Qstick Zero-Cross Trend Strategy with ATR Stops
Article Strategy library · Author: Chaos
Summary
This daily futures strategy calculates Qstick as the moving average of the difference between each bar's close and open. A move from negative to positive Qstick prompts a long entry when price is above its simple moving average; a move from positive to negative prompts a short entry when price is below that average. Positions close on an adverse Qstick zero-cross, an ATR-based stop, or a maximum holding period. The example applies the rules to a China 500 index futures contract and specifies position size, indicator periods, and a historical backtest window.
The document provides implementation details but no performance statistics, so it offers no evidence that the rules are profitable. Its stop is set from ATR at entry, and the stated holding limit is time-based; execution costs, slippage, contract specifics, and parameter sensitivity are not assessed. The code also relies on external trading-platform services and account authentication, which are operational details rather than evidence of strategy quality.
Key ideas
- Qstick is calculated as the rolling average of close minus open.
- Long entries require a negative-to-positive Qstick cross and price above its simple moving average.
- Short entries require a positive-to-negative cross and price below its simple moving average.
- An ATR-based stop, an opposite Qstick cross, and a maximum holding period can each close a position.
- The example includes backtest settings but does not report trading results or evaluate costs and slippage.
Tags
Full text
# Qstick
# Qstick
## Source (Apache-2.0)
```python
#!/usr/bin/env python
# -*- coding: utf-8 -*-
__author__ = "Chaos"
from datetime import date
import pandas as pd
from tqsdk import TqApi, TqAuth, TqBacktest, TargetPosTask, BacktestFinished
from tqsdk.ta import ATR
# ===== 全局参数设置 =====
SYMBOL = "CFFEX.IC2303" # 中证500指数期货
POSITION_SIZE = 30 # 持仓手数
START_DATE = date(2022, 8, 1) # 回测开始日期
END_DATE = date(2023, 1, 30) # 回测结束日期
# Qstick指标参数
QSTICK_PERIOD = 10 # Qstick周期
SMA_PERIOD = 8 # 价格SMA周期，用于确认趋势
# 风控参数
ATR_PERIOD = 14 # ATR计算周期
STOP_LOSS_MULTIPLIER = 2.0 # 止损ATR倍数
MAX_HOLDING_DAYS = 5 # 最大持仓天数
# ===== 全局变量 =====
current_direction = 0 # 当前持仓方向：1=多头，-1=空头，0=空仓
entry_price = 0 # 开仓价格
stop_loss_price = 0 # 止损价格
entry_date = None # 开仓日期
# ===== Qstick指标计算函数 =====
def calculate_qstick(open_prices, close_prices, period):
"""计算Qstick指标"""
# 计算收盘价与开盘价的差值
diff = close_prices - open_prices
# 计算移动平均
qstick = diff.rolling(window=period).mean()
return qstick
# ===== 策略开始 =====
print("开始运行Qstick趋势指标期货策略...")
# 创建API实例
api = TqApi(backtest=TqBacktest(start_dt=START_DATE, end_dt=END_DATE),
auth=TqAuth("快期账户", "快期密码"))
# 订阅合约的K线数据
klines = api.get_kline_serial(SYMBOL, 60 * 60 * 24) # 日线数据
# 创建目标持仓任务
target_pos = TargetPosTask(api, SYMBOL)
try:
while True:
# 等待更新
api.wait_update()
# 如果K线有更新
if api.is_changing(klines.iloc[-1], "datetime"):
# 确保有足够的数据计算指标
if len(klines) < max(QSTICK_PERIOD, SMA_PERIOD, ATR_PERIOD) + 5:
continue
# 计算Qstick指标
klines['qstick'] = calculate_qstick(klines.open, klines.close, QSTICK_PERIOD)
# 计算价格SMA，用于确认趋势
klines['price_sma'] = klines.close.rolling(window=SMA_PERIOD).mean()
# 计算ATR用于设置止损
atr_data = ATR(klines, ATR_PERIOD)
# 获取最新数据
current_price = float(klines.close.iloc[-1])
current_datetime = pd.to_datetime(klines.datetime.iloc[-1], unit='ns')
current_qstick = float(klines.qstick.iloc[-1])
previous_qstick = float(klines.qstick.iloc[-2])
current_price_sma = float(klines.price_sma.iloc[-1])
current_atr = float(atr_data.atr.iloc[-1])
# 输出调试信息
print(f"日期: {current_datetime.strftime('%Y-%m-%d')}, 价格: {current_price:.2f}")
print(f"Qstick: {current_qstick:.4f}")
# ===== 交易逻辑 =====
# 空仓状态 - 寻找开仓机会
if current_direction == 0:
# 多头开仓条件：Qstick从负值向上穿越零轴，价格在SMA上方
if previous_qstick < 0 and current_qstick > 0 and current_price > current_price_sma:
current_direction = 1
target_pos.set_target_volume(POSITION_SIZE)
entry_price = current_price
entry_date = current_datetime
# 设置止损价格
stop_loss_price = entry_price - STOP_LOSS_MULTIPLIER * current_atr
print(f"多头开仓: 价格={entry_price}, 止损={stop_loss_price:.2f}")
# 空头开仓条件：Qstick从正值向下穿越零轴，价格在SMA下方
elif previous_qstick > 0 and current_qstick < 0 and current_price < current_price_sma:
current_direction = -1
target_pos.set_target_volume(-POSITION_SIZE)
entry_price = current_price
entry_date = current_datetime
# 设置止损价格
stop_loss_price = entry_price + STOP_LOSS_MULTIPLIER * current_atr
print(f"空头开仓: 价格={entry_price}, 止损={stop_loss_price:.2f}")
# 多头持仓 - 检查平仓条件
elif current_direction == 1:
# 计算持仓天数
holding_days = (current_datetime - entry_date).days
# 1. 止损条件：价格低于止损价格
if current_price <= stop_loss_price:
profit_pct = (current_price - entry_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"多头止损平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
# 2. 反向信号平仓：Qstick从正值向下穿越零轴
elif previous_qstick > 0 and current_qstick < 0:
profit_pct = (current_price - entry_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"多头信号平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
# 3. 时间止损：持仓时间过长
elif holding_days >= MAX_HOLDING_DAYS:
profit_pct = (current_price - entry_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"多头时间平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
# 空头持仓 - 检查平仓条件
elif current_direction == -1:
# 计算持仓天数
holding_days = (current_datetime - entry_date).days
# 1. 止损条件：价格高于止损价格
if current_price >= stop_loss_price:
profit_pct = (entry_price - current_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"空头止损平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
# 2. 反向信号平仓：Qstick从负值向上穿越零轴
elif previous_qstick < 0 and current_qstick > 0:
profit_pct = (entry_price - current_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"空头信号平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
# 3. 时间止损：持仓时间过长
elif holding_days >= MAX_HOLDING_DAYS:
profit_pct = (entry_price - current_price) / entry_price * 100
target_pos.set_target_volume(0)
current_direction = 0
print(f"空头时间平仓: 价格={current_price}, 盈亏={profit_pct:.2f}%, 持仓天数={holding_days}")
except BacktestFinished as e:
print("回测结束")
api.close()
```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.