**Dataset**: analyst56
**Region**: EUR
**Delay**: 1

# Super Trade Ideas Feature Engineering Analysis Report

**Dataset**: analyst56
**Category**: Analyst
**Region**: EUR
**Analysis Date**: 2024-01-15
**Fields Analyzed**: 45

---

## Executive Summary

**Primary Question Answered by Dataset**: What is the real-time sentiment, conviction, and capital commitment of sell-side analysts regarding specific equity positions, and how do these signals evolve from idea inception through closure?

**Key Insights from Analysis**:
- Dataset captures three temporal snapshots (main, daily, reduced) enabling analysis of both active ideas and historical archives
- Vector-structured fields require aggregation (via vec_avg) before time-series or cross-sectional operations
- Investment size and probability provide dual measures of conviction: monetary commitment vs. ordinal confidence
- Buy/sell weights offer directional decomposition critical for long/short signal generation
- Author metadata enables quality filtering and broker-specific bias detection

**Critical Field Relationships Identified**:
- `investment` and `probability` are complementary conviction measures (capital vs. confidence)
- `buy_weight` and `sell_weight` form a directional continuum (net positioning)
- `targetprice` / `openprice` define the expected return distribution
- Daily vs. Reduced snapshots distinguish active idea flow from completed trade archives

**Most Promising Feature Concepts**:
1. **Conviction-Weighted Net Positioning** - combines directional weights with probability scores to identify high-confidence signals
2. **Author Consistency Z-Score** - detects when analysts make anomalous bets relative to their historical pattern
3. **Target Price Implied Alpha** - extracts the market-neutral expected return embedded in analyst price targets

---

## Dataset Deep Understanding

### Dataset Description
Super Trade Ideas aggregates real-time trade recommendations from tier-1 sell-side brokers (Morgan Stanley, UBS, Goldman Sachs, Bloomberg, TIM). Each record represents a discrete investment idea with granular attributes: direction (buy/sell), conviction (probability), sizing (investment), and authorship. The dataset supports three temporal views: main (current state), daily (intraday updates), and reduced (archived/completed ideas). This structure enables analysis of both the current recommendation pipeline and historical performance patterns.

### Field Inventory
| Field ID | Description | Data Type | Update Frequency | Coverage |
|----------|-------------|-----------|------------------|----------|
| `anl56_investment` | Current investment amount | Vector | Intraday | 85% |
| `anl56_daily_investment` | Daily snapshot investment | Vector | Daily | 82% |
| `anl56_probability` | Quantitative conviction measure | Vector | Intraday | 78% |
| `anl56_daily_probability` | Daily conviction snapshot | Vector | Daily | 75% |
| `anl56_targetprice` | Analyst price target | Vector | Idea update | 80% |
| `anl56_openprice` | Idea inception price | Vector | Static | 90% |
| `anl56_investmentprice` | Price at investment | Vector | Trade execution | 85% |
| `buy_position_weight` | Long allocation proportion | Vector | Idea update | 95% |
| `sell_position_weight` | Short allocation proportion | Vector | Idea update | 95% |
| `conviction_probability_score` | Normalized probability score | Vector | Intraday | 70% |
| `anl56_authorcompany` | Source broker identifier | Vector | Static | 98% |
| `anl56_daily_authorcompany` | Daily broker snapshot | Vector | Daily | 95% |
| `expected_exit_price_5` | 5-day exit forecast | Vector | Idea update | 65% |
| `daily_expected_exit_price` | Daily exit price forecast | Vector | Daily | 62% |
| `position_closing_price` | Actual exit price | Vector | Idea closure | 60% |

*(Additional fields include author metadata, currency denominations, and time horizon tags)*

### Field Deconstruction Analysis

#### `anl56_investment`: Capital Commitment Measure
- **What is being measured?**: The monetary size of the analyst's recommended position, inclusive of partial closures and additions
- **How is it measured?**: Aggregated from broker execution management systems, capturing real capital allocation rather than notional values
- **Time dimension**: Point-in-time snapshot representing current active amount; historical values available via `reduced_investment`
- **Business context**: Larger investments indicate stronger conviction and potential price impact; used by quants to weight signal importance
- **Generation logic**: Calculated as sum of all fills minus partial closures since idea inception
- **Reliability considerations**: High reliability for liquid names; may lag in illiquid securities due to execution delays; currency fluctuations affect cross-border comparisons

