Forge strategies through human-AI collaboration. You bring the edge, AutoForge stress-tests it, optimizes it, and tells you if it is real. See the case study: 200+ experiments, 8 phases, 1 validated strategy.
Universal noise model for superconducting quantum chips achieving 5.2-19.5× accuracy improvement over traditional methods through cross-platform parameter optimization.
AI-Driven Control Strategy for Differential Drive Wheeled Mobile Robots: Neural Network-Based Parameter Optimization and Real-Time Stabilization for Multi-Waypoint Navigation.
Parallel optimization engine for QuantConnect LEAN — a 679K-point parameter search in under 3 minutes via warm Docker workers, a persistent .NET harness, and smart search (GA / Bayesian / LHS).
A sophisticated PDF document analysis and question-answering application that leverages advanced AI models to provide detailed responses to user queries about PDF documents.
模块化量化回测框架 — 基于Claude Code最佳实践架构设计，支持可插拔策略、多策略对比、参数网格优化
Distributed GPU-accelerated MetaTrader 5 strategy backtester and parameter optimizer. Maps each parameter combo to a CUDA thread, shards across a Ray cluster of GPU workers. Numba CUDA + Ray + Polars + Pydantic.
This repository contains the modules implementing a Machine Learning-based solution for optimizing the execution of dislib algorithms. In particular, a stacked classification model is leveraged to predict the most suitable value of the block-size parameter for the execution of dislib algorithms.
Official implementation of DoLQ, a multi-agent LLM framework for discovering physically plausible ODEs from observational data.
Automate strategy research: turn trading ideas into code, test parameters, and validate edge with AI-driven analysis
Efficient reasoning under constrained compute.
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