Srirup Mitra

Consultant

Kolkata, West Bengal, India4 yrs experience

Key Highlights

Achieved 96% return with advanced trading strategies.
Expert in statistical modeling and machine learning techniques.
Proficient in Python and SQL for data-driven decision making.

Stackforce AI infers this person is a Fintech expert specializing in quantitative analysis and machine learning for trading strategies.

Contact

Skills

Core Skills

Machine LearningQuantitative Analysis

Other Skills

Algorithmic TradingAnomaly DetectionAsset PricingBlack Scholes ModelBond MarketsCVaRCredit Default Swaps (CDS)Credit Risk ManagementData AnalysisData MiningData ScienceDeep LearningDerivativesEADEconometrics

About

Hello Linkdin family! ⭐ Thanks for visiting my profile! Hi, I am Srirup, a data science enthusiast who is fond of diving deep into the complex datasets and retrospect to gain insights and identify patterns. I have expertise in Statistical modelling, Machine Learning, Deep Learning, Econometrics, NLP with proficiency in Python, SQL, R. My aim is to maintain the data clarity and to make strategic decisions to refine techniques, optimize processes, and enhance predictive capabilities. Interests : Statistical Inference, Machine Learning, Deep Learning, Econometrics, NLP, Predictive Analytics, Quantitative techniques.

Experience

Ey

Consultant

Sep 2025 – Present · 6 mos · Kolkata, West Bengal, India · Hybrid

Feedsense ai private limited

2 roles

Quantitative Research Executive

Promoted

Jul 2023 – Sep 2025 · 2 yrs 2 mos · Kolkata, West Bengal, India · On-site

Developed NIFTY50 intraday options short strategy using Logistic Regression for precised entry and LSTM for continuous
scanning, delivering 96% return, 6.8% max drawdown and Sharpe ratio of 2.42 on rolling 365 days.
Built power sector investment model (NTPC, Powergrid) with 55% return, 7.6% drawdown, Sharpe Ratio 2.11 and 84%
market beat by automated long signals using Logistic Regression on OHLCV and quarterly equity valuation methods.
Developed an intraday mean-reversion strategy using option chain Open Interest to identify key Support/Resistance zones
(65% accuracy), integrating Option Greeks (Gamma, Theta) and Implied Volatility for dynamic risk management.
Developed an optimized live trading system using Python, PySpark, PostgreSQL integrating broker side REST APIs and WebSocket conections for real time data processing and automated order execution in the Exchange.
Managed the portfolio consists of different Options' short models by adjusting the risk by calculating Conditional VaR(CVaR) and by Stress Testing for the post Covid scenarios and automated the allocation of weights to the models in different day of the week according to the risk return analysis.
Skills: Logistic Regression, XgBoost, LSTM, Equity & Derivatives, Options Greeks, Black Scholes Model, Equity Valuation, Implied Volatility, Python, SQL, PySpark, REST API, CVaR, Stress Testing, Portfolio Optimization.

Logistic RegressionXgBoostLSTMEquity & DerivativesOptions GreeksBlack Scholes Model+11

Quantitative Research Executive

Jan 2023 – Jun 2023 · 5 mos · Kolkata, West Bengal, India · On-site

Designed a two-step predictive modeling framework firstly by incorporating unsupervised clustering (K-Means) to identify market regimes.
Incorporated XgBoost thereafter with engineered features to generate Long and Short trading signals, achieving 82%
classification accuracy.
Developed predictive model for short-term Realized Volatility using Linear Regression maintaining 78% mean directional accuracy to enhance risk management and optimized option pricing to use in different intraday trading
strategies.
Skills: Machine Learning, Quantitative Analysis, Python, SQL, Derivatives pricing.

Machine LearningQuantitative AnalysisPythonSQL