Trading Expectancy Formula with R-Multiples

What is trading expectancy (and what it isn’t)

Expectancy is a way to describe your edge as an average outcome per trade. It’s not a promise about the next trade. It’s a summary of what your process has produced across a sample. If your expectancy is negative, the math says your current behavior set is losing money over time—no matter how “good” a few wins felt.

Expectancy vs win rate

Win rate is a single ingredient. Expectancy includes both the frequency of wins and the size of wins relative to losses. That’s why a trader can win 65% of trades and still lose money if the losers are larger (or if one tail loss wipes out many small wins).

Expectancy vs profit factor

Profit factor (gross profit ÷ gross loss) is useful—especially for quickly spotting “death by a thousand cuts.” But expectancy is often easier to act on because it maps directly to decision-making: it tells you what you’re making (in R) per trade. If you’re already tracking profit factor, pair this guide with the profit factor calculator and the deeper breakdown in Consistent Profit Factor Blueprint.

Why serious traders track expectancy in R-multiples

R-multiples normalize outcomes to your planned risk (1R). That matters because active traders often change size across instruments, sessions, or accounts (especially prop firm traders). If you only track dollars, performance can look “better” simply because you sized up—not because you improved.

The problem with dollar-based stats

• Size changes distort results: a bigger position makes average wins/losses look bigger without improving edge.
• Different tick values confuse comparisons: futures contracts have different multipliers—verify specs on the exchange (for example, CME contract specs).
• Harder to compare setups: “+$240 average win” means nothing unless we know risk per trade and stop distance.

What “1R” means (and how to define it)

1R is your planned risk on the trade—the amount you’re willing to lose if your stop is hit. If your risk per trade is inconsistent, expectancy becomes noisy. That’s why position sizing belongs in the same conversation as expectancy. If you need a clean baseline, start with a fixed percent or dollar risk and sanity-check it using a position size calculator.

Tip: R works for any market—futures, forex, crypto—so long as you define risk consistently and log trades in a proper trading journal.

How to calculate trading expectancy (step-by-step)

Step 1 — Pick a clean sample (and why it matters)

Don’t mix everything together. Expectancy is most useful when it’s calculated for a single setup or a tight family of setups. If you dump all trades into one bucket, you’ll average away the truth.

• Start with 50–100 trades for one setup tag (more is better).
• Keep conditions consistent (same session window, same instrument family where possible).
• Exclude obvious data errors (wrong size, missing stops, partial fills incorrectly logged).

Step 2 — Convert every trade into R

For each trade:

1. Define your planned risk: 1R = entry to stop distance × position size (in dollars or account currency).
2. Calculate outcome: trade P&L ÷ 1R = R-multiple.
3. Example: if you risked $100 (1R) and made $180, that trade is +1.8R. If you lost $95, it’s -0.95R.

If your strategy includes scaling, journaling needs to reflect the full realized P&L and the actual risk you took. (This is one reason a futures-first workflow helps, especially if you’re trading multiple contracts.) For futures-specific journaling, see the dedicated futures trading journal guide.

Step 3 — Compute win rate, average R win, average R loss

• Win%: number of winning trades ÷ total trades
• Avg Rwin: average R of winners (only winners)
• Avg Rloss: average absolute R of losers (only losers)

Step 4 — Plug into the expectancy formula

Now compute:

If you want to compute this instantly (and avoid spreadsheet errors), use the expectancy calculator.

Step 5 — Translate expectancy into dollars (optional)

R-based expectancy is best for analysis. But if you want planning clarity:

• Expected $ per trade ≈ E(R) × $ risk per trade
• Expected $ per week ≈ (E(R) × risk) × number of trades

Use this carefully. It’s not a forecast. It’s a way to sanity-check if your process has a positive edge and whether your sizing is reasonable.

Worked expectancy examples (with numbers)

Example A — Lower win rate, bigger winners

Suppose you have 100 trades tagged “Opening Range Breakout”:

• Win rate: 45% (W = 0.45)
• Loss rate: 55% (L = 0.55)
• Avg Rwin: 1.8R
• Avg Rloss: 1.0R

Expectancy: E(R) = (0.45 × 1.8) − (0.55 × 1.0) = 0.81 − 0.55 = +0.26R

If your planned risk is $100 per trade, that’s roughly $26 per trade on average. Again: average, not guaranteed.

Example B — Higher win rate, smaller winners

Another setup has:

• Win rate: 62% (W = 0.62)
• Loss rate: 38% (L = 0.38)
• Avg Rwin: 1.1R
• Avg Rloss: 1.0R

Expectancy: E(R) = (0.62 × 1.1) − (0.38 × 1.0) = 0.682 − 0.38 = +0.302R

This setup has a slightly higher expectancy—but it may feel psychologically harder if it requires holding winners longer, or easier if it fits your execution style. That’s why expectancy should be reviewed alongside drawdowns and rule adherence.

What “good expectancy” looks like in real trading

Many active traders are surprised how “small” good expectancy can look: +0.10R to +0.30R per trade can be meaningful if it’s stable, repeatable, and not driven by a few outliers. The goal isn’t a perfect number—it’s a process you can improve.

