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.

active investing

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.

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.

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

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

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

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.

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

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

Local desktop quantitative research system for A/H share Index & ETF trend analysis. Built with Rust + Tauri + ClickHouse, featuring technical indicators, macro regime classification, rotation ranking, backtesting, and LLM-powered report analysis.

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

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.

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.

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.

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

📊 Detect market regimes by clustering probability distributions using Wasserstein K-means for more accurate financial analysis and insights.

FX/CB回収EA向け市場レジーム判定→戦略許可/禁止の上位フィルター。Phase 1: ルールベース cb_run_score + persistence filter