Quantitative Researcher

at CTC Trading Group, L.L.C. in Chicago, Illinois, United States

Job Description

Partner directly with trading to turn market observation and intuition into working hypotheses. Talk with futures traders to determine current pricing problems. Partner with them directly on understanding particularities of a given market segment, e.g. what futures spreads are liquid, which intercommodity spreads have volume, which expiries are stable. Based on these details, propose a price modeling solution to address the particular challenges of a given product area. Implement the pricing solution once the proposal has been accepted. Collaborate with trading and technology to build world class models that help us describe the everchanging world around us. Creating new pricing models requires collaboration with C++ developers in our in-house pricing code. Work with C++ devs to get new model code into the C++ pricing solution. Work with C++ devs where necessary to track down bugs in the system correlation keys, feed handler and other data sources. Assess the strength of both current models and new candidate models by using intuition, data, and first principles. In order to make a price model for a given product area, need to create multiple candidate signals and then do a fit to predict price changes. This results in a cornucopia of candidate models. Selecting one to use in production is a combination of concerns, including predictivity, simulation performance and robustness. Creating signals requires an understanding of fundamental market principles in order to produce useful information. It also requires a knowledge of dimensional analysis to create signals that are invariant under various market events like stock splits. Once a model is in production, need to compute daily metrics about predictivity and profitability on option trades to continually assess the quality of the model. Assist in developing requirements and propose solutions - Participate in a rapid innovation cycle that produces demonstrable wins through the feedback loop of quantitative research, trading, posttrade analysis, and iterative workflows. Create and maintain a metrics application for tracking model performance in options trading. Work out the appropriate database queries to join price data with option trade data for this analysis. Use this information to decide when models need to be changed out or modified. Contribute to production trading through research - Conduct research and design experiments to improve existing strategies and deploy new strategies that monetize opportunities in a constantly evolving market environment. Run trading simulations using a combination of price models and strategy parameters to identify profitable strategies. Try variants with different model parameters and strategy parameters. Analyze market data and market microstructure - Build data-based tools and workflows to further systematize our approach to trading and risk management. The metrics application mentioned above is the primary example of this. Build queries to provide the market data required to train term structure models. Build a fitting procedure for stateful models. Collaborate with a multidisciplinary team - Learn from an experienced team with a diverse knowledge base across trading, quant, and technology. Requires direct collaboration with traders, quants and technologists. Succeeding in this effort requires learning from all of the above. Telecommuting is permitted.