Expectancy vs other metrics (comparison table)

Expectancy is best used as the “north star” metric, but it becomes far more actionable when paired with supporting stats—especially when you segment by setup tags. If you’re building out your analytics stack, start with Tradevia’s calculators and a clean logging workflow.

If your current tracking is mostly spreadsheet-based, compare that approach with purpose-built tooling in Tradevia vs Stonk Journal, then check the full features overview.

How to improve expectancy (the only 5 levers that matter)

Expectancy improves when you change one of these levers—ideally without breaking the others. The key is to test changes by setup tag, not by “vibes.”

1) Lift average winner (without forcing trades)

• Define where a trade is “allowed” to become a runner (structure-based, not hope-based).
• Track MFE/MAE or at least “exit reason” so you can see if you’re cutting winners early.
• Segment by market regime (trend vs chop) to avoid over-holding in the wrong environment.

2) Shrink average loser (without getting chopped)

• Use a consistent stop model (technical + volatility-informed) and log when you violate it.
• Cap “rule break” losses with hard limits—tail losses destroy expectancy.
• Audit sizing with a position sizing framework so “one bad trade” can’t undo weeks.

3) Improve selectivity to raise win rate

• Filter out your lowest-quality variants (time of day, context tags, confirmation rules).
• Review your best 20 trades and worst 20 trades—then find what differs (not what’s similar).
• Use a pre-trade checklist and log if you skipped it.

4) Reduce costs (commissions + slippage)

For short-horizon trading, costs matter. Track expectancy net of fees when possible. If you trade futures, verify contract tick size and multiplier details on the exchange (for example, CME contract specs).

5) Remove tail-risk losses (the “one trade” problem)

The fastest way to lift expectancy is often to eliminate a small number of outsized losses caused by rule breaks, revenge trades, or ignoring stops. If you’re building a process, link your execution rules to a repeatable workflow (see workflows).

Common expectancy mistakes (and fixes)

• Mistake: Mixing multiple strategies into one expectancy number.
  Fix: Tag by setup and calculate expectancy per tag.
• Mistake: Using inconsistent risk per trade.
  Fix: Define 1R and enforce sizing with a position size rule.
• Mistake: Ignoring commissions/slippage.
  Fix: Evaluate net results (especially for scalping).
• Mistake: Letting a few outliers dominate.
  Fix: Inspect distribution and cap rule-break behavior; review drawdown alongside expectancy.
• Mistake: Chasing “better expectancy” by moving exits randomly.
  Fix: Make one change at a time and test it over a clean sample.

Expectancy journaling checklist (what to log every trade)

Expectancy needs clean inputs. If you’re building your logging system from scratch, start with How to Start a Trading Journal and the free trading journal templates.

Minimum fields (non-negotiable)

• Instrument + session: ES/NQ/CL, London/NY open, etc.
• Entry, stop, exit, size: enough to compute 1R and the R-multiple outcome.
• Setup tag: the “reason you took the trade” (your playbook category).
• Rule adherence: did you follow plan? (yes/no + quick note)
• Exit reason: target, stop, time-based, discretionary, partials, etc.

The “edge tags” that unlock pattern-finding

• Market condition: trend / range / news / low liquidity
• Time window: first 30 minutes vs midday vs close
• Entry quality: A / B / C grade (define what those mean)
• Mental state: focused / rushed / tilted (short, honest label)

If you trade frequently, manual journaling becomes a bottleneck—especially for active futures traders. That’s why many traders use a dedicated day trading journal and import integrations where possible (see integrations).

A simple weekly review workflow (30–45 minutes)

This is the “no drama” way to use expectancy. Do it once a week, same time, same structure:

1. Filter by setup tag: choose your top 1–3 most traded setups.
2. Check expectancy + drawdown: confirm edge is positive and not driven by one outlier.
3. Identify the leakage: rule breaks, time-of-day issues, exit mistakes.
4. Pick one adjustment: one rule, one filter, or one execution improvement.
5. Document the test: what changed, when it starts, and how you’ll judge it.

If you’re also tracking portfolio-level health, use profit factor as a quick “are we bleeding?” check (see profit factor calculator) while expectancy remains your setup-level compass.

The Tradevia workflow (turn expectancy into a playbook)

Expectancy becomes useful when it’s connected to a repeatable journaling + review loop. Tradevia is built for that: fast capture, clean segmentation, and analytics that map back to decisions. Start with the trading journal foundation and explore the broader Tradevia features.

Segment expectancy by setup, time, and market

Instead of “my expectancy is +0.12R,” you want: “Setup A is +0.28R in the first 90 minutes, but negative midday,” or “Setup B is positive only when volatility is elevated.” That’s the kind of insight that comes from consistent tagging and review. For more on interpretation, see Mastering Your Analytics Dashboard.

Use workflows + playbooks to enforce process

A number alone doesn’t change behavior. A workflow does. Use workflows to formalize: pre-trade checks, journaling requirements, and weekly review steps—so expectancy improvements stick.

Stress-test results with simulations (optional)

If you’re performance-driven, you can use simulations (like Monte Carlo) to understand potential variance and drawdown behavior based on your historical distribution. This doesn’t predict the future—but it can help you ask better risk questions, especially when scaling or trading funded/prop-style rulesets.