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guskarls

Posted on • Originally published at guskarls.substack.com

Building a Polymarket Trading Bot in 2026: WebSockets, Order Books, CLOB Execution & Risk

#architecture #backend #crypto #web3

A technical walkthrough of the architecture I use for automated Polymarket trading - from real-time market data to strategy evaluation, execution, positions, and TWAP-aware resolution.

If you're a developer searching for how to build a Polymarket trading bot, the first version is relatively straightforward.

You can connect to market data, calculate a signal, and submit an order.

The production version is not straightforward.

Once a bot needs to operate continuously, you have to solve problems around:

  • Real-time market data
  • Order-book synchronization
  • Strategy evaluation
  • Slippage
  • Partial fills
  • Order management
  • Position state
  • Risk controls
  • Resolution
  • Monitoring
  • Recovery

This article walks through the architecture I use when thinking about a Polymarket trading bot.

Architecture

At a high level:

                    Polymarket
                        │
             ┌──────────┴──────────┐
             │                     │
        Market Data            Trading
             │                     │
             ↓                     ↓
       Market Scanner        Order Manager
             │                     │
             ↓                     ↓
       Strategy Engine       Execution
             │                     │
             └──────────┬──────────┘
                        ↓
                   Risk Engine
                        ↓
                  Position Manager
                        ↓
                    Monitoring
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I intentionally separate the strategy from execution.

This makes it possible to experiment with different strategies without rewriting the market-data and order-management infrastructure.

1. Market Discovery

The first problem is deciding which markets the bot should monitor.

You don't necessarily want to subscribe to every available market.

A scanner can filter markets based on:

Category
Market Type
Start Time
End Time
Liquidity
Volume
Status
Resolution
Token IDs
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The output might be:

type TradingMarket = {
  conditionId: string;
  question: string;
  yesTokenId: string;
  noTokenId: string;
  endTime: number;
  liquidity: number;
};
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The scanner then feeds selected markets into the market-data layer.

2. Real-Time Market Data

This is where WebSockets become important.

Polymarket's public market WebSocket provides real-time order-book, price, trade, and market-lifecycle updates.

The market endpoint is:

wss://ws-subscriptions-clob.polymarket.com/ws/market
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The architecture becomes:

WebSocket
    ↓
Raw Event
    ↓
Normalizer
    ↓
Market State
    ↓
Strategy
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The market channel can provide events such as:

book
price_change
last_trade_price
best_bid_ask
new_market
market_resolved
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The best_bid_ask, new_market, and market_resolved events are available when the relevant custom feature is enabled.

3. Maintain an In-Memory Order Book

I don't want the strategy to make a network request every time it needs the best bid or ask.

Instead, maintain local state:

type OrderBookState = {
  bids: Map<number, number>;
  asks: Map<number, number>;
  bestBid: number | null;
  bestAsk: number | null;
  lastTrade: number | null;
  timestamp: number;
};
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Then:

WebSocket
    ↓
Order Book State
    ↓
Strategy
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This makes strategy evaluation much faster and avoids unnecessary API calls.

For historical data, I can persist selected events separately.

4. Normalize Events

Different event types should not leak directly into the strategy.

Instead:

Polymarket Event
       ↓
Normalizer
       ↓
Internal Event
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For example:

type MarketEvent =
  | {
      type: "BOOK_UPDATE";
      marketId: string;
      timestamp: number;
    }
  | {
      type: "TRADE";
      marketId: string;
      price: number;
      size: number;
      timestamp: number;
    }
  | {
      type: "RESOLVED";
      marketId: string;
      timestamp: number;
    };
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Now the strategy doesn't need to know exactly how the external WebSocket payload is structured.

That's a useful abstraction.

5. Strategy Engine

The strategy should consume normalized market state.

For example:

const signal = strategy.evaluate({
  market,
  orderBook,
  externalPrice,
  position,
});
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The result should be something explicit:

type Signal = {
  side: "BUY" | "SELL" | "NONE";
  price: number;
  size: number;
  expectedEdge: number;
  confidence: number;
};
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This gives the risk layer something measurable to evaluate.

Signal vs Execution

This distinction is extremely important.

A strategy might return:

BUY
Price: 0.60
Size: 1000
Expected Edge: 5%
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That does not mean the bot should immediately buy 1,000 contracts.

The risk and execution layers still need to evaluate:

Liquidity
Spread
Slippage
Position
Exposure
Open Orders
Market State
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The flow should be:

Signal
  ↓
Validation
  ↓
Risk
  ↓
Execution
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not:

Signal
  ↓
BUY()
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6. Calculate Expected Execution Price

Suppose the ask side looks like:

Price    Size

0.60     100
0.61     300
0.62     500
0.63     1,000
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The bot wants:

Size = 1,000
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It cannot assume:

Execution Price = 0.60
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Instead, it should walk the book and calculate the expected average fill.

Conceptually:

Expected Fill
=
Σ(price × filled_size)
/
Σ(filled_size)
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Then compare:

Fair Value
vs
Expected Fill
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This is much more useful than comparing fair value with the last traded price.

