Evolutionary Metric Ranking

Methodology for systematically zooming into high-quality configurations across multiple evaluation metrics using per-metric percentile cutoffs, intersection-based filtering, and evolutionary optimization. Domain-agnostic principles with quantitative trading case studies.

Companion skills: rangebar-eval-metrics (metric definitions) | adaptive-wfo-epoch (WFO integration) | backtesting-py-oracle (SQL validation)

When to Use This Skill

Use this skill when:

Ranking and filtering configs/strategies/models across multiple quality metrics
Searching for optimal per-metric thresholds that select the best subset
Identifying which metrics are binding constraints vs inert dimensions
Running multi-objective optimization (Optuna TPE / NSGA-II) over filter parameters
Performing forensic analysis on optimization results (universal champions, feature themes)
Designing a metric registry for pluggable evaluation systems

Core Principles

P1 - Percentile Ranks, Not Raw Values

Raw metric values live on incompatible scales (Kelly in [-1,1], trade count in [50, 5000], Omega in [0.8, 2.0]). Percentile ranking normalizes every metric to [0, 100], making cross-metric comparison meaningful.

Rule: scipy.stats.rankdata(method='average') scaled to [0, 100]
      None/NaN/Inf -> percentile 0 (worst)
      "Lower is better" metrics -> negate before ranking (100 = best)

Why average ties: Tied values receive the mean of the ranks they would span. This prevents artificial discrimination between genuinely identical values.

P2 - Independent Per-Metric Cutoffs

Each metric gets its own independently-tunable cutoff. cutoff=20 means "only configs in the top 20% survive this filter." This creates a 12-dimensional (or N-dimensional) search space where each axis controls one quality dimension.

cutoff=100 -> no filter (everything passes)
cutoff=50  -> top 50% survives
cutoff=10  -> top 10% survives (stringent)
cutoff=0   -> nothing passes

Why independent, not uniform: Different metrics have different discrimination power. Uniform tightening (all metrics at the same cutoff) wastes filtering budget on inert dimensions while under-filtering on binding constraints.

P3 - Intersection = Multi-Metric Excellence

A config survives the final filter only if it passes ALL per-metric cutoffs simultaneously. This intersection logic ensures no single-metric champion sneaks through with terrible performance elsewhere.

survivors = metric_1_pass AND metric_2_pass AND ... AND metric_N_pass

Why intersection, not scoring: Weighted-sum scoring hides metric failures. A config with 99th percentile Sharpe but 1st percentile regularity would score well in a weighted sum but is clearly deficient. Intersection enforces minimum quality across every dimension.

P4 - Start Wide Open, Tighten Evolutionarily

All cutoffs default to 100% (no filter). The optimizer progressively tightens cutoffs to find the combination that best satisfies the chosen objective. This is the opposite of starting strict and relaxing.

Initial state:  All cutoffs = 100 (1008 configs survive)
After search:   Each cutoff independently tuned (11 configs survive)

Why start wide: Starting strict risks missing the global optimum by immediately excluding configs that would survive under a different cutoff combination. Wide-to-narrow exploration is characteristic of global optimization.

P5 - Multiple Objectives Reveal Different Truths

No single objective function captures "quality." Run multiple objectives and compare survivor sets. Configs that survive all objectives are the most robust.

Objective	Asks	Reveals
max_survivors_min_cutoff	Most configs at tightest cutoffs?	Efficient frontier of quantity vs stringency
quality_at_target_n	Best quality in top N?	Optimal cutoffs for a target portfolio size
tightest_nonempty	Absolute tightest with >= 1 survivor?	Universal champion (sole survivor)
pareto_efficiency	Survivors vs tightness trade-off?	Full Pareto front (NSGA-II)
diversity_reward	Are cutoffs non-redundant?	Which metrics provide independent information

Cross-objective consistency: A config that appears in ALL objective survivor sets is the most defensible selection. One that appears in only one is likely an artifact of that objective's bias.

P6 - Binding Metrics Identification

After optimization, identify binding metrics - those that would increase the intersection if relaxed to 100%. Non-binding metrics are either already loose or perfectly correlated with a binding metric.

