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Building a Polymarket Trading Bot: Arbitrage + Momentum Strategy (Twap-60s Available)

#polymarket #arbitrage #tutorial #architecture

Short-duration prediction markets move extremely quickly.

In Polymarket 5-minute and 15-minute crypto markets, BTC or ETH can move significantly within seconds, causing YES and NO probabilities to change rapidly.

You can watch this bot running video with this link.

That creates an interesting opportunity for automated trading:

Can we combine arbitrage structure with short-term momentum to identify better trading opportunities?

In this tutorial, I'll explain the architecture behind a Polymarket Trading Bot that combines:

  • YES/NO prices
  • Order-book imbalance
  • External crypto prices
  • Short-term momentum
  • On-chain signals
  • Balanced positioning
  • Automated limit orders

The goal isn't to predict every market correctly.

The goal is to continuously search for situations where the available information suggests a positive expected-value opportunity.

Disclaimer: This article is for educational purposes only. Trading prediction markets involves financial risk and does not guarantee profit.

What Is a Polymarket Arbitrage Momentum Bot?

A traditional arbitrage strategy looks for situations where YES and NO can be purchased at a favorable combined price.

For example:

YES = $0.47
NO  = $0.51

YES + NO = $0.98
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A momentum strategy takes a different approach.

It asks:

Is BTC moving up?

Is momentum accelerating?

Is the order book bullish?

Is Polymarket reacting quickly enough?
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The idea behind this bot is to combine both approaches.

Arbitrage provides the structure. Momentum provides directional bias.

For example:

Strong YES momentum
        ↓
Increase YES allocation

Weak signal
        ↓
Maintain balanced position

Strong NO momentum
        ↓
Increase NO allocation
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This is not risk-free arbitrage. It is a hybrid strategy that attempts to reduce excessive directional exposure while using momentum to improve capital allocation.

The Strategy Architecture

The bot continuously collects information from several sources:

          Polymarket
        YES / NO Prices
              │
              ▼
        Order Book Data
              │
              │
External ─────┼───── On-Chain
Crypto Data   │       Data
              ▼
        Signal Engine
              │
              ▼
     Momentum + Arbitrage
              │
              ▼
       Position Sizing
              │
              ▼
       Limit Order Engine
              │
              ▼
          Polymarket
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The important idea is that no single signal controls the entire strategy.

1. Monitor YES and NO Prices

The first layer is the Polymarket order book.

The bot monitors:

  • YES bid/ask
  • NO bid/ask
  • Spread
  • Recent trades
  • Available liquidity
  • Order-book depth

For example:

YES

Bid: $0.54
Ask: $0.56

NO

Bid: $0.43
Ask: $0.45
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The bot can use this information to determine whether the current market price provides a potential opportunity.

2. Order-Book Imbalance

Price alone doesn't tell us everything.

Consider a YES order book:

Bid volume = 2,000
Ask volume = 1,000
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A simple order-book imbalance is:

OBI =
(Bid Volume - Ask Volume)
-------------------------
(Bid Volume + Ask Volume)
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In this example:

OBI = (2000 - 1000) / (2000 + 1000)
    = 0.33
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A positive value suggests stronger displayed bid liquidity.

The bot can calculate OBI across multiple time windows:

OBI 1s
OBI 3s
OBI 5s
OBI 10s
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This can help identify short-term changes in buying and selling pressure.

3. External Crypto Price

For crypto prediction markets, external BTC or ETH prices can provide another important signal.

Suppose:

Polymarket reference = $117,080
External BTC         = $117,400
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Instead of simply saying:

BTC is bullish.

The bot asks:

Is the external market moving enough that Polymarket may not have fully reflected the information yet?

A simple feature can be:

price_gap =
(external_price - reference_price)
/
reference_price
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This feature can then be combined with momentum and order-book data.

For current market mechanics and API details, check the official Polymarket documentation.

4. Short-Term Momentum

The bot can calculate momentum over several time horizons.

For example:

Return 10s
Return 30s
Return 60s
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A simple momentum score could be:

Momentum =
    0.25 × Return10s
  + 0.35 × Return30s
  + 0.40 × Return60s
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The exact weights should be tested rather than assumed to be optimal.

The advantage of multiple windows is that the bot can distinguish between a very short price spike and a more persistent move.

For example:

10s return  = +0.03%
30s return  = +0.08%
60s return  = +0.15%
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This provides stronger evidence of sustained momentum than a single price change.

5. On-Chain Signals

On-chain data can provide another layer of market information.

Depending on the implementation, the bot can monitor features such as:

  • Transaction activity
  • Large transfers
  • Liquidity movements
  • Wallet activity
  • Market activity

These signals should not be treated as guaranteed predictors.

Instead:

On-chain data
      +
External price
      +
Momentum
      +
Order book
      ↓
Combined market signal
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The purpose is to give the strategy more context.

Combining the Signals

Now we can combine the individual features.

A simplified model might look like:

Signal Score =

Momentum
+
Order Book
+
External Price
+
On-Chain
+
Arbitrage
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After normalization, we might get:

Momentum:       +0.72
Order Book:     +0.41
External Price: +0.63
On-Chain:       +0.55
Arbitrage:      +0.38

Combined Score: +0.54
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A strongly positive score could create a YES bias.