7. Slippage-Aware Signals

A signal should ideally be based on expected execution, not simply the displayed market price.

For example:

Fair Value       = 0.66
Best Ask         = 0.61
Expected Fill    = 0.625
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Then:

Theoretical Edge = 0.05
Realistic Edge   = 0.035
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The second number is what the risk engine should care about.

8. Arbitrage

A Polymarket arbitrage bot can look for relationships between markets or outcomes.

A simplified example:

Market A = 0.52
Market B = 0.57
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If those markets represent sufficiently related outcomes, the price difference may indicate an opportunity.

But the bot needs to verify:

Liquidity
Correlation
Resolution Rules
Execution Timing
Partial Fills
Fees
Capital
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Arbitrage isn't:

Price A != Price B
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It's:

Price Difference
    ↓
Executable Difference
    ↓
Risk-Adjusted Edge
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9. Short-Duration Crypto Strategies

This is one of the most interesting areas for a Polymarket trading bot.

For short-duration BTC, ETH, SOL, or XRP markets, the bot can compare external crypto prices against prediction-market prices.

Example:

External Market
       ↓
Price Movement
       ↓
Probability Model
       ↓
Polymarket Probability
       ↓
Difference
       ↓
Execution
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The difficulty is speed.

If the external market moves 1% and Polymarket reprices almost immediately, the bot may have no remaining edge.

So latency becomes part of the strategy.

10. TWAP-Aware Trading

For affected short-duration crypto Up/Down markets, Polymarket has moved to TWAP-based resolution rather than relying solely on a single snapshot at the end of the market.

That changes the architecture.

A naive bot might think:

Current Price
     ↓
Final Outcome
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A resolution-aware bot thinks:

Resolution Window
       ↓
Underlying Price
       ↓
TWAP
       ↓
Resolution
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For a trading bot, that means the resolution mechanism needs to be represented explicitly in the market state.

For example:

type ResolutionState = {
  method: "TWAP" | "OTHER";
  startTime: number;
  endTime: number;
  referencePrice?: number;
  currentValue?: number;
};
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The exact market rules should always be read from the market itself rather than hardcoded globally.

I wrote a separate article about my own TWAP-related bot update because this deserves a deeper implementation discussion.

11. Risk Engine

The risk engine should sit between the strategy and execution layers.

Example:

const riskResult = riskEngine.validate({
  market,
  signal,
  position,
  portfolio,
});
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Potential rules:

MAX_POSITION_SIZE
MAX_MARKET_EXPOSURE
MAX_TOTAL_EXPOSURE
MAX_DAILY_LOSS
MAX_SLIPPAGE
MIN_EXPECTED_EDGE
MAX_OPEN_ORDERS
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The result can be:

{
  allowed: true,
  adjustedSize: 250,
  reason: "within limits"
}
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or:

{
  allowed: false,
  adjustedSize: 0,
  reason: "maximum exposure reached"
}
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This separation makes the system easier to test.

12. Order Manager

The order manager owns the lifecycle of an order.

CREATED
   ↓
SUBMITTED
   ↓
OPEN
   ↓
PARTIALLY_FILLED
   ↓
FILLED
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Or:

OPEN
  ↓
CANCELLED
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The strategy shouldn't need to know these implementation details.

It should simply receive:

Position Changed
Order Filled
Order Cancelled
Order Rejected
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13. Partial Fills

Partial fills are normal in order-book trading.

Suppose:

Requested = 1,000
Filled    = 400
Remaining = 600
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The order manager needs a policy.

Possible actions:

WAIT
CANCEL
REPRICE
TAKE LIQUIDITY
REDUCE SIZE
ABORT
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The correct behavior depends on the strategy.

For a latency-sensitive strategy, waiting 30 seconds might destroy the edge.

For a market-making strategy, waiting could be exactly what you want.

14. Position Manager

The position manager should be the source of truth for exposure.

Something like:

type Position = {
  marketId: string;
  outcome: "YES" | "NO";
  size: number;
  averageEntry: number;
  realizedPnl: number;
  unrealizedPnl: number;
};
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Then every strategy decision can include current exposure.

That prevents the classic problem:

Signal 1 → BUY
Signal 2 → BUY
Signal 3 → BUY
Signal 4 → BUY
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without realizing that the bot has accumulated too much exposure.

15. User WebSocket Updates

For authenticated trading activity, Polymarket also provides a user WebSocket channel for order and trade updates.

That allows the system to react to:

Order Matched
Order Confirmed
Order Updated
Order Cancelled
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instead of relying entirely on polling.

Credentials should remain server-side and should never be exposed in frontend code.

16. Market Resolution

Resolution deserves its own component.

Every market has resolution rules defining things such as:

Resolution Source
End Date
Edge Cases
Outcome
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Polymarket's documentation notes that markets are resolved through its resolution mechanism, with predefined rules determining the outcome.

A trading bot should therefore store resolution information alongside market metadata.

For example:

type MarketMetadata = {
  marketId: string;
  question: string;
  endTime: number;
  resolutionSource: string;
  resolutionMethod: string;
};
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This is especially important for strategies operating close to resolution.

