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Building Polymarket TWAP trading Bot : Winning Token niper

#polymarket #twap #bot #tutorial

Short-duration prediction markets create a very different trading environment from traditional markets.

A Polymarket Trading Bot operating on 5-minute and 15-minute crypto markets can sometimes find opportunities near resolution when the underlying asset has moved far enough away from the market's reference price that one outcome becomes highly likely to win.

The challenge is determining whether the corresponding prediction-market token is still cheap enough to buy.

This tutorial explains how to design a Polymarket TWAP Winning Token Sniper using Python.

We will cover:

  • How TWAP works
  • Why TWAP matters for short-duration markets
  • Spot price vs. reference price
  • Detecting high-probability outcomes
  • Calculating expected edge
  • Reading Chainlink TWAP data
  • Building a Python signal engine
  • Order-book validation
  • Risk management
  • Backtesting
  • Production architecture
  • Common failure modes

The goal isn't to blindly buy a token because it looks like the winner.

The goal is to identify situations where:

Estimated probability of winning > executable token price

Polymarket TWAP Trading bot

Polymarket TWAP Trading bot

Polymarket TWAP Trading bot

What Is a Winning Token Sniper?

Consider a simplified BTC market.

BTC 5-Minute Market

Reference Price: $100,000

Current BTC Price: $100,250

Time Remaining: 20 seconds
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The market has two outcomes:

UP
DOWN
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Because BTC is significantly above the reference price, the UP outcome may have a much higher probability of winning.

Suppose the order book shows:

UP token:   $0.94
DOWN token: $0.06
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The sniper bot asks:

Is the probability of UP winning sufficiently higher than $0.94 to justify buying it?

If the model estimates:

UP probability = 97%

UP token price = $0.94
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then the theoretical gross edge is:

0.97 - 0.94 = 0.03
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or 3 percentage points before execution costs.

That is the basic idea behind the strategy.

Understanding TWAP

TWAP means Time-Weighted Average Price.

Instead of using one instantaneous market price, a TWAP represents the asset price across a lookback window.

Polymarket's current documentation covers Chainlink-computed 30-second and 60-second TWAPs and exposes them through Chainlink Data Streams or Polymarket RTDS.

This is important because a short-duration market should not be evaluated using only the latest BTC tick.

Consider:

Reference = $100,000

BTC:
$100,000
   ↓
$100,300
   ↓
$100,050
   ↓
$99,980
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The latest price may temporarily suggest UP.

But the settlement-related TWAP can tell a different story.

A better trading system therefore looks at:

Spot Price
+
Reference Price
+
TWAP
+
Time Remaining
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rather than spot price alone.

Why a TWAP Sniper Can Work

The basic market structure looks like:

Market Opens
      ↓
Reference Price
      ↓
Crypto Price Moves
      ↓
One Outcome Becomes More Likely
      ↓
TWAP Window
      ↓
Settlement
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The sniper operates near the end of this process.

It searches for situations where the market has become highly asymmetric.

For example:

Reference:     $100,000
Spot:          $100,250
TWAP:          $100,180
Time left:     20 seconds
UP price:      $0.94
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Several signals agree:

Spot > Reference
TWAP > Reference
Large enough distance
Little time remaining
UP token still below estimated probability
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This is much stronger than:

if spot > reference:
    buy_up()
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Strategy Overview

The complete strategy can be represented as:

                 BTC / ETH / SOL
                       │
                       ▼
              Spot Price Feed
                       │
                       ▼
              Reference Price
                       │
                       ▼
                 TWAP Feed
                       │
                       ▼
              Signal Engine
                       │
             ┌─────────┴─────────┐
             ▼                   ▼
       Probability          Token Price
             │                   │
             └─────────┬─────────┘
                       ▼
                 Expected Edge
                       │
                       ▼
                 Risk Engine
                       │
                       ▼
              Order Book Check
                       │
                       ▼
                 Order Execution
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Each component should have a separate responsibility.

Step 1: Discover the Active Market

The first component searches for active short-duration markets.

