**Dataset**: analyst85
**Region**: EUR
**Delay**: 1

# Trade idea dataset Feature Engineering Analysis Report

**Dataset**: analyst85
**Category**: Analyst
**Region**: EUR
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 1

---

## Executive Summary

**Primary Question Answered by Dataset**: What is the revision state and versioning history of Goldman Sachs trade recommendations for European equities?

**Key Insights from Analysis**:
- The dataset captures version identifiers for trade ideas, indicating revision cycles and update frequency of analyst recommendations
- Single field structure suggests this is a metadata-rich vector field containing version histories or revision counters
- Version dynamics can proxy for analyst conviction changes (frequent updates = uncertainty, stable versions = conviction)
- The vector nature implies multiple concurrent versions or composite versioning across contributors

**Critical Field Relationships Identified**:
- Self-referential temporal relationships: Version values evolve through time, creating autoregressive patterns
- Vector internal structure: Distribution of versions within the vector indicates dispersion of opinions across sales contributors

**Most Promising Feature Concepts**:
1. **Version Update Momentum** - because rapid version changes signal shifting analyst views before price adjusts
2. **Version Dispersion Stability** - because consensus in versioning across contributors indicates high-conviction ideas
3. **Accumulated Revision Intensity** - because cumulative version increments measure total analytical activity on a stock

---

## Dataset Deep Understanding

### Dataset Description
The dataset provides access to Goldman Sachs' single stock trade ideas submitted by generalist global alpha capture sales contributors. It tracks version identifiers that represent revisions, updates, or vintages of trade recommendations for European equities (EUR region, TOPCS1600 universe). The version field captures the evolution of investment ideas through time, serving as a proxy for analyst activity and recommendation stability.

### Field Inventory
| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| `anl85_version` | version | Vector (Numeric/Categorical) | Daily | 100% |

### Field Deconstruction Analysis

#### anl85_version: Trade Idea Version Identifier
- **What is being measured?**: The revision state or version number assigned to trade ideas by Goldman Sachs sales contributors. Represents the iteration count or vintage identifier of investment recommendations.
- **How is it measured?**: System-generated identifiers assigned when trade ideas are submitted, updated, or revised within the Goldman Sachs alpha capture platform. Captured as vector data to represent multiple concurrent views or composite versioning.
- **Time dimension**: Point-in-time snapshot of version states with historical persistence. Versions accumulate or change as analysts update their views.
- **Business context**: Trade ideas evolve as new information emerges; version tracking allows identification of fresh vs. stale recommendations. Higher versions may indicate contested or rapidly evolving investment theses.
- **Generation logic**: Automatically incremented or assigned by the Goldman Sachs internal system when contributors submit updated trade ideas. May reset or follow sequential patterns depending on the idea lifecycle management protocol.
- **Reliability considerations**: Version numbers may have non-uniform distributions (clustering at initial versions). Jumps in version numbers may indicate data patches rather than organic updates. Missing values (NaN) may indicate inactive or withdrawn ideas.

### Field Relationship Mapping

**The Story This Data Tells**:
This dataset narrates the lifecycle of institutional trade recommendations - how frequently ideas are revised, whether analysts cluster around similar versions (consensus), and how recommendation intensity accumulates over time. The version field serves as a mechanical recording of analytical activity and conviction evolution.

**Key Relationships Identified**:
1. Temporal autocorrelation: Today's version strongly relates to yesterday's, but deviations signal events
2. Cross-sectional dispersion: The spread of versions within the vector indicates disagreement among contributors
3. Accumulation pattern: Sum of version changes reflects total analytical attention allocated to a stock

**Missing Pieces That Would Complete the Picture**:
- Directional bias of trade ideas (long/short/neutral)
- Price targets or expected returns associated with versions
- Timestamp of original idea generation vs. version date
- Contributor identity or confidence weighting

