# SOV.AI

## Sovai Documentation

- [Data & Screens](https://docs.sov.ai/readme.md)
- [Quick Start](https://docs.sov.ai/get-started/quickstart.md)
- [Tutorials](https://docs.sov.ai/get-started/tutorials.md): Here are markdown tables with links to the Google Colab, GitHub notebooks, and Jupyter Lab for each tutorial.
- [Installation](https://docs.sov.ai/get-started/installation.md)
- [Release Notes](https://docs.sov.ai/get-started/release-notes.md): This page shows release notes >= 0.0
- [About](https://docs.sov.ai/get-started/about.md)
- [Equity Datasets](https://docs.sov.ai/realtime-datasets/equity-datasets.md)
- [Accounting Data](https://docs.sov.ai/realtime-datasets/equity-datasets/accounting-data.md): Standardized financial accounting data across multiple US publicly traded firms.
- [Bankruptcy Predictions](https://docs.sov.ai/realtime-datasets/equity-datasets/bankruptcy-predictions.md): Chapter 7 and Chapter 11 bankruptcy predictions made easy for over 5,000 US publicly traded stocks.
- [Employee Visa](https://docs.sov.ai/realtime-datasets/equity-datasets/employee-visa.md): The H1B dataset offers quarterly insights into foreign hiring trends, job details, and wages for informed decision-making.
- [Earnings Surprise](https://docs.sov.ai/realtime-datasets/equity-datasets/earnings-surprise.md): Earnings announcements are obtained from external sources as well as estimate information leading up to the actual announcement.
- [Congressional Data](https://docs.sov.ai/realtime-datasets/equity-datasets/congressional-data.md): From filings we collect and match trades in the Senate and House and make them available within a day of processing.
- [Factor Signals](https://docs.sov.ai/realtime-datasets/equity-datasets/factor-signals.md): A financial factor dataset for in-depth company analysis and investment strategies.
- [Financial Ratios](https://docs.sov.ai/realtime-datasets/equity-datasets/financial-ratios.md): More than 80+ financial ratios calculated from financial statement and market data.
- [Government Contracts](https://docs.sov.ai/realtime-datasets/equity-datasets/government-contracts.md): The government spending data provides comprehensive information about government contracts, transactions, product specifications, entity details, locations, competition, and compensation.
- [Institutional Trading](https://docs.sov.ai/realtime-datasets/equity-datasets/institutional-trading.md): The dataset provides a comprehensive analysis of institutional investment behaviors, strategies, and portfolio dynamics assist professional investors in making informed decisions.
- [Insider Flow Prediction](https://docs.sov.ai/realtime-datasets/equity-datasets/insider-flow-prediction.md): More than 60+ insider trading features helpful for machine learning, including a flow prediction value.
- [Liquidity Data](https://docs.sov.ai/realtime-datasets/equity-datasets/liquidity-data.md): Various dataset that could help with the assesment of security liquidity to inform trading decisions.
- [Lobbying Data](https://docs.sov.ai/realtime-datasets/equity-datasets/lobbying-data.md): A ticker matched lobbying data to see fine-grained corporate lobbying behaviour.
- [News Sentiment](https://docs.sov.ai/realtime-datasets/equity-datasets/news-sentiment.md): Two types of news datasets have been developed, one is ticker-matched, and the next is theme-matched.
- [Price Breakout](https://docs.sov.ai/realtime-datasets/equity-datasets/price-breakout.md): A dataset with daily updated predictions of price breaking upwards for US Equities.
- [Risk Indicators](https://docs.sov.ai/realtime-datasets/equity-datasets/risk-indicators.md): Here we develop three tables to develop a final score of corporate risk to US equities.
- [SEC Edgar Search](https://docs.sov.ai/realtime-datasets/equity-datasets/sec-edgar-search.md): State of the art notebook tools to designed to search, retrieve, and analyze financial data from the SEC's EDGAR database. This is a work-in-progress.
- [SEC 10K Filings](https://docs.sov.ai/realtime-datasets/equity-datasets/sec-10k-filings.md): A very easily digestable dataframe format for all 10-K filings, with multiple sections, categories, and textual datapoints. This is not yet available, a work-in-progress.
- [Short Selling](https://docs.sov.ai/realtime-datasets/equity-datasets/short-selling.md): This section covers the usage of various short-selling datasets for risk analysis.
- [Wikipedia Views](https://docs.sov.ai/realtime-datasets/equity-datasets/wikipedia-views.md): A look at some of the largest firms and their daily wikipedia page views and trends.
