印度商品市场的随机价差对交易
Stochastic Spread Pairs Trading in the Indian Commodity Market
摘要：
在这项研究中，我们采用了随机价差对交易策略在印度商品市场。我们收集了2010年1月1日至2018年12月31日期间现货价格可用的全套商品，包括能源，金属和农产品部门。现货数据取自MCX汇集的17种商品的现货价格。数据分为训练期（2010年1月1日至2017年3月14日）和测试期（2017年15日至2018年12月31日）。使用80：20的分割进行分割。Johanssen Cointegration检验对商品对的训练数据进行检验以检查长期关系，并选择协整商品形成交易过程。我们发现共12协整对136可能的对。协整假设为测试期间。在训练期和测试期，对协整对的对数价差分别采用单因素随机交易方法，并利用差分进化算法对随机价差模型的参数进行估计。此外，交易规则的参数通过在训练期间进行回测来优化，并在测试期间进行假设。结果表明，在回测期内，所有商品协整对的夏普比率都在1.4以上。
英文摘要：
In this study, we applied a stochastic spread pairs trading strategy on the Indian commodity market. The complete set of commodities were taken whose spot price was available for the period of January 1st 2010 to December 31st 2018 including energy, metals and the agricultural commodity sector. Spot data was taken from the MCX pooled spot prices for 17 commodities. The data was split into training period (January 1st 2010 to 14th March 2017) and testing period(15th Match 2017 to 31st December 2018). The splitting was done using a 80:20 split.Johanssen Cointegration tests were done on training data for pairs of commodities to check for long-run relationship and the cointegrated commodities were selected for formation of the trading process. We found a total of 12 cointegrated pairs out of 136 possible pairs. Cointegration was assumed for the testing period. A single-factor stochastic trading approach was applied on the logarithmic spread of the cointegrated pairs for both the training and testing period.The parameters of stochastic spread model were estimated using differential evolution algorithm. Also parameters for the trading rule were optimized by backtesting on the training period and assumed for the testing period. The results show a sharpe ratio of above 1.4 for all the commodity cointegrated pairs in the backtesing period.
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