The Take-Profit / Re-Entry strategy combines two simple rules: sell when your target gain is reached, re-enter when price drops enough from the post-sell high. No indicator noise — just price and two parameters.

We wrote 500 lines of rules. Claude read them — and still wrote text-[13px] instead of text-xs. The mistake wasn't Claude. The mistake was confusing documentation with enforcement.

Grid bots are marketed as "low-risk" and, with AI, as a source of "consistent profits." We checked the claims against the mechanics: a grid earns on sideways oscillation, not trends — and "always trading" means trend-chasing. A fair breakdown, plus how to actually evaluate a grid bot.

High funding rates signal overheated longs — a warning. Negative funding means shorts capitulating — a buy signal. The logic seems solid. We tested it across 6.7 years of BTC data. The results were the opposite of what we expected.

A new strategy is live: John Bollinger's classic volatility-cycle play. Wait for the bands to squeeze tight, then ride the long breakout above the upper band. Default 20/2.0/0.1. Pro+.

Most traders writing their own backtests accidentally look into the future. The result: spectacular backtests, collapsing live performance. A look at the subtlest methodology mistake in systematic trading — from the common `shift(-N)` to the innocuous `.mean()` aggregation without rolling window — and why we manually check every Backtesting Arena strategy for bias before release.

"AI-crypto" trades as one sector but is at least four different things on different layers of the stack: payment rails (Solana, Base, Sui, Tempo), agent frameworks (Virtuals, ElizaOS, Fetch/ASI), decentralized intelligence (Bittensor) and decentralized compute (Render, Akash, io.net). A map of the most relevant projects per category — and one test cutting across all of them to show where real substance sits and where a narrative was simply built around a token: is the token structurally necessary, or just present?