- [Patents Data](https://docs.sov.ai/realtime-datasets/equity-datasets/patents-data.md)
- [Economic Datasets](https://docs.sov.ai/realtime-datasets/economic-datasets.md)
- [Asset Rotation](https://docs.sov.ai/realtime-datasets/economic-datasets/asset-rotation.md): This dataset provides historical and forecasted risk-parity asset allocation data for investors for five asset classes.
- [Core Economic Data](https://docs.sov.ai/realtime-datasets/economic-datasets/core-economic-data.md): Developed a core economic dataset that explain more than 90% of the variability in most economic outcomes. Forthcoming, December 2025
- [ETF Flows](https://docs.sov.ai/realtime-datasets/economic-datasets/etf-flows.md): Forthcoming, December 2025
- [Government Traffic](https://docs.sov.ai/realtime-datasets/economic-datasets/government-traffic.md): This dataset provides insights into web traffic patterns for various U.S. government agencies and domains.
- [Turing Risk Index](https://docs.sov.ai/realtime-datasets/economic-datasets/turing-risk-index.md): Use this indicator to understand the trajectory of global risks as perceived by investors. Here we supply the raw data, you might find it more favorable to use the dashboard.
- [Sectorial Datasets](https://docs.sov.ai/realtime-datasets/sectorial-datasets.md)
- [Airbnb Data](https://docs.sov.ai/realtime-datasets/sectorial-datasets/airbnb-data.md): Scraped Airbnb data (Real-time)
- [Box Office Stats](https://docs.sov.ai/realtime-datasets/sectorial-datasets/box-office-stats.md): This dataset contains information about movie producers, their movies, and the corresponding box office performance.
- [CFPB Complaints](https://docs.sov.ai/realtime-datasets/sectorial-datasets/cfpb-complaints.md): This section covers the usage of the Consumer Financial Complaint ticker-mapped dataset.
- [Pharma Clinical Trials](https://docs.sov.ai/realtime-datasets/sectorial-datasets/phrama-clinical-trials.md): This section covers a very unique dataset that tags clinical trials with their predicted outcome success.
- [Request Datasets](https://docs.sov.ai/realtime-datasets/sectorial-datasets/request-datasets.md): How to request the development of new datasets for the SovAI SDK.
- [Signal Evaluation](https://docs.sov.ai/asset-managment/signal-evaluation.md): This module provides a wide array of analytical tools and visualizations to help quantitative analysts and portfolio managers evaluate the quality, consistency, and robustness of their alpha signals.
- [Weight Optimization](https://docs.sov.ai/asset-managment/weight-optimization.md): This module provides a comprehensive set of tools for portfolio managers and quantitative analysts to optimize asset allocation strategies and evaluate their performance.
- [Screens and Filters](https://docs.sov.ai/asset-managment/screens-and-filters.md): This module allows users to apply various filters and screens to a comprehensive dataset of financial and market factors.
- [Pairwise Distance](https://docs.sov.ai/pattern-recognition/pairwise-distance.md): Pairwise statistics for distance and similarity between stocks in cross-section, time-series, and panel orientations.
- [Anomaly Detection](https://docs.sov.ai/pattern-recognition/anomaly-detection.md): It provides methods to detect global, local, and cluster anomalies in multivariate financial data
- [Clustering Panels](https://docs.sov.ai/pattern-recognition/clustering-panels.md): Clustering specifically designed for multivariate panel clustering of financial and time-series data
- [Extract Features](https://docs.sov.ai/feature-processing/extract-features.md): The feature extractor module generates features that can be categorized into several types based on the nature of the calculations.
- [Neutralize Features](https://docs.sov.ai/feature-processing/neutralize-features.md): The feature extractor module generates features that can be categorized into several types based on the nature of the calculations.
- [Select Features](https://docs.sov.ai/feature-processing/select-features.md): The feature selection module in the sovai library provides various methods to identify and select the most important features from financial datasets.
- [Dimensionality Reduction](https://docs.sov.ai/feature-processing/dimensionality-reduction.md): Implements multiple reduction techniques including PCA, SVD, Factor Analysis, Gaussian Random Projection, and UMAP.
- [Feature Importance](https://docs.sov.ai/feature-processing/feature-importance.md): The feature importance module in the sovai library offers multiple unsupervised algorithms to quantify the significance of each feature in financial datasets.
- [Nowcasting Series](https://docs.sov.ai/time-series/nowcasting-series.md): This module provides functionality for nowcasting financial data using a Multi-Frequency Long-term and Event-based forecasting method.