For each metric with cutoff < 100:
    Relax this metric to 100, keep others fixed
    If intersection grows: this metric IS binding
    If intersection unchanged: this metric is redundant at current cutoffs

Why this matters: Binding metrics are the actual constraints on your quality frontier. Effort to improve configs should focus on binding dimensions.

P7 - Inert Dimension Detection

A metric is inert if it provides zero discrimination across the population. Detect this before optimization to reduce dimensionality.

If max(metric) == min(metric) across all configs: INERT
If percentile spread < 5 points: NEAR-INERT

Action: Remove inert metrics from the search space or permanently set their cutoff to 100. Including them wastes optimization budget.

P8 - Forensic Post-Analysis

After optimization, perform forensic analysis to extract actionable insights:

Universal champions - configs surviving ALL objectives
Feature frequency - which features appear most in survivors
Metric binding sequence - order in which metrics become binding as cutoffs tighten
Tightening curve - intersection size vs uniform cutoff (100% -> 5%)
Metric discrimination power - which metric kills the most configs at each tightening step

Architecture Pattern

Metric JSONL files (pre-computed)
        |
        v
MetricSpec Registry  <-- Defines name, direction, source, cutoff var
        |
        v
Percentile Ranker    <-- scipy.stats.rankdata, None->0, flip lower-is-better
        |
        v
Per-Metric Cutoff    <-- Each metric independently filtered
        |
        v
Intersection         <-- Configs passing ALL cutoffs
        |
        v
Evolutionary Search  <-- Optuna TPE/NSGA-II tunes cutoffs
        |
        v
Forensic Analysis    <-- Cross-objective consistency, binding metrics

MetricSpec Registry

The registry is the single source of truth for metric definitions. Each entry is a frozen dataclass:

@dataclass(frozen=True)
class MetricSpec:
    name: str              # Internal key (e.g., "tamrs")
    label: str             # Display label (e.g., "TAMRS")
    higher_is_better: bool # Direction for percentile ranking
    default_cutoff: int    # Default percentile cutoff (100 = no filter)
    source_file: str       # JSONL filename containing raw values
    source_field: str      # Field name in JSONL records

Design principle: Adding a new metric = adding one MetricSpec entry. No other code changes required. The ranking, cutoff, intersection, and optimization machinery is fully generic.

Env Var Convention

Each metric's cutoff is controlled by a namespaced environment variable:

RBP_RANK_CUT_{METRIC_NAME_UPPER} = integer [0, 100]

This enables:

Shell-level override without code changes
Copy-paste of optimizer output directly into next run
CI/CD integration via environment configuration
Mise task integration via [env] blocks

Evolutionary Optimizer Design

Sampler Selection

Scenario	Sampler	Why
Single-objective	TPE (Tree-Parzen Estimator)	Bayesian, handles integer/categorical, good for 10-20 dimensions
Multi-objective (2+)	NSGA-II	Pareto-frontier discovery, population-based

Determinism: Always seed the sampler (seed=42). Optimization results must be reproducible.

Search Space Design

def suggest_cutoffs(trial):
    cutoffs = {}
    for spec in metric_registry:
        cutoffs[spec.name] = trial.suggest_int(spec.name, 5, 100, step=5)
    return cutoffs

Why step=5: Reduces the search space by 20x (20 values per metric vs 100) while maintaining sufficient granularity. For 12 metrics, this is 20^12 = 4 x 10^15 vs 100^12 = 10^24.

Why lower bound = 5: cutoff=0 always produces empty intersection. Values below 5 are too stringent to be useful in practice.

Data Pre-Loading (Critical Performance Pattern)

# Load metric data ONCE, share across all trials
metric_data = load_metric_data(results_dir, metric_registry)

def objective(trial):
    cutoffs = suggest_cutoffs(trial)
    # Pass pre-loaded data - avoids disk I/O per trial
    result = run_ranking_with_cutoffs(cutoffs, metric_data=metric_data)
    return obj_fn(result, cutoffs)

Why: Each trial evaluates in ~6ms when data is pre-loaded (pure NumPy/set operations). Without pre-loading, each trial incurs ~50ms of disk I/O. At 10,000 trials, this is 60 seconds vs 500 seconds.

