DEV Community

Benjamin-Cup
Benjamin-Cup

Posted on

Building a Probability-Driven Polymarket TWAP Trading Bot

#polymarket #trading #strategy #tutorial

Most Polymarket trading bots generate a simple signal:

TWAP > Strike → Buy UP
TWAP < Strike → Buy DOWN
Enter fullscreen mode Exit fullscreen mode

But what if instead of generating a signal, we could estimate the actual probability of each outcome?

That is the idea behind a probability-driven Polymarket Trading bot.

Instead of asking:

"Should I buy UP?"

we ask:

"What is the probability that UP wins, and is the market price too cheap?"

The architecture becomes:

TWAP
  +
Spot Price
  +
Momentum
  +
Volatility
  +
Time Remaining
        ↓
Probability Model
        ↓
P(UP) = 73%
        ↓
Compare With Market
        ↓
Edge = Probability - Price
        ↓
Trade
Enter fullscreen mode Exit fullscreen mode

1. From Signals to Probability

Suppose our model calculates:

P(UP) = 0.73
Enter fullscreen mode Exit fullscreen mode

This means the model estimates a 73% probability that UP will win.

But Polymarket currently offers the UP token at:

$0.61
Enter fullscreen mode Exit fullscreen mode

We can calculate:

edge = model_probability - market_price
Enter fullscreen mode Exit fullscreen mode

Therefore:

edge = 0.73 - 0.61
Enter fullscreen mode Exit fullscreen mode

Result:

Edge = +0.12
Enter fullscreen mode Exit fullscreen mode

The model believes the outcome is worth approximately $0.73, while the market is offering it for $0.61.

That difference is the potential trading opportunity.

2. Why This Is Better Than a Simple Indicator

Consider a traditional strategy:

if twap > strike:
    buy_up()
Enter fullscreen mode Exit fullscreen mode

It treats every bullish situation almost identically.

But these two situations are very different:

TWAP slightly above strike
P(UP) ≈ 52%
Enter fullscreen mode Exit fullscreen mode

and:

TWAP significantly above strike
P(UP) ≈ 78%
Enter fullscreen mode Exit fullscreen mode

A probability model allows the bot to understand the strength of the situation.

More importantly, it can compare that probability with the actual market price.

For example:

Model P(UP) = 0.55
Market      = 0.54
Edge        = +0.01
Enter fullscreen mode Exit fullscreen mode

Probably not attractive.

But:

Model P(UP) = 0.73
Market      = 0.61
Edge        = +0.12
Enter fullscreen mode Exit fullscreen mode

is much more interesting.

3. What Goes Into the Probability Model?

We don't want to rely on TWAP alone.

The model can combine several features:

TWAP distance from strike
Spot/TWAP difference
Short-term momentum
Realized volatility
Time remaining
Order-book imbalance
Enter fullscreen mode Exit fullscreen mode

For example:

features = {
    "twap_distance": twap_distance,
    "spot_twap_gap": spot_twap_gap,
    "return_10s": return_10s,
    "return_30s": return_30s,
    "return_60s": return_60s,
    "volatility": volatility,
    "time_remaining": time_remaining,
    "obi": orderbook_imbalance,
}
Enter fullscreen mode Exit fullscreen mode

These features are passed into a probability model.

4. A Simple Probability Model

A good starting point is logistic regression.

It is simple, fast, and produces a probability between 0 and 1.

from sklearn.linear_model import LogisticRegression

model = LogisticRegression()

model.fit(X_train, y_train)
Enter fullscreen mode Exit fullscreen mode

Then we can calculate the current probability:

p_up = model.predict_proba(
    X_current
)[0][1]
Enter fullscreen mode Exit fullscreen mode

For example:

p_up = 0.73
Enter fullscreen mode Exit fullscreen mode

Then:

p_down = 1 - p_up
Enter fullscreen mode Exit fullscreen mode

So:

P(UP)   = 73%
P(DOWN) = 27%
Enter fullscreen mode Exit fullscreen mode

5. Training the Model

The training dataset should contain historical market states.

For example:

TWAP Distance Momentum Volatility Time Left Outcome
+0.0012 +0.0003 0.0011 240s UP
-0.0008 -0.0004 0.0013 180s DOWN
+0.0021 +0.0008 0.0015 60s UP

The target is simple:

UP   → 1
DOWN → 0
Enter fullscreen mode Exit fullscreen mode

The model learns how these features relate to the eventual outcome.

Important: Avoid Look-Ahead Bias

Only use information that was available at the time of prediction.

You cannot use future TWAP values, future prices, or the final market outcome as model inputs.

Otherwise, your backtest will be unrealistic.

6. Probability Is Not the Same as Profit

This is one of the most important concepts.

Suppose:

Model P(UP) = 75%
Market price = $0.74
Enter fullscreen mode Exit fullscreen mode

The model may be correct that UP is more likely.

