Sal Abbasi | Co-Founder
Coatbridge Capital

Sal Abbasi, Co-Founder, Coatbridge Capital

Sal Abbasi is Co-Founder and Portfolio Manager at Coatbridge Capital, a quantitative hedge fund involved in algorithmic trading of US and international exchange traded futures, options and ETFs.
                                                                       
Prior to founding Coatbridge Capital, Mr. Abbasi served as Head of Quantitative Credit and Fundamental Credit Technology at Citadel. At Citadel, Mr. Abbasi designed and built algorithmic trading systems for Citadel’s Quantitative Credit business.
                                                                       
Mr. Abbasi was previously Head of Technology for Citadel’s Equity Statistical Arbitrage business. Mr. Abbasi also helped launch a business to trade same day natural gas options at Citadel.
                                                                       
Before Citadel, Mr. Abbasi worked at Morgan Stanley in New York, where he helped launch a profitable spread options trading strategy and a structured commodities trading strategy.
                                                                       
Mr. Abbasi has a Master of Business Administration from New York University, a Master of Science in Mechanical Engineering from Rensselaer Polytechnic Institute, and a Bachelor of Science from Lafayette College.

Appearances:



Day 2 @ 16:00

The art of backtesting – techniques & war stories from practitioners

Plenary
  • Filtering published research – appropriate benchmarks, survivorship bias, transaction costs, robustness over time, regime changes, scalability
  • Dealing with data – gathering and trusting data, identifying bad records, techniques for dealing with missing data, alignment and synchronization, data adjustment (dividends, splits, future rolls, etc.)
  • Making sensible assumptions – borrow costs, order types and transaction costs (timeliness and slippage, commissions, scalability)
  • Correctness – model complexity, reducing parameters, cross validation
  • Model robustness & stability – over time, sensitivity to parameters, regime changes, overcrowding
  • Performance measurement – appropriate use of various metrics, importance of drawdown data
  • Going live – walk forward testing, learning from live results, improving execution

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