portfolio-risk

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

Essential techniques to assess financial risks

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.

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

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

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.

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

Portfolio Drawdown Analyzer 2026 – Analyze your portfolio drawdowns via read-only exchange API. Identify which assets drive losses and reduce future risk. Setup.exe included.

Calculate VaR of Tesla Equity share with Historical, Variance-Covariance and MonteCarlo simulations methods

元启β是一套面向券商、财富管理机构和投顾服务场景的组合风险分析工作台。系统以客户持仓为入口，支持手动录入、Excel/CSV 导入和文本解析，运行压力测试后，可在风险仪表盘中展示组合总市值、Beta、最大回撤、最坏压力损失和关键风险提示。 在情景分析层，系统内置历史情景和假设情景，并支持 AI 根据自然语言生成定制化情景，将客户对宏观、政策、行业或地缘事件的担忧转化为结构化压力测试草稿。风险指标层进一步提供 VaR、CVaR、行业与资产大类暴露、因子与市场敏感性、蒙特卡洛模拟和模型验证。调仓建议层则基于风险优化策略输出调仓前后指标对比、资产配置变化和操作清单。 系统强调“辅助分析、人工确认、合规表达”，适合用于投顾服务提效、客户风险沟通、投资者教育和机构内部产品演示。

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

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

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

Financial risk MCP server for AI agents — portfolio risk analysis, crypto risk, stress testing, contagion detection and real-time market warnings. Read-only, observation not advice.

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

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

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