分模块写出股票强化学习代码

好的，我们可以将股票强化学习代码分成以下模块：

1. 数据获取
2. 环境建立
3. 强化学习算法
4. 训练流程

下面是每个模块的详细代码实现：

1. 数据获取

首先需要获取股票历史数据，这里使用tushare库来获取数据。可以在tushare官网申请token后，使用以下代码获取数据：

import tushare as ts

# 获取股票历史数据
def get_stock_data(code, start_date, end_date):
    data = ts.get_hist_data(code, start=start_date, end=end_date)
    return data

2. 环境建立

接下来我们需要建立强化学习的环境，这里我们以股票交易为例。环境包括股票价格、手续费等信息，以及交易策略等。

import numpy as np

class StockTradingEnv:
    def __init__(self, data, init_balance=1000000):
        self.data = data
        self.balance = init_balance
        self.shares = 0
        self.total_value = init_balance
        self.current_step = None
        self.max_steps = len(data) - 1
        self.reward_range = (0, self.balance * 0.05)
        self.action_space = spaces.Discrete(3) # 0: 不操作，1: 买入，2: 卖出
        self.observation_space = spaces.Box(low=0, high=np.inf, shape=(6,), dtype=np.float32)

    def reset(self):
        self.current_step = 0
        self.balance = 1000000
        self.shares = 0
        self.total_value = self.balance
        return self._get_obs()

    def step(self, action):
        assert self.action_space.contains(action), f"Invalid action {action}"
        prev_val = self._get_val()
        self.current_step += 1
        if action == 1: # 买入
            self._buy()
        elif action == 2: # 卖出
            self._sell()
        cur_val = self._get_val()
        reward = cur_val - prev_val
        done = self.current_step == self.max_steps
        obs = self._get_obs()
        return obs, reward, done, {}

    def _get_obs(self):
        obs = np.array([
            self.data['open'][self.current_step],
            self.data['high'][self.current_step],
            self.data['low'][self.current_step],
            self.data['close'][self.current_step],
            self.data['volume'][self.current_step],
            self.balance
        ])
        return obs

    def _get_val(self):
        return self.balance + self.shares * self.data['close'][self.current_step]

    def _buy(self):
        price = self.data['close'][self.current_step]
        shares = self.balance // price
        self.shares += shares
        self.balance -= shares * price

    def _sell(self):
        price = self.data['close'][self.current_step]
        self.balance += self.shares * price
        self.shares = 0

3. 强化学习算法

这里我们使用DQN（Deep Q-Network）算法来进行股票交易的强化学习。

import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from collections import deque

class DQNAgent:
    def __init__(self, env, gamma=0.99, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.9995, lr=0.001, batch_size=64):
        self.env = env
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.lr = lr
        self.batch_size = batch_size
        self.memory = deque(maxlen=2000)
        self.model = nn.Sequential(
            nn.Linear(env.observation_space.shape[0], 64),
            nn.ReLU(),
            nn.Linear(64, 64),
            nn.ReLU(),
            nn.Linear(64, env.action_space.n)
        )
        self.optimizer = optim.Adam(self.model.parameters(), lr=lr)
        self.loss_fn = nn.MSELoss()

    def act(self, state):
        if np.random.rand() < self.epsilon:
            return self.env.action_space.sample()
        q_values = self.model(torch.tensor(state).float())
        return torch.argmax(q_values).item()

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

    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        minibatch = np.array(random.sample(self.memory, self.batch_size))
        states = np.stack(minibatch[:, 0])
        actions = np.stack(minibatch[:, 1])
        rewards = np.stack(minibatch[:, 2])
        next_states = np.stack(minibatch[:, 3])
        dones = np.stack(minibatch[:, 4])
        current_q_values = self.model(torch.tensor(states).float())
        next_q_values = self.model(torch.tensor(next_states).float())
        target_q_values = current_q_values.clone().detach()
        for i in range(self.batch_size):
            if dones[i]:
                target_q_values[i][actions[i]] = rewards[i]
            else:
                target_q_values[i][actions[i]] = rewards[i] + self.gamma * torch.max(next_q_values[i])
        loss = self.loss_fn(current_q_values, target_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

    def decay_epsilon(self):
        self.epsilon = max(self.epsilon_min, self.epsilon * self.epsilon_decay)

    def train(self, episodes):
        for episode 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()
                self.decay_epsilon()

4. 训练流程

最后，我们将上述模块组合起来进行训练。训练过程中，我们将每个episode的总回报打印出来。

def main():
    # 获取股票历史数据
    data = get_stock_data('000001', '20200101', '20211231')

    # 建立股票交易环境
    env = StockTradingEnv(data)

    # 训练DQN智能体
    agent = DQNAgent(env)
    agent.train(100)

    # 测试交易策略
    state = env.reset()
    done = False
    while not done:
        action = agent.act(state)
        state, reward, done, _ = env.step(action)
    print(f"Total reward: {env.total_value - env.balance}")

以上就是股票强化学习代码的模块化实现。