diff --git a/SESSION.md b/SESSION.md
index fa49bbe..00b8359 100644
--- a/SESSION.md
+++ b/SESSION.md
@@ -2,7 +2,7 @@
## Current Goal
-Automation execution-layer PXX is closed. P15-P24 are all closed, current `HEAD` was deployed to the remote host, P24 remote ops smoke passed, and the PXX closure gate froze the known execution-layer scope. The default state is long-running analysis-decision monitoring with ops rollup; new product phases require explicit user direction.
+Automation execution-layer PXX remains closed and its known scope stays frozen. Under explicit user direction, the active product phase is a dense `Decision Assistant` operator workspace with grouped recall, broader replay baselines, note templates, quick-save shortcuts, and concrete OKX announcements/system-status read-only sources. This run closed H10/F11/G10/D12 by adding guided scan completion states, stale-to-fresh digest refresh prompts, saved-template reuse metrics, and setup-family peer baselines. Local validation passed, the current working tree was deployed, and remote smoke confirmed the latest Decision Assistant fields on the live host.
## Current Branch
@@ -10,12 +10,120 @@ Automation execution-layer PXX is closed. P15-P24 are all closed, current `HEAD`
## Last Updated
-2026-05-12 UTC
+2026-05-13 UTC
## Automation Status
- state: automation_execution_layer_pxx_known_scope_closed
- current_phase: Long-running Analysis Decision Monitor With Ops Rollup
+- active_product_phase: Decision Assistant Phase H/F/D/G/E Dense Recall + Templates + Announcements + Peer Baselines
+- latest_decision_assistant_slice: unified_cockpit_summary
+- latest_decision_assistant_change_slice: review_packet_delta_summary
+- latest_decision_assistant_evidence_slice: structured_evidence_chain
+- latest_decision_assistant_workflow_slice: workflow_digest
+- latest_decision_assistant_priority_slice: opportunity_ranking_and_no_trade_priority
+- latest_decision_assistant_timeline_slice: decision_timeline
+- latest_decision_assistant_explanation_slice: explanation_drilldown_and_reason_citations
+- latest_decision_assistant_learning_slice: learning_rollup
+- latest_decision_assistant_watchlist_slice: candidate_watchlist_tiers
+- latest_decision_assistant_external_context_slice: external_context_summary
+- latest_decision_assistant_digest_slice: digest_surface_and_personalized_reminders
+- latest_decision_assistant_setup_learning_slice: setup_learning_rollup
+- latest_decision_assistant_review_entry_slice: review_entrypoints
+- latest_decision_assistant_watchlist_action_slice: tier_action_commands
+- latest_decision_assistant_external_source_slice: expanded_external_context_cards
+- latest_decision_assistant_workspace_slice: sectioned_dense_workspace
+- latest_decision_assistant_replay_stats_slice: setup_session_replay_statistics
+- latest_decision_assistant_digest_history_slice: digest_history_preview
+- latest_decision_assistant_replay_compare_slice: comparative_replay_summary
+- latest_decision_assistant_exchange_status_slice: okx_public_system_status
+- latest_decision_assistant_scan_summary_slice: scan_summary_cards
+- latest_decision_assistant_digest_summary_slice: digest_history_summary
+- latest_decision_assistant_replay_story_slice: replay_comparison_story
+- latest_decision_assistant_note_template_slice: review_note_templates
+- latest_decision_assistant_digest_group_slice: digest_history_groups
+- latest_decision_assistant_replay_broad_baseline_slice: broader_replay_baseline_summary
+- latest_decision_assistant_announcement_slice: okx_announcements
+- latest_decision_assistant_note_shortcut_slice: template_quick_save_actions
+- latest_decision_assistant_history_filter_slice: digest_history_group_filtering
+- latest_decision_assistant_template_recommendation_slice: recommended_template_ergonomics
+- latest_decision_assistant_digest_recommendation_slice: recommended_digest_group
+- latest_decision_assistant_replay_percentile_slice: replay_percentile_visibility
+- latest_decision_assistant_scan_action_slice: scan_summary_jump_actions
+- latest_decision_assistant_digest_group_nav_slice: digest_history_group_navigation
+- latest_decision_assistant_template_action_slice: template_card_use_save_actions
+- latest_decision_assistant_replay_composite_rank_slice: composite_replay_rank
+- latest_decision_assistant_section_nav_slice: assistant_section_navigation
+- latest_decision_assistant_history_persistence_slice: digest_filter_persistence
+- latest_decision_assistant_replay_composite_slice: composite_replay_percentile
+- latest_decision_assistant_scan_hierarchy_slice: scan_card_hierarchy_context
+- latest_decision_assistant_digest_focus_slice: digest_group_focus_headlines
+- latest_decision_assistant_template_loop_slice: context_aware_review_templates
+- latest_decision_assistant_replay_risk_slice: replay_risk_discipline_verdict
+- latest_decision_assistant_scan_transition_slice: scan_to_action_transition_hints
+- latest_decision_assistant_digest_staleness_slice: digest_staleness_hints
+- latest_decision_assistant_template_reuse_slice: template_reuse_and_carry_over_context
+- latest_decision_assistant_replay_peer_slice: peer_replay_baseline_visibility
+- latest_decision_assistant_scan_completion_slice: guided_scan_completion_states
+- latest_decision_assistant_digest_refresh_slice: stale_to_fresh_refresh_prompts
+- latest_decision_assistant_template_reuse_metrics_slice: saved_template_reuse_metrics
+- latest_decision_assistant_replay_family_slice: setup_family_peer_baselines
+- latest_decision_assistant_summary_status: implemented
+- latest_decision_assistant_change_summary_status: implemented
+- latest_decision_assistant_evidence_status: implemented
+- latest_decision_assistant_workflow_status: implemented
+- latest_decision_assistant_priority_status: implemented
+- latest_decision_assistant_timeline_status: implemented
+- latest_decision_assistant_explanation_status: implemented
+- latest_decision_assistant_reason_citation_status: implemented
+- latest_decision_assistant_learning_status: implemented
+- latest_decision_assistant_watchlist_status: implemented
+- latest_decision_assistant_external_context_status: implemented
+- latest_decision_assistant_digest_status: implemented
+- latest_decision_assistant_setup_learning_status: implemented
+- latest_decision_assistant_review_entry_status: implemented
+- latest_decision_assistant_watchlist_action_status: implemented
+- latest_decision_assistant_external_source_status: implemented
+- latest_decision_assistant_workspace_status: implemented
+- latest_decision_assistant_replay_stats_status: implemented
+- latest_decision_assistant_digest_history_status: implemented
+- latest_decision_assistant_replay_compare_status: implemented
+- latest_decision_assistant_exchange_status_status: implemented
+- latest_decision_assistant_scan_summary_status: implemented
+- latest_decision_assistant_digest_summary_status: implemented
+- latest_decision_assistant_replay_story_status: implemented
+- latest_decision_assistant_note_template_status: implemented
+- latest_decision_assistant_digest_group_status: implemented
+- latest_decision_assistant_replay_broad_baseline_status: implemented
+- latest_decision_assistant_announcement_status: implemented
+- latest_decision_assistant_note_shortcut_status: implemented
+- latest_decision_assistant_history_filter_status: implemented
+- latest_decision_assistant_template_recommendation_status: implemented
+- latest_decision_assistant_digest_recommendation_status: implemented
+- latest_decision_assistant_replay_percentile_status: implemented
+- latest_decision_assistant_scan_action_status: implemented
+- latest_decision_assistant_digest_group_nav_status: implemented
+- latest_decision_assistant_template_action_status: implemented
+- latest_decision_assistant_replay_composite_rank_status: implemented
+- latest_decision_assistant_section_nav_status: implemented
+- latest_decision_assistant_history_persistence_status: implemented
+- latest_decision_assistant_replay_composite_status: implemented
+- latest_decision_assistant_scan_hierarchy_status: implemented
+- latest_decision_assistant_digest_focus_status: implemented
+- latest_decision_assistant_template_loop_status: implemented
+- latest_decision_assistant_replay_risk_status: implemented
+- latest_decision_assistant_scan_transition_status: implemented
+- latest_decision_assistant_digest_staleness_status: implemented
+- latest_decision_assistant_template_reuse_status: implemented
+- latest_decision_assistant_replay_peer_status: implemented
+- latest_decision_assistant_scan_completion_status: implemented
+- latest_decision_assistant_digest_refresh_status: implemented
+- latest_decision_assistant_template_reuse_metrics_status: implemented
+- latest_decision_assistant_replay_family_status: implemented
+- latest_decision_assistant_workbench_status: visible_local_smoke
+- latest_remote_decision_assistant_deploy_status: deployed_current_head
+- latest_remote_decision_assistant_smoke_status: passed
+- latest_remote_decision_assistant_review_packet_status: guided_scan_digest_refresh_template_reuse_metrics_setup_family_visible
- latest_p7_selector_choice: continue_periodic_analysis_decision_monitoring_with_maturity_rollup
- latest_p14_slice: webhook_transport_retry
- latest_p15_pxx_tasklist_status: ready
@@ -94,6 +202,91 @@ Automation execution-layer PXX is closed. P15-P24 are all closed, current `HEAD`
## Completed In This Run
+- Added guided scan completion state metadata (`completion_state`, `completion_hint`, `is_next_scan_step`) plus a top-level `scan_completion_summary` so the operator can see which cockpit scan item should be handled next.
+- Added stale-to-fresh digest refresh prompts (`refresh_needed`, `refresh_prompt`, `refresh_action_label`, `refresh_needed_count`) so aged recall groups clearly ask for refresh before being reused as live context.
+- Added saved-template reuse metrics (`template_reuse_metrics`, per-template `reuse_count` / `last_used_ts` / `reuse_status`) so review templates show whether the recommended template has already been used in recent notes.
+- Added setup-family peer replay baselines (`setup_family`, `setup_family_peer_sessions`, `setup_family_peer_summary`, `setup_family_focus`) so replay comparison can talk about the setup family, not only the current session ladder.
+- Passed focused validation: `unittest tests.unit.test_operator_workspace_service`, `unittest tests.unit.test_operator_workspace_service tests.unit.test_api_static_server tests.unit.test_api_server`, `scripts/run_script_static_checks.py`, `node --check src/web/app.js`, and a local `review-packet` smoke for the new H10/F11/G10/D12 fields.
+- Deployed the current working tree to `http://192.168.3.90:8000`; deploy smoke passed 11 endpoints, `run_remote_ops_smoke.py` passed 9 checks, and remote field smoke confirmed `scan_completion_summary`, digest refresh prompts, template reuse metrics, setup-family baseline fields, and matching `/static/app.js` UI strings.
+- Added scan-to-action `transition_hint` fields so scan cards tell the operator what the next click/action is for.
+- Added digest recall aging metadata (`age_seconds`, `age_band`, `stale`, `staleness_hint`) plus fresh/stale group counts on the digest summary.
+- Added review-template carry-over and reuse metadata (`carry_over_context`, `reuse_hint`, `recommended_reuse_hint`) so repeated note loops preserve context.
+- Added peer replay baseline visibility with `peer_sessions` and `peer_summary`, plus market-session fallback for setup replay when the decision packet has no session code.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local `review-packet` smoke confirming transition hints, digest staleness, template reuse hints, and peer baseline fields.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes transition hints, digest staleness hints, template reuse hints, and peer replay baseline fields; remote `app.js` includes the matching UI.
+- Added clearer `scan_summary_cards` hierarchy with rank, context, emphasis, and direct action labels so the top rail reads more like an operator scan order instead of five flat cards.
+- Added richer `digest_history_groups` focus metadata (`headline`, `latest_title`, `recommended`, `action_label`) and wired the workbench to filter/review groups with clearer recall intent.
+- Added context-aware review templates by introducing `next_review` and `queue_follow_up` note templates plus recommended-template reasoning/priority metadata.
+- Extended broader replay baselines with `risk_rank`, `risk_percentile`, `risk_band`, `top_safe_sessions`, and `replay_verdict`.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local `review-packet` smoke confirming `recommended_reason`, digest group headlines, scan action labels, and replay verdict fields.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `recommended_reason`, digest group headlines/action labels, scan hierarchy metadata, and replay verdict/risk fields; remote `app.js` / `styles.css` include the matching UI updates.
+- Added `note_templates` to review entrypoints so the assistant can prefill operator notes with top setup, blocker, and focus-mode templates.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `note_templates`.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `note_templates`, and remote app includes the prefill note helper and template actions.
+- Added recommended-template guidance and digest-history group filtering ergonomics to the assistant workspace.
+- Added replay percentile visibility so the assistant can show `framework_percentile` and `avg_r_percentile` alongside broader baseline ranks.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `recommended_label`, `recommended_kind`, `framework_percentile`, and `avg_r_percentile`.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `recommended_label`, `recommended_kind`, `framework_percentile`, and `avg_r_percentile`, and the remote app renders the corresponding UI labels.
+- Added section navigation, digest filter persistence, and composite replay percentile visibility to the assistant workspace.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local smoke confirming the updated section-nav/filter interactions.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes the template/digest/replay metadata and the remote app includes section navigation plus persisted digest filtering.
+- Revalidated and remotely confirmed that the current live `Decision Assistant` now exposes recommended templates, digest group guidance, replay percentiles, and the denser grouped workspace together.
+- Revalidated and remotely confirmed that the current live `Decision Assistant` now exposes grouped digest recall, broader replay baselines, OKX announcements, and quick-save note shortcuts alongside the earlier template and scan surfaces.
+- Added `scan_summary_cards` so the assistant can present a compact top scan rail with top opportunity, top blocker, next review, external context, and digest summary.
+- Added `history_summary` under the digest surface so the assistant can summarize recall kinds and latest timestamp, not just raw history rows.
+- Extended replay comparison with `comparison_status`, `confidence_band`, `session_alignment`, and a human-readable `story`.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `scan_summary_cards`, digest history summary, and replay comparison story fields.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `scan_summary_cards`, digest history summary, and replay comparison story; remote app/css include the matching dense workspace updates.
+- Added `comparative_summary` to setup/session replay statistics so the assistant can compare the current replay profile against weekly high/low session baselines.
+- Tightened the `Decision Assistant` surface into denser grouped sections for repeated operator use.
+- Added a concrete new read-only external source: OKX public system status, surfaced inside the assistant external context block.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `comparative_summary` and `exchange_status`.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `comparative_summary`, `digest_history_preview`, and `exchange_status`, and remote app/css include the latest dense assistant workspace updates.
+- Added `assistant-section-title` grouping so the Decision Assistant renders as a denser action-oriented workspace instead of one long undifferentiated surface.
+- Added setup/session `replay_statistics` inside `setup_learning_rollup`.
+- Added `digest_history_preview` so recent reminders, alerts, notes, and the previous packet can be recalled from the assistant surface.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `replay_statistics` and `digest_history_preview`.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `replay_statistics` and `digest_history_preview`, and remote app/css include the assistant sectioned workspace updates.
+- Added `review_entrypoints` so the assistant can expose notes / review / journal entry surfaces directly from the top-level panel.
+- Added per-tier watchlist action metadata (`suggested_action`, `action_path`, `action_method`) and workbench buttons for confirm-check flows.
+- Broadened `external_context_summary` with `operator_questions`, `next_confirmation_targets`, and structured `source_cards`.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `review_entrypoints`, `suggested_action`, `source_cards`, and `operator_questions`.
+- Deployed current `HEAD` again to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `review_entrypoints`, watchlist action metadata, and expanded external context cards; remote static app includes the same assistant surface changes.
+- Added `personalized_reminders` and `digest_surface` so the assistant can expose actionable reminder cards and a compact digest summary.
+- Added `setup_learning_rollup` so the assistant can surface setup-level lessons and session learning context for the current setup type.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `personalized_reminders`, `digest_surface`, and `setup_learning_rollup`.
+- Deployed current `HEAD` to `http://192.168.3.90:8000`.
+- Passed remote smoke for the latest Decision Assistant fields: remote `review_packet` exposes `candidate_watchlist_tiers`, `external_context_summary`, `personalized_reminders`, `digest_surface`, and `setup_learning_rollup`, and the remote workbench root still renders `Decision Assistant`.
+- Added `candidate_watchlist_tiers` and `candidate_watchlist_summary` so the assistant can group recent setups into `review_now`, `strong_watch`, `soft_watch`, and `archive_only`.
+- Added `external_context_summary` so the assistant can surface news-window status, provider freshness, active macro events, external waiting items, and council watch items.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `candidate_watchlist_tiers`, `candidate_watchlist_summary`, and `external_context_summary`.
+- Reconfirmed the real external blocker: remote deploy validation still requires `AI_ICT_DEPLOY_PASSWORD` or an explicit `--password`, which is absent in the current shell.
+- Added `explanation_drilldown` so the assistant can expose decision logic, blockers/guards, watch signals, and launch boundaries as separate drill-down cards.
+- Added structured `reason_citations` with source path / source type / severity so the assistant can show where each reason comes from.
+- Added `learning_rollup` so the assistant can surface review-entry count, pending feedback, quality flags, and next review actions.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `explanation_drilldown`, `reason_citations`, and `learning_rollup`.
+- Reconfirmed the real external blocker: remote deploy validation still requires `AI_ICT_DEPLOY_PASSWORD` or an explicit `--password`, which is absent in the current shell.
+- Added `workflow_digest` so the Decision Assistant now exposes pre-market / intraday / post-trade workflow cards.
+- Refined top opportunity ranking and structured `no_trade_reason_items` so the assistant can prioritize actionable setups over broad session wrappers and explain blocker severity.
+- Added `decision_timeline` so the assistant can show current packet, previous packet, latest alert, latest note, and latest execution time anchors when available.
+- Passed focused validation again: `unittest`, `node --check src/web/app.js`, and local HTTP smoke confirming `workflow_digest`, `top_opportunities`, `no_trade_reason_items`, and `decision_timeline`.
+- Confirmed the first real external blocker for this loop: remote deploy validation requires `AI_ICT_DEPLOY_PASSWORD` or an explicit `--password`, which is absent in the current shell.
+- Verified `GSTACK_OK` and reread the current Decision Assistant anchors.
+- Started `Decision Assistant Phase A Cockpit` under explicit user direction while preserving the closed PXX execution-layer state.
+- Added `DecisionAssistantSummaryService` and exposed `decision_assistant_summary` from `review_packet`.
+- Added a unified `Decision Assistant` workbench panel that surfaces focus mode, headline, market/risk posture, priority items, and next actions.
+- Added `changed_since_last` delta summary against the latest stored decision packet so the assistant can explain what materially changed.
+- Added a structured `evidence_chain` so the assistant summary exposes the key decision gate, launch mode, session context, operator queue, and weekly review evidence blocks.
+- Passed focused validation: `unittest` for operator/api/static surfaces, `node --check src/web/app.js`, and local HTTP smoke confirming the new panel text and summary payload.
- Verified `GSTACK_OK`.
- Promoted the visual-watch prewire into a P8 review loop slice.
- Added `VisualReviewService` to review reserved watch runs, create optional snapshot records, create visual reviews, and mark watch runs reviewed.
@@ -163,6 +356,18 @@ Automation execution-layer PXX is closed. P15-P24 are all closed, current `HEAD`
## Current Working State
+- Decision Assistant Phase H/F/D/G/E Dense Recall + Templates + Announcements is active as the current product-development line on top of the frozen execution-layer baseline.
+- The new assistant summary is implemented in `review_packet`: it synthesizes a single headline, focus mode, market posture, risk posture, no-trade reasons, priority items, next actions, and evidence summary.
+- The assistant summary now reports `changed_since_last` by diffing the current packet against the latest stored decision packet payload.
+- The assistant summary now also reports a structured `evidence_chain` so the top-level panel can show why the current stance exists without opening multiple secondary panels.
+- The assistant summary now also reports `workflow_digest`, `top_opportunities`, structured `no_trade_reason_items`, and `decision_timeline`.
+- The assistant summary now also reports `explanation_drilldown`, `reason_citations`, and `learning_rollup` so explainability and review-loop state are visible from the top-level panel.
+- The assistant summary now also reports `candidate_watchlist_tiers` / `candidate_watchlist_summary` and `external_context_summary`, so setup tiers and read-only macro/news context are visible from the same top-level surface.
+- The assistant summary now also reports `personalized_reminders`, `digest_surface`, and `setup_learning_rollup`, so reminder prioritization and setup-level learning are visible from the same top-level panel.
+- The assistant summary now also reports `review_entrypoints`, per-tier watchlist action metadata, `operator_questions`, `next_confirmation_targets`, and structured external `source_cards`.
+- The assistant summary now also reports setup/session `replay_statistics`, replay `comparative_summary`, `digest_history_preview`, and OKX public `exchange_status`.
+- The assistant summary now also reports `scan_summary_cards`, digest `history_summary`, replay comparison `story`, `digest_history_groups`, broader replay baseline summaries, `note_templates`, OKX announcements, plus recommended template and replay percentile guidance.
+- The workbench top section now includes a dedicated `Decision Assistant` panel with denser grouped sections plus note-template prefill, quick-save actions, digest-history group filtering, and replay percentile visibility, and both local and remote smoke confirmed the rendered surface plus `review_packet.decision_assistant_summary` with the latest scan/digest/replay/template/announcement fields.
- P7 is closed and remains a monitor-maturity routing layer, not a Research Council expansion.
- P8 review/capture/feedback surfaces remain implemented and unchanged in their execution guards.
- P9 quality rollup remains implemented and visible in both the dashboard and review packet.
@@ -210,6 +415,7 @@ Automation execution-layer PXX is closed. P15-P24 are all closed, current `HEAD`
## Current Risks Or Open Questions
+- Remote deploy is no longer blocked, but the current assistant still lacks richer external source coverage beyond the existing news/provider chain and council watch items.
- Browser screenshot capture adapter is a future enhancement, not a P8 closure blocker. The current adapter is an advisory review-packet HTML capture, and the review loop can still use an existing snapshot, an explicit `image_path`, or write an `unavailable` structured review.
- Visual review remains advisory and must not change execution permission, live readiness, trust bucket, risk governor, or operator confirmation.
- Alert-quality statistics remain advisory and must not change execution permission, live readiness, trust bucket, risk governor, or operator confirmation.