- [TS Decomposition](https://docs.sov.ai/time-series/institutional-1.md): This module provides powerful tools for analyzing financial time series data, offering insights that can be valuable for financial analysis, investment decision-making, and economic research.
- [Time Segmentation](https://docs.sov.ai/time-series/time-segmentation.md): Segments time series into different components according to statistical tests over the series. Helpful for understanding changes in regimes.
- [Bankruptcy Prediction](https://docs.sov.ai/dashboard-examples/bankruptcy-prediction.md): Example of the type of dashboard that can be built using the underlying bankruptcy data. Get in touch to develop your own dashboard.
- [Turing Risk Index](https://docs.sov.ai/dashboard-examples/turing-risk-index.md): Example of the type of dashboard that can be built using the underlying turing risk data. Get in touch to develop your own dashboard.
- [API Overview](https://docs.sov.ai/api-reference/api-reference.md): Complete API reference for the SovAI Python SDK, auto-generated from source code.
- [sovai (Package)](https://docs.sov.ai/api-reference/sovai.md): Main SovAI SDK Tool Kit package
- [Data Retrieval](https://docs.sov.ai/api-reference/data.md): API reference for sovai.get\_data
- [Plotting](https://docs.sov.ai/api-reference/plots.md): API reference for sovai.get\_plots
- [Reports](https://docs.sov.ai/api-reference/reports.md): API reference for sovai.get\_reports
- [Compute](https://docs.sov.ai/api-reference/compute.md): API reference for sovai.get\_compute
- [Tools (SEC, Explain)](https://docs.sov.ai/api-reference/tools.md): API reference for sovai.get\_tools
- [API Config](https://docs.sov.ai/api-reference/api-config.md): API reference for sovai.api\_config
- [Basic Auth](https://docs.sov.ai/api-reference/basic-auth.md): API reference for sovai.basic\_auth
- [Token Auth](https://docs.sov.ai/api-reference/token-auth.md): API reference for sovai.token\_auth
- [Error Classes](https://docs.sov.ai/api-reference/errors.md): API reference for sovai.errors.sovai\_errors
- [Extensions](https://docs.sov.ai/api-reference/extensions.md): DataFrame extensions for analytics, feature engineering, and signal evaluation.
- [Anomalies](https://docs.sov.ai/api-reference/extensions/anomalies.md): API reference for sovai.extensions.anomalies
- [Ask Df Llm](https://docs.sov.ai/api-reference/extensions/ask-df-llm.md): API reference for sovai.extensions.ask\_df\_llm
- [Change Point Generator](https://docs.sov.ai/api-reference/extensions/change-point-generator.md): API reference for sovai.extensions.change\_point\_generator
- [Chart Explainer](https://docs.sov.ai/api-reference/extensions/chart-explainer.md): Chart Explanation Module using Gemini via Ephemeral Token Broker
- [Clustering](https://docs.sov.ai/api-reference/extensions/clustering.md): API reference for sovai.extensions.clustering
- [Core Kshape](https://docs.sov.ai/api-reference/extensions/core-kshape.md): API reference for sovai.extensions.core\_kshape
- [Cusum](https://docs.sov.ai/api-reference/extensions/cusum.md): API reference for sovai.extensions.cusum
- [Dimensionality Reduction](https://docs.sov.ai/api-reference/extensions/dimensionality-reduction.md): API reference for sovai.extensions.dimensionality\_reduction
- [Feature Extraction](https://docs.sov.ai/api-reference/extensions/feature-extraction.md): API reference for sovai.extensions.feature\_extraction
- [Feature Importance](https://docs.sov.ai/api-reference/extensions/feature-importance.md): API reference for sovai.extensions.feature\_importance
- [Feature Neutralizer](https://docs.sov.ai/api-reference/extensions/feature-neutralizer.md): API reference for sovai.extensions.feature\_neutralizer
- [Filter Df](https://docs.sov.ai/api-reference/extensions/filter-df.md): API reference for sovai.extensions.filter\_df
- [Fractional Differencing](https://docs.sov.ai/api-reference/extensions/fractional-differencing.md): API reference for sovai.extensions.fractional\_differencing
- [Nowcasting](https://docs.sov.ai/api-reference/extensions/nowcasting.md): API reference for sovai.extensions.nowcasting
- [Overall Explainers](https://docs.sov.ai/api-reference/extensions/overall-explainers.md): Overall Explainers Module
- [Pairwise](https://docs.sov.ai/api-reference/extensions/pairwise.md): API reference for sovai.extensions.pairwise
