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Tom Sosnoff sits down on the Words of Wisdom podcast in Chicago—the floor-trader-turned-fintech builder behind thinkorswim (sold for ~$750M) and Tastytrade (sold for ~$1.1B). He’s famous for running hundreds of positions and still firing off ~100 trades a day—because he genuinely loves the game. In this interview, you’ll hear the mindset of a serial builder who helped reshape options for retail traders, and why he thinks charts and hot takes matter a lot less than probabilistic thinking and tight execution.
What you’ll learn here: how he manages that many positions without melting down, how he sizes and allocates risk off volatility (VIX) instead of vibes, and why the “edge” for retail isn’t secret info—it’s mechanical habits: trade small, trade often, diversify by underlying/strategy/duration, and let the law of large numbers do the heavy lifting. We’ll pull out the main points from the interview—portfolio allocation ranges, volatility-based deployment, and the difference between true edge vs. “mechanical optimization”—and spell out what another trader can actually use on Monday morning.
Tom Sosnoff Playbook & Strategy: How He Actually Trades
Before we jump into the details, here’s the big idea in plain English: this approach treats trading like running a well-oiled machine, not a guessing game. You keep positions small, place many independent bets, and let probabilities and clean execution do the heavy lifting. The bullets below translate that mindset into concrete rules you can follow day to day.
Core Philosophy
This is the mental model that powers every decision. If you’ve been tempted to chase headlines or a perfect chart, this re-centers you on math, repeatability, and process so results become reliable rather than random.
- Numbers over narratives. No macro guessing or chart worship; focus on probability, pricing, execution, and repeatability.
- Edge = mechanics + scale. Your advantage is process (small size, frequent trades, diversification, clean fills), not secret information.
- Law of large numbers. Many independent bets so results converge toward expected probabilities over time.
Portfolio Deployment & Market Selection
Here you decide how much risk to put on and where to put it. Volatility acts like a speedometer for exposure, and liquidity is the gatekeeper for good fills and quick adjustments.
- Volatility governs exposure. Deploy more capital when vol is high and less when it’s low; richer premiums and faster mean reversion make high-vol regimes more attractive.
- Only trade liquid underlyings. Tight spreads and deep open interest reduce slippage and enable swift adjustments.
Position Sizing (the safety net)
Sizing is the quiet hero of risk control. Keep each trade small enough that a full loss is tolerable, and spread risk so no single idea can dent your equity curve.
- Keep risk per trade tiny. Roughly 0.5%–5% of account buying power per position; avoid letting any single trade exceed ~5–7%.
- Prefer many small positions. A single adverse move can’t hurt much when risk is spread wide.
Diversification (the variance reducer)
Diversification is about lowering portfolio choppiness without predicting direction. Mix symbols, strategies, and expirations so your P&L isn’t tied to one outcome or one day.
- Diversify on three axes:
- Underlying: mix symbols/sectors (index ETFs, large caps, rates/FX proxies, commodities).
- Strategy: blend directional and market-neutral option structures; include both defined- and undefined-risk as appropriate.
- Duration: stagger expirations so P&L doesn’t hinge on one date.
- Goal: smooth the equity curve by lowering portfolio-level variance.
Entries (how trades get opened)
Entries are systematic rather than “perfect.” You align strikes and structures with probabilities, then adjust activity based on the volatility backdrop.
- Be systematic, not picky. Place trades consistently where liquidity and pricing are acceptable—don’t wait for “perfect.”
- Price with probabilities. Choose strikes/structures via probability of profit and risk/reward (credits that pay for the risk you actually assume).
- Scale with the environment. Higher IV → more activity/credit; low IV → lighter, choosier.
Risk Type: Defined vs. Undefined
Choose your “seatbelt” before the ride starts. Defined-risk trades cap loss by design; undefined-risk can boost expectancy if you keep size small and diversify.
- Understand the trade-off. Defined-risk = capped loss but smaller expected returns; undefined-risk can improve expectancy and win rate if size stays small and the portfolio is diversified.
- Match to account and comfort. Smaller accounts/low tolerance → lean defined-risk; larger/experienced → blend in some undefined-risk.
Exits, Targets, and Management
This is where edge turns into realized P&L. Use preset profit grabs, time-based choices, and thoughtful adjustments to keep capital rotating into fresher opportunities.
