Did feature selection, moved from RMSE to MAE for accuracy testing (maybe not a good idea after all), fine tuned hyperparameters a bit (need to do more)
This commit is contained in:
Simon Moisy
parent
8d3f045a92
commit
0bbb0e52af
@ -15,8 +15,8 @@ class CustomXGBoostGPU:
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'tree_method': 'hist',
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'device': 'cuda',
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'objective': 'reg:squarederror',
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'eval_metric': 'rmse',
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'verbosity': 1,
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'eval_metric': 'mae',
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'verbosity': 0,
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}
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params.update(xgb_params)
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self.params = params # Store params for later access
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@ -37,3 +37,12 @@ class CustomXGBoostGPU:
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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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def get_feature_importance(self, feature_names):
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if self.model is None:
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raise ValueError('Model not trained yet.')
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# get_score returns a dict with keys like 'f0', 'f1', ...
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score_dict = self.model.get_score(importance_type='weight')
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# Map to feature names
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importances = [score_dict.get(f'f{i}', 0.0) for i in range(len(feature_names))]
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return dict(zip(feature_names, importances))
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401
feature_engineering.py
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401
feature_engineering.py
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@ -0,0 +1,401 @@
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import os
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import numpy as np
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import pandas as pd
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import ta
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from technical_indicator_functions import *
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def feature_engineering(df, csv_prefix, ohlcv_cols, lags, window_sizes):
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feature_file = f'../data/{csv_prefix}_rsi.npy'
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features_dict = {}
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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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# 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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# EMA 14
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feature_file = f'../data/{csv_prefix}_ema_14.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['ema_14'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: ema_14')
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_, values = calc_ema(df['Close'])
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features_dict['ema_14'] = 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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# OBV
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feature_file = f'../data/{csv_prefix}_obv.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['obv'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: obv')
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_, values = calc_obv(df['Close'], df['Volume'])
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features_dict['obv'] = 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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# CMF
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feature_file = f'../data/{csv_prefix}_cmf.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['cmf'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: cmf')
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_, values = calc_cmf(df['High'], df['Low'], df['Close'], df['Volume'])
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features_dict['cmf'] = 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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# ROC 10
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feature_file = f'../data/{csv_prefix}_roc_10.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['roc_10'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: roc_10')
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_, values = calc_roc(df['Close'])
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features_dict['roc_10'] = 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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# DPO 20
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feature_file = f'../data/{csv_prefix}_dpo_20.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['dpo_20'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: dpo_20')
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_, values = calc_dpo(df['Close'])
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features_dict['dpo_20'] = 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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# Ultimate Oscillator
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feature_file = f'../data/{csv_prefix}_ultimate_osc.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['ultimate_osc'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: ultimate_osc')
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_, values = calc_ultimate(df['High'], df['Low'], df['Close'])
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features_dict['ultimate_osc'] = 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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# Daily Return
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feature_file = f'../data/{csv_prefix}_daily_return.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['daily_return'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: daily_return')
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_, values = calc_daily_return(df['Close'])
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features_dict['daily_return'] = 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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# Multi-column indicators
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# Bollinger Bands
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print('Calculating multi-column indicator: bollinger')
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result = calc_bollinger(df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Stochastic Oscillator
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print('Calculating multi-column indicator: stochastic')
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result = calc_stochastic(df['High'], df['Low'], df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# SMA
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print('Calculating multi-column indicator: sma')
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result = calc_sma(df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# PSAR
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print('Calculating multi-column indicator: psar')
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result = calc_psar(df['High'], df['Low'], df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Donchian Channel
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print('Calculating multi-column indicator: donchian')
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result = calc_donchian(df['High'], df['Low'], df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Keltner Channel
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print('Calculating multi-column indicator: keltner')
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result = calc_keltner(df['High'], df['Low'], df['Close'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Ichimoku
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print('Calculating multi-column indicator: ichimoku')
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result = calc_ichimoku(df['High'], df['Low'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Elder Ray
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print('Calculating multi-column indicator: elder_ray')
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result = calc_elder_ray(df['Close'], df['Low'], df['High'])
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for subname, values in result:
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print(f"Adding subfeature: {subname}")
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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if os.path.exists(sub_feature_file):
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print(f'B Loading cached feature: {sub_feature_file}')
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arr = np.load(sub_feature_file)
