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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}") |