- Manage winners mechanically. Take profits at pre-set thresholds (e.g., % of credit) to keep capital rotating.
- Use time as a lever. Harvest theta where it’s efficient; don’t let decaying trades linger once risk/reward degrades.
- Adjust losers thoughtfully. Roll, reduce, or convert to defined-risk when odds justify it and liquidity allows—avoid “repair by doubling.”
- Tight execution. Work limit orders near the mid; minimize give-up on entries/exits.
Stop-Loss Logic (how risk is actually controlled)
Stops are baked into structure and size rather than hard price triggers that can force bad fills. Exit when the original edge no longer exists or when your predefined risk budget is reached.
- Primary stop is position size. With options, hard price stops can force worst fills; sizing small makes a full-cycle loss tolerable.
- Structural stops via design. Defined-risk spreads embed a hard max loss—no separate stop order needed.
- Contextual exits. If the original probability edge is gone (e.g., regime/volatility shift), close and redeploy elsewhere.
Profit Targets (what “good” looks like)
Aim for frequent, bankable gains instead of home runs. The scoreboard is steady expectancy across many trades, not one epic winner.
- Bank frequent partial wins. Smaller, repeated gains beat chasing perfection; reallocate to new, higher-edge setups.
- Think portfolio, not hero trades. The win condition is steady expectancy across many trades.
Daily Process (your checklist)
Keep the machine humming with a short, repeatable routine. This sustains momentum without overtrading or concentrating risk.
- Open a few, close a few. Keep the flywheel spinning: recycle winners, seed new small positions.
- Check concentration. Trim outsized symbols/sectors; add where you’re under-exposed.
- Stay agnostic. Follow the rules regardless of headlines; you’re warehousing risk efficiently, not forecasting stories.
Trade Small, Trade Often: Risk Sizing That Survives Drawdowns
Trading small is the cheapest insurance you can buy against bad luck and bad timing. Size each position as a tiny slice of your account—think roughly 0.5%–2%—so a full loss is just background noise, not a crisis. When you keep individual bets small, you earn the right to make many independent trades, letting the law of large numbers pull your results toward your edge. Frequent, repeatable entries matter more than hunting unicorn setups that rarely appear. Cap exposure per symbol and per theme so one ticker or sector can’t hijack your P&L. Avoid martingale-style “add-to-loser” tactics; instead, predefine risk and recycle capital into fresher trades.
Use fixed take-profit thresholds to bank small wins and keep the engine turning over. If volatility jumps, you can place slightly more trades—still small—because premium and opportunity expand, but never relax your per-trade limits. This discipline turns drawdowns into shallow dents rather than portfolio-ending potholes.
Let Volatility Drive Exposure: Allocate More When IV Expands
Volatility is your on-ramp meter—when it’s green, more cars can enter. As implied volatility (IV) rises, option premiums fatten and mean reversion speeds up, creating better pay for the risk you take. Use a simple rule: scale your total portfolio deployment up in high IV regimes and scale it down when IV compresses. Keep per-trade size small, but increase the number of trades as opportunity expands. Prioritize liquid underlyings with tight spreads so you capture more of the theoretical edge and give up less in slippage. In lower IV, be choosier: fewer positions, closer to the money, or defined-risk structures to keep the reward/risk sensible.
Track a consistent IV gauge (index IV, IV rank/percentile) and map ranges to target deployment bands. Avoid binary jumps—adjust gradually so you don’t overshoot when vol snaps back. Always respect concentration limits; higher IV is not a pass to bet the farm on one ticker. The goal isn’t prediction—it’s matching the size of your opportunity to the size of your exposure.
Diversify by Underlying, Strategy, and Time to Smooth P&L
The trader from the podcast stresses that real diversification isn’t just “more tickers”—it’s spreading risk across what you trade, how you trade it, and when it matures. Start by diversifying underlyings so no single symbol, sector, or theme can dominate results; cap symbol exposure and avoid stacking correlated names that move as one. Next, diversify strategies: blend short-premium setups with defined-risk spreads and the occasional directional play so your P&L isn’t tied to a single payoff profile. Finally, diversify by duration: ladder expirations across near, mid, and far cycles to smooth theta harvest and reduce the chance that one volatile day sinks the book. This three-dimensional mix lowers portfolio variance without requiring you to predict direction.
