写出利用backtrader进行股票强化学习的代码

以下是使用backtrader进行股票强化学习的代码示例：

import backtrader as bt
import numpy as np
import pandas as pd

class StockTradingEnv(bt.Env):
    def __init__(self, data, max_stock=1e2, initial_capital=1e6):
        self.data = data
        self.max_stock = max_stock
        self.initial_capital = initial_capital
        self.cur_step = None
        self.stock_owned = None
        self.stock_price = None
        self.cash_in_hand = None
        self.action_space = np.arange(3)
        self.action_list = [0, 1, 2]
        self.reward_range = (0, self.initial_capital)

        # observation_space: [stock_owned, stock_price, cash_in_hand]
        self.observation_space = np.zeros((3,), dtype=np.float32)

        self.reset()

    def reset(self):
        self.cur_step = 0
        self.stock_owned = 0
        self.stock_price = self.data[self.cur_step]
        self.cash_in_hand = self.initial_capital
        return self._get_obs()

    def step(self, action):
        assert action in self.action_space

        prev_val = self._get_val()
        self.cur_step += 1
        self.stock_price = self.data[self.cur_step]

        if action == self.action_list[0]:  # Buy
            available = self.cash_in_hand // self.stock_price
            self.stock_owned += min(available, self.max_stock)
            self.cash_in_hand -= self.stock_price * self.stock_owned
        elif action == self.action_list[1]:  # Sell
            self.stock_owned = 0
            self.cash_in_hand += self.stock_price * self.stock_owned
        else:  # Hold
            pass

        cur_val = self._get_val()
        reward = cur_val - prev_val
        done = self.cur_step == len(self.data) - 1
        info = {'cur_val': cur_val}

        return self._get_obs(), reward, done, info

    def _get_obs(self):
        obs = np.zeros((3,), dtype=np.float32)
        obs[0] = self.stock_owned
        obs[1] = self.stock_price
        obs[2] = self.cash_in_hand
        return obs

    def _get_val(self):
        return self.stock_owned * self.stock_price + self.cash_in_hand


class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95  # discount rate
        self.epsilon = 1.0  # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        model = Sequential()
        model.add(Dense(64, input_dim=self.state_size, activation='relu'))
        model.add(Dense(32, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def memorize(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_size)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = reward + self.gamma * \
                         np.amax(self.model.predict(next_state)[0])
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

    def load(self, name):
        self.model.load_weights(name)

    def save(self, name):
        self.model.save_weights(name)

def train(agent, env, episodes, batch_size):
    for e in range(episodes):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        done = False
        i = 0
        while not done:
            action = agent.act(state)
            next_state, reward, done, info = env.step(action)
            next_state = np.reshape(next_state, [1, state_size])
            agent.memorize(state, action, reward, next_state, done)
            state = next_state
            i += 1
            if done:
                print("episode: {}/{}, score: {}, e: {:.2}"
                      .format(e, episodes, info['cur_val'], agent.epsilon))
                break
            if len(agent.memory) > batch_size:
                agent.replay(batch_size)

if __name__ == '__main__':
    data = pd.read_csv('AAPL.csv')['Close'].values[::-1]  # reverse order
    env = StockTradingEnv(data)
    state_size = env.observation_space.shape[0]
    action_size = len(env.action_space)
    agent = DQNAgent(state_size, action_size)
    train(agent, env, episodes=1000, batch_size=32)