Manas Baviskar
@manasbaviskarFinancial Engineering graduate from UCLA Anderson School of Management, specializing in quantitative research and development.
Language Breakdown
Lines of code distribution across 9 owned repositories
I-Shaped Developer
I-shapedSpecialist — deep expertise in Jupyter Notebook
Collaboration Network
Global Impact visualization
Repos
10
PRs
0
Growth
+18%
Top Collaborators
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Coding Streak
Contribution activity over the past year
Top Repositories
This project explores the optimization of trade execution strategies in high-frequency trading (HFT) by identifying cost-efficient time windows. Using a Time-Weighted Average Price (TWAP) strategy and incorporating market microstructure insights.
This project focuses on building predictive models to forecast future liquidity distribution in corporate bond markets. Using rolling-window frameworks, the study developed bond-level and trade-specific predictors to estimate future liquidity trends.
Develop TCA framework on TRACE data to compute expected transaction costs and price impact dynamics. Implemented regularized regression techniques to identify key determinants of trading costs, and improve execution
This project focuses on developing a statistical arbitrage strategy to identify and capitalize on temporary price divergences between pairs of technology stocks from the S&P 500. The approach uses backtesting and optimization techniques, along with dynamic enhancements like adaptive leverage and stop-loss adjustments.
This project focuses on interest rate modeling and option pricing using advanced numerical techniques. By implementing Monte Carlo simulations and finite difference methods, the project provides a robust framework for pricing bonds and options under various interest rate models, including Vasicek, CIR, and G2++.
Coding Assignment
A deep learning-based algorithmic trading strategy using various features such as Bollinger Bands (BB), Standard Moving Averages (SMA), Parabolic SAR (SAR), etc.
The project implements Optical Flow for tracking of obstacles. Optical flow is a technique which computes the flow vectors of all the pixels in the frame by monitoring the current frame and the previous frame. This technique has been traditionally used in analyzing flow states of fluids in various experiments related to thermodynamics. The proposed solution implements dense optical flow which is based on Farneback’s algorithm.
Open Source Impact
Contributions to external projects
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