
Arena Blog
Data-driven insights on trading strategies, backtests, and market analysis.
1–12 of 17 posts · page 1 of 2
Backtesting Arena Is Now a Claude Connector
Backtesting Arena can now be connected to claude.ai with a single click — 65 tools for Bitcoin cycle, on-chain data, macro regime and validated backtests. Instead of generic web-search answers, Claude pulls real, DSR-corrected and point-in-time data. Plus a new interactive tool: the Dip Decision calculator.
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.
The AI Copilot in Your Chart Is Reliable When It Measures and Invents When It Describes Itself
TradingView's chart copilot can connect to your own MCP servers. Tested on the Backtesting Arena connector: it answered market questions correctly and invented instructions about its own settings four times. Why that happens, and how to tell which answers to check.
Agent Payments: Why No Standard Checks Whether the Work Was Any Good
Agent payments are standardised: authorisation, identity, mandate, settlement. Whether the work was any good, nobody checks. Why that is a boundary and not a gap.
Building for AI Agents: 10 Lessons From Running 73 MCP Tools
Your most thorough reader gets the least to read. Ten places where building for agents differs from building for people, with numbers from production.
Consensus Is Not an Edge: Why a Quorum of Correlated Simulations Isn't One
An AI swarm turned $1,000 into nearly a million? The number falls apart at its own source. Why consensus from correlated simulations is not an edge.
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.
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.
The Agentic Brokerage: Why AI Signals Make Honest Backtesting More Important, Not Less
A thesis says trading platforms are going "agentic" — AI monitors, signals, executes. The structural part is right. The blind spot sits where money is lost.
Option vs. Annuity Machine: Two Ways to Be Invested in the Same AI-Healthcare Sector
"Invested in AI healthcare" can mean two almost opposite things. Two real companies show it: an annuity machine with ~86% recurring revenue — and an option with not a single approved drug. Same label, opposite risk DNA.
You're Building with AI Coding Agents. Your Codebase Is Drifting. Here's Why — and the Fix.
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.
AI-Crypto Is Not a Monolith: Four Categories — and the One Test That Separates Substance From Narrative
"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?
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