SENIOR VICE PRESIDENT at AMM E-FX TRADING, SENIOR RESEARCH FELLOW, QUANTITATIVE STRATEGIST ON QIS (Q

* Contributed nearly 1M$ per year to desk's P&L by researching and developing signals and strategies for hedging and trading of eFx book based
on classification of market and client data (Alpha research, Strategy design and backtest, FX 10- minute alpha from orderbook, price action, client
trades/order flow, CLS/EBS volume, news sentiment, events, other currency pairs, other asset prices, ...) successfully monetizing toxic client flow
using machine learning/predictive modeling, python
* Designed an Event-Driven model designed for post-event (2-20 min) market reaction based on surprise factor and other features with accuracy
score more than 0.65
* Designed a classifier for predicting Min Ask and Max Bid within a 5-10 min interval using market prices and orderbook states for use within the
market making framework, exiting trades passively (accuracy score>0.49)
* Used Ravenpack and Federal Reserve news analysis methodology for creating an online news sentiment score for currencies with 10-minute
Alpha (Incomplete)
* Worked closely with software developers in building the book for customized hedging of the toxic clients (Shared Memory,...)

* Applied Recurrent Neural Network to enhanced technical data (implied vol, sentiment) of Russel1000 in order to train the network in predicting
subsequent price move (Accuracy 0.21 out-of-sample vs. 0.1 random, Long-Short Equity based on this strategy produces 0.6% average daily
return without transaction/shorting costs)
* Enhanced OLMAR (Online Learning Moving Average Reversion) with a predictive model based on volume (interacting factor), momentum,
adjusting for Industry bias and Implementing transaction cost
* Applyed Deep Learning to options data for designing a volatility trading strategy (Not Completed)
* Worked on a CTA using standard systematic macro factors (Momentum, Volatility, Mean-Reversion around trend-line, ...) combined by a
Recurrent Neural Network (portfolio optimization wasn't completed)

* Contributed to Research on 2 equity factors based on Indication of Interest data& Intraday trade imbalance (alpha signals extracted were
implemented within the portfolio optimization signal set, with Sharpe ratios of 0.6 and 1.4)
* Researched and Implemented an orthogonal 6-factor model for a fast trading model enhancing the current trade execution system with a daily
up/down forecast signal (open to close)
* Implemented accounts based on the GTAA (Global Tactical Asset Allocation) model of the Macro team, enhancing and implementing the
accounts in the codebase in SECDB
* Implemented and migrated Equity Alpha Factors (Industry-Specific Data, Intraday Market Data, Value, Quality, Momentum, Sentiment,
Profitability, ...) of the Quantitative Equity team into SECDB codebase




* Conducted and completed Research projects using large data sets for investigating Alpha in certain market datasets using standard criteria for
verification of Alpha/Factor Interaction/Factor Turnover (IC, IR, U-chart,5-1 Decile Long-Short ,63-day Gamma, Residual regression, Turnover
heat map, ...)
* Implemented a Non-linear(L-1) filter with Mann-statistic for trend Filtering/Detection in equity trend