---

## Feature Concepts by Question Type

### Q1: "What is stable?" (Invariance Features)

**Concept**: Version Persistence Ratio
- **Sample Fields Used**: `anl85_version`
- **Definition**: Measures the stability of version numbers over time using rolling standard deviation of vector-averaged versions
- **Why This Feature**: Stable version numbers indicate high-conviction trade ideas that analysts rarely revise, suggesting strong fundamental theses. High volatility in versions indicates uncertainty or rapidly evolving situations.
- **is filling nan necessary**: NaN values likely indicate inactive or withdrawn trade ideas; these should not be filled blindly as they represent genuine absence of recommendation. Use `ts_backfill` only for temporary data gaps, not for structural NaNs indicating idea withdrawal.
- **Directionality**: Low values (high stability) suggest conviction and potential alpha persistence; high values suggest noise and potential reversal signals
- **Boundary Conditions**: Near-zero values indicate dormant or static recommendations; extremely high values indicate data errors or volatile idea churn
- **Implementation Example**: `ts_std_dev(vec_avg({version}), 20)`

**Concept**: Version Constancy Score
- **Sample Fields Used**: `anl85_version`
- **Definition**: Backfilled version values to maintain last known recommendation state, measuring how long ideas persist without revision
- **Why This Feature**: Captures the duration since last update, distinguishing between actively maintained ideas and forgotten legacy recommendations
- **is filling nan necessary**: Yes, use `ts_backfill` with limited lookback (5 days) to handle temporary data transmission gaps, but preserve NaNs for withdrawn ideas by checking `is_nan` flags
- **Directionality**: Higher backfilled values (older versions persisting) indicate stale recommendations; fresh values indicate current active research
- **Boundary Conditions**: Maximum lookback values indicate potentially stale or abandoned ideas; zero indicates very recent updates
- **Implementation Example**: `ts_backfill(vec_avg({version}), 5, k=1)`

---

### Q2: "What is changing?" (Dynamics Features)

**Concept**: Version Update Momentum
- **Sample Fields Used**: `anl85_version`
- **Definition**: Rate of change in average version numbers over a 5-day window, capturing acceleration of analyst updates
- **Why This Feature**: Rapid version increases signal fresh information or changing analyst views that may precede price movements. Momentum in versions indicates building or waning conviction.
- **is filling nan necessary**: NaN values in the input should be preserved through the calculation using `pasteurize` to prevent artificial momentum from data gaps
- **Directionality**: Positive values indicate accelerating revisions (potentially increasing uncertainty or new catalysts); negative values indicate version stabilization (consensus forming)
- **Boundary Conditions**: Extreme positive values indicate data discontinuities or batch updates; zero indicates static recommendation environment
- **Implementation Example**: `ts_delta(vec_avg({version}), 5)`

**Concept**: Version Acceleration
- **Sample Fields Used**: `anl85_version`
- **Definition**: Second derivative of version changes - the acceleration of revision activity calculated as change in momentum
- **Why This Feature**: Identifies inflection points where analyst activity shifts from increasing to decreasing or vice versa, signaling turning points in information flow
- **is filling nan necessary**: Apply `ts_backfill` with short window (2 days) before differencing to handle isolated NaNs, but preserve consecutive NaN blocks
- **Directionality**: Positive acceleration indicates escalating analyst attention (potential catalyst approaching); negative acceleration indicates settling views
- **Boundary Conditions**: Extreme values indicate single-day version jumps (likely data errors); moderate values indicate organic revision patterns
- **Implementation Example**: `ts_delta(ts_delta(vec_avg({version}), 5), 5)`

---

### Q3: "What is anomalous?" (Deviation Features)

**Concept**: Version Z-Score Deviation
- **Sample Fields Used**: `anl85_version`
- **Definition**: Standardized deviation of current version from its 20-day historical mean, identifying unusual version levels
- **Why This Feature**: Outlier version numbers indicate unusual analyst activity - either major revisions or data anomalies that require scrutiny
- **is filling nan necessary**: Calculate mean and std dev ignoring NaNs using standard operators, but return NaN if input is NaN to avoid false signals from missing data
- **Directionality**: High positive z-scores indicate unusually high version numbers (many revisions, potentially contested ideas); negative z-scores indicate unusually low/stable versions
- **Boundary Conditions**: Values beyond ±3 standard deviations suggest data errors or extraordinary corporate events driving multiple revisions
- **Implementation Example**: `(vec_avg({version}) - ts_mean(vec_avg({version}), 20)) / ts_std_dev(vec_avg({version}), 20)`

