new realt module
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Dobromir Popov
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NN/example.py
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261
NN/example.py
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#!/usr/bin/env python
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"""
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Example script for the Neural Network Trading System
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This shows basic usage patterns for the system components
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"""
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import os
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import sys
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import numpy as np
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import pandas as pd
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import tensorflow as tf
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import matplotlib.pyplot as plt
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from datetime import datetime
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import logging
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# Add project root to path
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# Import components
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from NN.utils.data_interface import DataInterface
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from NN.models.cnn_model import CNNModel
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from NN.models.transformer_model import TransformerModel, MixtureOfExpertsModel
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from NN.main import NeuralNetworkOrchestrator
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# Configure logging
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger('example')
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def example_data_interface():
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"""Show how to use the data interface"""
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logger.info("=== Data Interface Example ===")
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# Initialize data interface
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di = DataInterface(symbol="BTC/USDT", timeframes=['1h', '4h', '1d'])
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# Get historical data
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df_1h = di.get_historical_data(timeframe='1h', n_candles=100)
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if df_1h is not None and not df_1h.empty:
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logger.info(f"Retrieved {len(df_1h)} 1-hour candles")
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logger.info(f"Most recent candle: {df_1h.iloc[-1]}")
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# Prepare data for neural network
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X, y, timestamps = di.prepare_nn_input(timeframes=['1h'], n_candles=500, window_size=20)
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if X is not None and y is not None:
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logger.info(f"Prepared input shape: {X.shape}, target shape: {y.shape}")
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# Generate a dataset
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dataset = di.generate_training_dataset(
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timeframes=['1h', '4h'],
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n_candles=1000,
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window_size=20
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)
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if dataset:
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logger.info(f"Dataset generated and saved to: {list(dataset.values())}")
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return X, y, timestamps if X is not None else (None, None, None)
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def example_cnn_model(X=None, y=None):
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"""Show how to use the CNN model"""
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logger.info("=== CNN Model Example ===")
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# If no data provided, create dummy data
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if X is None or y is None:
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logger.info("Creating dummy data for CNN example")
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X = np.random.random((1000, 20, 5)) # 1000 samples, 20 time steps, 5 features
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y = np.random.randint(0, 2, size=(1000,)) # Binary labels
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# Split data into training and testing sets
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from sklearn.model_selection import train_test_split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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# Initialize and build the CNN model
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cnn = CNNModel(input_shape=(20, 5), output_size=1, model_dir='NN/models/saved')
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cnn.build_model(filters=(32, 64, 128), kernel_sizes=(3, 5, 7), dropout_rate=0.3)
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# Train the model (very small number of epochs for this example)
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history = cnn.train(
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X_train, y_train,
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batch_size=32,
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epochs=5, # Just a few epochs for the example
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validation_split=0.2
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)
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# Evaluate the model
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metrics = cnn.evaluate(X_test, y_test, plot_results=True)
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if metrics:
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logger.info(f"CNN Evaluation metrics: {metrics}")
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# Make a prediction
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y_pred, y_proba = cnn.predict(X_test[:1])
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logger.info(f"CNN Prediction: {y_pred[0]}, Probability: {y_proba[0]:.4f}")
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return cnn
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def example_transformer_model(X=None, y=None, cnn_model=None):
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"""Show how to use the Transformer model"""
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logger.info("=== Transformer Model Example ===")
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# If no data provided, create dummy data
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if X is None or y is None:
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logger.info("Creating dummy data for Transformer example")
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X = np.random.random((1000, 20, 5)) # 1000 samples, 20 time steps, 5 features
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y = np.random.randint(0, 2, size=(1000,)) # Binary labels
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# Generate high-level features (from CNN model or random if no CNN provided)
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if cnn_model is not None and hasattr(cnn_model, 'extract_hidden_features'):
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# Extract features from CNN model
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X_features = cnn_model.extract_hidden_features(X)
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logger.info(f"Extracted {X_features.shape[1]} features from CNN model")
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else:
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# Generate random features
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X_features = np.random.random((len(X), 128))
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logger.info("Generated random features for Transformer model")
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# Split data into training and testing sets
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from sklearn.model_selection import train_test_split
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X_train, X_test, X_feat_train, X_feat_test, y_train, y_test = train_test_split(
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X, X_features, y, test_size=0.2, random_state=42
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)
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# Initialize and build the Transformer model
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transformer = TransformerModel(
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ts_input_shape=(20, 5),
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feature_input_shape=X_features.shape[1],