A simplified interface could look like:

def get_active_markets():
    """
    Return currently active short-duration markets.
    """
    return markets
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For every market, collect:

market_id
asset
market_duration
reference_price
expiration_time
UP token
DOWN token
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Then filter:

def eligible_market(market):
    return (
        market.duration in [5, 15]
        and market.is_active
    )
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This prevents the strategy engine from processing irrelevant markets.

Step 2: Track Time Remaining

Time is one of the most important variables in the strategy.

Calculate:

time_remaining = (
    market.expiration_timestamp
    - current_timestamp
)
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Then define a trading window:

MAX_TIME_REMAINING = 60
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For example:

if time_remaining > MAX_TIME_REMAINING:
    return None
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The idea is to focus on markets close enough to resolution for the current price displacement and TWAP to be meaningful.

The exact threshold should be determined through backtesting rather than assumed to be optimal.

Step 3: Calculate Spot Distance

Now compare the underlying price with the reference price.

distance = abs(
    spot_price - reference_price
)
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A normalized version is better:

distance_pct = (
    abs(spot_price - reference_price)
    / reference_price
)
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For example:

Reference = $100,000
Spot      = $100,200
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Then:

Distance = $200

Distance % = 0.20%
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The bot can require a minimum displacement:

MIN_DISTANCE = 0.0015

if distance_pct < MIN_DISTANCE:
    return None
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Again, this value should be optimized using historical data.

Step 4: Determine the Direction

The basic directional signal is straightforward:

if spot_price > reference_price:
    side = "UP"

elif spot_price < reference_price:
    side = "DOWN"

else:
    return None
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But don't stop here.

We want TWAP confirmation.

Step 5: Add TWAP Confirmation

Suppose:

Reference = $100,000

Spot = $100,220
TWAP = $100,180
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Both are above the reference.

That is stronger than:

Spot = $100,220
TWAP = $99,990
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because the second situation shows disagreement between the latest price and the TWAP.

A simple confirmation function:

def twap_confirms(
    spot,
    twap,
    reference
):
    if spot > reference and twap > reference:
        return "UP"

    if spot < reference and twap < reference:
        return "DOWN"

    return None
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Then:

side = twap_confirms(
    spot,
    twap,
    reference
)

if side is None:
    return None
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This removes many weak signals.

Step 6: Get Chainlink TWAP Data

Polymarket's official documentation provides Chainlink TWAP data through two approaches:

  1. Chainlink Data Streams
  2. Polymarket RTDS

The current documentation supports 30-second and 60-second TWAP windows. It also provides Python examples using AsyncPublicClient and CryptoPricesChainlinkTwapSpec.

The Python package can be installed with:

python -m pip install --upgrade polymarket-client
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The official documentation currently specifies Python 3.11+ for this RTDS Python integration.

A basic subscription looks like:

import asyncio

from polymarket import AsyncPublicClient
from polymarket.streams import CryptoPricesChainlinkTwapSpec


async def main():

    async with AsyncPublicClient() as client:

        async with await client.subscribe(
            CryptoPricesChainlinkTwapSpec(
                window_seconds=30,
                symbols=["btc/usd"],
            )
        ) as stream:

            async for event in stream:

                print(
                    event.payload.symbol,
                    event.payload.value,
                    event.payload.window_seconds,
                    event.payload.timestamp,
                )


asyncio.run(main())
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Polymarket documents window_seconds values of 30 and 60. The Python payload provides the symbol, TWAP value, window, and observation timestamp.

For production systems, treat the observation timestamp as a freshness signal.

Step 7: Store the Latest TWAP

The trading engine should maintain the latest value.

For example:

latest_twap = {
    "btc/usd": {
        "value": None,
        "timestamp": None,
        "window": 30,
    }
}
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When an update arrives:

def update_twap(event):

    symbol = event.payload.symbol

    latest_twap[symbol] = {
        "value": event.payload.value,
        "timestamp": event.payload.timestamp,
        "window": event.payload.window_seconds,
    }
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Then the strategy can read:

twap = latest_twap["btc/usd"]["value"]
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Step 8: Check TWAP Freshness

Never trade on stale data.