**Concept**: Vector Version Dispersion Anomaly
- **Sample Fields Used**: `anl85_version`
- **Definition**: Deviation of current vector standard deviation from its historical median, identifying when contributor disagreement is unusually high
- **Why This Feature**: Anomalous dispersion across the vector indicates fragmentation of analyst views - potentially signaling controversial stocks or mixed signals from Goldman contributors
- **is filling nan necessary**: Use `vec_filter` to remove NaN values within the vector before calculating dispersion statistics to ensure accurate standard deviation
- **Directionality**: High positive values indicate unusually high disagreement among versions (divergent views); negative values indicate unusual consensus
- **Boundary Conditions**: Zero indicates typical disagreement levels; extreme values indicate either data quality issues or genuine analytical disputes
- **Implementation Example**: `vec_stddev({version}) - ts_median(vec_stddev({version}), 20)`

---

### Q4: "What is combined?" (Interaction Features)

**Concept**: Version Consistency Ratio
- **Sample Fields Used**: `anl85_version`
- **Definition**: Ratio of vector standard deviation to vector mean (coefficient of variation) of versions, measuring relative dispersion
- **Why This Feature**: Combines central tendency and dispersion to identify stocks where versions are both high (many revisions) and dispersed (disagreement) - indicating contested high-activity ideas
- **is filling nan necessary**: Filter NaN values within vector using `vec_filter` before computing statistics to ensure robust ratio calculation
- **Directionality**: High values indicate high revision activity with high disagreement (risky/contraian opportunities); low values indicate consensus regardless of version level
- **Boundary Conditions**: Near-zero values indicate perfect consensus; undefined values (mean near zero) indicate new ideas with minimal history
- **Implementation Example**: `vec_stddev({version}) / vec_avg({version})`

**Concept**: Version Range Momentum
- **Sample Fields Used**: `anl85_version`
- **Definition**: Product of version momentum and version range (max-min), combining direction of change with internal vector dispersion
- **Why This Feature**: Captures situations where the overall version is trending while internal contributor views span a wide range - indicating directional opportunity with high uncertainty
- **is filling nan necessary**: Apply `ts_backfill` to handle temporary gaps in the time series component, but preserve structural NaNs in the vector calculation
- **Directionality**: Positive values with high magnitude indicate strong trending revisions with wide disagreement (potential breakouts); negative values indicate converging views
- **Boundary Conditions**: Zero values indicate either no trend or no dispersion; extreme values indicate volatile revision environments
- **Implementation Example**: `ts_delta(vec_avg({version}), 5) * vec_range({version})`

---

### Q5: "What is structural?" (Composition Features)

**Concept**: Version Distribution Skewness
- **Sample Fields Used**: `anl85_version`
- **Definition**: Asymmetry of the version distribution within the vector, indicating whether most contributors cluster at low or high versions
- **Why This Feature**: Skewed distributions reveal structural biases - positive skew suggests most use old versions with few updates; negative skew suggests most updated to recent versions
- **is filling nan necessary**: Use `vec_filter` to remove NaN values before calculating skewness to prevent distortion from missing data points
- **Directionality**: Positive skew indicates tail of high versions (few active updaters, many stale views); negative skew indicates concentration at high versions (widespread updating)
- **Boundary Conditions**: Zero indicates symmetric version distribution; extreme values indicate polarized contributor bases
- **Implementation Example**: `vec_skewness({version})`

**Concept**: Version Percentile Concentration
- **Sample Fields Used**: `anl85_version`
- **Definition**: 90th percentile of versions within the vector minus 10th percentile, measuring the structural spread of contributor versions
- **Why This Feature**: Quantifies the structural diversity of opinion - wide percentiles indicate fragmented analytical views; narrow percentiles indicate uniform versioning
- **is filling nan necessary**: Filter NaN values before percentile calculation to ensure accurate tail measurement
- **Directionality**: High values indicate structural disagreement or asynchronous updates; low values indicates synchronized contributor behavior
- **Boundary Conditions**: Zero indicates all contributors on same version; high values indicate maximum dispersion across the versioning spectrum
- **Implementation Example**: `vec_percentage({version}, 0.9) - vec_percentage({version}, 0.1)`