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features_dict[subname] = pd.Series(arr, index=df.index)
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else:
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Prepare lags, rolling stats, log returns, and volatility features sequentially
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# Lags
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for col in ohlcv_cols:
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for lag in range(1, lags + 1):
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feature_name = f'{col}_lag{lag}'
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feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
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if os.path.exists(feature_file):
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print(f'C Loading cached feature: {feature_file}')
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features_dict[feature_name] = np.load(feature_file)
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else:
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print(f'Computing lag feature: {feature_name}')
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result = compute_lag(df, col, lag)
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features_dict[feature_name] = result
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np.save(feature_file, result.values)
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print(f'Saved feature: {feature_file}')
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# Rolling statistics
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for col in ohlcv_cols:
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for window in window_sizes:
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if (col == 'Open' and window == 5):
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continue
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if (col == 'High' and window == 5):
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continue
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if (col == 'High' and window == 30):
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continue
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if (col == 'Low' and window == 15):
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continue
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for stat in ['mean', 'std', 'min', 'max']:
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feature_name = f'{col}_roll_{stat}_{window}'
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feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
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if os.path.exists(feature_file):
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print(f'D Loading cached feature: {feature_file}')
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features_dict[feature_name] = np.load(feature_file)
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else:
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print(f'Computing rolling stat feature: {feature_name}')
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result = compute_rolling(df, col, stat, window)
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features_dict[feature_name] = result
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np.save(feature_file, result.values)
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print(f'Saved feature: {feature_file}')
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# Log returns for different horizons
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for horizon in [5, 15, 30]:
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feature_name = f'log_return_{horizon}'
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feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
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if os.path.exists(feature_file):
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print(f'E Loading cached feature: {feature_file}')
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features_dict[feature_name] = np.load(feature_file)
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else:
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print(f'Computing log return feature: {feature_name}')
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result = compute_log_return(df, horizon)
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features_dict[feature_name] = result
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np.save(feature_file, result.values)
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print(f'Saved feature: {feature_file}')
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# Volatility
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for window in window_sizes:
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feature_name = f'volatility_{window}'
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feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
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if os.path.exists(feature_file):
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print(f'F Loading cached feature: {feature_file}')
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features_dict[feature_name] = np.load(feature_file)
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else:
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print(f'Computing volatility feature: {feature_name}')
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result = compute_volatility(df, window)
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features_dict[feature_name] = result
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np.save(feature_file, result.values)
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print(f'Saved feature: {feature_file}')
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# --- Additional Technical Indicator Features ---
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# ADX
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adx_names = ['adx', 'adx_pos', 'adx_neg']
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adx_files = [f'../data/{csv_prefix}_{name}.npy' for name in adx_names]
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if all(os.path.exists(f) for f in adx_files):
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print('G Loading cached features: ADX')
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for name, f in zip(adx_names, adx_files):
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arr = np.load(f)
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features_dict[name] = pd.Series(arr, index=df.index)
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else:
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print('Calculating multi-column indicator: adx')
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result = calc_adx(df['High'], df['Low'], df['Close'])
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for subname, values in result:
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sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
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features_dict[subname] = values
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np.save(sub_feature_file, values.values)
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print(f'Saved feature: {sub_feature_file}')
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# Force Index
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feature_file = f'../data/{csv_prefix}_force_index.npy'
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if os.path.exists(feature_file):
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print(f'K Loading cached feature: {feature_file}')
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arr = np.load(feature_file)
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features_dict['force_index'] = pd.Series(arr, index=df.index)
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else:
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print('Calculating feature: force_index')
|
||||
_, values = calc_force_index(df['Close'], df['Volume'])
|
||||
features_dict['force_index'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Supertrend indicators
|
||||
for period, multiplier in [(12, 3.0), (10, 1.0), (11, 2.0)]:
|
||||
st_name = f'supertrend_{period}_{multiplier}'
|
||||
st_trend_name = f'supertrend_trend_{period}_{multiplier}'
|
||||
st_file = f'../data/{csv_prefix}_{st_name}.npy'
|
||||
st_trend_file = f'../data/{csv_prefix}_{st_trend_name}.npy'
|
||||
if os.path.exists(st_file) and os.path.exists(st_trend_file):
|
||||
print(f'L Loading cached features: {st_file}, {st_trend_file}')
|
||||
features_dict[st_name] = pd.Series(np.load(st_file), index=df.index)
|
||||
features_dict[st_trend_name] = pd.Series(np.load(st_trend_file), index=df.index)
|
||||
else:
|
||||
print(f'Calculating Supertrend indicator: {st_name}')
|
||||
st = ta.supertrend(df['High'], df['Low'], df['Close'], length=period, multiplier=multiplier)
|
||||
features_dict[st_name] = st[f'SUPERT_{period}_{multiplier}']
|
||||
features_dict[st_trend_name] = st[f'SUPERTd_{period}_{multiplier}']
|
||||
np.save(st_file, features_dict[st_name].values)
|
||||
np.save(st_trend_file, features_dict[st_trend_name].values)
|
||||
print(f'Saved features: {st_file}, {st_trend_file}')
|
||||
|
||||
return features_dict
|
||||
880
main.py
880
main.py
