Video summary

Trading $50M At 25 Using One SIMPLE Market Cycle Strategy (4 Stages) - Ted Zhang

Main summary

Key takeaways

Finance

Presenter + framing / disclaimers

  • Ted Zhang (referred to in subtitles as “Ted Seang/Seay”) presents a “stage analysis” framework to identify market regimes and align trades with the long-term trend.
  • He emphasizes: “impossible to predict the future” and calls claims of future prediction “full of crap.”
  • The framework views cycles as timeless and fractal across asset classes, including:
    • equities, commodities, fixed income, crypto, currencies
    • and possibly real estate (noted as tokenizable)
  • No formal “not financial advice” disclaimer is shown in the provided subtitles/text.

Core methodology: Stage Analysis (4 stages using 10/20/30/40-week SMAs)

Key moving averages (weekly)

  • 10-week SMA
  • 20-week SMA
  • 30-week SMA
  • 40-week SMA

Stage identification logic (main rules)

Stage 4 — Downtrending / bearish

  • Price structure: lower highs, lower lows
  • Bearish alignment:
    • 10-week < 30-week < 40-week
    • and price below these MAs
  • Action bias: “short, not to buy.”

Stage 1 — Basing / early reversal

  • Downtrend rate-of-change flattens (selling pressure stops)
  • MAs begin slicing through price, and price oscillates above/below them
  • News/sentiment: often worst at the bottom, but price isn’t pushed lower
  • Action bias: “avoid” (chop risk; institutions may be accumulating)
  • Typical duration: weeks to months (example mentioned: ~2 years in an ARK-related context)

Stage 2 — Uptrend / accumulation → trend continuation

  • Price “surfs” above the moving averages
  • Bullish alignment:
    • 10-week > 20-week > 30-week > 40-week
    • and price above them
  • Institutions often accumulate at base lows, with possible shakeouts/undercuts
  • Action bias: swing/position traders look to go long, aligned with the longer-term trend
  • He notes scaling tends to be managed rather than holding through all basing/chop phases

Stage 3 — Topping / distribution

  • Essentially the opposite of Stage 1, following Stage 2
  • Rate-of-change goes sideways / smooths, volatility can change
  • MAs begin “catching up” to price
  • More chop/looseness/wide gaps as strength fails
  • News pattern: “best of news” near tops (e.g., great earnings on TV/headlines), but price fails
  • Can transition from Stage 3 → Stage 4 after failed breakouts

Trade-management guidance

  • He advises avoiding Stage 1 and Stage 3 because they’re prone to being “chopped up” (“avoid stages”).
  • He implies Stage 2 and Stage 4 are more actionable for positioning:
    • Stage 4 bearish alignment → avoid longs / consider shorts

Execution discipline: “Eighths rule” (Jesse Livermore)

  • Forget the first eighth and last eighth of a move
  • Focus on the middle portion (often less chop)
  • Applies to both longs and shorts
  • “Expensive eighths” correspond to Stage 1 / Stage 3 regions

Key numbers / performance-risk metrics explicitly mentioned

ARKK example (top → down cycle)

  • Mentions a “stage-4 damage” range:
    • ~66% drop from the described peak / all-time-high region
    • ~78% drop from all-time high (stage-4 giveback risk context)

Abercrombie & Fitch (A&F)

  • Cited an ~800% move in 52 weeks (~1 year) after transitioning from downtrend → base/catalyst → Stage 2 uptrend

Tesla (TSLA)

  • From all-time highs to lows: ~76% decline

Moderna (failed breakout example)

  • If buying at a breakout high:
    • ~30% high-to-low move (by the described timeframe)
  • From failed breakout high to later low: ~86% drop
  • From “tippy-dippy top” to that point: ~75% drop
  • Then “another 80% drop” from another reference point (illustrating destruction risk)

Market pattern stats / rule of thumb

  • Mark Minervini “50/80 rule”:
    • 50% chance the leader drops 80%
    • 80% chance drops 50%

Time horizons

  • Weekly charts are used for regime/stage identification; entries are later refined with daily charts
  • Stage duration note:
    • Stage 1 can last weeks to months, including an example described as almost 2 years (ARK context)

Explicit tickers / instruments / assets mentioned

Equity ETFs / tickers

  • ARKK (Cathie Wood’s ARK ETF)
  • ARKG (Cathie Wood ETF family referenced)
  • ARK (general “ETF list” references; specifically includes ARKG)
  • A&F (Abercrombie & Fitch)
  • ELF (esteemed as “textbook” Stage-2 aligned stock; referenced as “ELF”)
  • NVDA (Nvidia)
  • SMCI (AI/data center supply chain context)
  • TSLA (Tesla)
  • IONQ (quantum computing)
  • RGTI (quantum computing)
  • FTAI (nuanced stage transition example)
  • AMR (example of “wide/loose” but returning to Stage 2)
  • Rocket Lab (company; not explicitly tickered)
  • Planet Labs (company; not explicitly tickered)
  • Moderna (company)