A strongly negative score could create a NO bias.

If the score is close to zero:

No clear edge
     ↓
Stay balanced
or
Do nothing
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This is important.

A good trading bot should know when not to trade.

Balanced YES/NO Positioning

The strategy doesn't need to make an all-or-nothing prediction.

Suppose the bot holds:

YES = 120 shares
NO  = 100 shares
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This creates a small YES bias.

If bullish momentum becomes stronger:

YES = 135
NO  = 100
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If momentum weakens:

YES = 120
NO  = 110
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If the market reverses:

YES = 100
NO  = 125
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The position can therefore adapt as new information arrives.

This is different from a simple momentum bot that might hold one large directional position.

Example: BTC 5-Minute Market

Imagine a BTC 5-minute market begins:

YES = $0.50
NO  = $0.50
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The bot detects:

BTC momentum       → bullish
Order-book         → bullish
External price     → bullish
On-chain signal    → bullish
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The combined signal becomes positive.

Instead of immediately putting all available capital into YES, the bot might gradually build a position:

BUY YES → 15 shares
BUY NO  → 10 shares
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Then it continuously reevaluates the market.

If BTC continues rising:

Increase YES allocation
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If momentum disappears:

Reduce new YES orders
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If BTC suddenly reverses:

Reduce YES
Increase NO
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The strategy is therefore dynamic.

Why Limit Orders Matter

Having a good signal isn't enough.

Execution can determine whether the theoretical edge actually becomes realized profit.

The bot therefore uses limit orders whenever appropriate.

The execution process can look like:

Signal detected
      ↓
Check liquidity
      ↓
Calculate target price
      ↓
Calculate position size
      ↓
Place limit order
      ↓
Monitor fill
      ↓
Recalculate signal
      ↓
Cancel / modify / place new order
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Limit orders provide price control, but they also create another risk:

the order may not fill.

Therefore, the bot needs to balance execution price against fill probability.

Position Sizing

Position size should depend on market conditions.

Useful inputs include:

Signal strength
Available capital
Current exposure
Liquidity
Spread
Volatility
Time remaining
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A simple framework is:

Weak signal
    → Small position

Medium signal
    → Medium position

Strong signal
    → Larger position

Extreme volatility
    → Reduce position
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The strategy should also enforce a maximum position size so that one signal cannot consume excessive capital.

Recommended Starting Capital

For the configuration described here, I recommend approximately:

$2,500 starting capital.

This is not a Polymarket requirement and does not guarantee profitability.

The reason is operational.

The bot may have multiple active orders at the same time, for example:

20–30 active orders
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with typical orders around:

10–20 shares
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Depending on market conditions, temporary capital usage can exceed:

$1,000+
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The remaining capital provides a buffer for:

  • Multiple markets
  • Pending orders
  • Position adjustments
  • Settlement delays
  • New opportunities

Your actual capital requirement should be determined by your own risk limits and testing.

Risk Management

The strategy is not risk-free.

Important risks include:

Directional Risk

Momentum signals can be wrong.

Execution Risk

Limit orders may not fill.

Liquidity Risk

Displayed liquidity can disappear quickly.

Latency Risk

External price information may reach different markets at different times.

Model Risk

A signal that worked historically may stop working.

Operational Risk

APIs, servers, networks, and order synchronization can fail.

Because of these risks, the bot should continuously monitor its exposure and trading environment.

The Complete Trading Loop

The strategy can be summarized as:

Discover market
      ↓
Collect market data
      ↓
Calculate momentum
      ↓
Calculate order-book imbalance
      ↓
Compare external price
      ↓
Process on-chain signals
      ↓
Calculate arbitrage relationship
      ↓
Generate combined signal
      ↓
Calculate target position
      ↓
Check risk limits
      ↓
Place limit order
      ↓
Monitor execution
      ↓
Recalculate
      ↓
Rebalance
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The key is the final step.

The bot doesn't make one prediction and wait.

It continuously updates its decision as new information arrives.

What's Next?

In Part 2, we'll look at the next layer of the system:

  • Position management
  • Risk controls
  • Arbitrage calculations
  • Execution logic
  • Backtesting
  • Live monitoring
  • Probability-based decision making
  • How the strategy can be improved over time

You can also explore my Polymarket Trading Bot Python repository on GitHub,

GitHub logo Benjam1nCup / Polymarket-trading-bot-python-V2

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Polymarket Trading Bot | Polymarket Arbitrage Bot | Polymarket TWAP Trading Bot

An open-source and Strong Strategy collection of Polymarket trading bot and Polymarket arbitrage bot and Polymarket TWAP trading bot in Python for high-performance automated trading on polymarket crypto 5min and 15min markets.

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Features

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Included Trading Bots

Designed for arbitrage, directional strategies, and ultra-short-term markets (including 5-minute and 15-minute rounds), this bot framework provides a robust…


View on GitHub


where I publish experiments and implementations around automated Polymarket trading.

The goal isn't to find one perfect indicator.

The goal is to build a system that continuously combines information, measures its edge, manages risk, and improves from real trading data.

You can check the this bot pnl with this account.

[https://polymarket.com/profile/0x2011550a8fd844aa22b78d0f039bb72befcb71fa]

If you are interested in my bot, Contact me.
https://t.me/BenjaminCup

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