pushing this all to git before cleaing it. putting this thing on my fucking Dell Poweredge bc god knows im not waiting 2 hours to run another ML test on this 12 core machine
This commit is contained in:
klein panic
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import os
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import sys
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import argparse # Added for argument parsing
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import seaborn as sns
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import logging
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from tabulate import tabulate
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from sklearn.model_selection import TimeSeriesSplit, GridSearchCV
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import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import LSTM, GRU, Dense, Dropout, Bidirectional
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from tensorflow.keras.optimizers import Adam, Nadam
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from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
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from tensorflow.keras.losses import Huber
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import xgboost as xgb
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import optuna
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from optuna.integration import KerasPruningCallback
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# For Reinforcement Learning
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import gym
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from gym import spaces
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from stable_baselines3 import DQN
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from stable_baselines3.common.vec_env import DummyVecEnv
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# To handle parallelization
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import multiprocessing
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# Suppress TensorFlow warnings
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress INFO and WARNING messages
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# Configure logging
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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# 1. Data Loading and Preprocessing
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def load_data(file_path):
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logging.info(f"Loading data from: {file_path}")
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try:
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# Parse 'time' column as dates
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data = pd.read_csv(file_path, parse_dates=['time'])
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except FileNotFoundError:
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logging.error(f"File not found: {file_path}")
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sys.exit(1)
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except pd.errors.ParserError as e:
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logging.error(f"Error parsing CSV file: {e}")
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sys.exit(1)
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except Exception as e:
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logging.error(f"Unexpected error: {e}")
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sys.exit(1)
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# Rename columns to match script expectations
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rename_mapping = {
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'time': 'Date',
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'open': 'Open',
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'high': 'High',
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'low': 'Low',
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'close': 'Close'
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}
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data.rename(columns=rename_mapping, inplace=True)
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logging.info(f"Data columns after renaming: {data.columns.tolist()}")
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# Sort and reset index
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data.sort_values('Date', inplace=True)
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data.reset_index(drop=True, inplace=True)
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logging.info("Data loaded and sorted successfully.")
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return data
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def compute_rsi(series, window=14):
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delta = series.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
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RS = gain / loss
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RSI = 100 - (100 / (1 + RS))
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return RSI
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def compute_macd(series, span_short=12, span_long=26, span_signal=9):
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ema_short = series.ewm(span=span_short, adjust=False).mean()
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ema_long = series.ewm(span=span_long, adjust=False).mean()
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MACD = ema_short - ema_long
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signal = MACD.ewm(span=span_signal, adjust=False).mean()
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return MACD - signal
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def compute_adx(df, window=14):
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# Placeholder for ADX calculation
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return df['Close'].rolling(window=window).std() # Simplistic placeholder
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def compute_obv(df):
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# On-Balance Volume calculation
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OBV = (np.sign(df['Close'].diff()) * df['Volume']).fillna(0).cumsum()
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return OBV
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def calculate_technical_indicators(df):
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logging.info("Calculating technical indicators...")
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df['SMA_5'] = df['Close'].rolling(window=5).mean()
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df['SMA_10'] = df['Close'].rolling(window=10).mean()
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df['EMA_5'] = df['Close'].ewm(span=5, adjust=False).mean()
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df['EMA_10'] = df['Close'].ewm(span=10, adjust=False).mean()
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df['STDDEV_5'] = df['Close'].rolling(window=5).std()
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df['RSI'] = compute_rsi(df['Close'], window=14)
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df['MACD'] = compute_macd(df['Close'])
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df['ADX'] = compute_adx(df)
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df['OBV'] = compute_obv(df)
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df.dropna(inplace=True) # Drop rows with NaN values after feature engineering
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logging.info("Technical indicators calculated successfully.")
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return df
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# Argument Parsing
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def parse_arguments():
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parser = argparse.ArgumentParser(description='Train LSTM and DQN models for stock trading.')
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parser.add_argument('csv_path', type=str, help='Path to the CSV data file.')
