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Trading post-mortems, AI automation write-ups, and infrastructure build logs from Gordon Technology — what worked and what didn't.
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A 0.70-AUC Crypto Model That Lost Money on Every Coin (AUC Is Not P&L)
I trained per-coin XGBoost models on 5-minute crypto bars. They hit ~0.70 out-of-sample AUC across 16 coins, walk-forward validated — genuinely predictive. Then I traded the signal and every single config lost money. Why a 'good' ML model isn't a profitable one: AUC measures direction, not magnitude.
Front-Loading a Crypto Grid Bot: More Wins, Less Money
I tried to beat my bot's uniform 3% grid by front-loading the rungs to catch more dips. The aggressive versions hit an 81% win rate — and earned about a third less money. A backtest post-mortem on why win rate is a vanity metric for grid bots.
Optimization: Scaling Your Bot Profitably
Scale from 1% to 5%+ position sizing, add multiple strategies, and optimize for consistent 6-10% monthly gains.
Backtesting Crypto Grid Bots: The 3% Step That Worked (And What the Backtest Couldn't See)
I backtested grid bots before risking real money — a 3% grid step was profitable across every coin and period tested. Then live trading revealed the one thing the backtest underweighted: regime. Here's both halves, honestly.
I Built a Solana Sniper Bot in Python — Here's the Real P&L (Losses Included)
A fully automated pump.fun sniper bot in Python, funded with real SOL and run live. The architecture, the bug that killed 74% of buys, and the honest result — including why a 0.78-AUC model still didn't make money.
Getting Started with Trading Bots
In this guide, we explore how to build your first automated trading bot using Python...
How to Build Trading Bots
In this guide, we'll cover the basics of algorithmic trading...