Liquidity Data

Various dataset that could help with the assesment of security liquidity to inform trading decisions.

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

Tutorials are the best documentation — Liquidity Data Tutorial

Input Datasets

Public Data from Financial Intermediaries

Models Used

Aggregate Calculations

Model Outputs

Price Improvement, Market Opportunity

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

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

Market Opportunity Dataset

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

df_market = sov.data("liquidity/market_opportunity")

Accessing Specific Tickers

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

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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