About SILTHRA

SILTHRA is an ongoing effort to build a professional intelligence layer for listed markets. We blend quantitative research, narrative, and a conversational interface to support faster, clearer decisions.

  • Active build — iterative releases, controlled surface area.
  • Server‑side computation; the interface presents conclusions, not raw noise.
  • Implementation details are deliberately private.

Data Sourcing

We are implementing resilient collectors from official endpoints with normalization and outage handling. Historical ranges are merged with recent prints when available.

  • Unified time series across history and live checks.
  • Issuer/ticker resolution for aliases and languages.
  • Backoff, caching, quota‑aware polling.

News Flow (Design)

A pipeline under design tags headlines by issuer and routes only relevant context to the language layer within seconds.

  • Symbol‑aware parsing and relevance scoring.
  • Time‑ordered digest with minimal duplication.
  • Latency‑first delivery to the interface.

Strategy & Tuning (Work in Progress)

We’re building zone‑based momentum engines designed to capture directional shifts with disciplined exits. Engines use a fast signal plus a slower context gate. Entries require alignment; exits may occur when evidence flips. Horizons and sensitivities are tunable per asset.

  • Zone logic: Adaptive bands with explicit cross/retest handling. No repainting — history is replayed prefix‑by‑prefix.
  • Targets vs. hold: One path projects momentum into price‑space levels; another uses target‑free, bar‑based holds. Signal generation and execution rules are kept separate.
  • Execution realism: Commission, slippage, and entry delay are modeled; stops may be time‑ or excursion‑based.

Tuning is under active R&D. We are experimenting with adaptive search methods and multi‑horizon walk‑forward evaluation to reduce leakage and overfitting.

  • Seeded exploration followed by focused refinement.
  • Supports asymmetric sensitivities across signals and context gates.
  • Parallel evaluation with immutable audit trails.
  • Per‑asset strategy artifacts for isolated deployment.
  • Periodic re‑tuning to keep drift under control.

Adaptive Code Factory (Vision)

We’re working toward a system that trains itself to produce and update strategy code aligned with current regimes — a disciplined “code factory.” How it works is intentionally not disclosed.

  • Generate and update per‑asset strategy clones.
  • Retune and redeploy on a cadence to manage regime drift.
  • Maintain reproducible artifacts and clear provenance.

Risk Engine (Prototype)

Signals are monitored in unified risk units. We study favorable/adverse excursions and progress toward levels to suggest realistic take‑profit bands — without exposing internals.

  • Excursion analytics and progress metrics.
  • Simple expected‑value views for context.
  • Designed for portfolio‑level roll‑ups.

System Architecture

  • Separation of collect → compute → serve.
  • Isolated workers, structured logging, timeouts.
  • Per‑asset catalogs for downstream consumption.

Quantitative Approach

A long‑horizon momentum view is blended with an adaptive mean and envelopes. Zone transitions and retests help infer phase and price‑space levels. Specific formulas are intentionally withheld.

  • Signals begin bias‑agnostic; final bias is determined by level vs. price.
  • Edge cases are handled explicitly to avoid false cues.
  • No repainting: backtests run prefix‑by‑prefix.

Research only: nothing here is investment advice.

Build Status & Roadmap

  • Now: collectors, zone engines, tuning experiments, and risk prototypes.
  • Next: harden walk‑forward evaluation and per‑asset artifacts; strengthen reporting.
  • Later: automate the adaptive code factory; expand instrument coverage.