Data Analysis

Data Analysis

When to use this skill

Use when the user asks about:

• Analyzing numerical data (prices, volumes, metrics)
• Calculating statistics (averages, percentiles, correlations)
• Identifying trends or anomalies in data
• Comparing performance across time periods or assets
• Presenting data in a clear, structured format

Analysis Framework

1. Data Understanding

Before analyzing, assess the data:

• Source: Where does the data come from? Is it reliable?
• Time range: What period does the data cover?
• Granularity: Daily, hourly, per-block?
• Completeness: Are there gaps or missing data points?
• Units: USD, ETH-denominated, percentage, raw count?
• Adjustments needed: Inflation adjustment, normalization, outlier handling?

2. Descriptive Statistics

Compute baseline statistics:

• Central tendency: Mean, median, mode — median is more robust for skewed crypto data
• Dispersion: Standard deviation, range, interquartile range (IQR)
• Distribution shape: Skewness (crypto returns are typically negatively skewed) and kurtosis (fat tails are common)
• Percentiles: 5th, 25th, 50th, 75th, 95th — useful for setting expectations

Present as a summary table:

3. Trend Analysis

Identify and quantify trends:

• Moving averages: 7-day, 30-day, 90-day to smooth noise
• Growth rates: Period-over-period percentage change (daily, weekly, monthly)
• CAGR: Compound Annual Growth Rate for longer-term performance
• Trend direction: Classify as uptrend, downtrend, or sideways based on moving average slopes
• Trend strength: How consistent is the trend? R-squared of linear regression

4. Comparative Analysis

When comparing across entities or time periods:

• Normalize data: Convert to percentage change from a common starting point for fair comparison
• Relative performance: Calculate alpha (excess return) relative to a benchmark (BTC, ETH, or market index)
• Correlation matrix: How closely do the compared items move together?
• Ratio analysis: Asset A / Asset B ratio to identify relative value trends
• Ranking: Order by performance metric with percentile rankings

5. Anomaly Detection

Flag unusual data points:

• Z-score method: Values beyond 2-3 standard deviations from the mean
• IQR method: Values below Q1 - 1.5IQR or above Q3 + 1.5IQR
• Volume spikes: Daily volume exceeding 3x the 30-day average
• Price gaps: Sudden moves exceeding 2x the average daily range
• Contextual check: Always check if an anomaly has a known cause (hack, listing, upgrade)

6. Data Presentation

Structure output for clarity:

Tables — best for exact values and multi-metric comparison:

• Align numbers to the right
• Use consistent decimal places
• Include units in column headers
• Sort by the most relevant column

Series summaries — when presenting time-series data textually:

• Start with the current value and direction
• Reference key inflection points (when did the trend change?)
• Compare to relevant time periods (YTD, QoQ, YoY)
• Highlight the single most significant data point

7. Caveats and Limitations

Always note:

• Survivorship bias: Analysis of "top tokens" ignores failed ones
• Look-ahead bias: Past data analysis doesn't predict future performance
• Sample size: Small samples (less than 30 data points) produce unreliable statistics
• Data quality: On-chain data may include wash trading, bots, or fake volume
• Correlation vs causation: Two metrics moving together doesn't mean one causes the other

8. Output Format

• Analysis type: Descriptive / Comparative / Trend / Anomaly
• Data summary: Key statistics in a table
• Main finding: The single most important insight from the data
• Supporting findings: 2-4 additional observations
• Trend assessment: Direction and strength
• Anomalies: Any flagged data points with context
• Confidence: High / Medium / Low based on data quality and sample size
• Limitations: Relevant caveats for this specific analysis