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}")