Eaglics forecasts are not opinions about where a pair might go. They are the output of a defined research pipeline: historical price data, engineered market features, a five-model ensemble, dynamic weighting, and a confidence score , all computed before the session opens, and all measured against what actually happens afterward.
Every daily forecast passes through the same defined sequence. Nothing is manually adjusted between stages , the pipeline runs the same way for every pair, every session.
Multi-year OHLC price history is pulled per pair as the raw input layer.
Raw prices are converted into statistical features the models can actually learn from.
LSTM, GRU, Transformer, XGBoost, and Ridge each independently forecast the session.
Each model's output is blended, weighted per component by recent reliability.
A blended confidence score and market regime classification is attached to the forecast.
Forecast High, Low, Close, and Range are published before the session opens.
Raw candles are noisy. Before any model touches a session, price history is converted into a set of statistical features that describe the underlying condition of the market , direction, volatility, and behavioral regime.
Measures the underlying directional bias of price over the recent window , is the pair trending or flat.
DirectionTracks how far volatility bands are stretching, signaling whether a range is compressing or breaking out.
VolatilityThe typical size of recent price movement, used to calibrate how wide a forecasted range should be.
VolatilityQuantifies how scattered recent price outcomes are relative to the mean , a proxy for market noise.
NoiseDistinguishes trending from mean-reverting behavior , a value above 0.5 favors trend, below favors reversal.
BehaviorSessions are tagged by prevailing market condition, so the ensemble adapts to the environment it is forecasting in.
ContextFive architecturally different models are trained independently on the same feature set. Each has different strengths , some read sequence and memory well, others read structured statistical relationships better. None of them vote equally by default.
Average contribution across a recent sample window, shown for illustration , actual weighting is recalculated per forecast, not fixed.
A model that forecasts the high well is not automatically trusted for the low, the close, or the range. Each of the six forecasted components gets its own independently calculated weighting across the ensemble.
Model agreement is not constant , some sessions produce tightly aligned outputs across the ensemble, others do not. That agreement is captured as a confidence score and paired with a regime read, so a forecast never arrives without context on how reliable it is likely to be.
A single blended figure derived from how closely the five models agree across every forecasted component.
Classifies the session as trend-persistent or mean-reverting, informing how the range forecast is shaped.
Sessions are tagged by prevailing volatility condition so forecasts adapt to calm versus expansive markets.
Confidence shifts session to session based on real model agreement , it is never a flat, marketed accuracy figure.
Once a session closes, the forecast is compared against what actually happened. Below is a sample of logged forecast-to-actual comparisons from a recent validation window , not curated for best results, shown in pips of deviation.
Sample drawn from a recent three-month validation window across two pairs. Deviation figures are illustrative of the tracking process, not a forward-looking accuracy guarantee.
A forecast is not the end of the research process , it is one data point feeding the next model update.
Produces a structured, probabilistic estimate of a session's likely high, low, close, and range, grounded in historical model performance and updated continuously as new sessions complete.
It does not predict the future with certainty, does not issue trade instructions, and does not guarantee that any given session will land inside the forecasted range.
Explore the live product to see the daily output of this exact research process.