Algorithmic Traders

Responsibilities
Analyze and develop quantitative trading strategies by developing electronic trading applications. Gather market data and develop market microstructure models using derivative Theory and Quantitative Methods to develop mathematical models for both linear and non-linear financial pricing and risk management. Implement and calibrate real-time trading algorithms. Develop and implement relative value and market trading execution strategies. Develop software applications and quantitative tools using statistical analysis methods. Develop, back-test and refine advanced quantitative financial and mathematical trading models. Perform statistical analysis on large data sets (over 5GB) and use statistical techniques to develop factor models for pricing and risk management.  Develop hedging algorithms to calculate optimal hedging product based on utility. Identify and combine alpha signals to improve portfolio returns, conduct relative value analysis in fixed income markets. Design, test and optimize trading strategies using simulation algorithms. Technical Environment: Stochastic Optimization Techniques, Principle Component Analysis, Machine Learning, Dynamic Conditional Correlations, MATLAB, R, Python, Dynamic Programming, Object Oriented Programming, Python (PyCharm, Jupyter Notebook, Spyder), Excel, SQL, VBA, C++, C++ in Linux, PuTTY, TT Xtrader, Bloomberg, factor models.
 
Skills and Experience
Master’s degree in Mathematics or Statistics or Financial Mathematics or Computational Finance or Financial Engineering plus two years of experience in the job offered or developing quantitative financial models OR Bachelor’s degree in Mathematics or Statistics or Financial Mathematics or Computational Finance or Financial Engineering plus five years of experience in the job offered or developing quantitative financial models required. Special Skills: developing, back-testing and refining advanced quantitative financial and mathematical trading models using linear and non-linear techniques (principal component analysis, dynamic conditional correlations) for financial markets; performing statistical analysis large data sets; identifying and combining alpha signals to improve portfolio returns; conduct relative value analysis in fixed income markets; market data analysis using machine learning techniques in MATLAB; developing and implementing relative value and market trading execution strategies using SQL, and Bloomberg, factor models. Must have created and optimized trading strategies used in live production. 

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