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BRAIN TIPS: 6 Ways to Quickly Evaluate a New Dataset

WorldQuant BRAIN Platform - Datafield Exploration Guide

Original Post: [BRAIN TIPS] 6 ways to quickly evaluate a new dataset
Author: KA64574
Date: 2 years ago
Followers: 265 people

🎯 Overview

WorldQuant BRAIN has thousands of datafields for you to create alphas. But how do you quickly understand a new datafield? Here are 6 proven methods to evaluate and understand new datasets efficiently.

Important: Simulate the below expressions in "None" neutralization and decay 0 setting and test_period P0Y0M. Obtain insights of specific parameters using the Long Count and Short Count in the IS Summary section of the results. Watch Out: - Data type (matrix/vector), please note, these are two special definition here and not similar as we knew in math. Different data types have different characteristics and usage rule; if it is a matrix data type, you can use the datafield directly, but if it is a vector data type, you should use a vector operator to convert the datafield to a matrix data type. Thus, for a vector data type, you should find proper vector operator via mcp then put it into the following test.

📊 The 6 Exploration Methods

1. Basic Coverage Analysis

Expression: datafield, for vector data type, the expression should be vector_operator(datafield), please note, the vector_operator is the operator that you found via mcp. Insight: % coverage, would approximately be ratio of (Long Count + Short Count in the IS Summary) / (Universe Size in the settings)

Purpose: Understand the basic availability of data across the universe What it tells you: How many instruments have data for this field on average

2. Non-Zero Value Coverage

Expression: datafield != 0 ? 1 : 0 , for vector data type, the expression should be vector_operator(datafield) != 0 ? 1 : 0, please note, the vector_operator is the operator that you found via mcp. Insight: Coverage. Long Count indicates average non-zero values on a daily basis

Purpose: Distinguish between missing data and actual zero values What it tells you: Whether the field has meaningful data vs. just coverage gaps

3. Data Update Frequency Analysis

Expression: ts_std_dev(datafield,N) != 0 ? 1 : 0 , for vector data type, the expression should be ts_std_dev(vector_operator(datafield),N) != 0 ? 1 : 0, please note, the vector_operator is the operator that you found via mcp. Insight: Frequency of unique data (daily, weekly, monthly etc.)

Key Points:

Some datasets have data backfilled for missing values, while some do not
This expression can be used to find the frequency of unique datafield updates by varying N (no. of days)
Datafields with quarterly unique data frequency would see a Long Count + Short Count value close to its actual coverage when N = 66 (quarter)
When N = 22 (month) Long Count + Short Count would be lower (approx. 1/3rd of coverage)
When N = 5 (week), Long Count + Short Count would be even lower

Purpose: Understand how often the data actually changes vs. being backfilled What it tells you: Data freshness and update patterns

4. Data Bounds Analysis

Expression: abs(datafield) > X , for vector data type, the expression should be abs(vector_operator(datafield)) > X, please note, the vector_operator is the operator that you found via mcp. Insight: Bounds of the datafield. Vary the values of X and see the Long Count

Example: X=1 will indicate if the field is normalized to values between -1 and +1

Purpose: Understand the range and scale of the data values What it tells you: Whether data is normalized, what the typical value ranges are

5. Central Tendency Analysis

Expression: ts_median(datafield, 1000) > X , for vector data type, the expression should be ts_median(vector_operator(datafield), 1000) > X, please note, the vector_operator is the operator that you found via mcp. Insight: Median of the datafield over 5 years. Vary the values of X and see the Long Count

Note: Similar process can be applied to check the mean of the datafield

Purpose: Understand the typical values and central tendency of the data What it tells you: Whether the data is skewed, what typical values look like

6. Data Distribution Analysis

Expression: X < scale_down(datafield) && scale_down(datafield) < Y , for vector data type, the expression should be X < scale_down(vector_operator(datafield)) && scale_down(vector_operator(datafield)) < Y, please note, the vector_operator is the operator that you found via mcp. Insight: Distribution of the datafield

Key Points:

scale_down acts as a MinMaxScaler that can preserve the original distribution of the data
X and Y are values that vary between 0 and 1 that allow us to check how the datafield distributes across its range

Purpose: Understand how data is distributed across its range What it tells you: Whether data is evenly distributed, clustered, or has specific patterns

🔍 Practical Example

Example: If you simulate [close <= 0], you will see Long and Short Counts as 0. This implies that closing price always has a positive value (as expected!)

What this demonstrates: The validation that your understanding of the data is correct

📋 Implementation Workflow

Step 1: Setup

Set neutralization to "None"
Set decay to 0
Choose appropriate universe and time period

Step 2: Run Basic Tests

Start with expression 1 (datafield) to get baseline coverage
Run expression 2 (datafield != 0 ? 1 : 0) to understand non-zero coverage

Step 3: Analyze Update Frequency

Test with N = 5 (weekly)
Test with N = 22 (monthly)
Test with N = 66 (quarterly)
Compare results to understand update patterns

Step 4: Explore Value Ranges

Test various thresholds for bounds analysis
Test various thresholds for central tendency
Test various ranges for distribution analysis

Step 5: Document Insights

Record Long Count and Short Count for each test
Calculate coverage ratios
Note patterns in update frequency
Document value ranges and distributions

🎯 When to Use Each Method

Method	Best For	When to Use
1. Basic Coverage	Initial assessment	First exploration of any new field
2. Non-Zero Coverage	Data quality check	After basic coverage to understand meaningful data
3. Update Frequency	Data freshness	When you need to understand how often data changes
4. Data Bounds	Value ranges	When you need to understand data scale and normalization
5. Central Tendency	Typical values	When you need to understand what "normal" looks like
6. Distribution	Data patterns	When you need to understand how data is spread

⚠️ Important Considerations

Neutralization Setting

Use "None" for these exploration tests
This ensures you're seeing the raw data behavior
Other neutralization settings may mask important patterns

Decay Setting

Use 0 for these exploration tests
This ensures you're seeing the actual data values
Decay can smooth out important variations

Universe Selection

Choose a universe that represents your target use case
Consider both coverage and representativeness
Large universes may have different patterns than smaller ones

Time Period

Use sufficient history to see patterns
Consider seasonal or cyclical effects
Ensure you have enough data for statistical significance

🚀 Advanced Applications

Combining Methods

Use multiple methods together for comprehensive understanding
Cross-reference results to validate insights
Look for inconsistencies that might indicate data quality issues

Custom Variations

Modify expressions to test specific hypotheses
Combine with other operators for deeper insights
Create custom metrics based on your findings

Automation

These tests can be automated for systematic dataset evaluation
Create standardized evaluation reports
Track changes in data quality over time

BRAIN Platform Documentation: Understanding Data concepts
Data Explorer Tool: Visual exploration of data fields
Simulation Results: Detailed analysis of field behavior
Community Forums: User experiences and best practices

This guide provides a systematic approach to understanding new datafields on the WorldQuant BRAIN platform. Use these methods to quickly assess data quality, coverage, and characteristics before incorporating fields into your alpha strategies.

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