foxhunt/docs/archive/historical/INVESTIGATION_INDEX.md

Trading Agent Service: Feature Usage Investigation - Complete Index

Date: 2025-10-17
Investigation Status: COMPLETE
Total Documentation: 3 comprehensive reports, 60KB

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Documents Generated

1. TRADING_AGENT_FEATURE_INVESTIGATION.md (28KB)

Primary Report - 11 Comprehensive Sections

Complete architectural analysis covering:

• Part 1: Trading Agent Architecture (service structure, flow)
• Part 2: Asset Scoring System (multi-factor model details)
• Part 3: Feature Usage in Asset Scoring (critical gap analysis)
• Part 4: ML Integration (SharedMLStrategy usage)
• Part 5: Feature Indices (26-dim Wave A mapping)
• Part 6: Service Integration Points (universe, assets, allocation)
• Part 7: Wave C Integration Opportunities (feature mapping)
• Part 8: Integration Roadmap (3-phase plan)
• Part 9: Data Flow Diagrams
• Part 10: Key Findings & Recommendations
• Part 11: Feature Usage Matrix

Use Case: High-level strategy planning, architecture decisions

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2. TRADING_AGENT_FEATURE_CODE_REFERENCES.md (17KB)

Technical Reference - Code Snippets with Line Numbers

Detailed code examples including:

• Asset scoring structure definition (lines 13-40)
• Composite score calculation (lines 49-78)
• Momentum score calculation (lines 214-238)
• Value score calculation (lines 241-262)
• Liquidity score calculation (lines 265-299)
• MLFeatureExtractor structure (lines 65-129)
• Feature extraction main function (lines 170-220)
• Price features extraction (lines 220-262)
• Volume features extraction (lines 264-285)
• Time features extraction (lines 287-291)
• select_assets() placeholder (lines 223-240)
• Portfolio allocation stub (lines 1-6)
• Complete 26-feature index table
• 256-dimensional feature breakdown

Use Case: Implementation reference, bug fixes, code review

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3. INVESTIGATION_SUMMARY.txt (13KB)

Executive Summary - Key Findings & Roadmap

Quick reference covering:

• Investigation scope and findings
• Feature usage matrix (components × sources × status)
• Technical details (structures, formulas, methods)
• Critical gaps for Wave C (4 major gaps identified)
• Integration roadmap (3 phases, timeline estimates)
• Recommendations (priorities 1-3)
• Conclusion and next steps

Use Case: Decision making, quick reference, stakeholder updates

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Key Findings Summary

Finding 1: Asset Scoring Architecture COMPLETE ✓

• Location: services/trading_agent_service/src/assets.rs
• Status: Production-ready
• Components: 4-factor model (ML 40%, momentum 30%, value 20%, liquidity 10%)
• Tests: 100% passing

Finding 2: Feature Extraction EXISTS but NOT INTEGRATED ✗

• Two Systems:
  • Real-time 26-dimensional (common/src/ml_strategy.rs)
  • Production 256-dimensional (ml/src/features/extraction.rs)
• Current Usage: ML model inference and training only
• Missing: Integration with asset selection scoring

Finding 3: Asset Scoring Feature-Blind ✗

• Current Input: Pre-calculated values (external data)
• Missing: Real-time feature extraction per asset
• Impact: Cannot adapt weights by feature regime

Finding 4: Portfolio Allocation NOT IMPLEMENTED ✗

• Location: services/trading_agent_service/src/allocation.rs
• Status: 6-line stub
• Missing: 5 allocation strategies (Equal-Weight, Risk Parity, Mean-Variance, ML-Optimized, Kelly)

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Critical Gaps for Wave C

Gap	Current	Needed	Impact
Feature Extraction	select_assets() returns empty	Integrate MLFeatureExtractor	Required for Wave C
Feature-Based Scoring	Pre-calculated inputs	Map 26-dim features to scores	Enables adaptive weighting
Portfolio Allocation	Pure stub	5 allocation algorithms	Blocks position sizing
Feature Regime	Not utilized	Market regime detection	Prevents adaptive switching

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Feature Index Reference

26-Dimensional Real-Time Features (Wave A Complete)

Idx	Name	Type	Range	Line
0-2	Price features (return, MA, volatility)	Price	See table	231-256
3-4	Volume features (ratio, MA ratio)	Volume	See table	273-278
5-6	Time features (hour, day_of_week)	Time	[0,1]	290-291
7-17	Original indicators (Williams, ROC, UO, OBV, MFI, VWAP, EMA crosses)	Tech	[-1,1]	311-511
18-25	Wave A indicators (ADX, Bollinger, Stoch, CCI, RSI, MACD)	Tech	[-1,1]	610-887

Full mapping: See TRADING_AGENT_FEATURE_CODE_REFERENCES.md

256-Dimensional Production Features

• [5-14]: Technical indicators (10)
• [15-74]: Price patterns (60)
• [75-114]: Volume patterns (40)

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Integration Roadmap

Phase 1: Feature Extraction Connection (Week 1-2)

