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Building a Low-Latency Polymarket TWAP Final-Cycle Sniper Bot

#polymarket #bot #tutorial #python

Combining Chainlink TWAP, Coinbase Market Data, Order-Book Analysis, and Real-Time Execution

Polymarket's crypto Up/Down markets became a significantly more interesting engineering problem after the transition to TWAP-based resolution.

Building Polymarket Final Sniper Bot

A simple strategy that watches the final spot price is no longer enough.

The settlement mechanism is based on the specified Chainlink TWAP, while external exchanges such as Coinbase can provide faster information about short-term market movements.

This creates an interesting systems problem:

Can we monitor multiple price feeds in real time, understand the remaining TWAP dynamics, detect a high-confidence end-cycle condition, and execute an order before the market resolves?

That is the idea behind the Polymarket TWAP Final-Cycle Sniper.

This article explains the architecture and strategy concept behind the bot.

1. What Is a TWAP Final-Cycle Sniper?

The basic idea is simple:

Coinbase Price
      │
      ▼
Fast Market Signal
      │
      ├──────────────┐
      │              │
      ▼              ▼
Chainlink/TWAP   Polymarket CLOB
      │              │
      └──────┬───────┘
             ▼
       Signal Engine
             │
             ▼
      Final-Cycle Check
             │
             ▼
       Risk Management
             │
             ▼
       Order Execution
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The bot continuously monitors:

  • Coinbase price data
  • Chainlink-related market data
  • Polymarket market state
  • market strike/reference price
  • UP/DOWN token prices
  • order-book liquidity
  • time remaining
  • price movement and direction

When the market enters its final cycle and the available information indicates a highly asymmetric outcome, the bot can attempt to buy the expected winning token at a predefined target price.

One target used by this strategy concept is around $0.99.

The important point is that the strategy is not simply:

BTC > Strike → Buy UP
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Instead, it combines price feeds + TWAP context + time + order-book conditions + execution constraints.

2. Why TWAP Changes the Trading Problem

With TWAP-based resolution, the final settlement is tied to a time-weighted Chainlink reference rather than simply taking the last exchange price.

That means there are several different prices to think about:

Coinbase
   │
   │
   ▼
External spot market

Chainlink
   │
   │
   ▼
Settlement data

TWAP
   │
   │
   ▼
Resolution reference

Polymarket
   │
   │
   ▼
Tradable UP/DOWN tokens
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These values are related, but they are not interchangeable.

This distinction is fundamental to the bot's architecture.

3. Why Monitor Coinbase?

Coinbase is used as an additional external market-data source.

The reason is latency.

A fast exchange feed can react to market movement before the corresponding Chainlink-derived information fully reflects that movement.

For example:

T0

Coinbase       110,000
Chainlink      109,990
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Then BTC moves rapidly:

T1

Coinbase       110,080
Chainlink      110,025
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Coinbase has provided an early indication of the move.

The bot can use that information as a leading signal, while still treating Chainlink/TWAP as the relevant settlement context.

So the architecture is:

Coinbase
   ↓
Fast signal

Chainlink
   ↓
Settlement context

Polymarket
   ↓
Execution environment
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This separation is important.

Coinbase does not determine the Polymarket outcome.

4. The End-Cycle Opportunity

The strategy focuses on the final portion of the market lifecycle.

Early in the market:

UP       $0.51
DOWN     $0.49
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There is significant uncertainty.

Later:

UP       $0.85
DOWN     $0.15
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Near expiration:

UP       $0.97+
DOWN     $0.03-
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The closer the market gets to resolution, the more information becomes available about the eventual outcome.

The bot therefore becomes increasingly interested in:

time_remaining
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For example:

time_remaining = market_end_time - current_time
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Time is not just another variable.

It changes the meaning of every other variable.

A $100 price difference from the strike with three minutes remaining is a different situation from the same difference with three seconds remaining.