@ -9,7 +9,13 @@ from plot_results import plot_prediction_error_distribution, plot_direction_tran
|
||||
import time
|
||||
from numba import njit
|
||||
import csv
|
||||
import ta
|
||||
import pandas_ta as ta
|
||||
from feature_engineering import feature_engineering
|
||||
from sklearn.feature_selection import VarianceThreshold
|
||||
|
||||
charts_dir = 'charts'
|
||||
if not os.path.exists(charts_dir):
|
||||
os.makedirs(charts_dir)
|
||||
|
||||
def run_indicator(func, *args):
|
||||
return func(*args)
|
||||
@ -24,254 +30,6 @@ def run_indicator_job(job):
|
||||
print(f'Indicator {indicator_name} computed in {elapsed:.4f} seconds')
|
||||
return result
|
||||
|
||||
def calc_rsi(close):
|
||||
from ta.momentum import RSIIndicator
|
||||
return ('rsi', RSIIndicator(close, window=14).rsi())
|
||||
|
||||
def calc_macd(close):
|
||||
from ta.trend import MACD
|
||||
return ('macd', MACD(close).macd())
|
||||
|
||||
def calc_bollinger(close):
|
||||
from ta.volatility import BollingerBands
|
||||
bb = BollingerBands(close=close, window=20, window_dev=2)
|
||||
return [
|
||||
('bb_bbm', bb.bollinger_mavg()),
|
||||
('bb_bbh', bb.bollinger_hband()),
|
||||
('bb_bbl', bb.bollinger_lband()),
|
||||
('bb_bb_width', bb.bollinger_hband() - bb.bollinger_lband())
|
||||
]
|
||||
|
||||
def calc_stochastic(high, low, close):
|
||||
from ta.momentum import StochasticOscillator
|
||||
stoch = StochasticOscillator(high=high, low=low, close=close, window=14, smooth_window=3)
|
||||
return [
|
||||
('stoch_k', stoch.stoch()),
|
||||
('stoch_d', stoch.stoch_signal())
|
||||
]
|
||||
|
||||
def calc_atr(high, low, close):
|
||||
from ta.volatility import AverageTrueRange
|
||||
atr = AverageTrueRange(high=high, low=low, close=close, window=14)
|
||||
return ('atr', atr.average_true_range())
|
||||
|
||||
def calc_cci(high, low, close):
|
||||
from ta.trend import CCIIndicator
|
||||
cci = CCIIndicator(high=high, low=low, close=close, window=20)
|
||||
return ('cci', cci.cci())
|
||||
|
||||
def calc_williamsr(high, low, close):
|
||||
from ta.momentum import WilliamsRIndicator
|
||||
willr = WilliamsRIndicator(high=high, low=low, close=close, lbp=14)
|
||||
return ('williams_r', willr.williams_r())
|
||||
|
||||
def calc_ema(close):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close=close, window=14)
|
||||
return ('ema_14', ema.ema_indicator())
|
||||
|
||||
def calc_obv(close, volume):
|
||||
from ta.volume import OnBalanceVolumeIndicator
|
||||
obv = OnBalanceVolumeIndicator(close=close, volume=volume)
|
||||
return ('obv', obv.on_balance_volume())
|
||||
|
||||
def calc_cmf(high, low, close, volume):
|
||||
from ta.volume import ChaikinMoneyFlowIndicator
|
||||
cmf = ChaikinMoneyFlowIndicator(high=high, low=low, close=close, volume=volume, window=20)
|
||||
return ('cmf', cmf.chaikin_money_flow())
|
||||
|
||||
def calc_sma(close):
|
||||
from ta.trend import SMAIndicator
|
||||
return [
|
||||
('sma_50', SMAIndicator(close, window=50).sma_indicator()),
|
||||
('sma_200', SMAIndicator(close, window=200).sma_indicator())
|
||||
]
|
||||
|
||||
def calc_roc(close):
|
||||
from ta.momentum import ROCIndicator
|
||||
return ('roc_10', ROCIndicator(close, window=10).roc())
|
||||
|
||||
def calc_momentum(close):
|
||||
return ('momentum_10', close - close.shift(10))
|
||||
|
||||
def calc_psar(high, low, close):
|
||||
# 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
|
||||
donchian = DonchianChannel(high, low, close, window=20)
|
||||
return [
|
||||
('donchian_hband', donchian.donchian_channel_hband()),
|
||||
('donchian_lband', donchian.donchian_channel_lband()),
|
||||
('donchian_mband', donchian.donchian_channel_mband())
|
||||
]
|
||||
|
||||
def calc_keltner(high, low, close):
|
||||
from ta.volatility import KeltnerChannel
|
||||
keltner = KeltnerChannel(high, low, close, window=20)
|
||||
return [
|
||||
('keltner_hband', keltner.keltner_channel_hband()),
|
||||
('keltner_lband', keltner.keltner_channel_lband()),
|
||||
('keltner_mband', keltner.keltner_channel_mband())
|
||||
]
|
||||
|
||||
def calc_dpo(close):
|
||||
from ta.trend import DPOIndicator
|
||||
return ('dpo_20', DPOIndicator(close, window=20).dpo())
|
||||
|
||||
def calc_ultimate(high, low, close):
|
||||
from ta.momentum import UltimateOscillator
|
||||
return ('ultimate_osc', UltimateOscillator(high, low, close).ultimate_oscillator())
|
||||
|
||||
def calc_ichimoku(high, low):
|
||||
from ta.trend import IchimokuIndicator
|
||||
ichimoku = IchimokuIndicator(high, low, window1=9, window2=26, window3=52)
|
||||
return [
|
||||
('ichimoku_a', ichimoku.ichimoku_a()),
|
||||
('ichimoku_b', ichimoku.ichimoku_b()),
|
||||
('ichimoku_base_line', ichimoku.ichimoku_base_line()),
|
||||
('ichimoku_conversion_line', ichimoku.ichimoku_conversion_line())
|
||||
]
|
||||
|
||||
def calc_elder_ray(close, low, high):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close, window=13).ema_indicator()
|
||||
return [
|
||||
('elder_ray_bull', ema - low),
|
||||
('elder_ray_bear', ema - high)
|
||||
]
|
||||
|
||||
def calc_daily_return(close):
|
||||
from ta.others import DailyReturnIndicator
|
||||
return ('daily_return', DailyReturnIndicator(close).daily_return())
|
||||
|
||||
@njit
|
||||
def fast_psar(high, low, close, af=0.02, max_af=0.2):
|
||||
length = len(close)
|
||||
psar = np.zeros(length)
|
||||
bull = True
|
||||
af_step = af
|
||||
ep = low[0]
|
||||
psar[0] = low[0]
|
||||
for i in range(1, length):
|
||||
prev_psar = psar[i-1]
|
||||
if bull:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if low[i] < psar[i]:
|
||||
bull = False
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = low[i]
|
||||
else:
|
||||
if high[i] > ep:
|
||||
ep = high[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
else:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if high[i] > psar[i]:
|
||||
bull = True
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = high[i]
|
||||
else:
|
||||
if low[i] < ep:
|
||||
ep = low[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
return psar
|
||||
|
||||
def compute_lag(df, col, lag):
|
||||
return df[col].shift(lag)
|
||||
|
||||
def compute_rolling(df, col, stat, window):
|
||||
if stat == 'mean':
|
||||
return df[col].rolling(window).mean()
|
||||
elif stat == 'std':
|
||||
return df[col].rolling(window).std()
|
||||
elif stat == 'min':
|
||||
return df[col].rolling(window).min()
|
||||
elif stat == 'max':
|
||||
return df[col].rolling(window).max()
|
||||
|
||||
def compute_log_return(df, horizon):
|
||||
return np.log(df['Close'] / df['Close'].shift(horizon))
|
||||
|
||||
def compute_volatility(df, window):
|
||||
return df['log_return'].rolling(window).std()
|
||||
|
||||
def run_feature_job(job, df):
|
||||
feature_name, func, *args = job
|
||||
print(f'Computing feature: {feature_name}')
|
||||
result = func(df, *args)
|
||||
return feature_name, result
|
||||
|
||||
def calc_adx(high, low, close):
|
||||
from ta.trend import ADXIndicator
|
||||
adx = ADXIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('adx', adx.adx()),
|
||||
('adx_pos', adx.adx_pos()),
|
||||
('adx_neg', adx.adx_neg())
|
||||
]
|
||||
|
||||
def calc_trix(close):
|
||||
from ta.trend import TRIXIndicator
|
||||
trix = TRIXIndicator(close=close, window=15)
|
||||
return ('trix', trix.trix())
|
||||
|
||||
def calc_vortex(high, low, close):
|
||||
from ta.trend import VortexIndicator
|
||||
vortex = VortexIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('vortex_pos', vortex.vortex_indicator_pos()),
|
||||
('vortex_neg', vortex.vortex_indicator_neg())
|
||||
]
|
||||
|
||||
def calc_kama(close):
|
||||
import pandas_ta as ta
|
||||
kama = ta.kama(close, length=10)
|
||||
return ('kama', kama)
|
||||
|
||||
def calc_force_index(close, volume):
|
||||
from ta.volume import ForceIndexIndicator
|
||||
fi = ForceIndexIndicator(close=close, volume=volume, window=13)
|
||||
return ('force_index', fi.force_index())
|
||||
|
||||
def calc_eom(high, low, volume):
|
||||
from ta.volume import EaseOfMovementIndicator
|
||||
eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
|
||||
return ('eom', eom.ease_of_movement())
|
||||
|
||||
def calc_mfi(high, low, close, volume):
|
||||
from ta.volume import MFIIndicator
|
||||
mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
|
||||
return ('mfi', mfi.money_flow_index())
|
||||
|
||||
def calc_adi(high, low, close, volume):
|
||||
from ta.volume import AccDistIndexIndicator
|
||||
adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
|
||||
return ('adi', adi.acc_dist_index())
|
||||
|
||||
def calc_tema(close):
|
||||
import pandas_ta as ta
|
||||
tema = ta.tema(close, length=10)
|
||||
return ('tema', tema)
|
||||
|
||||
def calc_stochrsi(close):
|
||||
from ta.momentum import StochRSIIndicator
|
||||
stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
|
||||
return [
|
||||
('stochrsi', stochrsi.stochrsi()),
|
||||
('stochrsi_k', stochrsi.stochrsi_k()),
|
||||
('stochrsi_d', stochrsi.stochrsi_d())
|
||||
]
|
||||
|
||||
def calc_awesome_oscillator(high, low):
|
||||
from ta.momentum import AwesomeOscillatorIndicator
|
||||
ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
|
||||
return ('awesome_osc', ao.awesome_oscillator())
|
||||
|
||||
if __name__ == '__main__':
|
||||
IMPUTE_NANS = True # Set to True to impute NaNs, False to drop rows with NaNs
|
||||
csv_path = '../data/btcusd_1-min_data.csv'
|
||||
@ -298,412 +56,38 @@ if __name__ == '__main__':
|
||||
|
||||
print('Starting feature computation...')