Objective Function Patterns

Pattern 1 - Ratio Optimization

def obj_max_survivors_min_cutoff(result, cutoffs):
    n = result["n_intersection"]
    if n == 0:
        return 0.0
    mean_cutoff = sum(cutoffs.values()) / len(cutoffs)
    return n / mean_cutoff  # More survivors per unit of looseness

Use when: Exploring the efficiency frontier - how much quality can you get for how much filtering?

Pattern 2 - Constrained Quality

def obj_quality_at_target_n(result, cutoffs, target_n=10):
    n = result["n_intersection"]
    avg_pct = result["avg_percentile"]
    if n < target_n:
        return avg_pct * (n / target_n)  # Partial credit
    return avg_pct  # Full credit: maximize quality

Use when: You have a target portfolio size and want the highest quality subset.

Pattern 3 - Minimum Budget

def obj_tightest_nonempty(result, cutoffs):
    n = result["n_intersection"]
    if n == 0:
        return 0.0
    total_budget = sum(cutoffs.values())
    return max_possible_budget - total_budget  # Lower budget = better

Use when: Finding the single most universally excellent config.

Pattern 4 - Diversity Reward

def obj_diversity_reward(result, cutoffs):
    n = result["n_intersection"]
    if n == 0:
        return 0.0
    n_binding = result["n_binding_metrics"]
    n_active = sum(1 for v in cutoffs.values() if v < 100)
    if n_active == 0:
        return 0.0
    efficiency = n_binding / n_active
    return n * efficiency

Use when: Ensuring that tightened cutoffs provide independent information, not redundant filtering.

Pattern 5 - Pareto (Multi-Objective)

study = optuna.create_study(
    directions=["maximize", "minimize"],  # max survivors, min cutoff
    sampler=optuna.samplers.NSGAIISampler(seed=42),
)
def objective(trial):
    cutoffs = suggest_cutoffs(trial)
    result = run_ranking_with_cutoffs(cutoffs, metric_data=metric_data)
    return result["n_intersection"], sum(cutoffs.values()) / len(cutoffs)

Use when: You want to see the full trade-off landscape between two competing objectives.

Forensic Analysis Protocol

After running all objectives, perform this analysis:

Step 1 - Cross-Objective Survivor Sets

For each objective:
    survivors_{objective} = set of configs in final intersection

universal_champions = survivors_1 AND survivors_2 AND ... AND survivors_K

If a config survives all K objective functions, it is robust to objective choice.

Step 2 - Feature Theme Extraction

Count feature appearances across all survivors:

feature_counts = Counter()
for config_id in quality_survivors:
    for feature in config_id.split("__"):
        feature_counts[feature.split("_")[0]] += 1

Dominant features reveal the underlying market microstructure that the ranking system is selecting for.

Step 3 - Uniform Tightening Curve

Apply the same cutoff to ALL metrics and plot intersection size:

@100%: 1008 survivors (no filter)
@80%:  502 survivors
@60%:  210 survivors
@40%:   68 survivors
@20%:   12 survivors
@10%:    3 survivors
@5%:     0 survivors

The shape of this curve reveals whether the metric space has natural clusters or is uniformly distributed.

Step 4 - Binding Sequence

Tighten uniformly and at each step identify which metric was the "tightest killer" - the metric that eliminated the most configs:

@90%: 410 survivors | tightest killer: rachev (-57)
@80%: 132 survivors | tightest killer: headroom (-27)
@70%:  29 survivors | tightest killer: n_trades (-12)
@60%:   6 survivors | tightest killer: dsr (-6)

This reveals the binding constraint hierarchy.