For example:

MAX_TWAP_AGE = 10
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Then:

def twap_is_fresh(timestamp):

    age = current_time_ms() - timestamp

    return age <= MAX_TWAP_AGE * 1000
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If the data is stale:

if not twap_is_fresh(twap_timestamp):
    return None
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The official Polymarket documentation notes that RTDS subscriptions begin with the next update and do not provide historical replay after a disconnect, so a production bot should explicitly handle stale data and reconnection.

Step 9: Estimate the Probability

Now we need to estimate how likely the selected outcome is to win.

A simple first version can be rule-based.

def estimate_probability(
    spot,
    twap,
    reference,
    time_remaining
):

    spot_distance = abs(
        spot - reference
    ) / reference

    twap_distance = abs(
        twap - reference
    ) / reference

    if (
        spot > reference
        and twap > reference
        and spot_distance > 0.002
        and twap_distance > 0.001
        and time_remaining < 30
    ):
        return 0.97

    if (
        spot < reference
        and twap < reference
        and spot_distance > 0.002
        and twap_distance > 0.001
        and time_remaining < 30
    ):
        return 0.97

    return 0.50
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This isn't a production probability model.

It is a starting point.

A real system should estimate probability from historical data.

Step 10: Calculate Expected Edge

Suppose:

Estimated probability = 0.97
Token price           = 0.94
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Then:

expected_edge = (
    estimated_probability
    - token_price
)
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Result:

0.03
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The strategy can require:

MIN_EDGE = 0.02
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Then:

if expected_edge < MIN_EDGE:
    return None
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This is one of the most important parts of the strategy.

A token isn't attractive simply because it is likely to win.

It is attractive when:

Probability > effective acquisition price

Step 11: Use the Executable Price

Don't use the last traded price.

Don't blindly use the midpoint.

Don't assume the best ask is the price for your entire order.

Instead, inspect the order book.

Example:

UP Order Book

$0.94 → 100 shares
$0.95 → 200 shares
$0.96 → 500 shares
$0.97 → 1,000 shares
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If you want to buy 700 shares, your actual average execution price will be higher than $0.94.

Therefore:

execution_price = calculate_vwap(
    asks,
    quantity
)
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Then calculate:

effective_edge = (
    estimated_probability
    - execution_price
)
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This is much closer to the real trading edge.

Step 12: Slippage Protection

Suppose:

Estimated probability = 0.97
Expected price         = 0.94
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Everything looks good.

But the order book changes:

Actual executable price = 0.975
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Now the trade no longer has meaningful edge.

So define:

MAX_ENTRY_PRICE = 0.97
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Then:

if execution_price > MAX_ENTRY_PRICE:
    return None
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This prevents the bot from chasing the market.

Step 13: Build the Signal Engine

Now we can combine the components.

def generate_signal(
    market,
    spot,
    twap,
    token_price
):

    reference = market.reference_price
    time_remaining = market.time_remaining

    if time_remaining > MAX_TIME_REMAINING:
        return None

    distance_pct = (
        abs(spot - reference)
        / reference
    )

    if distance_pct < MIN_DISTANCE:
        return None

    side = twap_confirms(
        spot,
        twap,
        reference
    )

    if side is None:
        return None

    probability = estimate_probability(
        spot=spot,
        twap=twap,
        reference=reference,
        time_remaining=time_remaining
    )

    edge = (
        probability
        - token_price
    )

    if edge < MIN_EDGE:
        return None

    return {
        "side": side,
        "probability": probability,
        "token_price": token_price,
        "edge": edge,
    }
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This function should only generate a trading signal.

It should not place an order.

Step 14: Add a Risk Engine

Before execution, validate the trade.

def risk_check(
    signal,
    market,
    position
):

    if signal["token_price"] > MAX_ENTRY_PRICE:
        return False

    if signal["edge"] < MIN_EDGE:
        return False

    if position >= MAX_POSITION:
        return False

    if market.liquidity < MIN_LIQUIDITY:
        return False

    return True
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This gives the strategy a separate safety layer.

Step 15: Final Validation Before Execution

Short-duration markets are extremely sensitive to latency.

The signal might be valid when generated but invalid 100 milliseconds later.