5. Core Data Model

A useful internal state object can look like:

market_state = {
    "market_id": "...",
    "strike": 110000.0,
    "coinbase_price": 110085.0,
    "chainlink_price": 110050.0,
    "twap": 110040.0,
    "time_remaining": 4.2,
    "up_bid": 0.97,
    "up_ask": 0.98,
    "down_bid": 0.02,
    "down_ask": 0.03,
}
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The strategy engine should operate on a synchronized snapshot of this state.

That makes the system easier to debug and backtest.

6. Real-Time Data Pipeline

A latency-sensitive architecture should be event-driven rather than relying entirely on repeated REST polling.

Conceptually:

             ┌─────────────────┐
             │ Coinbase WS     │
             └────────┬────────┘
                      │
                      ▼
              ┌───────────────┐
              │ Price State   │
              └───────┬───────┘
                      │
                      │
┌─────────────────┐   │   ┌─────────────────┐
│ Chainlink Data  │───┼──►│ Signal Engine   │
└─────────────────┘   │   └────────┬────────┘
                      │            │
┌─────────────────┐   │            ▼
│ Polymarket CLOB │───┘       Risk Engine
└─────────────────┘                 │
                                    ▼
                              Order Executor
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Each feed updates an internal state store.

The strategy engine then evaluates the latest synchronized information.

7. Coinbase + Chainlink Confirmation

A useful signal is not necessarily based on one price.

Instead, the bot can look for agreement.

For example:

Strike:       110,000

Coinbase:     110,115
Chainlink:    110,070

Time left:    5 seconds
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The system sees:

Coinbase > strike
Chainlink > strike
Time remaining is very small
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That is substantially different from:

Coinbase:     110,115
Chainlink:    109,920

Time left:    5 seconds
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The second situation contains greater disagreement between the data sources.

A professional implementation should therefore model feed agreement/disagreement rather than treating every tick as a signal.

8. Signal Persistence

One of the simplest ways to reduce noise is to require the signal to persist.

Instead of:

one tick → trade
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use something closer to:

multiple observations
        ↓
consistent direction
        ↓
signal confirmation
        ↓
trade evaluation
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For example:

Time       Coinbase     Chainlink

-10s       110,070      110,020
-9s        110,080      110,030
-8s        110,085      110,035
-7s        110,095      110,040
-6s        110,110      110,050
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This provides much more context than a single observation.

9. Order-Book Analysis

Prediction is only half of the problem.

Execution is the other half.

Suppose the bot decides that UP is the expected winner.

The order book might look like:

UP

$0.97    20 shares
$0.98    35 shares
$0.99    100 shares
$1.00    500 shares
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If the strategy wants 500 shares at $0.99, there may simply not be enough available liquidity.

Therefore the bot must monitor:

  • best bid
  • best ask
  • spread
  • depth
  • available quantity
  • recent trades
  • estimated slippage

This is why a backtest using only candle prices can be misleading for a strategy like this.

10. The $0.99 Entry Concept

The strategy can define an entry target around:

$0.99
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If the selected token resolves to $1.00, the theoretical gross difference is:

$1.00 - $0.99 = $0.01
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For example:

1,000 shares   → $10 gross difference
10,000 shares  → $100 gross difference
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But this is not guaranteed profit.

Real execution must account for:

Entry price
+ fees
+ slippage
+ failed fills
+ latency
+ losing signals
+ liquidity constraints
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The strategy therefore needs an execution model rather than assuming every order will fill at the target price.

11. Fill Rate Is Not the Same as Signal Accuracy

This is an important distinction when evaluating the bot.

Imagine:

100 signals

92 correct outcomes
70 successful executions
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Then:

Signal accuracy = 92%

Execution success = 70%
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These metrics describe completely different parts of the system.

A useful dashboard should therefore track:

Signals
Correct signals
Submitted orders
Filled orders
Partially filled orders
Cancelled orders
Average entry
Average slippage
Realized PnL
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This gives a much more realistic picture of strategy performance.

12. Stale Data Protection

Real-time trading systems need protection against stale feeds.