Put it to work with simple rules: set max allocation per symbol and per theme, maintain a minimum number of distinct tickers, and require at least two different strategy types in play. Use a basic correlation check or beta-weighted view to stop hidden concentration, and favor liquid products so your diversification doesn’t get taxed by wide spreads. Ladder roll dates—close winners early, seed new trades in later expiries—so capital constantly rotates while time exposure stays balanced. In calm markets, emphasize defined risk and fewer names; when volatility expands, widen the mix and add small, numerous positions while keeping each one tiny.
Mechanics Over Predictions: The Secret Sauce of Process Discipline
Tom Sosnoff argues that prediction is a trap and process is the edge. Instead of calling tops and bottoms, he codifies rules: small position size, consistent entries, and tight execution in liquid products. He focuses on measurable probabilities and expected value, not narratives, and lets the law of large numbers work by placing many independent, modest bets. Pre-set profit targets turn paper edge into realized gains, while predefined adjustments keep losers from spiraling.
The discipline shows up in the details: work limit orders near the mid, avoid chasing fills, and don’t let one thesis dominate your book. Time matters—harvest theta where it’s efficient, and don’t linger when risk/reward decays. Concentration caps prevent a single symbol or theme from hijacking P&L. When volatility expands, increase the count of small trades rather than the size of any one bet. The result is a repeatable engine that compounds consistency—process first, predictions never.
Defined Versus Undefined Risk: Choose Structures That Fit Your Account
Tom Sosnoff emphasizes that structure is your first line of defense, not a last-minute patch. Defined-risk trades (like debit and credit spreads or iron condors) cap the maximum loss at entry, making them friendlier for small accounts and calmer sleep. Undefined-risk trades (like naked short puts or strangles) can offer higher probability of profit and richer expectancy—but only if you keep size tiny, diversify widely, and respect portfolio heat. The choice isn’t moral; it’s mechanical: match structure to account size, margin rules, and your tolerance for gap and tail risk.
In practice, set hard limits: keep any single undefined-risk position small, beta-weight your book, and enforce symbol and theme caps. When IV is low, lean defined-risk to avoid asymmetric downside for thin credit; when IV expands, you can sprinkle in small undefined-risk positions for better return on risk. Manage winners mechanically (e.g., 25–50% of max credit) to reduce variance, and convert or roll losers early to re-cap risk. If a position challenges your max-loss threshold or breaks your sizing rules, close it—discipline beats bravado. The goal is simple: use structure to pre-decide pain, keep drawdowns shallow, and let position count—not position size—do the heavy lifting.
Conclusion
If you boil the interview down to one mandate, it’s this: make trading a repeatable machine. Tom Sosnoff’s through-line is consistency—small position size, lots of independent bets, clean execution, and a portfolio that’s diversified by underlying, strategy, and time. Volatility isn’t noise; it’s your throttle. When IV expands, deploy more (still small) trades across liquid names; when IV contracts, slow down, define risk more often, and stay selective.
Edge here isn’t a hot take—it’s mechanics. Entries are probability-anchored, exits are pre-planned, and losers are adjusted or closed before they metastasize. Your “stop” is decided up front via size and structure, not improvised mid-trade. The goal is steady expectancy across many trades, not hero calls. Do this long enough and drawdowns become shallow dents rather than portfolio events.
Put it into practice:
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Set hard sizing rules (≈0.5%–2% per position; cap any symbol/theme).
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Map IV bands to deployment ranges and stick to them.
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Diversify on three axes (underlying, strategy, duration) and keep everything liquid.
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Manage winners mechanically; convert/roll or cut losers early.
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Review concentration and beta-weighting daily; keep the flywheel turning—open a few, close a few, every session.
Trade small, trade often, and let the math—not the mood—do the heavy lifting.
He is an expert in financial niche, long-term trading, and weekly technical levels.
The primary field of Igor's research is the application of machine learning in algorithmic trading.
Education: Computer Engineering and Ph.D. in machine learning.
Igor regularly publishes trading-related videos on the Fxigor Youtube channel.
To contact Igor write on:
igor@forex.in.rs
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