Running a Quarterly Trading Review

Your weekly review catches the last five sessions. Your monthly review catches the last twenty. Neither one can see the slow-moving problems: an edge that is quietly eroding, a strategy that has drifted three steps from the rules you wrote down, or a market regime that changed underneath you while you kept trading the old one. Those trends only become visible when you zoom all the way out to 90 days.

A quarterly trading review is the longest horizon most retail traders ever look at deliberately. It answers a different question than the shorter reviews. Weekly and monthly reviews ask "am I following my process?" The quarterly review asks "is my process still the right one?" That is a harder question, and it is the one that separates traders who adapt from traders who repeat a system that stopped working two seasons ago.

This is a framework for the 90-day review: what to measure, how to read it, and how to change your rules without tinkering yourself into chaos.

• What it catches: edge decay, strategy drift, regime change, and process erosion, all invisible week to week.
• Why 90 days: enough trades to separate signal from noise, enough time for slow trends to appear.
• The core move: compare this quarter against prior quarters and your original baseline, not an all-time average.
• The output: one deliberate decision per strategy (keep, refine, remove, or cautiously add), then hold the line.

Why a quarter is the right horizon for edge detection

Short reviews are noisy. Over five or twenty trades, luck dominates. A profitable week can hide a broken strategy, and a losing month can hide a good one. You cannot tell signal from noise until you have enough trades for the numbers to mean something, and for most active retail traders that sample only accumulates over a quarter.

A quarter is also long enough to catch the thing shorter reviews structurally cannot: edge decay. An edge that was genuinely real can fade over time. Volatility regimes shift, more algorithmic participants crowd into the same behavior, and the market microstructure your rule exploited changes. A mean-reversion setup that printed money in one volatility environment can bleed slowly in the next, and the bleed is too gradual to notice week to week. You only see it when you compare this quarter against the baseline the strategy was validated on.

This is one of the most durable findings in quantitative finance. In The Journal of Finance, R. David McLean and Jeffrey Pontiff studied 97 characteristics that academic research had shown to predict stock returns. They found that portfolio returns were roughly 26% lower out-of-sample and about 58% lower after the predictor was published (McLean & Pontiff, 2016). Once a pattern becomes known and gets traded, it decays. Your personal edge is subject to the same physics, and the quarterly review is where you check whether yours is still alive.

If you have not yet settled on how these horizons fit together, start with a deliberate review cadence at the monthly level, then layer the quarterly review on top of it. The two are complements, not substitutes.

The four things a quarterly review is looking for

A quarterly review has a narrow job. You are not re-grading every trade. You are hunting for four slow-moving signals that only a wide window reveals.

• Edge decay. Is the strategy that carried you last quarter still producing the same expectancy, or has it quietly slipped?
• Strategy drift. Are you still trading the rules you wrote down, or have you added exceptions one at a time until the system on paper and the system in practice are two different things?
• Regime change. Did the market itself change (volatility, sector leadership, liquidity) in a way that invalidates an assumption your strategy depends on?
• Process erosion. Are the disciplines that used to be automatic (stops honored, sizing consistent, journal complete) starting to slip in ways too gradual to feel?

Everything below is in service of surfacing those four.

Step 1: Recompute your core metrics on the full quarter

Start with the arithmetic. Pull every closed trade from the last 90 days and compute the metrics that describe your edge as a whole:

• Expectancy (average profit or loss per trade). This is the single most important number in the review.
• Win rate and average win versus average loss. These two together explain how your expectancy is built.
• Profit factor (gross profit divided by gross loss).
• Maximum drawdown during the quarter.
• Trade count, so you know how much weight the numbers can bear.

Then do the step most traders skip: put this quarter next to the prior two or three. A single quarter's expectancy is a data point. Three quarters in a row is a trend. If your expectancy has declined each quarter while your rules have not changed, that is the fingerprint of edge decay, and it is only legible in the comparison.

