Best Multi-Asset Correlation Heatmap & Risk Matrix for MetaTrader 2026

Quantitative geopolitical risk dashboard tracking Iran-Israel conflict escalation via market signals, GDELT news analytics, and probabilistic portfolio regime guidance.

本地优先的 A 股研究与投资决策纪律工作台：账户复核、持仓风控、市场观察与盘后研究；不连接券商、不自动交易。

Risk attribution report for portfolio management, using MatLab and Excel

A market state factor based on graph automorphism group analysis. Detects market homogeneity through orbital structure of stock correlation networks, providing signals for risk management and portfolio construction.

Essential techniques to assess financial risks

Repository represents python usability of measuring and managing risks (practice tasks and real cases)

rGraph is an interactive Python and Streamlit app for portfolio risk analysis with many features.

Quantitative analysis techniques with Python and Pandas to determine which portfolio is performing best across many areas: volatility, returns, risk, and Sharpe Ratios.

Quantitative risk infrastructure for Meridian Private Bank. Three Monte Carlo methods - LHS for venture valuation, scenario-based correlated simulation for macro stress testing, and historical simulation for LP reporting. Calibrated against Q1 2026 Iran war oil shock.

Build a USE4-style (Barra) equity factor risk model from scratch on Sharadar data — specs + a full textbook; you write the code.

Real-time multi-symbol correlation matrix, MetaTrader currency strength meter and basket exposure heatmap for MT5 / MT4. Exposes hidden cross-pair risk, hedged positions and redundant trades across an entire forex portfolio.

Python portfolio risk engine for multi-asset portfolios, calculating VaR, Expected Shortfall, PnL contribution, volatility and stress-test losses.

Python framework for VaR, Expected Shortfall, volatility modelling, backtesting, stress testing, Gaussian-copula simulation, and model validation.

Reproducible Python portfolio risk dashboard with yfinance data loading, local CSV fallback, VaR, CVaR, drawdown, correlation analysis, and risk visualisation.

A Python-based portfolio optimization tool that implements Modern Portfolio Theory (MPT) to create efficient portfolios.

Research platform for MOEX liquidity, derivatives, risk, and execution analysis.

Reusable portfolio risk backend with FastAPI, CSV uploads, VaR/CVaR, drawdown, concentration, stress scenarios, Docker, and documented methodology.

Offline market risk analytics engine for VaR, ES, backtesting, stress testing, EVT, copulas, and risk attribution.

Python framework for multi-asset portfolio market risk analysis including rolling beta, VaR/CVaR, Monte Carlo simulation, stress testing, and risk decomposition.