The Problem With "Feel"
Every systematic trader eventually experiences a stretch where the market simply refuses to cooperate.
Signals fire.
Trades trigger.
But the market never expands.
Instead the session looks like this:
small move pause reversal
Repeated over and over.
During these periods, the psychological temptation is powerful:
"Maybe I should just trade the opposite."
But that thought is dangerous because it replaces data with emotion.
My goal has been to design a workflow where I can observe market behavior directly instead of guessing.
The Motto
One idea has become central to my trading process:
Stop guessing. Start observing.
Instead of asking:
"Why isn't the system working today?"
I ask:
"What is the market actually doing?"
To answer that question, I began tracking detailed trade behavior metrics.
Recording Trade Behavior
Every completed trade now records two key statistics:
Metric Meaning
MAE Maximum adverse excursion MFE Maximum favorable excursion
These values describe the actual path of a trade, not just the final profit or loss.
For example:
Trade MFE MAE Outcome
Trade A small large loss Trade B moderate moderate break-even Trade C large small winner
After a trade closes, these values are written into a database table.
The result is a continuously growing dataset describing how trades behave in different market conditions.
Turning Trade Data Into Market Observations
Once trade behavior is recorded, the next step is to compute rolling statistics.
For example:
Statistic What it tells me
Average MFE (last N trades) Is the market expanding? Average MAE (last N trades) How violent are reversals? Expansion rate How often trades actually move Consecutive losses Short-term variance context
These statistics provide a rough measure of market climate.
Instead of guessing whether conditions are good or bad, I can observe the data directly.
A Simple Example
One useful metric is the expansion rate.
Conceptually it looks like this:
SELECT
AVG(CASE WHEN mfe >= threshold THEN 1 ELSE 0 END)
FROM recent_trades;
This tells us:
What percentage of trades actually expanded.
When that percentage drops, the market is often in a compression regime.
Compression regimes typically produce:
- frequent reversals
- overlapping bars
- failed breakouts
None of those environments are friendly to trend-capture strategies.
Building a Market Climate Dashboard
To make these observations usable during trading, I added a small header dashboard to my execution spreadsheet.
The dashboard displays metrics like:
Metric Purpose
Trades today session activity Realized PnL risk awareness Win rate (rolling window) short-term context Average MFE expansion behavior Average MAE reversal pressure Market climate label compression / neutral / expansion
This information updates automatically using a simple SQL query.
Instead of relying on gut feeling, the spreadsheet shows what the market has actually been doing.
Why This Matters
The biggest benefit of this approach is psychological.
When a string of losses occurs, the brain naturally searches for explanations.
Without data, those explanations tend to be emotional:
- the system is broken
- the entries are wrong
- the strategy needs to be reversed
But when behavior statistics are visible, the interpretation changes.
A losing streak might simply mean:
- the market is rotating
- expansion probability has dropped
- volatility is compressed
In other words:
The system might be fine --- the environment changed.
Separating Process From Outcome
One of the most important distinctions in systematic trading is this:
Concept Meaning
Outcome profit or loss Process execution quality
A trade can lose money while still being executed perfectly.
By focusing on behavior metrics rather than only PnL, it becomes easier to judge the day based on process adherence, not just financial outcome.
A Small But Powerful Shift
Before implementing this workflow, difficult sessions often felt chaotic.
Now the question becomes much simpler:
What is the market doing right now?
The database already contains the answer.
My job is simply to look.
Final Thought
Systematic trading becomes much easier when we stop trying to predict every move.
Instead, we can build tools that help us observe the market clearly.
Trade data is not just a performance record.
Used correctly, it becomes a market behavior sensor.
And that small shift --- from guessing to observing --- can make an enormous difference in both decision quality and trader psychology.