Webinars This Blog

Algorithmic Trading in Commodity Markets by Sunil Guglani

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Thursday, 13 February, 2020

 8:00 AM ET | 6:30 PM IST | 9:00 PM SGT

Session Outline

  • Introduction
  • Stages of Algorithmic Trading:
  • Formulating the Trading Concept/Logic
  • Filtering criteria to choose the scripts
  • Verification of Logic (at High Level)
  • Backtesting
  • Optimization of Parameters
  • Paper Trading aka Forward Testing or Simulation Trading, in the real environment
  • Deployment in the real environment
  • Resources for learning Algorithmic Trading
  • A walkthrough with a sample strategy in Python

And so much more...

Who should attend?

  • Discretionary/manual traders (ex. professional traders, part-time traders) who are looking to upskill and get better returns
  • Commodity Traders looking to implement quant strategies
  • Technology professionals, who want to leverage their technical skills to invest wisely in the financial markets
  • Students and other enthusiasts who wish to make a career in quantitative finance

Speaker Profile

Sunil Guglani

Assistant Vice President at NCDEX

Sunil is currently working with NCDEX (National Commodity and Derivatives Exchange Ltd) as Assistant Vice President. He has a vast experience of around 20 years working within the IT industry. An Algorithmic Trader today, Sunil is an EPAT Alumni who changed his domain from IT management to the rapidly growing domain of Algo Trading 1.5 years back.

Sunil primarily uses Python for developing his Algorithms. His strategies are based on price pattern recognition, probability, correlation/cointegration, autocorrelation, pair trading, stationary series, momentum identification, Hedging and ML/DL algorithms. He developed his own backtesting and technical indicator libraries as well.


You can check out the powerpoint presentation for this webinar here:

Code files available on QuantInsti GitHub:

  • Chana Gram Futures Historical Data_2.csv
  • IAP Algo Trading Sep 2019_v3.pdf
  • algo_stages_commodity_article.py
  • algo_stages_commodity_article_optimization.py
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