Steelmaking Spread Mean Reversion

Article Strategy library · Author: Shinny (TqSdk)

Summary

This daily futures example constructs a synthetic steelmaking spread from rebar, iron ore, and coking coal prices, using weights of 1, 1.6, and 0.5. It calculates a 15-day moving average and a standard deviation, then places bands half a standard deviation above and below the average. A downward cross of the upper band triggers a short rebar and long input legs; an upward cross of the lower band triggers the opposite positions. A position-state variable prevents repeatedly issuing the same directional signal.

The code shows how to monitor daily bars and submit target volumes for three Chinese futures contracts. It does not provide backtest results, transaction costs, contract-ratio justification, or an exit rule beyond switching when the opposite threshold condition occurs. The spread deviation calculation uses the full available series, and the example’s fixed target volumes do not establish risk-balanced exposure. Its rules are therefore a strategy illustration rather than evidence of profitable convergence.

Key ideas

The method forms a weighted price spread from rebar, iron ore, and coking coal futures.
It compares the spread with a 15-day moving average and bands set half a standard deviation away.
A retreat from the upper band takes a short rebar and long input position; a rebound from the lower band reverses those legs.
The code updates signals on new daily bars and tracks position direction to limit duplicate entries.
No performance evidence, explicit risk sizing, or independent exit rule is provided.

Tags

Futures Commodities Mean reversion Pairs trading China markets

Full text

# rb-i-jt-spread


# rb-i-jt-spread









## Source (Apache-2.0)

```python
from tqsdk import TqApi, TargetPosTask
from tqsdk.tafunc import ma
import numpy as np

api = TqApi()

SYMBOL_rb = "SHFE.rb2001"
SYMBOL_i = "DCE.i2001"
SYMBOL_j = "DCE.j2001"

klines_rb = api.get_kline_serial(SYMBOL_rb, 86400)
klines_i = api.get_kline_serial(SYMBOL_i, 86400)
klines_j = api.get_kline_serial(SYMBOL_j, 86400)

target_pos_rb = TargetPosTask(api, SYMBOL_rb)
target_pos_i = TargetPosTask(api, SYMBOL_i)
target_pos_j = TargetPosTask(api, SYMBOL_j)


# 计算钢厂利润线，并将利润线画到副图
def cal_spread(klines_rb, klines_i, klines_j):
    index_spread = klines_rb.close - 1.6 * klines_i.close - 0.5 * klines_j.close
    # 使用15日均值，与注释保持一致
    ma_spread = ma(index_spread, 15)
    # 计算标准差
    spread_std = np.std(index_spread)
    klines_rb["index_spread"] = index_spread
    klines_rb["index_spread.board"] = "index_spread"

    return index_spread, ma_spread, spread_std


# 初始计算利润线
index_spread, ma_spread, spread_std = cal_spread(klines_rb, klines_i, klines_j)

print("ma_spread是%.2f,index_spread是%.2f,spread_std是%.2f" % (ma_spread.iloc[-1], index_spread.iloc[-1], spread_std))

# 记录当前持仓状态，避免重复发出信号
current_position = 0  # 0表示空仓，1表示多螺纹空焦炭焦煤，-1表示空螺纹多焦炭焦煤

while True:
    api.wait_update()

    # 每次有新日线生成时重新计算利润线
    if api.is_changing(klines_j.iloc[-1], "datetime"):
        index_spread, ma_spread, spread_std = cal_spread(klines_rb, klines_i, klines_j)

        # 计算上下轨
        upper_band = ma_spread.iloc[-1] + 0.5 * spread_std
        lower_band = ma_spread.iloc[-1] - 0.5 * spread_std

        print("ma_spread是%.2f,index_spread是%.2f,spread_std是%.2f" % (
            ma_spread.iloc[-1], index_spread.iloc[-1], spread_std))
        print("上轨是%.2f,下轨是%.2f" % (upper_band, lower_band))

        # 确保有足够的历史数据
        if len(index_spread) >= 2:
            # 1. 检测下穿上轨：前一个K线在上轨之上，当前K线在上轨之下或等于上轨
            if index_spread.iloc[-2] > upper_band and index_spread.iloc[-1] <= upper_band:
                if current_position != -1:  # 避免重复开仓
                    # 价差序列下穿上轨，利润冲高回落进行回复，策略空螺纹钢、多焦煤焦炭
                    target_pos_rb.set_target_volume(-100)
                    target_pos_i.set_target_volume(100)
                    target_pos_j.set_target_volume(100)
                    current_position = -1
                    print("下穿上轨：空螺纹钢、多焦煤焦炭")

            # 2. 检测上穿下轨：前一个K线在下轨之下，当前K线在下轨之上或等于下轨
            elif index_spread.iloc[-2] < lower_band and index_spread.iloc[-1] >= lower_band:
                if current_position != 1:  # 避免重复开仓
                    # 价差序列上穿下轨，利润过低回复上升，策略多螺纹钢、空焦煤焦炭
                    target_pos_rb.set_target_volume(100)
                    target_pos_i.set_target_volume(-100)
                    target_pos_j.set_target_volume(-100)
                    current_position = 1
                    print("上穿下轨：多螺纹钢、空焦煤焦炭")

    # 实时监控价差变化情况
    if api.is_changing(klines_rb.iloc[-1], "close") or api.is_changing(klines_i.iloc[-1], "close") or api.is_changing(
            klines_j.iloc[-1], "close"):
        # 实时更新价差
        current_spread = klines_rb.close.iloc[-1] - 1.6 * klines_i.close.iloc[-1] - 0.5 * klines_j.close.iloc[-1]
        # 可以添加实时监控输出
        # print("当前价差: %.2f" % current_spread)

```

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.

Mean-Reversion Trading on a Steelmaking Input-Output Spread

Summary

Key ideas

Tags