Polymarket Analytics Dashboard and Trade Simulator — Here's How Claude 4.5 Opus Handled It

Every time a new LLM launches, the ritual is the same. Benchmark screenshots flood social media — SWE-Bench scores, "state of the art performance" declarations, charts upon charts. And every time, I try the model and think: this is basically the same as the last one.

Claude 4.5 Opus broke that pattern. After testing it on real coding projects — not toy examples, but actual applications I wanted to exist — I'm convinced something genuinely shifted. Let me walk through what I built and why this felt different.

What Actually Changed

The feature list is solid on its own: a 200k context window, subagents, checkpoints. Nice upgrades. But features alone don't explain why this model feels like a different experience.

The difference lies in how it thinks about your problem. With older AI coding tools, you'd ask for something and receive technically correct code that completely lacked context. Say "build me a dashboard" and you'd get something that runs but is useless in any real-world sense — the kind of output that answers the letter of your request while missing the spirit entirely.

Claude 4.5 Opus understands what you're actually trying to accomplish. It thinks ahead, makes smart structural decisions, and catches problems before you encounter them. When you combine that deeper understanding with the expanded feature set, the tool stops feeling like sophisticated autocomplete and starts feeling like a developer who happens to work at machine speed.

That's why I think this is currently the strongest option for rapid prototyping — whether you're an experienced developer looking to move faster or someone who's never written code and wants to bring an idea to life.

The Project: Prediction Market Tools

Rather than build another to-do list, I wanted to test Claude Code on something with real complexity. Prediction markets seemed like the perfect domain — they involve multiple APIs, time-series data, financial logic, and genuinely interesting analytical questions.

For the uninitiated: prediction markets are platforms where people bet real money on future events. Prices reflect probability — if something trades at 70 cents, the market collectively believes there's a 70% chance it happens. Polymarket is the crypto-based platform that exploded during the 2024 election (though unavailable to US users), while Kalshi is its regulated American counterpart. Both produce surprisingly accurate forecasts by letting crowds put money behind their convictions.

I knew almost nothing about prediction markets going in, which made this a genuine learning-by-doing experiment. After some initial research, I identified two MVP ideas worth building.

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App 1: Polymarket Analytics Dashboard

The first application was an analytics tool for tracking and analyzing Polymarket data. The core question driving it: when prediction markets say there's a 70% chance of something, how often does it actually happen?

I started by drafting a seed prompt, then refined it into a detailed specification covering market tracking (auto-refreshing daily for markets resolving within 30 days), accuracy analysis (grouping markets by probability buckets and comparing predictions against outcomes), black swan detection (identifying surprising resolutions where low-probability events win), and a dashboard showing large odds movements that might signal breaking news or manipulation.

The tech stack Claude Code chose was sensible — FastAPI with SQLAlchemy on the backend, SQLite for local-first storage, Jinja2 templates on the frontend, all pulling from Polymarket's Gamma API. No external services, no accounts required. The kind of pragmatic architecture a thoughtful developer would pick for a local analytics tool.

App 2: Trade Simulation — and a Lesson in Domain Knowledge

The second application was more ambitious: a historical trade simulator where you pick a past date, see the top markets from that period, place hypothetical bets, and discover whether you'd have made or lost money.

This is where things got interesting — not because of Claude Code's failures, but because of what the debugging process revealed about how this tool handles real-world complexity.

The first iteration filtered markets by date and liquidity. Problem: only two markets showed up for most dates. Iteration two added an "any resolved" mode to bypass the date filter. Still almost nothing.

The breakthrough came in iteration three, when we investigated the market lifecycle more carefully. Here's the insight: "liquidity" measures money currently available for trading. Once a market resolves, all positions settle and liquidity drops to zero. This is expected behavior — there's nothing left to trade. So filtering resolved markets by liquidity is fundamentally the wrong approach.

The fix was simple once you understood the domain: use volume (total money traded over a market's lifetime) instead of liquidity as the quality metric. Suddenly the simulator surfaced meaningful data — Trump's 2024 election market with $1.5 billion in volume, major crypto and sports markets, the full richness of Polymarket's history.

This is the kind of product learning that usually takes days of head-scratching. The same metric means different things at different stages of a lifecycle. Active markets care about liquidity; historical analysis cares about volume. Claude Code didn't just implement the fix — the iterative process of working with it helped surface the conceptual error faster than I would have found it alone.

The Arbitrage Experiment (and Why It Didn't Pan Out)

I also built a cross-platform arbitrage tracker to find pricing inconsistencies between Polymarket, Kalshi, and Manifold. The idea was sound: if the same event trades at different prices on different platforms, there's a theoretical arbitrage opportunity.

Claude Code handled the technical implementation without issues. The disappointing discovery was purely about the market: there are currently very few genuine arbitrage opportunities across these platforms. The tool works; the opportunity doesn't exist at meaningful scale right now. Sometimes the most valuable output of building something is learning that the premise was wrong.

Practical Notes on Using Claude Code

A few things worth knowing if you're considering this for your own projects.

I started with the Pro subscription for Claude Code, but hit usage limits within about 30 minutes of active development. Serious building requires the higher-tier plan. If you're planning to use Claude Code for anything beyond quick experiments, budget at least $100–200 for the AI tooling alone. It's worth it — the velocity is extraordinary — but going in with the wrong expectations about the free tier will frustrate you.

The workflow that emerged naturally was: describe the problem in plain language, let Claude Code build a first pass, then iterate through conversation. No manual debugging sessions, no long searches through documentation. The gap between having an idea and having a working prototype collapsed to hours rather than days.

Why This Matters

We went from initial concept to multiple functional applications without writing code directly. That sentence would have sounded absurd two years ago, and even a year ago the reality never quite matched the promise. What's different now isn't any single feature — it's the compound effect of a model that genuinely understands context, combined with tools that let it operate autonomously within a codebase.

Whether you're an experienced developer looking to prototype faster or someone exploring what's possible without traditional coding skills, Claude 4.5 Opus with Claude Code represents a real step change. Not because the benchmarks say so, but because the experience of using it feels fundamentally different from everything that came before.

The interesting question — the one I'm still exploring — is what happens when you push it further. More complex applications, real databases, authentication flows, multiple interacting APIs. That's where the next round of testing begins.

Written by Lukas Kaminskis, CEO at Turing College