Therefore:

signal = generate_signal(...)

if signal is None:
    return

latest_state = refresh_market_state()

if not still_valid(
    signal,
    latest_state
):
    return

execute_order(signal)
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The final validation should check:

Spot price
TWAP
Token price
Order book
Time remaining
Market status
Position size
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Only then should the bot submit the order.

Step 16: The Complete Sniper Loop

The complete strategy becomes:

async def sniper_loop():

    while True:

        markets = get_active_markets()

        for market in markets:

            if not eligible_market(market):
                continue

            state = get_market_state(market)

            if not twap_is_fresh(
                state.twap_timestamp
            ):
                continue

            signal = generate_signal(
                market=market,
                spot=state.spot,
                twap=state.twap,
                token_price=state.token_price,
            )

            if signal is None:
                continue

            if not risk_check(
                signal,
                market,
                state.position
            ):
                continue

            latest = get_market_state(market)

            if not still_valid(
                signal,
                latest
            ):
                continue

            execute_order(
                market,
                signal
            )
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This is the basic architecture of the sniper.

Step 17: 5-Minute vs 15-Minute Markets

Don't assume the same parameters work for every market duration.

A configuration could look like:

CONFIG = {

    "5m": {
        "twap_window": 30,
        "max_time_remaining": 45,
    },

    "15m": {
        "twap_window": 60,
        "max_time_remaining": 90,
    },
}
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The exact values are strategy parameters, not guaranteed optimal settings.

They should be tested independently.

A 5-minute strategy may need:

Lower latency
Faster validation
Tighter execution
Smaller position sizes
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A 15-minute strategy may have:

More time for reversals
Different volatility characteristics
Different optimal entry windows
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The official Polymarket documentation currently supports 30-second and 60-second TWAP lookback windows.

Step 18: Example Trade

Let's walk through a hypothetical setup.

BTC 5M Market

Reference:       $100,000
Spot:            $100,240
30s TWAP:        $100,180

Time remaining:  20 seconds

UP best ask:     $0.94
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First:

spot_distance = (
    100240 - 100000
) / 100000
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Result:

0.24%
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Next:

Spot > Reference
TWAP > Reference
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So:

Direction = UP
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Suppose the probability model estimates:

P(UP wins) = 97%
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And the executable price is:

$0.94
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Then:

Expected edge = 0.97 - 0.94
              = 0.03
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The bot can now check:

Distance threshold       ✓
TWAP confirmation        ✓
Time window              ✓
Probability threshold    ✓
Expected edge            ✓
Liquidity                 ✓
Risk limit                ✓
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Only after all checks pass:

BUY UP
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Step 19: What Can Go Wrong?

The biggest mistake is treating this as a guaranteed strategy.

It isn't.

Price Reversal

BTC can move from:

$100,250
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to:

$99,950
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before settlement.

TWAP Divergence

Spot can remain above the reference while the TWAP remains closer to it.

Slippage

The token can move from:

$0.94
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to:

$0.97
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before the order fills.

Liquidity Disappears

The displayed price might not represent enough size.

Data Staleness

A stale TWAP can produce a false signal.

Latency

Your signal can become invalid before the order reaches the matching engine.

Bad Probability Calibration

A model that predicts 97% but actually wins only 92% of similar setups is systematically overconfident.

Step 20: Avoid the $0.99 Trap

One of the most important lessons in prediction-market trading is:

High probability does not automatically mean high expected value.

Consider:

Probability = 99%
Token price = $0.99
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The theoretical gross edge is only:

0.99 - 0.99 = 0
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If the token costs $0.991:

0.99 - 0.991 = -0.001
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The trade has negative theoretical edge before other costs.

Therefore, don't optimize for:

Highest win rate
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Optimize for:

Expected value after execution costs
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Step 21: Backtesting

Before deploying real capital, collect historical observations.

A useful dataset looks like:

timestamp
market_id
asset
duration
reference_price
spot_price
twap_price
spot_distance
twap_distance
time_remaining
token_price
executable_price
liquidity
estimated_probability
resolution
PnL
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Then test different thresholds.

