Summary

TrueRisk Capital is a quantitative, systematic CTA offering fully algo. automated Liquid Alternative strategies. It currently offers its Market Volatility strategy. Others in the pipeline include US Equity Long-Short and Diversified Managed Futures strategies.
Its managed account programs and funds offer advisors, intermediaries and individuals with unique standalone strategies as well as differentiated components to their overall investment portfolio allocation.
The firm is closely affiliated with TrueRisk Labs (truerisklabs.com) - a leading-edge quantitative investment research and technology firm that applies machine-learning engines to new data sources to generate forward-looking sentiment and risk metrics that enhance existing portfolio strategies or help to create entirely new ones.

Ritabrata "Rito" Bhattacharyya, Co-CIO and Chief Quant of TrueRisk Capital, is a professional trader and a pioneering technologist in machine learning based applications that include trading algorithm design.
Built over a decade, Rito’s trading algos (ML and non-ML-based,) have been licensed to 15+ hedge funds and prop. trading desks in the US, Europe and Asia. He has served in a lead quant role at many client managers and has founded/co-founded 3 prior investment and fintech firms, including TrueRisk Labs. TrueRisk Labs applies machine-learning engines on new data sources to generate forward-looking signal analytics that forecast security-level returns and volatility more accurately, compared with empirical and / or price-driven risk-return models.
Rito is a founding member of WorldQuant University and current faculty member for the final-year Capstone project course of their MS Financial Engineering program. He has authored or co-authored 15+ papers on contemporary topics in investment finance and AI/machine learning.
Rito holds 3 software patents in machine learning based loyalty program design modeling consumer behavior, designed for Oracle Int'l. - US11463879, US11463899 & US7837099.
Rito holds a Master’s degree in Engineering and Computational Biology (2014) from the Indian Institute of Technology (IIT), Bombay. His first exposure to machine learning modeling resulted in the journal publication of two papers - 1. Mathematical analysis and explanation of acetate metabolism in Escherichia coli, JM109 and BL21 - the identification of key genetic control steps, and 2. Glucose metabolism at high density growth of E.coli B and E.coli K: Efficient glucose utilization in E.coli B as determined by microarrays and northern blot analyses.
A sampling of his papers available on SSRN DB follow:
* Using Principal Component Analysis on Crypto Correlations to Build a Diversified Portfolio
* The Use of Deep Reinforcement Learning in Tactical Asset Allocation
* Efficient Market Hypothesis: Empirical Test to Debunk the Weak Form Using Selected Stocks
* Cryptocurrency Trading-Pair Forecasting, Using Machine Learning and Deep Learning Technique
* Short Term Trading Model for Asian Equity Index Futures – Using Hurst Exponent
* Short Term Trading Models – Mean Reversion Trading Strategies and the Black Swan Events
* Expected Shortfall – An Alternative Risk Measure to Value-at-Risk
* A Value Investment Strategy that Combines Security Selection and Market Timing Signals
* Dynamic Regime Strategy for Stress Testing and Optimizing Institutional Investor Portfolios
* Momentum, Market Regime and Stocks & Options Trading Strategies
* Evaluating the Building Blocks of a Dynamically Adaptive Systematic Trading Strategy

Kaushik Saha, CEO and Co-CIO of TrueRisk Capital, is an institutional investment researcher, portfolio manager and trader.
Kaushik cut his teeth on systematic investment research at Barclays Global Investors, a past pioneer in scientific investment research and industry leader in systematic/ hybrid investing and active portfolio management. He built trading models for systematic alpha generation for MBS and optimized portfolio allocation among model selected trades. He is practiced in quantitative techniques for active portfolio mgmt. including factor analysis & signal development, ex-ante excess return forecasting for security selection, optimizer-based portfolio construction & risk budgeting.
Prior to it, he held roles in quantitative research and analytics at Freddie Mac that involved the option adjusted valuation of mortgage backed securities and loans as well as securitization structuring tools for a range of asset backed products.