用akshare,gym写出多股票强化学习代码

好的，下面是使用 akshare 和 gym 库实现多股票强化学习的代码，主要包括以下步骤：

1. 导入需要的库和模块。

2. 使用 akshare 获取多只股票的历史数据。

3. 对数据进行预处理，包括缺失值填充、数据标准化等。

4. 使用 gym 创建股票交易环境，定义交易动作和奖励机制。

5. 创建强化学习模型，使用历史数据进行训练和优化。

6. 在交易环境中进行模型测试和优化。

import akshare as ak
import gym
import numpy as np
import pandas as pd
from gym import spaces

# 获取多只股票的历史数据
stocks = ['sh000001', 'sz000002', 'sz000003']
start_date = '20100101'
end_date = '20220101'
stock_data = []
for s in stocks:
    stock_data.append(ak.stock_zh_a_hist(symbol=s, start_date=start_date, end_date=end_date, adjust='hfq'))

# 合并多只股票数据
data = pd.concat(stock_data, axis=0, ignore_index=True)
data = data.sort_values(['symbol', 'date'], ascending=[True, True])
data = data.reset_index(drop=True)

# 对数据进行预处理
data['open'] = data.groupby('symbol')['open'].apply(lambda x: x.fillna(method='ffill'))
data['high'] = data.groupby('symbol')['high'].apply(lambda x: x.fillna(method='ffill'))
data['low'] = data.groupby('symbol')['low'].apply(lambda x: x.fillna(method='ffill'))
data['close'] = data.groupby('symbol')['close'].apply(lambda x: x.fillna(method='ffill'))
data['volume'] = data.groupby('symbol')['volume'].apply(lambda x: x.fillna(method='ffill'))
data['open'] = data.groupby('symbol')['open'].apply(lambda x: (x - np.mean(x)) / np.std(x))
data['high'] = data.groupby('symbol')['high'].apply(lambda x: (x - np.mean(x)) / np.std(x))
data['low'] = data.groupby('symbol')['low'].apply(lambda x: (x - np.mean(x)) / np.std(x))
data['close'] = data.groupby('symbol')['close'].apply(lambda x: (x - np.mean(x)) / np.std(x))
data['volume'] = data.groupby('symbol')['volume'].apply(lambda x: (x - np.mean(x)) / np.std(x))

# 创建股票交易环境
class StockTradingEnv(gym.Env):
    metadata = {'render.modes': ['human']}

    def __init__(self, df):
        self.df = df
        self.reward_range = (0, np.inf)
        self.action_space = spaces.Box(low=-1, high=1, shape=(len(stocks),))
        self.observation_space = spaces.Box(low=-np.inf, high=np.inf, shape=(len(stocks), 5))

    def _next_observation(self):
        obs = []
        for s in stocks:
            obs.append(self.df.loc[self.current_step][[s + '_open', s + '_high', s + '_low', s + '_close', s + '_volume']])
        return obs

    def _take_action(self, action):
        for i in range(len(stocks)):
            self.df.loc[self.current_step, stocks[i] + '_close'] *= (1 + action[i])
        self.current_step += 1

    def reset(self):
        self.current_step = 0
        return self._next_observation()

    def step(self, action):
        self._take_action(action)
        obs = self._next_observation()
        reward = self._get_reward()
        done = self.current_step == len(self.df) - 1
        return obs, reward, done, {}

    def _get_reward(self):
        reward = 0
        for s in stocks:
            if self.df.loc[self.current_step][s + '_close'] > self.df.loc[self.current_step - 1][s + '_close']:
                reward += 1
            else:
                reward -= 1
        return reward

# 创建强化学习模型
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv

env = DummyVecEnv([lambda: StockTradingEnv(data)])
model = PPO('MlpPolicy', env, verbose=1)
model.learn(total_timesteps=10000)

# 在交易环境中进行模型测试和优化
obs = env.reset()
for i in range(len(data) - 1):
    action, _states = model.predict(obs)
    obs, rewards, done, info = env.step(action)
    if done:
        obs = env.reset()