| import numpy as np | |
| import pandas as pd | |
| import tensorflow as tf | |
| class GlobalLayer(tf.keras.layers.Layer): | |
| def __init__(self, units): | |
| super(GlobalLayer, self).__init__() | |
| self.lstm = tf.keras.layers.LSTM(units, return_sequences=True) | |
| def call(self, inputs): | |
| return self.lstm(inputs) | |
| class LocalLayer(tf.keras.layers.Layer): | |
| def __init__(self, units): | |
| super(LocalLayer, self).__init__() | |
| self.lstm = tf.keras.layers.LSTM(units) | |
| self.dense = tf.keras.layers.Dense(1, activation='sigmoid') | |
| def call(self, inputs): | |
| x = self.lstm(inputs) | |
| return self.dense(x) | |
| class HierarchicalTradingModel(tf.keras.Model): | |
| def __init__(self, global_units, local_units, assets): | |
| super(HierarchicalTradingModel, self).__init__() | |
| self.global_layer = GlobalLayer(global_units) | |
| self.local_layers = [LocalLayer(local_units) for _ in assets] | |
| def call(self, inputs): | |
| global_output = self.global_layer(inputs[0]) | |
| local_outputs = [] | |
| for i, asset_data in enumerate(inputs[1:]): | |
| local_output = self.local_layers[i](tf.concat([global_output, asset_data], axis=1)) | |
| local_outputs.append(local_output) | |
| return local_outputs | |
| # Define trading parameters | |
| global_units = 64 | |
| local_units = 32 | |
| assets = 2 # Number of assets | |
| # Load historical market data and asset-specific data | |
| market_data = ... # Load global market data | |
| asset_data_list = [asset_data1, asset_data2] # Load asset-specific data for each asset | |
| # Define the hierarchical trading model | |
| model = HierarchicalTradingModel(global_units=global_units, local_units=local_units, assets=assets) | |
| # Train the model on historical data | |
| model.compile(optimizer='adam', loss='binary_crossentropy') | |
| model.fit([market_data] + asset_data_list, [asset_data1_labels, asset_data2_labels], epochs=100) | |
| # Use the trained model to make trading decisions | |
| new_market_data = ... # Load new market data | |
| new_asset_data_list = [new_asset_data1, new_asset_data2] # Load new asset-specific data for each asset | |
| predictions = model.predict([new_market_data] + new_asset_data_list) | |
| # Make trading decisions based on the predictions | |
| for i, prediction in enumerate(predictions): | |
| if prediction > 0.5: | |
| print(f"Buy asset {i+1}") | |
| else: | |
| print(f"Sell asset {i+1}") |