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a22914731f
| Author | SHA1 | Date | |
|---|---|---|---|
 Simon Moisy
|
a22914731f | ||
|
Simon Moisy
|
81e4b640a7 |
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+1
-1
@@ -1,5 +1,5 @@
|
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# ---> Python
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*.json
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/credentials/*.json
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*.csv
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*.png
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# Byte-compiled / optimized / DLL files
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@@ -0,0 +1 @@
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{"learner":{"attributes":{"best_iteration":"15","best_score":"6.720108134389e-05"},"feature_names":[],"feature_types":[],"gradient_booster":{"model":{"gbtree_model_param":{"num_parallel_tree":"1","num_trees":"26"},"iteration_indptr":[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],"tree_info":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"trees":[{"base_weights":[-3.8670794E-12,-1.2762184E-3,4.3431539E-4,-3.9894213E-3,-1.0002927E-3,2.0443962E-4,2.399732E-3,-7.335503E-3,-3.3200998E-3,-1.6589444E-3,-7.3369226E-4,-2.359659E-5,7.121429E-4,2.0261202E-3,5.7618795E-3,-6.589136E-3,-1.0167347E-2,-4.0106303E-3,-2.859653E-3,-2.069192E-3,-1.4351582E-3,-9.593777E-4,-5.8022403E-4,-2.2667211E-4,9.283359E-5,5.3504505E-4,1.0043513E-3,1.6946669E-3,2.8878527E-3,4.3291645E-3,7.194028E-3,-6.0462463E-3,-7.301802E-3,-1.4424558E-2,-8.898965E-3,-4.4059707E-3,-1.0844924E-3,-9.4653416E-4,-2.7118041E-3,-2.2472097E-3,-1.89112E-3,-1.5871366E-3,-1.3084923E-3,-1.0704146E-3,-8.606699E-4,-6.775895E-4,-5.037194E-4,-3.216747E-4,-1.440601E-4,1.759575E-5,2.265892E-4,4.384972E-4,6.508992E-4,8.859183E-4,1.149094E-3,1.505428E-3,1.9974257E-3,2.586768E-3,3.3393602E-3,1.2576575E-3,1.3056449E-3,6.3861436E-3,9.752441E-3,-1.7154933E-3,-1.9069613E-3,-2.6050785E-3,-2.0008117E-3,-3.790425E-3,-5.4830397E-3,-2.4164463E-3,-3.2439483E-3,-1.2794831E-3,-1.3290202E-3,-8.4991683E-4,-7.771017E-4,-7.195334E-4,-6.5145025E-4,-5.962644E-4,-5.528489E-4,-5.050309E-4,-4.568691E-4,-4.1120092E-4,-3.7388413E-4,-3.3835875E-4,-3.0388398E-4,-2.7420704E-4,-2.453918E-4,-2.1776417E-4,-1.9120124E-4,-1.6396039E-4,-1.3826939E-4,-1.1084072E-4,-8.216347E-5,-5.614325E-5,-3.1369636E-5,-3.1354841E-6,2.2107035E-5,5.222811E-5,8.493644E-5,1.16575524E-4,1.4652166E-4,1.7919521E-4,2.1363827E-4,2.4925088E-4,2.8560057E-4,3.2536112E-4,3.6893066E-4,4.1865403E-4,4.8459388E-4,5.6614267E-4,6.488298E-4,7.4328127E-4,8.41465E-4,9.352878E-4,1.0682496E-3,1.7701401E-3,2.0759057E-3,2.634289E-3,3.9918087E-3],"categories":[],"categories_nodes":[],"categories_segments":[],"categories_sizes":[],"default_left":[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],"id":0,"left_children":[1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,-1,-1,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103,105,107,109,111,113,-1,-1,115,117,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],"loss_changes":[1.8017862E0,6.1790013E-1,1.0957562E0,1.7060888E-1,1.3155204E-1,2.5138694E-1,3.1899834E-1,2.6773572E-2,2.0175636E-2,1.9815207E-2,1.8471032E-2,3.5427332E-2,3.482634E-2,6.527841E-2,5.208206E-2,3.8453639E-3,1.4164835E-2,3.9566457E-3,1.6555488E-3,2.4109185E-3,2.6868284E-3,2.365008E-3,2.3643076E-3,4.2852517E-3,9.583905E-3,4.686773E-3,4.3524206E-3,9.454727E-3,8.6224675E-3,2.1725893E-5,2.61873E-2,5.4489076E-4,3.7365109E-3,3.9473176E-3,3.2124668E-3,2.4273992E-5,0E0,0E0,3.6671758E-4,4.3095648E-4,1.7373264E-4,3.901869E-4,2.9300153E-4,3.342256E-4,2.5959313E-4,2.7104467E-4,3.2567978E-4,5.800463E-4,4.969351E-4,9.58928E-4,1.0174084E-3,5.6916475E-4,6.244555E-4,5.0763786E-4,5.938709E-4,1.2246817E-3,1.1536181E-3,9.0011954E-4,1.2360811E-3,0E0,0E0,2.461046E-3,1.0398626E-2,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0,0E0],"parents":[2147483647,0,0,1,1,2,2,3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,12,12,13,13,14,14,15,15,16,16,17,17,18,18,19,19,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,30,30,31,31,32,32,33,33,34,34,35,35,38,38,39,39,40,40,41,41,42,42,43,43,44,44,45,45,46,46,47,47,48,48,49,49,50,50,51,51,52,52,53,53,54,54,55,55,56,56,57,57,58,58,61,61,62,62],"right_children":[2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,-1,-1,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102,104,106,108,110,112,114,-1,-1,116,118,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1,-1],"split_conditions":[-4.2027403E-2,-2.4846634E-1,1.3075246E-1,-5.018099E-1,-1.1910506E-1,3.440461E-2,3.869802E-1,5.4798382E-3,-3.3490157E-1,-1.7547187E-1,-7.685892E-2,-6.51732E-3,7.7357925E-2,2.305668E-1,4.965987E-1,3.6002742E-3,-1.8539244E2,-3.914499E-1,-2.9733706E-1,-2.0401648E-1,-1.438832E-1,-9.60541E-2,-5.9275903E-2,-2.3060042E-2,1.2296796E-2,5.435789E-2,1.01490654E-1,1.756651E-1,2.950238E-1,1.9697219E-3,5.4798382E-3,2.8580925E-3,-1.809319E2,4.910954E-3,7.0174255E1,1.9697219E-3,-1.0844924E-3,-9.4653416E-4,-2.6971948E-1,-2.3109923E-1,-1.9319431E-1,-1Line truncated
