market-regime

2026 MT5 Gateway: Institutional Trend Classifier & Multi-Asset Regime Dashboard

🦞 AI 量化交易系统 — 37 因子选股 · 8 层风控 · 同花顺数据大屏 · 全自动盯盘

A research project on macro regime clustering using a compact monthly feature space and Jump Models, with stability analysis, external validation, and asset mapping across equities, bonds, oil, and gold.

An end-to-end machine learning trading system: ensemble of transformer models, hybrid RNN model and LightGBM with temperature calibration, and a live trading bot with Kelly Criterion position sizing.

active investing

Multi-model framework for market regime detection and dynamic asset allocation using HMM, XGBoost, LSTM, backtesting, and RL.

Machine learning overlay for SPY using ^GSPC regime signals, Jump Model labels, and XGBoost, focused on downside protection, recovery timing, and risk-adjusted performance improvement in a unified long-sample backtest.

Deterministic market regime detection agent with persistent state and transparent signal-based classification.

Identify regimes in financial markets based on multivariate time series data using multiple methodologies, including CNN, AutoEncoder, Siamese Model, Correlation Matrices, K-means++, and Hierarchical Clustering

A statistically rigorous framework for analyzing drawdown risk as a distribution problem rather than a single historical metric.

An institutional-grade, asynchronous High-Frequency Trading (HFT) node built in Python. This system reconstructs Level-2 (L2) Limit Order Book (LOB) updates, computes latency-critical microstructure features in O(1) time, and uses a Gaussian Hidden Markov Model (HMM) to infer latent market regimes.

Free REST API for the moneyfeel Macro & Geopolitical Risk Index (MRI) — market regime classifier covering 5 regions across Daily/Weekly/Monthly timeframes. Free for researchers, quants and portfolio managers.

Reproduced a research-backed Wasserstein k-means framework for market-regime detection by clustering rolling return windows as empirical probability distributions, implementing 1D optimal-transport distances, Wasserstein barycenters, baseline comparisons, validation metrics, and regime-aware visualization/backtesting in Python.

A framework for identifying, classifying, and visualizing bear markets, corrections, and bull markets from historical price data.

Pine Script v6 indicator that classifies market regime using Bandt-Pompe permutation entropy: adaptive Structure Rank, drift bias, five regimes, bar coloring and dashboard. Original, MPL-2.0.

Classification workflow for detecting short-term market regimes in S&P 500 data using technical indicators and supervised machine learning models.

Detects financial market regimes using semantic geometry of news embeddings (FinBERT) with clustering and statistical modeling to predict volatility and regime shifts ahead of price action.

Shazam for markets — match an asset's current 514-dim microstructure fingerprint against its entire history to name the regime ("that's the May-2021 crash setup"), over the Wickra core, in ten languages.

MacroPulse is a real-time macro intelligence platform that transforms fragmented global data into actionable investment insights through a unified decision layer.

Institutional-grade market regime decoding via Savitzky-Golay Kinematics and Hidden Markov Models (HMM). Engineered for zero-lag signal demodulation and structural alpha detection.