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

guskarls — Mon, 10 Aug 2026 13:54:28 +0000

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

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

The output might be:

type TradingMarket = {
  conditionId: string;
  question: string;
  yesTokenId: string;
  noTokenId: string;
  endTime: number;
  liquidity: number;
};

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

The architecture becomes:

WebSocket
    ↓
Raw Event
    ↓
Normalizer
    ↓
Market State
    ↓
Strategy

The market channel can provide events such as:

book
price_change
last_trade_price
best_bid_ask
new_market
market_resolved

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;
};

Then:

WebSocket
    ↓
Order Book State
    ↓
Strategy

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

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;
    };

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,
});

The result should be something explicit:

type Signal = {
  side: "BUY" | "SELL" | "NONE";
  price: number;
  size: number;
  expectedEdge: number;
  confidence: number;
};

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%

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

The flow should be:

Signal
  ↓
Validation
  ↓
Risk
  ↓
Execution

not:

Signal
  ↓
BUY()

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

The bot wants:

Size = 1,000

It cannot assume:

Execution Price = 0.60

Instead, it should walk the book and calculate the expected average fill.

Conceptually:

Expected Fill
=
Σ(price × filled_size)
/
Σ(filled_size)

Then compare:

Fair Value
vs
Expected Fill

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

Then:

Theoretical Edge = 0.05
Realistic Edge   = 0.035

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

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

Arbitrage isn't:

Price A != Price B

It's:

Price Difference
    ↓
Executable Difference
    ↓
Risk-Adjusted Edge

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

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

A resolution-aware bot thinks:

Resolution Window
       ↓
Underlying Price
       ↓
TWAP
       ↓
Resolution

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;
};

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,
});

Potential rules:

MAX_POSITION_SIZE
MAX_MARKET_EXPOSURE
MAX_TOTAL_EXPOSURE
MAX_DAILY_LOSS
MAX_SLIPPAGE
MIN_EXPECTED_EDGE
MAX_OPEN_ORDERS

The result can be:

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

or:

{
  allowed: false,
  adjustedSize: 0,
  reason: "maximum exposure reached"
}

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

Or:

OPEN
  ↓
CANCELLED

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

It should simply receive:

Position Changed
Order Filled
Order Cancelled
Order Rejected

13. Partial Fills

Partial fills are normal in order-book trading.

Suppose:

Requested = 1,000
Filled    = 400
Remaining = 600

The order manager needs a policy.

Possible actions:

WAIT
CANCEL
REPRICE
TAKE LIQUIDITY
REDUCE SIZE
ABORT

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;
};

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

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

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

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;
};

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?

A basic dashboard:

Markets          124
Signals           37
Orders            19
Filled            13
Open Positions     6
P&L              +$XXX
Errors             2
Latency          XX ms

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
}

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

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

and separately:

Events
    ↓
PostgreSQL
    ↓
Analytics

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

Better simulator:

Signal
  ↓
Order Book
  ↓
Expected Fill
  ↓
Slippage
  ↓
Partial Fill
  ↓
Position
  ↓
P&L

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

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

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:

Market discovery
WebSocket market data
Local order-book state
Strategy interface
Risk engine
Order manager
Position manager
Monitoring
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

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.

Building a Polymarket Arbitrage Bot: Architecture, Challenges, and Execution Strategies

guskarls — Wed, 05 Aug 2026 15:45:16 +0000

When people hear the phrase Polymarket arbitrage bot, they usually imagine finding pricing inefficiencies and collecting risk-free profits.

Reality is much more complicated.

Building an arbitrage bot requires solving problems that have very little to do with arbitrage itself.

Over the past several months I've been building trading infrastructure for Polymarket, and most of the engineering work has gone into making the system reliable rather than making it "smarter."

Core Components

My bot consists of several independent services:

Real-time market data
WebSocket subscriptions
Order book synchronization
Opportunity detection
Risk management
Order execution
Settlement monitoring

Separating these layers keeps the system maintainable as Polymarket evolves.

Arbitrage Is Mostly an Execution Problem

Finding opportunities isn't usually the hardest part.

Executing before those opportunities disappear is.

Execution introduces challenges such as:

Network latency
Order queue position
Partial fills
Liquidity constraints
Exchange downtime
Market rule changes

Each of these affects profitability far more than a simple arbitrage formula.

Infrastructure Never Stops Changing

Recent changes such as Polymarket's move toward TWAP settlement highlight why trading systems must be designed for change rather than hard-coded assumptions.

A flexible architecture makes these transitions significantly easier.

Final Thoughts

If you're building a Polymarket arbitrage bot, don't optimize only for opportunity detection.

Invest just as much time in:

System architecture
Execution quality
Risk management
Monitoring
Logging
Testing

A trading bot isn't just an algorithm.

It's an engineering system that has to keep working as the market evolves.

DEV Community: guskarls

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

Architecture

1. Market Discovery

2. Real-Time Market Data

3. Maintain an In-Memory Order Book

4. Normalize Events

5. Strategy Engine

Signal vs Execution

6. Calculate Expected Execution Price

7. Slippage-Aware Signals

8. Arbitrage

9. Short-Duration Crypto Strategies

10. TWAP-Aware Trading

11. Risk Engine

12. Order Manager

13. Partial Fills

14. Position Manager

15. User WebSocket Updates

16. Market Resolution

17. Monitoring

18. Structured Trade Logs

19. Suggested Project Structure

20. Database and Fast State

21. Paper Trading

22. What Makes a Polymarket Trading Bot Actually Interesting?

Final Architecture

Final Thoughts

Resources

Disclaimer

Building a Polymarket Arbitrage Bot: Architecture, Challenges, and Execution Strategies