How to Build a Polymarket BTC Momentum Trading Bot in Python (5-Minute Crypto Up/Down Market Strategy)
Source: Dev.to
Introduction
Crypto prediction markets move fast.
One interesting pattern I noticed while trading on Polymarket is that short-term crypto markets often follow Bitcoin’s direction, especially near market expiration. When Bitcoin shows strong directional momentum, assets such as Ethereum (ETH), Solana (SOL), and XRP frequently move in the same direction.
This observation led me to build a simple momentum-based Polymarket trading bot.
The core idea is straightforward:
-
Monitor BTC Up/Down markets.
-
Detect strong directional probability from the order book.
-
Confirm that ETH, SOL, or XRP markets agree with Bitcoin.
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Enter positions when confidence is high.
-
Hold until market settlement.
-
Redeem winnings automatically.
In this tutorial, you’ll learn how to build a Python bot that:
✅ Fetches Polymarket market data
✅ Reads order book probabilities
✅ Detects BTC momentum signals
✅ Places automated buy orders
✅ Waits for settlement
✅ Redeems winning positions
The goal is not to predict the future perfectly. The goal is to identify situations where multiple crypto prediction markets agree on direction and exploit that momentum.
Why Bitcoin Momentum Matters
Bitcoin is still the dominant asset in the cryptocurrency market.
When BTC experiences a strong move:
-
ETH often follows
-
SOL often follows
-
XRP often follows
-
Other altcoins frequently move in the same direction
This correlation is especially visible during short-duration prediction markets.
For example:
Market YES Probability
BTC Up 0.95
ETH Up 0.93
SOL Up 0.92
When all three markets strongly agree on direction, there may be an opportunity to enter the same side before settlement.
This is the basic principle behind the momentum bot.
Strategy Overview
The bot continuously watches several crypto markets.
Step 1: Monitor BTC Market
If BTC Up reaches:
BTC Up > 0.90
or
BTC Down > 0.90
the bot considers Bitcoin momentum strong.
Step 2: Confirm Altcoin Agreement
The bot then checks:
-
ETH
-
SOL
-
XRP
If at least one of these markets has the same directional probability above 0.90:
BTC Up = 0.95
ETH Up = 0.92
then a valid signal exists.
Step 3: Time Filter
The strategy focuses on the final minute before expiration.
Why?
Because market participants have already processed most available information.
Near settlement:
-
uncertainty decreases
-
probabilities become more accurate
-
momentum becomes more obvious
The bot only becomes active during the final 60 seconds.
Step 4: Execute Buy Order
Once conditions are satisfied:
-
identify winning side
-
buy corresponding token
-
hold position
Step 5: Settlement
The bot waits for market resolution and automatically redeems winnings.
System Architecture
The entire system consists of five components.
┌─────────────────┐
│ Polymarket API │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Market Scanner │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Signal Engine │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Order Executor │
└────────┬────────┘
│
▼
┌─────────────────┐
│ Redeem Module │
└─────────────────┘
Enter fullscreen mode
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We will use:
pip install requests
pip install websockets
pip install asyncio
pip install py-clob-client
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Python 3.11+
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Polymarket API
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Polymarket CLOB
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WebSocket streams
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Asyncio
Why WebSocket Instead of Polling?
Many beginners use REST polling.
Example:
while True:
requests.get(...)
Enter fullscreen mode
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latency
-
rate limits
-
missed opportunities
Instead, use WebSockets.
Benefits:
-
real-time updates
-
lower bandwidth
-
faster execution
The momentum strategy depends on receiving price updates instantly.
Market Data Collection
The bot subscribes to:
-
BTC Up
-
BTC Down
-
ETH Up
-
ETH Down
-
SOL Up
-
SOL Down
-
XRP Up
-
XRP Down
Whenever order books change:
async def on_book_update(data):
process_market(data)
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Exit fullscreen modeThe latest probability is stored in memory.
market_cache = {
"BTC_UP": 0.95,
"ETH_UP": 0.92,
"SOL_UP": 0.91
}
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Exit fullscreen modeBuilding the Signal Engine
The signal engine is the brain of the strategy.