|
||||
feature_start_time = time.time()
|
||||
|
||||
# --- Technical Indicator Features: Calculate or Load from Cache ---
|
||||
print('Calculating or loading technical indicator features...')
|
||||
# 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
|
||||
# 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:
|
||||
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 lags, rolling stats, log returns, and volatility features sequentially
|
||||
# Lags
|
||||
for col in ohlcv_cols:
|
||||
for lag in range(1, lags + 1):
|
||||
feature_name = f'{col}_lag{lag}'
|
||||
feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'C Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing lag feature: {feature_name}')
|
||||
result = compute_lag(df, col, lag)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# Rolling statistics
|
||||
for col in ohlcv_cols:
|
||||
for window in window_sizes:
|
||||
if (col == 'Open' and window == 5):
|
||||
continue
|
||||
if (col == 'High' and window == 5):
|
||||
continue
|
||||
if (col == 'High' and window == 30):
|
||||
continue
|
||||
if (col == 'Low' and window == 15):
|
||||
continue
|
||||
for stat in ['mean', 'std', 'min', 'max']:
|
||||
feature_name = f'{col}_roll_{stat}_{window}'
|
||||
feature_file = f'../data/{csv_prefix}_{feature_name}.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'D Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing rolling stat feature: {feature_name}')
|
||||
result = compute_rolling(df, col, stat, window)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# 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'E Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing log return feature: {feature_name}')
|
||||
result = compute_log_return(df, horizon)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
# 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'F Loading cached feature: {feature_file}')
|
||||
features_dict[feature_name] = np.load(feature_file)
|
||||
else:
|
||||
print(f'Computing volatility feature: {feature_name}')
|
||||
result = compute_volatility(df, window)
|
||||
features_dict[feature_name] = result
|
||||
np.save(feature_file, result.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# --- Additional Technical Indicator Features ---
|
||||
# ADX
|
||||
adx_names = ['adx', 'adx_pos', 'adx_neg']
|
||||
adx_files = [f'../data/{csv_prefix}_{name}.npy' for name in adx_names]
|
||||
if all(os.path.exists(f) for f in adx_files):
|
||||
print('G Loading cached features: ADX')
|
||||
for name, f in zip(adx_names, adx_files):
|
||||
arr = np.load(f)
|
||||
features_dict[name] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating multi-column indicator: adx')
|
||||
result = calc_adx(df['High'], df['Low'], df['Close'])
|
||||
for subname, values in result:
|
||||
sub_feature_file = f'../data/{csv_prefix}_{subname}.npy'
|
||||
features_dict[subname] = values
|
||||
np.save(sub_feature_file, values.values)
|
||||
print(f'Saved feature: {sub_feature_file}')
|
||||
|
||||
# Force Index
|
||||
feature_file = f'../data/{csv_prefix}_force_index.npy'
|
||||
if os.path.exists(feature_file):
|
||||
print(f'K Loading cached feature: {feature_file}')
|
||||
arr = np.load(feature_file)
|
||||
features_dict['force_index'] = pd.Series(arr, index=df.index)
|
||||
else:
|
||||
print('Calculating feature: force_index')
|
||||
_, values = calc_force_index(df['Close'], df['Volume'])
|
||||
features_dict['force_index'] = values
|
||||
np.save(feature_file, values.values)
|
||||
print(f'Saved feature: {feature_file}')
|
||||
|
||||
# Supertrend indicators
|
||||
for period, multiplier in [(12, 3.0), (10, 1.0), (11, 2.0)]:
|
||||
st_name = f'supertrend_{period}_{multiplier}'
|
||||
st_trend_name = f'supertrend_trend_{period}_{multiplier}'
|
||||
st_file = f'../data/{csv_prefix}_{st_name}.npy'
|
||||
st_trend_file = f'../data/{csv_prefix}_{st_trend_name}.npy'
|
||||
if os.path.exists(st_file) and os.path.exists(st_trend_file):
|
||||
print(f'L Loading cached features: {st_file}, {st_trend_file}')
|
||||
features_dict[st_name] = pd.Series(np.load(st_file), index=df.index)
|
||||
features_dict[st_trend_name] = pd.Series(np.load(st_trend_file), index=df.index)
|
||||
else:
|
||||
print(f'Calculating Supertrend indicator: {st_name}')
|
||||
st = ta.supertrend(df['High'], df['Low'], df['Close'], length=period, multiplier=multiplier)
|
||||
features_dict[st_name] = st[f'SUPERT_{period}_{multiplier}']
|
||||
features_dict[st_trend_name] = st[f'SUPERTd_{period}_{multiplier}']
|
||||
np.save(st_file, features_dict[st_name].values)
|
||||
np.save(st_trend_file, features_dict[st_trend_name].values)
|
||||
print(f'Saved features: {st_file}, {st_trend_file}')
|
||||
|
||||
# Concatenate all new features at once
|
||||
features_dict = feature_engineering(df, csv_prefix, ohlcv_cols, lags, window_sizes)
|
||||
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())
|
||||
# feature_cols_for_variance = [col for col in features_df.columns if features_df[col].dtype in [np.float32, np.float64, float, int, np.int32, np.int64]]
|
||||
# if feature_cols_for_variance:
|
||||
# selector = VarianceThreshold(threshold=1e-5)
|
||||
# filtered_features = selector.fit_transform(features_df[feature_cols_for_variance])
|
||||
# kept_mask = selector.get_support()
|
||||
# kept_feature_names = [col for col, keep in zip(feature_cols_for_variance, kept_mask) if keep]
|
||||
# print(f"Features removed by low variance: {[col for col, keep in zip(feature_cols_for_variance, kept_mask) if not keep]}")
