
Arena Blog
Data-driven insights on trading strategies, backtests, and market analysis.
We Just Shipped an API That Charges $0.01 Per Call — In USDC, On-Chain, No Account
Phase 4 is live: the same Bitcoin cycle data, on-chain indicators, and aggregated strategy insights now reachable through three channels — REST, MCP, and x402 pay-per-call in USDC on Base. No account required for the third. An AI agent gets HTTP 402, signs a USDC authorization, retries, and has the data three seconds later. What's actually live, why we built it this way, and the Coinbase detour that cost us a day.
We're Opening Our API: REST + MCP + (soon) x402
For 18 months we've been quietly building Backtesting Arena — a platform where 500+ users have run 10,000+ backtests across Bitcoin, stocks, ETFs, commodities, and forex. Daily cycle scores, on-chain indicators, sentiment dashboards, strategy insights. All powered by the same data layer that's been running on a private quasi-API.
AI Agents and Crypto Payments: Where This Is Really Heading
This is the crypto-rail deep-dive companion to our earlier piece [AI and the Future of Payment Systems](https://tradingstrategies.work/blog/ai-future-of-payment-systems-2026), which covered the broader fintech picture including Visa Intelligent Commerce and Mastercard Agent Pay. Here we zoom in on what's happening on the crypto layer specifically.
What Agents Actually Pay For in Finance Research — The Provider's View
Everyone talks about the agent economy. From the provider side — I run x402-paid financial endpoints — what agents actually buy today is sober: ingredients. And where the line runs between real and vision.
Bitcoin On-Chain: How Big Is the Wrapped-BTC Ecosystem Really?
"Bitcoin is going productive" — they say. Wrapped BTC brings BTC into DeFi. How big is it really, who dominates, where are the catches? A sober map.
On a Short Leash: How the US Pushes Its Debt to the Short End
The US Treasury increasingly funds itself with short-dated T-bills. That lowers cost today — and builds rollover risk for tomorrow. A methodical read on the mechanics behind the headlines.
When Fees Kill a Strategy: Why We Benched Our Second-Most-Popular One
Our backtest engine used to compute returns before costs. We built a net-of-cost layer that re-prices every fill at a realistic fee — and it flipped our second-most-popular strategy negative after fees. Here is the math, the numbers, and why "beats buy-and-hold" is half a truth until you pay the fees.
AI in the Drug-Discovery Race: Shovels, Bets, Giants
Asking which company is well-positioned in the AI drug-discovery race is really three questions at once. We sort the field soberly into shovels, bets, and giants — and show which layer earns regardless of any single trial outcome.
We Rebuilt Our Worst Strategy Correctly — It Worked — and Cut It Anyway
Our Bollinger Squeeze strategy lost to Buy & Hold by double digits on crypto. Before deleting it, we rebuilt it to the modern standard (TTM Squeeze). The rebuild turned positive — and we retired it anyway. Why "it works now" isn't enough.
We Fixed Our Fibonacci Strategy — and Retired It Anyway
Auditing 40,000 backtests, our Fibonacci strategy came up short. Before cutting it we asked: is it our config? A better exit made it 20 points better — and we retired it anyway. Why "beats Buy & Hold" isn't the bar.
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