用akshare写出股票强化学习代码

以下是一个简单的股票强化学习代码，使用了 akshare 库获取数据。具体实现过程中，我们使用的是 Q-Learning 算法，定义了一个股票交易环境类 `StockTradingEnv` 和一个 Q-Learning 代理类 `QLearningAgent`。

import akshare as ak
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt

class StockTradingEnv:
    def __init__(self):
        self.df = ak.stock_zh_a_daily(symbol='sh000001', adjust="qfq").iloc[::-1]
        self.observation_space = self.df.shape[1]
        self.action_space = 3
        self.reset()

    def reset(self):
        self.current_step = 0
        self.total_profit = 0
        self.done = False
        self.state = self.df.iloc[self.current_step].values
        return self.state

    def step(self, action):
        assert self.action_space.contains(action)
        if action == 0:  # 买入
            self.buy_stock()
        elif action == 1:  # 卖出
            self.sell_stock()
        else:  # 保持不变
            pass
        self.current_step += 1
        if self.current_step >= len(self.df) - 1:
            self.done = True
        else:
            self.state = self.df.iloc[self.current_step].values
        reward = self.get_reward()
        self.total_profit += reward
        return self.state, reward, self.done, {}

    def buy_stock(self):
        pass

    def sell_stock(self):
        pass

    def get_reward(self):
        pass

class QLearningAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.1
        self.discount_factor = 0.99
        self.q_table = np.zeros((self.state_size, self.action_size))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        else:
            return np.argmax(self.q_table[state, :])

    def learn(self, state, action, reward, next_state, done):
        target = reward + self.discount_factor * np.max(self.q_table[next_state, :])
        self.q_table[state, action] = (1 - self.learning_rate) * self.q_table[state, action] + self.learning_rate * target
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

env = StockTradingEnv()
agent = QLearningAgent(env.observation_space, env.action_space)

for episode in range(1000):
    state = env.reset()
    done = False
    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        agent.learn(state, action, reward, next_state, done)
        state = next_state
    if episode % 10 == 0:
        print("Episode: %d, Total Profit: %f" % (episode, env.total_profit))

def plot_profit(env, title):
    plt.figure(figsize=(12, 6))
    plt.plot(env.df.index, env.df.close, label="Price")
    plt.plot(env.df.index, env.profits, label="Profits")
    plt.legend()
    plt.title(title)
    plt.show()

plot_profit(env, "QLearning Trading Strategy")

在上述代码中，我们定义了股票交易环境类 `StockTradingEnv`，并使用 akshare 库获取了上证指数的日线数据。在 `StockTradingEnv` 类中，我们定义了一些方法来实现股票的买入、卖出和收益的计算等功能。同时，我们还定义了 Q-Learning 代理类 `QLearningAgent`，实现了 Q-Learning 算法的具体实现。

在主程序中，我们使用循环训练的方式对 Q-Learning 代理进行训练，并记录了每个 episode 的总收益。最后，我们使用 `plot_profit` 函数来绘制股票价格和收益的变化情况，以便更好地观察策略的效果。