For example:

Minimum spot distance:

0.05%
0.10%
0.15%
0.20%
0.25%
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And:

Minimum expected edge:

1%
2%
3%
4%
5%
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Measure:

Win rate
Average return
Expected value
Maximum drawdown
Trade frequency
Average slippage
Average execution price
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Step 22: Backtest the Order Book

A common backtesting mistake is assuming:

Signal price = execution price
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Suppose the historical signal occurred when:

UP ask = $0.94
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But the order book was:

$0.94 → 100
$0.95 → 200
$0.96 → 500
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A $500 order cannot necessarily execute entirely at $0.94.

Your backtest should simulate the actual order-book sweep.

This gives you:

Expected execution price
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rather than:

Best displayed price
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That distinction can completely change the profitability of a high-frequency strategy.

Step 23: Record Every Decision

Production trading systems need observability.

For example:

[12:30:01.220]

Market: BTC 5M

Reference: 100000
Spot:      100240
TWAP:      100180

Distance:  0.240%

Time left: 20s

UP price:  0.940
Probability: 0.970

Expected edge: 0.030

Liquidity: OK
Risk: OK

Decision: BUY UP
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And when skipping:

Decision: SKIP

Reason:
TWAP confirmation failed
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This makes it much easier to understand why the bot trades or doesn't trade.

Step 24: Production Architecture

A production version can be organized like this:

                Market Discovery
                       │
                       ▼
              ┌─────────────────┐
              │ Real-Time Data  │
              │                 │
              │ Spot            │
              │ TWAP            │
              │ Order Book      │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Signal Engine   │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Probability     │
              │ Model           │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Risk Engine     │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Execution       │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Position / PnL  │
              └────────┬────────┘
                       │
                       ▼
              ┌─────────────────┐
              │ Logging / Stats │
              └─────────────────┘
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Keep these components separate.

It makes the bot easier to test, debug, and extend.

Step 25: Project Structure

A Python project could look like:

polymarket-twap-sniper/
│
├── config.py
├── main.py
│
├── data/
│   ├── markets.py
│   ├── spot.py
│   ├── twap.py
│   └── orderbook.py
│
├── strategy/
│   ├── probability.py
│   ├── signal.py
│   └── twap_sniper.py
│
├── execution/
│   ├── orders.py
│   └── position.py
│
├── risk/
│   └── manager.py
│
└── analytics/
    ├── logger.py
    └── backtest.py
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This is much easier to maintain than putting the entire strategy into one Python file.

Step 26: Configuration

Don't hard-code strategy parameters throughout the code.

Use configuration:

MIN_SPOT_DISTANCE = 0.0015
MIN_TWAP_DISTANCE = 0.0010

MIN_PROBABILITY = 0.95
MIN_EDGE = 0.02

MAX_ENTRY_PRICE = 0.97
MAX_SLIPPAGE = 0.005

MAX_POSITION = 500
MIN_LIQUIDITY = 1000
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Then experiment with them through backtesting.

Step 27: Using an Existing Polymarket Python Bot

If you don't want to build the entire infrastructure from zero, you can use an existing Polymarket Python trading-bot codebase as the foundation.

My repository, Polymarket Trading Bot Python V2, is an open-source Python collection focused on automated Polymarket trading, including short-duration crypto markets and multiple trading strategies.

You can add the TWAP sniper as another strategy module:

strategies/
│
├── arbitrage.py
├── momentum.py
├── market_making.py
├── copy_trading.py
└── twap_sniper.py
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A clean strategy interface could be:

class TWAPSniper:

    def scan(self):
        pass

    def estimate_probability(self):
        pass

    def calculate_edge(self):
        pass

    def validate_risk(self):
        pass

    def execute(self):
        pass
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This makes the TWAP strategy independent from the rest of the trading infrastructure.

Step 28: A More Advanced Probability Model

Once the rule-based version works, replace fixed probabilities with a statistical model.

For example:

Features

spot_distance
twap_distance
time_remaining
recent_volatility
price_velocity
orderbook_imbalance
market_price
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Then estimate:

P(UP wins | features)
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A simple logistic model could be:

probability = model.predict_proba(
    features
)[0, 1]
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The model should be trained on historical market observations.

The important part is calibration.

