Awesome Quant

Automatic extraction of relevant features from time series:

The backtesting engine that gives you an unfair advantage. Run thousands of trading ideas before others finish one.

Probabilistic time series modeling in Python

Open source time series library for Python

A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.

ARCH models in Python

Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics

Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

Bringing financial analysis to the tidyverse

Quantitative Financial Modelling Framework

A python library for time-series smoothing and outlier detection in a vectorized way.

Time series analysis in the `tidyverse`

A Python 3 library making time series data mining tasks, utilizing matrix profile algorithms, accessible to everyone.

Financial market technical analysis & indicators in Julia

Extensible time series class that provides uniform handling of many R time series classes by extending zoo.

Time-aware tibbles

Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.

Quantitative systematic trading strategy development and backtesting in Julia

Time series market data

Use Python like a spreadsheet!

Python interface to Brazilian Central Bank web services

Time series implementation for the Julia language focused on efficiency and flexibility

R interface to Brazilian Central Bank web services

Automatic discovery of non-trivial statistical truths from 500+ public time series — mutual information, Granger causality, FDR correction

Distributional crypto-return forecasting via Wasserstein-geodesic extrapolation in quantile-function space. WGeo family wins 12/12 (asset × horizon) cells over 6.75y walk-forward CRPS vs GARCH and classical baselines. v0.4.

A high-performance execution engine utilizing LLVM-based JIT compilation to optimize mission-critical data processing. Engineered to handle high-throughput financial transactions and real-time infrastructure analysis.