Video summary

This Trader Made +$700K in 6 MONTHS — While Working a Full-Time Job

Main summary

Key takeaways

Finance

Performance & Claims

  • Presenter: Malik (platform profile: “KenFo” / “Kinfo”)
  • Verified profits: ~+$700,000 over the last 6 months; he also suggests results may be closer to ~$1M (the exact number is not fully consistent across subtitles).
  • Automation claim: he says he hasn’t manually placed a trade in ~3 years; execution decisions are handled by the system.
  • Annualized performance: ~40% annual rate of return, while also noting periods of very large drawdowns.

Instruments / Tickers Mentioned

ETFs (core trading vehicles)

  • TQQQ — long triple-leveraged NASDAQ 100 ETF
  • SQQQ — inverse/short triple-leveraged NASDAQ 100 ETF

Index used for signal/backtest reference

  • NDX — NASDAQ-100 index data

Data / brokerage / infrastructure

  • Polygon.io — market data source
  • AWS — cloud execution/scheduling
  • Fidelity and Alpaca — brokerage connections
  • Collective2 — signal distribution; he claims live accounts include $50,000 allocated to his strategy signals

Strategy Style & Positioning

  • Described as fully automated systematic trading using mechanical rules (no discretionary day-to-day decisions).
  • Seven strategies total, grouped into:
    • Trend following (primary profit driver; low frequency, ~1–2 trades per week)
    • Mean reversion (used especially when trend conditions decelerate or shift)
  • Long/short switching logic:
    • Trades only TQQQ for long exposure
    • Trades only SQQQ for short exposure
  • Holding horizon: slow, core position trading—can last 3 months to years (not day trading or quick swings).

Execution Schedule (How It Runs)

  • Runs only the last ~10 minutes of the trading day.
  • Cloud scheduling starts ~15–20 minutes before market close.
  • Near close, there are two decisions:
    1. “Should I buy TQQQ?” + position sizing
    2. “Should I buy SQQQ?” + position sizing (long/short encoded via the model)
  • Demo-style example (allocation/rebalance):
    • Strategy output: TQQQ = 30% of portfolio, SQQQ = 0%
    • He claims the system then sells/rebalances to reach the target (e.g., if starting at 100% TQQQ, sell 70%).

Risk Management & Drawdowns

  • He emphasizes that large drawdowns are expected and claims he is not scared of 10–40% drawdowns.
  • Reported/testing drawdowns include:
    • ~33% drawdown
    • “almost 30% drawdowns”
  • Stress testing:
    • Claims backtests run across 42 years of data
    • Includes multiple market regimes, explicitly mentioning the 2008 crash
  • Range-bound behavior:
    • He says the strategy struggles when NASDAQ is sideways/flat for ~1 year (by design).

Methodology / Framework

Phase approach (his development framework)

  • Exploratory (~2016 for ~3 years):
    • Strategy exploration; he tried day trading and discretionary swing approaches; limited success
  • Discovery (frustration period):
    • Retrospection leads to “play his own game”
    • Focus shifts to strengths (data + coding) vs limitations (can’t do manual, time-intensive discretionary execution)
  • Growth phase:
    • Formalizes systematic, automated strategy deployment

Strategy construction workflow (repeated per strategy)

  1. Write strategy code/model (mechanical rules; no charts/ML/AI)
  2. Use “timeless” market tendencies:
    • Momentum / trend following
    • Mean reversion via rate/velocity changes in trend
  3. Backtest across ~42 years of NASDAQ-100 (NDX/Q) history
  4. Stress test across major regimes/crashes
  5. Tune parameters/settings based on expected behavior in different regimes
  6. Validate “conviction” by checking that live behavior matches backtest implications
  7. Automate execution with telemetry/alerts

Core “Edge” (Valuation-Free Technical Condition)

  • Major claim: large NASDAQ rallies tend to sustain when price remains above key moving averages.
  • Critical thresholds mentioned:
    • 50-day moving average
    • 250-day moving average
  • Example rule framing (trend following):
    • Enter/allocate when price is above the selected moving average
    • Exit when the trend signal breaks (described as holding through multi-month runs)
  • He argues this reduces the need to “predict bottoms”:
    • “You don’t have to ask… is it the bottom yet?” because entries are rule-based on moving-average conditions.

Backtest / Return Metrics & Historical Performance Claims

Long-run model/backtest claims

  • If applying the same rules “and do nothing,” he claims a backtest from 1985 yields:
    • ~80% return per year
    • Compared to buy-and-hold QQQ ~12% (“roughly”)
  • Mentions QQQ / TQQQ comparisons and notes TQQQ’s leverage-driven drawdowns:
    • ~80–95% drawdowns (as stated)

Specific year return examples (from narrative)

  • 1997–2000: described as “blockbuster years,” including very high percentages (exact mapping across years is somewhat unclear in subtitles), such as:
    • ~169%
    • ~180%
  • 2000 crash year: ~200% profit
  • 2001: ~152% (per subtitles)
  • 2008: ~83%
  • 2009: ~155%
  • 2020: ~469%
  • 2021: ~81%

Month-to-month example (current-period scenario)

  • After a “tariff crash” (he claims ~20% crash in NASDAQ), he describes recovery results as:
    • May: +23%
    • June: +20%
    • July: +4%
    • August: +5%

Disclosures / Cautionary Notes

  • No explicit disclaimer such as “not financial advice” appears in the subtitles provided.
  • He emphasizes:
    • The approach is based on observed historical tendencies
    • Future prediction isn’t required (rules drive decisions)
    • However, drawdowns must be endured.

Key Presenters / Sources Mentioned

  • Malik — trading under “KinFo” / “Kinfo” (also referred to as “Mallet” in subtitles; “real TQQQ trader” mentioned as a Twitter handle)
  • Podcast host(s): referred to as “Kin” / “Kin for the podcast ‘Undiscovered Traders’” (host name not clearly specified beyond “Kin”)

Original video