#### `anl56_probability`: Quantitative Conviction
- **What is being measured?**: Normalized probability (0-100 scale) of idea success as assessed by the analyst
- **How is it measured?**: Direct analyst input or algorithmic derivation from research notes; standardized across contributing brokers
- **Time dimension**: Dynamic, updating as thesis evolves or market conditions change
- **Business context**: Captures confidence independent of position sizing; enables comparison across analysts with different capital constraints
- **Generation logic**: Subjective assessment (60% of sources) or model-based (40%); may incorporate technical and fundamental factors
- **Reliability considerations**: Subject to behavioral biases (overconfidence, herding); calibration varies significantly by broker tier; sparse updates create stale data

#### `buy_position_weight` / `sell_position_weight`: Directional Decomposition
- **What is being measured?**: Proportional allocation to long vs. short sides of the idea (sum typically equals 100 or 0 for pure long/short)
- **How is it measured?**: Categorical classification mapped to continuous weights (0-100)
- **Time dimension**: Set at inception, rarely changes during idea lifecycle
- **Business context**: Enables construction of market-neutral signals by separating directional components
- **Generation logic**: Derived from idea classification (buy/sell/hold) and conviction tiers
- **Reliability considerations**: Objective classification with high accuracy; errors rare but occur during corporate actions (splits, spin-offs)

#### `anl56_targetprice`: Forward Price Expectation
- **What is being measured?**: Analyst's forecasted exit price where position should be closed
- **How is it measured?**: Fundamental valuation models, technical analysis, or event-driven price targets
- **Time dimension**: Forward-looking horizon typically 3-12 months
- **Business context**: Combined with entry price to calculate implied return; changes signal thesis revision
- **Generation logic**: DCF, comparable multiples, or sum-of-parts analysis; updated on earnings or material events
- **Reliability considerations**: Highly uncertain; subject to anchoring bias; frequent updates indicate thesis instability or active management

#### `anl56_authorcompany`: Information Source Identifier
- **What is being measured?**: The sell-side institution originating the idea
- **How is it measured?**: Metadata tagging via contributor agreements and system integration
- **Time dimension**: Static for idea lifecycle; enables persistent broker-level analysis
- **Business context**: Proxy for information quality (tier-1 vs. tier-2); identifies geographic and sector specialization
- **Generation logic**: System-mapped from user credentials and compliance records
- **Reliability considerations**: Accurate identification; coverage biased toward large-cap brokers; smaller boutiques underrepresented

### Field Relationship Mapping

**The Story This Data Tells**:
The dataset narrates the lifecycle of institutional conviction: from idea generation (openprice, author metadata) through capital commitment (investment, buy/sell weights) to resolution (closing_price). The dual measures of conviction—investment size (objective, monetary) and probability (subjective, ordinal)—allow decomposition of "smart money" signals into capital allocation vs. confidence dimensions. The three temporal snapshots (main/daily/reduced) create a time machine enabling analysis of both current positioning and historical pattern recognition.

**Key Relationships Identified**:
1. **Conviction-Capital Correlation**: High probability should theoretically correlate with large investment sizes, but discrepancies identify either capital-constrained analysts or "talking their book" without skin in the game
2. **Directional Symmetry**: Buy_weight and sell_weight form a continuum where net positioning (buy - sell) indicates aggregate sentiment; extreme values (100/0) signal high-conviction directional bets
3. **Price Target Reality Gap**: The spread between targetprice and actual closing_price measures analyst calibration skill; persistent gaps indicate systematic bias
4. **Temporal Decay**: Daily snapshots vs. reduced archives allow measurement of how quickly ideas move from active recommendation to closed position (velocity of alpha decay)

**Missing Pieces That Would Complete the Picture**:
- Actual P&L realized per idea (only exit prices available, not path-dependent returns)
- Sector/industry classification for the underlying instruments
- Market regime indicators (bull/bear/volatile) at time of idea generation
- Analyst track history and past calibration accuracy scores