Comparing rolling windows against the baseline your strategy was originally validated on reveals genuine drift far more clearly than an all-time cumulative average, which mixes your best and worst periods into one flattering blur. If you are not sure which raw fields feed these calculations, the primer on what to log in a trading journal covers the entries a clean quarterly review depends on.

Step 2: Segment by strategy, setup, and regime

An aggregate number can lie by averaging. A quarter where one strategy quietly died and another quietly thrived can net out to "flat," and you would learn nothing. So break the quarter apart.

Segment your closed trades three ways:

1. By strategy or setup. Compute expectancy for each one separately. This is where edge decay usually hides. Your headline number can look fine while one specific setup has gone negative, dragged along by the others.
2. By time within the quarter. Split the 90 days into the first half and second half. A setup that was profitable in the first six weeks and unprofitable in the last six is decaying in front of you.
3. By market condition. Tag trades by the regime you were in (higher versus lower volatility, trending versus ranging). A strategy that only works in one regime is not broken, but you need to know its boundaries so you can stop trading it when the regime flips.

This segmentation is only possible if your trades carry the metadata to slice by. A consistent tagging system is what turns a pile of trades into a dataset you can question and lets you ask "which setup is dying?"

Step 3: Check your rules against your behavior for drift

Edge decay is the market changing on you. Strategy drift is you changing on the market without noticing. Both cost money, and drift is the one you can fix for free.

Pull out the written rules for each strategy (your entry criteria, your sizing, your stop and target logic) and read them cold. Then read a sample of the quarter's actual trades against them. You are looking for the gap between the system you designed and the system you are running. Common drift patterns:

• Exception creep. You added one "just this once" override, then another, until the exceptions outnumber the rule.
• Size creep. Your position sizes have crept up on conviction trades and the risk model no longer matches your account.
• Criteria loosening. Setups you would have skipped in January are getting taken in March because you were bored or chasing.

The point of catching drift is to decide, deliberately, whether each change should be adopted into the rules or reversed out. A drifted rule that quietly improves your results should be written down and made official. A drifted rule that hurts should be cut. What you cannot afford is a system that changes by accident.

Step 4: Look for regime change, not just your own mistakes

Sometimes the problem is neither decay nor drift. The market changed. A quarter is long enough to span a genuine regime shift: a jump or collapse in volatility, a rotation in sector leadership, a change in how your instruments trade.

Write down, in one paragraph, what the market did this quarter. Was volatility higher or lower than the quarter your strategy was built for? Did the trend character change? Then ask whether any of your strategy's core assumptions depend on the regime that just ended. A trend-following system in a suddenly choppy, mean-reverting tape is not broken. It is out of season, and the correct response is to reduce size or stand aside, not to blow up the rules.

Distinguishing "my edge decayed" from "the regime turned against a still-valid edge" is the hardest judgment in the whole review. The tell is usually in the segmentation: an edge that lost money across every regime is probably decaying, while an edge that lost money only in the new regime is probably fine and waiting.

Step 5: Audit your process, not just your P&L

The last thing a quarterly review checks is the review inputs themselves. If your journal has degraded over 90 days, every number above is built on sand.

Spot-check the quarter for process erosion:

• Are your journal entries as complete in the last month as the first, or did they thin out?
• Are stops being honored at the same rate, or is discipline slipping?
• Is your tagging still consistent, or did you get sloppy and break your own segmentation?

This is also where the behavioral dimension earns its place. Overconfidence is a documented and expensive trading cost, not a vague personality quirk. Barber and Odean's classic study of 66,465 households at a discount broker from 1991 to 1996 found that the households that traded the most earned an annual return of 11.4% while the market returned 17.9% (Barber & Odean, 2000). Overtrading, driven by overconfidence, was the cost. If your trade count crept up this quarter while your expectancy fell, you are living inside that finding, and the review is where you catch it.

