使用akshare获取股票数据强化学习完整代码

以下是使用akshare获取股票数据并进行强化学习的完整代码示例：

import akshare as ak
import pandas as pd
import numpy as np
import random
from collections import deque
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.keras.optimizers import Adam

# 获取股票数据
stock_zh_a_daily = ak.stock_zh_a_daily(symbol='sh600000', adjust="hfq")
df = pd.DataFrame(stock_zh_a_daily)
df = df[['open', 'high', 'low', 'close', 'volume']]
df['H-L'] = df['high'] - df['low']
df['O-C'] = df['open'] - df['close']
df = df[['close', 'H-L', 'O-C', 'volume']]
df = df[-1000:]  # 取最近1000天的数据
print(df.head())

# 定义强化学习环境
class Environment:
    def __init__(self, data, initial_investment=20000):
        self.actions = ["buy", "sell", "hold"]
        self.data = data
        self.n_step = len(self.data) - 1
        self.initial_investment = initial_investment
        self.current_step = None
        self.stock_owned = None
        self.cash_in_hand = None
        self.action_history = []
        self.state_history = []
        self.reward_history = []
        self.value_history = []

    def reset(self):
        self.current_step = 0
        self.stock_owned = 0
        self.cash_in_hand = self.initial_investment
        self.action_history = []
        self.state_history = []
        self.reward_history = []
        self.value_history = []
        return self._get_observation()

    def step(self, action):
        assert action in self.actions

        # 更新当前持仓
        prev_val = self._get_val()
        self.action_history.append(action)
        self.current_step += 1
        self.state_history.append(self._get_observation())

        if action == "sell":
            self.stock_owned -= 1
            self.cash_in_hand += self.data.iloc[self.current_step]['close']
            self.reward_history.append(self.data.iloc[self.current_step]['close'] - prev_val)
        elif action == "buy":
            self.stock_owned += 1
            self.cash_in_hand -= self.data.iloc[self.current_step]['close']
            self.reward_history.append(prev_val - self.data.iloc[self.current_step]['close'])
        else:
            self.reward_history.append(0)

        self.value_history.append(self._get_val())
        done = (self.current_step == self.n_step)
        info = {"stock_owned": self.stock_owned, "cash_in_hand": self.cash_in_hand}
        return self._get_observation(), sum(self.reward_history), done, info

    def _get_observation(self):
        obs = np.array([self.data.iloc[self.current_step]['close'], self.data.iloc[self.current_step]['H-L'], 
                        self.data.iloc[self.current_step]['O-C'], self.data.iloc[self.current_step]['volume']])
        return obs

    def _get_val(self):
        return self.stock_owned * self.data.iloc[self.current_step]['close'] + self.cash_in_hand

# 构建神经网络模型
class ReplayBuffer:
    def __init__(self, obs_dim, act_dim, size=int(1e6)):
        self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)
        self.act_buf = np.zeros([size, act_dim], dtype=np.float32)
        self.rew_buf = np.zeros(size, dtype=np.float32)
        self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)
        self.done_buf = np.zeros(size, dtype=np.float32)
        self.ptr, self.size, self.max_size = 0, 0, size

    def store(self, obs, act, rew, next_obs, done):
        self.obs_buf[self.ptr] = obs
        self.act_buf[self.ptr] = act
        self.rew_buf[self.ptr] = rew
        self.next_obs_buf[self.ptr] = next_obs
        self.done_buf[self.ptr] = done
        self.ptr = (self.ptr+1) % self.max_size
        self.size = min(self.size+1, self.max_size)

    def sample_batch(self, batch_size=32):
        idxs = np.random.randint(0, self.size, size=batch_size)
        return dict(obs=self.obs_buf[idxs],
                    act=self.act_buf[idxs],
                    rew=self.rew_buf[idxs],
                    next_obs=self.next_obs_buf[idxs],
                    done=self.done_buf[idxs])

def get_model(obs_dim, act_dim):
    model = Sequential([
        Dense(256, input_shape=(obs_dim,), activation='relu'),
        Dense(256, activation='relu'),
        Dense(256, activation='relu'),
        Dense(act_dim)
    ])
    return model

# 训练模型
def train():
    env = Environment(df)

    obs_dim = env.reset().shape[0]
    act_dim = len(env.actions)

    model = get_model(obs_dim, act_dim)
    target_model = get_model(obs_dim, act_dim)
    target_model.set_weights(model.get_weights())
    replay_buffer = ReplayBuffer(obs_dim=obs_dim, act_dim=act_dim)

    def get_action(state, epsilon):
        if random.random() < epsilon:
            return random.choice(env.actions)
        else:
            state = np.expand_dims(state, axis=0)
            q_values = model.predict(state)
            return env.actions[np.argmax(q_values)]

    def compute_loss(batch):
        obs, act, rew, next_obs, done = batch['obs'], batch['act'], batch['rew'], batch['next_obs'], batch['done']
        target_q = target_model.predict(next_obs).max(axis=1)
        target_q = rew + (1-done) * gamma * target_q
        q = model.predict(obs)
        q = tf.reduce_sum(q * tf.one_hot(act, act_dim), axis=1)
        loss = tf.reduce_mean((q - target_q)**2)
        return loss

    optimizer = Adam(learning_rate=lr)
    epsilon = 1.0
    gamma = 0.99
    batch_size = 32
    updates_per_step = 10
    update_target_every = 2000
    replay_start_size = 10000
    total_timesteps = 200000
    start_steps = 10000
    step_count = 0
    episode_reward = 0
    episode_timesteps = 0
    state = env.reset()
    for i in range(total_timesteps):
        if i < start_steps:
            action = random.choice(env.actions)
        else:
            action = get_action(state, epsilon)
        next_state, reward, done, info = env.step(action)
        replay_buffer.store(state, env.actions.index(action), reward, next_state, done)
        state = next_state
        episode_reward += reward
        episode_timesteps += 1
        step_count += 1

        if len(replay_buffer.obs_buf) < replay_start_size:
            continue

        if step_count % updates_per_step == 0:
            for j in range(updates_per_step):
                batch = replay_buffer.sample_batch(batch_size=batch_size)
                loss = compute_loss(batch)
                grads = tf.gradients(loss, model.trainable_variables)
                optimizer.apply_gradients(zip(grads, model.trainable_variables))

        if step_count % update_target_every == 0:
            target_model.set_weights(model.get_weights())

        if done or (episode_timesteps == env.n_step):
            print('Episode: {}, episode reward: {}, episode timesteps: {}'.format(i, episode_reward, episode_timesteps))
            state = env.reset()
            episode_reward, episode_timesteps = 0, 0

train()

这段代码中，我们首先使用akshare获取上证指数的股票数据，并对数据进行处理。然后我们定义了一个强化学习环境类`Environment`，可以根据当前的股票数据状态，选择买入、卖出或持有不动。接下来我们用神经网络模型来拟合这个环境，并使用经验回放缓存`ReplayBuffer`来加速训练。最后，我们使用`train()`函数来训练模型，并输出训练过程中的奖励和步数信息。