brain-data-feature-engineering

name	brain-data-feature-engineering
description	Automatically analyzes BRAIN dataset fields and generates feature engineering ideas for alpha creation. Input: data category, delay, region parameters; Output: markdown document with deep feature engineering suggestions. The skill performs autonomous analysis based on dataset and field information, proposing meaningful feature concepts.
allowed-tools	["Read","Grep","Glob","Write","mcp__brain-mcp__get_datasets","mcp__brain-mcp__get_datafields","mcp__brain-mcp__get_dataset_details"]

BRAIN Data Feature Engineering Workflow

Purpose: Automatically transform BRAIN dataset fields into deep, meaningful feature engineering ideas.

For Detailed Mindset Patterns: See reference.md for feature engineering philosophy. For Implementation Examples: See examples.md for case studies.

Input Requirements

Required Parameters:

• data_category: Dataset category (e.g., "fundamental", "analyst", "news", "model")
• delay: Data delay setting (0 or 1)
• region: Market region (e.g., "USA", "EUR", "ASI")

Optional Parameters:

• universe: Trading universe (default: "TOP3000")
• dataset_id: Specific dataset ID (if known, skips discovery phase)

Workflow Overview

Step 1: Dataset Discovery

Autonomous Action:

• Call mcp__brain-mcp__get_datasets with parameters (category, delay, region, universe)
• If dataset_id provided: Validate and use it
• If dataset_id not provided: Select the most relevant dataset based on metadata analysis
• Output: Locked dataset_id for analysis

Step 2: Field Extraction and Deconstruction

Autonomous Action:

• Call mcp__brain-mcp__get_datafields for the selected dataset
• For each field, extract: id, description, dataType, update frequency, coverage
• Deconstruct each field's meaning:
  • What is being measured? (the entity/concept)
  • How is it measured? (collection/calculation method)
  • Time dimension? (instantaneous, cumulative, rate of change)
  • Business context? (why does this field exist?)
  • Generation logic? (reliability considerations)
• Build field profiles: Structured understanding of each field's essence

Step 3: Autonomous Thinking and Analysis

The skill performs deep analysis based on collected information:

A. Field Relationship Mapping

• Analyze logical connections between fields
• Identify: independent fields, related fields, complementary fields
• Map the "story" the dataset tells
• Key question: What relationships are implied by these fields?

B. Question-Driven Feature Generation (Internal Process) The skill asks itself these questions and generates feature concepts:

1. "What is stable?" → Look for invariants

   • Which fields or combinations remain relatively constant?
   • What stability measures make sense?

2. "What is changing?" → Analyze change patterns

   • Rate of change, acceleration, volatility
   • Trend vs. noise separation

3. "What is anomalous?" → Identify deviations

   • Outliers, unusual patterns, breaks from normal
   • Deviation magnitude and significance

4. "What is combined?" → Examine interactions

   • How fields interact, amplify, or offset each other
   • Synthesis creates new meaning

5. "What is structural?" → Study compositions

   • Constituent parts, proportional relationships
   • Structural changes over time

6. "What is cumulative?" → Explore accumulation effects

   • Building up over time, decay effects
   • Memory and persistence in data

7. "What is relative?" → Make comparisons

   • Relative positioning, ranking, normalization
   • Context within dataset

8. "What is essential?" → Distill to core meaning

   • First principles thinking
   • Strip away assumptions, get to essence

C. Feature Concept Generation For each relevant question-field combination:

• Formulate feature concept that answers the question
• Define the concept clearly
• Identify the logical meaning
• Consider directionality (what high/low values mean)
• Identify boundary conditions
• Note potential issues/limitations

Step 4: Feature Documentation

For each generated feature concept, document:

• Concept Name: Clear, descriptive name
• Definition: One-sentence definition
• Logical Meaning: What phenomenon/concept does it represent?
• Why It's Meaningful: Why does this feature make sense?
• Directionality: Interpretation of high vs. low values
• Boundary Conditions: What extremes indicate
• Data Requirements: What fields are used and any constraints
• Potential Issues: Known limitations or concerns

Step 5: Output Generation

Generate structured markdown report including:

0. Write the report to ./output_report/region_delay_datasetID_ideas.md in the following format:

1. Dataset Understanding

   • Dataset description and characteristics
   • Field inventory (count, types, update patterns)
   • Key observations about data structure

