US Equities Daily Retail Flow

Historical daily retail investor turnover across US-listed equities derived from Vanda’s proprietary retail flow methodology.

This dataset provides institutional investors with daily estimates of retail buying, selling, net turnover, and total turnover across the US equity market.

• US-listed equities
• Daily frequency
• 2012–Present history
• 32M+ observations
• USD-denominated turnover metrics

Use retail positioning data as a behavioral factor within systematic investment models and cross-sectional equity research.

Monitor concentration of retail participation across individual equities, sectors, and thematic baskets.

Identify sustained retail buying and selling activity associated with momentum participation and speculative positioning.

Measure retail participation around earnings releases, macro events, product launches, and thematic rotations.

Identify inflection points where retail positioning begins reversing following extended participation trends.

Retail net turnover measures directional retail participation.

Sustained positive retail net turnover may indicate:

• growing speculative participation
• momentum-driven retail demand
• thematic adoption
• increasing retail crowding

Large reversals in retail net turnover may indicate:

• sentiment exhaustion
• retail capitulation
• positioning unwind risk
• reduced speculative participation

SELECT
    date,
    symbol,
    retail_net_turnover
FROM PUBLIC.EQUITY_1D_PUBLIC
WHERE symbol = 'NVDA'
ORDER BY date;

SELECT
    symbol,
    retail_buy_turnover
FROM PUBLIC.EQUITY_1D_PUBLIC
WHERE date = CURRENT_DATE - 1
ORDER BY retail_buy_turnover DESC
LIMIT 20;

SELECT
    symbol,
    date,
    SUM(retail_net_turnover) OVER (
        PARTITION BY symbol
        ORDER BY date
        ROWS BETWEEN 4 PRECEDING AND CURRENT ROW
    ) AS net_flow_5d
FROM PUBLIC.EQUITY_1D_PUBLIC;

SELECT
    date,
    SUM(retail_net_turnover) AS total_market_net_flow
FROM PUBLIC.EQUITY_1D_PUBLIC
GROUP BY date
ORDER BY date;

Vanda retail flow datasets are derived using proprietary behavioral modeling and aggregation techniques designed to estimate retail investor activity across US-listed securities.

Metrics are designed to capture relative retail participation dynamics and should be interpreted as modeled institutional-grade estimates rather than exchange-reported transaction feeds.

The dataset is designed for direct integration into:

• quantitative research pipelines
• factor research frameworks
• Snowflake notebooks
• Python workflows
• BI dashboards
• systematic strategy infrastructure

Access the dataset directly through Snowflake Marketplace.