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return parser.parse_args()
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def main():
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# Parse command-line arguments
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args = parse_arguments()
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csv_path = args.csv_path
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# Load and preprocess data
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data = load_data(csv_path)
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data = calculate_technical_indicators(data)
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# Feature selection
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feature_columns = ['SMA_5', 'SMA_10', 'EMA_5', 'EMA_10', 'STDDEV_5', 'RSI', 'MACD', 'ADX', 'OBV', 'Volume', 'Open', 'High', 'Low']
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target_column = 'Close'
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data = data[['Date'] + feature_columns + [target_column]]
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data.dropna(inplace=True)
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# Scaling
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scaler_features = MinMaxScaler()
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scaler_target = MinMaxScaler()
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scaled_features = scaler_features.fit_transform(data[feature_columns])
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scaled_target = scaler_target.fit_transform(data[[target_column]]).flatten()
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# Create sequences for LSTM
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def create_sequences(features, target, window_size=15):
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X, y = [], []
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for i in range(len(features) - window_size):
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X.append(features[i:i+window_size])
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y.append(target[i+window_size])
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return np.array(X), np.array(y)
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window_size = 15
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X, y = create_sequences(scaled_features, scaled_target, window_size)
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# Split data into training, validation, and testing sets
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train_size = int(len(X) * 0.7)
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val_size = int(len(X) * 0.15)
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test_size = len(X) - train_size - val_size
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X_train, X_val, X_test = X[:train_size], X[train_size:train_size+val_size], X[train_size+val_size:]
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y_train, y_val, y_test = y[:train_size], y[train_size:train_size+val_size], y[train_size+val_size:]
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logging.info(f"Scaled training features shape: {X_train.shape}")
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logging.info(f"Scaled validation features shape: {X_val.shape}")
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logging.info(f"Scaled testing features shape: {X_test.shape}")
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logging.info(f"Scaled training target shape: {y_train.shape}")
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logging.info(f"Scaled validation target shape: {y_val.shape}")
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logging.info(f"Scaled testing target shape: {y_test.shape}")
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# 2. Device Configuration
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def configure_device():
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gpus = tf.config.list_physical_devices('GPU')
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if gpus:
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try:
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logging.info(f"{len(gpus)} GPU(s) detected and configured.")
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except RuntimeError as e:
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logging.error(e)
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else:
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logging.info("No GPU detected, using CPU.")
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configure_device()
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# 3. Model Building
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def build_advanced_lstm(input_shape, hyperparams):
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model = Sequential()
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for i in range(hyperparams['num_lstm_layers']):
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return_sequences = True if i < hyperparams['num_lstm_layers'] - 1 else False
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model.add(Bidirectional(LSTM(
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hyperparams['lstm_units'],
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return_sequences=return_sequences,
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kernel_regularizer=tf.keras.regularizers.l2(0.001)
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)))
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model.add(Dropout(hyperparams['dropout_rate']))
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model.add(Dense(1, activation='linear'))
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if hyperparams['optimizer'] == 'Adam':
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optimizer = Adam(learning_rate=hyperparams['learning_rate'], decay=hyperparams['decay'])
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elif hyperparams['optimizer'] == 'Nadam':
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optimizer = Nadam(learning_rate=hyperparams['learning_rate'])
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else:
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optimizer = Adam(learning_rate=hyperparams['learning_rate'])
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model.compile(optimizer=optimizer, loss=Huber(), metrics=['mae'])
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return model
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def build_xgboost_model(X_train, y_train, hyperparams):
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model = xgb.XGBRegressor(
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objective='reg:squarederror',
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n_estimators=hyperparams['n_estimators'],
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max_depth=hyperparams['max_depth'],
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learning_rate=hyperparams['learning_rate'],
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subsample=hyperparams['subsample'],
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colsample_bytree=hyperparams['colsample_bytree'],
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random_state=42,
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n_jobs=-1
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)
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model.fit(X_train.reshape(X_train.shape[0], -1), y_train)
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return model
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# 4. Hyperparameter Tuning with Optuna
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def objective(trial):
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# Hyperparameter suggestions
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num_lstm_layers = trial.suggest_int('num_lstm_layers', 1, 3)
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lstm_units = trial.suggest_categorical('lstm_units', [32, 64, 96, 128])
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dropout_rate = trial.suggest_float('dropout_rate', 0.1, 0.5)
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learning_rate = trial.suggest_loguniform('learning_rate', 1e-5, 1e-2)
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optimizer_name = trial.suggest_categorical('optimizer', ['Adam', 'Nadam'])
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decay = trial.suggest_float('decay', 0.0, 1e-4)
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hyperparams = {
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'num_lstm_layers': num_lstm_layers,
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'lstm_units': lstm_units,
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'dropout_rate': dropout_rate,
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'learning_rate': learning_rate,
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'optimizer': optimizer_name,
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'decay': decay
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}
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model = build_advanced_lstm((X_train.shape[1], X_train.shape[2]), hyperparams)
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early_stop = EarlyStopping(monitor='val_loss', patience=10, restore_best_weights=True)
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lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-6)
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history = model.fit(
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X_train, y_train,
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epochs=100,
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batch_size=16,
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validation_data=(X_val, y_val),
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callbacks=[early_stop, lr_reduce, KerasPruningCallback(trial, 'val_loss')],
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verbose=0
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)
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val_mae = min(history.history['val_mae'])
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return val_mae
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# Optuna study
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logging.info("Starting hyperparameter optimization with Optuna...")
