Signal Evaluation
このセクションは、現在のブランチで AI、シグナル解釈、判断支援、リプレイ検証がどう結び付いているかを説明する文書をまとめます。
Core artifacts
Integrated flow
- live feeds and structured services create a current snapshot
- AI, event resolution, and graph layers build evidence-grounded context
- 判断支援ロジックがシグナルを候補に変換する
- リプレイと履歴検証がその候補の妥当性を確認する
- 検証結果が証拠品質と admission 品質を補正する
Public mock workbench
The public docs include a click-through mock replay workbench. It is not connected to private feeds, but it mirrors the product structure.
- point-in-time datasets
- replay and scenario comparison
- operator decision posture
- hot / warm / cold storage lifecycle
Mock Replay Studio
シナリオ・バックテスト workbench
この公開デモは実運用の replay stack に近い合成 point-in-time データを使います。データセットを切り替え、シナリオを比較し、保存層と評価結果がどう連動するかを確認できます。
Scenario
Middle East energy shock
Escalation lifts oil and shipping stress while safe-haven positioning turns defensive.
Replay curve
ACLED · conflict events · 91% coverageInput datasets
conflict events · 91% coverage
Conflict and protest events anchor the regime shift signal.
news / document stream · 78% coverage
News burst intensity confirms narrative acceleration around shipping routes.
price series · 96% coverage
USO, XLE, GLD, and TLT provide tradable exit points for the replay.
Decision posture
High conviction only when shipping stress and crude momentum confirm together.
Macro overlay prioritizes capital protection over fresh cyclic exposure.
Scenario timeline
ACLED and news spikes land in Redis and feed the current snapshot.
Transmission edges and hedge bias are recorded in the replay frame.
Max-hold fallback closes the position if no earlier clean exit appears.
Data lifecycle
Live conflict/news payloads stay in Redis with short TTL and schema checks.
Replay frames and run summaries persist into PostgreSQL for operator review.
Parquet snapshots archive the scenario window for later point-in-time reproduction.
Current limits
- いくつかの確率レイヤーはまだ実用的近似です
- リプレイ品質は point-in-time データの完全性に依存します
- 現在の main ブランチは完全自動売買スタックではありません