If the model says:

95%
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then markets assigned approximately 95% probability should actually win around 95% of the time over a sufficiently large sample.

Step 29: Volatility-Adjusted Distance

A fixed distance isn't always ideal.

For example:

BTC moves $200
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could be a huge move during low volatility but insignificant during high volatility.

Instead calculate:

normalized_distance = (
    abs(spot - reference)
    / recent_volatility
)
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This lets the strategy adapt to changing market conditions.

Step 30: Add Order-Book Imbalance

The token's order book can provide another confirmation signal.

For example:

imbalance = (
    bid_volume - ask_volume
) / (
    bid_volume + ask_volume
)
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A positive value means relatively more bid liquidity.

You could include it in the probability model:

Probability =
    f(
        spot distance,
        TWAP distance,
        volatility,
        momentum,
        order-book imbalance,
        time remaining
    )
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This transforms the strategy from a simple threshold system into a microstructure model.

Step 31: Handling Disconnects

Real-time data connections fail.

Your bot needs to handle:

WebSocket disconnect
        ↓
Reconnect
        ↓
Resubscribe
        ↓
Wait for fresh data
        ↓
Resume trading
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Don't immediately trade after reconnecting if the required TWAP state isn't fresh.

The official documentation specifically notes that RTDS doesn't provide historical replay after a disconnect.

Therefore:

if not twap_available:
    disable_trading = True
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Then re-enable only after receiving a fresh valid update.

Step 32: Risk Management

Never let the signal engine determine position size by itself.

Use a separate risk layer.

For example:

MAX_POSITION_PER_MARKET = 500
MAX_DAILY_LOSS = 1000
MAX_OPEN_MARKETS = 5
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Before every order:

if daily_loss >= MAX_DAILY_LOSS:
    disable_trading()
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Also consider:

Maximum position per market
Maximum simultaneous positions
Maximum order size
Maximum slippage
Maximum price
Maximum daily loss
Maximum number of trades
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Step 33: The Final Algorithm

Putting everything together:

def twap_sniper(market):

    state = get_market_state(market)

    if not state.active:
        return

    if state.time_remaining > MAX_TIME_REMAINING:
        return

    if not state.twap_fresh:
        return

    spot = state.spot
    twap = state.twap
    reference = state.reference

    spot_distance = (
        abs(spot - reference)
        / reference
    )

    twap_distance = (
        abs(twap - reference)
        / reference
    )

    if spot_distance < MIN_SPOT_DISTANCE:
        return

    if twap_distance < MIN_TWAP_DISTANCE:
        return

    if spot > reference and twap > reference:
        side = "UP"

    elif spot < reference and twap < reference:
        side = "DOWN"

    else:
        return

    execution_price = get_executable_price(
        side,
        state.orderbook
    )

    probability = estimate_probability(
        spot=spot,
        twap=twap,
        reference=reference,
        time_remaining=state.time_remaining
    )

    edge = (
        probability
        - execution_price
    )

    if edge < MIN_EDGE:
        return

    if execution_price > MAX_ENTRY_PRICE:
        return

    if not risk_check(
        market,
        side,
        execution_price
    ):
        return

    execute_order(
        market=market,
        side=side,
        price=execution_price
    )
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This is the core of the TWAP sniper.

The Most Important Concept

The strategy can be summarized in one equation:

Expected Edge =
Estimated Probability of Winning
-
Effective Acquisition Price
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Everything else exists to make those two numbers more accurate.

The data layer gives you:

Spot
TWAP
Reference
Order Book
Time
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The strategy layer turns those values into:

Probability
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The execution layer determines:

Actual acquisition price
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And the risk layer decides:

Whether the trade is allowed
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Common Mistakes

Mistake 1: Using spot price only

A single price tick can be misleading.

Use TWAP confirmation.

Mistake 2: Assuming the last price is executable

Use the order book.

Mistake 3: Optimizing only for win rate

A 99% win rate doesn't automatically mean positive expected value.

Mistake 4: Ignoring latency

A short-duration market can change before your order arrives.

Mistake 5: Trading stale data

Always validate timestamps.

