AI & Trading
Everyone wants to know how artificial intelligence fits into the markets. This is the place for it; discussions, videos, news, and honest explainers on what AI actually does for a systematic trader, with none of the hype.
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The mental models that keep AI useful instead of dangerous.
AI doesn't predict the market; it filters noise
The fantasy is a model that forecasts tomorrow's price. The reality that actually adds value is far more modest: AI helps classify regimes, flag anomalies, and remove low-quality signals so a disciplined system trades less often and more cleanly. If a tool promises forecasts, be skeptical. If it promises better filtering and faster research, listen.
Overfitting is the silent account-killer
Give a flexible model enough parameters and it will 'discover' an edge in pure randomness. The more powerful the AI, the easier it is to fool yourself. The defense never changes: large out-of-sample tests, walk-forward validation, and humility about how much of a backtest is real.
Where AI genuinely speeds you up
Summarizing earnings calls, drafting TradingView/Pine logic, sanity-checking your risk math, and turning a vague idea into a testable hypothesis. None of these touch the trade button. They compress the research loop so you spend your time on judgment, not grunt work.
Videos
Curated talks on AI, machine learning, and how it meets the market.
How AI Is Actually Used in Systematic Trading
A grounded look at where machine learning helps in a rules-based workflow; signal filtering, regime detection, execution; and where it quietly hurts.
Neural Networks, Explained Simply
The clearest intro to what a neural network actually does. Essential background before you trust any 'AI predicts the market' claim.
Large Language Models in Plain English
How LLMs work under the hood. Useful for understanding what an AI trading assistant can and cannot reason about.
Building Intuition for Backtesting AI Signals
Why most AI 'edges' vanish out of sample, and the discipline that separates a real signal from curve-fit noise.
News & reading
The market context behind the AI story, and how it touches the system.
The AI capex super-cycle and the semiconductor regime
Why the AI infrastructure buildout keeps volatility 'on' in semis; the exact conditions a momentum system like SSL6 feeds on.
HNW Trading · NewsWhy a sub-50% win rate still compounds
Before you ask an AI to 'increase your win rate', understand why win rate is the wrong target in the first place.
HNW Trading · NewsRecognizing edge decay before it costs you
AI models decay faster than rule-based systems. The signals that should trigger a pause in any data-driven strategy.
Share a tool, paper, or take on AI in trading.
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