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study = optuna.create_study(direction='minimize')
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study.optimize(objective, n_trials=50)
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best_params = study.best_params
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logging.info(f"Best Hyperparameters from Optuna: {best_params}")
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# 5. Train the Best LSTM Model
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best_model = build_advanced_lstm((X_train.shape[1], X_train.shape[2]), best_params)
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early_stop = EarlyStopping(monitor='val_loss', patience=20, restore_best_weights=True)
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lr_reduce = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, min_lr=1e-6)
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logging.info("Training the best LSTM model with optimized hyperparameters...")
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history = best_model.fit(
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X_train, y_train,
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epochs=300,
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batch_size=16,
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validation_data=(X_val, y_val),
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callbacks=[early_stop, lr_reduce],
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verbose=1
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)
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# 6. Evaluate the Model
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def evaluate_model(model, X_test, y_test):
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logging.info("Evaluating model...")
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y_pred_scaled = model.predict(X_test).flatten()
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y_pred_scaled = np.clip(y_pred_scaled, 0, 1) # Ensure predictions are within [0,1]
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y_pred = scaler_target.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
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y_test_actual = scaler_target.inverse_transform(y_test.reshape(-1, 1)).flatten()
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mse = mean_squared_error(y_test_actual, y_pred)
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rmse = np.sqrt(mse)
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mae = mean_absolute_error(y_test_actual, y_pred)
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r2 = r2_score(y_test_actual, y_pred)
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# Directional Accuracy
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direction_actual = np.sign(np.diff(y_test_actual))
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direction_pred = np.sign(np.diff(y_pred))
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directional_accuracy = np.mean(direction_actual == direction_pred)
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logging.info(f"Test MSE: {mse}")
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logging.info(f"Test RMSE: {rmse}")
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logging.info(f"Test MAE: {mae}")
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logging.info(f"Test R2 Score: {r2}")
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logging.info(f"Directional Accuracy: {directional_accuracy}")
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# Plot Actual vs Predicted
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plt.figure(figsize=(14, 7))
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plt.plot(y_test_actual, label='Actual Price')
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plt.plot(y_pred, label='Predicted Price')
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plt.title('Actual vs Predicted Prices')
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plt.xlabel('Time Step')
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plt.ylabel('Price')
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plt.legend()
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plt.grid(True)
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plt.savefig('actual_vs_predicted.png') # Save the plot
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plt.close()
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logging.info("Actual vs Predicted plot saved as 'actual_vs_predicted.png'")
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# Tabulate first 40 predictions
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table = [[i, round(actual, 2), round(pred, 2)] for i, (actual, pred) in enumerate(zip(y_test_actual[:40], y_pred[:40]))]
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headers = ["Index", "Actual Price", "Predicted Price"]
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print(tabulate(table, headers=headers, tablefmt="pretty"))
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return mse, rmse, mae, r2, directional_accuracy
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mse, rmse, mae, r2, directional_accuracy = evaluate_model(best_model, X_test, y_test)
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# 7. Save the Model and Scalers
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best_model.save('optimized_lstm_model.h5')
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import joblib
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joblib.dump(scaler_features, 'scaler_features.save')
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joblib.dump(scaler_target, 'scaler_target.save')
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logging.info("Model and scalers saved as 'optimized_lstm_model.h5', 'scaler_features.save', and 'scaler_target.save'.")
