triple-barrier-method

End-to-end crypto price direction prediction framework built on the Mamba Selective State Space architecture (Python/PyTorch). Covers the full lifecycle: data preparation, Triple Barrier labeling, training, evaluation & live inference — all from a single config. Linear-time sequence processing as a Transformer alternative.

This project explores Attention-Based Transformer Encoders to develop robust buy/sell classification models for financial time series. It addresses market non-stationarity and noise by combining De Prado-inspired preprocessing with a hybrid Transformer-LSTM architecture.

Crypto trading strategy backtesting & ML validation: triple-barrier labels, purged k-fold CV, deflated Sharpe ratio and a positive control. LightGBM, LSTM/BiLSTM, Binance + MEXC perpetual futures data.

EUR/USD H1 directional breakout prediction using RNNs (LSTM/GRU) with Triple-Barrier Method and Purged K-Fold Cross-Validation in PyTorch.

Implementation of the Lopez de Prado AFML toolchain end to end: dollar bars, fractional differentiation, triple-barrier and meta-labelling, purged and combinatorial-purged cross-validation, deflated Sharpe, wired to an OpenBB data layer and walk-forward backtester. No strategy result is claimed.