PMO BOT is an asynchronous multi-asset quant operating system built around proof journals, options-market structure diagnostics, alternative-data research overlays, and defensive capital controls before live capital is authorized. No promises. Only evidence.
Most trading systems ask for trust first, proof later. PMO BOT reverses this. Every lane must accumulate verified evidence before live capital is deployed, while the public layer discloses the same proof counters, risk gates, and architecture modules the backend actually runs. The architecture enforces discipline — not faith.
Proof gates lock live trading until statistical thresholds are met across win rate, profit factor, and trade count — per lane.
120K+ lines of Python. 640+ audited routes. 914 tests collected. Forensic logging. Regression checks before every restart. Built like a trading desk, not a side project.
Walk-forward validation, kill switches, bracket orders, drift measurement, and AI change audits. The system cannot override its own safety rules.
Proof counters update live from the PMO API. Every number you see is pulled directly from the system. No cherry-picking. No screenshots.
Every trading lane runs independent paper proof collection. Live trading unlocks only when a lane satisfies its evidence gate. These counters are live — pulled from PMO every 60 seconds.
"Twenty trades is not enough to call a system proven. PMO keeps live trading locked until the evidence is broad enough to survive real scrutiny."
PMO BOT is 120,000+ lines of institutional Python across 270+ modules — a full trading OS with its own proof journals, risk engine, forensic audit trail, and self-healing watchdog.
Institutional-grade math: VaR, CVaR (Basel FRTB), EWMA volatility, Monte Carlo risk of ruin, Half-Kelly sizing, Sharpe/Sortino/Calmar — calculated from journals and readiness reports.
pmo_quant_intelligence.pyLive trading is locked behind proof count gates, score rebuild validation, walk-forward testing, drift measurement, bracket orders, and hardware kill switches.
pmo_core/institutional_intelligence.pyEvery route, setting change, AI action, and broker reconciliation is logged and tested. PMO runs regression checks before restarting — no silent failures.
pmo_forensic_trade_audit.pyEight asset classes running simultaneously: Stock, ETF, Crypto Spot, Forex (OANDA), Futures (TopstepX), Stock Options, Commodity ETF, and Volatility ETP — each with public proof-lane tracking, disclosed canonical journal mapping, and risk controls.
8 asset lanes · 8 proof lanes · 5 canonical journalsPMO monitors its own health, detects degraded services, and recovers without bypassing safety rules. The system cannot override its own protection architecture.
pmo_watchdog.pyDeep per-asset intelligence: futures contract specs (MGC, MES), forex session windows, crypto on-chain signals, commodity seasonality, ETF decay mechanics.
pmo_asset_knowledge.pyPMO is documented as a multi-layer quantitative platform: ingestion, structural alpha, alternative-data research, capital controls, and low-latency readiness diagnostics. Research-only modules are labeled that way; execution authority remains behind proof gates.
Asynchronous broker and market-data adapters feed normalized tick/bar memory. PMO includes ClickHouse schema generation, baseline market-data field checks, and Z-score/MAD-style bad-tick rejection diagnostics for corrupted price packets.
pmo_core/enterprise_algo_infra.py · foundation_readiness.pyResearch overlays track options market-maker hedging corridors: gamma flip regimes, skew divergence, vanna/charm proxies, sweep diagnostics, max pain, put-call parity, and prediction-contract-to-options target mapping.
shadow_market_mechanics.py · institutional_strategy_candidates.pyPMO documents and tests research-only inputs for SEC Form 4 insider clusters, anonymized consumer transaction panels, geospatial packet anomalies, supply-chain telemetry, and maritime AIS-style departure signals.
shadow_market_mechanics.py · institutional_boardroom.pyAll intents route through proof locks, daily-loss checks, position limits, slippage/cost diagnostics, portfolio heat, Monte Carlo expectancy, walk-forward validation, and combine-specific guardrails before any live review.
multi_asset_risk.py · combine_guardrails.py · walkforward_diagnostics.pyPMO tracks co-location, OPRA/ITCH-style feed terms, kernel-bypass/OpenOnload readiness, randomized TWAP planning, IOC/leg-out recovery concepts, and clock-sync checks as diagnostics. These are not marketed as live deployed routing until verified.
enterprise_algo_infra.py · foundation_readiness.pyWebsite copy, proof counters, roadmap language, API health links, and platform descriptions are synchronized to PMO's real backend state: 8 asset lanes, 8 proof lanes, locked live trading, and audit-first operator governance.
deck/pmo_landing_v2.html · /public/proof.jsonEvery number below is pulled from the actual codebase and test suite.
PMO BOT is not presented as magic. Every gate, rule, and limit is documented, tested, and enforced in code. The system cannot lie to itself.
PMO BOT runs institutional session windows, slippage models, and proof journals across 8 distinct asset classes — each with its own execution engine and risk model.
Four tiers — from free public access to the funded-account readiness path. Each unlocks only what you're ready for.
PMO Bot does not guarantee trading results. Trading involves risk, including loss of capital.
Every milestone is documented publicly. PMO does not hide progress or failures.
Join the proof-first community. Free proof access is available immediately. Education and software access are routed through the canonical PMO subscription system.
PMO BOT doesn't ask for your trust. It earns it — in public, in code, in real time.