|
||||
# # Only keep the selected features in features_df and df
|
||||
# features_df = features_df[kept_feature_names]
|
||||
# for col in feature_cols_for_variance:
|
||||
# if col not in kept_feature_names:
|
||||
# df.drop(col, axis=1, inplace=True)
|
||||
# else:
|
||||
# print("No numeric features found for variance thresholding.")
|
||||
|
||||
# Remove highly correlated features (keep only one from each correlated group)
|
||||
# corr_matrix = features_df.corr().abs()
|
||||
# upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
|
||||
# to_drop = [column for column in upper.columns if any(upper[column] > 0.95)]
|
||||
# if to_drop:
|
||||
# print(f"Features removed due to high correlation: {to_drop}")
|
||||
# features_df = features_df.drop(columns=to_drop)
|
||||
# df = df.drop(columns=to_drop)
|
||||
# else:
|
||||
# print("No highly correlated features found for removal.")
|
||||
|
||||
# Downcast all float columns to save memory
|
||||
print('Downcasting float columns to save memory...')
|
||||
for col in df.columns:
|
||||
try:
|
||||
@ -729,76 +113,158 @@ if __name__ == '__main__':
|
||||
|
||||
# Exclude 'Timestamp', 'Close', 'log_return', and any future target columns from features
|
||||
print('Selecting feature columns...')
|
||||
exclude_cols = ['Timestamp', 'Close', 'log_return', 'log_return_5', 'log_return_15', 'log_return_30']
|
||||
exclude_cols = ['Timestamp', 'Close']
|
||||
exclude_cols += ['log_return_5', 'volatility_5', 'volatility_15', 'volatility_30']
|
||||
exclude_cols += ['bb_bbm', 'bb_bbh', 'bb_bbl', 'stoch_k', 'sma_50', 'sma_200', 'psar',
|
||||
'donchian_hband', 'donchian_lband', 'donchian_mband', 'keltner_hband', 'keltner_lband',
|
||||
'keltner_mband', 'ichimoku_a', 'ichimoku_b', 'ichimoku_base_line', 'ichimoku_conversion_line',
|
||||
'Open_lag1', 'Open_lag2', 'Open_lag3', 'High_lag1', 'High_lag2', 'High_lag3', 'Low_lag1', 'Low_lag2',
|
||||
'Low_lag3', 'Close_lag1', 'Close_lag2', 'Close_lag3', 'Open_roll_mean_15', 'Open_roll_std_15', 'Open_roll_min_15',
|
||||
'Open_roll_max_15', 'Open_roll_mean_30', 'Open_roll_min_30', 'Open_roll_max_30', 'High_roll_mean_15', 'High_roll_std_15',
|
||||
'High_roll_min_15', 'High_roll_max_15', 'Low_roll_mean_5', 'Low_roll_min_5', 'Low_roll_max_5', 'Low_roll_mean_30',
|
||||
'Low_roll_std_30', 'Low_roll_min_30', 'Low_roll_max_30', 'Close_roll_mean_5', 'Close_roll_min_5', 'Close_roll_max_5',
|
||||
'Close_roll_mean_15', 'Close_roll_std_15', 'Close_roll_min_15', 'Close_roll_max_15', 'Close_roll_mean_30',
|
||||
'Close_roll_std_30', 'Close_roll_min_30', 'Close_roll_max_30', 'Volume_roll_max_5', 'Volume_roll_max_15',
|
||||
'Volume_roll_max_30', 'supertrend_12_3.0', 'supertrend_10_1.0', 'supertrend_11_2.0']
|
||||
|
||||
feature_cols = [col for col in df.columns if col not in exclude_cols]
|
||||
print('Features used for training:', feature_cols)
