Anomaly Detection

It provides methods to detect global, local, and cluster anomalies in multivariate financial data

Tutorials are the best documentation — Anomaly Detection Tutorial

Key Features

1. Multiple anomaly detection methods:

   • Global anomalies: Identify outliers considering the entire dataset

   • Local anomalies: Detect outliers within local neighborhoods

   • Cluster anomalies: Find anomalies considering multi-dimensional data structure

2. Anomaly scoring: Compute anomaly scores for each data point

3. Feature-level anomaly analysis: Identify the most anomalous features for a given security

Usage

Load the accounting factors data:

import pandas as pd

df_factors = sov.data("factors/accounting", purge_cache=True)
df_last_3_years = df_factors.loc[(slice(None), slice(pd.Timestamp.now() - pd.DateOffset(years=3), None)), :]
df_last_3_years = df_last_3_years.percentile()

Anomaly Detection

Compute Anomaly Scores

df_anomaly_scores = df_last_3_years.anomalies("scores", ticker="TSLA")

Local Anomalies

df_local = df_last_3_years.anomalies("local", ticker="NVDA")

Global Anomalies

df_global = df_last_3_years.anomalies("global", ticker="NVDA")

Cluster Anomalies

df_cluster = df_last_3_years.anomalies("cluster", ticker="NVDA")

Notes

• The module uses the sovai library for data loading and processing. Ensure you have the necessary permissions and valid authentication token.

• Anomaly detection methods can be applied to different time ranges and tickers. Adjust the parameters as needed for your analysis.

• The module provides flexibility in analyzing anomalies at both the overall and feature level. Experiment with different combinations of methods for comprehensive insights.

• When working with large datasets, be mindful of computational resources, especially when applying multiple anomaly detection methods or creating complex visualizations.