DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
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Predict Cryptocurrency Price with Deep Learning
AuditBench: A Benchmark for Large Language Models in Financial Statement Auditing
Code for the paper "IPO Finance Agent: Benchmarking LLM Financial Analysts Beyond Finance Agent v2, with Automated Rubric Generation, on the SpaceX (SPCX) IPO"
From Generation to Decision: A Hierarchical Benchmark for Reliable Financial Reasoning with LLMs
FinTagging: an LLM-ready benchmark for XBRL tagging of financial reports (arXiv:2505.20650)
Deep learning platform for financial forecasting: phased CNN, LSTM, transformer and TFT training pipelines whose models are served by prediction-provider
First Version.. Cryptocurrency Prediction with Artificial Intelligence (Deep Learning via LSTM Neural Networks)- Emirhan BULUT
CryptoCurrency prediction using machine learning and deep learning
Stock price movement prediction using artificial neural networks
Official code for "RETuning: Upgrading Inference-Time Scaling for Stock Movement Prediction with Large Language Models"
Customize loss function by taking account of directional loss to make the LSTM model more applicable given limited resources
presents an in-depth exploration of a cutting-edge trading architecture that combines the predictive power of Long Short-Term Memory (LSTM) neural networks
Deep Learning - Neural network (RNN, LSTM & GRU)
Assay is a high-performance factor backtesting engine built for LLM agent-driven alpha mining
LLM-Powered Factor Mining and Evaluation Agent for Quantitative Research
This repository hosts an AI agent that autonomously researches quantitative trading strategies using machine learning. The agent analyzes datasets, experiments
Develop a quantitative trading algorithm using advanced financial forecasting and machine learning techniques. Explore methods like Bayesian estimation, neural
Can a machine learning model beat buy-and-hold on Indonesian stocks? This project builds, tests, and rigorously evaluates a trading strategy powered by XGBoost
Utilizando técnicas de Machine Learning e Deep Learning (IA), para o mercado financeiro
Code for the paper "Look-Ahead-Bench: a Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance" arXiv:2601.13770
An Adaptable, Multi-tiered RAG Framework for Multi-modal Financial Question Answering
Financial Literacy Gamification is a Minecraft-themed web app offering expert sessions, blogs, stock analysis games, and multi-player parties. It features a
RAG-Powered Financial Query Assistant for JPMorgan Chase documents with Hybrid Search, Docker support, and Streamlit UI
Notebooks demonstrating vector search & RAG design patterns with Redis Python clients
RAG for financial Analysis using SQL and calculator tool
Financial Document QA with Hallucination Detection
AI-powered XAUUSD (Gold) trading bot with XGBoost ML, Smart Money Concepts (SMC), and HMM regime detection for MetaTrader 5 xau ai trading bot gold…
Autonomous AI trading bot and digital assistant. Built for continuous market awareness and automated execution
I’ve just created my own bot and I’m excited to share my work with you!
AI-powered XAUUSD (Gold) trading bot with XGBoost ML, Smart Money Concepts (SMC), and HMM regime detection for MetaTrader 5
Design pattern for critical stages in the development process of an AI Stock Trading Bot
financial advisor LLM using llama 3.2 3B and RAG with PDF file compatibility
FinAgent-RAG: Agentic Retrieval-Augmented Generation for Financial Document Question Answering
RAG pipeline for Brazilian financial reports. Multi-agent system with LangGraph, hybrid search (Qdrant + BM25), structured outputs via Pydantic, FastAPI
MoziAI-35B: Local open-source financial AI LLM. 35B MoE model compressed to 15.5GB via MoziSmartBit quantization. 256K context, vision, tool calling, 140+
Open-source, privacy-first personal finance cockpit. Bring accounts, transactions, budgets, goals, assets & reports into one intelligent workspace — with