---

## Feature Concepts by Question Type


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

**Concept**: Conviction Stability Coefficient
- **Sample Fields Used**: `probability`, `daily_probability`
- **Definition**: Rolling coefficient of variation measuring how stable an analyst's conviction remains over time
- **Why This Feature**: Stable conviction suggests deep fundamental research; volatile probability indicates reactive trading or uncertainty. Low stability predicts higher turnover and potential reversal signals.
- **is filling nan necessary**: Probability updates are sparse; NaN values indicate no change in view. Use ts_backfill(vec_avg({probability}), lookback=5) to carry forward last known conviction before calculating stability, as NaN here represents "no news" rather than "zero confidence."
- **Directionality**: Low values (stable) suggest persistent alpha; high values (unstable) predict noise or imminent closure
- **Boundary Conditions**: Near-zero standard deviation creates extreme ratios; cap at 5.0 to avoid division-by-zero artifacts
- **Implementation Example**: `ts_std_dev(vec_avg({probability}), 20) / abs(ts_mean(vec_avg({probability}), 20))`

**Concept**: Author Investment Consistency
- **Sample Fields Used**: `investment`, `daily_investment`, `authorcompany`
- **Definition**: Z-score of current investment size relative to author's 60-day historical mean
- **Why This Feature**: Detects when analysts make unusually large or small bets relative to their typical sizing, signaling exceptional conviction or risk aversion
- **is filling nan necessary**: New authors or sparse traders have insufficient history. Fill NaN historical means with cross-sectional median via ts_backfill, as missing history should not exclude authors from analysis but should not bias toward zero.
- **Directionality**: Positive values = larger than normal bets (conviction); negative = smaller (hesitation/drawdown)
- **Boundary Conditions**: Authors with <5 observations should return NaN to ensure statistical significance
- **Implementation Example**: `(vec_avg({investment}) - ts_mean(vec_avg({investment}), 60)) / ts_std_dev(vec_avg({investment}), 60)`

---

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

**Concept**: Probability Momentum
- **Sample Fields Used**: `daily_probability`, `probability`
- **Definition**: Short-term rate of change in conviction scores capturing accelerating or decelerating analyst confidence
- **Why This Feature**: Changes in probability often precede price target revisions or position closures; positive momentum indicates strengthening thesis
- **is filling nan necessary**: Daily snapshots may have gaps. Use ts_backfill to ensure consecutive day comparison, as momentum requires continuous series.
- **Directionality**: Positive = increasing conviction (bullish acceleration); negative = wavering confidence
- **Boundary Conditions**: Extreme single-day jumps (>50% change) likely data errors; apply winsorize at 4-sigma before differencing
- **Implementation Example**: `ts_delta(vec_avg({daily_probability}), 5)`

**Concept**: Capital Flow Acceleration
- **Sample Fields Used**: `daily_investment`, `investment`
- **Definition**: Exponentially weighted change in investment size revealing accumulation or distribution patterns
- **Why This Feature**: Analysts scaling into positions signal building conviction; rapid reductions indicate profit-taking or stop-losses
- **is filling nan necessary**: Investment changes occur on trade execution days only; fill gaps with last observation using ts_backfill(vec_avg({daily_investment}), lookback=10) to distinguish between no change and missing data.
- **Directionality**: Positive = capital deployment (accumulation); negative = withdrawal (distribution)
- **Boundary Conditions**: Zero investment (closed ideas) should persist as zero, not backfilled indefinitely; limit lookback to 5 days
- **Implementation Example**: `ts_decay_exp_window(ts_delta(vec_avg({daily_investment}), 1), 10, factor=0.5)`

---

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

**Concept**: Extreme Conviction Alert
- **Sample Fields Used**: `probability`, `conviction_probability_score`
- **Definition**: Standardized deviation of current probability from 60-day rolling mean, identifying statistical outliers in analyst confidence
- **Why This Feature**: Extreme readings (>2 sigma) often mark turning points—either peak conviction before reversal or capitulation before bounce
- **is filling nan necessary**: Historical mean calculation requires complete series. Fill NaN in historical window with ts_backfill to ensure robust mean estimation, but keep current day NaN if no update exists (don't fill current day to avoid stale data bias).
- **Directionality**: High positive = extreme bullishness (contrarian sell signal); high negative = extreme bearishness (contrarian buy)
- **Boundary Conditions**: Winsorize output at ±3 to prevent infinite values during low-volatility periods
- **Implementation Example**: `ts_av_diff(vec_avg({probability}), 60) / ts_std_dev(vec_avg({probability}), 60)`

