Matrixport

The community volume represents the market's attention and information flow towards a particular cryptocurrency during a specific period. By quantifying its extreme volatility and structural changes, it can help identify market anomalies, emotional evolution, and potential trading signals.

1.1 Sample Scope

Time Interval: 2025-01-01 to 2025-07-26

Data Frequency: 1H

Currency Filtering Criteria:

• Remove cryptocurrencies with a combined percentage of NAN values and 0 values >10% in the community_volume column

• Exclude stablecoins, resulting in a list of 41 effective currencies

coin_list = [ 'T', 'NEO', 'VIRTUAL', 'TRX', 'OP', 'FLOW', 'S', 'BERA', 'DOT', 'LAYER', 'ALGO', 'ADA', 'ALT', 'DOGE', 'XRP', 'BNB', 'SUN', 'SUI', 'FORM', 'USDC', 'TON', 'BABY', 'ARB', 'APE', 'BTC', 'GAS', 'FUN', 'ETC', 'ETH', 'PEPE', 'ONE', 'HOT', 'LINK', 'ID', 'NEAR', 'NOT', 'ME', 'MOVE', 'SUPER', 'SOL', 'TRUMP' ]

• All NAN values are uniformly filled with 0

1.2 Data Cleaning

1. Data missing cases: Forward Filling

Time point 2025-03-13 01:00:00 Missing tokens (3): ['BABY', 'FORM', 'VIRTUAL']

Time point 2025-03-13 02:00:00 Missing tokens (3): ['BABY', 'FORM', 'VIRTUAL']

Time point 2025-03-13 03:00:00 Missing tokens (3): ['BABY', 'FORM', 'VIRTUAL']

2. close_price has 0 value cases

2.1 Data Transformation

Step 1: Perform log transformation on the original community_volume data (to prevent extreme values from affecting the results)

Step 2: Use RobustScaler for standardization (more robust to extreme values than z-score)

2.2 Distribution Division

Based on the standardized distribution pattern, 41 currencies are categorized into two types:

a）Approximately normally distributed (e.g., BTC)

b) High Skewness + Long Tail (e.g., BABY)

2.3 Distribution Feature Analysis

Why do normal vs. skewed distributions occur?Possible reasons include:

a) Skewed distributions may result from “pseudo-skewness” caused by a limited number of data sources or insufficient sampling. Currently, the monitored communities are primarily large discussion groups that seldom mention small-cap cryptocurrencies and tend to focus on major assets like BTC and ETH. As a result, small-cap coins exhibit low activity and sparse commentary, with the number of comments often remaining at zero or near-zero. Even minor increases in activity can significantly distort the overall distribution.

b) Some cryptocurrencies are inherently more susceptible to event-driven dynamics, leading to highly skewed and long-tailed distributions in their community comment volume. Several scenarios contribute to this:

i. Meme coins or narrative-driven tokens, such as DOGE, PEPE, TRUMP, NOT, SHIBA, etc. These communities are not supported by fundamentals but are fueled by viral events (e.g., Elon Musk tweets, trending topics). Comment volume is typically very low, with sudden spikes triggered by specific incidents. Their comment volume distribution is naturally explosive and right-skewed, even with complete datasets.

ii. Projects dominated by influential KOLs, such as TRX (Justin Sun), TRUMP (political ties), VIRTUAL, MOVE, etc. Each post by a key opinion leader triggers a wave of discussion that quickly fades, forming a "peak + baseline" pattern. This pattern reflects centralized community influence rather than broad market consensus, contributing to a skewed distribution.

iii. Projects driven by airdrop or governance events, such as ARB, SUI, TON. These are often new or airdrop-based blockchain projects, where community discussion clusters around specific milestones—Snapshot deadlines, airdrop distributions, DAO votes, etc. Comment volume between these events varies dramatically, creating pronounced cyclical peaks.

iv. Cryptocurrencies with short or highly unstable lifecycles, such as ALT, BERA, VIRTUAL. These are cold-start or hype-driven projects, where community engagement peaks early and then rapidly declines. Discussion is often concentrated within a few days, after which it remains minimal or nonexistent. This results in extreme data concentration and skewness—a form of “lifecycle skewness” that resembles "comet-like" behavior rather than a mean-reverting asset pattern.

3.1 Concept Terms

3.2 Definition of "Eruption Volume" under Different Distributions

High kurtosis and long-tail distributions are not coincidental—they reflect the inherent behavioral attributes of certain cryptocurrencies. This implies the following:

• These cryptocurrencies cannot be effectively analyzed using traditional factor construction methods, such as mean and standard deviation.

• Specialized modeling approaches are required, such as quantile-based methods, periodic signal detection, or event window analysis.

