This document provides a technical demonstration of the Risk Module, specifically designed to estimate the probability that an asset's price will remain within a specified range over a given timeframe. The primary objective is assessing market volatility through statistical techniques grounded in the normal distribution model. Furthermore, this document outlines a broader conceptual vision, highlighting how traditional financial methodologies may find new efficiencies within WEB3 environments, thanks to the unprecedented availability of open, transparent data.
The Risk Module evaluates the likelihood of an asset’s price remaining within a predetermined range (± threshold) after a set number of steps (minutes). The core concept involves constructing a probability distribution of future prices based on current volatility measurements.
[ToDo: Diagram illustrating how the Risk Module operates]
Distribution Modeling:
The Risk Module builds a normal distribution reflecting price volatility. The area under this distribution curve within a target range directly translates into the probability of price remaining in that range.
Example:
Calculating this area for ranges relevant to Uniswap CLMM pools produces precise probabilities essential for risk assessment.
Calculation Method:
For consecutive asset prices P[t] and P[t+1], the relative change is computed as:
$$ \delta_t = \left|\frac{P[t+1] - P[t]}{P[t]}\right| $$
Noise Reduction:
The module applies a rolling average to smooth short-term fluctuations and generate a stable volatility measure.
The Risk Module also outputs directional probabilities (upward vs. downward movement). While initial probabilities are set to equal (50/50), enhanced methods based on market conditions allow adjustments through:
Technical Analysis:
Metrics such as deviations from moving averages facilitate precise parameter adjustments, significantly enhancing prediction accuracy.
Machine Learning & Clustering:
By categorizing historical data into distinct clusters (e.g., volatility regimes), the module adaptively tunes its predictions based on recognized market states.
The model emphasizes extreme probabilities—values significantly deviating from the central 40–60% probability range—which are typically representative of market noise. By focusing on these extremes, the module identifies crucial signals indicating substantial volatility shifts.