Pseudo-code:
def generate_signal():
btc_up = get_probability("BTC_UP")
btc_down = get_probability("BTC_DOWN")
eth_up = get_probability("ETH_UP")
sol_up = get_probability("SOL_UP")
xrp_up = get_probability("XRP_UP")
if btc_up > 0.90:
if (
eth_up > 0.90 or
sol_up > 0.90 or
xrp_up > 0.90
):
return "BUY_UP"
return None
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Fast.
Easy to maintain.
Time-Based Entry Logic
The strategy only activates near expiration.
Example:
remaining = market_end_time - current_time
Enter fullscreen mode
Exit fullscreen modeEntry condition:
if remaining 0.90 ✓
ETH Up > 0.90 ✓
SOL Up > 0.90 ✓
Time Remaining < 60s ✓
Action:
BUY ETH UP BUY SOL UP
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Hold until settlement.
Redeem winnings after resolution.
Performance Considerations
If you want to scale the bot:
Async Processing
Use:
asyncio
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for all network operations.
In-Memory Cache
Avoid querying APIs repeatedly.
Store latest values:
market_cache = {}
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Event-Driven Design
React to updates.
Never poll unnecessarily.
Risk Factors
No strategy is perfect.
Important risks include:
Market Correlation Breakdown
Sometimes BTC moves while altcoins lag.
Low Liquidity
Thin markets can create slippage.
Resolution Delays
Settlement may take longer than expected.
Execution Risk
Fast-moving markets can change before orders are filled.
Always test with small amounts first.
Backtesting Ideas
Before deploying live capital:
- Collect historical order book data.
- Save probabilities.
- Simulate entries.
- Compare outcomes.
- Calculate:
- win rate
- average return
- maximum drawdown
- profit factor
A data-driven approach is more reliable than assumptions.
Project Structure
bot/ │ ├── websocket.py ├── signals.py ├── execution.py ├── redeem.py ├── config.py ├── main.py │ └── utils/
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This structure remains manageable as the project grows.
Useful Resources
Official Polymarket Documentation
[https://docs.polymarket.com](https://docs.polymarket.com/)
Example Open-Source Repository
[https://github.com/mateosoul/Polymarket-Trading-Bot-Python](https://github.com/mateosoul/Polymarket-Trading-Bot-Python)
Future Improvements
Possible upgrades:
- Multi-market confirmation
- Historical backtesting engine
- Database storage
- Telegram alerts
- Profit analytics dashboard
- Position sizing models
- Liquidity filters
Polymarket BTC Momentum Trading Bot Result Screenshot
[](https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fozbz06mm1ta5vlbbwbbp.png)
[](https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2Fa7r0j21legbnd1zhte0q.png)
[](https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Farticles%2F4t2ooxn7o6w6fz0u8fwp.png)
Conclusion
This tutorial demonstrated how to build a Bitcoin momentum-based trading bot for Polymarket using Python.
The strategy relies on a simple idea:
When Bitcoin shows strong directional probability and major altcoins agree, the market may be signaling a high-confidence outcome near settlement.
The complete workflow consists of:
- Real-time market monitoring
- Momentum detection
- Time filtering
- Automated execution
- Settlement redemption
Because the architecture is event-driven and relatively simple, it can be implemented in a surprisingly small amount of code while remaining effective and easy to maintain.
As always, perform extensive testing before trading with real funds and remember that past observations never guarantee future results.
Happy building and good luck experimenting with Polymarket automation.
FAQ
Is this simply explaining the strategy, or is it introducing an actual bot?
This article is a tutorial on how to build an actual bot. I have completed this bot and am generating stable revenue with this bot.
Why use Bitcoin as the primary signal?
Bitcoin is the dominant cryptocurrency and often influences short-term direction across major altcoins.
Why trade during the final 60 seconds?
Market uncertainty is usually lower near settlement, making directional probabilities clearer.
Why not use stop-losses?
The strategy is designed around short-duration prediction markets where positions are held until settlement. Whether additional risk controls improve performance should be evaluated through testing.
Can this strategy work on other assets?
Potentially. Similar momentum-confirmation logic may be applied to correlated markets.
Can I run the bot 24/7?
Yes. Deploy it on a VPS or cloud server with continuous WebSocket connectivity.
If you are interested in this bot, Please check the PNL with this public account.
@poll-sticky-test on Polymarket
Check out this profile on Polymarket.
polymarket.com
Contact
Telegram:
[https://t.me/mateosoul](https://t.me/mateosoul)
Tags: #polymarket #trading #bot #tutorial #guide #python