sovai/short_selling

Short Selling

Data Notice: This dataset provides academic research access with a 6-month data lag. For real-time data access, please visit sov.ai to subscribe. For market insights and additional subscription options, check out our newsletter at blog.sov.ai.

from datasets import load_dataset
df_over_shorted = load_dataset("sovai/short_selling", split="train").to_pandas().set_index(["ticker","date"])

Data is updated weekly as data arrives after market close US-EST time.

Tutorials are the best documentation — Short Selling Tutorial

Category	Details
Input Datasets	Financial Intermediaries, NASDAQ, NYSE, CME
Models Used	Parsing Techniques
Model Outputs	Predictions, Volume

---

Description

This dataset provides comprehensive information on short-selling activity for various stocks, including metrics on short interest, volume, and related indicators.

It offers investors and analysts insights into market sentiment, potential short squeezes, and overall risk assessment, enabling more informed decision-making in trading strategies and liquidity analysis.

Data Access

Over-shorted Dataset

The Over-Shorted dataset provides information on short interest and potentially over-shorted stocks, offering insights into short selling activity and related metrics.

Latest Data

import sov as sov
df_over_shorted = sov.data("short/over_shorted")

All Data

import sov as sov
df_over_shorted = sov.data("short/over_shorted", full_history=True)

Short Volume Dataset

The Short Volume dataset offers information on the short selling volume for specified stocks, including breakdowns by different types of market participants.

Latest Data

import sov as sov
df_short_volume = sov.data("short/volume")

All Data

import sov as sov
df_short_volume = sov.data("short/volume", full_history=True)

Accessing Specific Tickers

You can also retrieve data for specific tickers across these datasets. For example:

df_ticker_over_shorted = sov.data("short/over_shorted", tickers=["AAPL", "MSFT"])
df_ticker_short_volume = sov.data("short/volume", tickers=["AAPL", "MSFT"])

Data Dictionary

Over-Shorted Dataset:

Column Name	Description
ticker	Stock symbol
date	Date of the data point
over_shorted	Measure of how over-shorted a stock is
over_shorted_chg	Change in the over-shorted measure
short_interest	Number of shares sold short
number_of_shares	Total number of outstanding shares
short_percentage	Percentage of float sold short
short_prediction	Predicted short interest
days_to_cover	Number of days to cover short positions
market_cap	Market capitalization of the company
total_revenue	Total revenue of the company
volume	Trading volume

Short Volume Dataset:

Column Name	Description
ticker	Stock symbol
date	Date of the data point
short_volume	Volume of shares sold short
total_volume	Total trading volume
short_volume_ratio_exchange	Ratio of short volume to total volume on the exchange
retail_short_ratio	Ratio of short volume from retail traders
institutional_short_ratio	Ratio of short volume from institutional traders
market_maker_short_ratio	Ratio of short volume from market makers

Use Cases

• Short Squeeze Analysis: Identify potentially over-shorted stocks that might be candidates for a short squeeze.
• Risk Assessment: Evaluate the short interest in a stock as part of overall risk assessment.
• Market Sentiment Analysis: Use short volume data to gauge market sentiment towards specific stocks.
• Trading Strategy Development: Incorporate short selling data into quantitative trading strategies.
• Liquidity Analysis: Assess the liquidity of a stock by analyzing the days to cover metric.
• Sector Trends: Identify trends in short selling activity across different sectors or industries.

These datasets form a comprehensive toolkit for short selling analysis, enabling detailed examination of short interest, volume, and related metrics across different equities.