2. Field Deconstruction Analysis

   • For each field: what it truly measures and why
   • Logical relationships between fields
   • "Story" the data tells

3. Feature Engineering Suggestions by Question Type

   3.1 Stability Features

   • Concepts for measuring stability/invariance
   • Why stability matters in this dataset
   • Example implementations

   3.2 Change Features

   • Concepts for capturing change patterns
   • Rate, acceleration, volatility measures
   • Temporal dynamics

   3.3 Anomaly Features

   • Deviation and outlier detection concepts
   • Normal vs. abnormal identification
   • Significance measures

   3.4 Interaction Features

   • Cross-field interaction concepts
   • Amplification, offset, synthesis effects
   • Combined meaning creation

   3.5 Structure Features

   • Composition and relationship concepts
   • Proportional analysis
   • Structural change detection

   3.6 Cumulative Features

   • Accumulation and decay concepts
   • Memory/persistence measures
   • Time-weighted effects

   3.7 Relative Features

   • Comparison and normalization concepts
   • Ranking and percentile measures
   • Context-relative positioning

   3.8 Essential Features

   • First-principles derived concepts
   • Core meaning extraction
   • Fundamental measures

4. Implementation Considerations

   • Data quality notes
   • Coverage considerations
   • Computational complexity
   • Potential improvements/extensions

5. Critical Questions for Further Exploration

   • What aspects weren't covered?
   • What additional data would be helpful?
   • What assumptions should be challenged?

Core Analysis Principles

1. From Data Essence: Start with what data truly means, not what it's traditionally used for
2. Autonomous Reasoning: Skill performs all thinking, no user input required
3. Question-Driven: Internal question bank guides feature generation
4. Meaning Over Patterns: Prioritize logical meaning over conventional combinations
5. Transparency: Show reasoning process in output

Example Output Structure

When analyzing dataset 'BEME' (Balance Sheet and Market Data), the output would include:

Dataset Understanding

Fields Analyzed: book_value, market_cap, book_to_market, etc. Key Observations: Dataset compares accounting values with market valuations

Field Deconstruction

• book_value: Accountant's calculation of net asset value (quarterly, audited, historical cost-based)
• market_cap: Market participants' valuation (continuous, forward-looking, sentiment-influenced)
• book_to_market: Ratio comparing these two valuation perspectives

Feature Concepts Generated

From "What is stable?"

• "Market reevaluation stability": Rolling coefficient of variation of book_to_market
• Logic: Measures whether market opinion is stable or volatile
• Meaning: Stable values suggest consensus, volatile values suggest disagreement/uncertainty

From "What is changing?"

• "Value creation vs. market reevaluation decomposition": Separate book_value growth from market_cap growth
• Logic: Distinguish fundamental value creation from market sentiment changes
• Meaning: Which component drives changes in book_to_market?

From "What is combined?"

• "Intangible value proportion": (market_cap - book_value) / enterprise_value
• Logic: Quantify proportion of value from intangibles (brand, growth, etc.)
• Meaning: What percentage of valuation isn't captured on the balance sheet?

(Additional question-based features would follow...)

Implementation Notes

The skill should:

1. Analyze first, then generate: Fully understand dataset before proposing features
2. Show reasoning: Explain why each feature concept makes sense
3. Be specific: Reference actual field names and their characteristics
4. Be critical: Question assumptions and identify limitations
5. Be creative: Look beyond traditional financial metrics

The skill should NOT:

1. Ask users to think: All thinking is internal to the skill
2. Provide generic templates: Each analysis should be specific to the dataset
3. Rely on conventional wisdom: Challenge traditional approaches
4. Output patterns without meaning: Every suggestion must have clear logic

Quality Assurance

Self-Check Process:

• All fields analyzed, not just skimmed
• Field meanings understood beyond descriptions
• Multiple question types explored
• Each feature has clear logical meaning
• Reasoning is explicit, not implicit
• Limitations are acknowledged
• Output is dataset-specific, not generic

Validation Questions:

• Would this analysis help someone truly understand the data?
• Are feature concepts novel yet meaningful?
• Is the reasoning process transparent?
• Does it avoid conventional thinking traps?

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This skill performs autonomous deep analysis of BRAIN datasets, generating meaningful feature engineering concepts based on data essence and logical reasoning.