I've been building a whale copy-trading bot for Polymarket for about 5 weeks now. Started with a $500 bankroll. Here's where I'm at and what I learned.

Latest Screenshot (May 17)

First Bot Interaction April 11

The idea

Pretty simple concept: find wallets that consistently win on Polymarket, detect when they buy, and automatically mirror their trades with smaller sizes. Sounds easy. It is not.

What I actually built

The whole thing is Python running locally on my PC. No cloud, no paid APIs. Here's the architecture:

• Whale detection — dual-feed system. Primary: on-chain WebSocket subscriptions to Polygon OrderFilled events (~2-3s detection). Fallback: REST polling every 60s as a safety net.
• 7-gate safety array before every trade — price range check, slippage gate, balance/reserve, per-whale concentration cap, topic correlation cap, liquidity, and minimum viable exit size
• Autonomous exit engine — mirrors whale sells in real-time, plus take-profit, time-decay, and stop-loss as backup
• Auto-claim — automatically redeems resolved winning positions on-chain via Web3
• Telegram control plane — full dashboard with PnL, active positions, whale status, manual sell buttons, and a kill switch
• Wallet screener — offline forensic screener that discovered and qualified wallets from the Polymarket leaderboard. Started with 6,365 wallets → screened 2,303 → 72 passed (3.1% pass rate). Wilson confidence intervals on win rate, bankroll-proportional sizing gates, open-risk ratio caps, bot/dust filters.

The numbers (real, not backtested)

Metric	Value
Starting bankroll	$500
Realized PnL	+$256.85
Total notional traded	$391.31
Current free capital	~$577
Tracked wallets	7 (qualified through forensic screening)
Time running	~5 weeks

What went right

• On-chain detection is a game changer. REST polling has 30-60s latency. On-chain WebSocket catches fills in 2-3s. That alone was worth the engineering effort.
• The screening process is critical. My first set of wallets included some that looked amazing (90%+ win rate) but turned out to be resolution traders with 0% sell ratio, or had 60%+ of their capital locked in open positions. Removing bad wallets was more impactful than adding good ones.
• Auto-claim is free money. Resolved positions just sit there if you don't claim them. The bot handles this automatically.

What went wrong

• Gate tuning almost killed the bot. After swapping to my new screened wallets, the bot detected 69+ buy signals over 48 hours but executed zero trades. Every single one was rejected by safety gates that were too tight. Had to do a full forensic replay to figure out which gates were blocking profitable trades.
  • The lottery ticket gate (price < $0.05) was blocking high-asymmetry bets. One whale made +$860 on a $0.002 ceasefire bet that I blocked.
  • The slippage gate was set at 3 cents. But whales move thin order books — by the time I detect the fill, the ask has shifted 3-8 cents. Had to widen it.
• Sizing bugs are expensive. At one point the bot submitted a buy order calculated from the ask price instead of the limit price. Target was $9, it filled for $18. That's the kind of thing that makes you appreciate test coverage.
• "Ghost positions" are a real thing. After selling a position, the reconciliation loop would sometimes find leftover on-chain balance before settlement cleared and re-create a phantom position. Had to add exit cooldown timers.
• V2 migration was painful. Polymarket migrated from USDC.e to pUSD in late April. Had to rewrite the collateral handling, update all contract addresses, and deal with operator approval gaps.

Biggest lesson

Your safety gates need to match your whale's strategy, not your assumptions. I assumed lottery tickets (sub-$0.05 bets) were garbage. My best whale's entire edge was buying $0.01-0.03 positions in event markets. 50-100x payout when they hit. The gate I thought was protecting me was actually blocking the most profitable strategy.

Architecture diagram (simplified)

Polygon WebSocket ──→ OrderFilled decode ──→ Whale filter
                                                  ↓
REST /activity ────→ Fill clustering ──→ 7-gate evaluation
                                                  ↓
                                         FAK taker order ──→ CLOB API
                                                  ↓
                               Telegram notification + state update
                                                  ↓
                          Exit engine (whale-mirror / TP / SL / decay)
                                                  ↓
                              Auto-claim resolved → pUSD recovered

Tech stack

• Python 3.11, running on Windows 11 locally
• py-clob-client-v2 SDK for Polymarket CLOB
• web3.py for on-chain claims/approvals
• websockets for BBO price cache + on-chain event subscriptions
• Telegram Bot API for control plane
• No database — single JSON state file with atomic writes

Hey guys,

We've been working for some time on a polymarket extension product to help increase the expected value upside of any bets made. Would love some feedback on the concept if it is within this subreddit's rules to allow for such feedback.

Thanks

Spent the day improving the Imali-Defi UI and stress testing the paper trading system as a solo developer building the platform from the ground up.

The bots have been extremely active today.

📈 Current paper trading stats:

✅ Win Rate: 92.7%
💰 Total Paper PnL: $1368.79
🤖 Multiple bots actively managing trades
🛡 Automated trailing stop protection locking gains
📊 AI confidence scores consistently reaching 90–97%

Some paper trade examples from today:

📈 TRX-USDT
+1.61% closed gain

📈 EOS-USDT
+1.54% closed gain

📈 ZRX-USDT
+1.25% closed gain

📈 XLM-USDT
+0.88% closed gain

📈 AVAX-USDT
+0.53% trailing stop protected exit

Current Imali ecosystem includes:

✅ Crypto Spot Bot
✅ Futures Trading Bot
✅ Stock Trading Bot
✅ DEX Sniper Bot
✅ Shared AI strategy engine across all systems

The platform currently uses:

- AI confidence scoring
- automated entries/exits
- trailing stop logic
- risk-weighted trade sizing
- beginner + advanced strategies
- paper trading before live trading

One of my biggest focuses lately has been improving the UI and onboarding flow for beginners because most trading platforms feel overwhelming to normal users.

The goal with Imali-Defi is simple:

Make automated trading easier to understand before people risk real money.

Still actively improving the system daily, but users are now connecting APIs, running paper trades, and testing the dashboard in real conditions.

Looking for a few more early access testers and feedback from other builders/traders.

https://imali-defi.com