MGPraxis42,984 papers mapped

Deep Reinforcement Learning and Genetic Algorithm for a Pairs Trading Task on Commodities

Georgios Sermpinis, Charalampos Stasinakis, Xiangyu Zong·2021

“The average annualized returns of the traditional DM-ST, CA-ST and CA-DM-ST methods are not high, at 0.12%, 0.37% and 0.43%, respectively.”

AssetCommodities
StrategyPairs trading
TechniqueReinforcement learningsymbolic search
FrequencyAnnual

Also stated: 1980–2018, Sharpe ratio, Profit, out-of-sample test

Missing from abstract: benchmark, costs, multiple testing, universe

Institutional-grade audit of this strategy.
£5 — Request the audit
  • Audit of the paper claims accounting for costs, luck, crises, originality, geography, etc.
  • Reproduction code integratable into Bloomberg, IBKR, etc.
  • Bias-free datasets for backtesting included
  • Delivered in 2 working days
The paper's neighbourhoodPairs trading and statistical arbitrage by year
■ commodities■ equities■ crypto■ multi-asset■ other assets200020052010652015547This paper202111112026■ commodities■ equities■ crypto■ multi-asset■ other assets20002005201065201557This paper2021112026
The family is counted across the whole universe. Asset, method and authors are counted inside the family.
Similar paperscitationsame methodsame assetsame authors
Stochastic Spread Pairs Trading in the Indian Commodity Market by Mahajan and Chandra2019
Technical Analysis, Spread Trading and Data Snooping Control by Psaradellis et al.2018
The Case of Gold and Silver: A New Algorithm for Pairs Trading by Desai et al.2013
A Regime-Aware, Free-Boundary Framework for Statistical Arbitrage: Kalman-Filtered Synthetic Baskets, EGARCH Volatility, Markov Regime Detection, and Optimal Stopping via Hamilton-Jacobi-Bellman by Bektas2026
Pairs Trading: A Change Point Perspective by Dou et al.2026
Not yet audited by MarketGraphs Praxis. Tags from the abstract. Counts from the universe.
RSS · Contacts