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@@ -31,3 +31,9 @@ class CustomXGBoostGPU:
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raise ValueError('Model not trained yet.')
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dmatrix = xgb.DMatrix(X.astype(np.float32))
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return self.model.predict(dmatrix)
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def save_model(self, file_path):
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"""Save the trained XGBoost model to the specified file path."""
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if self.model is None:
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raise ValueError('Model not trained yet.')
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self.model.save_model(file_path)
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+347
-74
@@ -6,7 +6,7 @@ import numpy as np
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from sklearn.model_selection import train_test_split
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from custom_xgboost import CustomXGBoostGPU
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from sklearn.metrics import mean_squared_error
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from plot_results import display_actual_vs_predicted, plot_target_distribution, plot_predicted_vs_actual_log_returns
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from plot_results import display_actual_vs_predicted, plot_target_distribution, plot_predicted_vs_actual_log_returns, plot_predicted_vs_actual_prices
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import ta
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from cycles.supertrend import Supertrends
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from ta.trend import SMAIndicator, DPOIndicator, IchimokuIndicator, PSARIndicator
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@@ -14,7 +14,6 @@ from ta.momentum import ROCIndicator, KAMAIndicator, UltimateOscillator, Stochas
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from ta.volatility import KeltnerChannel, DonchianChannel
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from ta.others import DailyReturnIndicator
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import time
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import concurrent.futures
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from numba import njit
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def run_indicator(func, *args):
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@@ -101,13 +100,9 @@ def calc_momentum(close):
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return ('momentum_10', close - close.shift(10))
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def calc_psar(high, low, close):
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from ta.trend import PSARIndicator
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psar = PSARIndicator(high, low, close)
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return [
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('psar', psar.psar()),
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('psar_up', psar.psar_up()),
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('psar_down', psar.psar_down())
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]
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# Use the Numba-accelerated fast_psar function for speed
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psar_values = fast_psar(np.array(high), np.array(low), np.array(close))
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return [('psar', pd.Series(psar_values, index=close.index))]
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def calc_donchian(high, low, close):
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from ta.volatility import DonchianChannel
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@@ -220,17 +215,18 @@ if __name__ == '__main__':
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csv_path = './data/btcusd_1-min_data.csv'
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csv_prefix = os.path.splitext(os.path.basename(csv_path))[0]
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print('Reading CSV and filtering data...')