Consider:

Coinbase:       100 ms old
Polymarket:     50 ms old
Chainlink:      8 seconds old
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Using all three values as if they were equally current could create a bad signal.

A simple safety check might be:

if now - chainlink_timestamp > MAX_DATA_AGE:
    reject_signal()
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Similar checks can be applied to:

Coinbase
Chainlink
Polymarket
Order book
Market metadata
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The bot should know not only what the latest value is, but also how old that value is.

13. Latency Budget

For a final-cycle strategy, the complete execution path matters.

Market movement
      ↓
Exchange update
      ↓
WebSocket
      ↓
Local processing
      ↓
Signal calculation
      ↓
Risk checks
      ↓
Order creation
      ↓
Network
      ↓
Polymarket CLOB
      ↓
Matching
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Every stage consumes time.

This makes infrastructure part of the strategy.

Important engineering considerations include:

  • persistent WebSocket connections
  • low-latency VPS/networking
  • synchronized system clocks
  • efficient state updates
  • connection recovery
  • duplicate-message handling
  • order acknowledgement tracking
  • stale-data detection

14. Strategy Engine

A simplified strategy engine could look like:

def evaluate_market(state):

    if state.time_remaining > FINAL_CYCLE_THRESHOLD:
        return None

    if state.data_is_stale:
        return None

    direction = determine_direction(
        state.coinbase_price,
        state.chainlink_price,
        state.strike
    )

    if not direction:
        return None

    if not feeds_confirm(direction):
        return None

    if not sufficient_liquidity(direction):
        return None

    if not acceptable_execution_price(direction):
        return None

    return direction
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Then execution becomes a separate responsibility:

direction = evaluate_market(state)

if direction:
    execute_order(direction)
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Keeping signal generation and execution separate makes the system easier to test.

15. Risk Engine

A production bot should have a dedicated risk layer.

For example:

MAX_ORDER_SIZE
MAX_POSITION_SIZE
MAX_SLIPPAGE
MAX_DAILY_LOSS
MAX_DATA_AGE
MIN_LIQUIDITY
MIN_CONFIDENCE
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The architecture should be:

Signal
  ↓
Risk Engine
  ↓
Approved?
  │
  ├── No  → Ignore
  │
  └── Yes → Execute
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This is particularly important because a signal can be correct while the execution conditions are still unacceptable.

16. Handling Failed Execution

A serious bot must assume that orders can fail.

Possible cases include:

Order rejected
Order partially filled
Order not filled
Market moved
Connection lost
Data became stale
Market resolved
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The execution engine therefore needs explicit state transitions.

For example:

SIGNAL_DETECTED
      ↓
ORDER_SUBMITTED
      ↓
 ┌────┴─────────┐
 ▼              ▼
FILLED       NOT_FILLED
 ▼              │
POSITION        ▼
            CANCEL/RETRY
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Retry logic should be carefully bounded.

Blindly retrying an order during the final seconds can turn a missed opportunity into an unintended trade.

17. Backtesting

The strategy should be evaluated using historical market data.

A useful dataset might contain:

market_id
timestamp
strike
coinbase_price
chainlink_price
TWAP
UP bid
UP ask
DOWN bid
DOWN ask
order-book depth
time_remaining
signal
entry_price
fill_status
resolution
PnL
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The critical rule is:

Only use information that would have been available at the exact moment the strategy made its decision.

Otherwise the backtest can introduce look-ahead bias.

18. Paper Trading

After historical testing, paper trading provides the next validation layer.

The architecture becomes:

Real Market Data
       ↓
Real Signal Engine
       ↓
Virtual Order
       ↓
Simulated Execution
       ↓
Market Resolution
       ↓
Performance Report
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Paper trading can reveal problems that pure backtesting misses:

  • real-time latency
  • WebSocket disconnects
  • stale data
  • order-book changes
  • missed fills
  • signal timing
  • unexpected market behavior

19. Metrics

For this type of system, I would track at least these metrics.

