Next-Day Movers 🔒

The Next-Day Movers dataset provides a daily, model-ranked collection of equities most likely to experience large price moves in the following trading session.

Each observation contains one of the top names selected from a universe of liquid optionable equities, based on a predictive model designed to identify securities with elevated next-day realized movement potential.

Behind the scenes, the model evaluates features such as implied volatility, realized volatility, and related volatility structure information to rank candidates by expected next-day movement.

All observations are stored historically and remain fixed once published, allowing the dataset to be used safely in systematic research and backtesting workflows without lookahead bias.

Why it’s useful

Use this dataset to:

• Identify equities most likely to experience outsized next-day moves
• Build directional or non-directional volatility trading strategies
• Screen for high-movement candidates before the next session opens
• Study the relationship between implied volatility, realized volatility, and future movement
• Backtest systematic workflows built around next-day mover selection

Because the dataset is stored point-in-time and not retroactively altered, it can be used in research without introducing lookahead bias.

Endpoint

Base URL

Authentication

All requests require an API key.

Pass your key either:

As a query parameter

?api_key=your_key

Or via header

X-API-Key: your_key

Note

This endpoint is available to Pro plans only. Requests made with non-Pro API keys will return a 403 PREMIUM_ENDPOINT error.

Sample Request

Python

import requests

url = "https://api.alphanume.com/v1/next-day-movers"

params = {
    "api_key": "alp_abc123"
}

r = requests.get(url, params=params)
print(r.json())

cURL

curl "https://api.alphanume.com/v1/next-day-movers?api_key=alp_abc123"

Request Parameters

api_key (required)

Your Alphanume API key.

date (optional)

Return observations for a single trading date.

Format:

YYYY-MM-DD

Example:

?date=2026-03-05

date_gte / date_lte / date_gt / date_lt (optional)

Filter observations by date range.

All dates must be provided in YYYY-MM-DD format.

Any logically valid combination is accepted.

Examples:

?date_gte=2025-01-01&date_lte=2025-12-31

?date_gt=2025-06-01

If no date filters are provided, all available historical observations are returned.

Response Format

Responses are returned in JSON format.

Example:

{
   "count":2,
   "data":[
      {
         "date":"2026-03-09",
         "ticker":"AAOI",
         "return":8.92,
         "absolute_move":8.92
      },
      {
         "date":"2026-03-09",
         "ticker":"FIGR",
         "return":21.29,
         "absolute_move":21.29
      }
   ]
}

Forward Return Availability

For the most recent observation date, the fields return and absolute_move will appear as null.

This occurs because these fields represent the realized price movement during the following trading session. Until a full trading day has completed after the selection date, the outcome cannot yet be calculated.

Once the next trading session has closed, these values are populated automatically and remain fixed thereafter.

Example (most recent date):

{
  "date": "2026-03-10",
  "ticker": "NVDA",
  "return": null,
  "absolute_move": null
}

Example (historical observation):

{
  "date": "2026-03-09",
  "ticker": "FIGR",
  "return": 21.29,
  "absolute_move": 21.29
}

Key Behavior

• The most recent date will always have null values for forward outcome fields
• Values are populated after the next trading session completes
• Once populated, results are never retroactively modified

Response Fields

Dataset Definition

Each trading day, Alphanume evaluates a universe of liquid optionable equities and identifies the names most likely to experience elevated realized movement in the following session.

The ranking process incorporates signals such as:

• Implied volatility
• Realized volatility
• Related volatility term structure features

The endpoint returns the historically stored daily selections along with realized next-session outcome fields.

Field Definitions

return

The realized return of the selected ticker over the following trading session.

This value is signed:

• Positive values indicate an upward move
• Negative values indicate a downward move

absolute_move

The absolute value of the following session’s return.

This captures the size of the move regardless of direction and is useful for non-directional volatility research and ranking validation.

Notes on Data Behavior

• New selections are generated daily at 3:30 PM (America/New_York Time)
• Historical observations are stored point-in-time
• Once published, observations are not retroactively altered
• Dates are returned as YYYY-MM-DD
• Results are ordered by date DESC
• This endpoint is Pro-only

Typical Use Cases

• Building next-day volatility trading screens

• Researching which equities are most likely to experience large realized moves

• Studying the predictive relationship between implied volatility and realized movement

• Backtesting signal pipelines built around daily high-movement candidate selection

• Evaluating model quality by comparing selected names against realized next-session returns