Layered Grid Trading with Target Position Rebalancing
Summary
This example implements a two-sided grid for a futures contract. Starting from a reference price, it calculates successive levels at fixed percentage intervals: lower levels for a long grid and higher levels for a short grid. As price crosses levels, the strategy changes its target position to the size assigned to that layer. When price retraces, it steps back through the levels and adjusts the target position; after leaving the grid cycle, it targets a flat position.
The document provides executable strategy logic and example settings, but no performance results or discussion of market conditions. It does not describe safeguards for large moves beyond the outer grid levels, transaction costs, or how to choose spacing and position sizes. The example is explicitly presented as a demonstration that needs adaptation before live use. Its recursive price-waiting flow and position transitions should be examined carefully before relying on it in trading.
Key ideas
- The strategy builds long and short price levels by repeatedly applying fixed percentage changes to a starting price.
- Each grid layer maps to a target position size, so crossing a level changes the desired exposure.
- Price retracements move the strategy back toward earlier layers and their associated target positions.
- The sample offers no backtest evidence and leaves grid calibration and live-trading safeguards to the user.
Tags
Full text
# gridtrading
# gridtrading
## Source (Apache-2.0)
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = 'limin'
"""
网格交易策略 (难度：中级)
参考: https://www.shinnytech.com/blog/grid-trading/
注: 该示例策略仅用于功能示范, 实盘时请根据自己的策略/经验进行修改
"""
from functools import reduce
from tqsdk import TqApi, TqAuth, TargetPosTask
SYMBOL = "DCE.jd2011" # 合约代码
START_PRICE = 4247 # 起始价位
GRID_AMOUNT = 10 # 网格在多头、空头方向的格子(档位)数量
api = TqApi(auth=TqAuth("快期账户", "账户密码"))
grid_region_long = [0.005] * GRID_AMOUNT # 多头每格价格跌幅(网格密度)
grid_region_short = [0.005] * GRID_AMOUNT # 空头每格价格涨幅(网格密度)
grid_volume_long = [i for i in range(GRID_AMOUNT + 1)] # 多头每格持仓手数
grid_volume_short = [i for i in range(GRID_AMOUNT + 1)] # 空头每格持仓手数
grid_prices_long = [reduce(lambda p, r: p * (1 - r), grid_region_long[:i], START_PRICE) for i in
range(GRID_AMOUNT + 1)] # 多头每格的触发价位列表
grid_prices_short = [reduce(lambda p, r: p * (1 + r), grid_region_short[:i], START_PRICE) for i in
range(GRID_AMOUNT + 1)] # 空头每格的触发价位列表
print("策略开始运行, 起始价位: %f, 多头每格持仓手数:%s, 多头每格的价位:%s, 空头每格的价位:%s" % (
START_PRICE, grid_volume_long, grid_prices_long, grid_prices_short))
quote = api.get_quote(SYMBOL) # 行情数据
target_pos = TargetPosTask(api, SYMBOL)
position = api.get_position(SYMBOL) # 持仓信息
def wait_price(layer):
"""等待行情最新价变动到其他档位,则进入下一档位或回退到上一档位; 如果从下一档位回退到当前档位,则设置为当前对应的持仓手数;
layer : 当前所在第几个档位层次; layer>0 表示多头方向, layer<0 表示空头方向
"""
if layer > 0 or quote.last_price <= grid_prices_long[1]: # 是多头方向
while True:
api.wait_update()
# 如果当前档位小于最大档位,并且最新价小于等于下一个档位的价格: 则设置为下一档位对应的手数后进入下一档位层次
if layer < GRID_AMOUNT and quote.last_price <= grid_prices_long[layer + 1]:
target_pos.set_target_volume(grid_volume_long[layer + 1])
print("最新价: %f, 进入: 多头第 %d 档" % (quote.last_price, layer + 1))
wait_price(layer + 1)
# 从下一档位回退到当前档位后, 设置回当前对应的持仓手数
target_pos.set_target_volume(grid_volume_long[layer + 1])
# 如果最新价大于当前档位的价格: 则回退到上一档位
if quote.last_price > grid_prices_long[layer]:
print("最新价: %f, 回退到: 多头第 %d 档" % (quote.last_price, layer))
return
elif layer < 0 or quote.last_price >= grid_prices_short[1]: # 是空头方向
layer = -layer # 转为正数便于计算
while True:
api.wait_update()
# 如果当前档位小于最大档位层次,并且最新价大于等于下一个档位的价格: 则设置为下一档位对应的持仓手数后进入下一档位层次
if layer < GRID_AMOUNT and quote.last_price >= grid_prices_short[layer + 1]:
target_pos.set_target_volume(-grid_volume_short[layer + 1])
print("最新价: %f, 进入: 空头第 %d 档" % (quote.last_price, layer + 1))
wait_price(-(layer + 1))
# 从下一档位回退到当前档位后, 设置回当前对应的持仓手数
target_pos.set_target_volume(-grid_volume_short[layer + 1])
# 如果最新价小于当前档位的价格: 则回退到上一档位
if quote.last_price < grid_prices_short[layer]:
print("最新价: %f, 回退到: 空头第 %d 档" % (quote.last_price, layer))
return
while True:
api.wait_update()
wait_price(0) # 从第0层开始进入网格
target_pos.set_target_volume(0)
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.