Bukod sa AF Sm Blue saan pang branch merong gantong machine ?

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Gaya sa US at ibang western countries, pagtapak mo ng 18 kailangan mo na maghanap ng sariling titirhan at magtrabaho para sa sarili mo.

Sa tingin nyo ba mas gigising sa katotohanan ang mga kabataan natin para maging independent, o lalo lang silang maghihirap at masisira ang mental health dahil sa baba ng pasahod at taas ng rent sa Pinas ngayon?

Imagine yung SLEX, NLEX, SKYWAY, lahat libre na overnight. Magiging mas mabilis ba talaga ang buhay ng mga commuters or mas lalala pa ang traffic kasi mas maraming sasakyan na mag-aaway sa expressway? Anong ripple effect sa economy, sa mga toll company employees, sa construction ng bagong roads?

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

I published a free week of sub-second BTC orderbook data from Hyperliquid's L1 chain

I run an L1 node on Hyperliquid and have been parsing the native order status stream for a few months now. The pipeline writes everything to Parquet in real time.

I put together a free 7-day BTC sample and uploaded it to Kaggle for anyone doing microstructure research, execution analysis, or just wants to see how an on-chain perp exchange actually works under the hood.

What's in it

The package has four streams, all BTC only, all in Zstd-compressed Parquet:

• hl_book - L2 orderbook snapshots at roughly 550ms cadence, 20 levels deep on both sides. Includes order counts per level and a pre-computed OBI (order book imbalance). Each snapshot has both a local receipt timestamp and the exchange server timestamp.
• hl_orders - Every order event: placements, cancellations, ALO rejections, fills, triggered stops. Each event carries a wallet address, an exchange-assigned order_id, price, size, order type, and the raw L1 status enum. There are 14 different status values.
• hl_fills - Individual trade fills with wallet, maker/taker role, fee, and the order ID that generated the fill. You can join fills back to orders on oid = order_id for full lifecycle tracking.
• hl_funding - Funding rate, open interest, mark price, oracle price, premium, and 24h volume every 5 minutes.

The coverage window is May 8 through May 14, 2026 UTC. About 6 billion rows total across all four streams. You just load it with Polars or PyArrow, one line, no JSON parsing needed.

Why this might be useful

Hyperliquid is fully on-chain, so unlike centralized exchanges you get the wallet address on every order and fill. That means you can actually track individual accounts across their full trading lifecycle. You can see who placed an order, whether it got rejected or filled, and what role (maker or taker) they had on each fill.

Some things people have looked at with this kind of data:

• Spread dynamics and how top-of-book behaves around large fills
• Queue depth and how quickly levels get eaten during volatile periods
• Adverse selection costs for passive limit orders
• Wallet clustering to identify systematic vs retail flow
• ALO rejection rates as a proxy for liquidity stress

Link

https://www.kaggle.com/datasets/marvingozo/hyperliquid-btc-high-frequency-microstructure

It's completely free, no login wall beyond Kaggle itself. If you have questions about the schema or want to know more about how the data is captured, happy to answer.

May naka experience na po ba sa inyo nag deposit sa BPI Machine then nakita nyo nag reflect na sa balance nyo sa BPI Online App then after hours nawala ung balance na dineposit nyo tapos nawala ung record sa app?

Malas lang natapon ko na ung resibo.

Dalawang deposit machine ako nag deposit (sa loob ng EASTWOOD ) kasi ayaw kainin ung ibang pera sa unang machine. Parehas nawala ung record sa app at balance after an hours. Sana bug lang.