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volume-to-movement-characterization

Characterize the relationship between volume and price movement. This study explores how much traded volume is required to produce a given price displacement and how that efficiency varies over time.

Why​

Candle size alone tells us how far price moved, but not how much effort it took. By comparing volume against absolute price movement, we can measure the efficiency of market participation — when trades drive meaningful movement vs. when they merely churn.

This ratio becomes a powerful diagnostic for:

  • Detecting liquidity thinness or absorption phases.
  • Understanding intraday “effort vs. result” cycles.
  • Comparing how active different temporal regimes are.

Method​

  1. Compute absolute price movement per candle:

    [
    \Delta p_t = |close_t - open_t|
    ]

  2. Compute the volume-to-movement ratio:

    [
    \text{vm_ratio}_t = \frac{\text{volume}_t}{\Delta p_t}
    ]

  3. Optionally normalize both terms:

    • Use relative volume (volume / rolling_mean(volume, N))
    • Use percent price change instead of absolute difference.

  4. Create two complementary analyses:

    • Scatter Analysis — plot Δp_t (x-axis) vs. volume_t (y-axis). Look for density clusters and slope patterns.
    • Distribution Analysis — compute mean, std, and σ-bands of vm_ratio_t. Identify how stable or noisy the relationship is.

Interpretation​

RegimeMeaning
High vm_ratioHigh volume, small price change → absorption, congestion, or spoofing-like phases.
Low vm_ratioSmall volume, large price change → low-liquidity moves, high impact efficiency.
Stable σ-bandsPredictable liquidity behavior — consistent trading efficiency.
Expanding σ-bandsStructural change — volatility regime shift or participation shock.

Temporal View​

Combine this with the temporal heatmap approach:

  • Create an hour × day grid.
  • Fill each cell with the mean or std of vm_ratio_t.
  • This highlights when the market tends to move “cheaply” (low ratio) or “expensively” (high ratio) in terms of effort per move.

Goals​

  • Quantify how efficiently volume converts to price movement.
  • Identify temporal patterns in trading effort.
  • Establish a baseline distribution for DEX-relative data.
  • Provide a framework for comparing DEX-local vs. global volume data later.

Next Steps​

  • Add correlation analysis between vm_ratio_t and volatility measures.
  • Compare distributions across different timeframes.
  • Introduce conditional slicing (e.g., by Bollinger regime or volatility level).
  • Extend to DEX vs. global once broader volume APIs become available.