But:

Edge = 0.75 - 0.74
     = 0.01
Enter fullscreen mode Exit fullscreen mode

After fees and slippage, that may not be profitable.

Therefore, the bot should not trade every positive edge.

Instead:

net_edge = (
    model_probability
    - execution_price
    - estimated_costs
)

if net_edge > MIN_EDGE:
    buy_up()
Enter fullscreen mode Exit fullscreen mode

For example:

Model probability = 0.75
Execution price   = 0.62
Estimated costs    = 0.02

Net edge = 0.11
Enter fullscreen mode Exit fullscreen mode

If the minimum required edge is 5%:

11% > 5%
Enter fullscreen mode Exit fullscreen mode

The bot can consider entering.

7. Use the Executable Price

One important implementation detail is that you should not blindly compare the model probability with the last traded price.

If you want to buy UP, you care about the price you can actually purchase at.

For example:

Model P(UP) = 0.73
Last price  = 0.61
Best ask    = 0.64
Enter fullscreen mode Exit fullscreen mode

The realistic calculation is closer to:

0.73 - 0.64 = 0.09
Enter fullscreen mode Exit fullscreen mode

not:

0.73 - 0.61 = 0.12
Enter fullscreen mode Exit fullscreen mode

This makes the strategy much more realistic.

8. Trading Logic

The core strategy can be surprisingly simple:

def trading_decision(p_up, up_price, down_price):

    p_down = 1 - p_up

    edge_up = p_up - up_price
    edge_down = p_down - down_price

    if edge_up > MIN_EDGE and edge_up > edge_down:
        return "BUY_UP"

    if edge_down > MIN_EDGE and edge_down > edge_up:
        return "BUY_DOWN"

    return "NO_TRADE"
Enter fullscreen mode Exit fullscreen mode

The bot has three possible decisions:

BUY_UP
BUY_DOWN
NO_TRADE
Enter fullscreen mode Exit fullscreen mode

That last one is extremely important.

A good trading bot should be comfortable doing nothing when the market is fairly priced.

9. Backtesting the Strategy

Before using real money, test the model on historical data.

For every historical timestamp:

1. Calculate features
2. Generate probability
3. Get historical market price
4. Calculate edge
5. Apply trading rules
6. Simulate execution
7. Record PnL
Enter fullscreen mode Exit fullscreen mode

Don't only measure total profit.

Also examine:

Win rate
Average edge
Maximum drawdown
Profit factor
Fees
Slippage
Number of trades
PnL by probability range
Enter fullscreen mode Exit fullscreen mode

One particularly useful test is calibration.

If the model predicts:

P(UP) ≈ 70%
Enter fullscreen mode Exit fullscreen mode

then UP should occur roughly 70% of the time among similar predictions.

If the model consistently predicts 70% but UP only wins 55% of the time, the probability estimates are unreliable.

10. The Complete Architecture

The entire strategy can be summarized as:

              Market Data
                  │
        ┌─────────┴─────────┐
        │                   │
      TWAP              Spot Price
        │                   │
        └─────────┬─────────┘
                  ↓
             Features
                  ↓
          Probability Model
                  ↓
             P(UP) = 73%
                  ↓
           Market Price
                  ↓
             Edge = 12%
                  ↓
          Cost Adjustment
                  ↓
          Risk Management
                  ↓
              EXECUTE
Enter fullscreen mode Exit fullscreen mode

This is a major architectural improvement over:

Indicator → Buy
Enter fullscreen mode Exit fullscreen mode

The bot is now doing:

Features
   ↓
Probability
   ↓
Market Price
   ↓
Edge
   ↓
Risk
   ↓
Trade
Enter fullscreen mode Exit fullscreen mode

Conclusion

A simple Polymarket Trading bot tries to predict direction.

A probability-driven bot goes one step further.

It estimates:

P(UP)
P(DOWN)
Enter fullscreen mode Exit fullscreen mode

and compares those probabilities with the prices available in the market.

The core idea is:

edge = model_probability - market_price
Enter fullscreen mode Exit fullscreen mode

Then trade only when the net edge is large enough to justify the costs and risk.

The most important part isn't using the most complicated machine-learning model.

It's building a probability model that is:

  • Well-trained
  • Properly calibrated
  • Tested on unseen data
  • Resistant to overfitting
  • Combined with realistic execution costs

Once you have that foundation, you can start building much more advanced prediction-market systems.

Instead of simply asking:

"Where is the market going?"

your bot starts asking:

"What is this outcome actually worth, and is the market pricing it incorrectly?"

That is the foundation of probability-driven prediction-market trading.

Bot profit screenshot

🤝 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

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

Features

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

  • Increasing dominance of automated bots and AI in 5-minute and 15-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 and 15-minute rounds), this bot framework provides a robust…


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

Top comments (0)