What is the difference between expectancy in money and expectancy in R?

Read the unit before the value

Money expectancy = sum of net P&L / completed-trade count
Example: (+30 - 20 + 50) / 3 = 20 account units per trade

The example is a historical average across three fictional outcomes. It does not say that the next trade will make 20 units. Position sizes, instruments and costs can vary across the sample, so the number needs its scope as well as its unit.

An R average asks a different question: how large were outcomes relative to the risk reference used for each eligible trade? That normalisation can support a different comparison, but only when the risk inputs and calculation basis are understood.

Check which records are eligible

A completed trade can have a valid net monetary result while lacking a valid initial stop. It may contribute to money expectancy but be unavailable for price-R analysis. Comparing the two averages as if they described identical rows can hide that difference.

Some Chart Reporter analytics use net money R where derivable and planned price R as a fallback. Those values can differ because fees and risk inference matter. Use the widget's definition and eligible count to understand the reported average rather than assuming that every R label means the same calculation.

Use averages with distribution context

An average can conceal a small number of large gains or losses. Inspect the distribution, concentration and date coverage before concluding that a typical trade resembles the mean. Break down the sample using a documented question rather than searching until an attractive subgroup appears.

When recording a finding, write the unit, account scope, trade count and R basis where relevant. If one period has materially different position sizes, money expectancy and R expectancy may tell different stories for valid reasons. Neither measure alone establishes that a change in process caused an improvement or that the observed average will persist.