使用akshare获取股票数据强化学习获取最大收益完整代码

以下是一个简单的示例代码，使用 akshare 获取股票数据进行强化学习，以获取最大收益。

首先，我们需要安装 akshare 库。可以通过以下命令在命令行中安装：

pip install akshare

然后，我们需要准备股票数据。以下代码将获取某个股票的历史数据，并将其保存为 CSV 文件：

import akshare as ak
import pandas as pd

stock_code = "sh600000"
start_date = "20100101"
end_date = "20210101"

stock_data = ak.stock_zh_a_hist(symbol=stock_code, start_date=start_date, end_date=end_date, adjust="qfq")
stock_data.to_csv(f"{stock_code}.csv", index=False)

接下来，我们将使用 Gym 库来创建一个强化学习环境。以下是一个简单的示例代码，用于创建一个股票交易环境：

import gym
import numpy as np
import pandas as pd

class StockTradingEnv(gym.Env):
    metadata = {'render.modes': ['human']}
    
    def __init__(self, data, init_invest=20000):
        super(StockTradingEnv, self).__init__()
        self.data = data
        self.init_invest = init_invest
        self.current_step = None
        self.reward_range = (0, self.init_invest)
        
        self.action_space = gym.spaces.Discrete(3)
        self.observation_space = gym.spaces.Box(low=0, high=1, shape=(6,))
        
    def _next_observation(self):
        obs = np.array([
            self.data.loc[self.current_step, 'open'] / 1000,
            self.data.loc[self.current_step, 'high'] / 1000,
            self.data.loc[self.current_step, 'low'] / 1000,
            self.data.loc[self.current_step, 'close'] / 1000,
            self.data.loc[self.current_step, 'volume'] / 10000,
            self.invested / self.init_invest
        ])
        return obs
    
    def _take_action(self, action):
        current_price = np.random.uniform(
            self.data.loc[self.current_step, 'low'],
            self.data.loc[self.current_step, 'high']
        )
        sell_price = np.random.uniform(
            self.data.loc[self.current_step, 'low'],
            current_price
        )
        buy_price = np.random.uniform(
            current_price,
            self.data.loc[self.current_step, 'high']
        )
        
        if action == 0:
            self.invested -= self.init_invest
            self.balance += self.init_invest
        elif action == 1:
            self.invested += self.balance / current_price
            self.balance = 0
        elif action == 2:
            self.balance += self.invested * sell_price
            self.invested = 0
        
    def step(self, action):
        assert self.action_space.contains(action)
        
        self._take_action(action)
        self.current_step += 1
        
        if self.current_step > len(self.data) - 1:
            return self._next_observation(), 0, True, {}
        
        reward = self.balance + self.invested * np.random.uniform(
            self.data.loc[self.current_step, 'low'],
            self.data.loc[self.current_step, 'high']
        ) - self.init_invest
        
        done = self.balance + self.invested * self.data.loc[self.current_step, 'close'] <= 0
        
        obs = self._next_observation()
        
        return obs, reward, done, {}
    
    def reset(self):
        self.current_step = 0
        self.balance = self.init_invest
        self.invested = 0
        return self._next_observation()
    
    def render(self, mode='human', close=False):
        profit = self.balance + self.invested * np.random.uniform(
            self.data.loc[self.current_step, 'low'],
            self.data.loc[self.current_step, 'high']
        ) - self.init_invest
        
        print(f"Step: {self.current_step}")
        print(f"Cash: {self.balance:.2f}")
        print(f"Invested: {self.invested:.2f}")
        print(f"Total: {self.balance + self.invested:.2f}")
        print(f"Profit: {profit:.2f}")

最后，我们将使用 OpenAI Gym 和 TensorFlow 2.x 来训练一个神经网络，以学习交易策略。以下是一个简单的示例代码：

import gym
import numpy as np
import tensorflow as tf

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM

# Load data
data = pd.read_csv("sh600000.csv")

# Create environment
env = StockTradingEnv(data)

# Build model
model = Sequential()
model.add(LSTM(128, input_shape=(env.observation_space.shape[0],)))
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu'))
model.add(Dense(32, activation='relu'))
model.add(Dense(env.action_space.n, activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Train model
epochs = 10

for epoch in range(epochs):
    state = env.reset()
    state = np.reshape(state, [1, env.observation_space.shape[0]])
    
    for time in range(5000):
        action_probs = model.predict(state)[0]
        action = np.random.choice(env.action_space.n, p=action_probs)
        
        next_state, reward, done, _ = env.step(action)
        next_state = np.reshape(next_state, [1, env.observation_space.shape[0]])
        
        if done:
            target = reward
        else:
            target = reward + 0.95 * np.amax(model.predict(next_state)[0])
        
        target_f = model.predict(state)
        target_f[0][action] = target
        
        model.fit(state, target_f, epochs=1, verbose=0)
        
        state = next_state
        
        if done:
            print(f"Epoch: {epoch+1}/{epochs}, Profit: {reward:.2f}")
            break

这个示例代码仅仅是一个简单的示例，实际上，你需要自己根据不同的需求来修改和完善它。