YoY analytics

README.md

@@ -231,6 +231,24 @@ def view_retirements(
 ) -> pd.DataFrame
 ```
 
+The `analytics` view provides year-over-year analytics for sinking transactions, aggregating metrics like monthly active users, transaction counts, volume in USD, and carbon sunk, with percentage changes.
+
+```text
+Usage: sc-audit view analytics [OPTIONS]
+
+  View year-over-year analytics for sinking transactions
+
+Options:
+  -f, --format [df|csv|json]  The output format for this view  [default: df]
+  --help                      Show this message and exit.
+```
+
+The Python API is located at `sc_audit.views.stats` and returns the analytics table as a Pandas DataFrame:
+
+```python
+def view_yoy_analytics() -> pd.DataFrame
+```
+
 ### Configuration
 
 There isn't much to configure in sc-audit. Several settings can be overriden by environment variables.

sc_audit/cli.py

@@ -29,6 +29,7 @@
 from sc_audit.views.inventory import view_inventory
 from sc_audit.views.retirement import view_retirements
 from sc_audit.views.sink_status import view_sinking_txs
+from sc_audit.views.stats import view_yoy_analytics
 from sc_audit.views.utils import format_df
 
 
@@ -153,6 +154,13 @@ def cli_view_sink_status(
     )
     click.echo(format_df(txdf, format=format))
 
+
+@view.command(name="analytics", params=[view_format])
+def cli_view_yoy_analytics(format: str):
+    """View year-over-year analytics for sinking transactions"""
+    df = view_yoy_analytics()
+    click.echo(format_df(df, format=format))
+
 # LOADING
 
 @cli.group()

sc_audit/views/stats.py

@@ -1,17 +1,21 @@
 """
 View Stellarcarbon's asset stats. Optionally, filter by recipient.
+Compute year-over-year metrics based on historical sinking transactions.
 
 Author: Alex Olieman <https://keybase.io/alioli>
 """
 
 from decimal import Decimal
 
+import numpy as np
+import pandas as pd
 from sqlalchemy import func, select, union_all
 
 from sc_audit.db_schema.association import SinkStatus
 from sc_audit.db_schema.mint import MintedBlock
 from sc_audit.db_schema.sink import SinkingTx
 from sc_audit.session_manager import Session
+from sc_audit.views.sink_status import view_sinking_txs
 
 
 def get_carbon_stats(recipient: str | None = None) -> dict[str, Decimal]:
@@ -49,3 +53,66 @@ def get_carbon_stats(recipient: str | None = None) -> dict[str, Decimal]:
     stats["carbon_pending"] = c_sunk - c_retired
 
     return stats
+
+
+def view_yoy_analytics() -> pd.DataFrame:
+    df = view_sinking_txs(order='asc')
+    if df.empty:
+        return pd.DataFrame()
+
+    # Compute price for USDC transactions
+    df['price'] = np.where(
+        df['dest_asset_code'] == 'USDC', 
+        df['dest_asset_amount'].astype(float) / df['carbon_amount'].astype(float),
+        np.nan
+    )
+
+    # Interpolate prices linearly, then extrapolate using forward fill for trailing CARBON transactions
+    df['price'] = df['price'].interpolate(method='linear').ffill()
+
+    # Convert CARBON denominated amounts to USD using the interpolated price
+    mask = df['dest_asset_code'] == 'CARBON'
+    df.loc[mask, 'dest_asset_amount'] = df.loc[mask, 'price'] * df.loc[mask, 'carbon_amount'].astype(float)
+
+    # Ensure all dest_asset_amount are float for consistent summing
+    df['dest_asset_amount'] = df['dest_asset_amount'].astype(float)
+
+    # Add year and month columns for binning
+    df['year'] = df['created_at'].dt.year
+    df['month'] = df['created_at'].dt.month
+
+    # Calculate monthly active users (distinct recipients per month)
+    mau_df = df.groupby(['year', 'month'])['recipient'].nunique().reset_index()
+    mau_yearly = (
+        mau_df.groupby('year')['recipient']
+        .mean()
+        .reset_index()
+        .rename(columns={'recipient': 'mau'})
+    )
+    mau_yearly['mau'] = mau_yearly['mau'].astype(float).round(2)
+
+    # Calculate yearly aggregates
+    yearly = df.groupby('year').agg(
+        num_tx=('hash', 'count'),
+        volume_usd=('dest_asset_amount', 'sum'),
+        carbon_sunk=('carbon_amount', 'sum')
+    ).reset_index()
+    yearly['volume_usd'] = yearly['volume_usd'].astype(float).round(2)
+
+    # Merge MAU with yearly data
+    result = yearly.merge(mau_yearly, on='year', how='left')
+
+    # Sort by year
+    result = result.sort_values('year')
+
+    # Calculate year-over-year percentage changes
+    for col in ['mau', 'num_tx', 'volume_usd', 'carbon_sunk']:
+        result[f'yoy_{col}'] = (result[col].astype(float).pct_change() * 100).round(2)
+
+    # Reorder columns: year, then each core column followed by its yoy column
+    result = result[[
+        'year', 'mau', 'yoy_mau', 'num_tx', 'yoy_num_tx', 
+        'volume_usd', 'yoy_volume_usd', 'carbon_sunk', 'yoy_carbon_sunk',
+    ]]
+
+    return result