- [Pandas Extensions](https://docs.sov.ai/api-reference/extensions/pandas-extensions.md): API reference for sovai.extensions.pandas\_extensions
- [Parallel Functions](https://docs.sov.ai/api-reference/extensions/parallel-functions.md): API reference for sovai.extensions.parallel\_functions
- [Pfa Feature Selector](https://docs.sov.ai/api-reference/extensions/pfa-feature-selector.md): API reference for sovai.extensions.pfa\_feature\_selector
- [Regime Change](https://docs.sov.ai/api-reference/extensions/regime-change.md): API reference for sovai.extensions.regime\_change
- [Regime Change Pca](https://docs.sov.ai/api-reference/extensions/regime-change-pca.md): API reference for sovai.extensions.regime\_change\_pca
- [Shapley Global Importance](https://docs.sov.ai/api-reference/extensions/shapley-global-importance.md): API reference for sovai.extensions.shapley\_global\_importance
- [Shapley Importance](https://docs.sov.ai/api-reference/extensions/shapley-importance.md): API reference for sovai.extensions.shapley\_importance
- [Signal Evaluation](https://docs.sov.ai/api-reference/extensions/signal-evaluation.md): API reference for sovai.extensions.signal\_evaluation
- [Table Explainer](https://docs.sov.ai/api-reference/extensions/table-explainer.md): Table Explanation Module using Gemini via Ephemeral Token Broker
- [Technical Indicators](https://docs.sov.ai/api-reference/extensions/technical-indicators.md): API reference for sovai.extensions.technical\_indicators
- [Time Decomposition](https://docs.sov.ai/api-reference/extensions/time-decomposition.md): API reference for sovai.extensions.time\_decomposition
- [Plot Library](https://docs.sov.ai/api-reference/plots-1.md): Pre-built visualization functions organized by dataset category.
- [Accounting Plots](https://docs.sov.ai/api-reference/plots-1/accounting-plots.md): API reference for sovai.plots.accounting.accounting\_plots
- [Bankruptcy Plots](https://docs.sov.ai/api-reference/plots-1/bankruptcy-plots.md): API reference for sovai.plots.bankruptcy.bankruptcy\_plots
- [Breakout Plots](https://docs.sov.ai/api-reference/plots-1/breakout-plots.md): API reference for sovai.plots.breakout.breakout\_plots
- [Corp Risk Plots](https://docs.sov.ai/api-reference/plots-1/corp-risk-plots.md): API reference for sovai.plots.corp\_risk.corp\_risk\_plots
- [Insider Plots](https://docs.sov.ai/api-reference/plots-1/insider-plots.md): API reference for sovai.plots.insider.insider\_plots
- [Institutional Plots](https://docs.sov.ai/api-reference/plots-1/institutional-plots.md): API reference for sovai.plots.institutional.institutional\_plots
- [News Plots](https://docs.sov.ai/api-reference/plots-1/news-plots.md): API reference for sovai.plots.news.news\_plots
- [Ratios Plots](https://docs.sov.ai/api-reference/plots-1/ratios-plots.md): API reference for sovai.plots.ratios.ratios\_plots
- [Utilities](https://docs.sov.ai/api-reference/utils.md): Internal utility functions for data loading, caching, and format conversion.
- [Client Side](https://docs.sov.ai/api-reference/utils/client-side.md): API reference for sovai.utils.client\_side
- [Client Side S3](https://docs.sov.ai/api-reference/utils/client-side-s3.md): API reference for sovai.utils.client\_side\_s3
- [Client Side S3 Part High](https://docs.sov.ai/api-reference/utils/client-side-s3-part-high.md): Advanced S3 Partitioned Data Loader
- [Converter](https://docs.sov.ai/api-reference/utils/converter.md): API reference for sovai.utils.converter
- [File Management](https://docs.sov.ai/api-reference/utils/file-management.md): API reference for sovai.utils.file\_management
- [Get Tickers](https://docs.sov.ai/api-reference/utils/get-tickers.md): API reference for sovai.utils.get\_tickers
- [Helpers](https://docs.sov.ai/api-reference/utils/helpers.md): API reference for sovai.utils.helpers
- [Plot](https://docs.sov.ai/api-reference/utils/plot.md): API reference for sovai.utils.plot
- [Port Manager](https://docs.sov.ai/api-reference/utils/port-manager.md): API reference for sovai.utils.port\_manager
- [Stream](https://docs.sov.ai/api-reference/utils/stream.md): API reference for sovai.utils.stream
- [Verbose Utils](https://docs.sov.ai/api-reference/utils/verbose-utils.md): API reference for sovai.utils.verbose\_utils