diff --git a/TASKS.md b/TASKS.md
index cac75f0..c1fb1ff 100644
--- a/TASKS.md
+++ b/TASKS.md
@@ -10,13 +10,71 @@
- [x] Stable monitor quality may enter automation execution-layer P7
- [x] User explicitly requested the next P8 product-development round
- [x] User requested a finite P15-PXX task list so future phases can continue without per-phase stops
+- [x] User explicitly started `Decision Assistant Phase A Cockpit` from the new anchor plan
## In Progress
- [x] PXX Automation Execution Closure Gate complete
+- [x] Decision Assistant A1 unified cockpit summary slice complete
+- [x] Decision Assistant A2 review-packet delta summary slice complete
+- [x] Decision Assistant A3 structured evidence chain slice complete
+- [x] Decision Assistant A4 workflow digest slice complete
+- [x] Decision Assistant A5 opportunity ranking and no-trade priority slice complete
+- [x] Decision Assistant B1 decision timeline slice complete
+- [x] Decision Assistant B2 explanation drill-down and reason citations slice complete
+- [x] Decision Assistant D1 learning rollup slice complete
+- [x] Decision Assistant C1 watchlist / candidate tier slice complete
+- [x] Decision Assistant E1 external read-only context slice complete
+- [x] Decision Assistant F1 digest / personalized reminder slice complete
+- [x] Decision Assistant D2 setup learning rollup slice complete
+- [x] Decision Assistant G1 notes / journal review entrypoint slice complete
+- [x] Decision Assistant C2 per-tier watchlist action slice complete
+- [x] Decision Assistant E2 expanded external context card slice complete
+- [x] Decision Assistant H1 dense action-oriented workspace slice complete
+- [x] Decision Assistant D3 setup/session replay statistics slice complete
+- [x] Decision Assistant F2 digest history / reminder recall slice complete
+- [x] Decision Assistant H2 tightened dense section ordering slice complete
+- [x] Decision Assistant E3 OKX public system-status source slice complete
+- [x] Decision Assistant D4 replay comparison slice complete
+- [x] Decision Assistant H3 scan summary rail slice complete
+- [x] Decision Assistant F3 digest history summary slice complete
+- [x] Decision Assistant D5 replay comparison story slice complete
+- [x] Decision Assistant G2 review note template slice complete
+- [x] Decision Assistant G3 quick-save note shortcut slice complete
+- [x] Decision Assistant H4 dense grouping / history grid slice complete
+- [x] Decision Assistant F4 digest history group ergonomics slice complete
+- [x] Decision Assistant D6 broader replay baseline slice complete
+- [x] Decision Assistant E4 OKX announcements source slice complete
+- [x] Decision Assistant G4 recommended template ergonomics slice complete
+- [x] Decision Assistant H5 digest-history group filtering slice complete
+- [x] Decision Assistant F5 digest grouping ergonomics slice complete
+- [x] Decision Assistant D7 replay percentile visibility slice complete
+- [x] Decision Assistant G5 recommended template guidance slice complete
+- [x] Decision Assistant F6 recommended digest group slice complete
+- [x] Decision Assistant D8 composite replay rank slice complete
+- [x] Decision Assistant H7 scan-card jump action slice complete
+- [x] Decision Assistant F8 digest group navigation slice complete
+- [x] Decision Assistant G7 template card action slice complete
+- [x] Decision Assistant D9 composite replay leaderboard slice complete
+- [x] Decision Assistant H8 clearer scan hierarchy slice complete
+- [x] Decision Assistant F9 digest focus / recall headline slice complete
+- [x] Decision Assistant G8 context-aware review template loop slice complete
+- [x] Decision Assistant D10 replay risk / verdict slice complete
+- [x] Decision Assistant H9 scan-to-action transition slice complete
+- [x] Decision Assistant F10 digest staleness / aging hint slice complete
+- [x] Decision Assistant G9 template carry-over / reuse visibility slice complete
+- [x] Decision Assistant D11 peer replay baseline slice complete
+- [x] Decision Assistant H10 guided scan completion states slice complete
+- [x] Decision Assistant F11 stale-to-fresh digest refresh prompts slice complete
+- [x] Decision Assistant G10 saved-template reuse metrics slice complete
+- [x] Decision Assistant D12 setup-family peer baselines slice complete
## Next Up
+- [ ] H11 continue compact operator workspace polish with scan-state persistence / handoff markers
+- [ ] F12 deepen digest recall with refresh-action evidence anchors
+- [ ] G11 continue review template ergonomics with template outcome / usefulness scoring
+- [ ] D13 extend replay comparison with setup-family trend deltas
- [x] P8 local closure complete
- [x] P9 local closure complete
- [x] P10 local closure complete
@@ -43,6 +101,108 @@
- [x] state: automation_execution_layer_pxx_known_scope_closed
- [x] current_phase: Long-running Analysis Decision Monitor With Ops Rollup
+- [x] active_product_phase: Decision Assistant Phase H/F/D/G/E Dense Recall + Templates + Announcements + Peer Baselines
+- [x] latest_decision_assistant_slice: unified_cockpit_summary
+- [x] latest_decision_assistant_change_slice: review_packet_delta_summary
+- [x] latest_decision_assistant_evidence_slice: structured_evidence_chain
+- [x] latest_decision_assistant_workflow_slice: workflow_digest
+- [x] latest_decision_assistant_priority_slice: opportunity_ranking_and_no_trade_priority
+- [x] latest_decision_assistant_timeline_slice: decision_timeline
+- [x] latest_decision_assistant_explanation_slice: explanation_drilldown_and_reason_citations
+- [x] latest_decision_assistant_learning_slice: learning_rollup
+- [x] latest_decision_assistant_watchlist_slice: candidate_watchlist_tiers
+- [x] latest_decision_assistant_external_context_slice: external_context_summary
+- [x] latest_decision_assistant_digest_slice: digest_surface_and_personalized_reminders
+- [x] latest_decision_assistant_setup_learning_slice: setup_learning_rollup
+- [x] latest_decision_assistant_review_entry_slice: review_entrypoints
+- [x] latest_decision_assistant_watchlist_action_slice: tier_action_commands
+- [x] latest_decision_assistant_external_source_slice: expanded_external_context_cards
+- [x] latest_decision_assistant_workspace_slice: sectioned_dense_workspace
+- [x] latest_decision_assistant_replay_stats_slice: setup_session_replay_statistics
+- [x] latest_decision_assistant_digest_history_slice: digest_history_preview
+- [x] latest_decision_assistant_replay_compare_slice: comparative_replay_summary
+- [x] latest_decision_assistant_exchange_status_slice: okx_public_system_status
+- [x] latest_decision_assistant_scan_summary_slice: scan_summary_cards
+- [x] latest_decision_assistant_digest_summary_slice: digest_history_summary
+- [x] latest_decision_assistant_replay_story_slice: replay_comparison_story
+- [x] latest_decision_assistant_note_template_slice: review_note_templates
+- [x] latest_decision_assistant_digest_group_slice: digest_history_groups
+- [x] latest_decision_assistant_replay_broad_baseline_slice: broader_replay_baseline_summary
+- [x] latest_decision_assistant_announcement_slice: okx_announcements
+- [x] latest_decision_assistant_note_shortcut_slice: template_quick_save_actions
+- [x] latest_decision_assistant_history_filter_slice: digest_history_group_filtering
+- [x] latest_decision_assistant_template_recommendation_slice: recommended_template_ergonomics
+- [x] latest_decision_assistant_digest_recommendation_slice: recommended_digest_group
+- [x] latest_decision_assistant_replay_percentile_slice: replay_percentile_visibility
+- [x] latest_decision_assistant_scan_action_slice: scan_summary_jump_actions
+- [x] latest_decision_assistant_digest_group_nav_slice: digest_history_group_navigation
+- [x] latest_decision_assistant_template_action_slice: template_card_use_save_actions
+- [x] latest_decision_assistant_replay_composite_rank_slice: composite_replay_rank
+- [x] latest_decision_assistant_scan_hierarchy_slice: scan_card_hierarchy_context
+- [x] latest_decision_assistant_digest_focus_slice: digest_group_focus_headlines
+- [x] latest_decision_assistant_template_loop_slice: context_aware_review_templates
+- [x] latest_decision_assistant_replay_risk_slice: replay_risk_discipline_verdict
+- [x] latest_decision_assistant_scan_transition_slice: scan_to_action_transition_hints
+- [x] latest_decision_assistant_digest_staleness_slice: digest_staleness_hints
+- [x] latest_decision_assistant_template_reuse_slice: template_reuse_and_carry_over_context
+- [x] latest_decision_assistant_replay_peer_slice: peer_replay_baseline_visibility
+- [x] latest_decision_assistant_scan_completion_slice: guided_scan_completion_states
+- [x] latest_decision_assistant_digest_refresh_slice: stale_to_fresh_refresh_prompts
+- [x] latest_decision_assistant_template_reuse_metrics_slice: saved_template_reuse_metrics
+- [x] latest_decision_assistant_replay_family_slice: setup_family_peer_baselines
+- [x] latest_decision_assistant_summary_status: implemented
+- [x] latest_decision_assistant_change_summary_status: implemented
+- [x] latest_decision_assistant_evidence_status: implemented
+- [x] latest_decision_assistant_workflow_status: implemented
+- [x] latest_decision_assistant_priority_status: implemented
+- [x] latest_decision_assistant_timeline_status: implemented
+- [x] latest_decision_assistant_explanation_status: implemented
+- [x] latest_decision_assistant_reason_citation_status: implemented
+- [x] latest_decision_assistant_learning_status: implemented
+- [x] latest_decision_assistant_watchlist_status: implemented
+- [x] latest_decision_assistant_external_context_status: implemented
+- [x] latest_decision_assistant_digest_status: implemented
+- [x] latest_decision_assistant_setup_learning_status: implemented
+- [x] latest_decision_assistant_review_entry_status: implemented
+- [x] latest_decision_assistant_watchlist_action_status: implemented
+- [x] latest_decision_assistant_external_source_status: implemented
+- [x] latest_decision_assistant_workspace_status: implemented
+- [x] latest_decision_assistant_replay_stats_status: implemented
+- [x] latest_decision_assistant_digest_history_status: implemented
+- [x] latest_decision_assistant_replay_compare_status: implemented
+- [x] latest_decision_assistant_exchange_status_status: implemented
+- [x] latest_decision_assistant_scan_summary_status: implemented
+- [x] latest_decision_assistant_digest_summary_status: implemented
+- [x] latest_decision_assistant_replay_story_status: implemented
+- [x] latest_decision_assistant_note_template_status: implemented
+- [x] latest_decision_assistant_digest_group_status: implemented
+- [x] latest_decision_assistant_replay_broad_baseline_status: implemented
+- [x] latest_decision_assistant_announcement_status: implemented
+- [x] latest_decision_assistant_note_shortcut_status: implemented
+- [x] latest_decision_assistant_history_filter_status: implemented
+- [x] latest_decision_assistant_template_recommendation_status: implemented
+- [x] latest_decision_assistant_digest_recommendation_status: implemented
+- [x] latest_decision_assistant_replay_percentile_status: implemented
+- [x] latest_decision_assistant_scan_action_status: implemented
+- [x] latest_decision_assistant_digest_group_nav_status: implemented
+- [x] latest_decision_assistant_template_action_status: implemented
+- [x] latest_decision_assistant_replay_composite_rank_status: implemented
+- [x] latest_decision_assistant_scan_hierarchy_status: implemented
+- [x] latest_decision_assistant_digest_focus_status: implemented
+- [x] latest_decision_assistant_template_loop_status: implemented
+- [x] latest_decision_assistant_replay_risk_status: implemented
+- [x] latest_decision_assistant_scan_transition_status: implemented
+- [x] latest_decision_assistant_digest_staleness_status: implemented
+- [x] latest_decision_assistant_template_reuse_status: implemented
+- [x] latest_decision_assistant_replay_peer_status: implemented
+- [x] latest_decision_assistant_scan_completion_status: implemented
+- [x] latest_decision_assistant_digest_refresh_status: implemented
+- [x] latest_decision_assistant_template_reuse_metrics_status: implemented
+- [x] latest_decision_assistant_replay_family_status: implemented
+- [x] latest_decision_assistant_workbench_status: visible_local_smoke
+- [x] latest_remote_decision_assistant_deploy_status: deployed_current_head
+- [x] latest_remote_decision_assistant_smoke_status: passed
+- [x] latest_remote_decision_assistant_review_packet_status: guided_scan_digest_refresh_template_reuse_metrics_setup_family_visible
- [x] latest_p7_selector_choice: continue_periodic_analysis_decision_monitoring_with_maturity_rollup
- [x] latest_p14_slice: webhook_transport_retry
- [x] latest_p15_pxx_tasklist_status: ready
@@ -120,6 +280,66 @@
## Newly Done
+- [x] Added guided scan completion states and `scan_completion_summary`
+- [x] Added digest stale-to-fresh refresh prompts and refresh-needed counts
+- [x] Added saved-template reuse metrics and per-template reuse visibility
+- [x] Added setup-family peer replay baselines and family-focused replay summary
+- [x] Passed focused validation for the H10/F11/G10/D12 slice
+- [x] Deployed current working tree and passed remote Decision Assistant smoke for guided scan, digest refresh, template reuse metrics, and setup-family baseline fields
+- [x] Added scan-to-action transition hints on assistant scan cards
+- [x] Added digest staleness / aging hints and fresh/stale group counts
+- [x] Added template carry-over context and recommended reuse hints
+- [x] Added peer replay baseline summary and adjacent peer session cards
+- [x] Passed focused unit/static validation and local `review-packet` smoke for the H9/F10/G9/D11 slice
+- [x] Deployed current `HEAD` and passed remote Decision Assistant smoke for transition, staleness, reuse, and peer baseline fields
+- [x] Added scan-card hierarchy metadata (`rank`, `context`, `emphasis`, `action_label`) and matching dense UI emphasis
+- [x] Added digest group headlines / recommended focus / per-group review actions
+- [x] Added context-aware review templates including `next_review` and `queue_follow_up`
+- [x] Added replay risk ranking / percentile / verdict fields plus safest-session leaderboard
+- [x] Passed focused unit/static validation and local `review-packet` smoke for the new assistant slice
+- [x] Deployed current `HEAD` and passed remote smoke for scan hierarchy, digest focus, template reasoning, and replay verdict fields
+- [x] Verified `GSTACK_OK` and reread the current Decision Assistant anchors
+- [x] Added `DecisionAssistantSummaryService`
+- [x] Added `review_packet.decision_assistant_summary`
+- [x] Added a top-level `Decision Assistant` workbench panel
+- [x] Added `changed_since_last` diff summary against the latest stored decision packet
+- [x] Added structured `evidence_chain` blocks to the assistant summary and workbench panel
+- [x] Added `workflow_digest` blocks for pre-market / intraday / post-trade assistant flow
+- [x] Added `top_opportunities` ranking and structured `no_trade_reason_items`
+- [x] Added `decision_timeline` for current packet / previous packet / alert / note / execution anchors
+- [x] Added `explanation_drilldown` and structured `reason_citations`
+- [x] Added `learning_rollup` for review / feedback / quality-loop visibility
+- [x] Added `candidate_watchlist_tiers` and `candidate_watchlist_summary`
+- [x] Added `external_context_summary`
+- [x] Added `personalized_reminders` and `digest_surface`
+- [x] Added `setup_learning_rollup`
+- [x] Added `review_entrypoints`
+- [x] Added per-tier watchlist action metadata and buttons
+- [x] Expanded `external_context_summary` with operator questions, next confirmation targets, and source cards
+- [x] Added sectioned dense workspace headings for the assistant surface
+- [x] Added setup/session `replay_statistics`
+- [x] Added `digest_history_preview`
+- [x] Added replay `comparative_summary`
+- [x] Added OKX public `exchange_status` source into external context
+- [x] Added `scan_summary_cards`
+- [x] Added digest `history_summary`
+- [x] Added replay comparison `story`
+- [x] Added review `note_templates`
+- [x] Added quick-save note shortcut actions
+- [x] Added digest `history_groups`
+- [x] Added broader replay baseline summary
+- [x] Added OKX announcements read-only source
+- [x] Added digest history group filtering ergonomics
+- [x] Added recommended template guidance
+- [x] Added recommended digest group visibility
+- [x] Added replay percentile visibility
+- [x] Added recommended template guidance in review entrypoints
+- [x] Added scan-card jump actions
+- [x] Added digest group navigation
+- [x] Added template card use/save actions
+- [x] Added composite replay rank and top composite sessions
+- [x] Passed focused unit/static validation and local HTTP smoke for the new cockpit slice
+- [x] Deployed current `HEAD` and passed remote Decision Assistant smoke
- [x] Completed P7-4 bounded automation slice selector before opening P8
- [x] Added `VisualReviewService`
- [x] Added `scripts/run_visual_review.py`
diff --git a/docs/AI_ICT_TradingAgents融合实施方案_最终版.md b/docs/AI_ICT_TradingAgents融合实施方案_最终版.md
index cad70a5..0a54e7b 100644
--- a/docs/AI_ICT_TradingAgents融合实施方案_最终版.md
+++ b/docs/AI_ICT_TradingAgents融合实施方案_最终版.md
@@ -4,8 +4,8 @@
本文档综合以下两份已有方案，吸收其长处并统一为一个可执行版本：
-1. `AI_ICT_方案一系统边界图与模块融合图_v1.md`
-2. `AI_ICT_TradingAgents融合实施方案_v1.md`
+1. `docs/archive/AI_ICT_方案一系统边界图与模块融合图_v1.md`
+2. `docs/archive/AI_ICT_TradingAgents融合实施方案_v1.md`
本最终版采用以下合并原则：
diff --git a/docs/AI_ICT_全自动开发执行流程_v1.md b/docs/AI_ICT_全自动开发执行流程_v1.md
index 0d526e8..b06b554 100644
--- a/docs/AI_ICT_全自动开发执行流程_v1.md
+++ b/docs/AI_ICT_全自动开发执行流程_v1.md
@@ -17,8 +17,7 @@
本流程已经根据以下文档统一调整：
-- `docs/AI_ICT_实盘辅助决策系统开发方案_v2.md`
-- `docs/AI_ICT_TradingAgents融合实施方案_最终版.md`
+- `docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md`
后续该项目默认以本流程作为自动推进协议。
@@ -89,9 +88,7 @@
## 4.1 必读锚点
-- `docs/AI_ICT_实盘辅助决策系统开发方案_v2.md`
-- `docs/AI_ICT_TradingAgents融合实施方案_最终版.md`
-- `docs/AI_ICT_全自动开发执行流程_v1.md`
+- `docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md`
- `SESSION.md`
- `TASKS.md`
@@ -99,8 +96,8 @@
当需要判断当前历史阶段、上下文或旧计划映射时，补充读取：
-- `docs/AI_ICT_下一阶段开发计划_v1.md`
-- `docs/AI_ICT_下一阶段开发任务清单_v1.md`
+- `docs/archive/AI_ICT_下一阶段开发计划_v1.md`
+- `docs/archive/AI_ICT_下一阶段开发任务清单_v1.md`
- `docs/AI_ICT_TradingAgents_P0自动任务清单_v1.md`
- `docs/AI_ICT_TradingAgents_P1自动任务清单_v1.md`
- `docs/AI_ICT_TradingAgents_P2自动任务清单_v1.md`
@@ -190,9 +187,7 @@
必须读取：
-- `docs/AI_ICT_实盘辅助决策系统开发方案_v2.md`
-- `docs/AI_ICT_TradingAgents融合实施方案_最终版.md`
-- `docs/AI_ICT_全自动开发执行流程_v1.md`
+- `docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md`
- `SESSION.md`
- `TASKS.md`
diff --git a/docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md b/docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md
new file mode 100644
index 0000000..5746a29
--- /dev/null
+++ b/docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md
@@ -0,0 +1,323 @@
+# AI_ICT 实盘交易分析决策助手下一阶段开发方案 v1
+
+## 1. 目标
+
+本方案面向“实盘交易分析决策助手”，不做真实交易闭环，不做 OKX 真实下单，不做 live adapter 扩展。
+
+目标是把当前分散的行情、账户只读、运行时、决策包、告警、视觉复核、复盘信息，收敛成一个更像“分析助手”的产品：
+
+- 能看懂当前市场在讲什么
+- 能判断现在该不该看、该不该等、该不该做
+- 能解释为什么允许、为什么拒绝、为什么建议继续观察
+- 能把高价值机会、风险变化和待办动作按优先级推给人工
+- 能持续学习哪些 setup 值得盯，哪些 setup 只会制造噪声
+
+## 2. 明确边界
+
+本阶段明确不做：
+
+- 真实交易下单
+- OKX live submit
+- 实盘成交闭环
+- 带执行权限提升的自动化动作
+- 任何绕过人工确认的交易动作
+
+本阶段只做：
+
+- 分析
+- 解释
+- 提醒
+- 归纳
+- 复盘
+- 只读可视化
+
+## 3. 当前已具备的基础
+
+仓库里已经有的底座包括：
+
+- market truth / session / news / freshness
+- broker 只读同步
+- runtime scheduler / supervisor
+- decision packet
+- launch readiness / live readiness
+- alert dispatch / quality summary
+- operator digest
+- runtime incident journal
+- maintenance summary
+- visual watch / visual review / feedback
+- unattended harness / remote ops smoke
+
+所以接下来不需要再补“有无”的底层骨架，而是要补“一个分析助手该怎么组织这些能力”。
+
+## 4. 仍欠缺的核心功能
+
+### 4.1 统一分析助手总面板
+
+缺口：
+
+- 当前能力是分散的子面板，不像一个统一的分析助手
+- 缺少“今天该盯什么、为什么、优先级如何”的统一入口
+
+需要开发：
+
+- 一个 Analysis Cockpit / Decision Assistant 主面板
+- 把 market state、broker state、runtime、trust、decision packet、alerts、visual reviews、readiness 汇总到一屏
+- 输出“今日主叙事 / 主偏向 / 当前风险 / 当前可盯机会 / 当前不可做原因 / 下一步动作”
+
+### 4.2 决策解释与证据链
+
+缺口：
+
+- 现在能看到状态，但不够像一个可解释的分析助手
+- “为什么允许 / 为什么拒绝 / 证据是什么 / 哪一环没闭合”还不够统一
+
+需要开发：
+
+- 统一的 why-allowed / why-blocked / why-watch 解释块
+- 决策链路时间线
+- 关键证据引用
+- 变更对比：和上一次相比哪里变了
+- 失效条件与过期条件展示
+
+### 4.3 机会筛选与优先级排序
+
+缺口：
+
+- 有候选和决策包，但缺少“助手式排序”
+- 还没有把 setup 质量、上下文质量、时段、风险状态合成一个可读优先级
+
+需要开发：
+
+- setup ranking
+- watchlist 评分
+- no-trade reason 分类
+- 候选分层：强观察 / 弱观察 / 只留档 / 直接忽略
+- 面向人工的“最值得看 3 个机会”
+
+### 4.4 盘前 / 盘中 / 盘后分析工作流
+
+缺口：
+
+- 当前更像能力集合，缺一条固定工作流
+
+需要开发：
+
+- 盘前摘要
+- 盘中监控摘要
+- 盘后复盘摘要
+- 每日固定的分析节奏
+- session 级别的任务完成状态
+
+### 4.5 复盘与学习闭环
+
+缺口：
+
+- 已有 journal / incident / feedback，但还没有形成“分析助手的学习闭环”
+
+需要开发：
+
+- setup 级别复盘
+- 失败原因归因
+- 被忽略机会归因
+- 周度趋势总结
+- 逐 setup / 逐时段 / 逐市场状态的统计
+- 建议下一轮重点关注什么
+
+### 4.6 外部只读上下文补齐
+
+缺口：
+
+- 还缺更完整的只读市场上下文源
+
+需要开发：
+
+- 新闻日历 / 宏观日历的只读接入
+- 交易所公告 / 异常页的只读接入
+- 可选的外部 chart 只读源适配
+- 事件窗口 / 高波动窗口 / 低流动性窗口的统一呈现
+
+说明：
+
+- 这里不包含真实交易相关的 OKX 执行
+- 只做分析所需的只读上下文
+
+### 4.7 个性化提醒与摘要
+
+缺口：
+
+- 告警已经能发，但还不够像“分析助手给人的工作台”
+
+需要开发：
+
+- 今日要点 digest
+- 只推高价值变更
+- 用户偏好配置
+- 不同重要度的提醒模板
+- 可回看的摘要历史
+
+### 4.8 分析助手 UI 完整度
+
+缺口：
+
+- 目前是多个页面/面板并存，缺少一个更适合分析的连续工作区
+
+需要开发：
+
+- Decision Assistant 主视图
+- Watchlist 区
+- Evidence 区
+- Notes / Journal 区
+- Digest 区
+- 复盘入口
+
+## 5. 建议开发顺序
+
+### Phase A：统一分析 cockpit
+
+目标：
+
+- 让用户先在一个地方看懂“现在该看什么”
+
+产出：
+
+- 主面板
+- 今日摘要
+- 风险总览
+- 候选优先级
+
+验收：
+
+- 进入系统后 30 秒内能判断当天主线
+- 能直接看到当前最值得盯的机会和不该做的原因
+
+### Phase B：解释与证据链
+
+目标：
+
+- 让每个推荐都有可追踪的理由
+
+产出：
+
+- why-allowed / why-blocked
+- 决策链路时间线
+- 证据引用
+- 变化对比
+
+验收：
+
+- 用户能从页面直接复述“为什么现在看 / 为什么现在不做”
+
+### Phase C：机会排序与 watchlist
+
+目标：
+
+- 从“有很多状态”升级到“知道先看哪个”
+
+产出：
+
+- setup ranking
+- watchlist 分层
+- no-trade 原因分类
+- 机会优先级分数
+
+验收：
+
+- 同一时刻只能有少量真正优先项
+- 噪声候选不会淹没主机会
+
+### Phase D：复盘与学习闭环
+
+目标：
+
+- 让助手持续变得更会筛选
+
+产出：
+
+- setup 复盘
+- session 复盘
+- 周度趋势总结
+- 常见失败模式统计
+
+验收：
+
+- 用户能看到“哪些模式有效，哪些模式只是在制造噪声”
+
+### Phase E：只读外部上下文扩展
+
+目标：
+
+- 把更多市场背景补进分析助手
+
+产出：
+
+- 新闻/宏观日历
+- 公告页
+- 可选外部 chart 只读源
+
+验收：
+
+- 事件窗口、公告窗口、异常窗口能直接影响分析优先级
+
+### Phase F：提醒与摘要硬化
+
+目标：
+
+- 让分析助手更像日常可用的值班助手
+
+产出：
+
+- 个性化 digest
+- 高价值提醒
+- 历史摘要
+
+验收：
+
+- 用户不会被低价值重复提醒淹没
+
+## 6. 建议优先级
+
+### P0
+
+- 统一分析 cockpit
+- 解释与证据链
+- 机会排序与 watchlist
+
+### P1
+
+- 盘前 / 盘中 / 盘后分析工作流
+- 复盘与学习闭环
+- 个性化提醒与摘要
+
+### P2
+
+- 外部只读上下文扩展
+- UI 完整度增强
+
+## 7. 交付标准
+
+一套合格的“实盘交易分析决策助手”，至少要满足：
+
+1. 能告诉用户当前市场主叙事是什么
+2. 能告诉用户当前哪些机会值得盯
+3. 能告诉用户为什么现在不该做
+4. 能告诉用户当前风险在哪
+5. 能告诉用户应该先看哪一单、哪一类 setup
+6. 能在盘前、盘中、盘后形成连续工作流
+7. 能把复盘结论持续反馈回下一轮分析
+
+## 8. 不做事项
+
+- 不做真实交易下单
+- 不做 OKX live 交易执行
+- 不做自动提高执行权限
+- 不做绕过人工确认的决策替代
+
+## 9. 结论
+
+下一阶段最值得做的，不是再加更多 ICT 术语识别器，而是把现有的事实层、告警层、视觉层、复盘层统一成一个真正可用的分析决策助手。
+
+如果把它做成产品，核心不是“会不会下单”，而是：
+
+- 你是否一眼知道今天该看什么
+- 你是否一眼知道为什么不做
+- 你是否一眼知道下一步该补什么证据
diff --git a/docs/AI_ICT_盯盘告警架构图_v1.md b/docs/AI_ICT_盯盘告警架构图_v1.md
index b552c23..8c61c63 100644
--- a/docs/AI_ICT_盯盘告警架构图_v1.md
+++ b/docs/AI_ICT_盯盘告警架构图_v1.md
@@ -119,7 +119,7 @@
## 5. 额外吸收的实施边界
-结合 `AI_ICT_视觉盯盘与主动告警增强方案_v1.md`，补充以下边界：
+结合 `AI_ICT_盯盘告警技术设计稿_v1.md`，补充以下边界：
- 视觉层除了看图表，也适合看新闻日历页、公告页、工作台状态页
- 视觉层适合处理“候选接近可执行”“系统异常巡检”“session 切换复核”三类高价值场景
diff --git a/docs/AI_ICT_自动化流程使用教程_v1.md b/docs/AI_ICT_自动化流程使用教程_v1.md
index 981ee58..526eafc 100644
--- a/docs/AI_ICT_自动化流程使用教程_v1.md
+++ b/docs/AI_ICT_自动化流程使用教程_v1.md
@@ -35,6 +35,23 @@ P4: state writeback renderer
P7: monitor maturity rollup and bounded next-slice routing
```
+## 0.1 Default Kickoff Prompt
+
+Going forward, the default operator kickoff prompt for this repository is:
+
+```text
+启动 D:\ai-exchange 自动化流程。
+请先读取并以这些锚点为准：
+1. docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md
+2. SESSION.md
+3. TASKS.md
+全程按自动化闭环执行：读取锚点 -> 选择最小切片 -> 实现 -> focused validation -> 必要质量门 -> 必要时远端 deploy/smoke -> 回写 SESSION.md / TASKS.md。
+遇到真实外部阻塞再停下来说明，run-until-blocked。
+```
+
+`docs/AI_ICT_全自动开发执行流程_v1.md` remains the protocol specification, but it is no longer required in the user-facing kickoff prompt.
+`docs/AI_ICT_TradingAgents_P0-P6合理性审查与优化结论_v1.md` remains as a historical governance-freeze reference and is not part of the default kickoff anchors anymore.