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df = pd.read_csv(csv_path)
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df = df[df['Volume'] != 0]
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min_date = '2017-06-01'
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print('Converting Timestamp and filtering by date...')
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df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
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df = df[df['Timestamp'] >= min_date]
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lags = 3
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print('Calculating log returns as the new target...')
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df['log_return'] = np.log(df['Close'] / df['Close'].shift(1))
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ohlcv_cols = ['Open', 'High', 'Low', 'Close', 'Volume']
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@@ -243,55 +239,282 @@ if __name__ == '__main__':
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# --- Technical Indicator Features: Calculate or Load from Cache ---
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print('Calculating or loading technical indicator features...')
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indicator_jobs = [
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('rsi', calc_rsi, [df['Close']]),
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('macd', calc_macd, [df['Close']]),
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('atr', calc_atr, [df['High'], df['Low'], df['Close']]),
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('cci', calc_cci, [df['High'], df['Low'], df['Close']]),
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('williams_r', calc_williamsr, [df['High'], df['Low'], df['Close']]),
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('ema_14', calc_ema, [df['Close']]),
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('obv', calc_obv, [df['Close'], df['Volume']]),
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('cmf', calc_cmf, [df['High'], df['Low'], df['Close'], df['Volume']]),
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('roc_10', calc_roc, [df['Close']]),
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('dpo_20', calc_dpo, [df['Close']]),
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('ultimate_osc', calc_ultimate, [df['High'], df['Low'], df['Close']]),
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('daily_return', calc_daily_return, [df['Close']]),
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]
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# RSI
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feature_file = f'./data/{csv_prefix}_rsi.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['rsi'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: rsi')
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_, values = calc_rsi(df['Close'])
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features_dict['rsi'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# MACD
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feature_file = f'./data/{csv_prefix}_macd.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['macd'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: macd')
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_, values = calc_macd(df['Close'])
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features_dict['macd'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# ATR
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feature_file = f'./data/{csv_prefix}_atr.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['atr'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: atr')
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_, values = calc_atr(df['High'], df['Low'], df['Close'])
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features_dict['atr'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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# CCI
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feature_file = f'./data/{csv_prefix}_cci.npy'
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if os.path.exists(feature_file):
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print(f'A Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['cci'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: cci')
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_, values = calc_cci(df['High'], df['Low'], df['Close'])
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features_dict['cci'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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|
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# Williams %R
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feature_file = f'./data/{csv_prefix}_williams_r.npy'
|
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if os.path.exists(feature_file):
|
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print(f'A Loading cached feature: {feature_file}')
|
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arr = np.load(feature_file)
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features_dict['williams_r'] = pd.Series(arr, index=df.index)
|
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else:
|
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print('Calculating feature: williams_r')
|
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_, values = calc_williamsr(df['High'], df['Low'], df['Close'])
|
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features_dict['williams_r'] = values
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np.save(feature_file, values.values)
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print(f'Saved feature: {feature_file}')
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|
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# 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}')
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arr = np.load(feature_file)
|
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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)
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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])
|
||||
@@ -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')
|
||||
Reference in new issue
Block a user