Step 6: Decide, then hold the line for 90 days

The output of the review is a short list of deliberate decisions, one per strategy: keep, refine, remove, or cautiously add. A quarterly cadence shifts you from executor to system architect. You evaluate what belongs and what does not, then you stop touching it.

That last part matters as much as the analysis. The discipline of a fixed quarterly window prevents constant rule tinkering. You are allowed to change the system on review day. You are not allowed to change it on a Tuesday because a trade went against you. Write the decisions down, apply them, and let the next 90 days generate the evidence for the next review.

How TradeReveal fits the quarterly workflow

Most of this review is arithmetic and honest reading, and you can do it in a spreadsheet if you are disciplined. The friction is usually in the recomputation and segmentation: pulling 90 days of trades, computing expectancy and profit factor, then slicing by setup, by half-quarter, and by regime tag.

TradeReveal computes expectancy, profit factor, win rate, R-multiple, drawdown, and P&L over time from your logged trades for free, and its Trade Explorer lets you save the sliced views (by strategy, by tag, by time window) you will want to reopen next quarter. Its deterministic behavioral-insights card also surfaces patterns like hold-time asymmetry, streaks, and stop-loss discipline from your history, which is the process-erosion signal Step 5 looks for. The tool does the recomputation so the 90-day comparison is a click, not an afternoon. The judgment stays yours.

Frequently Asked Questions

How is a quarterly review different from a monthly review?

The monthly review is a process check on a smaller sample: are you following your rules, and what habits are forming. The quarterly review is a system check on a larger sample: is the process itself still correct. A quarter has enough trades to separate signal from noise and enough time to reveal edge decay and regime change, both of which are invisible over 20 trades. Run both. They answer different questions and do not replace each other.

How many trades do I need for a quarterly review to be meaningful?

There is no magic number, but the more trades per strategy, the more you can trust the segmented expectancy. If a specific setup only produced a handful of trades in the quarter, treat its numbers as directional rather than conclusive and confirm the read next quarter. This is why segmentation matters: your aggregate might have plenty of trades while an individual setup is too thin to judge. Log enough detail that you can always see the sample size behind any number.

How do I tell edge decay apart from a normal drawdown?

Segmentation and comparison. A normal drawdown is a string of losses inside a strategy whose longer-run expectancy is unchanged. Edge decay is expectancy that steps down and stays down across multiple quarters. Split the quarter in half and compare against your prior quarters. If the recent window is worse and the decline persists across time and across regimes, it is more likely decay than a rough patch. If the decline appears only in a new market regime, the edge may simply be out of season.

Can I automate the quarterly review?

You can automate the measurement, not the judgment. Recomputing metrics and building segmented views is the kind of arithmetic software should do for you, and journaling analytics tools handle it. Deciding whether a decayed setup should be tightened, paused, or retired depends on context no metric captures. Let the tool produce the evidence, then make the call yourself.

Final Thoughts

The quarterly review is where a trader stops being only an operator of a system and starts being its owner. Weekly and monthly reviews keep you honest about execution. The 90-day review keeps you honest about the thing execution serves: whether the edge is still there, whether you are still trading the rules you wrote, and whether the market you built for is the market you are in.

The finding that runs through the research is humbling and useful in equal measure. Edges decay, published or personal, and overtrading quietly taxes the confident. Neither is a reason for despair. Both are reasons to look up from the last five trades four times a year, measure what a quarter actually did, and decide on purpose what to keep.

Start your free TradeReveal account today

Happy Trading,

The TradeReveal Team

Sources

• R. David McLean and Jeffrey Pontiff (2016). "Does Academic Research Destroy Stock Return Predictability?" The Journal of Finance, 71(1), 5-32. Wiley Online Library
• Brad M. Barber and Terrance Odean (2000). "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors." The Journal of Finance, 55(2), 773-806. IDEAS/RePEc
• Brad M. Barber and Terrance Odean, working paper version. Berkeley Haas faculty page (PDF)
• McLean and Pontiff (2016), abstract and working paper. SSRN