python package for DFA (Detrended Fluctuation Analysis) and related algorithms

Markowitzify will implement a variety of portfolio and stock/cryptocurrency analysis methods to optimize portfolios or trading strategies. The two primary classes are "portfolio" and "stonks."

Epileptic Seizure Recognition System, In this project wavelet transform and Hurst exponent are used as an input of SVM, LSTM , Random Forest Models.

Python library for DFA, Hurst exponent, and fluctuation analysis of time series.

Computational Finance Lecture for MSc in AI, at University of Southampton

A mathematical framework for measuring the dynamics of consciousness. The framework uses a composite index of scale-free temporal organisation, cross-frequency organisation, and metastability.

High-Performance Fractal & Econophysics Tools for Financial Time Series using JAX (GPU-Accelerated).

Hurst value as a result of data smoothing

fractalVideoGuard

Bloomberg-style markets terminal — 4-tier data failover (Yahoo · CoinGecko · Stooq · Alpha Vantage) · Yang-Zhang / Garman-Klass volatility · Autocorrelation / Partial Autocorrelation · Hurst exponent · Carhart 4-factor with Newey-West HAC · Probabilistic / Deflated Sharpe · walk-forward cross-validation · decile Information Coefficient.

Quantum vs Classical, Autoregressive vs Reconstructive Encoder/Decoder Architectures on Non-Stationary Time-Series: Loss Landscapes and Latent Complexities with and without Recurrence

Institutional-grade quantitative finance calculator — Black-Scholes · Greeks · IV Solver · Avellaneda-Stoikov HFT · Kelly Criterion · GARCH · Hurst · OU Process · Ghana Cocoa Derivatives. Expo React Native (iOS · Android · Web).

Small CLI utility used to evaluate Hurst Exponent based on CSV files ( (this is just a mirror of https://hub.mos.ru/fominmv2498/hurst) )

Tests if EUR/USD daily moves are as random as coin flips using Monte Carlo and statistical tests (runs, Markov chains, Ljung–Box, Hurst, entropy). Results show returns look random, but volatility clusters, revealing structure beyond pure chance.

6-month learning journey from basic math to multifractal analysis (MF-DFA) of financial markets

A Python tool for calculating the Hurst Exponent of financial time series.

Hurst exponent: from mathematical foundations to a systematic basket trading strategy with statistical validation.

Python reproduction of the self-organizing 3D Ising model of financial markets (Guimaraes & Lima, PRE 2021). Generates fat-tailed return distributions, volatility clustering, and long-range memory via Monte Carlo simulation with Numba acceleration.

Browser-based market fractal dynamics engine. M-FDE equation: D^H P(t) = α(H±)·Caputo[P] + β·Σ D_f(ω_k)·cos(ω_k·t) + σ·dL_α^H(t)