Mean-Reversion Spread Trading Across Polyester, PTA, and Ethylene Glycol Futures
Summary
This example models a production-margin spread using polyester staple fiber futures against weighted PTA and ethylene glycol futures. It multiplies each contract price by its contract multiplier and a production ratio, then defines the spread as the polyester value minus the two input costs. A rolling history is used to calculate the spread mean and standard deviation. When the current spread's z-score exceeds a threshold, the strategy takes the opposite side of the margin move: short the output and long inputs when the spread is high, or the reverse when it is low. It closes when the z-score returns near its mean and also specifies a divergence-based stop.
The code sets contract ratios, a lookback, thresholds, and a backtest period, but reports no performance results. This is a simplified example: production ratios may not match actual exposure, and the displayed order sizes are not adjusted by contract multipliers even though the spread calculation is. The position state is tracked separately from actual holdings, and the stop condition checks the sign of only the output contract position, which may not reliably correspond to the trade's adverse direction. Transaction costs, liquidity, contract rolls, and execution risk are not addressed.
Key ideas
- The spread estimates output value minus weighted raw-material costs using contract multipliers and production ratios.
- A z-score threshold triggers trades against unusually high or low spread values.
- The example closes positions when the spread returns near its historical mean and includes a divergence stop.
- Order sizing does not incorporate contract multipliers, despite their use in spread calculations.
- No performance results are reported, and operational factors such as costs, liquidity, and contract rolls are omitted.
Tags
Full text
# pta_spread
# pta_spread
## Source (Apache-2.0)
```python
#!/usr/bin/env python
# coding=utf-8
__author__ = "Chaos"
from datetime import date
from tqsdk import TqApi, TqAuth, TargetPosTask, TqBacktest, BacktestFinished
import numpy as np
import time
# === 用户参数 ===
# 合约参数
PF = "CZCE.PF409" # 涤纶短纤期货合约
PTA = "CZCE.TA409" # PTA期货合约
EG = "DCE.eg2409" # 乙二醇期货合约
START_DATE = date(2024, 2, 1) # 回测开始日期
END_DATE = date(2024, 4, 30) # 回测结束日期
# 套利参数
LOOKBACK_DAYS = 30 # 计算历史价差的回溯天数
STD_THRESHOLD = 2.0 # 标准差阈值，超过此阈值视为套利机会
ORDER_VOLUME = 500 # 涤纶短纤的下单手数
CLOSE_THRESHOLD = 0.5 # 平仓阈值(标准差)
# 生产比例（可根据实际工艺调整）
PF_RATIO = 1
PTA_RATIO = 0.86
EG_RATIO = 0.34
# === 初始化API ===
api = TqApi(backtest=TqBacktest(start_dt=START_DATE, end_dt=END_DATE),
auth=TqAuth("快期账户", "快期密码"))
# 获取合约行情和K线
pf_quote = api.get_quote(PF)
pta_quote = api.get_quote(PTA)
eg_quote = api.get_quote(EG)
pf_klines = api.get_kline_serial(PF, 60*60*24, LOOKBACK_DAYS)
pta_klines = api.get_kline_serial(PTA, 60*60*24, LOOKBACK_DAYS)
eg_klines = api.get_kline_serial(EG, 60*60*24, LOOKBACK_DAYS)
# 创建目标持仓任务
pf_pos = TargetPosTask(api, PF)
pta_pos = TargetPosTask(api, PTA)
eg_pos = TargetPosTask(api, EG)
# 获取合约乘数
pf_volume_multiple = pf_quote.volume_multiple
pta_volume_multiple = pta_quote.volume_multiple
