在国信iquant上完成一段网格交易代码

很抱歉，作为AI语言模型，我不能直接在国信iquant上完成代码。但是，我可以提供一个网格交易的Python示例代码，您可以根据您的需要进行修改和优化。

import pandas as pd
import numpy as np
import datetime as dt

# 读取数据
data = pd.read_csv('data.csv')
data['时间'] = pd.to_datetime(data['时间'], format='%Y-%m-%d %H:%M:%S')
data = data.set_index('时间')

# 确定网格交易参数
start_price = data['收盘价'].iloc[0]
end_price = data['收盘价'].iloc[-1]
grid_size = 0.01
num_grids = int((end_price - start_price) / grid_size)
grid_upper_prices = [start_price + i * grid_size for i in range(num_grids)]
grid_lower_prices = [start_price + (i + 1) * grid_size for i in range(num_grids)]

# 网格交易
positions = []
for i in range(len(data)):
    price = data['收盘价'].iloc[i]
    if len(positions) == 0:
        if price <= grid_upper_prices[0]:
            positions.append(('buy', price))
        elif price >= grid_lower_prices[-1]:
            positions.append(('sell', price))
    else:
        if positions[-1][0] == 'buy':
            if price >= grid_lower_prices[positions[-1][1] - start_price]:
                positions.append(('sell', price))
        else:
            if price <= grid_upper_prices[positions[-1][1] - start_price]:
                positions.append(('buy', price))

# 输出交易记录
for i in range(1, len(positions), 2):
    buy_price = positions[i - 1][1]
    sell_price = positions[i][1]
    profit = (sell_price - buy_price) / buy_price
    print('买入价：{}，卖出价：{}，盈利：{:.2%}'.format(buy_price, sell_price, profit))

该代码实现了一个简单的网格交易策略，根据给定的价格范围和网格大小，进行买入和卖出交易。对于每一笔交易，计算盈利并输出交易记录。请根据您的实际需求修改和优化代码。