DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
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Discussions sur les stratégies de trading et le trading algorithmique. MQL5.community – le plus grand forum forex
Searching for JavaScript resources to help you develop applications on the AWS Cloud? Start here 🧑💻⬇️
A curated list of Python resources for building on AWS: Boto3, CDK, Lambda Powertools, Strands Agents, Bedrock AgentCore, MCP, and SageMaker for AI and…
QANplatform is the quantum-resistant Layer 1 hybrid blockchain where developers can code web3 solutions in any language on top of the QAN blockchain platform.
MT5 trading dashboard with AI-powered price forecasting using 8 deep learning models
A live trading dashboard to display multiple tickers on a portrait monitor. Uses the TradingView API.
A professional cryptocurrency trading dashboard inspired by Bloomberg Terminal, built with Next.js, React, and TypeScript. Features real-time market data, news aggregation, on-chain analytics, and…
A Terminal-style trading dashboard built entirely on free public market data. No API keys, no subscriptions.
tradingview mcp that exposes tradingview market data, multi-exchange scanners and ai backtesting engine to AI assistants and tradingview mcp automation tools
Public repository by LevKosnyrev. Primary language: JavaScript.
Explore this resource at its source website for documentation and current information.
This report on counterparty data includes background of the project, reporting expectations, and observations by supervisors and from firms’ self-assessments.
SABR stochastic volatility model: calibration, implied-volatility surfaces and conditional Monte Carlo (MSc thesis, BSE)
Market risk engine: VaR/ES (historical, parametric, Cornish-Fisher, Monte Carlo), Kupiec/Christoffersen/Basel backtests, stress tests, ex-ante tracking error
One-day VaR & ES for a cross-asset ETF portfolio via historical simulation, GJR-GARCH-t, and filtered historical simulation, with Kupiec, Christoffersen, & Acerbi–Székely backtesting.
Market risk analysis using historical, parametric and monte carlo VaR
Multi-asset portfolio risk model implementing VaR, Expected Shortfall, stress testing, model calibration, and risk decomposition
Market risk modeling using Value at Risk (VaR) and stress-conditioned probability of default with public financial data.
Eight VaR/ES models for a $10m multi-asset book, validated over 4,611 trading days with Kupiec, Christoffersen, Basel traffic-light and Acerbi-Szekely tests.
Rolling Var and expected shortfall using python
Public repository by ATHARV-IIT-BOMBAY. Primary language: Jupyter Notebook.
Market risk model implementing Historical, Parametric & Monte Carlo VaR + stress testing across crisis scenarios. Basel III framework in Python.
Public repository by AlexisAHG. Primary language: Jupyter Notebook.
Public repository by geheres-dotcom. Primary language: Python.
Market risk analysis using VaR and CVaR on a multi-asset ETF portfolio
VaR and Expected Shortfall with GARCH volatility forecasting and backtesting (Kupiec, Christoffersen, Basel)
Public repository by Parvmehra8755. Primary language: Python.
Public repository by SaahitKar. Primary language: Jupyter Notebook.
Market risk modeling using Value at Risk (VaR), Expected Shortfall and stress testing.
Public repository by Ruojieli. Primary language: Python.
Public repository by PhilippovRoman. Primary language: Python.
Market Risk Analysis using Historical, Parametric and Monte Carlo Value at Risk (VaR) with Python.
Python implementation of Value at Risk - Historical, Parametric & Monte Carlo simulation for NSE portfolio