eg_volume_multiple = eg_quote.volume_multiple
# 初始化状态变量
position_time = 0 # 建仓时间
in_position = False # 是否有持仓
mean_spread = 0 # 历史价差均值
std_spread = 0 # 历史价差标准差
print(f"策略启动，监控合约: {PF}, {PTA}, {EG}")
# === 主循环 ===
try:
# 初始计算历史统计值
spreads = []
for i in range(len(pf_klines) - 1):
pf_price = pf_klines.close.iloc[i] * pf_volume_multiple * PF_RATIO
pta_price = pta_klines.close.iloc[i] * pta_volume_multiple * PTA_RATIO
eg_price = eg_klines.close.iloc[i] * eg_volume_multiple * EG_RATIO
# 涤纶短纤生产利润 = 涤纶短纤价值 - (PTA成本 + EG成本)
spread = pf_price - (pta_price + eg_price)
spreads.append(spread)
mean_spread = np.mean(spreads)
std_spread = np.std(spreads)
print(f"历史涤纶短纤利润均值: {mean_spread:.2f}, 标准差: {std_spread:.2f}")
# 主循环
while True:
api.wait_update()
# 当K线数据有变化时进行计算
if api.is_changing(pf_klines) or api.is_changing(pta_klines) or api.is_changing(eg_klines):
# 重新计算历史价差统计
spreads = []
for i in range(len(pf_klines) - 1):
pf_price = pf_klines.close.iloc[i] * pf_volume_multiple * PF_RATIO
pta_price = pta_klines.close.iloc[i] * pta_volume_multiple * PTA_RATIO
eg_price = eg_klines.close.iloc[i] * eg_volume_multiple * EG_RATIO
spread = pf_price - (pta_price + eg_price)
spreads.append(spread)
mean_spread = np.mean(spreads)
std_spread = np.std(spreads)
# 计算当前利润价差
pf_price = pf_klines.close.iloc[-1] * pf_volume_multiple * PF_RATIO
pta_price = pta_klines.close.iloc[-1] * pta_volume_multiple * PTA_RATIO
eg_price = eg_klines.close.iloc[-1] * eg_volume_multiple * EG_RATIO
current_spread = pf_price - (pta_price + eg_price)
# 计算z-score (标准化的价差)
z_score = (current_spread - mean_spread) / std_spread
print(f"当前涤纶短纤利润: {current_spread:.2f}, Z-score: {z_score:.2f}")
# 获取当前持仓
pf_position = api.get_position(PF)
pta_position = api.get_position(PTA)
eg_position = api.get_position(EG)
current_pf_pos = pf_position.pos_long - pf_position.pos_short
current_pta_pos = pta_position.pos_long - pta_position.pos_short
current_eg_pos = eg_position.pos_long - eg_position.pos_short
# 计算实际下单手数（依据比例）
pta_volume = int(ORDER_VOLUME * PTA_RATIO / PF_RATIO)
eg_volume = int(ORDER_VOLUME * EG_RATIO / PF_RATIO)
# === 交易信号判断 ===
if not in_position: # 如果没有持仓
if z_score > STD_THRESHOLD: # 利润显著高于均值
# 做空利润：卖出PF，买入PTA和EG
print(f"做空利润：卖出PF{ORDER_VOLUME}手，买入PTA{pta_volume}手和EG{eg_volume}手")
pf_pos.set_target_volume(-ORDER_VOLUME)
pta_pos.set_target_volume(pta_volume)
eg_pos.set_target_volume(eg_volume)
position_time = time.time()
in_position = True
elif z_score < -STD_THRESHOLD: # 利润显著低于均值
# 做多利润：买入PF，卖出PTA和EG
print(f"做多利润：买入PF{ORDER_VOLUME}手，卖出PTA{pta_volume}手和EG{eg_volume}手")
pf_pos.set_target_volume(ORDER_VOLUME)
pta_pos.set_target_volume(-pta_volume)
eg_pos.set_target_volume(-eg_volume)
position_time = time.time()
in_position = True
else: # 如果已有持仓
# 检查是否应当平仓
if abs(z_score) < CLOSE_THRESHOLD: # 利润回归正常
print("利润回归正常，平仓所有头寸")
pf_pos.set_target_volume(0)
pta_pos.set_target_volume(0)
eg_pos.set_target_volume(0)
in_position = False
# 止损逻辑
if (z_score > STD_THRESHOLD * 1.5 and current_pf_pos > 0) or \
(z_score < -STD_THRESHOLD * 1.5 and current_pf_pos < 0):
print("止损：利润向不利方向进一步偏离")
pf_pos.set_target_volume(0)
pta_pos.set_target_volume(0)
eg_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.