature_name}')
             feature_jobs.append((feature_name, compute_volatility, window))
 
-    # Parallel computation for all non-cached features
+    # Sequential computation for all non-cached features
     if feature_jobs:
-        print(f'Computing {len(feature_jobs)} features in parallel...')
-        with concurrent.futures.ProcessPoolExecutor() as executor:
-            futures = [executor.submit(run_feature_job, job, df) for job in feature_jobs]
-            for future in concurrent.futures.as_completed(futures):
-                feature_name, result = future.result()
-                features_dict[feature_name] = result
-                feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
-                np.save(feature_file, result.values)
-        print('All parallel features computed.')
+        print(f'Computing {len(feature_jobs)} features sequentially...')
+        for job in feature_jobs:
+            print(f'Computing feature job: {job[0]}')
+            feature_name, result = run_feature_job(job, df)
+            features_dict[feature_name] = result
+            feature_file = f'./data/{csv_prefix}_{feature_name}.npy'
+            np.save(feature_file, result.values)
+            print(f'Saved computed feature: {feature_file}')
+        print('All features computed.')
     else:
         print('All features loaded from cache.')
 
     # Concatenate all new features at once
     print('Concatenating all new features to DataFrame...')
     features_df = pd.DataFrame(features_dict)
+    print("Columns in features_df:", features_df.columns.tolist())
+    print("All-NaN columns in features_df:", features_df.columns[features_df.isna().all()].tolist())
     df = pd.concat([df, features_df], axis=1)
 
+    # Print all columns after concatenation
+    print("All columns in df after concat:", df.columns.tolist())
+
     # Downcast all float columns to save memory
     print('Downcasting float columns to save memory...')
     for col in df.columns:
@@ -371,6 +603,7 @@ if __name__ == '__main__':
             pass
 
     # Drop intermediate features_df to free memory
+    print('Dropping intermediate features_df to free memory...')
     del features_df
     import gc
     gc.collect()
@@ -408,6 +641,10 @@ if __name__ == '__main__':
     print('Selecting feature columns...')
     exclude_cols = ['Timestamp', 'Close', 'log_return', 'log_return_5', 'log_return_15', 'log_return_30']
     feature_cols = [col for col in df.columns if col not in exclude_cols]
+
+    # Print the features used for training
+    print("Features used for training:", feature_cols)
+
     # Drop excluded columns to save memory
     print('Dropping excluded columns to save memory...')
     df = df[feature_cols + ['log_return', 'Timestamp']]
@@ -417,6 +654,7 @@ if __name__ == '__main__':
     y = df['log_return'].values.astype(np.float32)
   
     split_idx = int(len(X) * 0.8)
+    print(f'Splitting data: {split_idx} train, {len(X) - split_idx} test')
     X_train, X_test = X[:split_idx], X[split_idx:]
     y_train, y_test = y[:split_idx], y[split_idx:]
     test_timestamps = df['Timestamp'].values[split_idx:]
@@ -429,6 +667,10 @@ if __name__ == '__main__':
  
     print('Training complete.')
 
+    # Save the trained model
+    model.save_model('./data/xgboost_model.json')
+    print('Model saved to ./data/xgboost_model.json')
+
     if hasattr(model, 'params'):
         print("Model hyperparameters:", model.params)
     if hasattr(model, 'model') and hasattr(model.model, 'get_score'):
@@ -441,18 +683,49 @@ if __name__ == '__main__':
         for feat, score in sorted_importances:
             print(f'{feature_map.get(feat, feat)}: {score}')
 
+    print('Making predictions for first 5 test samples...')
     preds = model.predict(X_test[:5])
     print('Predictions for first 5 test samples:', preds)
     print('Actual values for first 5 test samples:', y_test[:5])
 
+    print('Making predictions for all test samples...')
     test_preds = model.predict(X_test)
     rmse = np.sqrt(mean_squared_error(y_test, test_preds))
     print(f'RMSE on test set: {rmse:.4f}')
 
+    print('Saving y_test and test_preds to disk...')
     np.save('./data/y_test.npy', y_test)
     np.save('./data/test_preds.npy', test_preds)
 