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# 8. Reinforcement Learning: Deep Q-Learning for Trading Actions
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class StockTradingEnv(gym.Env):
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"""
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A simple stock trading environment for OpenAI gym
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"""
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metadata = {'render.modes': ['human']}
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def __init__(self, df, initial_balance=10000):
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super(StockTradingEnv, self).__init__()
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self.df = df.reset_index()
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self.initial_balance = initial_balance
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self.balance = initial_balance
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self.net_worth = initial_balance
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self.max_steps = len(df)
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self.current_step = 0
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self.shares_held = 0
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self.cost_basis = 0
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# Actions: 0 = Sell, 1 = Hold, 2 = Buy
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self.action_space = spaces.Discrete(3)
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# Observations: [normalized features + balance + shares held + cost basis]
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self.observation_space = spaces.Box(low=0, high=1, shape=(len(feature_columns) + 3,), dtype=np.float32)
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def reset(self):
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self.balance = self.initial_balance
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self.net_worth = self.initial_balance
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self.current_step = 0
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self.shares_held = 0
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self.cost_basis = 0
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return self._next_observation()
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def _next_observation(self):
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obs = self.df.loc[self.current_step, feature_columns].values
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# Normalize features by their max to ensure [0,1] range
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obs = obs / np.max(obs)
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# Append balance, shares held, and cost basis
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additional = np.array([
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self.balance / self.initial_balance,
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self.shares_held / 100, # Assuming a maximum of 100 shares for normalization
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self.cost_basis / self.initial_balance
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])
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return np.concatenate([obs, additional])
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def step(self, action):
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current_price = self.df.loc[self.current_step, 'Close']
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if action == 2: # Buy
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total_possible = self.balance // current_price
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shares_bought = total_possible
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if shares_bought > 0:
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self.balance -= shares_bought * current_price
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self.shares_held += shares_bought
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self.cost_basis = (self.cost_basis * (self.shares_held - shares_bought) + shares_bought * current_price) / self.shares_held
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elif action == 0: # Sell
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if self.shares_held > 0:
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self.balance += self.shares_held * current_price
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self.shares_held = 0
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self.cost_basis = 0
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# Hold does nothing
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self.net_worth = self.balance + self.shares_held * current_price
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self.current_step += 1
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done = self.current_step >= self.max_steps - 1
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# Reward: change in net worth
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reward = self.net_worth - self.initial_balance
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obs = self._next_observation()
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return obs, reward, done, {}
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def render(self, mode='human', close=False):
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profit = self.net_worth - self.initial_balance
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print(f'Step: {self.current_step}')
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print(f'Balance: {self.balance}')
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print(f'Shares held: {self.shares_held} (Cost Basis: {self.cost_basis})')
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print(f'Net worth: {self.net_worth}')
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print(f'Profit: {profit}')
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def train_dqn_agent(env):
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logging.info("Training DQN Agent...")
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try:
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model = DQN(
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'MlpPolicy',
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env,
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verbose=1,
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learning_rate=1e-3,
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buffer_size=10000,
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learning_starts=1000,
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batch_size=64,
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||||
tau=1.0,
|
||||
gamma=0.99,
|
||||
train_freq=4,
|
||||
target_update_interval=1000,
|
||||
exploration_fraction=0.1,
|
||||
exploration_final_eps=0.02,
|
||||
tensorboard_log="./dqn_stock_tensorboard/"
|
||||
)
|
||||
model.learn(total_timesteps=100000)
|
||||
model.save("dqn_stock_trading")
|
||||
logging.info("DQN Agent trained and saved as 'dqn_stock_trading.zip'.")
|
||||
return model
|
||||
except Exception as e:
|
||||
logging.error(f"Error training DQN Agent: {e}")
|
||||
sys.exit(1)
|
||||
|
||||
# Initialize trading environment
|
||||
trading_env = StockTradingEnv(data)
|
||||
trading_env = DummyVecEnv([lambda: trading_env])
|
||||
|
||||
# Train DQN agent
|
||||
dqn_model = train_dqn_agent(trading_env)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
||||
Reference in New Issue
Block a user