+
## 1. 文档目标
本文档用于说明当前项目内自动化编排工具的用途、使用方式、运行模式、repair bridge 协议、常见命令和实践建议，作为后续学习和日常使用手册。
@@ -55,7 +72,7 @@ scripts/run_monitoring_alert_rollout.py
相关配套文档：
-- `docs/AI_ICT_下一阶段开发任务清单_v1.md`
+- `docs/archive/AI_ICT_下一阶段开发任务清单_v1.md`
- `docs/AI_ICT_repair_bridge_protocol_v1.md`
这个脚本的目标不是直接替你完成所有业务开发，而是把当前项目的阶段推进流程固化为：
diff --git a/docs/AI_ICT_TradingAgents融合实施方案_v1.md b/docs/archive/AI_ICT_TradingAgents融合实施方案_v1.md
similarity index 100%
rename from docs/AI_ICT_TradingAgents融合实施方案_v1.md
rename to docs/archive/AI_ICT_TradingAgents融合实施方案_v1.md
diff --git a/docs/AI_ICT_下一阶段开发任务清单_v1.md b/docs/archive/AI_ICT_下一阶段开发任务清单_v1.md
similarity index 99%
rename from docs/AI_ICT_下一阶段开发任务清单_v1.md
rename to docs/archive/AI_ICT_下一阶段开发任务清单_v1.md
index 5817eda..361754a 100644
--- a/docs/AI_ICT_下一阶段开发任务清单_v1.md
+++ b/docs/archive/AI_ICT_下一阶段开发任务清单_v1.md
@@ -447,7 +447,7 @@
- `session_focus_watch`
### 主要文件
-- `docs/AI_ICT_视觉盯盘与主动告警增强方案_v1.md`
+- `docs/AI_ICT_盯盘告警架构图_v1.md`
- `docs/AI_ICT_盯盘告警技术设计稿_v1.md`
- `docs/AI_ICT_盯盘告警开发任务清单_v1.md`
diff --git a/docs/AI_ICT_下一阶段开发计划_v1.md b/docs/archive/AI_ICT_下一阶段开发计划_v1.md
similarity index 100%
rename from docs/AI_ICT_下一阶段开发计划_v1.md
rename to docs/archive/AI_ICT_下一阶段开发计划_v1.md
diff --git a/docs/AI_ICT_方案一系统边界图与模块融合图_v1.md b/docs/archive/AI_ICT_方案一系统边界图与模块融合图_v1.md
similarity index 100%
rename from docs/AI_ICT_方案一系统边界图与模块融合图_v1.md
rename to docs/archive/AI_ICT_方案一系统边界图与模块融合图_v1.md
diff --git a/scripts/run_monitoring_alert_rollout.py b/scripts/run_monitoring_alert_rollout.py
index d07bfa2..9deb845 100644
--- a/scripts/run_monitoring_alert_rollout.py
+++ b/scripts/run_monitoring_alert_rollout.py
@@ -14,7 +14,7 @@ if str(ROOT) not in sys.path:
sys.path.insert(0, str(ROOT))
STATUS_PATH = ROOT / ".claude" / "monitoring_alert_rollout_status.json"
-DOC_PATH = ROOT / "docs" / "AI_ICT_下一阶段开发任务清单_v1.md"
+DOC_PATH = ROOT / "docs" / "archive" / "AI_ICT_下一阶段开发任务清单_v1.md"
SESSION_PATH = ROOT / "SESSION.md"
TASKS_PATH = ROOT / "TASKS.md"
ENV_LOCAL_PATH = ROOT / ".env.local"
@@ -256,7 +256,7 @@ PHASES = [
"N4-2 仅做代码预留点",
],
"files": [
- "docs/AI_ICT_视觉盯盘与主动告警增强方案_v1.md",
+ "docs/AI_ICT_盯盘告警架构图_v1.md",
"docs/AI_ICT_盯盘告警技术设计稿_v1.md",
"docs/AI_ICT_盯盘告警开发任务清单_v1.md",
"src/services/decision/decision_packet_service.py",
diff --git a/scripts/run_until_blocked.py b/scripts/run_until_blocked.py
index 886bc98..225da02 100644
--- a/scripts/run_until_blocked.py
+++ b/scripts/run_until_blocked.py
@@ -113,8 +113,7 @@ AUTOMATION_EXECUTION_GATE_STAGE = "automation_execution_layer:P6_governance_tick
AUTOMATION_EXECUTION_GATE_CLOSED_STATE = "automation_execution_layer_p0_governance_gate_closed"
AUTOMATION_EXECUTION_NEXT_SLICE = "automation_execution_layer_p1_anchor_slice_selector"
AUTOMATION_REQUIRED_DOC_PATHS = (
- ROOT / "docs" / "AI_ICT_全自动开发执行流程_v1.md",
- ROOT / "docs" / "AI_ICT_TradingAgents_P0-P6合理性审查与优化结论_v1.md",
+ ROOT / "docs" / "AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md",
)
AUTOMATION_EXECUTION_SLICE_SEQUENCE = [
("automation_execution_layer_p1_anchor_slice_selector", "Anchor-driven smallest-slice selector"),
@@ -3345,7 +3344,7 @@ def render_session(completed_stages: list[str], current_stage: Optional[str], la
"",
"## Current Goal",
"",
- "从现在开始按全自动模式持续推进交易模块主线，不再等待阶段确认，直到完成 `docs/AI_ICT_实盘辅助决策系统开发方案_v2.md` 中剩余开发目标。",
+ "启动 `D:\\ai-exchange` 自动化流程。请先读取并以 `docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md`、`SESSION.md`、`TASKS.md` 为准，全程按自动化闭环执行，遇到真实外部阻塞再停下来说明。",
"",
"## Current Branch",
"",
@@ -3361,10 +3360,13 @@ def render_session(completed_stages: list[str], current_stage: Optional[str], la
"",
"## Current Working State",
"",
- "- 当前主线执行协议已固化到：",
+ "- 当前默认启动锚点为：",
+ " - `docs/AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md`",
+ " - `SESSION.md`",
+ " - `TASKS.md`",
+ "- 当前执行协议说明保留在：",
" - `docs/AI_ICT_全自动开发执行流程_v1.md`",
- "- 当前阶段队列来源于：",
- " - `docs/AI_ICT_实盘辅助决策系统开发方案_v2.md`",
+ "- `docs/AI_ICT_TradingAgents_P0-P6合理性审查与优化结论_v1.md` 仅作为已完成冻结决策的历史治理参考，不再是每次启动必读锚点。",
"- 默认不再等待用户中途确认",
"- 每阶段必须完成：",
" - 实现",
diff --git a/src/services/market_state/exchange_status_provider.py b/src/services/market_state/exchange_status_provider.py
new file mode 100644
index 0000000..43c2818
--- /dev/null
+++ b/src/services/market_state/exchange_status_provider.py
@@ -0,0 +1,157 @@
+from __future__ import annotations
+
+from datetime import datetime, timezone
+from os import getenv
+from typing import Optional
+
+import httpx
+
+from src.config.settings import settings
+
+
+DEFAULT_OKX_SYSTEM_STATUS_URL = "https://www.okx.com/api/v5/system/status"
+DEFAULT_OKX_ANNOUNCEMENTS_URL = "https://www.okx.com/api/v5/support/announcements"
+
+
+class OkxSystemStatusProvider:
+ provider_name = "okx_system_status"
+
+ def __init__(
+ self,
+ *,
+ url: Optional[str] = None,
+ timeout: Optional[int] = None,
+ proxy_url: Optional[str] = None,
+ ) -> None:
+ self.url = url or DEFAULT_OKX_SYSTEM_STATUS_URL
+ self.timeout = timeout or settings.news_provider_timeout
+ self.proxy_url = proxy_url or settings.market_truth_proxy_url or getenv("HTTPS_PROXY") or getenv("HTTP_PROXY") or ""
+
+ def fetch_snapshot(self, *, now: Optional[datetime] = None, payload: Optional[dict] = None) -> dict:
+ observed_now = now or datetime.now(timezone.utc)
+ try:
+ data = dict(payload or self._fetch_payload())
+ events = [dict(item or {}) for item in list(data.get("data") or []) if isinstance(item, dict)]
+ active_events = [_normalize_status_event(item) for item in events if _status_event_is_active(item)]
+ return {
+ "provider_name": self.provider_name,
+ "fetched_ts": observed_now.isoformat(),
+ "status": "active" if active_events else "idle",
+ "event_count": len(events),
+ "active_events": active_events[:6],
+ "error": None,
+ "raw_payload": data,
+ }
+ except Exception as exc:
+ return {
+ "provider_name": self.provider_name,
+ "fetched_ts": observed_now.isoformat(),
+ "status": "error",
+ "event_count": 0,
+ "active_events": [],
+ "error": str(exc),
+ "raw_payload": {},
+ }
+
+ def _fetch_payload(self) -> dict:
+ kwargs = {"timeout": self.timeout, "follow_redirects": True}
+ if self.proxy_url:
+ kwargs["proxy"] = self.proxy_url
+ kwargs["trust_env"] = False
+ with httpx.Client(**kwargs) as client:
+ response = client.get(self.url, headers={"User-Agent": "ai-ict/exchange-status"})
+ response.raise_for_status()
+ payload = response.json()
+ if not isinstance(payload, dict):
+ raise ValueError("unsupported_okx_system_status_payload")
+ return payload
+
+
+def _status_event_is_active(row: dict) -> bool:
+ state = str(row.get("state") or "").lower()
+ return state in {"scheduled", "ongoing", "pre_open"}
+
+
+def _normalize_status_event(row: dict) -> dict:
+ return {
+ "title": str(row.get("title") or row.get("serviceType") or "system_status_event"),
+ "state": str(row.get("state") or "unknown"),
+ "begin": str(row.get("begin") or ""),
+ "end": str(row.get("end") or ""),
+ "description": str(row.get("scheDesc") or row.get("description") or ""),
+ "service_type": str(row.get("serviceType") or ""),
+ }
+
+
+class OkxAnnouncementsProvider:
+ provider_name = "okx_announcements"
+
+ def __init__(
+ self,
+ *,
+ url: Optional[str] = None,
+ timeout: Optional[int] = None,
+ proxy_url: Optional[str] = None,
+ language: str = "en-US",
+ ) -> None:
+ self.url = url or DEFAULT_OKX_ANNOUNCEMENTS_URL
+ self.timeout = timeout or settings.news_provider_timeout
+ self.proxy_url = proxy_url or settings.market_truth_proxy_url or getenv("HTTPS_PROXY") or getenv("HTTP_PROXY") or ""
+ self.language = language
+
+ def fetch_snapshot(self, *, payload: Optional[dict] = None, now: Optional[datetime] = None) -> dict:
+ observed_now = now or datetime.now(timezone.utc)
+ try:
+ data = dict(payload or self._fetch_payload())
+ details = []
+ for row in list(data.get("data") or []):
+ if isinstance(row, dict):
+ details.extend([dict(item or {}) for item in list(row.get("details") or []) if isinstance(item, dict)])
+ return {
+ "provider_name": self.provider_name,
+ "fetched_ts": observed_now.isoformat(),
+ "status": "active" if details else "idle",
+ "announcement_count": len(details),
+ "announcements": [_normalize_announcement_item(item) for item in details[:6]],
+ "error": None,
+ "raw_payload": data,
+ }
+ except Exception as exc:
+ return {
+ "provider_name": self.provider_name,
+ "fetched_ts": observed_now.isoformat(),
+ "status": "error",
+ "announcement_count": 0,
+ "announcements": [],
+ "error": str(exc),
+ "raw_payload": {},
+ }
+
+ def _fetch_payload(self) -> dict:
+ kwargs = {"timeout": self.timeout, "follow_redirects": True}
+ if self.proxy_url:
+ kwargs["proxy"] = self.proxy_url
+ kwargs["trust_env"] = False
+ with httpx.Client(**kwargs) as client:
+ response = client.get(
+ self.url,
+ headers={
+ "User-Agent": "ai-ict/exchange-announcements",
+ "Accept-Language": self.language,
+ },
+ )
+ response.raise_for_status()
+ payload = response.json()
+ if not isinstance(payload, dict):
+ raise ValueError("unsupported_okx_announcements_payload")
+ return payload
+
+
+def _normalize_announcement_item(row: dict) -> dict:
+ return {
+ "title": str(row.get("title") or "announcement"),
+ "ann_type": str(row.get("annType") or ""),
+ "published_ts": str(row.get("pTime") or ""),
+ "business_published_ts": str(row.get("businessPTime") or ""),
+ "url": str(row.get("url") or ""),
+ }
diff --git a/src/services/operator/__init__.py b/src/services/operator/__init__.py
index 7464ced..1c136dd 100644
--- a/src/services/operator/__init__.py
+++ b/src/services/operator/__init__.py
@@ -1,10 +1,12 @@
from src.services.operator.operator_workspace_service import (
+ DecisionAssistantSummaryService,
OperatorNoteService,
ReviewPacketService,
SessionFocusService,
)
__all__ = [
+ "DecisionAssistantSummaryService",
"OperatorNoteService",
"ReviewPacketService",
"SessionFocusService",
diff --git a/src/services/operator/operator_workspace_service.py b/src/services/operator/operator_workspace_service.py
index 95aa741..670916b 100644
--- a/src/services/operator/operator_workspace_service.py
+++ b/src/services/operator/operator_workspace_service.py
@@ -10,7 +10,9 @@ from src.repositories.trade_plan_candidate_repository import TradePlanCandidateR
from src.services.decision import DecisionPacketService
from src.services.execution import ExecutionService
from src.services.journal import WeeklyReviewService
-from src.services.alerts import AlertQualitySummaryService
+from src.services.alerts import AlertQualitySummaryService, OperatorDigestService
+from src.services.market_state.exchange_status_provider import OkxAnnouncementsProvider, OkxSystemStatusProvider
+from src.services.readiness import LaunchReadinessService
from src.services.research_council import CouncilOptimizationReviewService, ResearchCouncilService
from src.services.trust import ModelTrustService
@@ -113,6 +115,75 @@ class SessionFocusService:
}
+class DecisionAssistantSummaryService:
+ def __init__(
+ self,
+ *,
+ launch_readiness_service: Optional[LaunchReadinessService] = None,
+ session_focus_service: Optional[SessionFocusService] = None,
+ operator_digest_service: Optional[OperatorDigestService] = None,
+ decision_repository: Optional[DecisionPacketRepository] = None,
+ candidate_repository: Optional[TradePlanCandidateRepository] = None,
+ research_council_service: Optional[ResearchCouncilService] = None,
+ exchange_status_provider: Optional[OkxSystemStatusProvider] = None,
+ announcements_provider: Optional[OkxAnnouncementsProvider] = None,
+ ) -> None:
+ self.launch_readiness_service = launch_readiness_service or LaunchReadinessService()
+ self.session_focus_service = session_focus_service or SessionFocusService()
+ self.operator_digest_service = operator_digest_service or OperatorDigestService()
+ self.decision_repository = decision_repository or DecisionPacketRepository()
+ self.candidate_repository = candidate_repository or TradePlanCandidateRepository()
+ self.research_council_service = research_council_service or ResearchCouncilService()
+ self.exchange_status_provider = exchange_status_provider or OkxSystemStatusProvider()
+ self.announcements_provider = announcements_provider or OkxAnnouncementsProvider()
+
+ def build_summary(
+ self,
+ *,
+ instrument_id: int,
+ timeframe: str = "1m",
+ decision_packet: Optional[dict] = None,
+ weekly_review: Optional[dict] = None,
+ recent_alerts: Optional[list[dict]] = None,
+ recent_notes: Optional[list[dict]] = None,
+ recent_executions: Optional[list[dict]] = None,
+ ) -> dict:
+ launch_readiness = self.launch_readiness_service.build_summary(instrument_id=instrument_id, timeframe=timeframe)
+ session_focus = self.session_focus_service.build_summary(instrument_id=instrument_id, limit=6)
+ operator_digest = self.operator_digest_service.build_digest(instrument_id=instrument_id, top_limit=5)
+ previous_packet = self.decision_repository.fetch_latest_decision_packet(instrument_id=instrument_id)
+ recent_candidates = self.candidate_repository.fetch_recent_candidates(instrument_id=instrument_id, limit=20)
+ exchange_status = self.exchange_status_provider.fetch_snapshot()
+ announcements_snapshot = self.announcements_provider.fetch_snapshot()
+ fallback_session_code = str(
+ (((launch_readiness.get("market_state") or {}).get("session") or {}).get("session_code") or "")
+ )
+ setup_learning_rollup = _build_decision_assistant_setup_learning_rollup(
+ decision_packet=dict(decision_packet or {}),
+ weekly_review=dict(weekly_review or {}),
+ research_council_service=self.research_council_service,
+ instrument_id=instrument_id,
+ fallback_session_code=fallback_session_code or None,
+ )
+ return build_decision_assistant_summary(
+ instrument_id=instrument_id,
+ timeframe=timeframe,
+ decision_packet=dict(decision_packet or {}),
+ weekly_review=dict(weekly_review or {}),
+ launch_readiness=dict(launch_readiness or {}),
+ session_focus=dict(session_focus or {}),
+ operator_digest=dict(operator_digest or {}),
+ previous_packet=dict(previous_packet or {}),
+ recent_candidates=list(recent_candidates or []),
+ exchange_status=dict(exchange_status or {}),
+ announcements_snapshot=dict(announcements_snapshot or {}),
+ setup_learning_rollup=dict(setup_learning_rollup or {}),
+ recent_alerts=list(recent_alerts or []),
+ recent_notes=list(recent_notes or []),
+ recent_executions=list(recent_executions or []),
+ )
+
+
class ReviewPacketService:
def __init__(
self,
@@ -126,6 +197,7 @@ class ReviewPacketService:
alert_quality_summary_service: Optional[AlertQualitySummaryService] = None,
research_council_service: Optional[ResearchCouncilService] = None,
optimization_review_service: Optional[CouncilOptimizationReviewService] = None,
+ decision_assistant_service: Optional[DecisionAssistantSummaryService] = None,
) -> None:
self.note_service = note_service or OperatorNoteService()
self.decision_service = decision_service or DecisionPacketService()
@@ -138,23 +210,40 @@ class ReviewPacketService:
)
self.research_council_service = research_council_service or ResearchCouncilService()
self.optimization_review_service = optimization_review_service or CouncilOptimizationReviewService()
+ self.decision_assistant_service = decision_assistant_service or DecisionAssistantSummaryService(
+ research_council_service=self.research_council_service,
+ )
def build_packet(self, *, instrument_id: int) -> dict:
+ decision_packet = self.decision_service.build_preview(instrument_id=instrument_id)
+ weekly_review = self.weekly_review_service.build_summary(instrument_id=instrument_id)
+ recent_alerts = self.decision_repository.fetch_recent_decision_alerts(instrument_id=instrument_id, limit=10)
+ recent_notes = self.note_service.get_recent_notes(instrument_id=instrument_id, limit=10)
+ recent_executions = self.execution_service.build_execution_dashboard_items(instrument_id=instrument_id, limit=10)
research_council = self.research_council_service.get_latest_run(instrument_id=instrument_id)
optimization_review = self.optimization_review_service.build_review(instrument_id=instrument_id)
return {
"instrument_id": instrument_id,
"generated_ts": datetime.now(timezone.utc).isoformat(),
- "decision_packet": self.decision_service.build_preview(instrument_id=instrument_id),
- "recent_alerts": self.decision_repository.fetch_recent_decision_alerts(instrument_id=instrument_id, limit=10),
- "recent_notes": self.note_service.get_recent_notes(instrument_id=instrument_id, limit=10),
- "recent_executions": self.execution_service.build_execution_dashboard_items(instrument_id=instrument_id, limit=10),
- "weekly_review": self.weekly_review_service.build_summary(instrument_id=instrument_id),
+ "decision_packet": decision_packet,
+ "recent_alerts": recent_alerts,
+ "recent_notes": recent_notes,
+ "recent_executions": recent_executions,
+ "weekly_review": weekly_review,
"alert_quality_summary": self.alert_quality_summary_service.build_summary(instrument_id=instrument_id),
"trust_score": self.trust_service.build_summary(instrument_id=instrument_id),
"research_council": research_council,
"research_council_summary": _build_research_council_summary(research_council),
"research_council_optimization_review": optimization_review,
+ "decision_assistant_summary": self.decision_assistant_service.build_summary(
+ instrument_id=instrument_id,
+ timeframe="1m",
+ decision_packet=decision_packet,
+ weekly_review=weekly_review,
+ recent_alerts=recent_alerts,
+ recent_notes=recent_notes,
+ recent_executions=recent_executions,
+ ),
}
@@ -175,6 +264,2270 @@ def _build_research_council_summary(run: Optional[dict]) -> dict:
}
+def build_decision_assistant_summary(
+ *,
+ instrument_id: int,
+ timeframe: str,
+ decision_packet: dict,
+ weekly_review: dict,
+ launch_readiness: dict,
+ session_focus: dict,
+ operator_digest: dict,
+ previous_packet: dict,
+ recent_candidates: list[dict],
+ exchange_status: dict,
+ announcements_snapshot: dict,
+ setup_learning_rollup: dict,
+ recent_alerts: list[dict],
+ recent_notes: list[dict],
+ recent_executions: list[dict],
+) -> dict:
+ market_state = dict(launch_readiness.get("market_state") or {})
+ session_state = dict(market_state.get("session") or {})
+ freshness = dict(market_state.get("freshness") or {})
+ stream_state = dict(market_state.get("stream_state") or {})
+ gap_state = dict(market_state.get("data_gap_state") or {})
+ decision_action = str(decision_packet.get("action_recommendation") or "monitor")
+ decision_status = str(decision_packet.get("status") or "unknown")
+ why_blocked = [str(item) for item in list(decision_packet.get("why_blocked") or []) if item]
+ why_allowed = [str(item) for item in list(decision_packet.get("why_allowed") or []) if item]
+ launch_blockers = [str(item) for item in list(launch_readiness.get("blocking_reasons") or []) if item]
+ launch_warnings = [str(item) for item in list(launch_readiness.get("warning_reasons") or []) if item]
+ current_session = str(session_state.get("session_code") or "OFF_HOURS")
+ market_posture = _build_market_posture(
+ current_session=current_session,
+ session_state=session_state,
+ freshness=freshness,
+ stream_state=stream_state,
+ gap_state=gap_state,
+ decision_packet=decision_packet,
+ )
+ focus_mode = _resolve_decision_assistant_focus_mode(
+ launch_blockers=launch_blockers,
+ decision_action=decision_action,
+ operator_digest=operator_digest,
+ session_focus=session_focus,
+ )
+ no_trade_reason_items = _build_no_trade_reason_items(
+ why_blocked=why_blocked,
+ launch_blockers=launch_blockers,
+ launch_warnings=launch_warnings,
+ )
+ no_trade_reasons = [str(item.get("reason") or "") for item in no_trade_reason_items][:6]
+ risk_posture = _build_risk_posture(
+ no_trade_reason_items=no_trade_reason_items,
+ decision_status=decision_status,
+ )
+ top_priority_items = _build_decision_assistant_priority_items(
+ decision_packet=decision_packet,
+ session_focus=session_focus,
+ operator_digest=operator_digest,
+ )
+ candidate_watchlist_tiers = _build_candidate_watchlist_tiers(
+ candidates=recent_candidates,
+ session_focus=session_focus,
+ current_candidate_id=decision_packet.get("candidate_id"),
+ )
+ candidate_watchlist_summary = _build_candidate_watchlist_summary(candidate_watchlist_tiers)
+ workflow_digest = _build_decision_assistant_workflow_digest(
+ launch_readiness=launch_readiness,
+ decision_packet=decision_packet,
+ session_focus=session_focus,
+ operator_digest=operator_digest,
+ weekly_review=weekly_review,
+ top_priority_items=top_priority_items,
+ current_session=current_session,
+ )
+ next_actions = _build_decision_assistant_next_actions(
+ launch_readiness=launch_readiness,
+ decision_packet=decision_packet,
+ weekly_review=weekly_review,
+ operator_digest=operator_digest,
+ top_priority_items=top_priority_items,
+ )
+ headline = _build_decision_assistant_headline(
+ focus_mode=focus_mode,
+ current_session=current_session,
+ decision_packet=decision_packet,
+ market_posture=market_posture,
+ no_trade_reasons=no_trade_reasons,
+ top_priority_items=top_priority_items,
+ )
+ attention_reason = _build_attention_reason(
+ focus_mode=focus_mode,
+ why_allowed=why_allowed,
+ no_trade_reasons=no_trade_reasons,
+ operator_digest=operator_digest,
+ )
+ changed_since_last = _build_decision_assistant_change_summary(
+ current_packet=decision_packet,
+ previous_packet=previous_packet,
+ )
+ evidence_chain = _build_decision_assistant_evidence_chain(
+ launch_readiness=launch_readiness,
+ decision_packet=decision_packet,
+ operator_digest=operator_digest,
+ weekly_review=weekly_review,
+ current_session=current_session,
+ )
+ explanation_drilldown = _build_decision_assistant_explanation_drilldown(
+ decision_packet=decision_packet,
+ no_trade_reason_items=no_trade_reason_items,
+ operator_digest=operator_digest,
+ launch_readiness=launch_readiness,
+ )
+ reason_citations = _build_decision_assistant_reason_citations(
+ decision_packet=decision_packet,
+ no_trade_reason_items=no_trade_reason_items,
+ launch_readiness=launch_readiness,
+ )
+ learning_rollup = _build_decision_assistant_learning_rollup(
+ weekly_review=weekly_review,
+ operator_digest=operator_digest,
+ )
+ external_context_summary = _build_decision_assistant_external_context_summary(
+ launch_readiness=launch_readiness,
+ decision_packet=decision_packet,
+ exchange_status=exchange_status,
+ announcements_snapshot=announcements_snapshot,
+ )
+ assistant_top_opportunities = [item for item in top_priority_items if str(item.get("kind") or "") in {"decision_packet", "session_focus"}][:3]
+ review_entrypoints = _build_decision_assistant_review_entrypoints(
+ recent_notes=recent_notes,
+ weekly_review=weekly_review,
+ operator_digest=operator_digest,
+ focus_mode=focus_mode,
+ top_opportunities=assistant_top_opportunities,
+ no_trade_reason_items=no_trade_reason_items,
+ )
+ personalized_reminders = _build_decision_assistant_personalized_reminders(
+ top_opportunities=assistant_top_opportunities,
+ no_trade_reason_items=no_trade_reason_items,
+ operator_digest=operator_digest,
+ external_context_summary=external_context_summary,
+ learning_rollup=learning_rollup,
+ )
+ digest_history_preview = _build_decision_assistant_digest_history_preview(
+ personalized_reminders=personalized_reminders,
+ recent_alerts=recent_alerts,
+ recent_notes=recent_notes,
+ previous_packet=previous_packet,
+ )
+ digest_history_summary = _build_decision_assistant_digest_history_summary(digest_history_preview)
+ digest_history_groups = _build_decision_assistant_digest_history_groups(
+ digest_history_preview,
+ recommended_kind=digest_history_summary.get("recommended_kind"),
+ )
+ digest_surface = _build_decision_assistant_digest_surface(
+ focus_mode=focus_mode,
+ personalized_reminders=personalized_reminders,
+ learning_rollup=learning_rollup,
+ top_opportunities=assistant_top_opportunities,
+ digest_history_preview=digest_history_preview,
+ digest_history_summary=digest_history_summary,
+ )
+ decision_timeline = _build_decision_assistant_timeline(
+ decision_packet=decision_packet,
+ previous_packet=previous_packet,
+ recent_alerts=recent_alerts,
+ recent_notes=recent_notes,
+ recent_executions=recent_executions,
+ )
+ scan_summary_cards = _build_decision_assistant_scan_summary_cards(
+ top_opportunities=[item for item in top_priority_items if str(item.get("kind") or "") in {"decision_packet", "session_focus"}][:3],
+ no_trade_reason_items=no_trade_reason_items,
+ review_entrypoints=review_entrypoints,
+ external_context_summary=external_context_summary,
+ digest_surface=digest_surface,
+ )
+ scan_completion_summary = _build_decision_assistant_scan_completion_summary(scan_summary_cards)
+ return {
+ "instrument_id": instrument_id,
+ "timeframe": timeframe,
+ "generated_ts": datetime.now(timezone.utc).isoformat(),
+ "status": "completed",
+ "focus_mode": focus_mode,
+ "headline": headline,
+ "market_posture": market_posture,
+ "risk_posture": risk_posture,
+ "attention_reason": attention_reason,
+ "current_session": current_session,
+ "changed_since_last": changed_since_last,
+ "top_priority_items": top_priority_items,
+ "top_opportunities": assistant_top_opportunities,
+ "candidate_watchlist_tiers": candidate_watchlist_tiers,
+ "candidate_watchlist_summary": candidate_watchlist_summary,
+ "workflow_digest": workflow_digest,
+ "evidence_chain": evidence_chain,
+ "explanation_drilldown": explanation_drilldown,
+ "reason_citations": reason_citations,
+ "learning_rollup": learning_rollup,
+ "external_context_summary": external_context_summary,
+ "review_entrypoints": review_entrypoints,
+ "personalized_reminders": personalized_reminders,
+ "digest_history_preview": digest_history_preview,
+ "digest_history_summary": digest_history_summary,
+ "digest_history_groups": digest_history_groups,
+ "digest_surface": digest_surface,
+ "setup_learning_rollup": setup_learning_rollup,
+ "decision_timeline": decision_timeline,
+ "scan_summary_cards": scan_summary_cards,
+ "scan_completion_summary": scan_completion_summary,
+ "no_trade_reason_items": no_trade_reason_items,
+ "no_trade_reasons": no_trade_reasons,
+ "next_actions": next_actions,
+ "evidence_summary": {
+ "decision_status": decision_status,
+ "decision_action": decision_action,
+ "trust_classification": decision_packet.get("trust_classification"),
+ "operator_mode": decision_packet.get("operator_mode"),
+ "market_freshness": freshness.get("code"),
+ "stream_status": stream_state.get("code"),
+ "data_gap_status": gap_state.get("code"),
+ "launch_mode": launch_readiness.get("product_operating_mode"),
+ "digest_status": operator_digest.get("status"),
+ "high_value_alert_count": ((operator_digest.get("summary_counts") or {}).get("high_value_alert_count") or 0),
+ },
+ }
+
+
+def _build_market_posture(
+ *,
+ current_session: str,
+ session_state: dict,
+ freshness: dict,
+ stream_state: dict,
+ gap_state: dict,
+ decision_packet: dict,
+) -> str:
+ parts = [
+ current_session,
+ str(session_state.get("status") or "unknown"),
+ str(freshness.get("code") or "freshness_unknown"),
+ ]
+ if stream_state.get("code"):
+ parts.append(str(stream_state.get("code")))
+ if gap_state.get("code") and str(gap_state.get("code")) != "market_data_gap_clear":
+ parts.append(str(gap_state.get("code")))
+ if decision_packet.get("bias"):
+ parts.append(f"bias={decision_packet.get('bias')}")
+ return " | ".join(parts)
+
+
+def _resolve_decision_assistant_focus_mode(
+ *,
+ launch_blockers: list[str],
+ decision_action: str,
+ operator_digest: dict,
+ session_focus: dict,
+) -> str:
+ if launch_blockers:
+ return "stabilize_analysis_surface"
+ if decision_action in {"build_execution_ticket", "confirm_ticket", "manage_ticket"}:
+ return "review_top_setup"
+ queue = list(operator_digest.get("review_queue") or [])
+ if queue:
+ return "clear_review_queue"
+ for item in list(session_focus.get("items") or []):
+ if str(item.get("action_hint") or "") in {"review_alerts", "focus_now"}:
+ return "watch_priority_sessions"
+ return "monitor_and_wait"
+
+
+def _build_risk_posture(*, no_trade_reason_items: list[dict], decision_status: str) -> str:
+ hard_blockers = [str(item.get("reason") or "") for item in no_trade_reason_items if item.get("priority") == "high"]
+ advisory = [str(item.get("reason") or "") for item in no_trade_reason_items if item.get("priority") != "high"]
+ if hard_blockers:
+ return f"{decision_status}: {', '.join(hard_blockers[:3])}"
+ if advisory:
+ return f"{decision_status}: advisory {', '.join(advisory[:2])}"
+ return f"{decision_status}: no active no-trade blocker"
+
+
+def _build_decision_assistant_priority_items(*, decision_packet: dict, session_focus: dict, operator_digest: dict) -> list[dict]:
+ items: list[dict] = []
+ candidate_id = decision_packet.get("candidate_id")
+ if candidate_id is not None or decision_packet.get("narrative") or decision_packet.get("ai_explanation"):
+ items.append(
+ {
+ "kind": "decision_packet",
+ "title": f"Candidate #{candidate_id}" if candidate_id is not None else "Decision packet",
+ "status": decision_packet.get("action_recommendation") or decision_packet.get("status") or "monitor",
+ "detail": decision_packet.get("ai_explanation") or decision_packet.get("narrative") or "decision_packet_available",
+ "reason": ", ".join(list(decision_packet.get("why_allowed") or [])[:3]) or ", ".join(list(decision_packet.get("why_blocked") or [])[:3]) or "decision_packet_context",
+ "priority_score": _decision_packet_priority_score(decision_packet),
+ }
+ )
+ focus_items = sorted(
+ list(session_focus.get("items") or []),
+ key=lambda item: (
+ str(item.get("action_hint") or "") == "review_alerts",
+ str(item.get("action_hint") or "") == "focus_now",
+ int(item.get("alert_count") or 0),
+ int(item.get("executable_candidate_count") or 0),
+ float((item.get("session_recommendation") or {}).get("score") or 0.0),
+ ),
+ reverse=True,
+ )
+ for item in focus_items[:2]:
+ recommendation = dict(item.get("session_recommendation") or {})
+ items.append(
+ {
+ "kind": "session_focus",
+ "title": f"Session {item.get('session_code') or 'unknown'}",
+ "status": recommendation.get("classification") or item.get("status") or "observe",
+ "detail": f"alerts={item.get('alert_count') or 0}, executable={item.get('executable_candidate_count') or 0}, open={item.get('open_execution_count') or 0}",
+ "reason": item.get("action_hint") or "observe",
+ "priority_score": _session_focus_priority_score(item),
+ }
+ )
+ for item in list(operator_digest.get("review_queue") or [])[:2]:
+ items.append(
+ {
+ "kind": "review_queue",
+ "title": f"Queue {item.get('queue_type') or 'item'}",
+ "status": item.get("priority") or item.get("status") or "review",
+ "detail": item.get("reason") or item.get("label") or item.get("channel") or "digest_item",
+ "reason": f"subject={item.get('subject_type') or item.get('alert_type') or item.get('visual_state') or 'n/a'}",
+ "priority_score": _review_queue_priority_score(item),
+ }
+ )
+ return sorted(
+ items,
+ key=lambda item: (
+ float(item.get("priority_score") or 0.0),
+ str(item.get("title") or ""),
+ ),
+ reverse=True,
+ )[:5]
+
+
+def _build_candidate_watchlist_tiers(*, candidates: list[dict], session_focus: dict, current_candidate_id) -> dict:
+ session_items = {
+ str(item.get("session_code") or ""): dict(item)
+ for item in list(session_focus.get("items") or [])
+ if item
+ }
+ tiers = {
+ "review_now": [],
+ "strong_watch": [],
+ "soft_watch": [],
+ "archive_only": [],
+ }
+ for candidate in candidates:
+ session_item = session_items.get(str(candidate.get("session_code") or ""), {})
+ tier, tier_reason, priority_score = _classify_candidate_watchlist_tier(
+ candidate=candidate,
+ session_item=session_item,
+ current_candidate_id=current_candidate_id,
+ )
+ tiers[tier].append(
+ {
+ "candidate_id": candidate.get("id"),
+ "title": f"Candidate #{candidate.get('id')}",
+ "tier": tier,
+ "tier_reason": tier_reason,
+ "priority_score": priority_score,
+ "suggested_action": _candidate_tier_action_label(tier=tier, candidate_id=candidate.get("id")),
+ "action_path": _candidate_tier_action_path(tier=tier, candidate_id=candidate.get("id")),
+ "action_method": _candidate_tier_action_method(tier=tier),
+ "status": candidate.get("status") or candidate.get("candidate_state") or "unknown",
+ "session_code": candidate.get("session_code") or "OFF_HOURS",
+ "quality_score": candidate.get("quality_score"),
+ "model_code": candidate.get("model_code") or "n/a",
+ "detail": candidate.get("ai_explanation") or candidate.get("narrative") or "candidate_available",
+ "missing_confirmations": list(candidate.get("missing_confirmations") or []),
+ }
+ )
+ for key, rows in tiers.items():
+ rows.sort(
+ key=lambda item: (
+ float(item.get("priority_score") or 0.0),
+ float(item.get("quality_score") or 0.0),
+ int(item.get("candidate_id") or 0),
+ ),
+ reverse=True,
+ )
+ tiers[key] = rows[:5]
+ return tiers
+
+
+def _build_candidate_watchlist_summary(tiers: dict) -> dict:
+ return {
+ "review_now_count": len(list(tiers.get("review_now") or [])),
+ "strong_watch_count": len(list(tiers.get("strong_watch") or [])),
+ "soft_watch_count": len(list(tiers.get("soft_watch") or [])),
+ "archive_only_count": len(list(tiers.get("archive_only") or [])),
+ "top_review_candidate_id": (tiers.get("review_now") or [{}])[0].get("candidate_id") if list(tiers.get("review_now") or []) else None,
+ }
+
+
+def _classify_candidate_watchlist_tier(*, candidate: dict, session_item: dict, current_candidate_id) -> tuple[str, str, float]:
+ score = float(candidate.get("quality_score") or 0.0)
+ status = str(candidate.get("status") or candidate.get("candidate_state") or "")
+ action_hint = str(session_item.get("action_hint") or "")
+ recommendation = dict(session_item.get("session_recommendation") or {})
+ session_score = float(recommendation.get("score") or 0.0)
+ missing_count = len(list(candidate.get("missing_confirmations") or []))
+ priority_score = score
+ if candidate.get("id") == current_candidate_id:
+ priority_score += 120.0
+ return "review_now", "current_decision_packet_candidate", priority_score
+ if status == "executable" or bool(candidate.get("executable")):
+ priority_score += 100.0
+ return "review_now", "candidate_executable", priority_score
+ if action_hint in {"review_alerts", "focus_now"}:
+ priority_score += 20.0
+ if recommendation.get("classification") == "prefer":
+ priority_score += 10.0
+ if score >= 70.0 or (score >= 55.0 and action_hint == "focus_now"):
+ return "strong_watch", "quality_or_session_focus", priority_score
+ if score >= 40.0 or missing_count > 0 or recommendation.get("classification") == "prefer":
+ return "soft_watch", "needs_more_confirmation", priority_score
+ return "archive_only", "low_priority_or_low_context", priority_score
+
+
+def _candidate_tier_action_label(*, tier: str, candidate_id) -> str:
+ if tier == "review_now":
+ return f"run_confirm_check:{candidate_id}"
+ if tier == "strong_watch":
+ return f"monitor_confirmation:{candidate_id}"
+ if tier == "soft_watch":
+ return f"collect_missing_confirmation:{candidate_id}"
+ return f"archive_for_reference:{candidate_id}"
+
+
+def _candidate_tier_action_path(*, tier: str, candidate_id) -> str | None:
+ if candidate_id is None:
+ return None
+ if tier in {"review_now", "strong_watch"}:
+ return f"/trading/candidates/{candidate_id}/confirm-check"
+ return None
+
+
+def _candidate_tier_action_method(*, tier: str) -> str:
+ if tier in {"review_now", "strong_watch"}:
+ return "POST"
+ return "NONE"
+
+
+def _build_decision_assistant_next_actions(
+ *,
+ launch_readiness: dict,
+ decision_packet: dict,
+ weekly_review: dict,
+ operator_digest: dict,
+ top_priority_items: list[dict],
+) -> list[str]:
+ actions = _dedupe(
+ [
+ *[str(item) for item in list(launch_readiness.get("next_actions") or []) if item],
+ *[str(item) for item in list((decision_packet.get("research_council") or {}).get("follow_up_checks") or []) if item],
+ *[str(item) for item in list(weekly_review.get("next_week_actions") or []) if item],
+ ]
+ )
+ if not actions and top_priority_items:
+ actions = [f"review_{str(top_priority_items[0].get('kind') or 'assistant')}".lower()]
+ if list(operator_digest.get("review_queue") or []):
+ actions.append("clear_operator_review_queue")
+ return _dedupe(actions)[:5]
+
+
+def _build_decision_assistant_headline(
+ *,
+ focus_mode: str,
+ current_session: str,
+ decision_packet: dict,
+ market_posture: str,
+ no_trade_reasons: list[str],
+ top_priority_items: list[dict],
+) -> str:
+ if focus_mode == "stabilize_analysis_surface":
+ return f"Stabilize analysis surface before new decisions: {', '.join(no_trade_reasons[:2]) or market_posture}."