Signal Accuracy 

Correct signals / total signals
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Fill Rate 

Filled orders / submitted orders
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Average Entry Price 

Total execution cost / shares filled
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Slippage 

Actual execution price
-
Expected execution price
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Execution Latency 

Order submission timestamp
-
Signal timestamp
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Net PnL

After applicable trading costs and execution effects.

Maximum Drawdown

The largest peak-to-trough decline in the strategy's equity curve.

These metrics should be analyzed independently.

20. Complete Strategy Flow

The complete Final Sniper pipeline can be summarized as:

                Market Discovery
                       │
                       ▼
                Market Metadata
                       │
                       ▼
              ┌─────────────────┐
              │ Real-Time Feeds │
              └────────┬────────┘
                       │
          ┌────────────┼────────────┐
          ▼            ▼            ▼
      Coinbase      Chainlink    Polymarket
          │            │            │
          └────────────┼────────────┘
                       ▼
                 State Engine
                       │
                       ▼
               Time Remaining
                       │
                       ▼
                 Signal Engine
                       │
                       ▼
              Final-Cycle Filter
                       │
                       ▼
                 Risk Engine
                       │
                       ▼
              Order-Book Check
                       │
                       ▼
                Order Executor
                       │
                       ▼
                 Fill Tracking
                       │
                       ▼
                  Resolution
                       │
                       ▼
                Performance Data
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This is the architecture that turns a simple trading idea into a real trading system.

21. Repository

The implementation and related Polymarket trading-bot research are available here:

GitHub:

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

polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket trading bot polymarket bot polymarket twap bot polymarket arbitrage bot polymarket bot

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.

Polymarket-benjamincup-bot-dashboard

This repository is primarily intended for educational and research purposes. It includes strategy concepts, implementation approaches, and selected performance screenshots to help developers understand how different automated trading strategies can be designed and tested.

The repository does not provide a complete production-ready trading bot source code. Instead, it provides strategy descriptions and research materials that you can use as a foundation for developing your own system.

If you are interested in building a Polymarket Trading Bot, you can follow my tutorials and use the concepts in this repository to develop your own implementation.

For users who prefer a ready-to-deploy solution or require custom strategy development, commercial…


View on GitHub


The repository is intended as an educational and development reference for experimenting with Polymarket automation, market data, strategy logic, and execution infrastructure.

22. What Makes This Strategy Interesting?

The interesting part is not simply:

"Buy the winning token for $0.99."

The engineering challenge is determining:

When is the outcome sufficiently asymmetric, when is the underlying data trustworthy, and can the order actually be executed at the required price before resolution?

That requires combining several systems:

Market Data
+
TWAP Understanding
+
Real-Time Streaming
+
Signal Processing
+
Order-Book Analysis
+
Risk Management
+
Low-Latency Execution
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This is what makes the Final-Cycle Sniper an interesting project from a software-engineering perspective.

Conclusion

The TWAP transition changes how Polymarket crypto markets should be analyzed.

The settlement reference is important, but it is only one part of the trading system.

A practical automated strategy can combine:

  • Chainlink/TWAP context for understanding the settlement mechanism
  • Coinbase market data for faster external price information
  • Polymarket CLOB data for actual execution conditions
  • time remaining for end-cycle detection
  • order-book analysis for liquidity and slippage
  • risk controls for execution safety
  • low-latency infrastructure for time-sensitive orders

The result is a system designed around one specific question:

Can a trader identify a late-cycle asymmetric opportunity early enough to execute it under real market conditions?

That is the problem the Polymarket TWAP Final-Cycle Sniper is designed to investigate.

Disclaimer

This project is for educational and research purposes. Trading outcomes, fill rates, execution prices, and profitability are not guaranteed. Real-world performance depends on market conditions, liquidity, latency, competition, fees, and the behavior of the underlying settlement mechanism. Always perform independent testing and risk assessment before using real capital.

Contact

GitHub:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2

Telegram:
https://t.me/BenjaminCup

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