|
||||
|
||||
# from xgboost import XGBRegressor
|
||||
# from sklearn.model_selection import GridSearchCV
|
||||
|
||||
# # Prepare data for grid search
|
||||
# X = df[feature_cols].values.astype(np.float32)
|
||||
# y = df["log_return"].values.astype(np.float32)
|
||||
# split_idx = int(len(X) * 0.8)
|
||||
# X_train, X_test = X[:split_idx], X[split_idx:]
|
||||
# y_train, y_test = y[:split_idx], y[split_idx:]
|
||||
|
||||
# # Define parameter grid
|
||||
# param_grid = {
|
||||
# 'learning_rate': [0.01, 0.05, 0.1],
|
||||
# 'max_depth': [3, 5, 7],
|
||||
# 'n_estimators': [100, 200],
|
||||
# 'subsample': [0.8, 1.0],
|
||||
# 'colsample_bytree': [0.8, 1.0],
|
||||
# }
|
||||
|
||||
# print('Starting grid search for XGBoost hyperparameters...')
|
||||
# xgb_model = XGBRegressor(objective='reg:squarederror', tree_method='hist', device='cuda', eval_metric='mae', verbosity=0)
|
||||
# grid_search = GridSearchCV(xgb_model, param_grid, cv=3, scoring='neg_mean_absolute_error', verbose=2, n_jobs=-1)
|
||||
# grid_search.fit(X_train, y_train)
|
||||
# print('Best parameters found:', grid_search.best_params_)
|
||||
|
||||
# # Use best estimator for predictions
|
||||
# best_model = grid_search.best_estimator_
|
||||
# test_preds = best_model.predict(X_test)
|
||||
# rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
|
||||
# # Reconstruct price series from log returns
|
||||
# if 'Close' in df.columns:
|
||||
# close_prices = df['Close'].values
|
||||
# else:
|
||||
# close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
# start_price = close_prices[split_idx]
|
||||
# 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 = [start_price]
|
||||
# for r_ in test_preds:
|
||||
# predicted_prices.append(predicted_prices[-1] * np.exp(r_))
|
||||
# predicted_prices = np.array(predicted_prices[1:])
|
||||
|
||||
# mae = mean_absolute_error(actual_prices, predicted_prices)
|
||||
# r2 = r2_score(actual_prices, predicted_prices)
|
||||
# direction_actual = np.sign(np.diff(actual_prices))
|
||||
# direction_pred = np.sign(np.diff(predicted_prices))
|
||||
# directional_accuracy = (direction_actual == direction_pred).mean()
|
||||
# mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
|
||||
|
||||
# print(f'Grid search results: RMSE={rmse:.4f}, MAE={mae:.4f}, R2={r2:.4f}, MAPE={mape:.2f}%, DirAcc={directional_accuracy*100:.2f}%')
|
||||
|
||||
# plot_prefix = f'all_features_gridsearch'
|
||||
# plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
|
||||
# sys.exit(0)
|
||||
|
||||
# Prepare CSV for results
|
||||
results_csv = '../data/leave_one_out_results.csv'
|
||||
results_csv = '../data/cumulative_feature_results.csv'
|
||||
if not os.path.exists(results_csv):
|
||||
with open(results_csv, 'w', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow(['left_out_feature', 'used_features', 'rmse', 'mae', 'r2', 'mape', 'directional_accuracy'])
|
||||
writer.writerow(['num_features', 'added feature', 'rmse', 'mae', 'r2', 'mape', 'directional_accuracy', 'feature_importance'])
|
||||
|
||||
total_features = len(feature_cols)
|
||||
never_leave_out = {'Open', 'High', 'Low', 'Close', 'Volume'}
|
||||
for idx, left_out in enumerate(feature_cols):
|
||||
if left_out in never_leave_out:
|
||||
continue
|
||||
used = [f for f in feature_cols if f != left_out]
|
||||
print(f'\n=== Leave-one-out {idx+1}/{total_features}: left out {left_out} ===')
|
||||
try:
|
||||
# Prepare X and y for this combination
|
||||
X = df[used].values.astype(np.float32)
|
||||
y = df["log_return"].values.astype(np.float32)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
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:]
|
||||
try:
|
||||
X = df[feature_cols].values.astype(np.float32)
|
||||
y = df["log_return"].values.astype(np.float32)
|
||||
split_idx = int(len(X) * 0.8)
|
||||
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:]
|
||||
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
booster = model.train()
|
||||
model.save_model(f'../data/xgboost_model_wo_{left_out}.json')
|
||||
model = CustomXGBoostGPU(X_train, X_test, y_train, y_test)
|
||||
booster = model.train(
|
||||
colsample_bytree=1.0,
|
||||
learning_rate=0.05,
|
||||
max_depth=7,
|
||||
n_estimators=200,
|
||||
subsample=0.8
|
||||
)
|
||||
model.save_model(f'../data/xgboost_model_all_features.json')
|
||||
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
test_preds = model.predict(X_test)
|
||||
rmse = np.sqrt(mean_squared_error(y_test, test_preds))
|
||||
|
||||
# Reconstruct price series from log returns
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx]
|
||||
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 = [start_price]
|
||||
for r_ in test_preds:
|
||||
predicted_prices.append(predicted_prices[-1] * np.exp(r_))
|
||||
predicted_prices = np.array(predicted_prices[1:])
|
||||
# Reconstruct price series from log returns
|
||||
if 'Close' in df.columns:
|
||||
close_prices = df['Close'].values
|
||||
else:
|
||||
close_prices = pd.read_csv(csv_path)['Close'].values
|
||||
start_price = close_prices[split_idx]
|
||||
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 = [start_price]
|
||||
for r_ in test_preds:
|
||||
predicted_prices.append(predicted_prices[-1] * np.exp(r_))
|
||||
predicted_prices = np.array(predicted_prices[1:])
|
||||
|
||||
mae = mean_absolute_error(actual_prices, predicted_prices)
|
||||
r2 = r2_score(actual_prices, predicted_prices)
|
||||
direction_actual = np.sign(np.diff(actual_prices))
|
||||
direction_pred = np.sign(np.diff(predicted_prices))
|
||||
directional_accuracy = (direction_actual == direction_pred).mean()
|
||||
mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
|
||||
mae = mean_absolute_error(actual_prices, predicted_prices)
|
||||
r2 = r2_score(actual_prices, predicted_prices)
|
||||
direction_actual = np.sign(np.diff(actual_prices))
|
||||
direction_pred = np.sign(np.diff(predicted_prices))
|
||||
directional_accuracy = (direction_actual == direction_pred).mean()
|
||||
mape = np.mean(np.abs((actual_prices - predicted_prices) / actual_prices)) * 100
|
||||
|
||||
# Save results to CSV
|
||||
with open(results_csv, 'a', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
writer.writerow([left_out, "|".join(used), rmse, mae, r2, mape, directional_accuracy])
|
||||
print(f'Left out {left_out}: RMSE={rmse:.4f}, MAE={mae:.4f}, R2={r2:.4f}, MAPE={mape:.2f}%, DirAcc={directional_accuracy*100:.2f}%')
|
||||
# Save results to CSV for all features used in this run
|
||||
feature_importance_dict = model.get_feature_importance(feature_cols)
|
||||
with open(results_csv, 'a', newline='') as f:
|
||||
writer = csv.writer(f)
|
||||
for feature in feature_cols:
|
||||
importance = feature_importance_dict.get(feature, 0.0)
|
||||
fi_str = format(importance, ".6f")
|
||||
row = [feature]
|
||||
for val in [rmse, mae, r2, mape, directional_accuracy]:
|
||||
if isinstance(val, float):
|
||||
row.append(format(val, '.10f'))
|
||||
else:
|
||||
row.append(val)
|
||||
row.append(fi_str)
|
||||
writer.writerow(row)
|
||||
print('Feature importances and results saved for all features used in this run.')
|
||||
|
||||
# Plotting for this run
|
||||
plot_prefix = f'loo_{left_out}'
|
||||
print('Plotting distribution of absolute prediction errors...')
|
||||
plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
# Plotting for this run
|
||||
# plot_prefix = f'cumulative_{n}_features'
|
||||
# plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
# plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=plot_prefix)
|
||||
except Exception as e:
|
||||
print(f'Cumulative feature run failed: {e}')
|
||||
print(f'All cumulative feature runs completed. Results saved to {results_csv}')
|
||||
|
||||
plot_prefix = f'all_features'
|
||||
plot_prediction_error_distribution(predicted_prices, actual_prices, prefix=plot_prefix)
|
||||
|
||||
print('Plotting directional accuracy...')