+ if focus_mode == "review_top_setup":
+ title = top_priority_items[0]["title"] if top_priority_items else "decision packet"
+ return f"Review {title} in {current_session}: {decision_packet.get('ai_explanation') or decision_packet.get('narrative') or 'setup ready for operator review'}."
+ if focus_mode == "clear_review_queue":
+ queue_item = top_priority_items[0]["title"] if top_priority_items else "review queue"
+ return f"Clear the highest-priority operator queue item first: {queue_item}."
+ if focus_mode == "watch_priority_sessions":
+ title = top_priority_items[0]["title"] if top_priority_items else current_session
+ return f"Watch the priority session flow now: {title}."
+ return f"Monitor {current_session} and wait for a cleaner setup. {market_posture}."
+
+
+def _build_attention_reason(*, focus_mode: str, why_allowed: list[str], no_trade_reasons: list[str], operator_digest: dict) -> str:
+ if focus_mode == "review_top_setup" and why_allowed:
+ return ", ".join(why_allowed[:3])
+ if focus_mode == "stabilize_analysis_surface" and no_trade_reasons:
+ return ", ".join(no_trade_reasons[:3])
+ queue = list(operator_digest.get("review_queue") or [])
+ if focus_mode == "clear_review_queue" and queue:
+ return f"{queue[0].get('queue_type') or 'queue'} requires review"
+ return "observe_only_until_context_improves"
+
+
+def _build_decision_assistant_change_summary(*, current_packet: dict, previous_packet: dict) -> dict:
+ previous_payload = dict(previous_packet.get("payload") or {})
+ if not previous_payload:
+ return {
+ "summary": "first_visible_snapshot",
+ "items": ["no_previous_decision_packet"],
+ "previous_packet_id": previous_packet.get("id"),
+ }
+
+ changes: list[str] = []
+ if str(previous_payload.get("action_recommendation") or "") != str(current_packet.get("action_recommendation") or ""):
+ changes.append(
+ f"action:{previous_payload.get('action_recommendation') or 'n/a'}->{current_packet.get('action_recommendation') or 'n/a'}"
+ )
+ if str(previous_payload.get("status") or "") != str(current_packet.get("status") or ""):
+ changes.append(f"status:{previous_payload.get('status') or 'n/a'}->{current_packet.get('status') or 'n/a'}")
+ if str(previous_payload.get("candidate_id") or "") != str(current_packet.get("candidate_id") or ""):
+ changes.append(f"candidate:{previous_payload.get('candidate_id') or 'none'}->{current_packet.get('candidate_id') or 'none'}")
+ if str(previous_payload.get("bias") or "") != str(current_packet.get("bias") or ""):
+ changes.append(f"bias:{previous_payload.get('bias') or 'n/a'}->{current_packet.get('bias') or 'n/a'}")
+ if str(previous_payload.get("trust_classification") or "") != str(current_packet.get("trust_classification") or ""):
+ changes.append(
+ f"trust:{previous_payload.get('trust_classification') or 'n/a'}->{current_packet.get('trust_classification') or 'n/a'}"
+ )
+ previous_blocked = set(_dedupe([str(item) for item in list(previous_payload.get("why_blocked") or [])]))
+ current_blocked = set(_dedupe([str(item) for item in list(current_packet.get("why_blocked") or [])]))
+ cleared = sorted(previous_blocked - current_blocked)
+ added = sorted(current_blocked - previous_blocked)
+ if cleared:
+ changes.append(f"blocked_cleared:{','.join(cleared[:3])}")
+ if added:
+ changes.append(f"blocked_added:{','.join(added[:3])}")
+ return {
+ "summary": changes[0] if changes else "no_material_change_from_latest_stored_packet",
+ "items": changes[:6] if changes else ["no_material_change_from_latest_stored_packet"],
+ "previous_packet_id": previous_packet.get("id"),
+ }
+
+
+def _build_decision_assistant_workflow_digest(
+ *,
+ launch_readiness: dict,
+ decision_packet: dict,
+ session_focus: dict,
+ operator_digest: dict,
+ weekly_review: dict,
+ top_priority_items: list[dict],
+ current_session: str,
+) -> list[dict]:
+ first_priority = top_priority_items[0] if top_priority_items else {}
+ return [
+ {
+ "phase": "pre_market",
+ "title": "Pre-Market",
+ "status": launch_readiness.get("product_operating_mode") or "unknown",
+ "detail": decision_packet.get("narrative") or decision_packet.get("bias") or "prepare_daily_context",
+ "next_step": (launch_readiness.get("next_actions") or ["prepare_context"])[0],
+ },
+ {
+ "phase": "intraday",
+ "title": "Intraday",
+ "status": current_session,
+ "detail": first_priority.get("title") or "monitor_current_session",
+ "next_step": first_priority.get("reason") or "watch_priority_sessions",
+ },
+ {
+ "phase": "post_trade",
+ "title": "Post-Trade",
+ "status": f"entries={weekly_review.get('entry_count') or 0}",
+ "detail": f"pending feedback={dict(operator_digest.get('summary_counts') or {}).get('pending_feedback_count', 0)}",
+ "next_step": (weekly_review.get("next_week_actions") or ["collect_review_notes"])[0],
+ },
+ ]
+
+
+def _build_decision_assistant_explanation_drilldown(
+ *,
+ decision_packet: dict,
+ no_trade_reason_items: list[dict],
+ operator_digest: dict,
+ launch_readiness: dict,
+) -> list[dict]:
+ council = dict(decision_packet.get("research_council") or {})
+ return [
+ {
+ "title": "Decision Logic",
+ "status": decision_packet.get("action_recommendation") or decision_packet.get("status") or "monitor",
+ "detail": decision_packet.get("ai_explanation") or decision_packet.get("narrative") or "decision_packet_available",
+ "reasons": list(decision_packet.get("why_allowed") or [])[:4],
+ },
+ {
+ "title": "Blockers And Guards",
+ "status": "blocked" if no_trade_reason_items else "clear",
+ "detail": ", ".join([str(item.get("reason") or "") for item in no_trade_reason_items[:3]]) or "no_active_blocker",
+ "reasons": [str(item.get("reason") or "") for item in no_trade_reason_items[:4]],
+ },
+ {
+ "title": "Watch Signals",
+ "status": "watch" if council.get("follow_up_checks") or council.get("recommended_watch_items") else "idle",
+ "detail": council.get("operator_summary") or council.get("research_summary") or "no_watch_signal",
+ "reasons": _dedupe(
+ [
+ *[str(item) for item in list(council.get("follow_up_checks") or []) if item],
+ *[str(item) for item in list(council.get("recommended_watch_items") or []) if item],
+ ]
+ )[:4],
+ },
+ {
+ "title": "Launch Boundaries",
+ "status": launch_readiness.get("product_operating_mode") or "unknown",
+ "detail": ", ".join([str(item) for item in list(launch_readiness.get("blocking_reasons") or [])[:3]]) or "analysis_surface_ready",
+ "reasons": _dedupe(
+ [
+ *[str(item) for item in list(launch_readiness.get("blocking_reasons") or []) if item],
+ *[str(item) for item in list(launch_readiness.get("warning_reasons") or []) if item],
+ ]
+ )[:4],
+ },
+ ]
+
+
+def _build_decision_assistant_reason_citations(
+ *,
+ decision_packet: dict,
+ no_trade_reason_items: list[dict],
+ launch_readiness: dict,
+) -> list[dict]:
+ citations: list[dict] = []
+ for index, reason in enumerate(list(decision_packet.get("why_allowed") or [])[:4]):
+ citations.append(
+ {
+ "reason": str(reason),
+ "source_path": f"decision_packet.why_allowed[{index}]",
+ "source_type": "decision_packet",
+ "severity": "supporting",
+ "detail": decision_packet.get("ai_explanation") or "decision_support_reason",
+ }
+ )
+ for item in no_trade_reason_items[:4]:
+ source = str(item.get("source") or "")
+ source_path = "launch_readiness.blocking_reasons" if source == "launch_blocker" else (
+ "launch_readiness.warning_reasons" if source == "launch_warning" else "decision_packet.why_blocked"
+ )
+ citations.append(
+ {
+ "reason": str(item.get("reason") or ""),
+ "source_path": source_path,
+ "source_type": source or "decision_packet",
+ "severity": str(item.get("priority") or "low"),
+ "detail": str(item.get("category") or "reason"),
+ }
+ )
+ council = dict(decision_packet.get("research_council") or {})
+ for index, reason in enumerate(list(council.get("follow_up_checks") or [])[:2]):
+ citations.append(
+ {
+ "reason": str(reason),
+ "source_path": f"decision_packet.research_council.follow_up_checks[{index}]",
+ "source_type": "research_council",
+ "severity": "watch",
+ "detail": council.get("operator_summary") or council.get("research_summary") or "watch_follow_up",
+ }
+ )
+ return citations[:8]
+
+
+def _build_decision_assistant_learning_rollup(*, weekly_review: dict, operator_digest: dict) -> dict:
+ digest_counts = dict(operator_digest.get("summary_counts") or {})
+ quality_summary = dict(operator_digest.get("quality_summary") or {})
+ return {
+ "status": "active" if weekly_review or operator_digest else "idle",
+ "entry_count": int(weekly_review.get("entry_count") or 0),
+ "next_week_actions": list(weekly_review.get("next_week_actions") or [])[:4],
+ "quality_flags": list(operator_digest.get("quality_flags") or [])[:4],
+ "pending_feedback_count": int(digest_counts.get("pending_feedback_count") or 0),
+ "visual_mismatch_count": int(digest_counts.get("visual_mismatch_count") or 0),
+ "high_value_alert_count": int(digest_counts.get("high_value_alert_count") or 0),
+ "operator_summary": operator_digest.get("operator_summary") or "learning_rollup_idle",
+ "quality_summary_status": quality_summary.get("status") or operator_digest.get("status") or "unknown",
+ }
+
+
+def _build_decision_assistant_external_context_summary(
+ *,
+ launch_readiness: dict,
+ decision_packet: dict,
+ exchange_status: dict,
+ announcements_snapshot: dict,
+) -> dict:
+ market_state = dict(launch_readiness.get("market_state") or {})
+ news_window = dict(market_state.get("news_window") or market_state.get("news_state") or {})
+ council = dict(decision_packet.get("research_council") or {})
+ exchange_status = dict(exchange_status or {})
+ announcements_snapshot = dict(announcements_snapshot or {})
+ active_events = [dict(item or {}) for item in list(news_window.get("active_events") or []) if item]
+ external_waiting_items = list(launch_readiness.get("external_waiting_items") or [])[:4]
+ council_watch_items = list(council.get("recommended_watch_items") or [])[:4]
+ operator_questions = list(council.get("operator_questions") or [])[:4]
+ next_confirmation_targets = list(council.get("next_confirmation_targets") or [])[:4]
+ exchange_events = [dict(item or {}) for item in list(exchange_status.get("active_events") or []) if item]
+ announcements = [dict(item or {}) for item in list(announcements_snapshot.get("announcements") or []) if item]
+ return {
+ "status": "active" if active_events or external_waiting_items or council_watch_items or operator_questions or next_confirmation_targets or exchange_events or announcements else "idle",
+ "news_window_code": news_window.get("code") or "unknown",
+ "provider_name": news_window.get("provider_name") or "unknown",
+ "freshness_code": news_window.get("freshness_code") or "unknown",
+ "active_event_titles": [str(item.get("title") or item.get("event_id") or "macro_event") for item in active_events[:4]],
+ "event_count": int(news_window.get("event_count") or 0),
+ "external_waiting_items": external_waiting_items,
+ "council_watch_items": council_watch_items,
+ "operator_questions": operator_questions,
+ "next_confirmation_targets": next_confirmation_targets,
+ "exchange_status": exchange_status,
+ "exchange_status_titles": [str(item.get("title") or item.get("service_type") or "system_status_event") for item in exchange_events[:4]],
+ "announcements_snapshot": announcements_snapshot,
+ "announcement_titles": [str(item.get("title") or "announcement") for item in announcements[:4]],
+ "source_cards": [
+ {
+ "source_type": "news_provider",
+ "title": news_window.get("provider_name") or "news_provider",
+ "detail": news_window.get("freshness_code") or news_window.get("code") or "unknown",
+ },
+ {
+ "source_type": "exchange_status",
+ "title": exchange_status.get("provider_name") or "okx_system_status",
+ "detail": exchange_status.get("status") or "unknown",
+ },
+ {
+ "source_type": "okx_announcements",
+ "title": announcements_snapshot.get("provider_name") or "okx_announcements",
+ "detail": announcements_snapshot.get("status") or "unknown",
+ },
+ *[
+ {
+ "source_type": "external_waiting_item",
+ "title": str(item),
+ "detail": "launch_readiness.external_waiting_items",
+ }
+ for item in external_waiting_items
+ ],
+ *[
+ {
+ "source_type": "council_watch_item",
+ "title": str(item),
+ "detail": "decision_packet.research_council.recommended_watch_items",
+ }
+ for item in council_watch_items
+ ],
+ *[
+ {
+ "source_type": "announcement",
+ "title": str(item.get("title") or "announcement"),
+ "detail": str(item.get("ann_type") or item.get("published_ts") or "okx_announcement"),
+ }
+ for item in announcements[:2]
+ ],
+ ][:6],
+ "headline": _build_external_context_headline(
+ news_window=news_window,
+ active_events=active_events,
+ launch_readiness=launch_readiness,
+ exchange_status=exchange_status,
+ exchange_events=exchange_events,
+ announcements=announcements,
+ announcements_snapshot=announcements_snapshot,
+ ),
+ }
+
+
+def _build_decision_assistant_review_entrypoints(
+ *,
+ recent_notes: list[dict],
+ weekly_review: dict,
+ operator_digest: dict,
+ focus_mode: str,
+ top_opportunities: list[dict],
+ no_trade_reason_items: list[dict],
+) -> dict:
+ queue = list(operator_digest.get("review_queue") or [])
+ latest_note = dict(recent_notes[0] or {}) if recent_notes else {}
+ next_actions = list(weekly_review.get("next_week_actions") or [])
+ note_templates = _build_review_entry_note_templates(
+ focus_mode=focus_mode,
+ top_opportunities=top_opportunities,
+ no_trade_reason_items=no_trade_reason_items,
+ next_actions=next_actions,
+ review_queue=queue,
+ latest_note=latest_note,
+ )
+ recommended_template_id = _pick_recommended_review_template(
+ focus_mode=focus_mode,
+ note_templates=note_templates,
+ )
+ template_reuse_metrics = _build_review_template_reuse_metrics(
+ recent_notes=recent_notes,
+ note_templates=note_templates,
+ recommended_template_id=recommended_template_id,
+ )
+ template_usage = dict(template_reuse_metrics.get("template_usage") or {})
+ for item in note_templates:
+ template_id = str(item.get("template_id") or "")
+ usage = dict(template_usage.get(template_id) or {})
+ item["reuse_count"] = int(usage.get("count") or 0)
+ item["last_used_ts"] = usage.get("last_used_ts")
+ item["reuse_status"] = "saved_reuse" if item["reuse_count"] > 0 else "not_yet_reused"
+ return {
+ "status": "active",
+ "note_count": len(recent_notes),
+ "latest_note_author": latest_note.get("author") or None,
+ "latest_note_ts": latest_note.get("created_ts") or None,
+ "journal_entry_count": int(weekly_review.get("entry_count") or 0),
+ "review_queue_count": len(queue),
+ "next_review_action": next_actions[0] if next_actions else None,
+ "note_templates": note_templates,
+ "recommended_template_id": recommended_template_id,
+ "template_summary": _build_review_entry_template_summary(note_templates, recommended_template_id=recommended_template_id),
+ "template_reuse_metrics": template_reuse_metrics,
+ "actions": [
+ {"action_id": "open_operator_notes", "label": "Open Notes"},
+ {"action_id": "open_post_trade_review", "label": "Open Review"},
+ {"action_id": "generate_journal_backfill", "label": "Generate Journal"},
+ ],
+ }
+
+
+def _build_review_entry_note_templates(
+ *,
+ focus_mode: str,
+ top_opportunities: list[dict],
+ no_trade_reason_items: list[dict],
+ next_actions: list[str],
+ review_queue: list[dict],
+ latest_note: dict,
+) -> list[dict]:
+ latest_note_preview = _truncate_text(str(latest_note.get("body") or ""), limit=90)
+ templates = []
+ if top_opportunities:
+ top = top_opportunities[0]
+ templates.append(
+ {
+ "template_id": "top_opportunity",
+ "label": "Top setup note",
+ "body": f"[top-opportunity] {top.get('title') or 'candidate'} | {top.get('detail') or top.get('reason') or 'review next'}",
+ "note_scope": "workbench",
+ "tags": ["assistant-template", "top-opportunity"],
+ "priority": "high",
+ "reason": "best_current_opportunity",
+ "context_items": [top.get("status") or "observe", top.get("kind") or "decision_assistant"],
+ "follow_up_prompt": "Document what would confirm or invalidate this setup next.",
+ "reuse_hint": "Best when you want to carry the strongest current setup into the next operator loop.",
+ "carry_over_context": [latest_note_preview] if latest_note_preview else [],
+ }
+ )
+ hard_blockers = [item for item in no_trade_reason_items if item.get("priority") == "high"]
+ if hard_blockers:
+ blocker = hard_blockers[0]
+ templates.append(
+ {
+ "template_id": "top_blocker",
+ "label": "Blocker note",
+ "body": f"[blocker] {blocker.get('reason') or 'n/a'} | source={blocker.get('source') or 'unknown'}",
+ "note_scope": "workbench",
+ "tags": ["assistant-template", "blocker"],
+ "priority": "high",
+ "reason": "top_execution_blocker",
+ "context_items": [blocker.get("category") or "risk", blocker.get("source") or "assistant"],
+ "follow_up_prompt": "Record what would clear this blocker before the next review cycle.",
+ "reuse_hint": "Best when the next pass should start from why you are still not trading.",
+ "carry_over_context": [latest_note_preview] if latest_note_preview else [],
+ }
+ )
+ if next_actions:
+ next_action = str(next_actions[0] or "review_next_action")
+ templates.append(
+ {
+ "template_id": "next_review",
+ "label": "Next review note",
+ "body": f"[next-review] {next_action} | checkpoint for next operator pass",
+ "note_scope": "session",
+ "tags": ["assistant-template", "next-review"],
+ "priority": "medium",
+ "reason": "weekly_follow_up_action",
+ "context_items": [focus_mode or "monitor_and_wait", next_action],
+ "follow_up_prompt": "Use this when you want the next operator loop to pick up a concrete follow-up.",
+ "reuse_hint": "Best when you need the next review loop to inherit one explicit pending action.",
+ "carry_over_context": [latest_note_preview] if latest_note_preview else [],
+ }
+ )
+ if review_queue:
+ queue_item = dict(review_queue[0] or {})
+ queue_type = str(queue_item.get("queue_type") or "review_queue")
+ queue_reason = str(queue_item.get("reason") or queue_item.get("label") or "review pending")
+ templates.append(
+ {
+ "template_id": "queue_follow_up",
+ "label": "Queue follow-up note",
+ "body": f"[queue-follow-up] {queue_type} | {queue_reason}",
+ "note_scope": "workbench",
+ "tags": ["assistant-template", "queue-follow-up"],
+ "priority": "medium",
+ "reason": "operator_review_queue_anchor",
+ "context_items": [queue_type, queue_item.get("priority") or "review"],
+ "follow_up_prompt": "Capture the exact queue item that still needs operator attention.",
+ "reuse_hint": "Best when the operator queue is driving what should happen next.",
+ "carry_over_context": [latest_note_preview] if latest_note_preview else [],
+ }
+ )
+ templates.append(
+ {
+ "template_id": "focus_mode",
+ "label": "Focus note",
+ "body": f"[focus-mode] {focus_mode or 'monitor_and_wait'} | next review checkpoint",
+ "note_scope": "workbench",
+ "tags": ["assistant-template", "focus-mode"],
+ "priority": "medium",
+ "reason": "current_focus_mode_anchor",
+ "context_items": [focus_mode or "monitor_and_wait"],
+ "follow_up_prompt": "Use this when you only need a quick state anchor for the next pass.",
+ "reuse_hint": "Best when you only need a compact checkpoint note.",
+ "carry_over_context": [latest_note_preview] if latest_note_preview else [],
+ }
+ )
+ return templates[:5]
+
+
+def _pick_recommended_review_template(*, focus_mode: str, note_templates: list[dict]) -> str | None:
+ template_by_id = {
+ str(item.get("template_id") or ""): dict(item)
+ for item in note_templates
+ if item
+ }
+ if focus_mode == "clear_review_queue" and template_by_id.get("queue_follow_up"):
+ return "queue_follow_up"
+ if focus_mode == "stabilize_analysis_surface" and template_by_id.get("top_blocker"):
+ return "top_blocker"
+ if focus_mode == "review_top_setup" and template_by_id.get("top_opportunity"):
+ return "top_opportunity"
+ if template_by_id.get("next_review"):
+ return "next_review"
+ if note_templates:
+ return str(note_templates[0].get("template_id") or "")
+ return None
+
+
+def _build_review_template_reuse_metrics(
+ *,
+ recent_notes: list[dict],
+ note_templates: list[dict],
+ recommended_template_id: str | None,
+) -> dict:
+ template_prefixes = {
+ str(item.get("template_id") or ""): _review_template_prefix(item)
+ for item in note_templates
+ if item
+ }
+ template_usage: dict[str, dict] = {
+ template_id: {"count": 0, "last_used_ts": None}
+ for template_id in template_prefixes
+ if template_id
+ }
+ assistant_template_note_count = 0
+ latest_template_note_ts = None
+ for note in recent_notes:
+ body = str(note.get("body") or "")
+ tags = [str(tag) for tag in note.get("tags") or []]
+ author = str(note.get("author") or "")
+ note_ts = note.get("created_ts")
+ matched_template_ids: list[str] = []
+ for template_id, prefix in template_prefixes.items():
+ if prefix and prefix in body:
+ matched_template_ids.append(template_id)
+ usage = template_usage.setdefault(template_id, {"count": 0, "last_used_ts": None})
+ usage["count"] = int(usage.get("count") or 0) + 1
+ if note_ts and (usage.get("last_used_ts") is None or str(note_ts) > str(usage.get("last_used_ts"))):
+ usage["last_used_ts"] = note_ts
+ is_template_note = bool(matched_template_ids) or author == "assistant_template" or "assistant-template" in tags
+ if is_template_note:
+ assistant_template_note_count += 1
+ if note_ts and (latest_template_note_ts is None or str(note_ts) > str(latest_template_note_ts)):
+ latest_template_note_ts = note_ts
+
+ recommended_usage = dict(template_usage.get(str(recommended_template_id or "")) or {})
+ recommended_reuse_count = int(recommended_usage.get("count") or 0)
+ if not note_templates:
+ reuse_status = "no_templates"
+ elif recommended_reuse_count > 0:
+ reuse_status = "recommended_reused"
+ elif assistant_template_note_count > 0:
+ reuse_status = "other_template_reused"
+ else:
+ reuse_status = "available_no_saved_reuse"
+ return {
+ "assistant_template_note_count": assistant_template_note_count,
+ "latest_template_note_ts": latest_template_note_ts,
+ "template_usage": template_usage,
+ "recommended_template_id": recommended_template_id,
+ "recommended_reuse_count": recommended_reuse_count,
+ "recommended_last_used_ts": recommended_usage.get("last_used_ts"),
+ "reuse_status": reuse_status,
+ }
+
+
+def _review_template_prefix(template: dict) -> str:
+ body = str(template.get("body") or "")
+ if "]" in body and body.startswith("["):
+ return body.split("]", 1)[0] + "]"
+ template_id = str(template.get("template_id") or "")
+ if template_id:
+ return f"[{template_id.replace('_', '-')}]"
+ return ""
+
+
+def _build_review_entry_template_summary(note_templates: list[dict], *, recommended_template_id: str | None) -> dict:
+ counts = {}
+ recommended_template = None
+ for item in note_templates:
+ priority = str(item.get("priority") or "unknown")
+ counts[priority] = int(counts.get(priority) or 0) + 1
+ if str(item.get("template_id") or "") == str(recommended_template_id or ""):
+ recommended_template = dict(item)
+ return {
+ "count": len(note_templates),
+ "priorities": counts,
+ "labels": [str(item.get("label") or "template") for item in note_templates],
+ "recommended_label": (recommended_template or {}).get("label"),
+ "recommended_priority": (recommended_template or {}).get("priority"),
+ "recommended_reason": (recommended_template or {}).get("reason"),
+ "recommended_reuse_hint": (recommended_template or {}).get("reuse_hint"),
+ }
+
+
+def _build_external_context_headline(
+ *,
+ news_window: dict,
+ active_events: list[dict],
+ launch_readiness: dict,
+ exchange_status: dict,
+ exchange_events: list[dict],
+ announcements: list[dict],
+ announcements_snapshot: dict,
+) -> str:
+ if active_events:
+ return f"External macro context active: {', '.join([str(item.get('title') or item.get('event_id') or 'macro_event') for item in active_events[:2]])}."
