Eaglics is built as a quantitative research process, not a trading opinion. Raw price history is transformed into an orthogonalized factor library, conditioned on the prevailing volatility regime, passed through a multi model ensemble, checked at every stage for point in time data integrity, and graded against realized outcomes once each session closes. Nothing in that process is discretionary.
The research process is built on a point in time data discipline, the same standard institutional quant desks hold their own pipelines to. Every observation is bound to the information that was genuinely available at that moment, survivorship and revision bias are controlled for, and cross instrument scaling is normalized so that pairs with different tick and pip conventions are directly comparable inside the same factor space.
Price history is ingested across multiple sampling frequencies per instrument.
A structural boundary prevents any observation beyond the evaluation point from entering the factor set.
Price scale conventions are reconciled so every pair sits on a comparable footing.
Each instrument is conditioned against a curated set of correlated macro drivers.
Verified, boundary safe data flows into the factor construction layer.
No currency pair trades in isolation. Each instrument carries systematic exposure to broader macro factors, rate differentials, risk sentiment, and cross asset flow, and that exposure is incorporated as conditioning context rather than treated as noise.
Coverage: EURUSD · GBPUSD · USDCHF · GBPCHF · AUDUSD · GBPJPY · EURGBP (Free)
Price alone carries almost no usable information for a learning system. A broad candidate factor set is constructed across seven domains of market behavior, statistical dispersion, directional persistence, microstructure, liquidity geometry, session conditioning, denoised price structure, and long memory regime. Every candidate factor is subject to strict ex ante lag enforcement, so a factor evaluated at a given point can only reflect information that was already realized. Collinear factors are then removed through a systematic orthogonalization process, leaving a compact library where each surviving factor carries distinct, non redundant information.
↑ Systematic correlation pruning reduces the candidate set to an orthogonalized factor library
Volatility clusters through time, calm periods tend to persist, and turbulent periods tend to persist. A regime classifier ranks current dispersion against its own trailing distribution, and a parallel conditional variance model provides an independent forward looking read. Together they recalibrate the width and confidence of every forecast, so a quiet session and a post event, high dispersion session are never scored against the same yardstick.
The regime read itself is subject to the same ex ante lag enforcement as every other factor, so today's forecast is only ever conditioned on a regime that was already confirmed as of the prior close.
No single estimator is trusted in isolation. Five architecturally distinct model families are trained independently on the same orthogonalized factor library, then combined across six forecast targets, expected close, high and low boundaries, expected range, trend slope, and dispersion expansion. Each family contributes a different statistical lens on the same data, and a regime aware meta learner determines how much weight each one receives depending on the prevailing memory regime.
The long memory regime read determines how much say each model family gets at any point in time. Tree based and sequence based estimators are weighted up in persistent, trending conditions, while the linear baseline is weighted up in mean reverting conditions. The latent factor model contributes a continuous nonlinear check across every regime. None of the five ever forecasts alone.
A forecasting process that performs well in backtest and fails in production has almost always leaked future information into training somewhere. This process is built around three structural guardrails designed specifically to close that gap.
Evaluation proceeds strictly in chronological order with a purge and embargo applied around every split, so no observation adjacent to a test period can bleed information across the boundary.
Every factor is bound to information that was already realized as of the prior close. The model is structurally prevented from ever touching a still forming observation.
Any feature that could encode a realized result or downstream outcome is excluded at the earliest stage of construction, so the process can never condition on its own prior performance.
A forecast is not graded once and forgotten. Once a session closes, a recursive calibration process runs automatically ahead of the next forecast cycle.
Realized outcomes are compared against prior forecasts, and ensemble weights update to minimize forecast error.
A rolling calibration window checks for systematic bias in either direction, and any persistent offset is corrected out of future predictions.
Directional accuracy is tracked continuously, and a correction mechanism engages automatically if performance degrades beyond an internally defined threshold.
Once a forecast horizon is established, a separate microstructure layer evaluates order flow imbalance to condition the timing of implementation against that horizon. Rather than a fixed trigger, an adaptive threshold is derived statistically from recent order flow behavior, so the bar for confirmation relaxes in quiet conditions and rises in expanding, high dispersion conditions to guard against premature signals.
The session level forecast sets the directional context.
Live order flow is evaluated for directional pressure in real time.
A rolling distribution of pressure readings is maintained.
A statistically derived, regime sensitive confirmation threshold is applied.
Timing is confirmed only once the adaptive threshold is cleared.
A raw pip error means little on its own, since it is tight for one instrument and loose for another. Forecast error is instead normalized against realized volatility, producing a standardized accuracy read that is directly comparable across every instrument in the coverage set.
Forecast error is expressed as a proportion of realized volatility rather than as an absolute figure. An error on the scale of typical dispersion for that instrument scores low, while an error well inside typical dispersion scores high, placing every pair on one standardized scale regardless of its native volatility profile.
Tracked independently, this measures whether the session closed in the direction the forecast implied, and feeds directly into the drift guard described in Section 06.
A rolling out of sample verification database of paired forecasts and realized outcomes is maintained continuously, and it is what drives the recursive weight tuning process.
Eaglics runs the full pipeline, forecasting and execution, across seven major and cross currency pairs. Each pair keeps its own trained weights, its own feature whitelist, and its own validation history, rather than sharing a single generic model across all of them.
Produces a structured, volatility conditioned forecast of a session's likely high, low, close, and range across ten pairs, built on leakage controlled historical data and recalibrated after every closed session.
It does not predict the future with certainty, does not issue trade instructions, and does not guarantee any session will land inside its forecasted range. Forecasts are research prices, not signals.
Explore the live product to see the daily output of this exact research process.