DUCKQUANT / THE QUANT & AI DIRECTORY
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Developed an end-to-end ML system on Azure to predict loan defaults, leveraging advanced data preprocessing, feature engineering, and machine learning models
Developed a machine learning model to detect fraudulent financial transactions using classification algorithms. Includes data preprocessing, feature
Combine your new algorithmic trading skills with your existing skills in financial Python programming and machine learning to create an algorithmic trading bot
CASE STUDY: Financial Institution's ML Trading Bot, using the SVC Classifier Model for Support Vector Machine (SVM)
This repository showcases my AI projects spanning Agentic AI, Generative AI, Deep Learning, Machine Learning, and Fintech - from autonomous agents and trading
Welcome to WarrenGPT a trading AI & the worlds first opensource AI ML trading AI bot the white paper coming soon goal of WarrenGPT…
Intelligent Trading Bot: Automatically generating signals and trading based on machine learning and feature engineering
A Commission-Free Algo Trading Bot By Machine Learning Company SEC Filing Language
Resources
Credit Risk Analysis with Machine Learning
This project detects fraudulent bank transactions using machine learning by analyzing patterns and anomalies. It enhances financial security and reduces fraud
A machine learning project designed to detect fraudulent credit card transactions. Utilizing advanced anomaly detection algorithms and data preprocessing
An experiment with Double Deep Q Networks algorithm applied to the FinRL trading environment (Pytorch)
Simulated markets based on Zero-Intelligence agent
Simulation platform for facilitating agent-based modeling of financial markets
This is an end to end machine learning project using Random Forest to predict credit risk of German Bank's customers. By employing predictive models,…
Credit Risk Analysis using Machine Learning models
Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA, GARCH, Markov Regime
LSTM-RNN Tutorial with LSTM and RNN Tutorial with Demo with Demo Projects such as Stock/Bitcoin Time Series Prediction, Sentiment Analysis, Music Generation
TKstock uses multi-model AI to analyze markets and generate smart investment insights—helping you trade with confidence. Key Features: • AI-powered
Official implementation of our IEEE IJCNN 2024 paper: a complex-valued Transformer that forecasts financial multivariate time series in the frequency domain
Unsupervised regime detection for financial time series using embeddings and clustering
MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series
Auction-based market simulation using market making and market taking zero intelligence agents. It's controlled using various basic variables that focus
A historically authentic dynamic price generator for Dungeons & Dragons in-game purchases. Implemented with agent-based trade simulation where local events
model and simulate a virtual stock market to investigate market volatility and wealth inequality
drillr — the financial data and research MCP for AI agents: filings with full-text and fact search, statements as reported, earnings, ownership, corporate
LLM-powered A-share (Chinese stock) market analysis platform — technical analysis, prediction, strategy backtesting, and an autonomous research/decision agent
LLMRoboFund is a powerful chatbot empowered with Multi-document RAG. The chatbot is equipped with RetrievalQA and SQL Agents to ease the investment research
Multi-agent AI system for financial analysis and investment research. Uses LLMs, APIs, and agentic workflows (prompt chaining, routing, evaluator–optimizer) to
An investment research committee that runs inside Claude — analysts, a bull-vs-bear debate, risk audit, trader call, and PM sign-off in one conversation
Created full-stack AI platform generating stock and financial analysis reports (LaTeX/PDF) using LLMs (DeepSeek) and real-time data pipelines, reducing
Local-first LLM Wiki template for investment research — Obsidian vault + Claude Code agents (Munger, Peter Lynch, Feynman, Reviewer, Darwin, Leopold...) that
Resources for Invest like Apo: AI investment research, LLM Wiki, Book-to-Skill, portfolio tools and QuantCorner Webull referral
EventSphere Pro is a proposed full-stack SaaS-style Event Management Platform designed as the third portfolio project for a developer building a Full-Stack +
AI-powered quantitative investment platform with 7-agent swarm orchestration, fractal debate algorithm, 3-tier LLM routing, and automated eToro trading — built
