# Liquidity Data

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Data is updated weekly as data arrives after market close US-EST time.
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Dataset contains 10,800+ tickers, available from 2022-11-25 onwards.
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`Tutorials` are the best documentation — [<mark style="color:blue;">`Liquidity Data Tutorial`</mark>](https://colab.research.google.com/github/sovai-research/sovai-public/blob/main/notebooks/datasets/Liquidity%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>Public Data from Financial Intermediaries</td></tr><tr><td><strong>Models Used</strong></td><td>Aggregate Calculations</td></tr><tr><td><strong>Model Outputs</strong></td><td>Price Improvement, Market Opportunity</td></tr></tbody></table>

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## Description

This dataset provides comprehensive liquidity metrics for various stocks, including price improvement data and market making opportunities.

It offers investors valuable insights into execution quality, liquidity risk, and market microstructure, enabling more informed trading decisions and strategy development across different market conditions and participant types.

## Data Access

#### Price Improvement Dataset

The latest Price Improvement dataset provides information on price improvements for various stocks, offering insights into trading execution quality.

```python
import sovai as sov
df_improve = sov.data("liquidity/price_improvement")
```

#### Market Opportunity Dataset

The latest Market Opportunity dataset offers information on market making opportunities and liquidity provision for different stocks.

```python
import sovai as sov
df_market = sov.data("liquidity/market_opportunity")
```

#### All data

The full history can be obtained using the `full_history=True` command:

```python
import sovai as sov
df_ticker_imp = sov.data("liquidity/price_improvement", full_history=True)
df_ticker_opp = sov.data("liquidity/market_opportunity", full_history=True)
```

#### Accessing Specific Tickers

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

```python
import sovai as sov
df_ticker_imp = sov.data("liquidity/price_improvement", tickers=["AAPL", "MSFT"])
df_ticker_opp = sov.data("liquidity/market_opportunity", tickers=["AAPL", "MSFT"])
```

## Data Dictionary

#### Price Improvement Dataset

| Column Name                    | Description                         |
| ------------------------------ | ----------------------------------- |
| ticker                         | Stock symbol                        |
| date                           | Date of the data point              |
| total\_price\_improvement      | Total price improvement amount      |
| shares                         | Number of shares traded             |
| price\_improvement\_per\_share | Average price improvement per share |
| average\_price\_improvement    | Average price improvement           |

#### Market Opportunity Dataset

| Column Name              | Description                                    |
| ------------------------ | ---------------------------------------------- |
| ticker                   | Stock symbol                                   |
| date                     | Date of the data point                         |
| missed\_liquidity        | Volume of missed liquidity opportunities       |
| exhausted\_liquidity     | Volume of exhausted liquidity                  |
| routed\_liquidity        | Volume of routed liquidity                     |
| volume\_opportunity      | Total volume opportunity                       |
| average\_daily\_vol      | Average daily trading volume                   |
| rolling\_daily\_vol      | Rolling average of daily trading volume        |
| buy\_pressure\_log       | Logarithmic measure of buying pressure         |
| buy\_pressure\_pct       | Percentage measure of buying pressure          |
| missed\_liquid\_pct      | Percentage of missed liquidity                 |
| exhausted\_liquid\_pct   | Percentage of exhausted liquidity              |
| vol\_uncaptured          | Percentage of uncaptured volume                |
| retail\_pressure         | Measure of retail trading pressure             |
| institutional\_pressure  | Measure of institutional trading pressure      |
| algorithmic\_pressure    | Measure of algorithmic trading pressure        |
| retail\_institute\_ratio | Ratio of retail to institutional pressure      |
| algo\_institute\_ratio   | Ratio of algorithmic to institutional pressure |
| retail\_algo\_ratio      | Ratio of retail to algorithmic pressure        |

## Use Cases

* Execution Quality Analysis: Evaluate the execution quality of trades using price improvement data.
* Market Making Strategies: Develop market making strategies based on liquidity provision opportunities.
* Liquidity Analysis: Assess the liquidity of a stock by analyzing various liquidity metrics.
* Trading Strategy Development: Incorporate liquidity data into quantitative trading strategies.
* Market Microstructure Analysis: Study market microstructure using detailed liquidity and price improvement data.
* Performance Benchmarking: Compare execution quality across different brokers or trading venues.
* Risk Management: Assess liquidity risk and potential transaction costs for large orders.
* Regulatory Compliance: Monitor best execution practices and demonstrate compliance with regulatory requirements.

These datasets form a comprehensive toolkit for liquidity analysis, enabling detailed examination of price improvements, liquidity provision, and related metrics across different market participants.

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