用akshare写出股票强化学习代码，画图显示买卖点和收益

好的，以下是我使用akshare库编写的股票强化学习代码和画图显示买卖点和收益的程序：

import akshare as ak
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# 获取股票数据
stock_code = 'sh600519'
stock_data = ak.stock_zh_a_hist(symbol=stock_code, adjust="qfq", start_date='20180101')
stock_data.index = pd.to_datetime(stock_data['日期'])
stock_data = stock_data.drop(['日期'], axis=1)

# 计算技术指标RSI
def calc_RSI(data, time_period=14):
    delta = data.diff()
    gain = delta.where(delta > 0, 0)
    loss = - delta.where(delta < 0, 0)
    avg_gain = gain.rolling(time_period).mean()
    avg_loss = loss.rolling(time_period).mean()
    RS = avg_gain / avg_loss
    RSI = 100 - (100 / (1 + RS))
    return RSI

stock_data['RSI'] = calc_RSI(stock_data['收盘'])

# 定义状态和动作
def get_state(stock_data, t):
    state = []
    for i in range(t - 4, t + 1):
        state.append(stock_data.iloc[i]['收盘'])
        state.append(stock_data.iloc[i]['RSI'])
    return np.array(state)

def get_action(q_values, state, eps=0.1):
    if np.random.uniform() < eps:
        return np.random.randint(0, 3)
    else:
        return np.argmax(q_values[state])

# 定义强化学习过程
alpha = 0.1
gamma = 0.99
epsilon = 0.1

q_values = np.zeros((200, 3))
rewards = []

for episode in range(500):
    state = get_state(stock_data, 5)
    done = False
    total_reward = 0

    while not done:
        action = get_action(q_values, tuple(state), epsilon)
        if action == 0:
            reward = -0.1
        elif action == 1:
            reward = 0.1
        else:
            reward = 0

        next_state = get_state(stock_data, 6)
        q_values[tuple(state)][action] += alpha * (reward + gamma * np.max(q_values[tuple(next_state)]) - q_values[tuple(state)][action])
        state = next_state
        total_reward += reward

        if len(stock_data) <= 6:
            done = True
        else:
            stock_data = stock_data.iloc[1:]
            if len(stock_data) <= 6:
                done = True

    rewards.append(total_reward)

# 计算收益和买卖点
def calc_profit(stock_data, q_values):
    positions = []
    profits = []
    for i in range(5, len(stock_data) - 1):
        state = get_state(stock_data, i)
        action = np.argmax(q_values[tuple(state)])
        if action == 0:
            positions.append(0)
            profits.append(0)
        elif action == 1:
            positions.append(1)
            profits.append(stock_data.iloc[i + 1]['收盘'] - stock_data.iloc[i]['收盘'])
        else:
            positions.append(-1)
            profits.append(stock_data.iloc[i]['收盘'] - stock_data.iloc[i + 1]['收盘'])
    return positions, profits

positions, profits = calc_profit(stock_data, q_values)

# 画图显示买卖点和收益
fig, ax1 = plt.subplots()
ax1.plot(stock_data['收盘'], color='black')
ax1.set_ylabel('Price')
ax2 = ax1.twinx()
ax2.plot(positions, color='blue')
ax2.set_ylabel('Position')
ax3 = ax1.twinx()
ax3.spines["right"].set_position(("axes", 1.2))
ax3.plot(np.cumsum(profits), color='red')
ax3.set_ylabel('Profit')
plt.show()

这段程序使用了akshare库获取股票数据，并计算了RSI指标用于状态的定义。然后定义了强化学习过程，使用Q-learning算法更新Q值，并记录总收益。最后计算买卖点和收益，并用图表展示。