Agentic coding skills for backtesting trading strategies using VectorBT. Supports Indian, US, and Crypto markets with realistic transaction cost modeling, TA-Lib indicators, QuantStats tearsheets, and 12 ready-made strategy templates.

An implementation of Giuseppe Paleologo's Rademacher Antiserum, designed to assess strategy performance consistency through Rademacher complexity and RAS-adjusted Sharpe Ratios. This code evaluates strategy robustness by applying Rademacher random vectors for anti-overfitting analysis.

Crypto-currencies (Bitcoin) trading & Transfer Learning applications

Utilized time series, statistic, ML and NLP models to practice. Topics include stock forecasting (algorithm trading), US 2024 presidential election and customer sentimental review.

Method to use rolling optimization with 3D LSTM arrays. Explores integrating multi-asset portfolio with embedding layer.

Quant Whale is a systematic long/short crypto strategy trading BTC and SOL on 1-hour bars. Entries require three-layer confirmation: a MACD crossover as the trigger, RSI within a lookback window confirming momentum exhaustion, and a higher-timeframe trend filter for directional alignment.

Reproducible regime-aware multi-strategy trading system for BTC/USDT, with walk-forward validation and stress-tested risk controls.

A financial ML pipeline that analyzes earnings call transcripts using FinBERT sentiment analysis and predicts post-earnings stock price movements with XGBoost, validated with expanding-window walk-forward backtesting.

Intraday mean‑reversion trading strategy with realistic execution costs, turnover caps, and walk‑forward backtesting.

Oil Production Flow Rate Prediction with Deep Neural Network Algorithm such as Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM). This Model will testing with Validation method called Walk-Forward Validation (WFV). Basically the validation seperate in two part, the first WFV over actual data and WFV over predicted data.

SiraEdge Portfolio Optimization Module - Complete implementation with 7 optimization models, ML integration, and comprehensive documentation