+ if exchange_events:
+ return f"Exchange status context active: {', '.join([str(item.get('title') or item.get('service_type') or 'system_status_event') for item in exchange_events[:2]])}."
+ if announcements:
+ return f"OKX announcement context active: {', '.join([str(item.get('title') or 'announcement') for item in announcements[:2]])}."
+ waiting = list(launch_readiness.get("external_waiting_items") or [])
+ if waiting:
+ return f"External read-only context waiting items: {', '.join([str(item) for item in waiting[:2]])}."
+ if exchange_status.get("status") == "error":
+ return f"Exchange status source degraded: {exchange_status.get('error') or 'unknown_error'}."
+ if announcements_snapshot.get("status") == "error":
+ return f"Announcement source degraded: {announcements_snapshot.get('error') or 'unknown_error'}."
+ return f"External context stable: provider={news_window.get('provider_name') or 'unknown'} freshness={news_window.get('freshness_code') or 'unknown'}."
+
+
+def _build_decision_assistant_personalized_reminders(
+ *,
+ top_opportunities: list[dict],
+ no_trade_reason_items: list[dict],
+ operator_digest: dict,
+ external_context_summary: dict,
+ learning_rollup: dict,
+) -> list[dict]:
+ reminders: list[dict] = []
+ if top_opportunities:
+ item = top_opportunities[0]
+ reminders.append(
+ {
+ "severity": "high",
+ "title": f"Review {item.get('title') or 'top opportunity'}",
+ "message": item.get("detail") or item.get("reason") or "top_opportunity_ready",
+ "source": item.get("kind") or "decision_assistant",
+ }
+ )
+ high_blockers = [item for item in no_trade_reason_items if item.get("priority") == "high"]
+ if high_blockers:
+ reminders.append(
+ {
+ "severity": "high",
+ "title": "Resolve no-trade blockers first",
+ "message": ", ".join([str(item.get("reason") or "") for item in high_blockers[:2]]),
+ "source": "no_trade_reason_items",
+ }
+ )
+ review_queue = list(operator_digest.get("review_queue") or [])
+ if review_queue:
+ reminders.append(
+ {
+ "severity": "medium",
+ "title": "Clear operator review queue",
+ "message": f"{review_queue[0].get('queue_type') or 'queue'} needs review",
+ "source": "operator_digest",
+ }
+ )
+ if list(external_context_summary.get("active_event_titles") or []):
+ reminders.append(
+ {
+ "severity": "medium",
+ "title": "Watch external macro context",
+ "message": ", ".join([str(item) for item in list(external_context_summary.get("active_event_titles") or [])[:2]]),
+ "source": "external_context_summary",
+ }
+ )
+ if list(learning_rollup.get("next_week_actions") or []):
+ reminders.append(
+ {
+ "severity": "low",
+ "title": "Capture learning follow-up",
+ "message": str((learning_rollup.get("next_week_actions") or [])[0]),
+ "source": "learning_rollup",
+ }
+ )
+ return reminders[:5]
+
+
+def _build_decision_assistant_digest_surface(
+ *,
+ focus_mode: str,
+ personalized_reminders: list[dict],
+ learning_rollup: dict,
+ top_opportunities: list[dict],
+ digest_history_preview: list[dict],
+ digest_history_summary: dict,
+) -> dict:
+ return {
+ "status": "active" if personalized_reminders or top_opportunities else "idle",
+ "focus_mode": focus_mode,
+ "reminder_count": len(personalized_reminders),
+ "high_priority_count": sum(1 for item in personalized_reminders if str(item.get("severity") or "") == "high"),
+ "top_opportunity_title": top_opportunities[0].get("title") if top_opportunities else None,
+ "learning_status": learning_rollup.get("status") or "idle",
+ "history_count": len(digest_history_preview),
+ "history_summary": digest_history_summary,
+ "summary": personalized_reminders[0].get("title") if personalized_reminders else "digest_surface_idle",
+ }
+
+
+def _build_decision_assistant_digest_history_preview(
+ *,
+ personalized_reminders: list[dict],
+ recent_alerts: list[dict],
+ recent_notes: list[dict],
+ previous_packet: dict,
+) -> list[dict]:
+ history = []
+ for item in personalized_reminders[:2]:
+ history.append(
+ {
+ "kind": "reminder",
+ "title": item.get("title") or "reminder",
+ "detail": item.get("message") or "",
+ "ts": None,
+ }
+ )
+ if recent_alerts:
+ latest_alert = dict(recent_alerts[0] or {})
+ history.append(
+ {
+ "kind": "alert",
+ "title": latest_alert.get("alert_type") or "alert",
+ "detail": latest_alert.get("status") or "queued",
+ "ts": latest_alert.get("created_ts"),
+ }
+ )
+ if recent_notes:
+ latest_note = dict(recent_notes[0] or {})
+ history.append(
+ {
+ "kind": "note",
+ "title": latest_note.get("author") or "operator_note",
+ "detail": latest_note.get("body") or latest_note.get("note_scope") or "note",
+ "ts": latest_note.get("created_ts"),
+ }
+ )
+ if previous_packet:
+ history.append(
+ {
+ "kind": "previous_packet",
+ "title": "Previous packet",
+ "detail": previous_packet.get("action_recommendation") or previous_packet.get("status") or "previous_packet",
+ "ts": previous_packet.get("packet_ts"),
+ }
+ )
+ return history[:5]
+
+
+def _build_decision_assistant_digest_history_summary(history: list[dict]) -> dict:
+ counts = {}
+ latest_ts = None
+ stale_kind_count = 0
+ fresh_kind_count = 0
+ for item in history:
+ kind = str(item.get("kind") or "history")
+ counts[kind] = int(counts.get(kind) or 0) + 1
+ ts = item.get("ts")
+ if ts and (latest_ts is None or str(ts) > str(latest_ts)):
+ latest_ts = ts
+ groups = _build_decision_assistant_digest_history_groups(history, recommended_kind=None)
+ for group in groups:
+ if bool(group.get("stale")):
+ stale_kind_count += 1
+ if str(group.get("age_band") or "") in {"fresh_now", "monitor_window"}:
+ fresh_kind_count += 1
+ refresh_prompts = [
+ {
+ "kind": str(group.get("kind") or "history"),
+ "prompt": group.get("refresh_prompt"),
+ "action_label": group.get("refresh_action_label"),
+ }
+ for group in groups
+ if bool(group.get("refresh_needed"))
+ ]
+ sorted_kinds = [kind for kind, _count in sorted(counts.items(), key=lambda row: (row[1], row[0]), reverse=True)]
+ recommended_kind = sorted_kinds[0] if sorted_kinds else None
+ return {
+ "counts": counts,
+ "latest_ts": latest_ts,
+ "kinds": list(counts.keys()),
+ "sorted_kinds": sorted_kinds,
+ "recommended_kind": recommended_kind,
+ "recommended_count": counts.get(recommended_kind, 0) if recommended_kind else 0,
+ "stale_kind_count": stale_kind_count,
+ "fresh_kind_count": fresh_kind_count,
+ "refresh_needed_count": len(refresh_prompts),
+ "refresh_prompts": refresh_prompts[:3],
+ }
+
+
+def _build_decision_assistant_digest_history_groups(history: list[dict], *, recommended_kind: str | None) -> list[dict]:
+ buckets: dict[str, list[dict]] = {}
+ for item in history:
+ kind = str(item.get("kind") or "history")
+ buckets.setdefault(kind, []).append(dict(item))
+ groups = []
+ for kind, items in buckets.items():
+ latest_ts = None
+ for item in items:
+ ts = item.get("ts")
+ if ts and (latest_ts is None or str(ts) > str(latest_ts)):
+ latest_ts = ts
+ age = _resolve_digest_age(latest_ts)
+ refresh_needed = bool(age.get("stale"))
+ groups.append(
+ {
+ "kind": kind,
+ "count": len(items),
+ "latest_ts": latest_ts,
+ "group_status": _digest_history_group_status(kind=kind, count=len(items)),
+ "recommended": bool(recommended_kind and kind == recommended_kind),
+ "headline": _digest_history_group_headline(kind=kind, items=items),
+ "latest_title": items[0].get("title") if items else None,
+ "action_label": _digest_history_group_action_label(kind=kind),
+ "age_seconds": age.get("age_seconds"),
+ "age_band": age.get("age_band"),
+ "stale": age.get("stale"),
+ "staleness_hint": age.get("staleness_hint"),
+ "refresh_needed": refresh_needed,
+ "refresh_prompt": _digest_history_refresh_prompt(kind=kind, age=age),
+ "refresh_action_label": _digest_history_refresh_action_label(kind=kind, refresh_needed=refresh_needed),
+ "items": items[:3],
+ }
+ )
+ groups.sort(
+ key=lambda item: (
+ bool(item.get("recommended")),
+ str(item.get("latest_ts") or ""),
+ str(item.get("kind") or ""),
+ ),
+ reverse=True,
+ )
+ return groups
+
+
+def _digest_history_group_headline(*, kind: str, items: list[dict]) -> str:
+ latest = dict(items[0] or {}) if items else {}
+ latest_title = str(latest.get("title") or kind or "history")
+ if len(items) <= 1:
+ return f"{latest_title} is the only recent {kind} item."
+ return f"{latest_title} leads {len(items)} recent {kind} items."
+
+
+def _digest_history_group_action_label(*, kind: str) -> str:
+ if kind == "reminder":
+ return "Review reminders"
+ if kind == "alert":
+ return "Review alerts"
+ if kind == "note":
+ return "Review notes"
+ if kind == "previous_packet":
+ return "Review previous packet"
+ return "Review history"
+
+
+def _digest_history_refresh_prompt(*, kind: str, age: dict) -> str:
+ age_band = str(age.get("age_band") or "unknown_age")
+ if bool(age.get("stale")):
+ return f"Refresh {kind} before treating this recall group as live operator context."
+ if age_band == "monitor_window":
+ return f"Recheck {kind} if you are about to use it for the next decision note."
+ if age_band == "fresh_now":
+ return f"{kind} is fresh enough for the current assistant loop."
+ if age_band in {"untimed", "unknown_age"}:
+ return f"{kind} has weak timing metadata; verify freshness before relying on it."
+ return f"Review {kind} freshness before the next operator pass."
+
+
+def _digest_history_refresh_action_label(*, kind: str, refresh_needed: bool) -> str:
+ if refresh_needed:
+ if kind == "alert":
+ return "Refresh Alerts"
+ if kind == "note":
+ return "Refresh Notes"
+ if kind == "previous_packet":
+ return "Refresh Packet"
+ if kind == "reminder":
+ return "Refresh Reminders"
+ return "Refresh History"
+ return _digest_history_group_action_label(kind=kind)
+
+
+def _resolve_digest_age(latest_ts) -> dict:
+ if not latest_ts:
+ return {
+ "age_seconds": None,
+ "age_band": "untimed",
+ "stale": False,
+ "staleness_hint": "This history group does not carry a concrete timestamp.",
+ }
+ parsed = _parse_iso_timestamp(latest_ts)
+ if parsed is None:
+ return {
+ "age_seconds": None,
+ "age_band": "unknown_age",
+ "stale": False,
+ "staleness_hint": "This history group has a timestamp we could not normalize.",
+ }
+ age_seconds = max(0, int((datetime.now(timezone.utc) - parsed).total_seconds()))
+ if age_seconds <= 900:
+ return {
+ "age_seconds": age_seconds,
+ "age_band": "fresh_now",
+ "stale": False,
+ "staleness_hint": "Recent enough to trust as current operator context.",
+ }
+ if age_seconds <= 3600:
+ return {
+ "age_seconds": age_seconds,
+ "age_band": "monitor_window",
+ "stale": False,
+ "staleness_hint": "Still inside the current monitoring window, but worth rechecking before acting.",
+ }
+ if age_seconds <= 21600:
+ return {
+ "age_seconds": age_seconds,
+ "age_band": "same_session",
+ "stale": True,
+ "staleness_hint": "From the same session, but old enough that conditions may have drifted.",
+ }
+ return {
+ "age_seconds": age_seconds,
+ "age_band": "stale",
+ "stale": True,
+ "staleness_hint": "Old enough that this group should be treated as recall, not live context.",
+ }
+
+
+def _digest_history_group_status(*, kind: str, count: int) -> str:
+ if kind == "reminder":
+ return "actionable" if count else "idle"
+ if kind in {"alert", "previous_packet"}:
+ return "review"
+ if kind == "note":
+ return "context"
+ return "history"
+
+
+def _build_decision_assistant_setup_learning_rollup(
+ *,
+ decision_packet: dict,
+ weekly_review: dict,
+ research_council_service: ResearchCouncilService,
+ instrument_id: int,
+ fallback_session_code: str | None,
+) -> dict:
+ model_code = str(decision_packet.get("setup_type") or "")
+ packet_session_code = str(
+ decision_packet.get("raw", {}).get("candidate", {}).get("session_code")
+ or decision_packet.get("raw", {}).get("signal", {}).get("session_code")
+ or ""
+ )
+ session_code = packet_session_code or str(fallback_session_code or "")
+ session_code_source = "decision_packet" if packet_session_code else ("market_session_fallback" if fallback_session_code else "unknown")
+ if hasattr(research_council_service, "get_compact_lessons") and model_code:
+ lessons = list(
+ research_council_service.get_compact_lessons(
+ instrument_id=instrument_id,
+ session_code=session_code or None,
+ setup_type=model_code or None,
+ model_code=model_code or None,
+ limit=5,
+ )
+ or []
+ )
+ else:
+ lessons = []
+ session_performance = dict(weekly_review.get("session_performance") or {})
+ current_session_stats = dict(session_performance.get(session_code) or {})
+ replay_statistics = _build_setup_replay_statistics(
+ setup_type=model_code,
+ session_code=session_code,
+ current_session_stats=current_session_stats,
+ session_performance=session_performance,
+ high_quality_session=dict(weekly_review.get("high_quality_session") or {}),
+ low_quality_session=dict(weekly_review.get("low_quality_session") or {}),
+ )
+ return {
+ "status": "active" if lessons or current_session_stats else "idle",
+ "setup_type": model_code or "unknown",
+ "session_code": session_code or "OFF_HOURS",
+ "session_code_source": session_code_source,
+ "lesson_count": len(lessons),
+ "top_lessons": [
+ {
+ "title": str(item.get("title") or item.get("lesson_key") or "lesson"),
+ "summary": str(item.get("lesson_summary") or ""),
+ "lesson_type": str(item.get("lesson_type") or "lesson"),
+ }
+ for item in lessons[:3]
+ ],
+ "current_session_performance": current_session_stats,
+ "high_quality_session": dict(weekly_review.get("high_quality_session") or {}),
+ "low_quality_session": dict(weekly_review.get("low_quality_session") or {}),
+ "replay_statistics": replay_statistics,
+ }
+
+
+def _build_decision_assistant_scan_summary_cards(
+ *,
+ top_opportunities: list[dict],
+ no_trade_reason_items: list[dict],
+ review_entrypoints: dict,
+ external_context_summary: dict,
+ digest_surface: dict,
+) -> list[dict]:
+ cards = []
+ if top_opportunities:
+ cards.append(
+ {
+ "title": "Top Opportunity",
+ "status": top_opportunities[0].get("status") or "observe",
+ "detail": top_opportunities[0].get("title") or "n/a",
+ "context": top_opportunities[0].get("reason") or top_opportunities[0].get("detail") or "review next",
+ "rank": 1,
+ "emphasis": "primary",
+ "action_label": "Open Setup",
+ "transition_hint": "Open the setup section and decide whether it is worth a template-backed note.",
+ "completion_state": "ready_to_review",
+ "completion_hint": "Review this setup, then either capture a note or leave it in watch mode.",
+ "target_section": "opportunities",
+ }
+ )
+ hard_blockers = [item for item in no_trade_reason_items if item.get("priority") == "high"]
+ if hard_blockers:
+ cards.append(
+ {
+ "title": "Top Blocker",
+ "status": hard_blockers[0].get("priority") or "high",
+ "detail": hard_blockers[0].get("reason") or "n/a",
+ "context": f"{hard_blockers[0].get('category') or 'risk'} / {hard_blockers[0].get('source') or 'assistant'}",
+ "rank": 2,
+ "emphasis": "warning",
+ "action_label": "Open Blockers",
+ "transition_hint": "Open blockers first and confirm what must clear before the next pass.",
+ "completion_state": "blocked_until_resolved",
+ "completion_hint": "This scan step is incomplete until the blocker is explicitly cleared or accepted as observe-only context.",
+ "target_section": "no-trade-reasons",
+ }
+ )
+ if review_entrypoints:
+ cards.append(
+ {
+ "title": "Next Review",
+ "status": review_entrypoints.get("status") or "active",
+ "detail": review_entrypoints.get("next_review_action") or "open_operator_notes",
+ "context": review_entrypoints.get("template_summary", {}).get("recommended_label") or "notes ready",
+ "rank": 3,
+ "emphasis": "action",
+ "action_label": "Open Review",
+ "transition_hint": "Jump into review entry and either use or save the recommended template.",
+ "completion_state": "ready_to_capture",
+ "completion_hint": "Capture the recommended review note to make the next operator loop easier to resume.",
+ "target_section": "review-entry",
+ }
+ )
+ if external_context_summary:
+ external_status = str(external_context_summary.get("status") or "idle")
+ freshness_code = str(external_context_summary.get("freshness_code") or "unknown")
+ external_needs_refresh = external_status == "error" or freshness_code in {"stale", "unknown", "unavailable", "missing"}
+ cards.append(
+ {
+ "title": "External Context",
+ "status": external_context_summary.get("status") or "idle",
+ "detail": external_context_summary.get("headline") or "n/a",
+ "context": f"{external_context_summary.get('provider_name') or 'unknown'} / {external_context_summary.get('freshness_code') or 'unknown'}",
+ "rank": 4,
+ "emphasis": "context",
+ "action_label": "Open Context",
+ "transition_hint": "Open context and check whether the latest external source is still fresh enough.",
+ "completion_state": "needs_refresh" if external_needs_refresh else "fresh_context",
+ "completion_hint": "Refresh or recheck read-only context before relying on it." if external_needs_refresh else "External context is usable for the current analysis pass.",
+ "target_section": "external-context",
+ }
+ )
+ if digest_surface:
+ digest_history_summary = dict(digest_surface.get("history_summary") or {})
+ digest_needs_refresh = int(digest_history_summary.get("refresh_needed_count") or digest_history_summary.get("stale_kind_count") or 0) > 0
+ cards.append(
+ {
+ "title": "Digest",
+ "status": digest_surface.get("status") or "idle",
+ "detail": digest_surface.get("summary") or "n/a",
+ "context": f"history={digest_surface.get('history_summary', {}).get('recommended_kind') or 'n/a'} reminders={digest_surface.get('reminder_count') or 0}",
+ "rank": 5,
+ "emphasis": "summary",
+ "action_label": "Open Digest",
+ "transition_hint": "Open digest recall and confirm whether the active history group is still fresh.",
+ "completion_state": "needs_refresh" if digest_needs_refresh else "fresh_context",
+ "completion_hint": "Refresh stale recall groups before using them as current context." if digest_needs_refresh else "Digest recall is fresh enough for the current assistant loop.",
+ "target_section": "digest",
+ }
+ )
+ return _mark_next_scan_step(cards[:5])
+
+
+def _mark_next_scan_step(cards: list[dict]) -> list[dict]:
+ priority = {
+ "blocked_until_resolved": 0,
+ "needs_refresh": 1,
+ "ready_to_review": 2,
+ "ready_to_capture": 3,
+ "fresh_context": 4,
+ }
+ next_card_title = None
+ candidates = [
+ dict(item)
+ for item in cards
+ if str(item.get("completion_state") or "") not in {"complete", "fresh_context"}
+ ]
+ if candidates:
+ selected = sorted(candidates, key=lambda item: (priority.get(str(item.get("completion_state") or ""), 9), int(item.get("rank") or 99)))[0]
+ next_card_title = str(selected.get("title") or "")
+ for index, item in enumerate(cards, start=1):
+ item["scan_step_index"] = index
+ item["is_next_scan_step"] = bool(next_card_title and str(item.get("title") or "") == next_card_title)
+ if item["is_next_scan_step"]:
+ item["completion_hint"] = f"Next scan step: {item.get('completion_hint') or item.get('transition_hint') or 'review this card.'}"
+ return cards
+
+
+def _build_decision_assistant_scan_completion_summary(cards: list[dict]) -> dict:
+ state_counts: dict[str, int] = {}
+ for item in cards:
+ state = str(item.get("completion_state") or "unknown")
+ state_counts[state] = int(state_counts.get(state) or 0) + 1
+ next_card = next((item for item in cards if bool(item.get("is_next_scan_step"))), {})
+ refresh_needed_count = int(state_counts.get("needs_refresh") or 0)
+ blocked_count = int(state_counts.get("blocked_until_resolved") or 0)
+ ready_count = sum(
+ int(state_counts.get(state) or 0)
+ for state in ("ready_to_review", "ready_to_capture")
+ )
+ return {
+ "status": "active" if cards else "idle",
+ "total_count": len(cards),
+ "state_counts": state_counts,
+ "refresh_needed_count": refresh_needed_count,
+ "blocked_count": blocked_count,
+ "ready_count": ready_count,
+ "open_count": blocked_count + refresh_needed_count + ready_count,
+ "next_title": next_card.get("title"),
+ "next_target_section": next_card.get("target_section"),
+ "next_completion_state": next_card.get("completion_state"),
+ "next_hint": next_card.get("completion_hint"),
+ }
+
+
+def _build_setup_replay_statistics(
+ *,
+ setup_type: str,
+ session_code: str,
+ current_session_stats: dict,
+ session_performance: dict,
+ high_quality_session: dict,
+ low_quality_session: dict,
+) -> dict:
+ framework_rate = float(current_session_stats.get("framework_execution_rate") or 0.0)
+ model_outside_rate = float(current_session_stats.get("model_outside_rate") or 0.0)
+ avg_r_multiple = float(current_session_stats.get("avg_r_multiple") or 0.0)
+ risk_violation_count = int(current_session_stats.get("risk_violation_count") or 0)
+ comparison = _build_setup_replay_comparison(
+ session_code=session_code,
+ current_session_stats=current_session_stats,
+ high_quality_session=high_quality_session,
+ low_quality_session=low_quality_session,
+ )
+ broader_baseline_summary = _build_setup_replay_broader_baseline_summary(
+ setup_type=setup_type,
+ session_code=session_code,
+ current_session_stats=current_session_stats,
+ session_performance=session_performance,
+ )
+ return {
+ "setup_type": setup_type or "unknown",
+ "session_code": session_code or "OFF_HOURS",
+ "sample_count": int(current_session_stats.get("count") or 0),
+ "framework_execution_rate": framework_rate,
+ "model_outside_rate": model_outside_rate,
+ "risk_violation_count": risk_violation_count,
+ "avg_r_multiple": avg_r_multiple,
+ "high_quality_session_code": high_quality_session.get("session_code"),
+ "low_quality_session_code": low_quality_session.get("session_code"),
+ "comparative_summary": comparison,
+ "broader_baseline_summary": broader_baseline_summary,
+ }
+
+
+def _build_setup_replay_comparison(
+ *,
+ session_code: str,
+ current_session_stats: dict,
+ high_quality_session: dict,
+ low_quality_session: dict,
+) -> dict:
+ current_framework = float(current_session_stats.get("framework_execution_rate") or 0.0)
+ current_r = float(current_session_stats.get("avg_r_multiple") or 0.0)
+ high_framework = float(high_quality_session.get("framework_execution_rate") or 0.0)
+ high_r = float(high_quality_session.get("avg_r_multiple") or 0.0)
+ low_framework = float(low_quality_session.get("framework_execution_rate") or 0.0)
+ low_r = float(low_quality_session.get("avg_r_multiple") or 0.0)
+ sample_count = int(current_session_stats.get("count") or 0)
+ notes = []
+ comparison_status = "insufficient_samples"
+ if sample_count == 0:
+ notes.append("no_replay_samples_for_current_session")
+ if session_code and session_code == str(high_quality_session.get("session_code") or ""):
+ notes.append("current_session_matches_weekly_high_quality_session")
+ if session_code and session_code == str(low_quality_session.get("session_code") or ""):
+ notes.append("current_session_matches_weekly_low_quality_session")
+ if sample_count > 0 and current_framework >= high_framework and current_r >= high_r:
+ notes.append("current_setup_matches_or_exceeds_high_quality_session")
+ comparison_status = "stronger_than_high_baseline"
+ elif sample_count > 0 and (current_framework < low_framework or current_r < low_r):
+ notes.append("current_setup_is_below_low_quality_session_baseline")
+ comparison_status = "below_low_baseline"
+ elif sample_count > 0:
+ notes.append("current_setup_sits_between_high_and_low_session_baselines")
+ comparison_status = "between_baselines"
+ confidence_band = "low"
+ if sample_count >= 8:
+ confidence_band = "high"
+ elif sample_count >= 3:
+ confidence_band = "medium"
+ session_alignment = "neutral"
+ if session_code and session_code == str(high_quality_session.get("session_code") or ""):
+ session_alignment = "high_quality_session"
+ elif session_code and session_code == str(low_quality_session.get("session_code") or ""):
+ session_alignment = "low_quality_session"
+ return {
+ "status": "active" if sample_count > 0 else "idle",
+ "comparison_status": comparison_status,
+ "confidence_band": confidence_band,
+ "session_alignment": session_alignment,
+ "framework_execution_gap_vs_high": current_framework - high_framework,
+ "avg_r_gap_vs_high": current_r - high_r,
+ "framework_execution_gap_vs_low": current_framework - low_framework,
+ "avg_r_gap_vs_low": current_r - low_r,
+ "notes": notes[:4],
+ "story": _build_setup_replay_story(
+ comparison_status=comparison_status,
+ confidence_band=confidence_band,
+ session_alignment=session_alignment,
+ sample_count=sample_count,
+ ),
+ }
+
+
+def _build_setup_replay_story(
+ *,
+ comparison_status: str,
+ confidence_band: str,
+ session_alignment: str,
+ sample_count: int,
+) -> str:
+ if comparison_status == "insufficient_samples":
+ return "Replay comparison is advisory because the current session has no local replay samples."
+ if comparison_status == "stronger_than_high_baseline":
+ return f"Current replay profile is at or above the weekly high-quality baseline with {confidence_band} confidence from {sample_count} samples."
+ if comparison_status == "below_low_baseline":
+ return f"Current replay profile is below the weekly low-quality baseline with {confidence_band} confidence from {sample_count} samples."
+ alignment_text = "current session aligns with known baselines" if session_alignment != "neutral" else "current session sits between known baselines"
+ return f"Current replay profile sits between weekly high/low baselines; {alignment_text} with {confidence_band} confidence from {sample_count} samples."
