Day Trading Journal

An AI agent that can place orders isn't a chatbot anymore. It's a new hire with access to your account. Most people ask what the agent can do. The better question is what it's not allowed to do, and who decides. That's what an approval gate is: a checkpoint between an idea and a live order. Something has to pass it, or nothing happens. Here's a must-have checklist for building them, in plain English. It's process education from my own journal, not a trade idea or a broker or tool recommendation. Where I name a feature, I link its source. Why gates exist The default story: give the model good instructions, and it will follow them. Research says otherwise. In a 2023 paper , Scheurer, Balesni and Hobbhahn ran GPT-4 as an autonomous stock-trading agent in a simulated environment. The agent got an insider tip and acted on it, even though it knew company management disapproved of insider trading. When it reported to its manager, it consistently hid th...

Welcome to the first weekly roundup from the Agentic Trading Use Cases hub . Each week I collect a handful of real, dated items about AI agents, broker connections, testing and review. I only include links I opened and read myself. This is education about process. It is not a list of trade ideas, and nothing here says any setup makes money. This week's theme: when an AI agent can reach a brokerage account, who holds the keys, and how do you know your tests match what actually runs? Approval gates: who holds the keys? Four items on how much authority an outside agent should get, and how brokers and tool makers are drawing the lines. Schwab and JPMorgan keep outside agents away from moving money Oct 16, 2026 · The Next Web, reporting a Bloomberg TV interview Schwab's CEO told Bloomberg Television that Schwab is blocking outside AI agents that try to move client money. The Next Web reports these agents are stopped tens of times a week. Instead, Schwab plans contro...

Morgan Housel's The Psychology of Money isn't a trading manual. There are no setups, no indicators and no stock picks in it. It's a collection of short stories about how people actually behave with money. As a day trader, I found it more useful than most trading books I've read, because almost every chapter describes a mistake I've made in front of a chart. As an Amazon Associate I earn from qualifying purchases. The core idea of the book fits in one line. Housel writes that doing well with money "has little to do with how smart you are and a lot to do with how you behave." That's the trading problem in a sentence. Most of us don't lose because we don't know enough. We lose because we break our own rules when we're scared, bored or on a hot streak. So instead of a chapter-by-chapter summary, this review pulls out five lessons from the book and turns each one into something you can use in your trading process. About the book. ...

Before the Wright brothers flew a powered airplane for 12 seconds, they spent more than four years running small tests. They used a kite, three gliders, a homemade wind tunnel and notebooks full of measurements. When the first flight came on December 17, 1903, it was short and low, and that was on purpose. I first read about the Wrights in David McCullough's book The Wright Brothers , and it changed how I think about building a trading process. They weren't fearless daredevils who got lucky. They tested every piece of the machine before they trusted their lives to it. That is the same problem a trader faces before taking a new system live, whether it's a manual playbook or an agent-assisted workflow. This post walks through how the Wrights tested and what each step can teach us about testing a trading system before real money is on the line. Key takeaways Test one idea at a time, at the smallest scale that still teaches you something. A backtest or simulator is ...