ARIMA_SAX_Hybrid_Forecaster/ARCHITECTURE.md

ARCHITECTURE.md

1. Principle

Clean separation and a common forecast interface. Both sorting well-defined and independently testable components.

/docs
/src
  /forecasting     common target + interface + record schema
  /baselines       naive + drift
  /arima           ARIMA component (statsmodels)
  /sax             SAX component (pure numpy)
  /hybrid          transparent evidence layer + decision states
  /data            data integrity checks
  /evaluation      forecast / distribution / economic metrics
  /validation      chronological walk-forward
  /pipeline.py     orchestration of variants -> structured records
/tests
/experiments
/results  (git-ignored)
/configs
/scripts

2. Component responsibilities

ARIMA

• accept a closed-bar series up to the origin
• optional stationarity transform (differencing / log-return target)
• configured (p,d,q); baseline config selected in configs/
• produce H-step forecast on the common target
• prediction interval where supported
• sequential no-lookahead out-of-sample evaluation
• refit policy (fixed vs rolling) controlled

SAX

Preserve reference properties:

1. z-normalisation
2. PAA dimensionality reduction
3. SAX symbolic encoding
4. historical analog search
5. MINDIST-based pruning where used
6. Euclidean re-rank/validate where used
7. strict no-lookahead
8. forward-outcome extraction only from historical analogs whose outcome is known at the forecast origin (analog_end + H <= origin)
9. ATR-normalized outcome
10. analog count, median, P25/P75, up-rate / direction

SAX must never use the current pattern's future outcome while selecting analogs.

Hybrid evidence layer

Not a learned-weight black box in stage one. Deterministic transparent states:

• CASE1 AGREEMENT (both bullish / both bearish)
• CASE2 DISAGREEMENT (opposite directions)
• CASE3 PARTIAL (one directional, other neutral/insufficient)
• CASE4 NO EVIDENCE (both neutral/insufficient)

Outputs STRONG_AGREEMENT / WEAK_AGREEMENT / DISAGREEMENT / NO_EDGE / INSUFFICIENT.

3. Common forecast record

Each forecast record includes at minimum: timestamp, symbol, timeframe, horizon H, target definition, direction, expected return, normalized expected return, uncertainty, confidence/evidence, model id + version, train/eval boundary, data snapshot id.

SAX adds: sax_word, analog_count, analog distance stats, up_rate, forward outcome (median + q25/q75).

ARIMA adds: p,d,q, fit_window, refit_policy, forecast_interval, status.

4. MQL5/Python boundary (phase 1)

• Python: statistical engine (ARIMA fitting, SAX experiments, walk-forward, evaluation, reporting).
• MQL5: closed-bar event capture, market data source adapter (later phase). No live trading execution in this bootstrap. The interface is a documented MarketDataSource contract (see src/data/).