self.observation_space = spaces.Box(low=0, high=np.inf, shape=(6,), dtype=np.float32)

这行代码定义了环境的观测空间（observation space），即智能体可以观测到的状态的取值范围。这里采用的是一个 Box 空间，形状为 (6,)，表示智能体可以观测到一个由 6 个数值组成的状态。具体来说，这个状态包括当前的股价、持有的股票数量、当前的现金余额、当前的总资产、历史收益率和历史动作收益率。其中，当前的股价、持有的股票数量、当前的现金余额、当前的总资产以及历史收益率都是非负数，因此下界被设为 0。而历史动作收益率可以取任意实数，因此上下界都被设为正无穷。数据类型为 np.float32。

相关问题

self.observation_space.append(spaces.Box(low=-np.inf, high=+np.inf, shape=(obs_dim,), dtype=np.float32)) TypeError: __init__() got an unexpected keyword argument 'dtype'怎么解决

这个错误通常是因为您正在使用的 Gym 版本较低，不支持 `dtype` 参数。您可以尝试更新 Gym 到最新版本，或者使用以下代码来替换您的现有代码：

import gym
import numpy as np
from gym import spaces

low = np.array([-np.inf] * obs_dim, dtype=np.float32)
high = np.array([+np.inf] * obs_dim, dtype=np.float32)
self.observation_space.append(spaces.Box(low=low, high=high))

这里，我们使用了 `numpy` 库来创建低和高的数组，然后将其传递给 Box 空间。这应该可以解决您遇到的问题。

利用backtrader进行股票强化学习，完整代码

这里提供一个简单的示例代码，用于利用backtrader进行股票强化学习。

首先，我们需要安装backtrader和其他依赖项：

!pip install backtrader
!pip install tensorboardX
!pip install gym

接下来，我们将定义我们的环境类：

import backtrader as bt
import gym
from gym import spaces
import numpy as np

class StockTradingEnv(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self, df):
        super(StockTradingEnv, self).__init__()

        self.df = df
        self.reward_range = (0, np.inf)
        self.action_space = spaces.Discrete(3)  # 0: Buy, 1: Sell, 2: Hold
        self.observation_space = spaces.Box(low=0, high=1, shape=(6,))

        self.data = bt.feeds.PandasData(dataname=self.df)

        self.model = None
        self.total_reward = 0
        self.current_step = 0
        self.principal = 10000
        self.share_price = None
        self.share_held = None
        self.transaction_cost = None

    def _next_observation(self):
        obs = np.array([
            self.data.open[0] / 300,
            self.data.high[0] / 300,
            self.data.low[0] / 300,
            self.data.close[0] / 300,
            self.share_price / 300,
            self.share_held / 300
        ])

        return obs

    def _take_action(self, action):
        current_price = self.data.close[0]

        if action == 0:  # Buy
            self.share_held = self.principal / current_price
            self.principal -= self.share_held * current_price
            self.transaction_cost = self.share_held * current_price * 0.001
            self.principal -= self.transaction_cost
        elif action == 1:  # Sell
            self.principal += self.share_held * current_price
            self.transaction_cost = self.share_held * current_price * 0.001
            self.principal -= self.transaction_cost
            self.share_held = 0
        elif action == 2:  # Hold
            pass

    def step(self, action):
        self._take_action(action)

        self.current_step += 1

        if self.current_step > len(self.df.loc[:, 'Open'].values) - 1:
            self.current_step = 0

        self.data = bt.feeds.PandasData(dataname=self.df.loc[self.current_step:, :])
        self.share_price = self.data.close[0]

        reward = self.principal - 10000
        self.total_reward += reward

        obs = self._next_observation()

        done = self.current_step == len(self.df.loc[:, 'Open'].values) - 1

        return obs, reward, done, {}

    def reset(self):
        self.model = None
        self.total_reward = 0
        self.current_step = 0
        self.principal = 10000
        self.share_price = self.df.loc[0, 'Open']
        self.share_held = 0
        self.transaction_cost = 0

        self.data = bt.feeds.PandasData(dataname=self.df)

        return self._next_observation()

    def render(self, mode='human', close=False):
        print(f'Step: {self.current_step}')
        print(f'Principal: {self.principal}')
        print(f'Total Reward: {self.total_reward}')
        print(f'Share Price: {self.share_price}')
        print(f'Shares Held: {self.share_held}')
        print(f'Transaction Cost: {self.transaction_cost}')

然后，我们将定义我们的强化学习代理类：

import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from tensorboardX import SummaryWriter

class DQNAgent:
    def __init__(self, env):
        self.env = env
        self.memory = []
        self.gamma = 0.9
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.tau = 0.01
        self.model = self.create_model()
        self.target_model = self.create_model()

        self.writer = SummaryWriter()

        self.optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate)
        self.criterion = nn.MSELoss()

    def create_model(self):
        model = nn.Sequential(
            nn.Linear(6, 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, 3)
        )

        return model

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return self.env.action_space.sample()

        state = torch.tensor(state, dtype=torch.float32).unsqueeze(0)
        q_values = self.model(state)
        action = q_values.argmax().item()

        return action

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

    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return

        batch = random.sample(self.memory, batch_size)

        states = torch.tensor([x[0] for x in batch], dtype=torch.float32)
        actions = torch.tensor([x[1] for x in batch], dtype=torch.int64)
        rewards = torch.tensor([x[2] for x in batch], dtype=torch.float32)
        next_states = torch.tensor([x[3] for x in batch], dtype=torch.float32)
        dones = torch.tensor([x[4] for x in batch], dtype=torch.bool)

        q_values = self.model(states)
        next_q_values = self.target_model(next_states)
        next_q_values[dones] = 0.0
        target_q_values = (next_q_values * self.gamma) + rewards.unsqueeze(1)

        loss = self.criterion(q_values.gather(1, actions.unsqueeze(1)), target_q_values.detach())

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        for param, target_param in zip(self.model.parameters(), self.target_model.parameters()):
            target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

        self.epsilon = max(self.epsilon_min, self.epsilon_decay * self.epsilon)

    def train(self, episodes, batch_size):
        for e in range(episodes):
            state = self.env.reset()
            done = False

            while not done:
                action = self.act(state)
                next_state, reward, done, _ = self.env.step(action)
                self.remember(state, action, reward, next_state, done)
                state = next_state

            self.replay(batch_size)

            self.writer.add_scalar('epsilon', self.epsilon, e)
            self.writer.add_scalar('total_reward', self.env.total_reward, e)

            if (e + 1) % 10 == 0:
                print(f'Episode: {e + 1}, Reward: {self.env.total_reward}')

            self.env.render()

        self.writer.close()

最后，我们将加载数据并创建环境和代理对象，并训练代理：

import pandas as pd

df = pd.read_csv('AAPL.csv')
df = df.dropna().reset_index(drop=True)

env = StockTradingEnv(df)
agent = DQNAgent(env)
agent.train(episodes=100, batch_size=32)

注意，这是一个非常简单的示例，需要更多的调整和改进以获得更好的结果。