Recursive Quant Supervisory Swarm

1. The Death of Static Rule-Sets: Strategy Decay & Survivorship Bias

Legacy algorithmic trading models suffer from a fundamental epistemic blindspot: they only log the performance of trades they actually execute. If a strategy's filter eliminates 90% of incoming signals, traditional analytics can never determine whether those rejections saved capital from ruin or discarded the system's highest-Sharpe opportunities.

To eliminate this survivorship bias, BayesianPivot deploys an autonomous Recursive Quant Supervisory Swarm. Instead of treating execution as a linear trigger, the architecture operates a continuous dual-stream pipeline: every signal vetoed by the AI or filtered by risk rules is routed to an isolated CounterfactualTracker. The shadow engine models live fills, slippage, and price action walk-forward trajectories in real-time, calculating the exact mathematical alpha generated by the algorithmic veto.

2. Multi-Agent Supervisory Architecture

The supervisory layer is decoupled into specialized autonomous agents running as native macOS LaunchAgent daemons and Python event loops:

• QAQuantAgent (src/qa/quant_agent.py): Continuously audits mathematical setup coherence, HTF (Higher-Timeframe) Point of Interest (POI) alignment, and dynamic ATR stop buffers (calibrated at 2.25x ATR). It executes sensitivity sweeps across AI conviction cutoffs (4.5 through 8.5) to detect model calibration drift before capital is exposed.
• Supervisory Missed Opportunity Daemon (com.sovereign.supervisor): Intercepts rejected signals and tracks their shadow equity curve across a 30-minute polling cadence. If the shadow stream outperforms the live funnel, the agent issues real-time Telegram telemetry alerting the architect to filter over-tightening.
• Watchdog Sentinel (com.sovereign.watchdog): Monitors system health, SQLite WAL database concurrency, and pending intent anti-hedging locks to prevent race conditions during high-frequency volatility spikes.

3. The 8-Account Multi-Mandate Capital Mesh ($204,933 NAV)

Managing single-account retail bots provides zero systemic resilience. The supervisory swarm orchestrates a unified execution mesh across 8 distinct TradeLocker accounts ($204,933 total NAV) divided into three tactical mandates:

1. Legacy Anchor (25k): Conservative low-frequency swing execution with tight risk ceilings. 2. Volume Operator (50k): High-turnover execution harvesting liquidity across major FX/crypto pairs. 3. Oracle Instant Operators (10k–50k accounts): Rapid-scaling multi-pair evaluation accounts governed by dynamic trailing risk controls and automated position sizing.

The MultiAccountFunnelManager dynamically handles session JWT authentication under stealth headers, queues orders to prevent WAF throttling, and halts execution instantly if account-level trailing drawdowns approach firm boundaries.

4. Mathematical Edge: Recursive Calibration

$Alpha_Veto = Σ_i ∈ Vetoed (Loss_Counterfactual - Win_Counterfactual)$

By turning discarded trade signals into structured telemetry, the system achieves Recursive Calibration: the model does not sit passively against changing market physics—it uses the supervisory swarm to continuously refine its own decision boundaries.