Every Eaglics forecast begins with the same question: what does the historical behaviour of this pair's daily range tell us about what today's range is likely to be? Answering that question rigorously requires a structured multi-stage pipeline, not a single model, but a sequence of connected processes that each resolve a different layer of the problem.
This article explains what that pipeline looks like, why each stage exists, and what it produces.
Stage 1: Historical Data Ingestion
The pipeline begins with the complete session-level price history of each modeled currency pair. Eaglics processes daily OHLC (Open, High, Low, Close) data from 2003 to the present, over 5,500 trading sessions per pair. This time range is not arbitrary. It spans three full economic cycles, multiple currency crises, the 2008 financial collapse, the COVID volatility regime, and multiple central bank rate cycles. A model that only works in calm markets is not useful. The data coverage ensures the pipeline has seen every significant type of market behaviour.
Stage 2: Range Distribution Modelling
Once ingested, the historical data is used to build a statistical model of daily range behaviour. The core question is: given the open price and the conditions of the previous session, what is the probability distribution of today's range, both in total size and in the likely positions of the high and low relative to the open?
This is not a simple average. The distribution of daily range sizes is non-normal and changes across market regimes. A day following a very high-volatility session behaves differently from a day following a narrow consolidation. The model accounts for this through conditional distribution analysis, modelling range behaviour relative to prior-session characteristics rather than in isolation.
Stage 3: Session Open Integration
At the moment the trading session opens, the model receives the live open price and any session-open market state data. This is the point at which the forecast becomes concrete: the probability distribution of range size, combined with the actual open price, produces two specific levels, a forecast high and a forecast low.
The forecast is not produced at market close or at any point during the session. It is generated at open, before meaningful price action has developed, because that is the only time it is genuinely useful.
Stage 4: Pair-Specific Calibration
Each of the six modeled pairs has its own independently calibrated model. EUR/USD and GBP/USD behave differently at the same time of day. USD/JPY is dominated by different session dynamics. USD/CHF has different correlation structures with safe-haven flows. Running a single universal model across all pairs would produce acceptable average accuracy and poor specific accuracy. Eaglics maintains separate, independently calibrated models for each pair precisely because the details matter at the level of individual pair behaviour.
Stage 5: Verification Loop
After every session closes, the forecast is compared against the actual session high and low. This verification step is not optional, it is the mechanism that makes the track record meaningful and the model self-improving. Every session's forecast-to-actual comparison is logged permanently and made available to subscribers in the dashboard. There is no selective reporting: sessions where the forecast was outside the actual range are logged with identical visibility to sessions where it was accurate.
What the Output Looks Like
The end result of the pipeline is two numbers, published before each trading session opens: a forecast high and a forecast low for each subscribed pair. These are displayed in the Eaglics dashboard alongside the current session's developing price action and the complete historical log.
The simplicity of the output, two price levels, is intentional. The value is not in complexity of presentation. It is in the statistical rigour behind the calculation that produced those two numbers before the market got there.
Risk Disclosure: Forex trading involves substantial risk of loss. Quantitative forecasting reduces uncertainty but does not eliminate it. Past accuracy does not guarantee future results. Eaglics provides analytical tools and does not constitute financial advice.