1import numpy as np
2import tensorflow as tf
3from sklearn.ensemble import RandomForestRegressor
4from sklearn.metrics import mean_squared_error
5
6class PortfolioOptimizer:
7    def __init__(self):
8        self.model = None
9        self.features = []
10
11    def train_model(self, features, targets):
12        model = RandomForestRegressor(n_estimators=100)
13        model.fit(features, targets)
14        self.model = model
15        return {
16            'model': model,
17            'accuracy': model.score(features, targets)
18        }
19
20    def predict_returns(self, portfolio_data):
21        if self.model is None:
22            raise ValueError("Model not trained")
23        return self.model.predict(portfolio_data)
24
25    def optimize_portfolio(self, risk_tolerance):
26        weights = np.random.dirichlet(np.ones(10))
27        expected_return = np.dot(weights, self.expected_returns)
28        portfolio_risk = np.sqrt(weights @ self.cov_matrix @ weights)
29        return {'weights': weights, 'return': expected_return}