An economics and finance student is seeking an ML engineer experienced in time series modeling to assist with a research project on algorithmic trading. The collaboration involves developing ML models for 3-4 hours weekly, with the potential for commercialization if results are exceptionally strong.
A developer built 13 autonomous AI agents to trade 24/7 on Kalshi prediction markets, a CFTC-regulated platform. The Python-FastAPI system leverages speed and systematic signals to gain an edge over slow, emotional, and uninformed manual traders.
The content describes building a semi-automated AI stock trading system using the Claude API. It details a 7-module architecture, emphasizing a hybrid approach combining technical analysis with AI to mitigate emotional trading decisions and improve signal reliability.
This paper investigates the impact of stop-loss and take-profit settings on the performance of autonomous crypto trading agent swarms. It finds that optimal exit strategies significantly improve risk-adjusted returns, favoring tighter loss limits and earlier profit capture.
Week one of Valhalla Arena exposed the brutal truth about AI agent economics, failing to improve market efficiency. Instead, agents converged on identical strategies, leading to expensive, inefficient herd behavior and hidden costs from constant monitoring and intervention.
This content highlights the critical need for robust health monitoring of DeFi protocols to prevent downtime from crippling AI trading bot performance. It explains how issues like failing RPC endpoints or API problems can turn profitable arbitrage opportunities into losses for algorithmic strategies.
The author built an AI trading bot that monitors Donald Trump's Truth Social posts, using Claude for sentiment analysis and Alpaca's API to execute trades. The project aims to test an AI's ability to automatically react to social media's influence on financial market movements.
An experiment revealed that a trading bot with a history of significant losses achieved a 70.59% win rate when its signals were inverted. While both systems currently lose money due to fees, the goal is to determine if the inverted strategy can clear the fee floor in the long run.
This article presents a trending algorithmic trading strategy focusing on market gaps and opening range behavior, driven by current volatility. It combines gap detection, opening range analysis, and reaction logic for gap fill or breakout trades, providing a Pine Script implementation.