I Built an AI-Powered Polymarket Wallet Scanner and Copy Trading Workflow

ItsRagnar — Tue, 16 Jun 2026 16:24:53 +0000

I Built an AI-Powered Polymarket Wallet Scanner and Copy Trading Workflow

Prediction markets have become one of the most interesting areas in crypto, and platforms like Polymarket have created a new category of traders who consistently generate profits by identifying market inefficiencies.

One question many traders ask is:

What if instead of trying to outperform everyone, you simply found the best traders and copied them?

In this article, I'll break down an AI-powered workflow that scans hundreds of Polymarket wallets, ranks the most profitable traders, and helps identify potential wallets for copy trading.

This article is inspired by the following YouTube experiment:

YouTube Video: https://www.youtube.com/watch?v=sPhZqKYUFLQ

The Idea

The system automatically analyzes hundreds (or even thousands) of Polymarket wallets every day and ranks them based on:

Historical profitability
Consistency
Trading activity
Risk profile
Market specialization
Win rate
Position sizing behavior

Instead of manually researching thousands of traders, the AI narrows the list down to the top candidates.

The workflow looks like this:

Polymarket Wallets
        ↓
AI Wallet Scanner
        ↓
Wallet Ranking System
        ↓
Top 10 Candidates
        ↓
Manual Review
        ↓
Copy Trading Selection

The key insight is that AI isn't making the trades.

It's filtering data and helping identify traders worth following.

Why Use AI for Wallet Discovery?

The biggest challenge in copy trading isn't execution.

It's finding traders who:

Have a proven track record
Are consistently profitable
Don't rely on a single lucky trade
Manage risk appropriately
Trade markets you understand

Reviewing hundreds of wallets manually can take hours.

AI can do it in minutes.

Daily Wallet Scanning Process

A typical daily workflow might look like:

Step 1: Collect Wallet Data

Gather:

Wallet addresses
Historical positions
Closed trades
Open positions
P&L history
Market categories

Step 2: Apply Filters

Remove wallets that:

Have insufficient trading history
Show extremely high risk
Have low activity
Demonstrate inconsistent performance

Step 3: Score Wallets

Example scoring metrics:

score = (
    profit_score * 0.4 +
    consistency_score * 0.3 +
    activity_score * 0.2 +
    risk_score * 0.1
)

Step 4: Generate Report

The AI produces a ranked list:

Rank	Wallet	Score
1	Wallet A	95
2	Wallet B	92
3	Wallet C	90
...	...	...

Only the highest-ranked wallets are reviewed manually.

Example Trading Categories

One interesting observation is that profitable traders often specialize.

Examples include:

Geopolitics

Traders focused on:

Elections
International conflicts
Government decisions

Sports

Specialists in:

NBA
NFL
Soccer
Tennis

Crypto Markets

Prediction markets related to:

Bitcoin
Ethereum
ETF approvals
Regulatory developments

Rather than following generalists, many successful copy traders focus on specialists.

Risk Management Lessons

One of the biggest takeaways from the experiment was position sizing.

A trader being copied might place:

A $5 trade
A $50 trade
A $5,000 trade

all within the same day.

Blindly mirroring trade size can create problems.

A safer configuration includes:

Fixed Position Sizes

Example:

Wallet A → $7 per trade
Wallet B → $10 per trade
Wallet C → $6 per trade

Maximum Exposure Per Market

Maximum Per Market = $50

This prevents over-allocation to a single prediction market.

Ignore Tiny Trades

Example:

Ignore trades under $5

This reduces noise and unnecessary execution costs.

Results of the Experiment

During the experiment:

Starting balance: ~$490
Mid-experiment peak: ~$610
Final balance after one week: ~$400

At one point the portfolio was up more than $100.

Later, several large sports favorites lost unexpectedly, causing a significant drawdown.

This highlights an important reality:

Even when copying profitable traders, losses are inevitable.

Copy trading is not a guaranteed strategy.

The quality of the wallets matters, but risk management matters even more.

