CCS is a governed capital intelligence and execution control system for crypto and traditional markets. Market strategies generate candidate allocations. Deterministic risk gates evaluate them. Treasury policy authorizes execution. Every transaction is receipted and auditable. Currently operating in paper-validation mode on Hyperliquid, testing whether a governed, evidence-first trading system can evolve safely before real capital deployment is authorized.
26 registered signal engines (momentum, mean-reversion, regime detection, funding-rate analysis, etc.) generate candidate trades across 6 crypto pairs. Signals may propose; governance does not pre-filter them.
Deterministic gates evaluate: position limits, drawdown controls, Kelly criterion, confidence thresholds, kappa filters, cost analysis. Binary outcome. No probabilistic judgment. Gates may ACCEPT, DENY, or DEFER.
Approved trades route to paper or live execution (paper-only in validation phase). Every execution is bounded: stop-loss enforced, exit logic deterministic, partial scale-out rules explicit.
Every trade entry, exit, stop-loss, and gate decision is hash-chained in the ledger. Operators can replay the full audit trail, measure signal alpha per engine, and diagnose edge failures offline.
Ingests OHLCV data, funding rates, and order-book state across BTC, ETH, SOL, DOGE, ADA, XRP. Supports multi-timeframe analysis (hourly, 4-hour, daily aggregation). All data is timestamped and reproducible for offline diagnostics.
26 registered strategy engines (boustrophedon, micro_momentum, satstacker, vol_squeeze, zenith_regime, lead_lag, and others). Each engine can identify different market conditions. Engines do not control capital — they propose.
Deterministic gates enforce: position count limits (max 2), drawdown ceilings, Kelly fraction limits, kappa confidence thresholds (85% baseline), stop-loss honor (no override), cost-benefit analysis. All gate evaluations are logged.
Routes approved trades to paper execution (current validation mode) or future live execution. Stop-loss is enforced by the kernel, not the strategy. Exit logic is deterministic: no discretionary mid-trade changes.
CCS currently runs in paper-validation mode ($30K simulated capital on Hyperliquid). This layer collects evidence without exposing real capital: trade fills, exit behavior, slippage impact, and edge-case handling under live market conditions.
Post-trade forensics: win-rate analysis per engine, MFE (maximum favorable excursion) tracking, stop-loss bleed diagnosis, exit quality scoring, and partial scale-out research. Identifies whether edge failures are systematic or noise.
126 paper trades logged with full entry/exit/stop/reason chain. Hash-chained ledger: operators can verify trade provenance, replicate gate decisions offline, and audit whether risk governors actually blocked bad signals or let them through.
CCS runs alongside a live market monitoring system that continuously tracks exchange health, macroeconomic conditions, liquidity stress, security exploits, social panic signals, and prediction market probabilities (Polymarket, Kalshi, Manifold). These signals are advisory only — they inform the operator, they do not modify positions, gates, configuration, or capital allocation. Every intelligence event is receipted. The operator decides. Execution remains gated.
CCS operates alongside a governed intelligence monitoring layer that runs 9 advisory scouts every 30 minutes. No scout can place a trade, modify a gate, or change configuration. Intelligence informs. Authority remains external.
Exchange health, macro regime, stablecoin stress, security exploits, liquidity conditions, social sentiment, technical signals, news, and prediction markets. All receipted. None can execute.
Fear & Greed index, trending assets, BTC dominance, and market cap change are live in the CCS runtime. Context logged per trade cycle. Persistent extreme detection with 6-hour decay model.
Polymarket, Kalshi, and Manifold probabilities for macro and crypto events surface as advisory context. What smart money is betting on — not as a trading signal, but as operator awareness. Advisory-only until validated.
When scouts detect HIGH or CRITICAL conditions, the operator is pushed a Telegram alert automatically — with receipt ID, suggested checks, and an explicit "no trade authority" declaration. CRITICAL alerts repeat until acknowledged.
CCS is NOT live-ready. It is NOT being marketed as profitable. It is a research and validation system designed to answer one question before capital deployment: "Can a governed, evidence-first trading system evolve safely?"
Running on Hyperliquid paper-trading pair. Live market conditions, zero real capital at risk. Enforces same risk gates whether capital is paper or real.
Six crypto pairs (BTC, ETH, SOL, DOGE, ADA, XRP). Multi-timeframe: hourly, 4-hour, daily. Live market conditions, continuous validation.
Position limits, drawdown control, Kelly criterion, kappa thresholds, cost analysis, stop-loss honor, regime detection. All decisions binary and logged.
Full entry/exit/stop/reason ledger. Officers can replay every trade decision offline. Gate logs available for forensic analysis. No trade hidden from audit.
Most bots: signal → trade → hope. CCS: signal → governance gate → risk check → execution control → receipt chain → forensic analysis. CCS separates the producer of a trade idea from the judge of its admissibility. The idea generator cannot override the risk boundary.
CCS asks: "Does this system handle fees, slippage, drawdown, and adverse paths reliably?" Paper validation builds evidence before live capital is considered. Offline forensics identify whether improvements are real or noise. Deployment requires evidence, not optimism.
The doctrine (signal → gate → receipt → execute) works in robotics, vehicles, medical, and infrastructure. CCS proves it in the hardest domain: continuous feedback, zero tolerance for breakdown, real losses. If the architecture survives markets, it works elsewhere.
CCS is actively evolving. Recent work includes multi-engine signal blending, partial scale-out research, stop-loss path analysis, and per-asset regime tuning. All changes are validated offline before deployment. Current open questions:
Can CCS improve take-profit logic so favorable trades aren't given back to the market? MFE-based research underway. Target: reduce exit slippage by 15–25% without sacrificing runner upside.
Kelly criterion currently static. Can dynamic Kelly (based on realized regime, volatility regime, correlation matrices) improve risk-adjusted returns? Under shadow testing.
Do different engines fire at different times? Can ensemble weighting improve signal quality? Currently studying inter-engine timing correlation and win-rate variance.
What evidence is sufficient to unlock live capital? Defining N-trade benchmarks, edge-case coverage, and gate-hold forensics. Live deployment gated on N=30 clean validation cycles.
CCS is available for research partnerships, collaborative signal development, and academic study. We can share anonymized trade logs, gate decision forensics, and offline diagnostics. Real evidence from real market conditions.