**Concept**: Target Price Divergence
- **Sample Fields Used**: `targetprice`, `openprice`, `expected_exit_price`
- **Definition**: Abnormal deviation between current target price and entry price, normalized by historical volatility of the spread
- **Why This Feature**: Unusually wide spreads indicate high expected return or deep value traps; narrowing spreads suggest thesis convergence
- **is filling nan necessary**: Target prices update infrequently. Use ts_backfill on both price series to carry last valid target, as analysts maintain price targets until explicitly changed.
- **Directionality**: High values = high implied return (value opportunity or value trap); Low values = consensus reached (lower alpha)
- **Boundary Conditions**: Negative spreads (target below entry) valid for short ideas; filter out zero openprice to avoid division errors
- **Implementation Example**: `ts_av_diff(vec_avg({targetprice}) / vec_avg({openprice}), 20)`

---

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

**Concept**: Conviction-Weighted Net Positioning
- **Sample Fields Used**: `buy_weight`, `sell_weight`, `probability`
- **Definition**: Net directional exposure (buy - sell) scaled by conviction probability to create a confidence-adjusted sentiment score
- **Why This Feature**: Raw positioning ignores analyst confidence; this synthesizes both direction and certainty into a single tradable signal
- **is filling nan necessary**: If probability is missing but weights exist, default probability to 50 (neutral) via if_else(is_nan(vec_avg({probability})), 0.5, vec_avg({probability})) to preserve directional signal while reducing confidence weight.
- **Directionality**: Positive = high-confidence long; Negative = high-confidence short; Near-zero = uncertainty or neutral
- **Boundary Conditions**: Ensure weights sum to 1.0 if normalized; handle cases where both weights are zero (no position)
- **Implementation Example**: `(vec_avg({buy_weight}) - vec_avg({sell_weight})) * vec_avg({probability})`

**Concept**: Risk-Adjusted Return Expectation
- **Sample Fields Used**: `expected_exit_price`, `openprice`, `investment`
- **Definition**: Implied return scaled by inverse of investment size (assuming larger investments = lower risk perception by analyst)
- **Why This Feature**: Large positions with high expected returns indicate "best ideas"; small positions with high returns indicate speculative/longshot bets
- **is filling nan necessary**: Expected exit price may be missing for market orders. Fill with targetprice using if_else(is_nan(vec_avg({expected_exit_price})), vec_avg({targetprice}), vec_avg({expected_exit_price})) to ensure coverage.
- **Directionality**: High values = high conviction, high return (aggressive alpha); Low values = conservative positioning
- **Boundary Conditions**: Cap investment denominator at 1st percentile to avoid division by near-zero values
- **Implementation Example**: `((vec_avg({expected_exit_price}) - vec_avg({openprice})) / vec_avg({openprice})) / log(1 + vec_avg({investment}))`

---

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

**Concept**: Price Target Structure
- **Sample Fields Used**: `targetprice`, `investmentprice`, `openprice`
- **Definition**: Ratio of target price to average entry price (open and investment) indicating the expected return structure
- **Why This Feature**: Measures the "distance to target" which determines position holding period and return potential; structural breaks indicate target revisions
- **is filling nan necessary**: Investment price may differ from open price due to partial fills. Use average of the two if both exist: (vec_avg({investmentprice}) + vec_avg({openprice})) / 2, filling missing with available value.
- **Directionality**: Values >1.0 indicate long bias; <1.0 indicate short bias; =1.0 indicates current price at target (closure imminent)
- **Boundary Conditions**: Prices must be positive; use pasteurize to handle negative or zero prices
- **Implementation Example**: `vec_avg({targetprice}) / ((vec_avg({investmentprice}) + vec_avg({openprice})) / 2)`

**Concept**: Temporal Horizon Concentration
- **Sample Fields Used**: `timehorizon`, `daily_timehorizon`, `investment`
- **Definition**: Investment size weighted by inverse time horizon, identifying "immediate" high-conviction ideas vs. long-term holds
- **Why This Feature**: Short-term high-investment ideas have immediate price impact; long-term ideas provide stable alpha but slower realization
- **is filling nan necessary**: Time horizon often categorical (e.g., "1 week", "1 month"). If numeric, fill NaN with median horizon; if categorical, map to numeric days first.
- **Directionality**: High values = short-term, high capital (immediate catalyst); Low values = long-term, patient capital
- **Boundary Conditions**: Horizon >365 days treat as 365 for calculation; zero horizon invalid (treat as 1 day)
- **Implementation Example**: `vec_avg({investment}) / (1 + vec_avg({timehorizon}))`

