ROCS™
Regime-Optimised Calibration
Continuously recalibrates prediction confidence against realised settlement accuracy, per regime.
EDGX GPU Field Engine runs a real-time WebGL probability-field renderer alongside a BiRNN temporal network, 20-asset AgentNet, GJR-GARCH volatility engine, ROCS calibration system, and APEX five-algorithm ensemble — all from a single application with zero external dependencies — covering 28 assets across four time horizons.
EDGX designs and architects complex financial systems from the ground up — transforming granular market data into structured intelligence pipelines that operate at institutional precision and scale. Every system is grounded in custom-engineered mathematical frameworks: from proprietary volatility engines and Bayesian inference models to multi-regime detection algorithms that classify market states with quantified, reproducible certainty. Deep-learning modules — bidirectional recurrent networks, ensemble neural architectures, and adaptive retraining schedulers — are built without black-box dependencies, so every weight, gradient, and decision boundary remains traceable from raw feed to final signal. By computing direction votes through Bayesian weighted-factor consensus, ATR-scaled noise gates, and HMM regime-conditioned Hurst suppression, the engine converts raw Binance WebSocket feeds into probability-calibrated, ATR-normalised predictions — where mathematical derivation, not estimation, defines every signal.
Each engine models a distinct facet of market behaviour, then reconciles into a single probability-calibrated forecast. No synthetic data, no black boxes — every value is mathematically traceable. The engines run concurrently on every tracked asset, so the picture you see is the whole market in motion rather than a single instrument in isolation. Where most platforms bolt indicators together, EDGX treats each engine as an independent estimator whose output is weighed, scored, and held accountable against what the market actually did.
Continuously recalibrates prediction confidence against realised settlement accuracy, per regime.
Factor weights adapt from actual outcomes — the model learns which signals matter now.
A bidirectional recurrent network reads sequence structure forward and backward across the tape.
Asymmetric conditional volatility feeding every horizon from two minutes to seven days.
Hidden Markov state decoding via Viterbi identifies the regime driving price action.
Twenty specialised agents, scored by regime-conditional accuracy, voting in concert.
A 12-factor decomposition of market-moving news across institutional sources, scored per asset.
Five algorithms combine into a single optimal-stopping allocation and entry decision.
A 13-factor ML ensemble synthesising every engine into portfolio-level allocation signals.
Raw Binance WebSocket feeds become probability-calibrated, ATR-normalised predictions through a deterministic chain — each stage inspectable, reproducible, and traceable. The same inputs always produce the same outputs, which means a signal can be audited end to end rather than taken on trust. Nothing in the pipeline is hidden behind a vendor API or a stochastic shortcut — the path from raw tick to final probability is fully open to inspection.
Raw WebSocket feeds — trades, depth, klines — stream in for 28 assets simultaneously, with no polling and no third-party data intermediaries.
GJR-GARCH estimates conditional volatility; HMM Viterbi decoding classifies the prevailing market regime to condition every downstream factor.
Direction votes are computed through Bayesian weighted-factor consensus, ATR-scaled noise gates, and Hurst suppression to strip sub-noise movement.
ROCS recalibrates confidence against realised accuracy per regime, so a stated probability reflects observed settlement, not a raw model score.
Every prediction is recorded, settled against the tape, and scored — feeding the calibration loop so the system sharpens continuously.
The intelligence matrix tracks all assets in parallel and continuously: order-flow, volatility, regime, news impact, and calibrated direction across four horizons — not just the asset in focus. Every cell updates in real time as fresh ticks arrive, so cross-asset rotation and divergence surface the moment they begin. This breadth turns the terminal from a single-chart tool into a market-wide situational display you can scan at a glance.
DEMONSTRATION The price values, percentage changes, sparkline graphs, NII readings and signal strengths shown in the cards above are illustrative sample data generated in your browser for presentation purposes only. They are not live market quotes and do not reflect real cryptocurrency prices, real trading signals, or actual engine output. Live market data is available inside the terminal to authenticated account holders.
ROCS — the Regime-Optimised Calibration System — measures realised accuracy per regime and rescales confidence accordingly. When EDGX states a probability, it reflects observed settlement performance under comparable conditions, not an uncalibrated model output. Every prediction is logged, settled, and scored in the Prediction Tracker, closing the loop between forecast and outcome. Because calibration is conditioned on regime, a 65% reading in a trending market and a 65% reading in a choppy one are both honest about their own context. Over time the system tightens itself: persistent over- or under-confidence in any regime is detected and corrected without manual tuning.
Every tier runs the live engine stack. Higher tiers unlock more assets, the full horizon set, portfolio intelligence, and programmatic access. Access is granted on approval — request below and our team activates your account. There are no usage surprises: each tier states its asset coverage and request limits up front. Upgrade or change tier at any renewal as your desk and coverage needs grow.
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