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Institutional-Grade Options Flow Analytics

This document describes the institutional-grade enhancements to the options flow pipeline.

Overview

The pipeline has been refactored to convert static retail-style flow detection into dynamic, dealer-aware, time-sequenced signals suitable for intraday momentum and 1-5 day swing trades.

New Analytics Modules

1. Relative Premium Scoring (relative_premium_scorer.py)

Purpose: Replace static premium filter (minPremium = $80K) with context-aware relative scoring.

New Fields:

  • premium_zscore: Z-score of premium relative to 20-day rolling window per ticker
  • premium_percentile_intraday: Percentile rank within same-day flow
  • relative_premium_score: Composite score (0-100) combining z-score, intraday percentile, and median normalization

Usage: Premium of $80K might be significant for AAPL but noise for TSLA. This module computes relative significance.

2. Signal Component Scoring (signal_component_scorer.py)

Purpose: Convert binary badge logic (💎 ⭐ 🟢 🔴) into continuous numeric signal components.

New Fields:

  • aggression_score: Measures trade aggression (ITM premiums, ask-side trades)
  • size_concentration_score: Measures size concentration (single large trade vs many small ones)
  • repeat_trade_velocity: Measures repeat trade frequency (urgency building)
  • strike_clustering_score: Measures strike clustering (laddering patterns)
  • signal_strength: Composite score = 0.30 * aggression + 0.30 * size_concentration + 0.20 * repeat_velocity + 0.20 * strike_clustering

Note: Badges remain display-only. Signal strength is computed from components.

3. Tier-0 Noise Rejection (noise_rejector.py)

Purpose: Reject low-quality signals before enrichment to reduce processing overhead.

New Fields:

  • early_noise_reject: Boolean flag indicating if signal should be rejected as noise

Rejection Criteria:

  • Single isolated trade (no repeat activity within 30 minutes)
  • Far OTM weekly lottos (>15% OTM with <7 days to expiry)
  • Delta-adjusted premium below threshold (<$50K)

4. Time-Sequenced Flow Analysis (time_sequenced_analyzer.py)

Purpose: Analyze flow patterns over time to detect urgency, distribution, and continuation.

New Fields:

  • flow_acceleration: Change in premium per minute (Δ premium / minute)
  • time_between_hits: Average time between consecutive trades (minutes)
  • follow_on_ratio: Fraction of trades in same direction after initial trade (0-1)
  • strike_laddering_detected: Boolean indicating sequential strike accumulation

Interpretation:

  • Escalating premium + decreasing time gaps = urgency
  • Flat premium + widening gaps = distribution

5. Intent Classification (intent_classifier.py)

Purpose: Replace naive direction (BULL/BEAR) with nuanced volatility and hedging intent.

New Fields:

  • delta_exposure: Delta exposure (contracts * delta * 100 * spot_price)
  • gamma_exposure: Gamma exposure (contracts * gamma * 100 * spot_price^2)
  • volatility_intent: Enum (LONG_VOL, SHORT_VOL, DIRECTIONAL, HEDGE_UNWIND)

Note: Direction (BULL/BEAR) becomes secondary metadata.

6. Dealer-Aware Flow Context (dealer_flow_context.py)

Purpose: Track dealer hedging pressure and gamma exposure.

New Fields:

  • net_gamma_exposure_per_symbol: Sum of gamma exposures for symbol (positive = long gamma, negative = short gamma)
  • gamma_flip_proximity: Proximity to gamma flip point (-1 to 1)
  • dealer_hedge_pressure_score: Dealer hedge pressure score (0-100)

Usage: Validates flow continuation, flow reversals, and gamma squeeze setups.

7. Market Regime Detection (market_regime_detector.py)

Purpose: Identify market regime to gate trade signal generation.

New Fields:

  • market_regime: Enum (TREND, RANGE, HIGH_VOL_EVENT)

Trade Signal Gating:

  • Trend → continuation bias
  • Range → fade or vol-sell bias
  • Event → volatility expansion bias

8. Flow Decay & Reversal Validation (flow_decay_validator.py)

Purpose: Validate flow decay/reversal signals with anchors.

New Fields:

  • flow_state: Enum (ACTIONABLE, INFORMATIONAL)

Validation Criteria: Flow decay/reversal is actionable ONLY IF:

  • Premium contracts (relative_premium_score >= 60)
  • Dealer hedge pressure decreases
  • Price fails near VWAP / opening range / key level
  • Otherwise marked as INFORMATIONAL

9. Institutional Confidence Metrics (institutional_confidence.py)

Purpose: Calculate confidence scores for institutional flow signals.

New Fields:

  • confidence_score: Overall confidence score (0-100)
  • institutional_likelihood: Likelihood flow is institutional (0-1)
  • dealer_pain_level: Dealer pain level (0-100)
  • expected_move_vs_implied: Expected move vs implied move ratio

Integration

All modules are integrated into the main processing pipeline in main.py:

  1. Basic flow processing (normalization, badges, rocket score)
  2. Price context enrichment
  3. Alert matching
  4. Institutional analytics pipeline (NEW):
    • Tier-0 noise rejection
    • Relative premium scoring
    • Signal component scoring
    • Time-sequenced analysis
    • Intent classification
    • Dealer flow context
    • Market regime detection
    • Flow decay validation
    • Confidence metrics
  5. Filtering (premium, relative premium, badges, direction)
  6. Output formatting

Filtering Changes

Before:

  • Static premium filter: premium_num > 80000
  • Badge requirements: 🟢/🔴 + 💎 + ⭐

After:

  • Static premium filter: premium_num > min_premium (still applied)
  • Relative premium filter: relative_premium_score >= 60.0 (NEW)
  • Noise rejection filter: early_noise_reject == False (NEW)
  • Badge requirements: 🟢/🔴 + 💎 + ⭐ (still applied, but badges are now display-only)

API Response

All new fields are included in the API response. The response maintains backward compatibility - existing fields remain unchanged, new fields are additive.

Design Philosophy

  1. Flow represents pressure, not prediction: Signals indicate who is forced to act next (dealers hedging)
  2. Institutions trade urgency and forced hedging: Focus on dealer pain and gamma exposure
  3. Fewer, higher-quality signals > more alerts: Noise rejection and relative premium filtering reduce false positives
  4. Every signal must answer: "Who is forced to act next?"

Success Criteria

If implemented correctly:

  • Signal count decreases (noise filtered out)
  • Average signal quality increases (relative premium, signal strength)
  • False positives reduce (noise rejection, dealer context validation)
  • Trades align with intraday momentum and short-term swing horizons (time-sequenced analysis)