How to Make a Polymarket Bot: A Practical 2026 Guide.

01What a Polymarket bot actually does.

A Polymarket bot is a program that reads market data, decides on a trade according to fixed rules, and places orders through Polymarket's API without a human clicking anything. It does not predict the future. It executes a strategy faster, more consistently, and at more hours than a person can. The edge - if there is one - comes from the strategy and the execution quality, not from the fact that code is running.

That distinction matters because most people arrive at this question after watching a video about a wallet that turned a small stake into a large one. The honest framing is that a bot is a tool for applying a known edge repeatedly. If the underlying idea has no edge, automating it just loses money more efficiently. This guide assumes you want to build something real, so it spends as much time on the parts that fail as on the parts that work.

A bot turns a strategy into a process. If the strategy is sound, automation compounds it. If it isn't, automation accelerates the loss.

02Check eligibility before you write a line of code.

Polymarket restricts access by jurisdiction, and that list has grown through 2026. Several countries - including Portugal, Spain, Italy, France, and others - have moved to block or restrict the platform, and Polymarket's own Terms of Service exclude users in those regions. Before investing time in a bot, confirm that you can legally and contractually access the platform from where you live and operate. If you are in the United States, the equivalent regulated venue is Kalshi, and a bot there follows the same engineering principles against a different API.

This is not a formality. A bot that runs from a restricted jurisdiction risks frozen funds, a terminated account, and tax or regulatory exposure in your home country. Predikted builds bots only for clients in jurisdictions where the relevant venue is legally accessible. If you are unsure of your status, get local advice before building.

03Prerequisites: wallet, funds, and a development setup.

Assuming you are eligible, here is what you need in place before the first commit:

• A funded Polymarket account. Polymarket settles on Polygon and trades in USDC. Your trading balance lives in a proxy wallet that Polymarket controls on your behalf, signed for by your own key.
• A wallet private key you control. The bot signs orders with it. Treat this key like the keys to a vault - it should live in an environment variable or a secrets manager, never in source code or a screenshot.
• A Polygon RPC endpoint. The free public RPC works for testing; a paid endpoint (Alchemy, Infura, or similar) is worth it once you go live, because dropped calls cost fills.
• Python 3.10+ and the official client. Polymarket maintains py-clob-client, which handles request signing and the awkward parts of authentication. There is a TypeScript client too if you prefer.
• A little USDC for gas and a lot of patience. Polygon gas is cheap - fractions of a cent per transaction - but it is not zero, and you will spend more than you expect during testing.

If those words are unfamiliar, start with our explainer on what Polymarket is and the CLOB API glossary entry before going further. The rest of this guide assumes you can read async Python.

04Pick a strategy - this is the real decision.

The code is the easy part. Choosing what the bot does is where most projects succeed or fail. The four strategies people most often automate on Polymarket:

Arbitrage

The cleanest edge to reason about is sum-to-one arbitrage: buy YES and NO on the same market for a combined price under $1.00 and capture the difference at resolution. It is riskless in theory and full of execution traps in practice - thin book depth, leg failure, and an edge that decays as more bots arrive. We wrote a full breakdown in our arbitrage bot guide. It is a good first project because the logic is simple and the failure modes teach you everything else.

Copy trading

A copy-trading bot watches one or more profitable wallets and mirrors their positions with your own sizing rules. The engineering is straightforward; the hard part is choosing wallets that are genuinely skilled rather than lucky, and sizing so a copied bad day does not wreck you. Survivorship bias is the silent killer here.

Market making

A market-making bot posts two-sided quotes and earns the spread plus any liquidity rewards, while managing the inventory risk of holding both sides. It is the most operationally demanding strategy and the one most sensitive to API reliability, but it does not depend on directional prediction.

Directional and event-driven

The hardest category: the bot takes a side based on a signal - a news feed, a polling model, an on-chain trigger. This is where most retail attempts lose money, because you are now betting that your model beats the market's consensus price. Automate this last, if at all.

For a first build, arbitrage or copy trading give you a working, testable system without requiring you to be right about the world.

05Set up CLOB API access.

