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AUDITVECTOR FORENSIC SUITE
AI REASONS. CODE PROVES. EVIDENCE EXPLAINS.
SYSTEM OPERATIONAL
ADK 2.7 Gemini 3.5 Flash DuckDB Cloud Run Pub/Sub Firestore
RUNTIME: LOCAL
TASKMASTER TRACK β€’ AUTONOMOUS FINANCIAL INTEGRITY INVESTIGATOR

AI Reasons. Code Proves. Evidence Explains.

Quantitative trading models and execution systems silently decay through sign inversions, disguised losses, and double-counted fees. AuditVector orchestrates 6 Google ADK agents to autonomously investigate code ASTs, extract claims, reconstruct bottom-up deterministic FIFO lot matching, and formulate verified code patches tested in an isolated sandbox ($21,960.00 → $0.00) with strict human authorization guards.

🎯 User Sets Mission Goal
πŸ€– ADK Dynamically Routes Next Step
πŸ“ Code Deterministically Proves Math
πŸ› οΈ Sandbox Verifies Patches to $0.00
πŸ”’ Strict Human Approval Gate
FAILURE BENCHMARK 4 Planted Contradictions β€’ FIS: 0/100 (Grade F)

IntegrityLab Alpha

Synthetic backtester containing 4 critical flaws: PnL sign inversion, disguised positive return on loss, fee double-deduction, and configuration fee drift ($44,140.00 capital at risk).

Target Code: strategy_alpha.py
Dataset: trades_alpha_failure.csv
Auto-Remediation: 4 Verified Patches ($0.00 Delta)
TRUST BENCHMARK 0 False Positives β€’ FIS: 100/100 (Grade A+)

IntegrityLab Control

Mathematically sound quantitative baseline demonstrating that AuditVector verifies clean systems with zero hallucinations and dynamically skips remediation when variance is $0.00.

Target Code: control_strategy.py
Dataset: trades_control_case.csv
Integrity Verdict: 100% Mathematically Sound
REAL-WORLD DOGFOOD Production Quant System β€’ FIS: 65.5/100 (Grade C)

AI-BIP Quantitative Engine

Real-world multi-token momentum engine. Proves $16,286.24 discrepancy (a 30.0484% erosion of reported $54,200.00 profit) against canonical transaction fills.

Target Code: backtest_engine.py
Dataset: trades_aibip_real.csv
Proven Drag: $16,286.24 (30.0484% Erosion)
βš™οΈ Configure Custom Target Repository & Transaction Dataset β–Ό
AUTONOMOUS AUDIT MISSION IN PROGRESS

Target: IntegrityLab-Alpha

JOB: audit-init
ADAPTIVE INVESTIGATION PIPELINE PROGRESS 0%
02 / ACTIVE INVESTIGATION TELEMETRY LIVE STREAM
CURRENT OPERATION
Initializing Google ADK Multi-Agent Session...
FORENSIC EVENT STREAM 0 Events Logged
[00:00:00] [INIT] AuditVector asynchronous worker initialized.
FINANCIAL CLAIM VS. DETERMINISTIC REALITY GROUNDED PROOF
SOFTWARE'S REPORTED CLAIM report.json
Reported Net Realized PnL +$18,240.00
Reported Return: +18.24%
MATHEMATICAL VARIANCE
-$21,960.00
Capital Misstatement Discovered
CRITICAL CONTRADICTION
INDEPENDENT DETERMINISTIC PROOF pnl_recalculator_v2.2
Bottom-Up FIFO Reconstructed PnL -$3,720.00
Deterministic Return: -3.72%
CRITICAL CONTRADICTIONS
2 Polarity inversions & major PnL discrepancies
HIGH SEVERITY
1 Fee double-counting & aggregation flaws
CONFIG WARNINGS
1 Fee rate model parameter mismatches
VERIFIED SOUND
0 Claims matching deterministic recalculation
FILTER SEVERITY:

AUTONOMOUS REMEDIATION & VERIFICATION SANDBOX

Verified surgical unified diffs tested inside an isolated sandbox. Proven to drop discrepancy to $0.00 without modifying repository.

100% ISOLATED SANDBOX PROOF

AUDIT MISSION REPLAY & ADAPTIVE DECISION LOG

Step backward and forward through the 6-stage investigation to review intermediate evidence and ADK routing decisions.

Stage 1 of 6

ADK ADAPTIVE ROUTING DECISION LOG

CRYPTOGRAPHIC PROVENANCE GRAPH

Interactive Node-and-Edge Evidence Chains. Click any node to open the Forensic Inspector.

Source Code Raw Data Normalizer Deterministic Verifier Verified Finding

ASYNCHRONOUS AUDIT EXECUTION TIMELINE

Exact millisecond event log tracing each agent's execution, tool invocation, and verification state.

EXECUTIVE AUDIT REPORT (.MD) Ready for institutional risk and compliance archiving
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DuckDB In-Memory Analytical Execution

High-throughput deterministic SQL execution profile

DuckDB 1.5.5