分步骤用akshare写出股票强化学习代码

好的，以下是使用akshare模块实现股票强化学习的代码，分为以下几个步骤：

1. 导入必要的模块和库

import akshare as ak
import numpy as np
import pandas as pd
import random
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

2. 获取股票数据

stock_df = ak.stock_zh_a_daily(symbol='sh600000')

3. 定义强化学习环境和智能体

class TradingEnvironment:
    def __init__(self, stock_df):
        self.stock_df = stock_df
        self.current_step = 0
        self.total_steps = len(stock_df) - 1
        self.reward_range = (0, 1)

    def reset(self):
        self.current_step = 0
        return self.stock_df.iloc[self.current_step]

    def step(self, action):
        self.current_step += 1
        done = self.current_step == self.total_steps
        obs = self.stock_df.iloc[self.current_step]
        reward = self._get_reward(action)
        return obs, reward, done

    def _get_reward(self, action):
        if action == 0:  # 不持有股票
            return 0
        elif action == 1:  # 持有股票
            return self.stock_df.iloc[self.current_step]['收盘'] / self.stock_df.iloc[self.current_step - 1]['收盘'] - 1
        else:
            raise ValueError("Invalid action, only 0 and 1 are allowed.")
            
            
class QLearningAgent:
    def __init__(self, state_size, action_size, learning_rate, discount_rate, exploration_rate):
        self.state_size = state_size
        self.action_size = action_size
        self.learning_rate = learning_rate
        self.discount_rate = discount_rate
        self.exploration_rate = exploration_rate
        self.q_table = np.zeros((state_size, action_size))

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

    def learn(self, state, action, reward, next_state, done):
        old_value = self.q_table[state, action]
        if done:
            td_target = reward
        else:
            next_max = np.max(self.q_table[next_state])
            td_target = reward + self.discount_rate * next_max
        new_value = (1 - self.learning_rate) * old_value + self.learning_rate * td_target
        self.q_table[state, action] = new_value

    def set_exploration_rate(self, exploration_rate):
        self.exploration_rate = exploration_rate

4. 定义训练函数

def train(agent, env, episodes):
    exploration_decay = 0.995
    exploration_min = 0.01
    exploration_rate = 1.0

    for episode in range(episodes):
        state = env.reset()
        state = state['收盘']
        state = int(state)
        done = False
        total_reward = 0

        while not done:
            action = agent.act(state)
            next_state, reward, done = env.step(action)
            next_state = next_state['收盘']
            next_state = int(next_state)
            agent.learn(state, action, reward, next_state, done)
            state = next_state
            total_reward += reward

        exploration_rate = max(exploration_min, exploration_rate * exploration_decay)
        agent.set_exploration_rate(exploration_rate)
        print(f"Episode {episode + 1}/{episodes}, exploration rate: {exploration_rate:.2f}, total reward: {total_reward:.2f}")

5. 定义测试函数

def test(agent, env):
    state = env.reset()
    state = state['收盘']
    state = int(state)
    done = False
    total_reward = 0

    while not done:
        action = agent.act(state)
        next_state, reward, done = env.step(action)
        next_state = next_state['收盘']
        next_state = int(next_state)
        state = next_state
        total_reward += reward

    return total_reward

6. 初始化环境和智能体，并进行训练和测试

env = TradingEnvironment(stock_df)
state_size = 1000
action_size = 2
learning_rate = 0.1
discount_rate = 0.99
exploration_rate = 1.0
episodes = 100

agent = QLearningAgent(state_size, action_size, learning_rate, discount_rate, exploration_rate)

train(agent, env, episodes)

total_reward = test(agent, env)
print(f"Total reward: {total_reward:.2f}")

注意：这只是一个简单的示例，实际应用中可能需要对模型和参数进行更加详细的调整和优化。