Chicago, United States (U.S)
•Speaks English
Software Engineering | Quantitative Trading | Applied Machine Learning | English | United States | MCS
Ricardo Lachman works with corporate and trading firms in United States to develop and monitor low latency quantitative trading strategies using his expertise in quantitative finance, software engineering/development and applied machine learning and data sciences.
Ricardo has more than 16 years of professional working experience and he holds Masters degree in Computer Science from University of Illinois.
He worked with Quantsoft Advisers as Cofounder and Trading System Developer for 10 years to manage the portfolio of thousands of listed U.S. cash equities.
Prior to his current work as Engineering Consultant at A&W Technical Consulting, Ricardo was the Trader/Quantitative Trading Developer for XR Trading.
Ricardo has more than 16 years of professional working experience and he holds Masters degree in Computer Science from University of Illinois.
He worked with Quantsoft Advisers as Cofounder and Trading System Developer for 10 years to manage the portfolio of thousands of listed U.S. cash equities.
Prior to his current work as Engineering Consultant at A&W Technical Consulting, Ricardo was the Trader/Quantitative Trading Developer for XR Trading.
Questions I can answer:
How can I model this phenomenon in data and build a software system around it?
Keywords
Other
Experience
A&W Technical Consulting • United States (U.S)
Engineering Consultant
2021 - Present (3 years, 4 months)
XR TradingTrader/Quantitative Trading Developer • United States (U.S)
Trader/Quantitative Trading Developer
2017 - 2020 (3 years, 7 months)
Maintained analysis framework with enriched CME market by order data for quick exploration (C++, Cling, SQL, and Python);
Elaborate order history details (queue position, timestamps, complete order lifecycle, internal flow integration);
Place/modify/trade classification (fill informed, FGPA, or software actions);
Detailed packet information for trade summary analysis;
Trade summary details (self-match prevention, iceberg interaction, stop orders, implied activity);
Order value modelling for passive order evaluation (C++/R);
Leveraged stateful order book snapshots with order/level features;
Determination of filled subset given an event time after snapshot;
Order value modeled as a combination of fill probability and conditional fill performance;
CME to cash equity competitiveness (C++);
Maintained CME Equity FPGA trading software;
Yield curve trading analysis .
Elaborate order history details (queue position, timestamps, complete order lifecycle, internal flow integration);
Place/modify/trade classification (fill informed, FGPA, or software actions);
Detailed packet information for trade summary analysis;
Trade summary details (self-match prevention, iceberg interaction, stop orders, implied activity);
Order value modelling for passive order evaluation (C++/R);
Leveraged stateful order book snapshots with order/level features;
Determination of filled subset given an event time after snapshot;
Order value modeled as a combination of fill probability and conditional fill performance;
CME to cash equity competitiveness (C++);
Maintained CME Equity FPGA trading software;
Yield curve trading analysis .
Quantsoft Advisers • United States (U.S)
Cofounder and Trading System Developer
2008 - 2016 (8 years, 11 months)
Co-portfolio manager of Quantsoft’s investment vehicle: Quantsoft Equity Fund, LP;
Managed portfolio of thousands of listed U.S. cash equities;
Mean reversion signal-based market neutral statistical arbitrage strategy ;
Maintained complete U.S. cash equity back testing, trading, and analysis framework (C++);
Special attention to event handling for overnight trading (CUSIP tracking and corporate actions database);
Parameterized algorithmic order simulation using trade price histogram bars;
Linked market data feeds to signal generators and order management systems;
Signal exploration for alpha research;
Parallel, synchronized, event driven callback framework (OpenMP/C++)
Covariance Modelling for risk estimation (C++/Eigen);
Random Matrix Theory filtering and M estimators
Managed portfolio of thousands of listed U.S. cash equities;
Mean reversion signal-based market neutral statistical arbitrage strategy ;
Maintained complete U.S. cash equity back testing, trading, and analysis framework (C++);
Special attention to event handling for overnight trading (CUSIP tracking and corporate actions database);
Parameterized algorithmic order simulation using trade price histogram bars;
Linked market data feeds to signal generators and order management systems;
Signal exploration for alpha research;
Parallel, synchronized, event driven callback framework (OpenMP/C++)
Covariance Modelling for risk estimation (C++/Eigen);
Random Matrix Theory filtering and M estimators
Fermilab National Accelerator Laboratory • United States (U.S)
MIT Undergraduate Researcher
2002 - 2004 (2 years, 1 month)
Studied fundamentals of particle physics, emphasizing the roles of software design and statistics;
Designed a simplified simulation of Fermilab’s CDF detector (QuickSim) to increase Monte Carlo production (C++)
Designed a simplified simulation of Fermilab’s CDF detector (QuickSim) to increase Monte Carlo production (C++)
Education
Bachelor of Science, Mathematics and Physics
Massachusetts Institute of Technology
2000 - 2004
Master of Computer Science in Data Science
University of Illinois
? - 2020