Uranium / nuclear theme (spot + miners + ETFs)

  • Spot uranium (no ticker; pivotal region cited ~33.8–33.9, possible entry ~27.2)
  • URNM (uranium miners ETF)
  • URA (uranium miners ETF)
  • “Nuclear stocks” also referenced (no individual tickers listed)

Fixed income / rates

  • US 10-year note futures
  • US 10-year yield (referenced conceptually via price–yield relationship)

Currency / macro instrument

  • DXY (US dollar index futures/chart referenced)

Commodities / futures

  • Gold
  • Silver
  • Cocoa beans
  • Orange juice
  • Coffee

Crypto

  • Bitcoin
  • Ethereum

Macro / narrative overlays (catalyst context, not full fundamental analysis)

The episode frames technical regime reading as powerful (“the tape tells all”), then adds narrative/catalysts to improve confidence for clients and money management.

Fed / monetary policy

  • Rate hikes strengthen the dollar (noted as to ~5% during the inflation period)
  • QE / trillions in printing cited as drivers for crypto/tech-type strength (e.g., ARK, Bitcoin)

Earnings / guidance

  • A failed breakout attributed to earnings/guidance dynamics:
    • “great earnings” on TV, but institutions look through to worse future earnings
  • Stage transitions can reverse around earnings

Technology / AI / energy

  • Semis/AI cycle ties to data centers and GPUs (Nvidia)
  • Uranium thesis: AI needs massive energy; nuclear is framed as safe/efficient/low-polluting; uranium as the fuel

Supply/demand shocks

  • Crypto: emphasis on supply/demand and human psychology
  • Commodities: mentions farm impacts (e.g., viruses/fungus affecting cocoa/orange trees)

Performance / risk-management recommendations and cautions

  • Avoid choppy regimes
    • Stage 1 (basing/chop) and Stage 3 (topping/distribution/chop) are where investors can get “chopped up” and suffer avoidable losses
  • Align with long-term regime
    • Use a higher-timeframe regime filter (weekly) before entries (daily refinement)
  • Risk control
    • Stop losses are discussed when discussing potential Stage 2 entries and risk of reverting to Stage 4
  • Don’t try to catch absolute bottoms/tops
    • Reiterates Livermore’s “forget first/last eighths”
    • Notes execution realism: it’s not feasible to enter/exist at extremes every time
  • Portfolio objective
    • Focus on protecting clients from large drawdowns
    • He links this to client mission language (especially retirement accounts)
    • Cites stage-4 declines including 30–40% drawdowns as something clients “can’t afford”
    • Uses stage alignment to reduce exposure during those regimes

Step-by-step process (how to apply the framework)

  1. Determine long-term stage using weekly
    • Plot 10/20/30/40-week SMAs
    • Check:
      • price structure (higher highs/lows vs lower highs/lows)
      • whether price is above/below the SMAs
      • whether moving averages are stacked bullishly/bearishly
      • MA slope direction (turning up/down)
  2. Identify “Action” vs “Avoid” zones
    • Stage 2 & 4: more aligned with positioning (long vs short bias)
    • Stage 1 & 3: “avoid/chop management” with higher noise and reversal risk
  3. Refine entries with daily
    • Entries are placed using the daily chart (while the stage/regime is identified with weekly)
  4. Add catalyst/narrative confirmation (optional)
    • earnings/guidance, Fed/macro policy, and theme catalysts (AI/energy)
  5. Manage risk and scaling
    • scale out/in around transitions
    • avoid holding through high-chop basing spans
    • use stops
    • expect rechecks (e.g., Stage 3 ↔ Stage 2)

Presenter / sources mentioned

  • Ted Zhang — managing over $50M at Ritholtz Asset Management
    • Subtitles also mention “Revere Asset Management” (likely a subtitle error)
  • Riz — podcast host
  • Stan Weinstein — attributed as the source of stage analysis via Secrets of Profiting in Bull and Bear Markets
    • also referenced as related to course creation at Trader Lion
  • Jesse Livermore
    • referenced for the “first/last eighth” execution idea
    • also mentions the “Weinstein investor buy” concept
  • R. Druckenmiller / Stanley Druckenmiller
    • referenced for rate-of-change/top detection and “contrarianism” commentary
  • Mark Minervini
    • referenced for the 50/80 rule
  • Large asset managers referenced in the accumulation/distribution narrative:
    • BlackRock, Vanguard, Fidelity

Original video