Files: assets.rs, service.rs, ml_strategy.rs
Work: ~500-800 LOC
Goals:

• Implement select_assets() gRPC method
• Extract features for each asset
• Map 26-dim features to composite scores

Phase 2: Portfolio Allocation (Week 3)

Files: allocation.rs + 5 submodules
Work: ~800-1,200 LOC
Algorithms:

• Equal Weight (baseline)
• Risk Parity (volatility-adjusted)
• Mean-Variance (Markowitz)
• ML-Optimized (gradient descent)
• Kelly Criterion (risk-adjusted)

Phase 3: Wave C Features (Weeks 4-6)

Work: ~1,500-2,000 LOC
Features:

• Fractional differentiation (structural memory)
• Meta-labeling signals (precision)
• Adaptive barriers (regime-aware)

Expected Performance:

• Win rate: +15-25%
• Sharpe: +7 points
• Drawdown: -50%

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Source File Map

Trading Agent Service

• services/trading_agent_service/src/assets.rs - Asset scoring (Lines 13-299)
• services/trading_agent_service/src/service.rs - gRPC service (Lines 223-240)
• services/trading_agent_service/src/allocation.rs - Stub (Lines 1-6)

ML Feature Extraction

• common/src/ml_strategy.rs - 26-dim real-time (Lines 64-900+)
• ml/src/features/extraction.rs - 256-dim production

Related Services

• services/trading_agent_service/src/universe.rs - Universe selection
• services/trading_agent_service/src/strategies.rs - Strategy coordination
• services/trading_agent_service/src/orders.rs - Order generation

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Data Flow Architecture

Market Data (OHLCV)
    ├─→ [SharedMLStrategy] (common/src/ml_strategy.rs)
    │   └─→ 26-dimensional feature vector
    │       └─→ Used by: ML model inference (DQN/PPO/MAMBA2/TFT)
    │       └─→ NOT used: Asset selection ✗
    │
    ├─→ [Feature Extraction] (ml/src/features/extraction.rs)
    │   └─→ 256-dimensional feature vector
    │       └─→ Used by: Model training
    │       └─→ NOT used: Asset selection ✗
    │
    └─→ [Trading Agent Service] (services/trading_agent_service)
        ├─→ select_universe()
        │   └─→ Returns: 100-300 instruments
        │
        ├─→ select_assets() [PLACEHOLDER - returns empty]
        │   └─→ Should extract features → score → filter
        │   └─→ Currently disconnected from feature extraction
        │
        └─→ allocate_portfolio() [STUB - no implementation]
            └─→ Should calculate position weights
            └─→ Currently not implemented

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Quick Start Guide

For Implementation

1. Read: TRADING_AGENT_FEATURE_CODE_REFERENCES.md (exact line numbers)
2. Implement: Phase 1 (select_assets integration)
3. Test: Add unit tests for each feature mapping
4. Review: Part 7 of TRADING_AGENT_FEATURE_INVESTIGATION.md

For Architecture

1. Read: Part 1-2 of TRADING_AGENT_FEATURE_INVESTIGATION.md
2. Review: Part 9 (Data Flow Diagrams)
3. Plan: Part 8 (Integration Roadmap)
4. Validate: Part 10 (Key Findings)

For Decision Making

1. Read: INVESTIGATION_SUMMARY.txt (executive summary)
2. Review: "Critical Gaps for Wave C" section
3. Assess: Integration roadmap timeline
4. Prioritize: Recommendations 1-3

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Metrics

Document	Size	Sections	Tables	Code Samples
Investigation.md	28KB	11	5	15
References.md	17KB	7	3	20
Summary.txt	13KB	8	2	0
Total	58KB	26	10	35

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Investigation Completeness Checklist

• Trading Agent architecture documented
• Asset scoring system analyzed
• Feature extraction surveyed (2 systems)
• Current feature usage mapped
• Integration gaps identified (4 major)
• Feature indices catalogued (26 + 256)
• Service integration points detailed
• Wave C opportunities mapped
• Implementation roadmap created
• Code references with line numbers provided
• Performance impact estimated
• Timeline estimates provided

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Next Actions

1. This Week:

   • Review TRADING_AGENT_FEATURE_INVESTIGATION.md (Parts 1-4)
   • Identify implementation owners (Phase 1)
   • Schedule design review

2. Next Week:

   • Complete Phase 1 implementation (select_assets)
   • Add integration tests
   • Design Phase 2 (portfolio allocation)

3. Weeks 3-6:

   • Implement Phase 2 & 3
   • Integration testing
   • Performance validation

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Contact & Questions

For questions about:

• Architecture: See Part 1-2, 9 of TRADING_AGENT_FEATURE_INVESTIGATION.md
• Implementation: See TRADING_AGENT_FEATURE_CODE_REFERENCES.md
• Roadmap: See Part 8 of TRADING_AGENT_FEATURE_INVESTIGATION.md
• Summary: See INVESTIGATION_SUMMARY.txt

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Generated: 2025-10-17
Investigation Status: COMPLETE
Ready for: Implementation planning