Temporal Volume Characterization

Characterize temporal patterns of trading volume. This study examines how relative volume behaves across hours, days, and weeks to reveal participation cycles and local liquidity rhythms on the DEX.

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Why

Volume isn’t constant — it pulses with time, volatility, and trader behavior. Understanding these temporal patterns helps you answer questions like:

• When is liquidity deepest or thinnest?
• Are there predictable “quiet hours” or “burst hours”?
• Does activity cluster around certain days of the week or after key events?

Even with DEX-local data, the time structure of activity carries valuable information about the micro-seasons of your market.

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Data Requirements & Limitations

Current Scope: This study uses DEX-local volume data as the input source. At present, no global aggregate or cross-exchange volume feeds are integrated.

Limitations:

• The analysis reflects local participation patterns specific to this DEX, not total ecosystem liquidity.
• Temporal cycles may be influenced by DEX-specific user base, incentives, or market-making activity.
• Absolute values should not be interpreted as total market volume — only relative intra-DEX patterns are reliable.

Future Integration: Once additional APIs or oracles become available:

• Compare DEX-local patterns to aggregate ecosystem volume.
• Quantify correlation and lag between DEX and global temporal signatures.
• Use cross-exchange weighting to refine the global view of participation rhythms.

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Method

1. Compute relative volume

   $\mathrm{rel\_vol}_t = \frac{\text{volume}_t}{\operatorname{rolling\_mean(volume, N)}}$

   This normalizes local fluctuations and removes long-term drift.

2. Extract temporal components

   • Hour of day
   • Day of week
   • (Optional) Month or event-phase tag

3. Aggregate

   • Compute mean, median, and std of rel_vol for each (day, hour) pair.

   • Generate a heatmap table:

     day_of_week ↓    hour_of_day →   0  1  2  ...  23
     ---------------------------------------------------
     Mon                           ████░░░░░░░░░
     Tue                           ░░███░░░░░░░░
     ...
     Sun                           ░░░░░░░██████

4. Visualization

   • Use Tableau or matplotlib to render heatmaps and highlight hotspots.
   • Optionally overlay key volatility or funding events.

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Interpretation

Pattern	Possible Meaning
Consistent peaks	Regular high-activity hours — e.g., U.S. or Asia overlap.
Weekend drop-off	Retail inactivity or liquidity providers offline.
Temporal skew	Asymmetry between early and late-session activity.
Volatility-coupled bursts	Spikes aligning with external catalysts.

These temporal “signatures” can later be compared against other datasets (global feeds, oracles, CEXs) to test whether the same rhythms hold true market-wide.

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Temporal Anomalies & Behavioral Inference

Beyond describing patterns, this study also aims to infer the nature of market activity — distinguishing likely human-driven from bot-driven behavior based on temporal anomalies.

Anomalous Conditions

Look for periods when relative volume deviates strongly from expectations:

• High activity on low-participation holidays (e.g., Christmas Day).
• Sustained or elevated volume during traditionally quiet hours.
• Repetitive bursts that occur with machine-like regularity.

Inference Framework

Observation	Likely Mechanism
High volume + flat price	Market makers rebalancing or arbitrage bots maintaining spreads.
Low volume + large price moves	Low-liquidity environment, thin books, or manual intervention.
Volume spikes with no event correlation	Internal system mechanics (oracle updates, liquidations, incentives).
Recurrent off-hour bursts	Scheduled bots, funding resets, or automated re-hedging.

Analytical Steps

1. Compute expected volume per (hour, day) cell and its σ range.
2. Flag cells exceeding (mean + 2σ) as anomalies.
3. Cross-check against volatility and price movement to contextualize.
4. Annotate potential causes based on behavioral patterns.
5. Overlay external data (holiday calendar, liquidation events, AMM incentives).

This approach allows for behavioral inference without needing explicit trader identifiers — you can observe how the system behaves and infer who is active based on temporal fingerprints.

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Goals

• Establish a baseline temporal volume profile for the DEX.
• Quantify intra-day and intra-week participation cycles.
• Provide visual tools (heatmaps) for quick pattern recognition.
• Identify temporal anomalies suggestive of automated activity.
• Lay groundwork for comparing DEX vs. global temporal structures later.

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Next Steps

• Integrate volatility overlays — see how participation intensity shifts across volatility regimes.
• Cross-compare with volume-to-movement ratio to find efficient vs. inefficient periods.
• Add event-anchored alignment (FED, CPI, liquidation windows).
• When broader APIs are available, expand the analysis to include aggregate ecosystem volume.