Trade Accuracy by Setup Type

Trade Accuracy by Setup Type measures win rate and expectancy individually for each trade setup or strategy a trader uses — rather than blending all trades into a single aggregate number. This consistency metric reveals whether each setup in your playbook is generating or destroying edge, and by exactly how much.

Formula & Calculation

Setup Expectancy = (Win Rate × Avg Winner) − (Loss Rate × Avg Loser)

Where:

• Win Rate = winning trades ÷ total trades for that setup (e.g., 0.61 for 61%)
• Loss Rate = 1 − Win Rate (e.g., 0.39 for 39%)
• Avg Winner = mean dollar profit on winning trades for that setup
• Avg Loser = mean dollar loss on losing trades for that setup

Calculate this separately for each setup tag in your trade log. A positive result means the setup has positive expectancy; a negative result means it is losing money on average, regardless of how often it wins. Win rate alone is insufficient: a setup with a 60% win rate can still carry negative expectancy if average losers are significantly larger than average winners.

Benchmarks

Level	Range	What It Means
Strong Edge	Above +$20/trade	High-conviction setup — allocate maximum capital and trade frequency
Positive Edge	+$5 to +$20/trade	Solid setup worth keeping in your playbook
Marginal	$0 to +$5/trade	Minimal edge — monitor closely; small execution slippage can turn it negative
Negative	Below $0/trade	Losing setup — cut from playbook after 50+ sample trades

Note: dollar benchmarks scale with account size. Express results in R multiples (expectancy ÷ avg risk per trade) to compare setups across different position sizes.

Practical Example

A day trader runs three setups on QQQ and individual tech stocks over six months:

Opening Range Breakout (BO): 38 trades, 34% win rate, avg winner $210, avg loser $75. Expectancy = (0.34 × $210) − (0.66 × $75) = $71.40 − $49.50 = +$21.90/trade

VWAP Pullback (PB): 52 trades, 61% win rate, avg winner $95, avg loser $90. Expectancy = (0.61 × $95) − (0.39 × $90) = $57.95 − $35.10 = +$22.85/trade

Pre-Market Gap Fill (GAP): 29 trades, 45% win rate, avg winner $80, avg loser $100. Expectancy = (0.45 × $80) − (0.55 × $100) = $36.00 − $55.00 = −$19.00/trade

The aggregate win rate across all 119 trades is 47% — looks marginal but plausible. The GAP setup alone generated −$551 in losses over 29 trades. Eliminating it and reallocating those 29 trades to the VWAP pullback setup produces an expected +$663 in additional profit — a meaningful improvement achieved by trading less, not more. The breakout setup at 34% wins also outperforms the gap-fill setup at 45% wins, confirming that win rate without R multiple context is a vanity number.

How to Track Trade Accuracy by Setup

1. Tag every trade before entry — Assign a short label at the point of entry: BO, PB, GAP, FADE, or whatever fits your playbook. Pre-entry tagging prevents post-hoc rationalization.
2. Log entry price, exit price, and outcome — You need winner/loser status and dollar P&L per trade to calculate both win rate and expectancy.
3. Accumulate 30+ trades per setup before drawing conclusions — At 50 trades, the 95% confidence interval on win rate narrows to roughly ±10%. Below 30, the data is too noisy to act on.
4. Calculate setup expectancy monthly — Filter your trade log by tag, compute win rate, average winner, average loser, and apply the formula. A spreadsheet or dedicated journal handles this automatically.
5. Annotate market regime — Record whether conditions were trending, choppy, or ranging. Setup performance is regime-dependent; a breakout setup that excels in Q1 2024 trending conditions may underperform in choppy mid-year markets.

How to Improve Trade Accuracy by Setup

1. Cut negative-expectancy setups after 50 trades — This is the single highest-leverage action available. Reallocating those trades to your best setup typically adds more profit than any entry refinement.
2. Narrow to 2-3 setups maximum — Each additional setup splits mental bandwidth and slows sample accumulation. Fewer setups means faster feedback loops and tighter execution.
3. Activate setups conditionally by regime — If your breakout setup has positive expectancy in trending markets but negative expectancy in choppy conditions, pause it — don’t cut it. Annotate market type alongside setup tags.
4. Audit losing trades within positive-expectancy setups — If a setup has good expectancy but mediocre win rate, review the losing trades for patterns: late stops, poor entry timing, or wrong market conditions. Fixing one root cause can shift a good setup into a strong one.

Common Mistakes

1. Tracking only aggregate win rate — A 48% aggregate win rate across 200 trades looks like marginal edge. It may actually be one strong setup (positive expectancy) and one losing setup (negative expectancy) blended together. The aggregate is useless for decision-making.
2. Cutting setups with fewer than 30 trades — At 20 trades, a true 45% win rate can look like 30% or 60% by chance alone. Premature cuts remove setups before you have enough data to evaluate them.
3. Using win rate alone to evaluate setups — A breakout trader running 35% wins at 3:1 R:R has expectancy of +0.4R. A pullback setup at 62% wins and 0.8:1 R:R has expectancy of +0.12R. The lower win rate setup is three times more profitable per trade.
4. Ignoring market regime — A setup with negative expectancy in the current market may have strong expectancy in a different regime. Regime-blind analysis leads to premature culling of valid strategies.
5. Keeping a marginally negative setup due to occasional big wins — One outlier trade can push a negative-expectancy setup into positive territory for a given month. Expectancy is only meaningful over 50+ trades; single-month samples mislead.

How JournalPlus Calculates Trade Accuracy by Setup

JournalPlus supports custom setup tags applied at trade entry or imported via broker data. The analytics dashboard automatically segments performance by tag — displaying per-setup win rate, average winner, average loser, and expectancy without manual filtering. Traders see a setup breakdown table updated in real time as new trades are logged, with the ability to filter by date range and market session to isolate regime-specific performance. The trade log filter allows side-by-side comparison of any two setups — showing the exact dollar difference in expectancy that makes culling decisions straightforward rather than intuitive.