+
+
+def _build_setup_replay_broader_baseline_summary(
+ *,
+ setup_type: str,
+ session_code: str,
+ current_session_stats: dict,
+ session_performance: dict,
+) -> dict:
+ setup_family = _setup_family_label(setup_type)
+ rows = [
+ {"session_code": code, **dict(stats or {})}
+ for code, stats in dict(session_performance or {}).items()
+ if isinstance(stats, dict)
+ ]
+ active_rows = [row for row in rows if int(row.get("count") or 0) > 0]
+ if not active_rows:
+ return {
+ "status": "idle",
+ "session_count": 0,
+ "framework_execution_global_avg": 0.0,
+ "avg_r_global_avg": 0.0,
+ "risk_violation_global_avg": 0.0,
+ "framework_gap_vs_global_avg": 0.0,
+ "avg_r_gap_vs_global_avg": 0.0,
+ "risk_gap_vs_global_avg": 0.0,
+ "framework_rank": None,
+ "avg_r_rank": None,
+ "risk_rank": None,
+ "framework_percentile": None,
+ "avg_r_percentile": None,
+ "risk_percentile": None,
+ "composite_percentile": None,
+ "risk_band": "unranked",
+ "composite_band": "unranked",
+ "composite_rank": None,
+ "position_band": "unranked",
+ "top_sessions": [],
+ "top_r_sessions": [],
+ "top_safe_sessions": [],
+ "top_composite_sessions": [],
+ "peer_sessions": [],
+ "peer_summary": "No peer baseline is available until the current session is ranked.",
+ "setup_family": setup_family,
+ "setup_family_status": "idle",
+ "setup_family_peer_sessions": [],
+ "setup_family_peer_summary": f"No setup-family peer baseline is available yet for {setup_family}.",
+ "setup_family_focus": "Wait for active session statistics before comparing this setup family.",
+ "replay_verdict": "unranked",
+ "story": "No broader replay baseline is available yet because there are no active session statistics.",
+ }
+
+ framework_avg = sum(float(row.get("framework_execution_rate") or 0.0) for row in active_rows) / len(active_rows)
+ avg_r_avg = sum(float(row.get("avg_r_multiple") or 0.0) for row in active_rows) / len(active_rows)
+ risk_avg = sum(float(row.get("risk_violation_count") or 0.0) for row in active_rows) / len(active_rows)
+ current_framework = float(current_session_stats.get("framework_execution_rate") or 0.0)
+ current_r = float(current_session_stats.get("avg_r_multiple") or 0.0)
+ current_risk = float(current_session_stats.get("risk_violation_count") or 0.0)
+ framework_rank = _rank_session_metric(active_rows, session_code=session_code, metric="framework_execution_rate")
+ avg_r_rank = _rank_session_metric(active_rows, session_code=session_code, metric="avg_r_multiple")
+ risk_rank = _rank_session_metric(active_rows, session_code=session_code, metric="risk_violation_count", reverse=False)
+ framework_percentile = _rank_to_percentile(rank=framework_rank, total=len(active_rows))
+ avg_r_percentile = _rank_to_percentile(rank=avg_r_rank, total=len(active_rows))
+ risk_percentile = _rank_to_percentile(rank=risk_rank, total=len(active_rows))
+ composite_percentile = _combine_percentiles(framework_percentile, avg_r_percentile)
+ risk_band = _percentile_band(risk_percentile)
+ composite_band = _percentile_band(composite_percentile)
+ composite_rank = _rank_session_composite(active_rows, session_code=session_code)
+ peer_sessions = _session_composite_peer_lane(active_rows, session_code=session_code, window=1)
+ sample_count = int(current_session_stats.get("count") or 0)
+ position_band = "unranked"
+ if framework_rank and len(active_rows):
+ if framework_rank <= max(1, len(active_rows) // 3):
+ position_band = "top_third"
+ elif framework_rank >= max(1, len(active_rows) - max(1, len(active_rows) // 3) + 1):
+ position_band = "bottom_third"
+ else:
+ position_band = "middle_band"
+ story = "Current session has no replay samples against broader baselines."
+ if sample_count > 0:
+ story = (
+ f"Current session ranks framework #{framework_rank or 'n/a'} and avg-R #{avg_r_rank or 'n/a'} "
+ f"with risk discipline #{risk_rank or 'n/a'} across {len(active_rows)} active sessions; "
+ f"it sits in the {position_band.replace('_', ' ')}."
+ )
+ replay_verdict = _build_replay_verdict(
+ composite_band=composite_band,
+ risk_band=risk_band,
+ composite_percentile=composite_percentile,
+ risk_percentile=risk_percentile,
+ )
+ peer_summary = _build_peer_baseline_summary(
+ session_code=session_code,
+ composite_rank=composite_rank,
+ peer_sessions=peer_sessions,
+ )
+ setup_family_peer_sessions = _build_setup_family_peer_sessions(
+ setup_family=setup_family,
+ peer_sessions=peer_sessions,
+ top_composite_sessions=_session_composite_leaderboard(active_rows, limit=3),
+ )
+ setup_family_peer_summary = _build_setup_family_peer_summary(
+ setup_family=setup_family,
+ session_code=session_code,
+ sample_count=sample_count,
+ peer_sessions=setup_family_peer_sessions,
+ )
+ return {
+ "status": "active",
+ "session_count": len(active_rows),
+ "framework_execution_global_avg": framework_avg,
+ "avg_r_global_avg": avg_r_avg,
+ "risk_violation_global_avg": risk_avg,
+ "framework_gap_vs_global_avg": current_framework - framework_avg,
+ "avg_r_gap_vs_global_avg": current_r - avg_r_avg,
+ "risk_gap_vs_global_avg": current_risk - risk_avg,
+ "framework_rank": framework_rank,
+ "avg_r_rank": avg_r_rank,
+ "risk_rank": risk_rank,
+ "framework_percentile": framework_percentile,
+ "avg_r_percentile": avg_r_percentile,
+ "risk_percentile": risk_percentile,
+ "composite_percentile": composite_percentile,
+ "risk_band": risk_band,
+ "composite_band": composite_band,
+ "composite_rank": composite_rank,
+ "position_band": position_band,
+ "top_sessions": _session_metric_leaderboard(active_rows, metric="framework_execution_rate", limit=3),
+ "top_r_sessions": _session_metric_leaderboard(active_rows, metric="avg_r_multiple", limit=3),
+ "top_safe_sessions": _session_metric_leaderboard(active_rows, metric="risk_violation_count", limit=3, reverse=False),
+ "top_composite_sessions": _session_composite_leaderboard(active_rows, limit=3),
+ "peer_sessions": peer_sessions,
+ "peer_summary": peer_summary,
+ "setup_family": setup_family,
+ "setup_family_status": "active" if sample_count > 0 else "watch_only",
+ "setup_family_peer_sessions": setup_family_peer_sessions,
+ "setup_family_peer_summary": setup_family_peer_summary,
+ "setup_family_focus": f"Compare {setup_family} against adjacent session lanes before promoting this setup family in the next review.",
+ "replay_verdict": replay_verdict,
+ "story": f"{story} Verdict: {replay_verdict.replace('_', ' ')}.",
+ }
+
+
+def _rank_session_metric(rows: list[dict], *, session_code: str, metric: str, reverse: bool = True) -> int | None:
+ if not session_code:
+ return None
+ ranked = sorted(
+ rows,
+ key=lambda row: (float(row.get(metric) or 0.0), str(row.get("session_code") or "")),
+ reverse=reverse,
+ )
+ for index, row in enumerate(ranked, start=1):
+ if str(row.get("session_code") or "") == str(session_code):
+ return index
+ return None
+
+
+def _setup_family_label(setup_type: str) -> str:
+ normalized = str(setup_type or "").strip().upper()
+ if not normalized:
+ return "UNKNOWN_SETUP_FAMILY"
+ if "SWEEP" in normalized and ("MSS" in normalized or "FVG" in normalized):
+ return "LIQUIDITY_REVERSAL_FAMILY"
+ if "FVG" in normalized:
+ return "IMBALANCE_RETEST_FAMILY"
+ if "BREAK" in normalized or "MSS" in normalized:
+ return "STRUCTURE_SHIFT_FAMILY"
+ return f"{normalized}_FAMILY"
+
+
+def _build_setup_family_peer_sessions(
+ *,
+ setup_family: str,
+ peer_sessions: list[dict],
+ top_composite_sessions: list[dict],
+) -> list[dict]:
+ source_rows = peer_sessions or top_composite_sessions
+ family_peers = []
+ for item in source_rows[:3]:
+ family_peers.append(
+ {
+ "setup_family": setup_family,
+ "session_code": str(item.get("session_code") or "unknown"),
+ "rank": item.get("rank"),
+ "relative_position": item.get("relative_position") or "leaderboard",
+ "framework_execution_rate": float(item.get("framework_execution_rate") or 0.0),
+ "avg_r_multiple": float(item.get("avg_r_multiple") or 0.0),
+ "risk_violation_count": int(item.get("risk_violation_count") or 0),
+ "count": int(item.get("count") or 0),
+ }
+ )
+ return family_peers
+
+
+def _build_setup_family_peer_summary(
+ *,
+ setup_family: str,
+ session_code: str,
+ sample_count: int,
+ peer_sessions: list[dict],
+) -> str:
+ if sample_count <= 0:
+ return f"{setup_family} has no current-session replay samples, so peer baselines remain watch-only."
+ if not peer_sessions:
+ return f"{setup_family} has current samples in {session_code or 'unknown'}, but no peer lane is available yet."
+ peer_codes = ", ".join([str(item.get("session_code") or "unknown") for item in peer_sessions[:3]])
+ return f"{setup_family} in {session_code or 'unknown'} should be compared against peer sessions {peer_codes} before reuse."
+
+
+def _rank_session_composite(rows: list[dict], *, session_code: str) -> int | None:
+ if not session_code:
+ return None
+ ranked = sorted(
+ rows,
+ key=lambda row: (
+ float(row.get("framework_execution_rate") or 0.0),
+ float(row.get("avg_r_multiple") or 0.0),
+ -int(row.get("risk_violation_count") or 0),
+ str(row.get("session_code") or ""),
+ ),
+ reverse=True,
+ )
+ for index, row in enumerate(ranked, start=1):
+ if str(row.get("session_code") or "") == str(session_code):
+ return index
+ return None
+
+
+def _session_metric_leaderboard(rows: list[dict], *, metric: str, limit: int, reverse: bool = True) -> list[dict]:
+ ranked = sorted(
+ rows,
+ key=lambda row: (float(row.get(metric) or 0.0), str(row.get("session_code") or "")),
+ reverse=reverse,
+ )
+ return [
+ {
+ "session_code": str(row.get("session_code") or "unknown"),
+ "metric": float(row.get(metric) or 0.0),
+ "count": int(row.get("count") or 0),
+ }
+ for row in ranked[:limit]
+ ]
+
+
+def _session_composite_leaderboard(rows: list[dict], *, limit: int) -> list[dict]:
+ ranked = sorted(
+ rows,
+ key=lambda row: (
+ float(row.get("framework_execution_rate") or 0.0),
+ float(row.get("avg_r_multiple") or 0.0),
+ -int(row.get("risk_violation_count") or 0),
+ str(row.get("session_code") or ""),
+ ),
+ reverse=True,
+ )
+ return [
+ {
+ "session_code": str(row.get("session_code") or "unknown"),
+ "framework_execution_rate": float(row.get("framework_execution_rate") or 0.0),
+ "avg_r_multiple": float(row.get("avg_r_multiple") or 0.0),
+ "risk_violation_count": int(row.get("risk_violation_count") or 0),
+ }
+ for row in ranked[:limit]
+ ]
+
+
+def _session_composite_peer_lane(rows: list[dict], *, session_code: str, window: int) -> list[dict]:
+ if not session_code:
+ return []
+ ranked = sorted(
+ rows,
+ key=lambda row: (
+ float(row.get("framework_execution_rate") or 0.0),
+ float(row.get("avg_r_multiple") or 0.0),
+ -int(row.get("risk_violation_count") or 0),
+ str(row.get("session_code") or ""),
+ ),
+ reverse=True,
+ )
+ current_index = None
+ for index, row in enumerate(ranked):
+ if str(row.get("session_code") or "") == str(session_code):
+ current_index = index
+ break
+ if current_index is None:
+ return []
+ peers: list[dict] = []
+ start = max(0, current_index - window)
+ end = min(len(ranked), current_index + window + 1)
+ for index in range(start, end):
+ if index == current_index:
+ continue
+ row = ranked[index]
+ relative_position = "above" if index < current_index else "below"
+ peers.append(
+ {
+ "session_code": str(row.get("session_code") or "unknown"),
+ "rank": index + 1,
+ "relative_position": relative_position,
+ "framework_execution_rate": float(row.get("framework_execution_rate") or 0.0),
+ "avg_r_multiple": float(row.get("avg_r_multiple") or 0.0),
+ "risk_violation_count": int(row.get("risk_violation_count") or 0),
+ "count": int(row.get("count") or 0),
+ }
+ )
+ return peers
+
+
+def _rank_to_percentile(*, rank: int | None, total: int) -> float | None:
+ if rank is None or total <= 0:
+ return None
+ return max(0.0, (total - rank) / total)
+
+
+def _combine_percentiles(framework_percentile: float | None, avg_r_percentile: float | None) -> float | None:
+ values = [value for value in (framework_percentile, avg_r_percentile) if value is not None]
+ if not values:
+ return None
+ return sum(values) / len(values)
+
+
+def _percentile_band(percentile: float | None) -> str:
+ if percentile is None:
+ return "unranked"
+ if percentile >= 0.66:
+ return "top_band"
+ if percentile <= 0.33:
+ return "bottom_band"
+ return "middle_band"
+
+
+def _build_replay_verdict(
+ *,
+ composite_band: str,
+ risk_band: str,
+ composite_percentile: float | None,
+ risk_percentile: float | None,
+) -> str:
+ if composite_percentile is None:
+ return "unranked"
+ if composite_band == "top_band" and risk_band == "top_band":
+ return "repeatable_high_quality"
+ if composite_band == "bottom_band" or risk_band == "bottom_band":
+ return "needs_tighter_selection"
+ if composite_percentile >= 0.5 and (risk_percentile or 0.0) >= 0.5:
+ return "balanced_operator_ok"
+ return "mixed_profile_watch_closely"
+
+
+def _build_peer_baseline_summary(*, session_code: str, composite_rank: int | None, peer_sessions: list[dict]) -> str:
+ if not session_code or composite_rank is None:
+ return "Current session is not ranked tightly enough to compare against nearby peers yet."
+ if not peer_sessions:
+ return f"{session_code} is ranked, but there are no adjacent peer sessions around its current composite position."
+ descriptions = [
+ f"{item.get('session_code')} ({item.get('relative_position')}, rank {item.get('rank')})"
+ for item in peer_sessions[:2]
+ ]
+ return f"{session_code} sits near {' and '.join(descriptions)} on the current composite replay ladder."
+
+
+def _parse_iso_timestamp(value) -> datetime | None:
+ if not value:
+ return None
+ raw = str(value)
+ normalized = raw.replace("Z", "+00:00")
+ try:
+ parsed = datetime.fromisoformat(normalized)
+ except ValueError:
+ return None
+ if parsed.tzinfo is None:
+ return parsed.replace(tzinfo=timezone.utc)
+ return parsed.astimezone(timezone.utc)
+
+
+def _truncate_text(value: str, *, limit: int) -> str:
+ text = str(value or "").strip()
+ if len(text) <= limit:
+ return text
+ return f"{text[: max(0, limit - 3)].rstrip()}..."
+
+
+def _build_decision_assistant_timeline(
+ *,
+ decision_packet: dict,
+ previous_packet: dict,
+ recent_alerts: list[dict],
+ recent_notes: list[dict],
+ recent_executions: list[dict],
+) -> list[dict]:
+ timeline = []
+ packet_ts = decision_packet.get("packet_ts")
+ if packet_ts:
+ timeline.append({"title": "Current Packet", "ts": str(packet_ts), "detail": decision_packet.get("action_recommendation") or decision_packet.get("status") or "packet"})
+ previous_ts = previous_packet.get("packet_ts")
+ if previous_ts:
+ timeline.append({"title": "Previous Packet", "ts": str(previous_ts), "detail": previous_packet.get("action_recommendation") or previous_packet.get("status") or "previous_packet"})
+ if recent_alerts:
+ latest_alert = dict(recent_alerts[0] or {})
+ timeline.append({"title": "Latest Alert", "ts": str(latest_alert.get("created_ts") or ""), "detail": latest_alert.get("alert_type") or latest_alert.get("status") or "alert"})
+ if recent_notes:
+ latest_note = dict(recent_notes[0] or {})
+ timeline.append({"title": "Latest Note", "ts": str(latest_note.get("created_ts") or ""), "detail": latest_note.get("author") or latest_note.get("note_scope") or "note"})
+ if recent_executions:
+ latest_execution = dict(recent_executions[0] or {})
+ timeline.append(
+ {
+ "title": "Latest Execution",
+ "ts": str(
+ latest_execution.get("updated_ts")
+ or latest_execution.get("closed_ts")
+ or latest_execution.get("opened_ts")
+ or latest_execution.get("created_ts")
+ or ""
+ ),
+ "detail": latest_execution.get("execution_status") or latest_execution.get("side") or "execution",
+ }
+ )
+ return [item for item in timeline if item.get("ts")]
+
+
+def _build_decision_assistant_evidence_chain(
+ *,
+ launch_readiness: dict,
+ decision_packet: dict,
+ operator_digest: dict,
+ weekly_review: dict,
+ current_session: str,
+) -> list[dict]:
+ blockers = [str(item) for item in list(launch_readiness.get("blocking_reasons") or []) if item]
+ warnings = [str(item) for item in list(launch_readiness.get("warning_reasons") or []) if item]
+ quality_flags = [str(item) for item in list(operator_digest.get("quality_flags") or []) if item]
+ digest_counts = dict(operator_digest.get("summary_counts") or {})
+ return [
+ {
+ "title": "Decision Gate",
+ "status": decision_packet.get("action_recommendation") or decision_packet.get("status") or "monitor",
+ "detail": decision_packet.get("ai_explanation") or decision_packet.get("narrative") or "decision_packet_available",
+ "reason": ", ".join(list(decision_packet.get("why_allowed") or [])[:3]) or ", ".join(list(decision_packet.get("why_blocked") or [])[:3]) or "decision_gate_context",
+ },
+ {
+ "title": "Launch Mode",
+ "status": launch_readiness.get("product_operating_mode") or "unknown",
+ "detail": ", ".join(blockers[:3]) if blockers else ", ".join(warnings[:3]) or "analysis_surface_ready",
+ "reason": "blocking_reasons" if blockers else ("warning_reasons" if warnings else "no_launch_blockers"),
+ },
+ {
+ "title": "Session Context",
+ "status": current_session,
+ "detail": launch_readiness.get("market_state", {}).get("freshness", {}).get("code") or "market_state_available",
+ "reason": launch_readiness.get("market_state", {}).get("session", {}).get("status") or "session_status_unknown",
+ },
+ {
+ "title": "Operator Queue",
+ "status": operator_digest.get("status") or "idle",
+ "detail": f"high_value={digest_counts.get('high_value_alert_count', 0)}, queue={len(list(operator_digest.get('review_queue') or []))}",
+ "reason": ", ".join(quality_flags[:3]) or "digest_queue_visible",
+ },
+ {
+ "title": "Weekly Review",
+ "status": f"entries={weekly_review.get('entry_count') or 0}",
+ "detail": ", ".join(list(weekly_review.get("next_week_actions") or [])[:3]) or "weekly_review_available",
+ "reason": f"framework_rate={weekly_review.get('framework_execution_rate') if weekly_review.get('framework_execution_rate') is not None else 'n/a'}",
+ },
+ ]
+
+
+def _build_no_trade_reason_items(*, why_blocked: list[str], launch_blockers: list[str], launch_warnings: list[str]) -> list[dict]:
+ items = []
+ for reason in _dedupe([*launch_blockers, *why_blocked]):
+ items.append(
+ {
+ "reason": reason,
+ "priority": _reason_priority(reason),
+ "category": _reason_category(reason),
+ "source": "launch_blocker" if reason in launch_blockers else "decision_packet",
+ }
+ )
+ for reason in _dedupe(launch_warnings):
+ if any(item["reason"] == reason for item in items):
+ continue
+ items.append(
+ {
+ "reason": reason,
+ "priority": "low",
+ "category": "advisory",
+ "source": "launch_warning",
+ }
+ )
+ order = {"high": 3, "medium": 2, "low": 1}
+ return sorted(items, key=lambda item: (order.get(str(item.get("priority")), 0), str(item.get("reason") or "")), reverse=True)[:8]
+
+
+def _decision_packet_priority_score(decision_packet: dict) -> float:
+ action = str(decision_packet.get("action_recommendation") or "")
+ base = {
+ "confirm_ticket": 120.0,
+ "build_execution_ticket": 115.0,
+ "manage_ticket": 110.0,
+ "restricted_decision_support": 70.0,
+ "decision_support_only": 65.0,
+ "observe_only": 35.0,
+ }.get(action, 50.0)
+ return base + float(decision_packet.get("quality_score") or 0.0)
+
+
+def _session_focus_priority_score(item: dict) -> float:
+ recommendation = dict(item.get("session_recommendation") or {})
+ score = float(recommendation.get("score") or 0.0)
+ score += float(item.get("alert_count") or 0) * 5.0
+ score += float(item.get("executable_candidate_count") or 0) * 8.0
+ if str(item.get("action_hint") or "") == "review_alerts":
+ score += 15.0
+ if str(item.get("action_hint") or "") == "focus_now":
+ score += 10.0
+ return score
+
+
+def _review_queue_priority_score(item: dict) -> float:
+ return {
+ "high": 92.0,
+ "medium": 78.0,
+ "low": 55.0,
+ }.get(str(item.get("priority") or ""), 60.0)
+
+
+def _reason_priority(reason: str) -> str:
+ text = str(reason or "")
+ if any(token in text for token in ("market_", "runtime_", "broker_", "readiness_", "live_", "gap_", "stream_")):
+ return "high"
+ if any(token in text for token in ("trust_", "candidate_", "confirmation", "risk_")):
+ return "medium"
+ return "low"
+
+
+def _reason_category(reason: str) -> str:
+ text = str(reason or "")
+ if any(token in text for token in ("market_", "stream_", "gap_")):
+ return "market_truth"
+ if any(token in text for token in ("runtime_", "broker_", "live_", "readiness_")):
+ return "execution_surface"
+ if any(token in text for token in ("trust_", "risk_")):
+ return "policy_guard"
+ return "setup_quality"
+
+
+def _dedupe(items: list[str]) -> list[str]:
+ seen = set()
+ result = []
+ for item in items:
+ value = str(item or "").strip()
+ if not value or value in seen:
+ continue
+ seen.add(value)
+ result.append(value)
+ return result
+
+
def aggregate_session_recommendations(models: list[dict]) -> dict[str, dict]:
buckets: dict[str, dict] = {}
for model in models:
diff --git a/src/web/app.js b/src/web/app.js
index fc0c3f8..67ee7d9 100644
--- a/src/web/app.js
+++ b/src/web/app.js
@@ -11,6 +11,7 @@ const trustClassFilterInput = document.getElementById('trustClassFilter');
const lastUpdated = document.getElementById('lastUpdated');
const activeFilters = document.getElementById('activeFilters');
const overviewCards = document.getElementById('overviewCards');
+const decisionAssistantPanel = document.getElementById('decisionAssistantPanel');
const marketStatePanel = document.getElementById('marketStatePanel');
const runtimeStatusPanel = document.getElementById('runtimeStatusPanel');
const decisionPacketPanel = document.getElementById('decisionPacketPanel');
@@ -31,6 +32,7 @@ const canvas = document.getElementById('candlesChart');
const ctx = canvas.getContext('2d');
let autoRefreshTimer = null;
+let assistantHistoryKindFilter = loadAssistantHistoryKindFilter();
async function fetchJson(url, options = {}) {
const fetchOptions = { ...options };
@@ -69,6 +71,67 @@ function clearNode(node) {
}
}
+function revealPanel(node) {
+ const panel = node?.closest?.('.panel') || node;
+ if (panel && typeof panel.scrollIntoView === 'function') {
+ panel.scrollIntoView({ behavior: 'smooth', block: 'start' });
+ }
+}
+
+function loadAssistantHistoryKindFilter() {
+ try {
+ return window.localStorage?.getItem('assistantHistoryKindFilter') || 'all';
+ } catch {
+ return 'all';
+ }
+}
+
+function persistAssistantHistoryKindFilter(value) {
+ assistantHistoryKindFilter = value || 'all';
+ try {
+ window.localStorage?.setItem('assistantHistoryKindFilter', assistantHistoryKindFilter);
+ } catch {
+ // no-op
+ }
+}
+
+function normalizeAssistantSectionId(title) {
+ return String(title || '')
+ .toLowerCase()
+ .replace(/[^a-z0-9]+/g, '-')
+ .replace(/^-+|-+$/g, '');
+}
+
+function revealAssistantSection(sectionId) {
+ if (!decisionAssistantPanel) return;
+ const target = decisionAssistantPanel.querySelector(`[data-assistant-section="${sectionId}"]`);
+ if (target && typeof target.scrollIntoView === 'function') {
+ target.scrollIntoView({ behavior: 'smooth', block: 'start' });
+ }
+}
+
+function scanCardActionLabel(sectionId) {
+ if (sectionId === 'review-entry') return 'Open Review';
+ if (sectionId === 'digest') return 'Open Digest';
+ if (sectionId === 'external-context') return 'Open Context';
+ return 'Open';
+}
+
+function prefillOperatorNote(body) {
+ revealPanel(operatorConsolePanel);
+ const textarea = operatorConsolePanel.querySelector('textarea');
+ if (textarea) {
+ textarea.value = body || '';
+ textarea.focus();
+ }
+}
+
+function appendSectionTitle(container, title) {
+ const element = createElement('div', 'assistant-section-title', title);
+ element.dataset.assistantSection = normalizeAssistantSectionId(title);
+ container.appendChild(element);
+}
+
function createElement(tag, className, text) {
const element = document.createElement(tag);
if (className) element.className = className;
@@ -229,6 +292,22 @@ async function runAction(url, body = {}) {
}
}
+async function saveOperatorTemplateNote(template) {
+ try {
+ await postJson('/trading/operator-notes', {
+ instrument_id: Number(instrumentIdInput.value || 1),
+ note_scope: template.note_scope || 'workbench',
+ author: 'assistant_template',
+ body: template.body || '',
+ session_code: sessionFilterInput.value === 'all' ? null : sessionFilterInput.value,
+ tags: Array.isArray(template.tags) ? template.tags : ['assistant-template'],
+ });
+ await refreshDashboard();
+ } catch (error) {
+ showErrorPanel(String(error));
+ }
+}
+
function renderHealth(health) {
setText(
healthStatus,
@@ -256,6 +335,743 @@ function renderOverview(weeklyReview, candidates, tickets, executions) {
cards.forEach((card) => overviewCards.appendChild(card));
}
+function renderDecisionAssistant(summary) {
+ clearNode(decisionAssistantPanel);
+ decisionAssistantPanel.classList.add('assistant-dense');
+ window.__latestDecisionAssistantSummary = summary;
+ if (!summary || summary.status === 'unavailable') {
+ decisionAssistantPanel.appendChild(createElement('div', 'empty-state', 'Decision assistant summary unavailable.'));
+ return;
+ }
+ const evidence = summary.evidence_summary || {};
+ const scanSummaryCards = summary.scan_summary_cards || [];
+ const scanCompletionSummary = summary.scan_completion_summary || null;
+ const workflowDigest = summary.workflow_digest || [];
+ const opportunities = summary.top_opportunities || [];
+ const watchlistTiers = summary.candidate_watchlist_tiers || {};
+ const watchlistSummary = summary.candidate_watchlist_summary || {};
+ const noTradeItems = summary.no_trade_reason_items || [];
+ const decisionTimeline = summary.decision_timeline || [];
+ const explanationDrilldown = summary.explanation_drilldown || [];
+ const reasonCitations = summary.reason_citations || [];
+ const learningRollup = summary.learning_rollup || null;
+ const externalContext = summary.external_context_summary || null;
+ const reviewEntrypoints = summary.review_entrypoints || null;
+ const personalizedReminders = summary.personalized_reminders || [];
+ const digestHistoryPreview = summary.digest_history_preview || [];
+ const digestHistoryGroups = summary.digest_history_groups || [];
+ const digestSurface = summary.digest_surface || null;
+ const setupLearningRollup = summary.setup_learning_rollup || null;
+ const assistantSections = [
+ scanSummaryCards.length ? { title: 'Scan' } : null,
+ digestSurface ? { title: 'Digest' } : null,
+ reviewEntrypoints ? { title: 'Review Entry' } : null,
+ opportunities.length ? { title: 'Opportunities' } : null,
+ (watchlistTiers.review_now || []).length ? { title: 'Watchlist: Review Now' } : null,
