Video summary

BLP #38: Gini loh cara manfaatin AI buat trading yang benar!

Main summary

Key takeaways

Finance

Finance-focused summary (from the subtitles)

Core market / trading views (no explicit backtested performance metrics)

  • The speakers argue that markets are (largely) efficient, meaning:
    • If everyone could get the same “alpha,” it wouldn’t be alpha anymore.
    • In a highly accessible environment, informational edges get competed away.
  • AI cannot reliably make market predictions (directional forecasting), even with sophisticated models—outputs can change based on confidence/temperature and re-prompting.
  • The practical framing is to shift from “prediction” to:
    • Probabilities
    • Risk management
    • Analytics

Explicit recommendations / cautions

  • Don’t believe “fully automated trading agents will make money while you sleep.”
    • The guest calls this “bullshit”, noting that automation-only claims often ignore market complexity and changing regimes.
  • Evaluate any “beat-the-market” goal versus Bitcoin buy-and-hold (BTC).
    • The guest claims strategies that try to outperform often underperform BTC after:
      • costs,
      • invalidation,
      • and regime changes.
    • They emphasize that long-term BTC holders have a simpler path, with drawdowns they view as comparatively “not that big” relative to returns.
  • Backtests and “holy grail” strategies are treated skeptically.
    • Mentions include unreliable backtest results (“power test garbage”).
    • Even strategies that work early can fail later because:
      • the market changes,
      • and strategies require ongoing updates.
  • Strategy applicability is pair-dependent.
    • One approach won’t transfer cleanly across Bitcoin vs. Hyperliquid vs. ETFs/others.
    • Parameters must be re-tuned for regimes (uptrend/downtrend/sideways).
  • Risk management matters more as time in the market increases.
    • “Season traders” are described as thinking in probabilities and possibilities, not certainties.
    • The goal is to manage risk so outcomes remain survivable.

Crypto / instruments mentioned (tickers/assets)

  • Bitcoin (BTC): repeatedly used as the benchmark for buy-and-hold and as the target outperform baseline.
  • Hyperliquid: treated as a trading venue / pair context (not necessarily as a direct ticker in the subtitles).
  • “ETs” / “ET”: mentioned generically (no specific ticker).
  • US “XBT / Z XBT”: referenced as a “detective” account used for on-chain clustering (not clearly presented as an instrument).
  • Trump tweeting: referenced as a potential news/narrative input (macro/event catalyst).
  • CFTC: referenced in a regulatory/macro shock context.
  • Funding rate: referenced conceptually for long/short positioning decisions.

On-chain / smart money / analytics approach (methodology framework)

The guest describes an analytics-first AI use case:

  • Not prediction, but monitoring and scoring on-chain actions—especially for Hyperliquid traders.

Implied workflow / framework

  • Track on-chain smart money / wallet positions, especially wallets with large portfolios.
  • Use a scoring system to identify which traders are “best.”
  • Send notifications when top/scored traders open positions.
  • Emphasis: decision support, not fully automated execution.

Specific construction details mentioned

  • A Hyperliquid tracker that:
    • Monitors traders above stated portfolio thresholds ($2M / $5M).
    • Alerts on positions opened above a notional threshold ($10M).
    • Includes metrics such as:
      • win rate
      • EV (expected value)
      • confidence score / conviction
      • average size
      • total size
  • An additional example described:
    • Watch “Wales” (large participants):
      • 60–70% are said to be shorting everything.
      • That behavior later reversed into a short squeeze after a catalyst (e.g., “Trump said something” and “CFTC flew”).

Tools / automation (relevant for trading ops)

While the broader discussion is about AI agents, the finance-adjacent angle includes:

  • Tracking wallet balances across on-chain/off-chain wallets:
    • aggregating personal portfolio holdings and daily changes.
  • Building a data aggregator / analytics layer for trading insights:
    • The subtitles repeatedly stress it’s “just a data aggregator… news / smart money position… not something you can automate that easily.”

Numbers & thresholds explicitly stated

  • Event dates (promo/conference context):
    • November 11–12, 2026 (Jakarta)
  • Trading/analytics thresholds:
    • Portfolio size: $2M / $5M
    • Position notional alert threshold: $10M
    • Positioning example: 60–70% shorting everything → later short squeeze after a catalyst
  • AI trading/indicator subscription claim:
    • A subscription mentioned as 2,000 (currency unclear; later implied to be an expensive subscription type)

No explicit BTC price, yield, multiple, or portfolio return figures were provided.

Disclosures / disclaimers

  • No explicit “not financial advice” disclaimer appears in the provided subtitles.
  • The discussion is framed largely as personal opinion (e.g., “in my opinion,” “I don’t believe it,” “bullshit”).

Presenters / sources mentioned (at end)

  • Josh Gult (main guest)
  • Host / other participant repeatedly referenced as “Andreas / Andreas Ar topic”
    • Website: andreasartopic.com
  • Event promo source: Web Thweek Asia / Web3 Week Asia
  • Other mentioned figures (not presented as co-hosts): Bis Yugo, Trump (as a news driver), CFTC (regulator)

Original video