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output_size=1,
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model_dir='NN/models/saved'
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)
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transformer.build_model(
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embed_dim=32,
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num_heads=2,
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ff_dim=64,
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num_transformer_blocks=2,
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dropout_rate=0.2
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)
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# Train the model (very small number of epochs for this example)
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history = transformer.train(
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X_train, X_feat_train, y_train,
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batch_size=32,
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epochs=5, # Just a few epochs for the example
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validation_split=0.2
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)
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# Make a prediction
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y_pred, y_proba = transformer.predict(X_test[:1], X_feat_test[:1])
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logger.info(f"Transformer Prediction: {y_pred[0]}, Probability: {y_proba[0]:.4f}")
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return transformer
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def example_moe_model(X=None, y=None, cnn_model=None, transformer_model=None):
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"""Show how to use the Mixture of Experts model"""
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logger.info("=== Mixture of Experts Example ===")
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# If no data provided, create dummy data
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if X is None or y is None:
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logger.info("Creating dummy data for MoE example")
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X = np.random.random((1000, 20, 5)) # 1000 samples, 20 time steps, 5 features
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y = np.random.randint(0, 2, size=(1000,)) # Binary labels
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# If models not provided, create them
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if cnn_model is None:
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logger.info("Creating a new CNN model for MoE")
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cnn_model = CNNModel(input_shape=(20, 5), output_size=1)
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cnn_model.build_model()
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if transformer_model is None:
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logger.info("Creating a new Transformer model for MoE")
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transformer_model = TransformerModel(ts_input_shape=(20, 5), feature_input_shape=128, output_size=1)
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transformer_model.build_model()
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# Initialize MoE model
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moe = MixtureOfExpertsModel(output_size=1, model_dir='NN/models/saved')
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# Add expert models
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moe.add_expert('cnn', cnn_model)
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moe.add_expert('transformer', transformer_model)
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# Build the MoE model (this is a simplified implementation - in a real scenario
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# you would need to handle the interfaces between models more carefully)
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moe.build_model(
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ts_input_shape=(20, 5),
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expert_weights={'cnn': 0.7, 'transformer': 0.3}
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)
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# In a real implementation, you would train the MoE model here
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logger.info("MoE model built - in a real implementation, you would train it here")
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return moe
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def example_orchestrator():
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"""Show how to use the Orchestrator"""
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logger.info("=== Orchestrator Example ===")
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# Configure the orchestrator
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config = {
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'symbol': 'BTC/USDT',
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'timeframes': ['1h', '4h'],
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'window_size': 20,
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'n_features': 5,
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'output_size': 3, # BUY/HOLD/SELL
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'batch_size': 32,
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'epochs': 5, # Small number for example
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'model_dir': 'NN/models/saved',
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'data_dir': 'NN/data'
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}
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# Initialize the orchestrator
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orchestrator = NeuralNetworkOrchestrator(config)
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# Prepare training data
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X, y, timestamps = orchestrator.prepare_training_data(
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timeframes=['1h'],
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n_candles=200
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)
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if X is not None and y is not None:
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logger.info(f"Prepared training data: X shape {X.shape}, y shape {y.shape}")
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# Train CNN model
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logger.info("Training CNN model with orchestrator...")
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history = orchestrator.train_cnn_model(X, y, epochs=2) # Very small for example
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# Make a prediction
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result = orchestrator.run_inference_pipeline(
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model_type='cnn',
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timeframe='1h'
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)
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if result:
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logger.info(f"Inference result: {result}")
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else:
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logger.warning("Could not prepare training data - this is expected if no real data is available")
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logger.info("The orchestrator would normally handle training and inference")
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def main():
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"""Run all examples"""
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logger.info("Starting Neural Network Trading System Examples")
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# Example 1: Data Interface
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X, y, timestamps = example_data_interface()
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# Example 2: CNN Model
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cnn_model = example_cnn_model(X, y)
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# Example 3: Transformer Model
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transformer_model = example_transformer_model(X, y, cnn_model)
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# Example 4: Mixture of Experts
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moe_model = example_moe_model(X, y, cnn_model, transformer_model)
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# Example 5: Orchestrator
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example_orchestrator()
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logger.info("Examples completed")
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if __name__ == "__main__":
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main()
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