How to Find Mispriced Odds in Polymarket with a Probability Model
Most Polymarket bots start with a simple idea:
TWAP > Strike → Buy UP
TWAP < Strike → Buy DOWN
That can work as a basic signal, but there is a much more powerful way to think about prediction-market trading:
Don't just predict the direction. Estimate the probability, then compare it with the market price.
This is the foundation of a probability-driven Polymarket Trading bot.
The architecture is:
TWAP
+
Spot Price
+
Momentum
+
Volatility
+
Time Remaining
↓
Probability Model
↓
P(UP) = 73%
↓
Market Price = $0.61
↓
Edge = +12%
↓
Trade
From Signal to Probability
Imagine a 5-minute BTC Up/Down market.
Our model analyzes the current market conditions and estimates:
P(UP) = 0.73
P(DOWN) = 0.27
At the same moment, the UP token is trading around:
$0.61
We can calculate the estimated edge:
edge = model_probability - market_price
So:
0.73 - 0.61 = 0.12
The model estimates a 73% chance of UP, while the market price implies roughly 61%.
That 12 percentage-point difference is the potential opportunity.
Why This Is Different
A traditional bot might use:
if twap > strike:
buy_up()
The problem is that this treats every bullish situation similarly.
Consider:
TWAP slightly above strike
→ P(UP) = 52%
versus:
TWAP significantly above strike
→ P(UP) = 78%
Both are technically bullish.
But they are clearly not the same opportunity.
A probability model gives the bot a way to quantify the strength of the situation.
More importantly, it allows the bot to compare that probability with what the market is charging.
What Does the Model Use?
The model can combine several features:
TWAP distance from strike
Spot/TWAP difference
10s momentum
30s momentum
60s momentum
Realized volatility
Time remaining
Order-book imbalance
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,
}
These features become the input to the probability model.
Start With a Simple Model
You don't need a complicated AI system to build the first version.
Logistic regression is a good starting point because it is fast, interpretable, and naturally produces probabilities.
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
model.fit(X_train, y_train)
Then:
p_up = model.predict_proba(
X_current
)[0][1]
p_down = 1 - p_up
For example:
P(UP) = 0.73
P(DOWN) = 0.27
The important thing is not the complexity of the model.
The important thing is whether its probabilities are reliable.
The Real Trading Decision
Suppose:
Model P(UP) = 0.73
UP ask = $0.63
Then:
Raw edge = 0.73 - 0.63
= 0.10
But we shouldn't immediately buy.
Real trading has costs:
Fees
Spread
Slippage
Latency
Execution uncertainty
So the bot should think in terms of net edge:
net_edge = (
model_probability
- execution_price
- estimated_costs
)
For example:
Model probability = 0.73
Execution price = 0.63
Estimated costs = 0.02
Net edge = 0.08
If our minimum required edge is 5%:
8% > 5%
the trade passes the filter.
Don't Use the Last Price Blindly
One common mistake is comparing the model probability with the last traded price.
Suppose:
Model probability = 0.73
Last price = 0.61
Best ask = 0.64
You cannot necessarily buy at $0.61.
If you're taking liquidity, the relevant number is closer to the executable ask:
0.73 - 0.64 = 0.09
not:
0.73 - 0.61 = 0.12
This small implementation detail can make a huge difference to a short-term trading strategy.
Simple Trading Logic
The core decision can be very small:
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"
Notice the third option:
NO_TRADE
This is extremely important.
A probability model doesn't mean the bot should constantly trade.
If:
P(UP) = 0.70
Market = $0.69
the model might be correct about the direction, but the opportunity may be too small after costs.
Correct prediction does not automatically mean profitable trade.
Training the Model
The training dataset should contain historical snapshots of the market.
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:
UP → 1
DOWN → 0
The model learns the relationship between the market state and the eventual outcome.
But there is one critical rule:
Never use information from the future.
If you're making a prediction with 30 seconds remaining, the model can only use information that existed at that moment.
Otherwise, your backtest can look extremely profitable while the live strategy fails.
Probability Calibration
This is arguably more important than model accuracy.
Suppose the model says:
P(UP) = 70%
Ideally, situations where the model predicts around 70% should actually result in UP roughly 70% of the time.
You can test this by grouping predictions:
Predicted Probability Actual UP Rate
50–55% 53%
55–60% 58%
60–65% 63%
65–70% 68%
70–75% 73%
If the numbers are reasonably close, the model is better calibrated.
Why does this matter?
Because your entire strategy depends on:
Probability - Market Price
If the probability estimate is wrong, the estimated edge is wrong too.
Backtest the Edge, Not Just the PnL
A good backtest should measure more than total profit.
Track:
Win rate
Average predicted edge
Average realized return
Maximum drawdown
Fees
Slippage
Number of trades
PnL by probability range
PnL by time remaining
Most importantly, investigate whether larger predicted edges actually produce better results.
For example:
Edge Average Return
0–2% Negative
2–5% Near zero
5–10% Positive
10%+ Stronger
If this relationship disappears on unseen data, the model probably isn't producing a reliable trading advantage.
The Complete Architecture
The strategy can be summarized as:
Market Data
│
┌───────────┴───────────┐
│ │
TWAP BTC Spot
│ │
└───────────┬───────────┘
↓
Features
↓
Probability Model
↓
P(UP) = 73%
↓
Market Price
↓
Edge
↓
Cost Adjustment
↓
Risk Management
↓
Execute
This is a major step beyond:
Indicator → Buy
The bot is now doing:
Features
↓
Probability
↓
Market Price
↓
Edge
↓
Risk
↓
Trade
Final Thoughts
The most interesting evolution of a Polymarket bot is moving from signal generation to probability estimation.
Instead of saying:
"TWAP is above the strike, so buy UP."
the bot says:
"Based on TWAP, spot price, momentum, volatility, and time remaining, I estimate a 73% probability of UP. The market is offering UP at $0.63, creating an estimated 10% raw edge."
That is a much more powerful framework.
The ultimate goal isn't simply to predict winners.
It is to identify situations where:
Your estimated probability
>
Market-implied probability
+
Trading costs
+
Risk margin
When that difference is large enough, the bot has a reason to trade.
When it isn't, the best decision may simply be:
NO TRADE
And that is the foundation for building a more sophisticated probability-driven Polymarket Trading bot.
🤝 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:
https://github.com/Benjam1nCup/Polymarket-trading-bot-python-V2
💬 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