-    # display_actual_vs_predicted(y_test, test_preds, test_timestamps)
-    # plot_target_distribution(y_train, y_test)
+    # Reconstruct price series from log returns
+    print('Reconstructing price series from log returns...')
+    # Get the last available Close price before the test set
+    # The DataFrame df has been reset, so use split_idx to get the right row
+    if 'Close' in df.columns:
+        close_prices = df['Close'].values
+    else:
+        # Reload original CSV to get Close prices if not present
+        close_prices = pd.read_csv(csv_path)['Close'].values
+    start_price = close_prices[split_idx]  # This is the price at the split point
+    # Actual prices
+    actual_prices = [start_price]
+    for r in y_test:
+        actual_prices.append(actual_prices[-1] * np.exp(r))
+    actual_prices = np.array(actual_prices[1:])
+    # Predicted prices
+    predicted_prices = [start_price]
+    for r in test_preds:
+        predicted_prices.append(predicted_prices[-1] * np.exp(r))
+    predicted_prices = np.array(predicted_prices[1:])
 
-    plot_predicted_vs_actual_log_returns(y_test, test_preds, test_timestamps)
+    print('Plotting predicted vs actual prices...')
+    plot_predicted_vs_actual_prices(actual_prices, predicted_prices, test_timestamps)
+
+    print("Final features used for training:", feature_cols)
+
+    print("Shape of X:", X.shape)
+    print("First row of X:", X[0])
+    print("stoch_k in feature_cols?", "stoch_k" in feature_cols)
+    if "stoch_k" in feature_cols:
+        idx = feature_cols.index("stoch_k")
+        print("First 10 values of stoch_k:", X[:10, idx])

xgboost/plot_results.py

@@ -109,3 +109,61 @@ def plot_predicted_vs_actual_log_returns(y_test, test_preds, timestamps=None, n_
     )
     fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
     pyo.plot(fig_scatter, filename='log_return_scatter_plot.html')
+
+def plot_predicted_vs_actual_prices(actual_prices, predicted_prices, timestamps=None, n_plot=200):
+    import plotly.offline as pyo
+    import plotly.graph_objs as go
+    n_plot = min(n_plot, len(actual_prices))
+    actual = actual_prices[:n_plot]
+    predicted = predicted_prices[:n_plot]
+    if timestamps is not None:
+        x_axis = timestamps[:n_plot]
+        x_label = 'Timestamp'
+    else:
+        x_axis = list(range(n_plot))
+        x_label = 'Index'
+
+    # Line plot: Actual vs Predicted over time
+    trace_actual = go.Scatter(x=x_axis, y=actual, mode='lines', name='Actual Price')
+    trace_predicted = go.Scatter(x=x_axis, y=predicted, mode='lines', name='Predicted Price')
+    data_line = [trace_actual, trace_predicted]
+    layout_line = go.Layout(
+        title='Actual vs. Predicted BTC Prices (Test Set)',
+        xaxis={'title': x_label},
+        yaxis={'title': 'BTC Price'},
+        legend={'x': 0, 'y': 1},
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_line = go.Figure(data=data_line, layout=layout_line)
+    pyo.plot(fig_line, filename='price_line_plot.html')
+
+    # Scatter plot: Predicted vs Actual
+    trace_scatter = go.Scatter(
+        x=actual,
+        y=predicted,
+        mode='markers',
+        name='Predicted vs Actual',
+        opacity=0.5
+    )
+    # Diagonal reference line
+    min_val = min(np.min(actual), np.min(predicted))
+    max_val = max(np.max(actual), np.max(predicted))
+    trace_diag = go.Scatter(
+        x=[min_val, max_val],
+        y=[min_val, max_val],
+        mode='lines',
+        name='Ideal',
+        line=dict(dash='dash', color='red')
+    )
+    data_scatter = [trace_scatter, trace_diag]
+    layout_scatter = go.Layout(
+        title='Predicted vs Actual Prices (Scatter)',
+        xaxis={'title': 'Actual Price'},
+        yaxis={'title': 'Predicted Price'},
+        showlegend=True,
+        margin={'l': 40, 'b': 40, 't': 40, 'r': 10},
+        hovermode='closest'
+    )
+    fig_scatter = go.Figure(data=data_scatter, layout=layout_scatter)
+    pyo.plot(fig_scatter, filename='price_scatter_plot.html')