Concept
Distribution-of-returns Profiling
Distribution-of-returns Profiling is a Statistics concept. The Library holds 5 implementations, each one a working definition you can pull into Quant.
Top Distribution-of-returns Profiling indicators
5 total
What is Distribution-of-returns Profiling?
Distribution-of-returns profiling builds the empirical distribution of a market's moves from history and reads its shape directly, instead of assuming a bell curve. Collect returns per bar, per session, or per week, optionally conditioned on context such as weekday or regime, then examine the histogram and its summary statistics: center, spread, skew, and tail weight, the classic distribution moments, along with percentiles and exceedance frequencies such as the share of days that travel more than a given distance.
The exercise matters because market returns are famously non-Gaussian: fat tails and volatility clustering make extreme moves far more common than a normal curve implies. Profiling the observed sample grounds targets, stops, and expectations in measured frequencies rather than idealized math, and it supplies the raw material for tools such as probability cones and exceedance tables. The estimates are only as good as the sample: tails need a lot of data, and old regimes may not represent current ones.
How traders use it
- To size targets and stops from observed frequencies: if only a small share of sessions travel beyond some multiple of ATR, a target parked past that distance is, by the data's own account, a low-frequency outcome.
- To test for conditional edges: split the sample by session, weekday, or volatility regime and compare the distributions; a bias that survives the split, with adequate sample size in each slice, is worth further study.
- To sanity-check model assumptions: comparing the sample's tails against a fitted normal curve shows how badly sigma-based bands and thresholds will understate extremes before you rely on them.
More Distribution-of-returns Profiling implementations
Related concepts · Normalization & distribution
Concept family
Statistics
45 concepts mapped · 37 in the Library
Distribution-of-returns Profiling FAQ
Are market returns normally distributed?
No. Empirical return distributions are fat-tailed and often skewed, and volatility clusters in time, so large moves arrive far more often than a normal distribution predicts. That is exactly why profiling the observed distribution is useful: rules calibrated to a Gaussian assumption, such as fixed sigma thresholds, systematically understate how frequent and how large the extremes really are.
How much history do you need to profile returns?
It depends on which part of the distribution you care about. Estimates of the center and middle percentiles stabilize with modest samples, while tail frequencies need far more data, and every conditioning split (by weekday, session, or regime) thins the sample further. There is no universal number: report sample size alongside the estimate, and remember that old data may describe a regime that no longer exists.
Build Distribution-of-returns Profiling your way.
Quant writes, tests, and refines it with you — then it runs on LuxAlgo charting or ports to TradingView.