Kiploks Trading Robustness Engine is an open-source TypeScript engine for deterministic backtest and walk-forward analysis (WFA) of algorithmic trading strategies, published as @kiploks/engine-* packages under Apache 2.0.

Code to generate all tables and figures of "Dissecting Market Expectations in the Cross-Section of Book-to-Market Ratios", Critical Finance Review (forthcoming).

Prove a trading edge survives real costs out-of-sample, or kill it. An honest edge-validation harness for CME micro futures & prop challenges.

This course delves into financial forecasting, covering predictive regression, multi-horizon models, and principal components for improved accuracy, with a focus on rigorous out-of-sample analysis.

It contains codes and data to replicate paper on Spatial bootstrapped microeconometrics forecasting for out-of-sample geo-locations in big data

Factor-based MVP portfolio optimizer using Fama-French 5-factor model. Achieves 11% Sharpe improvement over sample covariance with 27% lower max drawdown in 10-year out-of-sample backtest. Python | scipy | optimization

Empirical forecasting of the equity risk premium using historical financial predictors and time-series regression methods.

This project aims to predict future yen prices using time-series models such as ARIMA as well as making out of sample and in sample predictions on Python

Pre-registered empirical study of whether in-sample Sharpe-surface smoothness (σ_micro under the perturbation suite) predicts next-window OOS Sharpe. Pilot SOL → replicate DOGE+BTC. Result: H₀ retained at the population level; ATR is the only family with consistent within-family support.

Synthetic equity factor research and validation workflow demo.

Usage of model averaging to improve total credit gap as predictor of financial crises

Research backtest of a multi-component breadth-thrust signal computed from point-in-time ETF constituents. Tested across SOXX, CNDX, CSP1 (S&P 500), IUES (energy), IUFS (financials) with full reproducibility from iShares + yfinance data.