|
||||
plot_direction_transition_heatmap(actual_prices, predicted_prices, prefix=plot_prefix)
|
||||
except Exception as e:
|
||||
print(f'Leave-one-out failed for {left_out}: {e}')
|
||||
print(f'All leave-one-out runs completed. Results saved to {results_csv}')
|
||||
sys.exit(0)
|
||||
@ -8,6 +8,7 @@ dependencies = [
|
||||
"dash>=3.0.4",
|
||||
"numba>=0.61.2",
|
||||
"pandas>=2.2.3",
|
||||
"pandas-ta>=0.3.14b0",
|
||||
"scikit-learn>=1.6.1",
|
||||
"ta>=0.11.0",
|
||||
"xgboost>=3.0.2",
|
||||
|
||||
251
technical_indicator_functions.py
Normal file
251
technical_indicator_functions.py
Normal file
@ -0,0 +1,251 @@
|
||||
from numba import njit
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
def calc_rsi(close):
|
||||
from ta.momentum import RSIIndicator
|
||||
return ('rsi', RSIIndicator(close, window=14).rsi())
|
||||
|
||||
def calc_macd(close):
|
||||
from ta.trend import MACD
|
||||
return ('macd', MACD(close).macd())
|
||||
|
||||
def calc_bollinger(close):
|
||||
from ta.volatility import BollingerBands
|
||||
bb = BollingerBands(close=close, window=20, window_dev=2)
|
||||
return [
|
||||
('bb_bbm', bb.bollinger_mavg()),
|
||||
('bb_bbh', bb.bollinger_hband()),
|
||||
('bb_bbl', bb.bollinger_lband()),
|
||||
('bb_bb_width', bb.bollinger_hband() - bb.bollinger_lband())
|
||||
]
|
||||
|
||||
def calc_stochastic(high, low, close):
|
||||
from ta.momentum import StochasticOscillator
|
||||
stoch = StochasticOscillator(high=high, low=low, close=close, window=14, smooth_window=3)
|
||||
return [
|
||||
('stoch_k', stoch.stoch()),
|
||||
('stoch_d', stoch.stoch_signal())
|
||||
]
|
||||
|
||||
def calc_atr(high, low, close):
|
||||
from ta.volatility import AverageTrueRange
|
||||
atr = AverageTrueRange(high=high, low=low, close=close, window=14)
|
||||
return ('atr', atr.average_true_range())
|
||||
|
||||
def calc_cci(high, low, close):
|
||||
from ta.trend import CCIIndicator
|
||||
cci = CCIIndicator(high=high, low=low, close=close, window=20)
|
||||
return ('cci', cci.cci())
|
||||
|
||||
def calc_williamsr(high, low, close):
|
||||
from ta.momentum import WilliamsRIndicator
|
||||
willr = WilliamsRIndicator(high=high, low=low, close=close, lbp=14)
|
||||
return ('williams_r', willr.williams_r())
|
||||
|
||||
def calc_ema(close):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close=close, window=14)
|
||||
return ('ema_14', ema.ema_indicator())
|
||||
|
||||
def calc_obv(close, volume):
|
||||
from ta.volume import OnBalanceVolumeIndicator
|
||||
obv = OnBalanceVolumeIndicator(close=close, volume=volume)
|
||||
return ('obv', obv.on_balance_volume())
|
||||
|
||||
def calc_cmf(high, low, close, volume):
|
||||
from ta.volume import ChaikinMoneyFlowIndicator
|
||||
cmf = ChaikinMoneyFlowIndicator(high=high, low=low, close=close, volume=volume, window=20)
|
||||
return ('cmf', cmf.chaikin_money_flow())
|
||||
|
||||
def calc_sma(close):
|
||||
from ta.trend import SMAIndicator
|
||||
return [
|
||||
('sma_50', SMAIndicator(close, window=50).sma_indicator()),
|
||||
('sma_200', SMAIndicator(close, window=200).sma_indicator())
|
||||
]
|
||||
|
||||
def calc_roc(close):
|
||||
from ta.momentum import ROCIndicator
|
||||
return ('roc_10', ROCIndicator(close, window=10).roc())
|
||||
|
||||
def calc_momentum(close):
|
||||
return ('momentum_10', close - close.shift(10))
|
||||
|
||||
def calc_psar(high, low, close):
|
||||
# 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
|
||||
donchian = DonchianChannel(high, low, close, window=20)
|
||||
return [
|
||||
('donchian_hband', donchian.donchian_channel_hband()),
|
||||
('donchian_lband', donchian.donchian_channel_lband()),
|
||||
('donchian_mband', donchian.donchian_channel_mband())
|
||||
]
|
||||
|
||||
def calc_keltner(high, low, close):
|
||||
from ta.volatility import KeltnerChannel
|
||||
keltner = KeltnerChannel(high, low, close, window=20)
|
||||
return [
|
||||
('keltner_hband', keltner.keltner_channel_hband()),
|
||||
('keltner_lband', keltner.keltner_channel_lband()),
|
||||
('keltner_mband', keltner.keltner_channel_mband())
|
||||
]
|
||||
|
||||
def calc_dpo(close):
|
||||
from ta.trend import DPOIndicator
|
||||
return ('dpo_20', DPOIndicator(close, window=20).dpo())
|
||||
|
||||
def calc_ultimate(high, low, close):
|
||||
from ta.momentum import UltimateOscillator
|
||||
return ('ultimate_osc', UltimateOscillator(high, low, close).ultimate_oscillator())
|
||||
|
||||
def calc_ichimoku(high, low):
|
||||
from ta.trend import IchimokuIndicator
|
||||
ichimoku = IchimokuIndicator(high, low, window1=9, window2=26, window3=52)
|
||||
return [
|
||||
('ichimoku_a', ichimoku.ichimoku_a()),
|
||||
('ichimoku_b', ichimoku.ichimoku_b()),
|
||||
('ichimoku_base_line', ichimoku.ichimoku_base_line()),
|
||||
('ichimoku_conversion_line', ichimoku.ichimoku_conversion_line())
|
||||
]
|
||||
|
||||
def calc_elder_ray(close, low, high):
|
||||
from ta.trend import EMAIndicator
|
||||
ema = EMAIndicator(close, window=13).ema_indicator()
|
||||
return [
|
||||
('elder_ray_bull', ema - low),
|
||||
('elder_ray_bear', ema - high)
|
||||
]
|
||||
|
||||
def calc_daily_return(close):
|
||||
from ta.others import DailyReturnIndicator
|
||||
return ('daily_return', DailyReturnIndicator(close).daily_return())
|
||||
|
||||
@njit
|
||||
def fast_psar(high, low, close, af=0.02, max_af=0.2):
|
||||
length = len(close)
|
||||
psar = np.zeros(length)
|
||||
bull = True
|
||||
af_step = af
|
||||
ep = low[0]
|
||||
psar[0] = low[0]
|
||||
for i in range(1, length):