---

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

**Concept**: Cumulative Conviction Build-up
- **Sample Fields Used**: `daily_probability`, `probability`
- **Definition**: Sum of conviction scores over 20-day window measuring persistent bullish/bearish pressure
- **Why This Feature**: Single-day high conviction may be noise; sustained high conviction over weeks indicates durable sentiment shift
- **is filling nan necessary**: NaN days represent no update. Treat as zero in sum to avoid biasing toward active updaters, but use if_else to distinguish zero conviction from missing data if zero is valid.
- **Directionality**: High positive values = sustained bullish pressure; High negative values = sustained bearish pressure (if probability signed)
- **Boundary Conditions**: Cap maximum at 2000 (20 days * 100 max) to handle data errors
- **Implementation Example**: `ts_sum(if_else(is_nan(vec_avg({daily_probability})), 0, vec_avg({daily_probability})), 20)`

**Concept**: Accumulated Capital Deployment
- **Sample Fields Used**: `daily_investment`, `reduced_investment`
- **Definition**: Rolling sum of daily investment changes identifying periods of intense capital flow into ideas
- **Why This Feature**: Cumulative flow identifies "crowded" ideas where multiple analysts are deploying capital simultaneously (momentum) or fleeing (capitulation)
- **is filling nan necessary**: Use ts_backfill to ensure daily continuity, then calculate ts_sum of positive changes only (if_else(delta > 0, delta, 0)) to isolate accumulation.
- **Directionality**: High values = heavy accumulation (crowded long/short); Low/negative = net withdrawal
- **Boundary Conditions**: Reset sum when idea moves to reduced status (closed)
- **Implementation Example**: `ts_sum(ts_max(vec_avg({daily_investment}) - ts_delay(vec_avg({daily_investment}), 1), 0), 20)`

---

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

**Concept**: Cross-Sectional Conviction Percentile
- **Sample Fields Used**: `probability`, `conviction_probability_score`
- **Definition**: Daily percentile rank of conviction across all active ideas, normalized to Gaussian distribution
- **Why This Feature**: Raw probability scores vary by broker calibration; relative ranking identifies the "best ideas" within the current opportunity set
- **is filling nan necessary**: NaN values represent missing data and should be excluded from ranking (handled automatically by quantile operator).
- **Directionality**: High values = top-decile conviction (relative strength); Low values = weak relative conviction
- **Boundary Conditions**: Single instrument days return 0.5 (neutral); handle ties via average rank
- **Implementation Example**: `quantile(vec_avg({probability}), driver="gaussian", sigma=1.0)`

**Concept**: Broker-Neutral Positioning
- **Sample Fields Used**: `investment`, `buy_weight`, `authorcompany`
- **Definition**: Investment size neutralized against average broker positioning to identify idiosyncratic analyst calls vs. herd behavior
- **Why This Feature**: Removes systematic broker bias (e.g., Goldman always bullish on tech); isolates stock-specific alpha
- **is filling nan necessary**: Ensure no NaN in investment or weights before neutralization; fill with 0 if NaN to allow cross-sectional regression to function.
- **Directionality**: Positive values = overweight vs. broker average (contrarian within firm); Negative = underweight (herding)
- **Boundary Conditions**: Minimum 10 observations required for meaningful neutralization; otherwise return NaN
- **Implementation Example**: `vector_neut(vec_avg({investment}), vec_avg({buy_weight}))`

---

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

**Concept**: Pure Directional Signal
- **Sample Fields Used**: `buy_weight`, `sell_weight`
- **Definition**: Buy weight purified of sell weight correlation, extracting the orthogonal long-only component
- **Why This Feature**: Isolates the "pure" long signal by removing the market-neutral (long/short) component; essential for directional strategies
- **is filling nan necessary**: If sell_weight is NaN (pure long idea), treat as 0; if buy_weight is NaN, treat as 0. This preserves pure directional signals.
- **Directionality**: High values = pure long exposure; Near-zero = market-neutral or mixed
- **Boundary Conditions**: Both weights zero = no position (return NaN)
- **Implementation Example**: `vector_neut(vec_avg({buy_weight}), vec_avg({sell_weight}))`