---

### Q6: "What is cumulative?" (Accumulation Features)

**Concept**: Accumulated Revision Intensity
- **Sample Fields Used**: `anl85_version`
- **Definition**: Sum of absolute version changes over 20 days, measuring total analytical activity and revision intensity
- **Why This Feature**: Cumulative measure of "analyst attention" - high values indicate stocks undergoing intense scrutiny and frequent updates, potentially preceding volatility
- **is filling nan necessary**: Treat NaNs as zeros in the summation using conditional logic, or use `ts_sum` which ignores NaNs, to prevent activity undercounting during data gaps
- **Directionality**: High values indicate high analytical churn (potentially overcrowded or uncertain ideas); low values indicate neglected or stable recommendations
- **Boundary Conditions**: Zero indicates no revisions in window; maximum values indicate daily version updates
- **Implementation Example**: `ts_sum(abs(ts_delta(vec_avg({version}), 1)), 20)`

**Concept**: Version Decay Weight
- **Sample Fields Used**: `anl85_version`
- **Definition**: Exponentially weighted average of versions over 20 days, giving higher weight to recent revisions while maintaining memory of historical versions
- **Why This Feature**: Captures the persistence of version information with decay - recent updates matter more, but old versions influence the signal gradually fading
- **is filling nan necessary**: Use `ts_backfill` before decay calculation to ensure continuous time series, then apply decay to prevent NaN propagation
- **Directionality**: Higher values indicate recent high-version activity; lower values indicate recent low-version or old high-version activity decaying
- **Boundary Conditions**: Approaches current value quickly if factor is high; maintains long memory if factor is low
- **Implementation Example**: `ts_decay_exp_window(vec_avg({version}), 20, factor=0.1)`

---

### Q7: "What is relative?" (Comparison Features)

**Concept**: Cross-Sectional Version Rank
- **Sample Fields Used**: `anl85_version`
- **Definition**: Gaussian quantile transformation of vector-averaged versions across the universe, creating relative positioning
- **Why This Feature**: Identifies stocks with unusually high or low version numbers relative to peers, normalizing for market-wide shifts in versioning behavior
- **is filling nan necessary**: NaN values should remain NaN through the quantile transformation to avoid false relative rankings for inactive ideas
- **Directionality**: High values (positive tail) indicate most revised ideas relative to universe (consensus shorts/long candidates); low values indicate least revised (undiscovered ideas)
- **Boundary Conditions**: ±3 sigma represents extreme percentiles; zero indicates median revision activity
- **Implementation Example**: `quantile(vec_avg({version}), driver="gaussian", sigma=1.0)`

**Concept**: Version Irregularity Ratio
- **Sample Fields Used**: `anl85_version`
- **Definition**: Vector information ratio (mean/standard deviation) of versions, measuring signal-to-noise in contributor versioning
- **Why This Feature**: High IR indicates consistent versioning across contributors (reliable signal); low IR indicates noisy, inconsistent versioning
- **is filling nan necessary**: Filter NaN values before calculating vector statistics to ensure accurate IR computation
- **Directionality**: High values indicate consensus and stability; low values indicate disagreement and uncertainty
- **Boundary Conditions**: Infinity indicates zero dispersion (perfect consensus); zero indicates zero mean or infinite dispersion (chaos)
- **Implementation Example**: `vec_ir({version})`

---

### Q8: "What is essential?" (Essence Features)

**Concept**: Essential Version Signal
- **Sample Fields Used**: `anl85_version`
- **Definition**: Sigmoid transformation of mean-centered versions, compressing extreme values while preserving rank information
- **Why This Feature**: Distills the essential signal from version data - extreme high/low versions are compressed to prevent outlier dominance while maintaining the ordinal relationship
- **is filling nan necessary**: Preserve NaNs through transformation to avoid artificial signal generation from missing data
- **Directionality**: Values near 0.5 indicate average version activity; approaching 1 indicates extremely high versions; approaching 0 indicates minimal versioning
- **Boundary Conditions**: Asymptotically approaches 0 or 1 for extreme inputs; exactly 0.5 when input equals zero (after centering)
- **Implementation Example**: `sigmoid(vec_avg({version}))`

