Copper-Aluminum Ratio Mean Reversion

Article Strategy library · Author: Shinny (TqSdk)

Summary

This calendar-spread style strategy trades the ratio between copper and aluminum futures. It multiplies each contract’s close by its volume multiplier, computes a rolling historical mean and standard deviation, and converts the current ratio to a z-score. When the ratio moves beyond a two-standard-deviation threshold, it opens opposing positions in the two contracts: long copper and short aluminum for a high ratio, or the reverse for a low ratio. It exits when the z-score returns within 0.5 of the mean and includes a further-deviation stop condition.

The source specifies daily observations, a 30-day lookback, fixed order volume, and a backtest window from November 2023 through April 2024. It gives no performance statistics. The position directions in the stop condition appear inconsistent with the entry directions, so the stop logic may not reliably represent adverse ratio movement. The fixed leg quantities also do not guarantee dollar or risk neutrality, and the method assumes the ratio is sufficiently mean reverting over the chosen window.

Key ideas

The strategy measures copper relative to aluminum using contract-adjusted prices.
A rolling mean and standard deviation convert the ratio into a z-score.
Extreme positive or negative z-scores trigger opposite positions in the two futures contracts.
Positions close near the historical mean, with an additional threshold intended as a stop.
Fixed quantities and potentially inconsistent stop directions limit the reliability of the hedge.

Tags

Futures Commodities Pairs trading Mean reversion Backtesting

Full text

# cu-al-spread


# cu-al-spread









## Source (Apache-2.0)

```python
from tqsdk import TqApi, TqAuth, TargetPosTask, TqBacktest, BacktestFinished
import numpy as np
from datetime import date

CU = "SHFE.cu2407"
AL = "SHFE.al2407"
START_DATE = date(2023, 11, 1)
END_DATE = date(2024, 4, 30)
LOOKBACK_DAYS = 30
STD_THRESHOLD = 2.0
ORDER_VOLUME = 30
CLOSE_THRESHOLD = 0.5

api = TqApi(backtest=TqBacktest(start_dt=START_DATE, end_dt=END_DATE), auth=TqAuth("快期账户", "快期密码"))

cu_quote = api.get_quote(CU)
al_quote = api.get_quote(AL)
cu_klines = api.get_kline_serial(CU, 60*60*24, LOOKBACK_DAYS)
al_klines = api.get_kline_serial(AL, 60*60*24, LOOKBACK_DAYS)
cu_pos = TargetPosTask(api, CU)
al_pos = TargetPosTask(api, AL)

try:
    # 计算历史铜铝比
    ratios = []
    for i in range(len(cu_klines) - 1):
        cu_price = cu_klines.close.iloc[i] * cu_quote.volume_multiple
        al_price = al_klines.close.iloc[i] * al_quote.volume_multiple
        ratios.append(cu_price / al_price)
    mean_ratio = np.mean(ratios)
    std_ratio = np.std(ratios)
    print(f"历史铜铝比均值: {mean_ratio:.4f}, 标准差: {std_ratio:.4f}")

    in_position = False
    while True:
        api.wait_update()
        if api.is_changing(cu_klines) or api.is_changing(al_klines):
            # 重新计算
            ratios = []
            for i in range(len(cu_klines) - 1):
                cu_price = cu_klines.close.iloc[i] * cu_quote.volume_multiple
                al_price = al_klines.close.iloc[i] * al_quote.volume_multiple
                ratios.append(cu_price / al_price)
            mean_ratio = np.mean(ratios)
            std_ratio = np.std(ratios)
            cu_price = cu_klines.close.iloc[-1] * cu_quote.volume_multiple
            al_price = al_klines.close.iloc[-1] * al_quote.volume_multiple
            current_ratio = cu_price / al_price
            z_score = (current_ratio - mean_ratio) / std_ratio
            print(f"当前铜铝比: {current_ratio:.4f}, Z-score: {z_score:.2f}")

            cu_position = api.get_position(CU)
            al_position = api.get_position(AL)
            current_cu_pos = cu_position.pos_long - cu_position.pos_short
            current_al_pos = al_position.pos_long - al_position.pos_short

            if not in_position:
                if z_score > STD_THRESHOLD:
                    # 做多铜铝比：买入铜，卖出铝
                    print(f"做多铜铝比：买入铜{ORDER_VOLUME}手，卖出铝{ORDER_VOLUME}手")
                    cu_pos.set_target_volume(ORDER_VOLUME)
                    al_pos.set_target_volume(-ORDER_VOLUME)
                    in_position = True
                elif z_score < -STD_THRESHOLD:
                    # 做空铜铝比：卖出铜，买入铝
                    print(f"做空铜铝比：卖出铜{ORDER_VOLUME}手，买入铝{ORDER_VOLUME}手")
                    cu_pos.set_target_volume(-ORDER_VOLUME)
                    al_pos.set_target_volume(ORDER_VOLUME)
                    in_position = True
            else:
                if abs(z_score) < CLOSE_THRESHOLD:
                    print("比率回归正常，平仓所有头寸")
                    cu_pos.set_target_volume(0)
                    al_pos.set_target_volume(0)
                    in_position = False
                # 止损逻辑
                if (z_score > STD_THRESHOLD * 1.5 and current_cu_pos < 0) or \
                   (z_score < -STD_THRESHOLD * 1.5 and current_cu_pos > 0):
                    print("止损：比率向不利方向进一步偏离")
                    cu_pos.set_target_volume(0)
                    al_pos.set_target_volume(0)
                    in_position = False

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.

Copper-Aluminum Ratio Mean Reversion with Z-Score Exits

Summary

Key ideas

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