17. Monitoring

Production monitoring should answer:

Is the bot running?

Is WebSocket connected?

How many markets are active?

How many signals were generated?

How many orders were submitted?

How many filled?

What is the current exposure?

What is the P&L?

What errors occurred?

What is the execution latency?
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A basic dashboard:

Markets          124
Signals           37
Orders            19
Filled            13
Open Positions     6
P&L              +$XXX
Errors             2
Latency          XX ms
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But metrics aren't enough.

You also need structured logs.

18. Structured Trade Logs

For every trade, I want something similar to:

{
  "market": "BTC",
  "side": "BUY",
  "signalPrice": 0.61,
  "expectedFill": 0.625,
  "expectedEdge": 0.035,
  "size": 250,
  "riskApproved": true,
  "orderId": "...",
  "fillPrice": 0.623,
  "timestamp": 1760000000000
}
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This makes post-trade analysis much easier.

You can answer:

Why did the bot enter?

What did it expect?

What actually happened?

That's essential for improving a strategy.

19. Suggested Project Structure

A clean TypeScript project could look like:

src/
│
├── markets/
│   ├── discovery.ts
│   ├── scanner.ts
│   └── filters.ts
│
├── market-data/
│   ├── websocket.ts
│   ├── orderbook.ts
│   └── normalizer.ts
│
├── strategy/
│   ├── base.ts
│   ├── arbitrage.ts
│   ├── momentum.ts
│   ├── market-maker.ts
│   └── fair-value.ts
│
├── execution/
│   ├── order-manager.ts
│   ├── fill-manager.ts
│   └── position-manager.ts
│
├── risk/
│   ├── risk-engine.ts
│   ├── limits.ts
│   └── exposure.ts
│
├── wallet/
│   └── signer.ts
│
├── monitoring/
│   ├── metrics.ts
│   ├── logger.ts
│   └── alerts.ts
│
└── config/
    └── index.ts
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This gives each component one clear responsibility.

20. Database and Fast State

I wouldn't write every WebSocket event directly to PostgreSQL.

Instead:

WebSocket
    ↓
Memory / Redis
    ↓
Strategy
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and separately:

Events
    ↓
PostgreSQL
    ↓
Analytics
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Use fast state for the trading path.

Use persistent storage for historical analysis.

21. Paper Trading

Before deploying real capital, I recommend running the bot in paper-trading mode.

But the simulator needs to be realistic.

Bad simulator:

Signal
  ↓
Instant Fill
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Better simulator:

Signal
  ↓
Order Book
  ↓
Expected Fill
  ↓
Slippage
  ↓
Partial Fill
  ↓
Position
  ↓
P&L
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Otherwise, the backtest can make the strategy look much better than it really is.

22. What Makes a Polymarket Trading Bot Actually Interesting?

The interesting part isn't the API call.

It's the complete feedback loop:

Market
  ↓
Data
  ↓
State
  ↓
Signal
  ↓
Risk
  ↓
Execution
  ↓
Fill
  ↓
Position
  ↓
P&L
  ↓
Analysis
  ↓
Strategy Improvement
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That's what turns a script into a trading system.

Final Architecture

Putting everything together:

                  MARKET DISCOVERY
                         │
                         ↓
                  REAL-TIME DATA
                         │
                         ↓
                   ORDER BOOK
                         │
                         ↓
                   STRATEGY
                         │
                         ↓
                  EXPECTED EDGE
                         │
                         ↓
                      RISK
                         │
                         ↓
                    EXECUTION
                         │
                         ↓
                      FILLS
                         │
                         ↓
                    POSITIONS
                         │
                         ↓
                    MONITORING
                         │
                         ↓
                   ANALYTICS
                         │
                         └──────→ STRATEGY
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That feedback loop is the core of the Polymarket trading bot architecture I'm interested in building.

Final Thoughts

If you're starting your first Polymarket trading bot, don't begin with the most complicated strategy you can think of.

Start with infrastructure.

Build:

  1. Market discovery
  2. WebSocket market data
  3. Local order-book state
  4. Strategy interface
  5. Risk engine
  6. Order manager
  7. Position manager
  8. Monitoring
  9. Paper trading

Then add the strategy.

This approach makes debugging dramatically easier because you can isolate whether a problem comes from:

Data
Strategy
Risk
Execution
Position State
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rather than debugging everything at once.

The most important lesson I've learned is that a trading signal is only the beginning.

The real engineering challenge is converting that signal into an executable, risk-controlled trade.

That's what makes building a Polymarket trading bot such an interesting problem.

Resources

Polymarket Trading Bot - TWAP

Source code for my TWAP trading-bot project.

YouTube - std0d

I also share Polymarket development and trading-bot content on YouTube.

Previous articles

  • Building a Polymarket Trading Bot
  • Building a Polymarket Arbitrage Bot: Architecture, Challenges, and Execution Strategies
  • How I Updated My Polymarket Trading Bot for TWAP Resolution

Disclaimer

This article is for educational and software-development purposes only. It is not financial advice. Automated trading involves substantial risk, and past or simulated performance does not guarantee future results.

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