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Coinbase's New AI Tool for Trading and Research
Coinbase's agent can use x402 protocol to get access to data and APIs.
Catalyst Maze: AI-Powered Biotech Trading Game
Catalyst Maze: AI Powered Biotech Trading Game Catalyst Maze stands as a cutting edge AI driven trading game that merges the biology of biotechnological researc…
AI Trading Assistant: Vibe-Trading by HKUDS
"Vibe-Trading: Your Personal Trading Agent"
Machine Learning for Algorithmic Trading: 2nd Edition Code
Code for Machine Learning for Algorithmic Trading, 2nd edition.
Google Engineer Charged with Insider Trading on Polymarket
According to the complaint, a Google engineer risked over $2.7 million on wagers related to Google's 2025 Year in Search campaign.
Infinite Swap: AI Tool for Trading Up to a House
Infinite Swap: Revolutionizing Real Estate Trading In the dynamic world of real estate, innovative tools are constantly emerging to simplify and enhance the tra…
WallStreetBets Slams SEC's Plan to Reduce Quarterly Reporting
The retail trading subreddit submitted the sharpest criticism yet of the financial regulator's idea of letting companies report twice per year.
AI-Trader: Fully Automated AI Trading Agent
"AI-Trader: 100% Fully-Automated Agent-Native Trading"
Trading System V2: AI's Role in Deterministic Execution
Thanks to the incredible feedback on my last post, I’m officially moving away from the "distributed veto" system (where 8 LLM agents argue until they agree to trade). For v2, I am implementing a strict State Machine using a deterministic runtime (llm-nano-vm). The new rule is simple: Python owns the math and the execution contract. The LLM only interprets the context. I've sketched out a 5-module architecture, but before I start coding the new Python feature extractors, I want to sanity-check the exact roles I’m giving to the AI. Here is the blueprint: 1. The HTF Agent (Higher Timeframe - D1/H4) Python: Extracts structural levels, BOS/CHoCH, and premium/discount zones. LLM Role: Reads this hard data to determine the institutional narrative and select the most relevant Draw on Liquidity (DOL). 2. The Structure Agent (H1) Python: Identifies all valid Order Blocks (OB) and Fair Value Gaps (FVG) with displacement. LLM Role: Selects the highest-probability Point of Interest (POI) based on the HTF Agent's narrative. 3. The Trigger Agent (M15/M5) 100% Python (NO LLM): Purely deterministic. It checks for liquidity sweeps and LTF CHoCH inside the selected POI. 4. The Context Agent LLM Role: Cross-references active killzones, news blackouts, and currency correlations to either greenlight or veto the setup. 5. The Risk Agent 100% Python (NO LLM): Calculates Entry, SL, TP, Expected Value (EV), and position sizing. The state machine will only transition to EXECUTING if the deterministic Trigger and Risk modules say yes. The LLMs are basically just "context providers" for the state machine. My questions for the quants/architects here: Does this division of labor make sense? Am I giving the LLMs too much or too little responsibility in step 1 and 2? By making the Trigger layer (M15/M5) 100% deterministic, am I losing the core advantage of having an AI, or is this the standard way to avoid execution paralysis? Would you merge the HTF and Structure agents to reduce token constraints/hallucinations, or is separating them better for debugging? Would love to hear your thoughts before I dive into the codebase.
Rogue AI Agents: Predicting the First Major Catastrophe
After reading about the PocketOS situation it got me thinking that sometime in the near future a rogue AI agent will do something so catastrophic and damaging that it goes down in the history books as being “The Incident”. A real turning point when we realize we’ve created something we can no longer control. Yes, agents have already deleted entire codebases (PocketOS and others), hacked into things, and blackmailed people. I’m taking about something way worse though. I think it’ll be a global stock market crash caused by a group of trading agents getting stuck in a hallucination loop and dumping all stock on fire sale or something. Or will it be something more sinister like a complete power grid collapse or intentionally blowing up a refinery or something crazy like that. Or a true black swan event that’s impossible to comprehend right now. What do you guys think?
TauricResearch TradingAgents: Multi-Agent LLM Financial Trading
TradingAgents: Multi-Agents LLM Financial Trading Framework
X-Energy Stock Surges 27% in Nasdaq Debut
Investors flocked to nuclear power startup X-energy in its first day of public trading on the Nasdaq.