Scaling Beyond 500 Wallets

A more advanced system could scan:

5,000 wallets
50,000 wallets
Entire prediction market ecosystems

Additional metrics could include:

Sharpe ratio
Market specialization score
Drawdown analysis
Trade timing accuracy
Liquidity impact
Correlation between wallets

This is where AI becomes particularly powerful.

Humans can review 20 wallets.

AI can review 20,000.

Open-Source Projects for Inspiration

If you're interested in building your own Polymarket bot, here are several open-source repositories worth exploring.

Polymarket Copy Trading Bot

GitHub:

https://github.com/phoneixtrade/polymarket-copy-trading-bot

Features:

Copy trading automation
Wallet monitoring
Position replication

Polymarket Copytrading

GitHub:

https://github.com/ducksybils/polymarket-copytrading

Features:

Automated trade mirroring
Wallet tracking
Event-driven execution

Polymarket BTC / ETH Trading Bot

GitHub:

https://github.com/nahuelvivas/Polymarket-Trading-BTC-ETH-M-Bot

Features:

Market-making strategies
BTC/ETH prediction trading
Automated execution

Future Improvements

Some enhancements I'd add:

AI Wallet Classification

Automatically categorize traders into:

Sports experts
Political experts
Crypto experts
Macro experts

Performance Forecasting

Use historical behavior to estimate:

Future risk
Expected profitability
Drawdown probability

Multi-Wallet Portfolio Optimization

Instead of copying individual traders:

Wallet A → 30%
Wallet B → 25%
Wallet C → 20%
Wallet D → 25%

This creates diversification across trading styles.

Final Thoughts

The most interesting takeaway isn't the profit or loss.

It's that AI can dramatically reduce the research required to discover high-quality traders.

Rather than spending hours searching through wallets manually, AI can:

Analyze thousands of traders
Filter out poor performers
Rank the strongest candidates
Present only the best opportunities for human review

In my opinion, this is one of the most practical applications of AI in prediction markets today.

The AI isn't replacing the trader.

It's helping the trader focus on the most valuable information.

Have you built any Polymarket bots or copy trading systems? I'd love to hear about your approach in the comments.

AI #Polymarket #Crypto #PredictionMarkets #TradingBots #MachineLearning #Automation #Python #OpenSource #Web3

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ItsRagnar — Wed, 11 Mar 2026 15:37:54 +0000

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Really solid project. Open-source tools like this will help push forward prediction market automation and crypto trading research.

ItsRagnar — Thu, 05 Mar 2026 06:45:33 +0000

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DEV Community: ItsRagnar

I Built an AI-Powered Polymarket Wallet Scanner and Copy Trading Workflow

I Built an AI-Powered Polymarket Wallet Scanner and Copy Trading Workflow

The Idea

Why Use AI for Wallet Discovery?

Daily Wallet Scanning Process

Step 1: Collect Wallet Data

Step 2: Apply Filters

Step 3: Score Wallets

Step 4: Generate Report

Example Trading Categories

Geopolitics

Sports

Crypto Markets

Risk Management Lessons

Fixed Position Sizes

Maximum Exposure Per Market

Ignore Tiny Trades

Results of the Experiment

Scaling Beyond 500 Wallets

Open-Source Projects for Inspiration

Polymarket Copy Trading Bot

Polymarket Copytrading

Polymarket BTC / ETH Trading Bot

Future Improvements

AI Wallet Classification

Performance Forecasting

Multi-Wallet Portfolio Optimization

Final Thoughts

AI #Polymarket #Crypto #PredictionMarkets #TradingBots #MachineLearning #Automation #Python #OpenSource #Web3

Strong liquidity reduces slippage, improves trade execution, and enables larger position sizes without significantly impacting prices.

Building a Liquidity Monitoring Engine for a Polymarket Trading bot: Architecture, Strategy, and Real-Time Market Intelligence

Polymarket trading Ladder strategy bot

Building a Polymarket Liquidity Maker Bot in Python (Crypto 5m–1d Markets) - DEV Community

Really solid project. Open-source tools like this will help push forward prediction market automation and crypto trading research.

Polymarket Trading Bot : Real-Time Arbitrage, Momentum Strategies, and Production Features (Open Source) - DEV Community