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# BRAIN TIPS: 6 Ways to Quickly Evaluate a New Dataset
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## WorldQuant BRAIN Platform - Datafield Exploration Guide
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**Original Post**: [BRAIN TIPS] 6 ways to quickly evaluate a new dataset
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**Author**: KA64574
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**Date**: 2 years ago
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**Followers**: 265 people
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---
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## 🎯 **Overview**
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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.
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**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.
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**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.
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---
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## 📊 **The 6 Exploration Methods**
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### **1. Basic Coverage Analysis**
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**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.
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**Insight**: % coverage, would approximately be ratio of (Long Count + Short Count in the IS Summary) / (Universe Size in the settings)
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**Purpose**: Understand the basic availability of data across the universe
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**What it tells you**: How many instruments have data for this field on average
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---
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### **2. Non-Zero Value Coverage**
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**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.
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**Insight**: Coverage. Long Count indicates average non-zero values on a daily basis
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**Purpose**: Distinguish between missing data and actual zero values
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**What it tells you**: Whether the field has meaningful data vs. just coverage gaps
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---
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### **3. Data Update Frequency Analysis**
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**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.
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**Insight**: Frequency of unique data (daily, weekly, monthly etc.)
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**Key Points**:
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- Some datasets have data backfilled for missing values, while some do not
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- This expression can be used to find the frequency of unique datafield updates by varying N (no. of days)
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- Datafields with quarterly unique data frequency would see a Long Count + Short Count value close to its actual coverage when N = 66 (quarter)
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- When N = 22 (month) Long Count + Short Count would be lower (approx. 1/3rd of coverage)
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- When N = 5 (week), Long Count + Short Count would be even lower
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**Purpose**: Understand how often the data actually changes vs. being backfilled
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**What it tells you**: Data freshness and update patterns
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---
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### **4. Data Bounds Analysis**
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**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.
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**Insight**: Bounds of the datafield. Vary the values of X and see the Long Count
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**Example**: X=1 will indicate if the field is normalized to values between -1 and +1
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**Purpose**: Understand the range and scale of the data values
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**What it tells you**: Whether data is normalized, what the typical value ranges are
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---
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### **5. Central Tendency Analysis**
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**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.
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**Insight**: Median of the datafield over 5 years. Vary the values of X and see the Long Count
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**Note**: Similar process can be applied to check the mean of the datafield
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**Purpose**: Understand the typical values and central tendency of the data
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**What it tells you**: Whether the data is skewed, what typical values look like
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---
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### **6. Data Distribution Analysis**
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**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.
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**Insight**: Distribution of the datafield
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**Key Points**:
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- `scale_down` acts as a MinMaxScaler that can preserve the original distribution of the data
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- X and Y are values that vary between 0 and 1 that allow us to check how the datafield distributes across its range
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**Purpose**: Understand how data is distributed across its range
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**What it tells you**: Whether data is evenly distributed, clustered, or has specific patterns
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---
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## 🔍 **Practical Example**
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**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!)
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**What this demonstrates**: The validation that your understanding of the data is correct
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---
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## 📋 **Implementation Workflow**
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### **Step 1: Setup**
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1. Set neutralization to "None"
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2. Set decay to 0
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3. Choose appropriate universe and time period
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### **Step 2: Run Basic Tests**
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1. Start with expression 1 (`datafield`) to get baseline coverage
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2. Run expression 2 (`datafield != 0 ? 1 : 0`) to understand non-zero coverage
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### **Step 3: Analyze Update Frequency**
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1. Test with N = 5 (weekly)
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2. Test with N = 22 (monthly)
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3. Test with N = 66 (quarterly)
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4. Compare results to understand update patterns
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### **Step 4: Explore Value Ranges**
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1. Test various thresholds for bounds analysis
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2. Test various thresholds for central tendency
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3. Test various ranges for distribution analysis
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### **Step 5: Document Insights**
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1. Record Long Count and Short Count for each test
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2. Calculate coverage ratios
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3. Note patterns in update frequency
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4. Document value ranges and distributions
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---
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## 🎯 **When to Use Each Method**
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| Method | Best For | When to Use |
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|--------|----------|-------------|
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| **1. Basic Coverage** | Initial assessment | First exploration of any new field |
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| **2. Non-Zero Coverage** | Data quality check | After basic coverage to understand meaningful data |
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| **3. Update Frequency** | Data freshness | When you need to understand how often data changes |
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| **4. Data Bounds** | Value ranges | When you need to understand data scale and normalization |
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| **5. Central Tendency** | Typical values | When you need to understand what "normal" looks like |
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| **6. Distribution** | Data patterns | When you need to understand how data is spread |
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---
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## ⚠️ **Important Considerations**
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### **Neutralization Setting**
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- **Use "None"** for these exploration tests
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- This ensures you're seeing the raw data behavior
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- Other neutralization settings may mask important patterns
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### **Decay Setting**
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- **Use 0** for these exploration tests
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- This ensures you're seeing the actual data values
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- Decay can smooth out important variations
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### **Universe Selection**
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- Choose a universe that represents your target use case
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- Consider both coverage and representativeness
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- Large universes may have different patterns than smaller ones
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### **Time Period**
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- Use sufficient history to see patterns
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- Consider seasonal or cyclical effects
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- Ensure you have enough data for statistical significance
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---
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## 🚀 **Advanced Applications**
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### **Combining Methods**
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- Use multiple methods together for comprehensive understanding
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- Cross-reference results to validate insights
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- Look for inconsistencies that might indicate data quality issues
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### **Custom Variations**
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- Modify expressions to test specific hypotheses
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- Combine with other operators for deeper insights
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- Create custom metrics based on your findings
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### **Automation**
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- These tests can be automated for systematic dataset evaluation
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- Create standardized evaluation reports
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- Track changes in data quality over time
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---
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## 📚 **Related Resources**
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- **BRAIN Platform Documentation**: Understanding Data concepts
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- **Data Explorer Tool**: Visual exploration of data fields
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- **Simulation Results**: Detailed analysis of field behavior
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- **Community Forums**: User experiences and best practices
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---
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*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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