|
||||
prev_psar = psar[i-1]
|
||||
if bull:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if low[i] < psar[i]:
|
||||
bull = False
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = low[i]
|
||||
else:
|
||||
if high[i] > ep:
|
||||
ep = high[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
else:
|
||||
psar[i] = prev_psar + af_step * (ep - prev_psar)
|
||||
if high[i] > psar[i]:
|
||||
bull = True
|
||||
psar[i] = ep
|
||||
af_step = af
|
||||
ep = high[i]
|
||||
else:
|
||||
if low[i] < ep:
|
||||
ep = low[i]
|
||||
af_step = min(af_step + af, max_af)
|
||||
return psar
|
||||
|
||||
def compute_lag(df, col, lag):
|
||||
return df[col].shift(lag)
|
||||
|
||||
def compute_rolling(df, col, stat, window):
|
||||
if stat == 'mean':
|
||||
return df[col].rolling(window).mean()
|
||||
elif stat == 'std':
|
||||
return df[col].rolling(window).std()
|
||||
elif stat == 'min':
|
||||
return df[col].rolling(window).min()
|
||||
elif stat == 'max':
|
||||
return df[col].rolling(window).max()
|
||||
|
||||
def compute_log_return(df, horizon):
|
||||
return np.log(df['Close'] / df['Close'].shift(horizon))
|
||||
|
||||
def compute_volatility(df, window):
|
||||
return df['log_return'].rolling(window).std()
|
||||
|
||||
def run_feature_job(job, df):
|
||||
feature_name, func, *args = job
|
||||
print(f'Computing feature: {feature_name}')
|
||||
result = func(df, *args)
|
||||
return feature_name, result
|
||||
|
||||
def calc_adx(high, low, close):
|
||||
from ta.trend import ADXIndicator
|
||||
adx = ADXIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('adx', adx.adx()),
|
||||
('adx_pos', adx.adx_pos()),
|
||||
('adx_neg', adx.adx_neg())
|
||||
]
|
||||
|
||||
def calc_trix(close):
|
||||
from ta.trend import TRIXIndicator
|
||||
trix = TRIXIndicator(close=close, window=15)
|
||||
return ('trix', trix.trix())
|
||||
|
||||
def calc_vortex(high, low, close):
|
||||
from ta.trend import VortexIndicator
|
||||
vortex = VortexIndicator(high=high, low=low, close=close, window=14)
|
||||
return [
|
||||
('vortex_pos', vortex.vortex_indicator_pos()),
|
||||
('vortex_neg', vortex.vortex_indicator_neg())
|
||||
]
|
||||
|
||||
def calc_kama(close):
|
||||
import pandas_ta as ta
|
||||
kama = ta.kama(close, length=10)
|
||||
return ('kama', kama)
|
||||
|
||||
def calc_force_index(close, volume):
|
||||
from ta.volume import ForceIndexIndicator
|
||||
fi = ForceIndexIndicator(close=close, volume=volume, window=13)
|
||||
return ('force_index', fi.force_index())
|
||||
|
||||
def calc_eom(high, low, volume):
|
||||
from ta.volume import EaseOfMovementIndicator
|
||||
eom = EaseOfMovementIndicator(high=high, low=low, volume=volume, window=14)
|
||||
return ('eom', eom.ease_of_movement())
|
||||
|
||||
def calc_mfi(high, low, close, volume):
|
||||
from ta.volume import MFIIndicator
|
||||
mfi = MFIIndicator(high=high, low=low, close=close, volume=volume, window=14)
|
||||
return ('mfi', mfi.money_flow_index())
|
||||
|
||||
def calc_adi(high, low, close, volume):
|
||||
from ta.volume import AccDistIndexIndicator
|
||||
adi = AccDistIndexIndicator(high=high, low=low, close=close, volume=volume)
|
||||
return ('adi', adi.acc_dist_index())
|
||||
|
||||
def calc_tema(close):
|
||||
import pandas_ta as ta
|
||||
tema = ta.tema(close, length=10)
|
||||
return ('tema', tema)
|
||||
|
||||
def calc_stochrsi(close):
|
||||
from ta.momentum import StochRSIIndicator
|
||||
stochrsi = StochRSIIndicator(close=close, window=14, smooth1=3, smooth2=3)
|
||||
return [
|
||||
('stochrsi', stochrsi.stochrsi()),
|
||||
('stochrsi_k', stochrsi.stochrsi_k()),
|
||||
('stochrsi_d', stochrsi.stochrsi_d())
|
||||
]
|
||||
|
||||
def calc_awesome_oscillator(high, low):
|
||||
from ta.momentum import AwesomeOscillatorIndicator
|
||||
ao = AwesomeOscillatorIndicator(high=high, low=low, window1=5, window2=34)
|
||||
return ('awesome_osc', ao.awesome_oscillator())
|
||||
11
uv.lock
generated
11
uv.lock
generated
@ -314,6 +314,7 @@ dependencies = [
|
||||
{ name = "dash" },
|
||||
{ name = "numba" },
|
||||
{ name = "pandas" },
|
||||
{ name = "pandas-ta" },
|
||||
{ name = "scikit-learn" },
|
||||
{ name = "ta" },
|
||||
{ name = "xgboost" },
|
||||
@ -324,6 +325,7 @@ requires-dist = [
|
||||
{ name = "dash", specifier = ">=3.0.4" },
|
||||
{ name = "numba", specifier = ">=0.61.2" },
|
||||
{ name = "pandas", specifier = ">=2.2.3" },
|
||||
{ name = "pandas-ta", specifier = ">=0.3.14b0" },
|
||||
{ name = "scikit-learn", specifier = ">=1.6.1" },
|
||||
{ name = "ta", specifier = ">=0.11.0" },
|
||||
{ name = "xgboost", specifier = ">=3.0.2" },
|
||||
@ -372,6 +374,15 @@ wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/ab/5f/b38085618b950b79d2d9164a711c52b10aefc0ae6833b96f626b7021b2ed/pandas-2.2.3-cp313-cp313t-musllinux_1_2_x86_64.whl", hash = "sha256:ad5b65698ab28ed8d7f18790a0dc58005c7629f227be9ecc1072aa74c0c1d43a", size = 13098436, upload-time = "2024-09-20T13:09:48.112Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "pandas-ta"
|
||||
version = "0.3.14b0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pandas" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/f7/0b/1666f0a185d4f08215f53cc088122a73c92421447b04028f0464fabe1ce6/pandas_ta-0.3.14b.tar.gz", hash = "sha256:0fa35aec831d2815ea30b871688a8d20a76b288a7be2d26cc00c35cd8c09a993", size = 115089, upload-time = "2021-07-28T20:51:17.456Z" }
|
||||
|
||||
[[package]]
|
||||
name = "plotly"
|
||||
version = "6.1.2"
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user