rolling-window

Command line script and Python library to work with time-series data.

Given two 2D arrays (grids), perform a windowed linear regression between the two. Aim: removal of correlated component between two variables.

Rolling window statistics for numpy arrays.

Annualised risk/return metrics for multi-asset portfolios: Sharpe, Sortino, max drawdown and rolling variants, measured against any benchmark. Live Yahoo Finance data with an offline parquet fallback, exposed three ways: a CLI, a Streamlit dashboard and a FastAPI JSON API. Dockerised, typed and tested.

A collection of substantial projects I completed for the CS156 Machine Learning course.

SQLite SQL practice project covering daily metrics, date spine, running totals, rolling windows, and late-arriving data patterns (GROUP BY + window functions).

A Python deque that expires items by age instead of length.

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

Rolling returns in 5 years, using reference currency and distributions

End-to-End Python replication of Wu et al.'s (2026) Sentiment Networks methodology. Quantifies directed sentiment contagion across 34 tech equities via Transfer Entropy, Markov bootstrap significance filtering, weighted PageRank centrality, and Chu-Liu/Edmonds Maximum Spanning Arborescence. Compares News vs. Social Media information topologies.

A-share 5-model rolling ensemble (LightGBM, XGBoost, SVR, RF, Linear) on JoinQuant. Research backtest v2 Sharpe 2.499, return 70.90%, initial capital ¥100,000.

End-to-End Python implementation of "Reflexivity as Prompt" (Park, 2026). Evaluates LLMs as financial forecasters during boom-bust cycles using George Soros's Reflexivity Theory as a zero-shot prompt scaffold. Implements P/E-ratio-invariant normalization, buy-and-hold calibration gates, and a structured diagnostic reproducibility audit taxonomy.

Plan low-cost primer automations for Codex and Claude Code rolling quota windows.

Rolling-window monitoring of synthetic project request response times using Python, Polars, and Matplotlib.

Sliding window rate limiter implemented in Golang/ Go using redis

VaR/TCE-based optimal capital allocation and rolling tail-risk attribution in MATLAB.

Simulates a rolling window strategy that continuously optimizes and deploys the bot with updated thresholds. This project serves as the backtester for the original strategy.