Ask most traders which of their setups performs best and they will give you an answer based on gut feel — the setup they are most confident in, the one that has the most recent memorable wins, the one they've read the most about. Ask them to show you the data, and most cannot.
This matters because gut feel about strategy performance is systematically unreliable. Human memory overweights recent events, vivid wins, and emotionally memorable trades. It underweights small, consistent gains from simple setups and overweights spectacular-but-inconsistent performance from complex ones.
The result: most traders are spending their best trading hours on setups that their data, if they looked at it, would show are mediocre — while undertrading their most profitable setups because those setups feel "too simple" or "not exciting enough."
Your trade data already contains the information you need to correct this. This guide shows you exactly how to extract it.
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Before you can analyze performance by setup type, you need to have labeled your trades consistently. If you have recorded "breakout," "Breakout," "BO setup," and "range breakout" as four different setups for the same pattern, your data is fragmented and your analysis will be meaningless.
Create a specific, finite list of setup names that covers every type of trade you take. Examples for an Indian intraday trader:
Every trade you log should be categorized into one of these setups. If a trade doesn't fit, either add a new category or use "Discretionary/Other" and review it later.
This categorization work, done consistently from the first day of journaling, is what makes setup-level analysis possible. If you are starting a journal now, define your setup list on day one. If you have existing journal data with inconsistent labels, spend an hour standardizing the labels before running analysis.
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With consistently categorized trades, you can now calculate expectancy for each setup type. As covered in the performance analysis methods guide, expectancy = (Win Rate × Average Win) – (Loss Rate × Average Loss).
Run this calculation for each setup category where you have at least 20 trades (fewer than 20 trades produces statistically unreliable results — the sample is too small to distinguish genuine edge from variance).
A typical result for an Indian intraday trader might look like this:
| Setup | Trades | Win Rate | Avg Win | Avg Loss | Expectancy |
|-------|--------|----------|---------|----------|------------|
| Opening Range Breakout | 48 | 54% | ₹2,800 | ₹1,600 | +₹780 |
| Trend Continuation | 35 | 62% | ₹2,100 | ₹1,900 | +₹580 |
| Resistance Breakout | 52 | 41% | ₹3,400 | ₹1,500 | +₹507 |
| Reversal at Key Level | 29 | 34% | ₹2,200 | ₹1,800 | –₹438 |
| Gap Fill | 18 | 44% | ₹1,600 | ₹2,100 | –₹474 |
| Event-Based | 12 | 33% | ₹4,100 | ₹3,200 | +₹214 |
This table tells you everything you need to know about which setups to trade more and which to trade less. The Opening Range Breakout has the highest expectancy by a wide margin. The Reversal at Key Level and Gap Fill setups have negative expectancy — they are loss sources that should be eliminated.
The prescription: trade the top three setups more, eliminate the bottom two, and monitor the Event-Based setup (positive expectancy but small sample size — more data needed before committing more capital).
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Knowing which setups work overall is valuable. Knowing which setups work under specific market conditions is even more valuable.
For each of your positive-expectancy setups, break the data down by market condition:
Trending vs. ranging markets: Many momentum setups (like Opening Range Breakout and Trend Continuation) perform well in trending markets and poorly in ranging ones. If you can identify which market condition you're in before the session, you can adjust your setup selection accordingly.
High volatility vs. low volatility: Options strategies, in particular, perform very differently in high-IV environments compared to low-IV ones. Breakout setups in high-volatility conditions may produce larger winners but also larger losers.
Time of day: Many Indian intraday setups have strong time-of-day dependency. The same ORB setup that produces +₹780 expectancy overall might show +₹1,200 before 11 AM and –₹300 after 2 PM.
Day of week: Some traders find their performance varies significantly by day of week — often related to institutional activity patterns or specific recurring events (like weekly F&O expiry on Thursdays).
Each of these conditional analyses narrows your trading to the specific combinations of setup, market condition, time, and context where your edge is strongest.
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Once you have identified your highest-expectancy setups and the conditions where they perform best, you can improve returns without changing your strategy at all — simply by sizing your best setups larger.
This is one of the most underused improvements in retail trading. Most traders use uniform position sizing across all setups. But if your Opening Range Breakout has 3x the expectancy of your Gap Fill setup, putting the same capital at risk on both is an inefficient use of your edge.
Consider a tiered sizing approach:
This asymmetric sizing concentrates capital in your best opportunities without taking on additional overall risk — because you are reducing size on weaker setups to offset the increase on stronger ones.
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Trading strategies are not static. A setup that worked well last year may underperform this year due to changing market conditions, increased competition from algorithmic traders, or shifts in institutional behavior. An evolving market requires an evolving strategy — or at minimum, regular verification that your edge is still intact.
Track the trailing expectancy of each setup across rolling 3-month periods. If a setup's expectancy has been declining for 6 months, that is a signal to reduce its weight in your trading or investigate what has changed. If a setup's expectancy has been improving, consider whether you are giving it enough capital allocation.
[Improve trading performance through data analysis](/blog/improve-trading-performance-data-analysis) covers the rolling performance analysis framework in detail, including how to distinguish genuine edge decay from short-term variance.
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The analysis described in this guide — expectancy by setup, conditional performance breakdown, time-of-day segmentation — requires either significant spreadsheet work or a purpose-built analytics platform.
TradeFix AI automates this entire process. When you log trades with setup categories (which the entry form prompts you for), the analytics dashboard automatically calculates expectancy by setup, segments performance by time of day and market condition, and tracks how each setup's performance evolves over time.
The AI Coach goes further: it reads your complete pattern history and provides specific recommendations. "Your Trend Continuation trades have a 40% higher win rate when Nifty is above its 20-day moving average — consider adding this as a prerequisite filter." "Your Gap Fill trades are consistently negative after 12 PM — consider cutting this setup after noon."
These insights are the output of exactly the analysis described in this guide — but generated automatically from your data, without requiring you to build spreadsheet formulas or run manual queries.
[Trading performance tracker India](/blog/trading-performance-tracker-india) explains how systematic performance tracking enables the kind of strategy identification and optimization described in this guide — and why consistent data entry is the prerequisite for all of it.