Video summary

Former Poker Player: The Exact Math That Helped My Trading!

Main summary

Key takeaways

Finance

Finance-focused summary (Sep 3, 2026 interview)

Who / where

  • The video is an interview within the CMR community (with a helpdesk mentioned).
  • Jeremiah discusses trading performance using probabilistic/statistical “edge” concepts drawn from poker and gambling markets.
  • Presenters:
    • Jason Shapiro (host)
    • Jeremiah (guest)

Core finance / investing ideas & key recommendations

Emphasize EV and payout structure (not direction alone)

  • Jeremiah stresses evaluating trades by:
    • win rate
    • expected value (EV)
    • payout structure
  • The goal is to avoid focusing only on whether the market goes up or down.

Manage downside tightly

  • He controls risk such that he is typically risking below 1% per trade about 99% of the time.
  • He also acknowledges occasional larger mistakes:
    • sometimes risking around 100 basis points (bps) per trade.

Use stats to diagnose negative performance, then adjust incrementally

  • If results are negative, use trade statistics to identify why (not just accept the outcome).
  • Make small, incremental changes rather than sweeping redesigns.

Avoid over-trading (timing/regime matters)

  • He suggests that sometimes “now is not the best time to trade.”
  • This implies that:
    • trade frequency and
    • market regime/timing can strongly affect outcomes.

Prefer lower variability (lower standard deviation)

  • He contrasts:
    • high-variability approaches (e.g., some trend-following behavior) vs.
    • smoother performance
  • His preference: lower standard deviation / lower variability.

Jeremiah’s step-by-step framework (turning stats into an improved system)

1) Collect clean performance data

  • Keep accounts separate for clean datasets.
  • He references tracking via:
    • FundSeeder connected to IBKR
  • He also advocates separating accounts by strategy.

2) Compute and interpret metrics

Track (at minimum):

  • % successful trades
  • average win
  • average loss
  • expected value per trade
  • (Implicitly) variance / standard deviation

3) Identify the binding constraint (what is limiting EV?)

If overall EV is negative, determine what’s “causing” it:

  • Too-low win rate
    • Improve trade selection (better odds of winning)
    • Make fewer/better trades
  • Wins are too small / exits mistimed
    • Consider whether you’re:
      • selling too soon in uptrends
      • selling too late in downtrends
  • Losses are too large
    • Consider narrowing stops
    • Exit losing positions faster

4) Change one variable at a time

  • Use small changes.
  • Ideally adjust only one parameter per test so you can attribute cause more confidently.

5) Validate using larger samples

  • He emphasizes that systems that behave like “coin flips” require large sample sizes for statistical reliability.

6) Use a trading journal

  • Journal every trade so you can analyze patterns over time.
  • Focus on whether categories/themes work consistently—not just on remembered winners.

Key numbers & performance metrics cited (stocks)

Trade quality / distribution

  • Success rate (stocks): just over 30%
  • Average win (stocks): 7.4%
  • Average loss (stocks): 3.59%

EV outcome (negative overall expectation)

  • Even though wins are larger than losses, the overall result is still negative EV.
  • He describes this as roughly ~4 bps loss per trade (EV ≈ -4 bps per trade).

Time horizon & year-by-year results

  • 18-month stock trading period
  • EV: -4 bps per trade
  • Approx. performance over ~200 deals: about -8%
  • Breakdown mentioned:
    • Prior year: +3%
    • 2026: about -10%
    • Net: about -8%

Futures comparison (mentioned, not fully aligned dataset)

  • He says futures success rate is very similar
  • Futures average profit is higher
  • Main takeaway: stocks still show negative expectation

Illustrative counterfactual improvement

  • If average loss could be reduced from 3.59% → 2.5%, holding other metrics constant:
    • EV rises to +67 bps per trade
    • Over 250 trades: described as ~+17% profit
  • He attributes the improvement specifically to reducing average loss, not increasing win rate.

Trading mechanics / implementation details mentioned

Charting & execution style

  • Uses daily charts for evaluation.
  • Executes with real order types:
    • Stop-limit for entry
    • Stop-market for exit
    • Limit orders for take-profit

Position sizing & risk

  • Typical risk: < 1%
  • He admits occasional larger bets (around 100 bps per trade), contributing to drawdowns (notably referenced around March and July).

Macro / market discussion

  • He argues against outcome-chasing narratives like “predict what the S&P will do tomorrow.”
  • Even if someone is “right” 50% of the time, that may not indicate real knowledge—it may just reflect coin-flip odds.
  • Strategy implication:
    • optimize EV/risk-reward and execution discipline
    • avoid relying on directional prediction.

Instruments / tools / examples mentioned

  • S&P (referenced generically)
  • Polymarket (prediction markets)
  • American Idol prediction market (used as a forecasting/edge example; not treated as a financial instrument)
  • IBKR (broker platform)
  • FundSeeder (analytics / tracking)

Disclosures / cautions (as implied by the subtitles)

  • No explicit “not financial advice” disclaimer was present in the provided subtitles.
  • Implicit cautions include:
    • Results depend on market conditions and metrics can change by year (e.g., strategies that worked in 2022 may fail in 2026).
    • Strategy changes are interconnected—adjusting one metric can affect others.
    • His analysis is based on his own data; outcomes are not guaranteed.

Presenters / sources mentioned (end of video)

  • Jason Shapiro (host)
  • Jeremiah (CMR participant/guest)
  • Hazem (credited as a creator of CMR; last name not provided)
  • Nate Silver (analogy to statistics/polling via 538)
  • Wikipedia (referenced for “Dial Idol” page)
  • Freakonomics (article about Dial Idol in 2008)
  • Dial Idol (software / past dataset example)
  • FundSeeder and IBKR (tools referenced for performance tracking)

Original video