**Concept**: Purified Version Increment
- **Sample Fields Used**: `anl85_version`
- **Definition**: Cleansed version changes with infinite values removed and extreme jumps dampened using jump decay
- **Why This Feature**: Removes data artifacts and extreme outliers to reveal the true underlying rate of version updates without noise from data errors
- **is filling nan necessary**: Use `purify` to clear infinities, then `jump_decay` to handle extreme values, preserving NaNs for genuine missing data
- **Directionality**: Positive values indicate genuine version increases; near-zero indicates stability; negative values rare (version rollbacks)
- **Boundary Conditions**: Capped by jump_decay parameters; zero indicates no meaningful change after purification
- **Implementation Example**: `jump_decay(purify(ts_delta(vec_avg({version}), 1)), 5, sensitivity=0.5, force=0.1)`

---

## Implementation Considerations

### Data Quality Notes
- **Coverage**: Field available for all instruments in TOPCS1600 universe with daily updates, but vector completeness may vary by date
- **Timeliness**: 1-day delay ensures all Goldman contributors' updates are captured and validated before distribution
- **Accuracy**: Version numbers are system-generated reducing manual input errors, but batch updates may create artificial version jumps
- **Potential Biases**: Survivorship bias toward actively covered stocks; withdrawn ideas may leave NaN gaps rather than explicit closure signals

### Computational Complexity
- **Lightweight features**: `vec_avg`, `vec_count`, `quantile` - O(1) per instrument per day
- **Medium complexity**: `ts_mean`, `ts_std_dev`, `ts_decay_exp_window` - O(d) where d=lookback days
- **Heavy computation**: `ts_sum` with nested `ts_delta`, `jump_decay` combined with `purify` - O(d) with multiple passes

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Version Update Momentum** - Direct measure of analyst activity changes, high interpretability
2. **Cross-Sectional Version Rank** - Normalized signal comparable across time periods and market conditions
3. **Version Z-Score Deviation** - Robust outlier detection for identifying unusual analyst behavior

**Tier 2 (Secondary Priority)**:
1. **Version Consistency Ratio** - Captures dispersion effects but requires careful handling of near-zero means
2. **Accumulated Revision Intensity** - Good measure of attention but may lag price action

**Tier 3 (Requires Further Validation)**:
1. **Version Distribution Skewness** - Interpretation depends heavily on underlying version distribution assumptions
2. **Version Irregularity Ratio** - May be noisy for instruments with few active contributors

---

## Critical Questions for Further Exploration

### Unanswered Questions:
1. Do version numbers reset for new trade ideas or do they continue incrementing indefinitely across idea refreshes?
2. Are version numbers comparable across different Goldman Sachs sales contributors or do different contributors use different versioning schemes?
3. Does the vector structure represent multiple simultaneous trade ideas per stock or different versioning perspectives?

### Recommended Additional Data:
- Trade idea direction (long/short) to combine with version activity
- Original idea generation timestamp to calculate version velocity (versions per day since inception)
- Contributor confidence scores or weightings to refine vector aggregation

### Assumptions to Challenge:
- That higher versions indicate more analysis (could indicate data corrections rather than substantive updates)
- That version changes are uniformly meaningful (major vs. minor revisions may have different information content)
- That the vector represents homogeneous data (different contributors may have different versioning granularities)

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## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand data essence (version as revision tracking)
2. Question-driven feature generation (8 fundamental questions)
3. Logical validation of each feature concept against allowed operators
4. Vector operator integration for statistical aggregation

**Design Principles**:
- Focus on logical meaning over conventional patterns (version as analyst activity proxy)
- Every feature must answer a specific question
- Clear documentation of "why" for each suggestion
- Emphasis on data understanding over prediction

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*Report generated: 2024-01-15*
*Analysis depth: Comprehensive field deconstruction + 8-question framework*
*Next steps: Implement Tier 1 features, validate assumptions about version semantics, gather additional data as needed*