Direct answer
“Decreasing loss” in forex trading usually means improving the outcomes so that losses become smaller on average and/or occur less often over time. In the average win loss framing, you look at the relationship between average win size and average loss size, and you track whether the overall pattern becomes less damaging as you collect more completed trades.
Important limitation: you cannot know the future results of any new trade. The only defensible approach is to define what “loss” means, measure it across a sufficiently large set of trades, and check whether your win–loss balance is improving.
How average win loss is used to reduce loss
Average win loss is a way to summarize trading performance using two quantities:
- Average win: the mean size of profitable trades (often measured in pips or account currency).
- Average loss: the mean size of losing trades (also measured consistently).
“Decreasing loss” can be addressed through two measurable channels:
- Lowering average loss: If losing trades are, on average, smaller than before, then average loss decreases.
- Improving the win–loss relationship: Even if average loss does not change much, the pattern can worsen or improve depending on how average win compares to average loss.
A practical way to “make it work” is to choose consistent measurement rules. For example, define loss by the executed outcome of the trade (not by what might have happened). Then compute average win and average loss from completed trades only. When you update these values with new data, you can verify whether loss is actually decreasing.
You can also use loss coverage as a concept: evaluate whether the typical win is large enough to offset the typical loss over your tracked sample. This is not a promise; it is a comparison based on past outcomes.
Example checks and what to verify
Use checks that confirm whether your process is reducing harm:
- Compare average loss over two samples: For example, compute average loss from an earlier set of trades, then recompute from a later set of completed trades. Look for a consistent reduction rather than a single outlier.
- Check loss size distribution: Average loss can hide extremes. If a few very large losses appear, the mean may not tell the whole story.
- Verify measurement consistency: Ensure “loss” is measured the same way each time (same instrument conventions, same unit such as pips, and same method for recording outcomes).
These checks are ways to test claims about decreasing loss without relying on predictions.
Limitations and risks
Several limitations apply:
- No certainty for future trades: Even if average loss has decreased historically, new market conditions can change results.
- Small samples mislead: Early changes in average win loss can be driven by randomness rather than process improvement.
- Execution and costs matter: Real outcomes depend on how trades are executed and how costs are reflected in your measurement.
- Different definitions change conclusions: If “loss” is defined inconsistently, comparisons across time become unreliable.
Because of this uncertainty, treat decreasing loss as an outcome you must measure and verify using completed trade data and clear definitions, rather than something you can guarantee.