Implementation Checklist

When implementing this methodology in a new domain:

Anti-Patterns

Anti-Pattern	Symptom	Fix	Severity
Weighted-sum scoring	Single metric dominates, others ignored	Use intersection (P3)	CRITICAL
Starting strict	Miss global optimum, premature convergence	Start at 100%, tighten (P4)	HIGH
Uniform cutoffs only	Over-filters inert metrics, under-filters binding ones	Per-metric independent cutoffs (P2)	HIGH
Single objective	Artifact of objective bias	Run 5+ objectives, check consistency (P5)	HIGH
Raw value comparison	Scale-dependent, misleading	Always use percentile ranks (P1)	HIGH
Including inert metrics	Wastes optimization budget	Detect and remove inert dimensions (P7)	MEDIUM
No data pre-loading	Optimizer 10x slower	Pre-load once, share across trials	MEDIUM
Unseeded optimizer	Non-reproducible results	Always seed sampler (seed=42)	MEDIUM
Missing forensic analysis	Raw numbers without insight	Run full forensic protocol (P8)	MEDIUM

References

Topic	Reference File
Range Bar Case Study	case-study-rangebar-ranking.md
Objective Functions	objective-functions.md
Metric Design Guide	metric-design-guide.md

Related Skills

Skill	Relationship
rangebar-eval-metrics	Metric definitions (TAMRS, Omega, DSR, etc.) fed into ranking
adaptive-wfo-epoch	Walk-Forward metrics that could be ranked
backtesting-py-oracle	Validates trade outcomes used in metric computation

Dependencies

pip install scipy numpy optuna>=4.7

TodoWrite Task Templates

Template A - Implement Ranking System (New Project)

1. [Preflight] Identify all evaluation metrics and their JSONL sources
2. [Preflight] Define MetricSpec registry (name, direction, source_file, source_field)
3. [Execute] Implement percentile_ranks() with scipy.stats.rankdata
4. [Execute] Implement apply_cutoff() and intersection()
5. [Execute] Add env var override for each metric cutoff (RANK_CUT_{NAME})
6. [Execute] Create run_ranking_with_cutoffs() API for optimizer
7. [Execute] Add binding metric detection and tightening analysis
8. [Execute] Write markdown report generator
9. [Verify] Unit tests for all pure functions (14+ tests)
10. [Verify] Run with default cutoffs (100%) - all configs should survive

Template B - Add Evolutionary Optimizer

1. [Preflight] Verify ranking module has run_ranking_with_cutoffs() API
2. [Preflight] Add optuna>=4.7 dependency
3. [Execute] Implement 5 objective functions
4. [Execute] Create suggest_cutoffs() with step=5 search space
5. [Execute] Pre-load metric data once, share across trials
6. [Execute] Handle pareto_efficiency (NSGA-II) as special case
7. [Execute] Write JSONL output with provenance (git commit, timestamp)
8. [Verify] POC with 10 trials - verify non-trivial cutoffs found
9. [Verify] Full run with 10K trials per objective

Template C - Forensic Analysis

1. [Preflight] Collect optimization results from all 5 objectives
2. [Execute] Extract survivor sets per objective
3. [Execute] Compute cross-objective intersection (universal champions)
4. [Execute] Run uniform tightening analysis (100% -> 5%)
5. [Execute] Identify binding metrics at each tightening step
6. [Execute] Extract feature themes from quality survivors
7. [Execute] Detect inert dimensions (zero discrimination)
8. [Verify] Document findings in structured summary table

Post-Change Checklist (Self-Maintenance)

After modifying this skill:

Principles P1-P8 remain internally consistent
Anti-patterns table covers new patterns discovered
References in references/ are up to date
Case study reflects latest production results
Implementation checklist is complete and ordered
Plugin README updated if description changed

Troubleshooting

Issue	Cause	Solution
All cutoffs converge to 100%	Metrics are all correlated	Check for metric redundancy (Spearman r > 0.95)
Zero intersection at mild cutoffs	One metric has near-zero variance	Detect inert dimensions (P7)
Optimizer takes too long	Disk I/O per trial	Pre-load metric data (see Performance section)
Different objectives give same answer	Objectives poorly differentiated	Verify objective formulas test different trade-offs
Universal champion is mediocre	Survival != excellence	Check raw values, not just survival
Binding sequence changes across runs	Unseeded optimizer	Always use seed=42
Too many survivors	Cutoffs too loose	Increase n_trials, lower step size
Zero survivors	Cutoffs too tight	Check for inert metrics inflating dimensionality

evolutionary-metric-ranking