icon: sort-down
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This section covers the usage of various short-selling datasets for risk
analysis.
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.