An AI Hedge Fund Team

HedgeDoc - Ideas grow better together

Resources to Prepare for Quant Developers/ Quantitative Researcher/ Quantitative Trader/ Quant Analyst/ Software Engineers in Quant Trading Firms, HFTs and Hedge Funds

Run, compile and execute JavaScript for Scientific Computing and Data Visualization TOTALLY TOTALLY TOTALLY in your BROWSER! An open source scientific computing environment for JavaScript TOTALLY in your browser, matrix operations with GPU acceleration, TeX support, data visualization and symbolic computation.

Fault tolerance and resilience patterns for Go

Build your autonomous hedge fund in minutes. AutoHedge harnesses the power of swarm intelligence and AI agents to automate market analysis, risk management, and trade execution.

An endeavor to create an analytics tool to democratize the information hedge funds are creating teams to collect.

Release with confidence, state-of-the-art property testing for Haskell.

A GUI named hedge for smartwatch like devices based on ESP32. Currently support for T-Watch2020 (V1,V2,V3), T-Watch2021, M5Paper, M5Core2 and native Linux support for testing.

AI-Hedge-Fund for Crypto 🚀 AI-powered hedge fund for cryptocurrency trading, leveraging LLM agents for intelligent decision-making.

A mod manager for Sonic Generations, Sonic Lost World, Sonic Forces, Sonic Colours Ultimate and other PC Hedgehog Engine games.

🦔 Build DApps like Apps -- A Metamask alternative that empowers you to build good UX

PyTorch-based framework for Deep Hedging

Implementation of the vanilla Deep Hedging engine

Option pricing based on Black-Scholes processes, Monte-Carlo simulations with Geometric Brownian Motion, historical volatility, implied volatility, Greeks hedging

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Trading-enabled Polymarket skill for OpenClaw - browse markets, execute trades, track positions, discover hedges

Release with confidence, state-of-the-art property testing for .NET.

Release with confidence, state-of-the-art property testing for Scala.

A pytorch tutorial for DRL(Deep Reinforcement Learning)

Backtester for evaluating options and equity portfolio strategies over historical data. Includes tools for strategy sweeps, tail-risk hedge analysis, and signal-based timing research.

PyTorch for Quantitative Finance : Refine Derivatives Hedging and Pricing with Architecture Alightment in Operators

Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning

Adaptive hedged requests for Go. Cut your p99 latency with zero configuration. Based on Google's "The Tail at Scale" paper.

Deep Hedging Demo - An Example of Using Machine Learning for Derivative Pricing.

Multiplatform rewrite of Hedge Mod Manager

Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks

Hedged HTTP client which helps to reduce tail latency at scale.

A Kotlin Multiplatform library providing resilience patterns (Timeout, Retry, Circuit Breaker, Rate Limiter, Bulkhead, Hedging, Cache, Fallback) for suspend functions. Compose them declaratively with a small DSL and observe runtime telemetry via Flow.

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