Falling Down the Polymarket Bot Rabbit Hole

A field report on prediction-market arbitrage, crypto micro-markets, and building my first automated trader.

I’ve been going deep into the world of automated arbitrage bots on prediction markets, especially Polymarket and its ultra-fast crypto markets. What started as casual curiosity quickly turned into a full-blown research and build sprint: I mapped the “bot meta” I kept seeing on Twitter, reverse-engineered the recurring patterns, and shipped multiple bot prototypes (built with Claude) that aim to exploit pricing inefficiencies in a systematic way.

This article is a structured recap of my discoveries, what I built, and how I’m planning to test and iterate from here.

1) What I noticed on Twitter: bots are quietly printing

There’s a strong trend right now: people are posting profiles of traders/bots showing very high win rates and six-figure monthly profits, especially on 15-minute crypto direction markets (BTC/ETH up or down, etc.).

The recurring narrative is always the same:

• Humans react slowly: read news, think, click, place bet.

• Bots react instantly: monitor prices, order books, spreads, and execute without hesitation.

• The edge is not “having a better opinion,” it’s exploiting bad pricing.

A few archetypes kept repeating:

A) Two-leg arbitrage style (YES/NO hedging over time)

Core idea: each market resolves to $1.00 on the winning side at expiry.
So if you can build a combined position where your total cost for both outcomes is < $1.00, you lock a mathematical edge (once both legs are in).

The trick these bots use is asymmetry in timing:

• Buy one side when it becomes unusually cheap.

• Wait for the market to overreact the other way.

• Buy the opposite side later when that becomes cheap.

• If the combined average stays under $1, you’ve created a “hedged coupon” that should settle above your cost.

Important nuance: it’s not truly riskless until both legs are filled. Your first entry has exposure.

B) Liquidity window / spread hunter style

Some bots don’t “predict” anything. They act like snipers:

• show up only when liquidity conditions create a mispriced window

• take small, high-probability shots

• disappear

C) “AI assistant + execution” tooling

I also found tools like polymarket-mcp-server being talked about as a way to plug an AI into market data:

• live price watching (websockets)

• scanning events/news fast

• checking liquidity, slippage, order books

• flagging opportunities faster than a human

Even if you don’t let the AI trade directly, it can act as a real-time analyst.

2) My own work: from research to a real bot prototype

After seeing the same patterns over and over, I built 2–3 versions of a Polymarket bot. The goal wasn’t “copy trading,” it was learning the mechanics and building my own system.

What my bot is designed to do

Mission: detect short-lived inefficiencies and execute a disciplined two-leg approach (or opportunistic single-leg entries) with strict controls.

High-level loop:

1. Select markets (starting with crypto 15m, then expanding to other events).

2. Watch live prices and order books.

3. Detect moments where an outcome becomes “too cheap” vs recent price and/or implied fair band.

4. Enter one side with a strict max size.

5. Wait for the opposite side to become cheap enough to complete the hedge.

6. Track combined cost basis, fees, and exit conditions.

7. Log everything.

Why I’m starting small

Because the market is fast and reality is messy:

• partial fills happen

• latency exists

• spreads collapse quickly

• fees and slippage can eat theoretical edge

So I’m testing with $10–$15 first, just to validate execution and behavior before scaling anything.

3) UX philosophy: “no .env, no terminal pain”

A huge part of this project is not only the strategy, but the product.

Most bots die because they’re annoying:

• messy env files

• cryptic setup

• fragile scripts

• unclear errors

So my approach is: ultra user-friendly GUI, where a user can:

• enter credentials in-app (no .env required)

• choose markets and presets

• run in paper mode / dry-run mode

• see logs, positions, and state clearly

• start/stop safely

Think “trading cockpit,” not “developer console.”

4) Testing plan: how I’m going live today

My short test plan is intentionally simple:

Phase 1: Paper / dry-run (behavioral validation)

• Ensure signals trigger correctly

• Ensure state machine logic makes sense

• Ensure risk rules are enforced

• Ensure logs are complete and readable

Phase 2: Micro live test ($10–$15)

• Place tiny orders

• Verify fills, partial fills, and cancellations

• Measure real slippage and fee impact

• Validate that “edge” survives real execution

Phase 3: Iteration + hardening

• improve market filters

• improve sizing logic

• add better kill-switch behavior

• add reporting and performance analytics

5) Operational realities: things I’m watching closely

This project can look like “free money” on screenshots, but the devil lives in the plumbing. Key watch-outs:

A) Fees + slippage can erase the edge

A theoretical “cost < $1.00” is useless if:

• you cross the spread twice

• you pay meaningful fees

• you get bad partial fills

B) Execution risk on the first leg

Until both sides are acquired, you can get:

• stuck holding one side during a trend

• forced to hedge at a worse price later

• forced to cut the position

C) Market selection matters

This seems to work best when:

• volatility is high

• pricing lags

• spreads temporarily widen

• liquidity is sufficient to enter/exit without self-slippage

D) Security is non-negotiable

If your bot handles keys:

• never log secrets

• encrypt locally

• limit permissions

• build safe defaults

E) Compliance and access constraints

Polymarket access varies by jurisdiction and rules can change. I’m being careful to respect platform terms and local requirements, and I’m treating testing as a controlled experiment, not a promise of outcomes.

6) Roadmap: what I’m adding next

If I want this to be more than a toy, these upgrades are high leverage:

• Backtesting module (replay market data, stress test parameters)

• Paper trading mode that simulates fills realistically (spread + depth)

• Smart alerting (Telegram/Discord notifications for setups)

• Market scanner (auto-detect high-volatility / high-liquidity windows)

• Risk engine (max exposure, max daily loss, cooldowns, kill switch)

• Performance dashboard (PnL, win rate, average edge, slippage stats)

• User presets (safe conservative mode vs aggressive mode)

7) The big takeaway

Most people trade narratives.
These bots trade pricing mistakes.

The real edge is not “being right.” It’s:

• being fast

• being systematic

• being disciplined on risk

• and building a product that actually runs reliably

Now the real test begins: putting a small amount live, watching how it behaves in the wild, and iterating until it’s bulletproof.