股票强化学习代码

股票强化学习是一种应用强化学习的方法，用于预测股票价格和进行交易决策。以下是一个简单的股票强化学习代码示例：

1. 导入必要的库和模块

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, LSTM
from tensorflow.keras.optimizers import Adam

2. 准备数据

# 从csv文件中读取股票数据
data = pd.read_csv('stock_data.csv')

# 将数据分成训练集和测试集
train_size = int(len(data) * 0.8)
train_data = data.iloc[:train_size, :]
test_data = data.iloc[train_size:, :]

# 对数据进行归一化处理
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
train_data = scaler.fit_transform(train_data)
test_data = scaler.transform(test_data)

3. 定义环境和代理

class StockEnvironment:
    def __init__(self, data, window_size=30):
        self.data = data
        self.window_size = window_size
        self.action_space = 2 # 买入或卖出
        self.observation_space = (window_size, 1) # 输入是window_size个价格

    def reset(self):
        self.current_step = self.window_size
        return self.data[self.current_step - self.window_size:self.current_step, :]

    def step(self, action):
        reward = 0
        done = False

        if action == 0: # 买入
            reward = -self.data[self.current_step, 0]
            self.current_step += 1
        elif action == 1: # 卖出
            reward = self.data[self.current_step, 0]
            self.current_step += 1

        if self.current_step >= len(self.data):
            done = True

        return self.data[self.current_step - self.window_size:self.current_step, :], reward, done

class StockAgent:
    def __init__(self, env):
        self.env = env
        self.gamma = 0.95 # 折扣因子
        self.epsilon = 1.0 # 探索率
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.model = self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(LSTM(64, input_shape=self.env.observation_space, return_sequences=True))
        model.add(Dropout(0.5))
        model.add(LSTM(32, return_sequences=False))
        model.add(Dropout(0.5))
        model.add(Dense(self.env.action_space, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=0.001))
        return model

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.env.action_space)
        else:
            return np.argmax(self.model.predict(state)[0])

    def learn(self, state, action, reward, next_state, done):
        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

4. 训练代理

env = StockEnvironment(train_data)
agent = StockAgent(env)

batch_size = 32
num_episodes = 1000

for episode in range(num_episodes):
    state = env.reset()
    state = np.reshape(state, [1, env.observation_space[0], env.observation_space[1]])
    done = False
    total_reward = 0

    while not done:
        action = agent.act(state)
        next_state, reward, done = env.step(action)
        next_state = np.reshape(next_state, [1, env.observation_space[0], env.observation_space[1]])
        agent.learn(state, action, reward, next_state, done)
        state = next_state
        total_reward += reward

    print("Episode: %d, Reward: %f, Epsilon: %f" % (episode, total_reward, agent.epsilon))

5. 测试代理

test_env = StockEnvironment(test_data)
state = test_env.reset()
state = np.reshape(state, [1, test_env.observation_space[0], test_env.observation_space[1]])
done = False
total_reward = 0

while not done:
    action = agent.act(state)
    next_state, reward, done = test_env.step(action)
    next_state = np.reshape(next_state, [1, test_env.observation_space[0], test_env.observation_space[1]])
    state = next_state
    total_reward += reward

print("Test Reward: %f" % total_reward)