+ noTradeItems.length ? { title: 'No-Trade Reasons' } : null,
+ setupLearningRollup ? { title: 'Setup Replay' } : null,
+ externalContext ? { title: 'External Context' } : null,
+ decisionTimeline.length ? { title: 'Timeline' } : null,
+ explanationDrilldown.length ? { title: 'Explain' } : null,
+ ].filter(Boolean);
+ if (scanSummaryCards.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Scan');
+ const scanGrid = createElement('div', 'cards assistant-scan-grid');
+ scanSummaryCards.forEach((item) => {
+ const card = buildItemCard(
+ item.title || 'summary',
+ item.status || 'n/a',
+ [
+ ['detail', item.detail || 'n/a'],
+ ['context', item.context || 'n/a'],
+ ['rank', String(item.rank ?? 'n/a')],
+ ['scan step', item.is_next_scan_step ? 'next' : String(item.scan_step_index ?? 'n/a')],
+ ['completion', item.completion_state || 'n/a'],
+ ['completion hint', item.completion_hint || 'n/a'],
+ ['next step', item.transition_hint || 'n/a'],
+ ],
+ item.target_section
+ ? [{
+ label: item.action_label || scanCardActionLabel(item.target_section),
+ onClick: () => revealAssistantSection(item.target_section),
+ }]
+ : []
+ );
+ if (item.target_section) {
+ card.classList.add('clickable-card');
+ card.addEventListener('click', () => revealAssistantSection(item.target_section));
+ }
+ if (item.emphasis) {
+ card.classList.add(`assistant-scan-${item.emphasis}`);
+ }
+ if (item.is_next_scan_step) {
+ card.classList.add('assistant-scan-next');
+ }
+ scanGrid.appendChild(card);
+ });
+ decisionAssistantPanel.appendChild(scanGrid);
+ if (scanCompletionSummary) {
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['scan completion', scanCompletionSummary.status || 'idle'],
+ ['open scan steps', String(scanCompletionSummary.open_count ?? 0)],
+ ['blocked scan steps', String(scanCompletionSummary.blocked_count ?? 0)],
+ ['refresh-needed steps', String(scanCompletionSummary.refresh_needed_count ?? 0)],
+ ['ready steps', String(scanCompletionSummary.ready_count ?? 0)],
+ ['next scan card', scanCompletionSummary.next_title || 'n/a'],
+ ['next scan target', scanCompletionSummary.next_target_section || 'n/a'],
+ ['next scan hint', scanCompletionSummary.next_hint || 'n/a'],
+ ])
+ );
+ }
+ }
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['focus mode', summary.focus_mode || 'monitor_and_wait'],
+ ['headline', summary.headline || 'n/a'],
+ ['market posture', summary.market_posture || 'n/a'],
+ ['risk posture', summary.risk_posture || 'n/a'],
+ ['attention reason', summary.attention_reason || 'n/a'],
+ ['current session', summary.current_session || 'n/a'],
+ ['changed since last', summary.changed_since_last?.summary || 'n/a'],
+ ['decision', `${evidence.decision_status || 'unknown'} / ${evidence.decision_action || 'monitor'}`],
+ ['trust / mode', `${evidence.trust_classification || 'unknown'} / ${evidence.operator_mode || 'unknown'}`],
+ ['market truth', `${evidence.market_freshness || 'unknown'} | ${evidence.stream_status || 'unknown'} | ${evidence.data_gap_status || 'unknown'}`],
+ ['launch mode', evidence.launch_mode || 'unknown'],
+ ['top opportunity', opportunities[0]?.title || 'n/a'],
+ ['watchlist tiers', `review ${watchlistSummary.review_now_count ?? 0} | strong ${watchlistSummary.strong_watch_count ?? 0} | soft ${watchlistSummary.soft_watch_count ?? 0}`],
+ ['section nav', assistantSections.map((item) => item.title).join(' | ') || 'n/a'],
+ ['no-trade reasons', formatList(summary.no_trade_reasons || [])],
+ ['next actions', formatList(summary.next_actions || [])],
+ ])
+ );
+ if (assistantSections.length) {
+ decisionAssistantPanel.appendChild(
+ buildActionBar(
+ assistantSections.map((item) => ({
+ label: item.title,
+ onClick: () => revealAssistantSection(normalizeAssistantSectionId(item.title)),
+ }))
+ )
+ );
+ }
+ if (digestSurface) {
+ appendSectionTitle(decisionAssistantPanel, 'Digest');
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['digest status', digestSurface.status || 'idle'],
+ ['digest summary', digestSurface.summary || 'n/a'],
+ ['reminders', String(digestSurface.reminder_count ?? 0)],
+ ['high priority reminders', String(digestSurface.high_priority_count ?? 0)],
+ ['digest history', String(digestSurface.history_count ?? 0)],
+ ['digest kinds', formatList(digestSurface.history_summary?.kinds || [])],
+ ['recommended history kind', digestSurface.history_summary?.recommended_kind || 'n/a'],
+ ['recommended history count', String(digestSurface.history_summary?.recommended_count ?? 0)],
+ ['fresh history groups', String(digestSurface.history_summary?.fresh_kind_count ?? 0)],
+ ['stale history groups', String(digestSurface.history_summary?.stale_kind_count ?? 0)],
+ ['refresh-needed groups', String(digestSurface.history_summary?.refresh_needed_count ?? 0)],
+ ['refresh prompts', formatList((digestSurface.history_summary?.refresh_prompts || []).map((item) => `${item.kind}: ${item.prompt}`))],
+ ['history filter', assistantHistoryKindFilter || 'all'],
+ ['digest latest', formatTimestamp(digestSurface.history_summary?.latest_ts)],
+ ['digest top opportunity', digestSurface.top_opportunity_title || 'n/a'],
+ ['learning status', digestSurface.learning_status || 'n/a'],
+ ])
+ );
+ }
+ if (personalizedReminders.length) {
+ const reminderGrid = createElement('div', 'cards assistant-tight-grid');
+ personalizedReminders.forEach((item) => {
+ reminderGrid.appendChild(
+ buildItemCard(
+ item.title || 'reminder',
+ item.severity || 'info',
+ [
+ ['message', item.message || 'n/a'],
+ ['source', item.source || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(reminderGrid);
+ }
+ if (digestHistoryGroups.length) {
+ const recommendedKind = digestSurface?.history_summary?.recommended_kind || 'all';
+ const groupKinds = digestHistoryGroups.map((item) => item.kind);
+ if (assistantHistoryKindFilter !== 'all' && !groupKinds.includes(assistantHistoryKindFilter)) {
+ persistAssistantHistoryKindFilter(recommendedKind);
+ }
+ const historyFilterBar = buildActionBar([
+ {
+ label: 'All History',
+ onClick: () => {
+ persistAssistantHistoryKindFilter('all');
+ renderDecisionAssistant(window.__latestDecisionAssistantSummary || summary);
+ },
+ },
+ ...(recommendedKind && recommendedKind !== 'all'
+ ? [{
+ label: `Recommended ${recommendedKind}`,
+ onClick: () => {
+ persistAssistantHistoryKindFilter(recommendedKind);
+ renderDecisionAssistant(window.__latestDecisionAssistantSummary || summary);
+ },
+ }]
+ : []),
+ ...digestHistoryGroups.map((item) => ({
+ label: `${item.kind} (${item.count})${item.recommended ? ' *' : ''}`,
+ onClick: () => {
+ persistAssistantHistoryKindFilter(item.kind || 'all');
+ renderDecisionAssistant(window.__latestDecisionAssistantSummary || summary);
+ },
+ })),
+ ]);
+ decisionAssistantPanel.appendChild(historyFilterBar);
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['active history focus', assistantHistoryKindFilter || 'all'],
+ ['recommended history focus', recommendedKind || 'n/a'],
+ ])
+ );
+ const groupGrid = createElement('div', 'cards assistant-history-grid');
+ const visibleGroups = assistantHistoryKindFilter === 'all'
+ ? digestHistoryGroups
+ : digestHistoryGroups.filter((item) => String(item.kind || '') === assistantHistoryKindFilter);
+ visibleGroups.forEach((item) => {
+ const card = buildItemCard(
+ item.recommended ? `Recommended ${item.kind || 'history'}` : `History: ${item.kind || 'history'}`,
+ item.group_status || String(item.count ?? 0),
+ [
+ ['headline', item.headline || 'n/a'],
+ ['latest', formatTimestamp(item.latest_ts)],
+ ['latest item', item.latest_title || 'n/a'],
+ ['age band', item.age_band || 'n/a'],
+ ['staleness', item.staleness_hint || 'n/a'],
+ ['refresh', item.refresh_prompt || 'n/a'],
+ ['count', String(item.count ?? 0)],
+ ['items', formatList((item.items || []).map((row) => row.title || row.kind || 'item'))],
+ ],
+ [{
+ label: item.refresh_action_label || item.action_label || 'Review Group',
+ onClick: () => {
+ persistAssistantHistoryKindFilter(item.kind || 'all');
+ renderDecisionAssistant(window.__latestDecisionAssistantSummary || summary);
+ },
+ }]
+ );
+ if (item.recommended) {
+ card.classList.add('assistant-history-recommended');
+ }
+ groupGrid.appendChild(card);
+ });
+ if (visibleGroups.length) {
+ decisionAssistantPanel.appendChild(groupGrid);
+ } else {
+ decisionAssistantPanel.appendChild(createElement('div', 'empty-state', 'No history items match the current digest filter.'));
+ }
+ }
+ if (reviewEntrypoints) {
+ appendSectionTitle(decisionAssistantPanel, 'Review Entry');
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['review entrypoints', reviewEntrypoints.status || 'idle'],
+ ['notes', String(reviewEntrypoints.note_count ?? 0)],
+ ['latest note author', reviewEntrypoints.latest_note_author || 'n/a'],
+ ['latest note time', formatTimestamp(reviewEntrypoints.latest_note_ts)],
+ ['journal entries', String(reviewEntrypoints.journal_entry_count ?? 0)],
+ ['review queue', String(reviewEntrypoints.review_queue_count ?? 0)],
+ ['next review action', reviewEntrypoints.next_review_action || 'n/a'],
+ ['recommended template', reviewEntrypoints.recommended_template_id || 'n/a'],
+ ['recommended label', reviewEntrypoints.template_summary?.recommended_label || 'n/a'],
+ ['recommended priority', reviewEntrypoints.template_summary?.recommended_priority || 'n/a'],
+ ['recommended reason', reviewEntrypoints.template_summary?.recommended_reason || 'n/a'],
+ ['recommended reuse hint', reviewEntrypoints.template_summary?.recommended_reuse_hint || 'n/a'],
+ ['template reuse status', reviewEntrypoints.template_reuse_metrics?.reuse_status || 'n/a'],
+ ['template notes saved', String(reviewEntrypoints.template_reuse_metrics?.assistant_template_note_count ?? 0)],
+ ['recommended reuse count', String(reviewEntrypoints.template_reuse_metrics?.recommended_reuse_count ?? 0)],
+ ['recommended last used', formatTimestamp(reviewEntrypoints.template_reuse_metrics?.recommended_last_used_ts)],
+ ['latest template note', formatTimestamp(reviewEntrypoints.template_reuse_metrics?.latest_template_note_ts)],
+ ['template priorities', formatJsonInline(reviewEntrypoints.template_summary?.priorities || {})],
+ ])
+ );
+ decisionAssistantPanel.appendChild(
+ buildActionBar([
+ {
+ label: 'Open Notes',
+ onClick: () => prefillOperatorNote(''),
+ },
+ {
+ label: 'Open Review',
+ onClick: () => revealPanel(postTradeReviewPanel),
+ },
+ {
+ label: 'Generate Journal',
+ onClick: () => runAction('/trading/journal/generate', {}),
+ },
+ ])
+ );
+ const noteTemplates = reviewEntrypoints.note_templates || [];
+ if (noteTemplates.length) {
+ const recommendedTemplate = noteTemplates.find((item) => item.template_id === reviewEntrypoints.recommended_template_id) || noteTemplates[0];
+ const templateGrid = createElement('div', 'cards assistant-tight-grid');
+ noteTemplates.forEach((item) => {
+ const isRecommended = item.template_id === reviewEntrypoints.recommended_template_id;
+ templateGrid.appendChild(
+ buildItemCard(
+ isRecommended ? `Recommended: ${item.label || 'Template'}` : (item.label || 'Template'),
+ item.priority || 'medium',
+ [
+ ['reason', item.reason || 'n/a'],
+ ['scope', item.note_scope || 'workbench'],
+ ['tags', formatList(item.tags || [])],
+ ['context', formatList(item.context_items || [])],
+ ['carry-over', formatList(item.carry_over_context || [])],
+ ['reuse count', String(item.reuse_count ?? 0)],
+ ['reuse status', item.reuse_status || 'n/a'],
+ ['last used', formatTimestamp(item.last_used_ts)],
+ ],
+ [
+ {
+ label: 'Use',
+ onClick: () => prefillOperatorNote(item.body || ''),
+ },
+ {
+ label: 'Save',
+ onClick: () => saveOperatorTemplateNote(item),
+ },
+ ],
+ [item.body || 'n/a', item.follow_up_prompt || '', item.reuse_hint || ''].filter(Boolean).join(' | '),
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(templateGrid);
+ decisionAssistantPanel.appendChild(
+ buildActionBar([
+ {
+ label: 'Use Recommended',
+ onClick: () => prefillOperatorNote(recommendedTemplate?.body || ''),
+ },
+ {
+ label: 'Save Recommended',
+ onClick: () => saveOperatorTemplateNote(recommendedTemplate),
+ },
+ ])
+ );
+ const templateBar = buildActionBar(
+ noteTemplates.map((item) => ({
+ label: item.template_id === reviewEntrypoints.recommended_template_id
+ ? `${item.label || 'Use Template'}`
+ : (item.label || 'Use Template'),
+ onClick: () => prefillOperatorNote(item.body || ''),
+ }))
+ );
+ decisionAssistantPanel.appendChild(templateBar);
+ const quickSaveBar = buildActionBar(
+ noteTemplates.map((item) => ({
+ label: `Save ${item.label || 'Template'}`,
+ onClick: () => saveOperatorTemplateNote(item),
+ }))
+ );
+ decisionAssistantPanel.appendChild(quickSaveBar);
+ }
+ }
+ if (workflowDigest.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Workflow');
+ const workflowGrid = createElement('div', 'cards');
+ workflowDigest.forEach((item) => {
+ workflowGrid.appendChild(
+ buildItemCard(
+ item.title || item.phase || 'workflow',
+ item.status || 'n/a',
+ [
+ ['detail', item.detail || 'n/a'],
+ ['next step', item.next_step || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(workflowGrid);
+ }
+ if (opportunities.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Opportunities');
+ const opportunityGrid = createElement('div', 'cards');
+ opportunities.forEach((item) => {
+ opportunityGrid.appendChild(
+ buildItemCard(
+ item.title || 'opportunity',
+ item.status || 'observe',
+ [
+ ['kind', item.kind || 'n/a'],
+ ['detail', item.detail || 'n/a'],
+ ['priority', String(item.priority_score ?? 'n/a')],
+ ['reason', item.reason || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(opportunityGrid);
+ }
+ [
+ ['review_now', 'Review Now'],
+ ['strong_watch', 'Strong Watch'],
+ ['soft_watch', 'Soft Watch'],
+ ['archive_only', 'Archive Only'],
+ ].forEach(([key, label]) => {
+ const items = watchlistTiers[key] || [];
+ if (!items.length) return;
+ appendSectionTitle(decisionAssistantPanel, `Watchlist: ${label}`);
+ const tierGrid = createElement('div', 'cards assistant-tight-grid');
+ items.forEach((item) => {
+ tierGrid.appendChild(
+ buildItemCard(
+ `${label}: ${item.title || 'candidate'}`,
+ item.status || key,
+ [
+ ['session', item.session_code || 'n/a'],
+ ['model', item.model_code || 'n/a'],
+ ['quality', String(item.quality_score ?? 'n/a')],
+ ['tier reason', item.tier_reason || 'n/a'],
+ ['suggested action', item.suggested_action || 'n/a'],
+ ['detail', item.detail || 'n/a'],
+ ['missing confirmations', formatList(item.missing_confirmations || [])],
+ ],
+ item.action_path && item.action_method === 'POST'
+ ? [{
+ label: 'Run Confirm Check',
+ onClick: () => runAction(item.action_path, {}),
+ }]
+ : []
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(tierGrid);
+ });
+ if (noTradeItems.length) {
+ appendSectionTitle(decisionAssistantPanel, 'No-Trade Reasons');
+ const noTradeGrid = createElement('div', 'cards assistant-tight-grid');
+ noTradeItems.forEach((item) => {
+ noTradeGrid.appendChild(
+ buildItemCard(
+ item.reason || 'no-trade',
+ item.priority || 'low',
+ [
+ ['category', item.category || 'n/a'],
+ ['source', item.source || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(noTradeGrid);
+ }
+ if (decisionTimeline.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Timeline');
+ const timelineGrid = createElement('div', 'cards');
+ decisionTimeline.forEach((item) => {
+ timelineGrid.appendChild(
+ buildItemCard(
+ item.title || 'timeline',
+ item.ts || 'n/a',
+ [
+ ['detail', item.detail || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(timelineGrid);
+ }
+ if (explanationDrilldown.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Explain');
+ const drilldownGrid = createElement('div', 'cards');
+ explanationDrilldown.forEach((item) => {
+ drilldownGrid.appendChild(
+ buildItemCard(
+ item.title || 'explanation',
+ item.status || 'n/a',
+ [
+ ['detail', item.detail || 'n/a'],
+ ['reasons', formatList(item.reasons || [])],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(drilldownGrid);
+ }
+ if (reasonCitations.length) {
+ appendSectionTitle(decisionAssistantPanel, 'Reason Citations');
+ const citationGrid = createElement('div', 'cards');
+ reasonCitations.forEach((item) => {
+ citationGrid.appendChild(
+ buildItemCard(
+ item.reason || 'citation',
+ item.severity || 'supporting',
+ [
+ ['source', item.source_type || 'n/a'],
+ ['path', item.source_path || 'n/a'],
+ ['detail', item.detail || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(citationGrid);
+ }
+ if (learningRollup) {
+ appendSectionTitle(decisionAssistantPanel, 'Learning');
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['learning status', learningRollup.status || 'idle'],
+ ['review entries', String(learningRollup.entry_count ?? 0)],
+ ['pending feedback', String(learningRollup.pending_feedback_count ?? 0)],
+ ['visual mismatches', String(learningRollup.visual_mismatch_count ?? 0)],
+ ['high-value alerts', String(learningRollup.high_value_alert_count ?? 0)],
+ ['quality flags', formatList(learningRollup.quality_flags || [])],
+ ['next review actions', formatList(learningRollup.next_week_actions || [])],
+ ['learning summary', learningRollup.operator_summary || 'n/a'],
+ ])
+ );
+ }
+ if (externalContext) {
+ appendSectionTitle(decisionAssistantPanel, 'External Context');
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['external context', externalContext.status || 'idle'],
+ ['external headline', externalContext.headline || 'n/a'],
+ ['news provider', `${externalContext.provider_name || 'unknown'} / ${externalContext.freshness_code || 'unknown'}`],
+ ['news window', externalContext.news_window_code || 'unknown'],
+ ['active events', formatList(externalContext.active_event_titles || [])],
+ ['exchange status', `${externalContext.exchange_status?.provider_name || 'unknown'} / ${externalContext.exchange_status?.status || 'unknown'}`],
+ ['exchange events', formatList(externalContext.exchange_status_titles || [])],
+ ['announcements', `${externalContext.announcements_snapshot?.provider_name || 'unknown'} / ${externalContext.announcements_snapshot?.status || 'unknown'}`],
+ ['announcement titles', formatList(externalContext.announcement_titles || [])],
+ ['waiting items', formatList(externalContext.external_waiting_items || [])],
+ ['council watch items', formatList(externalContext.council_watch_items || [])],
+ ['operator questions', formatList(externalContext.operator_questions || [])],
+ ['next confirmation targets', formatList(externalContext.next_confirmation_targets || [])],
+ ])
+ );
+ const sourceCards = externalContext.source_cards || [];
+ if (sourceCards.length) {
+ const sourceGrid = createElement('div', 'cards');
+ sourceCards.forEach((item) => {
+ sourceGrid.appendChild(
+ buildItemCard(
+ item.title || item.source_type || 'context source',
+ item.source_type || 'source',
+ [
+ ['detail', item.detail || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(sourceGrid);
+ }
+ }
+ if (setupLearningRollup) {
+ appendSectionTitle(decisionAssistantPanel, 'Setup Replay');
+ decisionAssistantPanel.appendChild(
+ buildInfoGrid([
+ ['setup learning', setupLearningRollup.status || 'idle'],
+ ['setup type', setupLearningRollup.setup_type || 'unknown'],
+ ['setup session', setupLearningRollup.session_code || 'n/a'],
+ ['session source', setupLearningRollup.session_code_source || 'n/a'],
+ ['lesson count', String(setupLearningRollup.lesson_count ?? 0)],
+ ['replay samples', String(setupLearningRollup.replay_statistics?.sample_count ?? 0)],
+ ['framework rate', formatPercent(setupLearningRollup.replay_statistics?.framework_execution_rate)],
+ ['model outside rate', formatPercent(setupLearningRollup.replay_statistics?.model_outside_rate)],
+ ['avg R', formatNumber(setupLearningRollup.replay_statistics?.avg_r_multiple, 2)],
+ ['risk violations', String(setupLearningRollup.replay_statistics?.risk_violation_count ?? 0)],
+ ['framework gap vs high', formatPercent(setupLearningRollup.replay_statistics?.comparative_summary?.framework_execution_gap_vs_high)],
+ ['avg R gap vs high', formatNumber(setupLearningRollup.replay_statistics?.comparative_summary?.avg_r_gap_vs_high, 2)],
+ ['framework gap vs low', formatPercent(setupLearningRollup.replay_statistics?.comparative_summary?.framework_execution_gap_vs_low)],
+ ['avg R gap vs low', formatNumber(setupLearningRollup.replay_statistics?.comparative_summary?.avg_r_gap_vs_low, 2)],
+ ['comparison status', setupLearningRollup.replay_statistics?.comparative_summary?.comparison_status || 'n/a'],
+ ['confidence band', setupLearningRollup.replay_statistics?.comparative_summary?.confidence_band || 'n/a'],
+ ['session alignment', setupLearningRollup.replay_statistics?.comparative_summary?.session_alignment || 'n/a'],
+ ['comparison notes', formatList(setupLearningRollup.replay_statistics?.comparative_summary?.notes || [])],
+ ['comparison story', setupLearningRollup.replay_statistics?.comparative_summary?.story || 'n/a'],
+ ['global framework avg', formatPercent(setupLearningRollup.replay_statistics?.broader_baseline_summary?.framework_execution_global_avg)],
+ ['global avg R', formatNumber(setupLearningRollup.replay_statistics?.broader_baseline_summary?.avg_r_global_avg, 2)],
+ ['global risk avg', formatNumber(setupLearningRollup.replay_statistics?.broader_baseline_summary?.risk_violation_global_avg, 2)],
+ ['framework rank', String(setupLearningRollup.replay_statistics?.broader_baseline_summary?.framework_rank ?? 'n/a')],
+ ['framework percentile', formatPercent(setupLearningRollup.replay_statistics?.broader_baseline_summary?.framework_percentile)],
+ ['avg R rank', String(setupLearningRollup.replay_statistics?.broader_baseline_summary?.avg_r_rank ?? 'n/a')],
+ ['avg R percentile', formatPercent(setupLearningRollup.replay_statistics?.broader_baseline_summary?.avg_r_percentile)],
+ ['risk rank', String(setupLearningRollup.replay_statistics?.broader_baseline_summary?.risk_rank ?? 'n/a')],
+ ['risk percentile', formatPercent(setupLearningRollup.replay_statistics?.broader_baseline_summary?.risk_percentile)],
+ ['composite percentile', formatPercent(setupLearningRollup.replay_statistics?.broader_baseline_summary?.composite_percentile)],
+ ['risk band', setupLearningRollup.replay_statistics?.broader_baseline_summary?.risk_band || 'n/a'],
+ ['composite band', setupLearningRollup.replay_statistics?.broader_baseline_summary?.composite_band || 'n/a'],
+ ['composite rank', String(setupLearningRollup.replay_statistics?.broader_baseline_summary?.composite_rank ?? 'n/a')],
+ ['position band', setupLearningRollup.replay_statistics?.broader_baseline_summary?.position_band || 'n/a'],
+ ['replay verdict', setupLearningRollup.replay_statistics?.broader_baseline_summary?.replay_verdict || 'n/a'],
+ ['peer summary', setupLearningRollup.replay_statistics?.broader_baseline_summary?.peer_summary || 'n/a'],
+ ['setup family', setupLearningRollup.replay_statistics?.broader_baseline_summary?.setup_family || 'n/a'],
+ ['setup family status', setupLearningRollup.replay_statistics?.broader_baseline_summary?.setup_family_status || 'n/a'],
+ ['setup family peers', setupLearningRollup.replay_statistics?.broader_baseline_summary?.setup_family_peer_summary || 'n/a'],
+ ['setup family focus', setupLearningRollup.replay_statistics?.broader_baseline_summary?.setup_family_focus || 'n/a'],
+ ['broader story', setupLearningRollup.replay_statistics?.broader_baseline_summary?.story || 'n/a'],
+ ['high-quality session', setupLearningRollup.high_quality_session?.session_code || 'n/a'],
+ ['low-quality session', setupLearningRollup.low_quality_session?.session_code || 'n/a'],
+ ])
+ );
+ const broadTopSessions = setupLearningRollup.replay_statistics?.broader_baseline_summary?.top_sessions || [];
+ const broadTopRSessions = setupLearningRollup.replay_statistics?.broader_baseline_summary?.top_r_sessions || [];
+ const broadTopSafe = setupLearningRollup.replay_statistics?.broader_baseline_summary?.top_safe_sessions || [];
+ const broadTopComposite = setupLearningRollup.replay_statistics?.broader_baseline_summary?.top_composite_sessions || [];
+ const peerSessions = setupLearningRollup.replay_statistics?.broader_baseline_summary?.peer_sessions || [];
+ const setupFamilyPeers = setupLearningRollup.replay_statistics?.broader_baseline_summary?.setup_family_peer_sessions || [];
+ if (broadTopSessions.length || broadTopRSessions.length || broadTopSafe.length || broadTopComposite.length || peerSessions.length || setupFamilyPeers.length) {
+ const leaderboardGrid = createElement('div', 'cards assistant-tight-grid');
+ broadTopSessions.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Top Framework: ${item.session_code || 'session'}`,
+ formatPercent(item.metric),
+ [
+ ['samples', String(item.count ?? 0)],
+ ]
+ )
+ );
+ });
+ broadTopRSessions.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Top Avg R: ${item.session_code || 'session'}`,
+ formatNumber(item.metric, 2),
+ [
+ ['samples', String(item.count ?? 0)],
+ ]
+ )
+ );
+ });
+ broadTopSafe.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Safest: ${item.session_code || 'session'}`,
+ formatNumber(item.metric, 0),
+ [
+ ['samples', String(item.count ?? 0)],
+ ]
+ )
+ );
+ });
+ peerSessions.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Peer ${String(item.relative_position || 'nearby')}: ${item.session_code || 'session'}`,
+ `rank ${String(item.rank ?? 'n/a')}`,
+ [
+ ['framework', formatPercent(item.framework_execution_rate)],
+ ['avg R', formatNumber(item.avg_r_multiple, 2)],
+ ['risk violations', String(item.risk_violation_count ?? 0)],
+ ['samples', String(item.count ?? 0)],
+ ]
+ )
+ );
+ });
+ setupFamilyPeers.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Family Peer: ${item.session_code || 'session'}`,
+ item.setup_family || 'setup family',
+ [
+ ['relative', item.relative_position || 'n/a'],
+ ['rank', String(item.rank ?? 'n/a')],
+ ['framework', formatPercent(item.framework_execution_rate)],
+ ['avg R', formatNumber(item.avg_r_multiple, 2)],
+ ['risk violations', String(item.risk_violation_count ?? 0)],
+ ['samples', String(item.count ?? 0)],
+ ]
+ )
+ );
+ });
+ broadTopComposite.forEach((item) => {
+ leaderboardGrid.appendChild(
+ buildItemCard(
+ `Top Composite: ${item.session_code || 'session'}`,
+ `${formatPercent(item.framework_execution_rate)} | ${formatNumber(item.avg_r_multiple, 2)}`,
+ [
+ ['risk violations', String(item.risk_violation_count ?? 0)],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(leaderboardGrid);
+ }
+ const lessonItems = setupLearningRollup.top_lessons || [];
+ if (lessonItems.length) {
