MapleLeavesKrish/short_selling

---
icon: sort-down
description: >-
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](https://sov.ai) to subscribe.
> For market insights and additional subscription options, check out our newsletter at [blog.sov.ai](https://blog.sov.ai).

```python
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 — [<mark style="color:blue;">`Short Selling Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Short%20Data.ipynb)

<table data-column-title-hidden data-view="cards"><thead><tr><th>Category</th><th>Details</th></tr></thead><tbody><tr><td><strong>Input Datasets</strong></td><td>Financial Intermediaries, NASDAQ, NYSE, CME</td></tr><tr><td><strong>Models Used</strong></td><td>Parsing Techniques</td></tr><tr><td><strong>Model Outputs</strong></td><td>Predictions, Volume</td></tr></tbody></table>

***

## Description

This dataset provides comprehensive information on short-selling activity for various stocks, including metrics on short interest, volume, and related indicators.&#x20;

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

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

#### All Data

```python
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

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

#### All Data

```python
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:

```python
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.