Polymarket exposes three APIs: Gamma for market metadata, Data for positions and history, and the CLOB API for live order books and order placement. The CLOB uses a two-level authentication model. L1 is your wallet key signing an EIP-712 message, used once to create API credentials. L2 is the resulting API key and secret, which sign each trading request so your private key is not exposed on every call. Reading public order-book data needs no authentication at all.

In practice the sequence is:

1. Initialise the client with your private key and the Polygon chain ID.
2. Derive or create your L2 API credentials with one signed request.
3. Confirm you can read an order book for a known market - if that works, your auth is correct.
4. Place a tiny test order well away from the touch so it rests without filling, then cancel it. If both succeed, you are ready to build logic on top.

Resist the urge to skip the resting-order test. It is the cheapest possible confirmation that signing, funding, and the proxy wallet are all wired correctly, and it surfaces the most common setup mistakes before any real money is at risk.

06The core loop, conceptually.

Every trading bot, regardless of strategy, is the same shape: observe, decide, act, reconcile. For an arbitrage bot the loop reads both order books, checks whether the edge clears a minimum threshold, fires both legs, then reconciles in case only one filled.

async def run(client, markets, min_edge=0.012):
    while True:
        # observe
        books = await asyncio.gather(*[
            client.get_orderbook(m.yes_token, m.no_token)
            for m in markets
        ])
        for mkt, (yes, no) in zip(markets, books):
            # decide
            edge = 1.0 − (yes.best_ask + no.best_ask)
            if edge < min_edge: continue
            size = risk.max_size(mkt, yes, no)
            # act
            y, n = await asyncio.gather(
                client.place_limit(mkt.yes_token, yes.best_ask, size, tif="FOK"),
                client.place_limit(mkt.no_token,  no.best_ask,  size, tif="FOK"),
            )
            # reconcile
            await reconciler.resolve(y, n)

Three things this snippet hides, which decide whether the bot is profitable:

• Rate limits. The public order-book endpoint is capped at roughly 100 requests per minute. Watching many markets means batching through the multi-market endpoint or subscribing over WebSocket instead of polling each one.
• Leg failure. The two orders are separate transactions on separate books. Around 3-7% of the time only one fills, and you are suddenly holding directional risk. The reconciler that unwinds the orphan is the part DIY bots most often get wrong.
• Risk sizing. risk.max_size is doing real work - capping per-trade exposure, respecting book depth, and refusing to trade when conditions are abnormal. A bot without a hard risk layer is a bot waiting for one bad day.

07Test, paper trade, then go live - in that order.

The transition from "it runs on my machine" to "it trades my money" is where discipline pays. A sane progression:

1. Unit-test the decision logic against recorded order books. You should be able to prove the bot fires exactly when the edge clears your threshold and never otherwise.
2. Paper trade for at least two weeks. Run the full loop live but log intended orders instead of placing them. Compare what it would have done against what actually happened in the market. This catches latency and fill-rate assumptions that backtests flatter.
3. Go live at minimum size. Trade the smallest size the platform allows. Your first live week is for confirming that fills, fees, gas, and reconciliation behave as your paper run predicted - not for making money.
4. Scale slowly, with monitoring. Add size only once you have alerting on failed legs, stalled loops, balance drift, and API errors. A bot you cannot observe is a liability the moment Polymarket ships an API change, which it does several times a year.

The single most common way these projects lose money is not a clever bug - it is trading size the bankroll cannot survive a bad day on, before the operator has proven the system end to end. Start small enough that an unexpected loss is a lesson, not a disaster.

08Build it yourself, or commission it?

If you can write async Python, manage a Polygon RPC, and have time to babysit the bot through three or four API revisions a year, you can build this yourself. Public repositories get you a meaningful fraction of the way, and the learning is genuinely useful. The remaining work - the reconciler, the risk layer, the rate-limit choreography, and the unglamorous operations and monitoring - is what separates a bot that stays break-even from one that nets positive.

If you would rather hand over a specification and receive a tested, documented bot with the source code transferred to you at delivery, that is exactly what we do at Predikted. Either path is legitimate. Whichever you choose, the advice does not change: confirm eligibility, pick a strategy with a real edge, paper trade before live, and never let the bot size beyond what you can afford to be wrong about.