+ const lessonGrid = createElement('div', 'cards');
+ lessonItems.forEach((item) => {
+ lessonGrid.appendChild(
+ buildItemCard(
+ item.title || 'lesson',
+ item.lesson_type || 'lesson',
+ [
+ ['summary', item.summary || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(lessonGrid);
+ }
+ }
+ if (digestHistoryPreview.length) {
+ const filteredHistory = assistantHistoryKindFilter === 'all'
+ ? digestHistoryPreview
+ : digestHistoryPreview.filter((item) => String(item.kind || '') === assistantHistoryKindFilter);
+ const historyGrid = createElement('div', 'cards assistant-history-grid');
+ filteredHistory.forEach((item) => {
+ historyGrid.appendChild(
+ buildItemCard(
+ item.title || item.kind || 'history',
+ item.kind || 'history',
+ [
+ ['detail', item.detail || 'n/a'],
+ ['time', formatTimestamp(item.ts)],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(historyGrid);
+ }
+ const items = summary.top_priority_items || [];
+ const evidenceChain = summary.evidence_chain || [];
+ if (!items.length) {
+ decisionAssistantPanel.appendChild(createElement('div', 'empty-state', 'No priority items yet. Monitor and wait.'));
+ } else {
+ const grid = createElement('div', 'cards');
+ items.forEach((item) => {
+ grid.appendChild(
+ buildItemCard(
+ item.title || item.kind || 'priority',
+ item.status || 'observe',
+ [
+ ['kind', item.kind || 'n/a'],
+ ['detail', item.detail || 'n/a'],
+ ['reason', item.reason || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(grid);
+ }
+ if (!evidenceChain.length) {
+ return;
+ }
+ const evidenceGrid = createElement('div', 'cards');
+ evidenceChain.forEach((item) => {
+ evidenceGrid.appendChild(
+ buildItemCard(
+ item.title || 'evidence',
+ item.status || 'n/a',
+ [
+ ['detail', item.detail || 'n/a'],
+ ['reason', item.reason || 'n/a'],
+ ]
+ )
+ );
+ });
+ decisionAssistantPanel.appendChild(evidenceGrid);
+}
+
function renderMarketState(payload, executions) {
clearNode(marketStatePanel);
const marketState = payload?.market_state || {};
@@ -1364,6 +2180,7 @@ function renderDashboardData({
window.__latestTrustScorePayload = trustScore;
renderHealth(health);
renderOverview(weeklyReview, tradeCandidates.items || [], executionTickets.items || [], tradingExecutions.items || []);
+ renderDecisionAssistant(reviewPacket?.decision_assistant_summary || null);
renderMarketState(marketState, tradingExecutions.items || []);
renderRuntimeStatus(runtimeStatus, runtimeIncidents, maintenanceSummary);
renderDecisionPacket(decisionPacket, decisionAlerts);
diff --git a/src/web/index.html b/src/web/index.html
index 756a285..92ae4a9 100644
--- a/src/web/index.html
+++ b/src/web/index.html
@@ -72,6 +72,11 @@
+
+
Trading Status
diff --git a/src/web/styles.css b/src/web/styles.css
index 737525a..7ccbffa 100644
--- a/src/web/styles.css
+++ b/src/web/styles.css
@@ -188,6 +188,68 @@ th { color: #9db0d3; }
font-size: 12px;
line-height: 1.5;
}
+.assistant-section-title {
+ margin: 16px 0 10px;
+ font-size: 11px;
+ font-weight: 700;
+ letter-spacing: 0.04em;
+ text-transform: uppercase;
+ color: #8fb0ff;
+}
+.assistant-dense .info-grid {
+ grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+ gap: 7px 9px;
+}
+.assistant-dense .info-row {
+ padding: 8px 9px;
+ border: 1px solid #233154;
+ border-radius: 10px;
+ background: #0c132b;
+}
+.assistant-dense .cards {
+ grid-template-columns: repeat(auto-fit, minmax(170px, 1fr));
+ gap: 9px;
+}
+.assistant-scan-grid {
+ grid-template-columns: repeat(auto-fit, minmax(145px, 1fr));
+}
+.assistant-scan-primary {
+ border-color: #4f7cff;
+ background: linear-gradient(180deg, rgba(79, 124, 255, 0.18), rgba(15, 21, 48, 0.95));
+}
+.assistant-scan-warning {
+ border-color: rgba(239, 68, 68, 0.45);
+ background: linear-gradient(180deg, rgba(239, 68, 68, 0.14), rgba(15, 21, 48, 0.95));
+}
+.assistant-scan-action {
+ border-color: rgba(56, 189, 248, 0.4);
+}
+.assistant-scan-context {
+ border-color: rgba(148, 163, 184, 0.35);
+}
+.assistant-scan-summary {
+ border-color: rgba(250, 204, 21, 0.35);
+}
+.assistant-scan-next {
+ box-shadow: inset 0 0 0 1px rgba(56, 189, 248, 0.45), 0 0 0 1px rgba(56, 189, 248, 0.18);
+}
+.assistant-tight-grid {
+ grid-template-columns: repeat(auto-fit, minmax(150px, 1fr));
+}
+.assistant-history-grid {
+ grid-template-columns: repeat(auto-fit, minmax(160px, 1fr));
+}
+.assistant-history-recommended {
+ border-color: #4f7cff;
+ box-shadow: inset 0 0 0 1px rgba(79, 124, 255, 0.25);
+}
+.assistant-dense .card {
+ padding: 11px;
+}
+.assistant-dense .actions {
+ margin-top: 0;
+ margin-bottom: 5px;
+}
.clickable-row {
cursor: pointer;
}
diff --git a/tests/unit/test_api_static_server.py b/tests/unit/test_api_static_server.py
index 48413d9..07319ec 100644
--- a/tests/unit/test_api_static_server.py
+++ b/tests/unit/test_api_static_server.py
@@ -78,6 +78,7 @@ class ApiStaticServerTests(unittest.TestCase):
app_js = app_file.read_text(encoding="utf-8")
for expected in (
+ "Decision Assistant",
"Trading Status",
"Pre-Market Plan",
"Intraday Candidates",
diff --git a/tests/unit/test_documentation_consistency.py b/tests/unit/test_documentation_consistency.py
index 320ae75..a664d52 100644
--- a/tests/unit/test_documentation_consistency.py
+++ b/tests/unit/test_documentation_consistency.py
@@ -17,6 +17,13 @@ TRADING_APP_JS = ROOT / "src" / "web" / "app.js"
TRAINING_HTML = ROOT / "src" / "web" / "training.html"
TRAINING_JS = ROOT / "src" / "web" / "training.js"
OLD_TRAINING_DOC = ROOT / "docs" / "training_case_engine_m1_m6_product_readme.md"
+NONEXISTENT_VISUAL_PLAN_NAME = "AI_ICT_" + "视觉盯盘与主动告警增强方案_v1.md"
+ARCHIVED_DORMANT_DOCS = {
+ "AI_ICT_下一阶段开发计划_v1.md",
+ "AI_ICT_下一阶段开发任务清单_v1.md",
+ "AI_ICT_TradingAgents融合实施方案_v1.md",
+ "AI_ICT_方案一系统边界图与模块融合图_v1.md",
+}
OPTIONAL_DORMANT_DOCS = {
"AI_ICT_TradingAgents_P0自动任务清单_v1.md",
"AI_ICT_TradingAgents_P1自动任务清单_v1.md",
@@ -26,18 +33,14 @@ OPTIONAL_DORMANT_DOCS = {
"AI_ICT_TradingAgents_P5自动任务清单_v1.md",
"AI_ICT_TradingAgents_P6自动任务清单_v1.md",
"AI_ICT_TradingAgents_P0-P6合理性审查与优化结论_v1.md",
- "AI_ICT_TradingAgents融合实施方案_v1.md",
"AI_ICT_TradingAgents融合实施方案_最终版.md",
"AI_ICT_盯盘告警开发任务清单_v1.md",
"AI_ICT_盯盘告警技术设计稿_v1.md",
"AI_ICT_盯盘告警架构图_v1.md",
- "AI_ICT_下一阶段开发计划_v1.md",
- "AI_ICT_下一阶段开发任务清单_v1.md",
"AI_ICT_自动化流程使用教程_v1.md",
"AI_ICT_外部repair_agent接入说明_v1.md",
"AI_ICT_repair_bridge_protocol_v1.md",
"AI_ICT_research_council脚手架与数据表草案_v1.md",
- "AI_ICT_方案一系统边界图与模块融合图_v1.md",
"AI_ICT_自动化执行层_P7监控成熟度与晋级路由_v1.md",
"AI_ICT_自动化执行层_P15-PXX闭环任务清单_v1.md",
}
@@ -54,6 +57,7 @@ class DocumentationConsistencyTests(unittest.TestCase):
top_level_docs,
{
"AI_ICT_全自动开发执行流程_v1.md",
+ "AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md",
"AI_ICT_实盘交易分析系统开发计划_v1.md",
"AI_ICT_实盘辅助决策系统开发方案_v2.md",
"ICT交易全书_出版终校版.md",
@@ -61,6 +65,12 @@ class DocumentationConsistencyTests(unittest.TestCase):
} | OPTIONAL_DORMANT_DOCS,
)
self.assertTrue(TRAINING_PACKAGE_SMOKE_DOC.is_file())
+ archive_docs = {
+ path.name
+ for path in (ROOT / "docs" / "archive").iterdir()
+ if path.is_file()
+ }
+ self.assertTrue(ARCHIVED_DORMANT_DOCS <= archive_docs)
self.assertFalse(OLD_TRAINING_DOC.exists())
def test_readme_and_runbook_use_current_training_entry_and_not_old_doc(self) -> None:
@@ -71,6 +81,21 @@ class DocumentationConsistencyTests(unittest.TestCase):
self.assertNotIn("training_case_engine_m1_m6_product_readme.md", text)
self.assertIn("Training Console at `/training`", README.read_text(encoding="utf-8"))
+ def test_repo_no_longer_references_nonexistent_visual_plan_doc(self) -> None:
+ search_roots = [
+ ROOT / "docs",
+ ROOT / "scripts",
+ ROOT / "tests",
+ ]
+ allowed_suffixes = {".md", ".py", ".html", ".js", ".json"}
+ for root in search_roots:
+ for path in root.rglob("*"):
+ if not path.is_file() or path.suffix not in allowed_suffixes:
+ continue
+ text = path.read_text(encoding="utf-8")
+ with self.subTest(path=str(path.relative_to(ROOT))):
+ self.assertNotIn(NONEXISTENT_VISUAL_PLAN_NAME, text)
+
def test_training_public_doc_matches_current_console_and_routes(self) -> None:
text = TRAINING_PUBLIC_DOC.read_text(encoding="utf-8")
for snippet in (
diff --git a/tests/unit/test_operator_workspace_service.py b/tests/unit/test_operator_workspace_service.py
index e9d70e8..b003191 100644
--- a/tests/unit/test_operator_workspace_service.py
+++ b/tests/unit/test_operator_workspace_service.py
@@ -4,7 +4,7 @@ from unittest.mock import patch
os.environ.setdefault("DATABASE_URL", "postgresql+psycopg://user:pass@localhost:5432/ai_ict_test")
-from src.services.operator import OperatorNoteService, ReviewPacketService, SessionFocusService
+from src.services.operator import DecisionAssistantSummaryService, OperatorNoteService, ReviewPacketService, SessionFocusService
class OperatorWorkspaceServiceTests(unittest.TestCase):
@@ -50,6 +50,7 @@ class OperatorWorkspaceServiceTests(unittest.TestCase):
alert_quality_summary_service=_AlertQualitySummaryService(),
research_council_service=_ResearchCouncilService(),
optimization_review_service=_OptimizationReviewService(),
+ decision_assistant_service=_DecisionAssistantService(),
)
packet = service.build_packet(instrument_id=1)
@@ -61,6 +62,7 @@ class OperatorWorkspaceServiceTests(unittest.TestCase):
self.assertIn("trust_score", packet)
self.assertIn("research_council", packet)
self.assertIn("research_council_optimization_review", packet)
+ self.assertIn("decision_assistant_summary", packet)
self.assertEqual(packet["research_council"]["id"], 31)
self.assertEqual(packet["research_council_optimization_review"]["health_state"], "healthy")
@@ -75,6 +77,7 @@ class OperatorWorkspaceServiceTests(unittest.TestCase):
alert_quality_summary_service=_AlertQualitySummaryService(),
research_council_service=_EmptyResearchCouncilService(),
optimization_review_service=_OptimizationReviewService(),
+ decision_assistant_service=_DecisionAssistantService(),
)
packet = service.build_packet(instrument_id=1)
@@ -85,6 +88,174 @@ class OperatorWorkspaceServiceTests(unittest.TestCase):
self.assertIn("trust_score", packet)
self.assertIsNone(packet["research_council"])
+ def test_decision_assistant_summary_prioritizes_current_setup(self) -> None:
+ service = DecisionAssistantSummaryService(
+ launch_readiness_service=_LaunchReadinessService(),
+ session_focus_service=_SessionFocusSummaryService(),
+ operator_digest_service=_OperatorDigestSummaryService(),
+ decision_repository=_PreviousPacketRepo(),
+ candidate_repository=_CandidateWatchlistRepo(),
+ research_council_service=_ResearchCouncilSummaryService(),
+ exchange_status_provider=_ExchangeStatusProvider(),
+ announcements_provider=_AnnouncementsProvider(),
+ )
+
+ summary = service.build_summary(
+ instrument_id=1,
+ timeframe="5m",
+ decision_packet={
+ "status": "actionable",
+ "action_recommendation": "confirm_ticket",
+ "candidate_id": 18,
+ "packet_ts": "2026-05-12T02:00:00+00:00",
+ "ai_explanation": "London sweep is confirmed and risk stays bounded.",
+ "narrative": "London draw into discount then MSS reclaim.",
+ "bias": "bullish",
+ "setup_type": "SWEEP_MSS_FVG",
+ "why_allowed": ["candidate_confirmed", "risk_governor_passed"],
+ "why_blocked": ["trust_soft_block"],
+ "trust_classification": "observe",
+ "operator_mode": "decision_support_only",
+ "research_council": {
+ "follow_up_checks": ["watch_london_open"],
+ "recommended_watch_items": ["news_window_review"],
+ "operator_questions": ["Should London wait for CPI to clear?"],
+ "next_confirmation_targets": ["wait_for_mss_reclaim"],
+ },
+ },
+ weekly_review={
+ "next_week_actions": ["review_london_session_playbook"],
+ "session_performance": {
+ "LONDON": {
+ "count": 5,
+ "framework_execution_rate": 0.8,
+ "model_outside_rate": 0.1,
+ "risk_violation_count": 1,
+ "avg_r_multiple": 1.4,
+ },
+ "NY_AM": {
+ "count": 4,
+ "framework_execution_rate": 0.5,
+ "model_outside_rate": 0.3,
+ "risk_violation_count": 2,
+ "avg_r_multiple": 0.4,
+ },
+ },
+ "high_quality_session": {"session_code": "LONDON", "framework_execution_rate": 0.8, "avg_r_multiple": 1.4},
+ "low_quality_session": {"session_code": "NY_AM", "framework_execution_rate": 0.5, "avg_r_multiple": 0.4},
+ },
+ recent_alerts=[{"created_ts": "2026-05-12T01:59:00+00:00", "alert_type": "candidate_actionable"}],
+ recent_notes=[
+ {
+ "created_ts": "2026-05-12T01:58:00+00:00",
+ "author": "assistant_template",
+ "body": "[top-opportunity] Candidate #18 | prior handoff",
+ "tags": ["assistant-template"],
+ }
+ ],
+ recent_executions=[{"updated_ts": "2026-05-12T01:57:00+00:00", "execution_status": "open"}],
+ )
+
+ self.assertEqual(summary["focus_mode"], "review_top_setup")
+ self.assertIn("Candidate #18", summary["headline"])
+ self.assertEqual(summary["top_priority_items"][0]["kind"], "decision_packet")
+ self.assertEqual(summary["top_opportunities"][0]["kind"], "decision_packet")
+ self.assertEqual(summary["candidate_watchlist_tiers"]["review_now"][0]["candidate_id"], 18)
+ self.assertEqual(summary["candidate_watchlist_tiers"]["strong_watch"][0]["candidate_id"], 21)
+ self.assertEqual(summary["candidate_watchlist_tiers"]["soft_watch"][0]["candidate_id"], 22)
+ self.assertEqual(summary["candidate_watchlist_tiers"]["review_now"][0]["action_method"], "POST")
+ self.assertIn("/trading/candidates/18/confirm-check", summary["candidate_watchlist_tiers"]["review_now"][0]["action_path"])
+ self.assertEqual(summary["workflow_digest"][0]["phase"], "pre_market")
+ self.assertEqual(summary["workflow_digest"][1]["phase"], "intraday")
+ self.assertEqual(summary["workflow_digest"][2]["phase"], "post_trade")
+ self.assertEqual(summary["decision_timeline"][0]["title"], "Current Packet")
+ self.assertEqual(summary["evidence_chain"][0]["title"], "Decision Gate")
+ self.assertEqual(summary["evidence_chain"][1]["title"], "Launch Mode")
+ self.assertEqual(summary["explanation_drilldown"][0]["title"], "Decision Logic")
+ self.assertEqual(summary["reason_citations"][0]["source_path"], "decision_packet.why_allowed[0]")
+ self.assertEqual(summary["learning_rollup"]["status"], "active")
+ self.assertEqual(summary["external_context_summary"]["status"], "active")
+ self.assertEqual(summary["review_entrypoints"]["status"], "active")
+ self.assertEqual(summary["review_entrypoints"]["actions"][0]["action_id"], "open_operator_notes")
+ self.assertTrue(summary["review_entrypoints"]["note_templates"])
+ self.assertEqual(summary["review_entrypoints"]["note_templates"][0]["note_scope"], "workbench")
+ self.assertTrue(summary["review_entrypoints"]["note_templates"][0]["tags"])
+ self.assertEqual(summary["review_entrypoints"]["recommended_template_id"], "top_opportunity")
+ self.assertEqual(summary["review_entrypoints"]["template_summary"]["recommended_label"], "Top setup note")
+ self.assertEqual(summary["review_entrypoints"]["template_summary"]["recommended_reason"], "best_current_opportunity")
+ self.assertTrue(summary["review_entrypoints"]["template_summary"]["recommended_reuse_hint"])
+ self.assertEqual(summary["review_entrypoints"]["template_reuse_metrics"]["reuse_status"], "recommended_reused")
+ self.assertEqual(summary["review_entrypoints"]["template_reuse_metrics"]["recommended_reuse_count"], 1)
+ self.assertEqual(summary["review_entrypoints"]["note_templates"][0]["reuse_count"], 1)
+ self.assertEqual(summary["review_entrypoints"]["note_templates"][0]["reuse_status"], "saved_reuse")
+ self.assertTrue(any(item["template_id"] == "next_review" for item in summary["review_entrypoints"]["note_templates"]))
+ self.assertTrue(any(item["template_id"] == "queue_follow_up" for item in summary["review_entrypoints"]["note_templates"]))
+ self.assertTrue(any(item["reuse_hint"] for item in summary["review_entrypoints"]["note_templates"]))
+ self.assertEqual(summary["digest_surface"]["status"], "active")
+ self.assertIn("history_summary", summary["digest_surface"])
+ self.assertIn("recommended_kind", summary["digest_surface"]["history_summary"])
+ self.assertIn("stale_kind_count", summary["digest_surface"]["history_summary"])
+ self.assertIn("refresh_needed_count", summary["digest_surface"]["history_summary"])
+ self.assertIn("refresh_prompts", summary["digest_surface"]["history_summary"])
+ self.assertIn("digest_history_groups", summary)
+ self.assertIn("group_status", summary["digest_history_groups"][0])
+ self.assertIn("headline", summary["digest_history_groups"][0])
+ self.assertIn("action_label", summary["digest_history_groups"][0])
+ self.assertIn("age_band", summary["digest_history_groups"][0])
+ self.assertIn("staleness_hint", summary["digest_history_groups"][0])
+ self.assertIn("refresh_prompt", summary["digest_history_groups"][0])
+ self.assertIn("refresh_action_label", summary["digest_history_groups"][0])
+ self.assertTrue(summary["digest_history_preview"])
+ self.assertEqual(summary["digest_history_preview"][0]["kind"], "reminder")
+ self.assertEqual(summary["personalized_reminders"][0]["severity"], "high")
+ self.assertEqual(summary["setup_learning_rollup"]["status"], "active")
+ self.assertEqual(summary["setup_learning_rollup"]["top_lessons"][0]["lesson_type"], "journal_pattern")
+ self.assertEqual(summary["setup_learning_rollup"]["session_code"], "LONDON")
+ self.assertEqual(summary["setup_learning_rollup"]["session_code_source"], "market_session_fallback")
+ self.assertIn("replay_statistics", summary["setup_learning_rollup"])
+ self.assertIn("sample_count", summary["setup_learning_rollup"]["replay_statistics"])
+ self.assertIn("comparative_summary", summary["setup_learning_rollup"]["replay_statistics"])
+ self.assertIn("broader_baseline_summary", summary["setup_learning_rollup"]["replay_statistics"])
+ self.assertIn("notes", summary["setup_learning_rollup"]["replay_statistics"]["comparative_summary"])
+ self.assertIn("story", summary["setup_learning_rollup"]["replay_statistics"]["comparative_summary"])
+ self.assertIn("story", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("framework_percentile", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("composite_percentile", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("composite_band", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("composite_rank", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("risk_rank", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("risk_percentile", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("replay_verdict", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertIn("peer_summary", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertEqual(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["setup_family"], "LIQUIDITY_REVERSAL_FAMILY")
+ self.assertIn("setup_family_peer_summary", summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"])
+ self.assertTrue(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["setup_family_peer_sessions"])
+ self.assertTrue(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["top_sessions"])
+ self.assertTrue(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["top_safe_sessions"])
+ self.assertTrue(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["top_composite_sessions"])
+ self.assertTrue(summary["setup_learning_rollup"]["replay_statistics"]["broader_baseline_summary"]["peer_sessions"])
+ self.assertTrue(summary["scan_summary_cards"])
+ self.assertEqual(summary["scan_summary_cards"][0]["action_label"], "Open Setup")
+ self.assertEqual(summary["scan_summary_cards"][0]["emphasis"], "primary")
+ self.assertTrue(summary["scan_summary_cards"][0]["context"])
+ self.assertTrue(summary["scan_summary_cards"][0]["transition_hint"])
+ self.assertIn("completion_state", summary["scan_summary_cards"][0])
+ self.assertIn("completion_hint", summary["scan_summary_cards"][0])
+ self.assertIn("scan_completion_summary", summary)
+ self.assertEqual(summary["scan_completion_summary"]["status"], "active")
+ self.assertTrue(summary["scan_completion_summary"]["next_target_section"])
+ self.assertTrue(summary["external_context_summary"]["operator_questions"])
+ self.assertTrue(summary["external_context_summary"]["source_cards"])
+ self.assertEqual(summary["external_context_summary"]["exchange_status"]["status"], "active")
+ self.assertEqual(summary["external_context_summary"]["announcements_snapshot"]["status"], "active")
+ self.assertTrue(summary["external_context_summary"]["announcement_titles"])
+ self.assertIn("CPI", summary["external_context_summary"]["headline"])
+ self.assertTrue(summary["no_trade_reason_items"])
+ self.assertIn("watch_london_open", summary["next_actions"])
+ self.assertIn("review_london_session_playbook", summary["next_actions"])
+ self.assertIn("action:observe_only->confirm_ticket", summary["changed_since_last"]["items"])
+ self.assertIn("candidate:none->18", summary["changed_since_last"]["items"])
+
class _NoteRepo:
def insert_note(self, **kwargs):
@@ -162,6 +333,17 @@ class _OptimizationReviewService:
}
+class _DecisionAssistantService:
+ def build_summary(self, **kwargs):
+ return {
+ "status": "completed",
+ "focus_mode": "monitor_and_wait",
+ "headline": "Monitor session flow.",
+ "top_priority_items": [],
+ "next_actions": [],
+ }
+
+
class _SessionRepo:
def fetch_recent_sessions(self, **kwargs):
return [
@@ -170,5 +352,170 @@ class _SessionRepo:
]
+class _LaunchReadinessService:
+ def build_summary(self, *, instrument_id: int, timeframe: str = "1m"):
+ return {
+ "instrument_id": instrument_id,
+ "timeframe": timeframe,
+ "analysis_only_ready": True,
+ "decision_support_ready": True,
+ "blocking_reasons": [],
+ "warning_reasons": ["trust_observe_mode"],
+ "next_actions": ["review_priority_setup"],
+ "product_operating_mode": "decision_support_only",
+ "market_state": {
+ "session": {"session_code": "LONDON", "status": "active"},
+ "freshness": {"code": "market_data_fresh"},
+ "stream_state": {"code": "market_stream_connected"},
+ "data_gap_state": {"code": "market_data_gap_clear"},
+ "news_window": {
+ "code": "news_window_active",
+ "provider_name": "tradingeconomics_snapshot",
+ "freshness_code": "news_provider_fresh",
+ "event_count": 1,
+ "active_events": [{"title": "CPI"}],
+ },
+ },
+ "external_waiting_items": ["real_trade_sample_verified"],
+ }
+
+
+class _SessionFocusSummaryService:
+ def build_summary(self, *, instrument_id: int, limit: int = 6):
+ return {
+ "instrument_id": instrument_id,
+ "items": [
+ {
+ "session_code": "LONDON",
+ "status": "active",
+ "alert_count": 1,
+ "executable_candidate_count": 1,
+ "open_execution_count": 0,
+ "action_hint": "focus_now",
+ "session_recommendation": {"classification": "prefer", "score": 82.0},
+ }
+ ],
+ }
+
+
+class _OperatorDigestSummaryService:
+ def build_digest(self, *, instrument_id: int, top_limit: int = 5):
+ return {
+ "instrument_id": instrument_id,
+ "status": "completed",
+ "summary_counts": {"high_value_alert_count": 1},
+ "quality_flags": ["visual_mismatch_present"],
+ "quality_summary": {"status": "completed"},
+ "operator_summary": "pending feedback=2",
+ "review_queue": [
+ {
+ "queue_type": "high_value_alert",
+ "priority": "medium",
+ "alert_type": "candidate_actionable",
+ "reason": "needs_operator_review",
+ }
+ ],
+ }
+
+
+class _CandidateWatchlistRepo:
+ def fetch_recent_candidates(self, *, instrument_id: int, limit: int = 20):
+ return [
+ {
+ "id": 18,
+ "session_code": "LONDON",
+ "status": "candidate",
+ "quality_score": 81.0,
+ "model_code": "SWEEP_MSS_FVG",
+ "ai_explanation": "Current focus candidate.",
+ "missing_confirmations": ["trust_soft_block"],
+ },
+ {
+ "id": 21,
+ "session_code": "LONDON",
+ "status": "candidate",
+ "quality_score": 72.0,
+ "model_code": "SWEEP_MSS_FVG",
+ "ai_explanation": "Strong session-aligned watch.",
+ "missing_confirmations": ["mss"],
+ },
+ {
+ "id": 22,
+ "session_code": "NY_AM",
+ "status": "candidate",
+ "quality_score": 44.0,
+ "model_code": "SWEEP_MSS_FVG",
+ "ai_explanation": "Needs more confirmation.",
+ "missing_confirmations": ["fvg_retrace"],
+ },
+ {
+ "id": 23,
+ "session_code": "OFF_HOURS",
+ "status": "candidate",
+ "quality_score": 12.0,
+ "model_code": "SWEEP_MSS_FVG",
+ "ai_explanation": "Low-priority background candidate.",
+ "missing_confirmations": [],
+ },
+ ]
+
+
+class _PreviousPacketRepo:
+ def fetch_latest_decision_packet(self, *, instrument_id: int):
+ return {
+ "id": 7,
+ "instrument_id": instrument_id,
+ "packet_ts": "2026-05-12T01:55:00+00:00",
+ "action_recommendation": "observe_only",
+ "status": "observe_only",
+ "payload": {
+ "status": "observe_only",
+ "action_recommendation": "observe_only",
+ "candidate_id": None,
+ "bias": "neutral",
+ "trust_classification": "observe",
+ "why_blocked": ["candidate_not_confirmed"],
+ },
+ }
+
+
+class _ResearchCouncilSummaryService:
+ def get_compact_lessons(
+ self,
+ *,
+ instrument_id: int,
+ session_code=None,
+ setup_type=None,
+ model_code=None,
+ limit: int = 5,
+ ):
+ return [
+ {
+ "title": "London setup lesson",
+ "lesson_key": "lesson:london",
+ "lesson_summary": "Confirmed London sweep setups perform better after MSS reclaim.",
+ "lesson_type": "journal_pattern",
+ }
+ ]
+
+
+class _ExchangeStatusProvider:
+ def fetch_snapshot(self):
+ return {
+ "provider_name": "okx_system_status",
+ "status": "active",
+ "active_events": [{"title": "API maintenance", "service_type": "api"}],
+ }
+
+
+class _AnnouncementsProvider:
+ def fetch_snapshot(self):
+ return {
+ "provider_name": "okx_announcements",
+ "status": "active",
+ "announcements": [{"title": "Listing schedule update", "ann_type": "listing"}],
+ }
+
+
if __name__ == "__main__":
unittest.main()
diff --git a/tests/unit/test_run_until_blocked.py b/tests/unit/test_run_until_blocked.py
index c396247..35e19d4 100644
--- a/tests/unit/test_run_until_blocked.py
+++ b/tests/unit/test_run_until_blocked.py
@@ -61,6 +61,31 @@ class RunUntilBlockedTests(unittest.TestCase):
self.assertTrue(selection["requires_code"])
self.assertTrue(selection["already_in_progress"])
+ def test_required_automation_anchor_paths_match_new_kickoff_prompt(self) -> None:
+ anchors = run_until_blocked.get_automation_required_anchor_paths()
+
+ self.assertEqual(
+ [path.name for path in anchors],
+ [
+ "AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md",
+ "SESSION.md",
+ "TASKS.md",
+ ],
+ )
+
+ def test_render_session_uses_new_kickoff_anchors(self) -> None:
+ text = run_until_blocked.render_session(
+ completed_stages=[],
+ current_stage="P0-2",
+ last_result={},
+ blocked_reason=None,
+ )
+
+ self.assertIn("AI_ICT_实盘交易分析决策助手下一阶段开发方案_v1.md", text)
+ self.assertIn("SESSION.md", text)
+ self.assertIn("TASKS.md", text)
+ self.assertNotIn("AI_ICT_实盘辅助决策系统开发方案_v2.md", text)
+
def test_selector_blocks_when_required_anchors_are_missing(self) -> None:
selection = run_until_blocked.select_automation_execution_slice(
anchor_state={