flexible and efficient rolling window operations

A Proximal Policy Optimization Approach to Detect Spoofing in Algorithmic Trading

Time series forecasting: ARIMA/SARIMA, Prophet, LSTM networks and ensemble methods along with automated feature engineering for real-time predictive analytics

Fast Robust Moments in R with Rcpp

Analysis of Classical Machine Learning Algorithms for Anomaly Detection in Time Series Data

librstats is a C library for efficient online computation of statistics, such as for mean, variance, skewness and kurtosis.

NumPy based Python library for online calculation of moments .

An improved temporal data pipeline with foundational model for battery State of Health (SOH) prediction (R²->0.99) using advanced time series decomposition (D3R, CEEMDAN) and transformer-based methods. Utilized 100-150 features (ARIMA-based, Rolling statistics, Degradation indicators)

Quantitative Analysis; Custom Portfolio

In this assignment, I'll get to use what I've learned this week to evaluate the performance among various algorithmic, hedge, and mutual fund portfolios and compare them against the S&P 500 Index.

This repository is for me to practice with Pandas to use them in a financial environment.

A Whale off the Port(folio)

🔋 Predict battery State of Health (SOH) with a cutting-edge model using time series decomposition and transformer methods for accurate results.