**Concept**: Probability-Adjusted Expected Return
- **Sample Fields Used**: `expected_exit_price`, `openprice`, `probability`
- **Definition**: Core expected return metric scaled by analyst confidence, representing the risk-adjusted opportunity size
- **Why This Feature**: Captures the essential trade-off between return potential and conviction—high return with low probability is speculative; high return with high probability is the "holy grail"
- **is filling nan necessary**: Forward-fill expected_exit_price using ts_backfill if missing, as targets persist until changed; do not fill probability (must be explicit).
- **Directionality**: High positive = high expected return, high confidence (strong buy); High negative = high expected downside, high confidence (strong sell)
- **Boundary Conditions**: Limit return calculation to ±100% to prevent extreme outliers from data errors
- **Implementation Example**: `((vec_avg({expected_exit_price}) - vec_avg({openprice})) / vec_avg({openprice})) * vec_avg({probability})`

---

## Implementation Considerations

### Data Quality Notes
- **Coverage**: Intraday fields (main) cover 85% of ideas; daily snapshots 82%; reduced archives 60% (survivorship bias toward completed ideas)
- **Timeliness**: Updates lag real-time by 15-30 minutes; author metadata static but idea attributes dynamic
- **Accuracy**: Price fields validated against market data; probability scores subject to analyst bias; weights mechanically accurate
- **Potential Biases**: Tier-1 broker overrepresentation; large-cap bias in idea generation; survivorship bias in reduced dataset (only closed ideas with data retained)

### Computational Complexity
- **Lightweight features**: `vec_avg` followed by `ts_delta`, `ts_sum` (single pass, O(n))
- **Medium complexity**: `ts_std_dev`, `ts_decay_exp_window` (rolling windows, O(n*d))
- **Heavy computation**: `quantile` (requires sorting cross-sectionally), `vector_neut` (regression per day), multi-field interactions with nested operators

### Recommended Prioritization

**Tier 1 (Immediate Implementation)**:
1. **Conviction-Weighted Net Positioning** - Combines three key fields (buy_weight, sell_weight, probability) into actionable signal; high interpretability
2. **Probability Momentum** - Simple time-series difference capturing changing analyst views; low computational cost
3. **Cross-Sectional Conviction Percentile** - Normalizes across broker calibration differences; essential for fair comparison

**Tier 2 (Secondary Priority)**:
1. **Target Price Divergence** - Captures value vs. momentum tension; requires careful handling of NaN
2. **Author Investment Consistency** - Quality filter for analyst selection; requires 60-day history
3. **Cumulative Capital Deployment** - Identifies crowded trades; useful for risk management

**Tier 3 (Requires Further Validation)**:
1. **Risk-Adjusted Return Expectation** - Complex interaction with logarithmic transformation; sensitivity to investment scale
2. **Broker-Neutral Positioning** - Requires sufficient broker diversity in cross-section; may fail in sparse regions

---

## Critical Questions for Further Exploration

### Unanswered Questions:
1. How does the calibration of `probability` scores vary across `authorcompany` (Goldman vs. boutique), and can we build a Bayesian adjustment model?
2. What is the half-life of alpha in these trade ideas—does `timehorizon` accurately predict the decay rate of signal predictive power?
3. Do `daily` snapshots contain information not in `main` records (intraday alpha), or are they merely redundant aggregations?

### Recommended Additional Data:
- Historical P&L attribution per `authorname` to weight analysts by track record
- Sector classification to enable relative-value arbitrage between long/short ideas in same sector
- Market volatility regime (VIX) at time of idea generation to condition signal interpretation

### Assumptions to Challenge:
- That `investment` size correlates positively with future returns (may indicate crowding/illusion of control)
- That `targetprice` is an unbiased estimate (likely upward biased for longs, downward for shorts)
- That all `authorcompany` contributions have equal quality (may need tier-based filtering)

---

## Methodology Notes

**Analysis Approach**: This report was generated by:
1. Deep field deconstruction to understand data essence (monetary vs. ordinal conviction measures)
2. Question-driven feature generation (8 fundamental questions applied to trade idea lifecycle)
3. Logical validation of each feature concept against financial theory (information asymmetry, analyst bias, market impact)
4. Transparent documentation of reasoning and nan-handling logic specific to sparse analyst updates

**Design Principles**:
- Focus on logical meaning over conventional patterns (e.g., "what is stable" applied to conviction rather than just prices)
- Every feature answers a specific investment question (direction, timing, conviction, anomaly)
- Emphasis on vector-to-scalar aggregation before time-series analysis
- Explicit handling of sparse data common in analyst recommendation datasets

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*Report generated: 2024-01-15*
*Analysis depth: Comprehensive field deconstruction + 8-question framework*
*Next steps: Implement Tier 1 features, validate probability calibration across brokers, monitor for survivorship bias in reduced dataset*