为学习交流，以下是一段基于Python的简单的股市量化交易代码示例，用于计算股票价格的涨跌幅，并基于涨跌幅限制进行交易：

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# 计算股票的涨跌幅
def calculate_price_move(df):
    df['open_diff'] = df['open'].diff()
    df['close_diff'] = df['close'].diff()
    df['high_diff'] = df['high'].diff()
    df['low_diff'] = df['low'].diff()
    df['gain_percent'] = df['gain'] / df['close'] * 100
    df['loss_percent'] = (df['loss'] / df['close'] * 100) * 100
    return df
# 计算股票的涨跌幅限制
def limit_price_move(df, limit):
    df['open_diff'] = df['open'].diff()
    df['close_diff'] = df['close'].diff()
    df['high_diff'] = df['high'].diff()
    df['low_diff'] = df['low'].diff()
    df['gain_percent'] = df['gain'] / df['close'] * 100
    df['loss_percent'] = (df['loss'] / df['close'] * 100) * 100
    if (df['gain_percent'] > limit or df['loss_percent'] < (100 - limit)):
        return df
    else:
        return df.head(1)
# 获取股票数据
df = pd.read_csv('stock_data.csv')
# 计算涨跌幅限制
limit = 5  # 股票价格的涨跌幅限制，例如5%
df = limit_price_move(df, limit)
# 绘制股票价格和涨跌幅的图表
df = calculate_price_move(df)
df.plot(kind='scatter', x='close', y='gain_percent')
df.plot(kind='scatter', x='close', y='loss_percent')
plt.show()

上述代码中，我们使用pandas_datareader库的read_csv函数来读取某中股票相关数据，并使用calculate_price_move函数来计算股票的涨跌幅。limit_price_move函数用于计算股票价格的涨跌幅限制，并基于限制进行交易。在示例中，我们设定股票价格的涨跌幅限制为5%。最后，我们使用plot函数来绘制股票价格和涨跌幅的图表。您可以根据需要修改股票数据、涨跌幅限制以及绘图参数。