用akshare,gym写出股票强化学习代码，包含模型保存和应用，完整代码

好的，以下是基于akshare和gym实现股票强化学习的完整代码示例：

import akshare as ak
import gym
import numpy as np
import os
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

# 获取股票历史数据
stock_df = ak.stock_zh_a_hist(symbol='sh600000', adjust="qfq", start_date='20000101', end_date='20211231')

# 定义强化学习环境
class StockEnv(gym.Env):
    def __init__(self, stock_df):
        self.stock_df = stock_df
        self.observation_space = gym.spaces.Box(low=0, high=np.inf, shape=(len(stock_df.columns),))
        self.action_space = gym.spaces.Discrete(2)
        self.reward_range = (-np.inf, np.inf)
        self.reset()

    def reset(self):
        self.current_step = 0
        self.profit = 0
        self.done = False
        self.bought = False
        self.sold = False
        obs = np.array(self.stock_df.iloc[self.current_step])
        return obs

    def step(self, action):
        obs = np.array(self.stock_df.iloc[self.current_step])
        self.current_step += 1
        if action == 0 and not self.bought:
            self.bought = True
            self.buy_price = obs[3]
        elif action == 1 and self.bought and not self.sold:
            self.sold = True
            self.sell_price = obs[3]
            self.profit = self.sell_price - self.buy_price
            if self.profit > 0:
                reward = 1
            else:
                reward = -1
            self.done = True
        else:
            reward = 0
        return obs, reward, self.done, {}

# 定义深度强化学习模型
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
        self.epsilon = 1.0
        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(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

    def remember(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 = np.array(self.memory)
        minibatch = minibatch[np.random.choice(minibatch.shape[0], batch_size, replace=False), :]
        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)

# 训练模型
env = StockEnv(stock_df)
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = DQNAgent(state_size, action_size)
done = False
batch_size = 32
num_episodes = 1000
for e in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, state_size])
    for time in range(500):
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        reward = reward if not done else -10
        next_state = np.reshape(next_state, [1, state_size])
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        if done:
            print("episode: {}/{}, score: {}, e: {:.2}".format(e+1, num_episodes, env.profit, agent.epsilon))
            break
    if len(agent.memory) > batch_size:
        agent.replay(batch_size)
    if e % 50 == 0:
        agent.save("model.h5")

# 加载模型并预测
agent.load("model.h5")
state = env.reset()
state = np.reshape(state, [1, state_size])
while True:
    action = agent.act(state)
    next_state, reward, done, _ = env.step(action)
    next_state = np.reshape(next_state, [1, state_size])
    state = next_state
    if done:
        print("profit: ", env.profit)
        break

以上代码通过股票历史数据构建了一个强化学习环境，使用深度强化学习模型进行训练，并保存模型。最后加载模型并预测股票收益。