To accurately detect "outburst events" (i.e., instances of extreme community activity), a distribution-aware approach must be adopted based on the statistical characteristics of the cryptocurrency’s total community message volume (community_volume).

We categorize cryptocurrencies into two types:

1. Those with approximately normal distributions

2. Those with high kurtosis and long-tail distributions

Each type requires a distinct screening strategy.

a) Cryptocurrencies with Normal DistributionsDetection

Method: Z-score standard deviation approach

Computation: Calculate the rolling 30-day mean and standard deviation of community_volume within the same time window.

General Rule: If the current value ≥ Outburst Threshold,→ Label as “Outburst Volume”

b) Cryptocurrencies with Skewed and Long-Tail Distributions

Detection Method: Quantile-Based Thresholding

Computation: Calculate the 90th percentile (Q90) of community_volume over a rolling 30-day window (same time slot each day).

Classification Rule:If the current value ≥ Q90 and ≥ 100 (minimum activity threshold), then the data point is labeled as an "Outburst Volume".

3.3 Concept Definitions

Based on the methods introduced above, abnormal surges in message volume can be effectively identified. These burst volumes can then be further classified according to their temporal continuity.

• If the current time period exhibits a burst in message volume, while both the preceding and following periods show moderate levels, it is classified as an isolated burst.

• If the current period and at least one adjacent period also display burst volumes, it is classified as a continuous burst.

Peaks, Ridges, and Valleys

The classification of burst patterns can be analogized to geographic formations:

• A "peak" refers to a solitary, sharp elevation—analogous to an isolated burst.

• A "ridge" describes a connected range of elevated areas—mirroring a continuous burst.

• A "valley" represents a low-lying region between elevations—similar to periods of moderate or baseline message volume.

By applying this analogy, we define:

• Volume Peaks as isolated bursts,

• Volume Ridges as continuous bursts, and

• Volume Valleys as periods of typical or subdued community activity.

3.4 Economic Explanations and Modeling Principles

1. Core Concept: Inferring Information Flow from Volume Spikes Spikes in message volume—extreme surges in community activity—often coincide with external information shocks, such as breaking news, significant on-chain transactions, or protocol-level anomalies. These spikes serve as signals of shifting market attention and potential inflection points in sentiment.

2. Economic Interpretation of Volume Valleys, Peaks, and Hills

4.1 Statistical Factors (30-Day Rolling Window)

4.2 Factor Applicability Explanation

Note：

• NORMAL_COINS: Tokens such as BTC, ETH, LINK, and SOL. These mainstream assets exhibit stable trading activity. The factor performs well on this category, as it better captures structural trading patterns in the market.

• SKEWED_COINS: Tokens such as DOGE, TRX, OP, and USDC. These are more susceptible to community-driven hype or event-based activity, often showing more extreme volume peaks and valleys.

We found that the factor performs effectively on Normal coins, but not on All or Skewed categories.This underperformance on Skewed coins may not be due to the factor itself, but rather due to our inability to accurately distinguish between “genuinely skewed” coins and “pseudo-skewed” ones.

In fact, the Skewed category contains two fundamentally different types of tokens:

1. Genuinely skewed coins – These have inherently skewed volume distributions due to tokenomics or community dynamics.

2. Pseudo-skewed coins – Apparent skewness is caused by incomplete or low-quality data, resulting in frequent false labeling of volume peaks/valleys.

Although both types may appear similar in distributional shape, they are mechanistically different. Mixing them undermines the factor's interpretability and reduces the effectiveness of cross-sectional grouping.

4.3 The performance of factors on normal token types

1）Performance of the Volume Valley Count Factor on Normal Tokens

Performance Summary

Factors Validity Assessment Indicator (Cross-sectional Predictive Ability)

2）Performance of the Ridge Frequency Factor on Normal Tokens

Performance

Factors Validity Assessment Indicator (Cross-sectional Predictive Ability)

4.4 Economic Logic Explanation

Valley Volume refers to periods of significantly depressed trading volume over a given timeframe. When these “quiet periods” occur frequently, it may indicate that the token is currently in a state of:

1. Low market attention – Investors are ignoring or underestimating the asset; its information may be overlooked or misunderstood.

2. Low turnover rate → Strategic capital has yet to enter. Funds/arbitrageurs have not built positions, offering a potential first-mover advantage. When such assets experience a volume surge, it often signals institutional entry or news-driven moves, typically followed by strong rebounds.

3. Weak market sentiment → Higher risk premium.

Therefore: More frequent valley volume periods → Prolonged weakness and inactivity → Greater likelihood of future value recovery or mean-reversion → Presents alpha opportunities.