Mistake 6: Using one threshold for every market

5-minute and 15-minute markets should be tested separately.

Mistake 7: Overfitting

A threshold that works perfectly on historical data may fail live.

Always use out-of-sample testing.

Production Checklist

Before deploying the strategy:

[ ] Market discovery
[ ] Correct reference price
[ ] Live spot feed
[ ] Live TWAP feed
[ ] TWAP freshness validation
[ ] Correct TWAP window
[ ] Accurate time remaining
[ ] Order-book data
[ ] Probability model
[ ] Edge calculation
[ ] Slippage protection
[ ] Position limits
[ ] Daily loss limit
[ ] Duplicate-order protection
[ ] Partial-fill handling
[ ] WebSocket reconnect
[ ] Market-resolution detection
[ ] Structured logging
[ ] Backtesting
[ ] Paper trading
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Don't skip the boring infrastructure.

In short-duration automated trading, infrastructure can be just as important as the strategy.

Conclusion

Building a Polymarket TWAP Trading Bot is not simply about finding the side that is likely to win.

The real problem is identifying when the market gives you a sufficiently attractive price for that probability.

The strategy can be summarized as:

Find active short-duration market
             ↓
Read reference price
             ↓
Read live spot price
             ↓
Calculate spot distance
             ↓
Read Chainlink TWAP
             ↓
Confirm direction
             ↓
Estimate winning probability
             ↓
Read executable token price
             ↓
Calculate expected edge
             ↓
Apply risk controls
             ↓
Execute
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The strongest version of this strategy isn't:

"BTC is above the strike, so buy UP."

It is:

"The spot price and TWAP strongly support UP, the market is close to resolution, the estimated probability is high, and the executable UP price still provides sufficient expected edge after trading costs."

That difference is what turns a simple prediction into a systematic trading strategy.

For the official TWAP implementation details, including Chainlink Data Streams, Polymarket RTDS, 30-second and 60-second windows, and the Python subscription interface, see the Polymarket Chainlink TWAP documentation.

For a Python foundation for automated Polymarket trading and short-duration crypto strategies, see the Polymarket Trading Bot Python V2 repository.

Disclaimer: This tutorial describes an automated trading strategy for educational purposes. It does not guarantee profitability. Prediction-market prices, crypto prices, liquidity, execution conditions, and settlement outcomes can change rapidly. Always test with historical data and paper trading before risking real capital.

🤝 Collaboration & Contact
If you’re interested in building trading bots, buy trading bots, collaborating, exploring strategy improvements, or discussing about this system, feel free to reach out.

I’m especially open to connecting with:

Quant traders
Engineers building trading infrastructure
Researchers in prediction markets
Investors interested in market inefficiencies

📌 GitHub Repository
This repo has some Polymarket several bots in this system.
You can explore the full implementation, strategy logic, and ongoing updates about 5 min crypto market here:

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

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Polymarket Trading Bot | Polymarket Arbitrage Bot

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

Polymarket benjamincup bot dashboard

Features

  • Explosive growth of Polymarket with surging trading volume and new short-term markets

  • Increasing dominance of automated bots and AI in 5-minute crypto prediction markets

  • Higher profitability potential through advanced arbitrage and market-making strategies

  • Stronger edge for Python-based bots with real-time orderbook intelligence and low-latency execution

  • Continuous evolution of sniper, ladder, stair, momentum, and copy trading strategies

  • Scalable daily profits as prediction markets move toward hundreds of billions in annual volume

  • Full future-proof architecture for new features, contracts, and high-frequency trading environments

Included Trading Bots

Designed for arbitrage, directional strategies, and ultra-short-term markets (including 5-minute rounds), this bot framework provides a robust foundation for building and scaling automated trading strategies on Polymarket .

Demo Video

Polymarket Benjamin trading Bot video

Documentation

Throughout this…

View on GitHub

💬 Get in Touch

If you have ideas, questions, or would like to collaborate or want these trading bots, don’t hesitate to reach out directly.
Feedback on your repo (based on your description & strategy)

Contact Info
Telegram
https://t.me/BenjaminCup

tags: polymarket,trading,bot,architecture,tutorial,TWAP

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