From e63344b6328fadef4d1308bc7df32df96d7c53f5 Mon Sep 17 00:00:00 2001
From: aaron <>
Date: Mon, 18 May 2026 16:31:45 +0800
Subject: [PATCH] update
---
app/db/altcoin_db.py | 151 +++++----
app/db/review_center.py | 304 ++++++++++++++++++
app/web/routes_pages.py | 11 +
app/web/routes_review_center.py | 13 +
app/web/web_server.py | 2 +
static/base.html | 5 +-
static/iteration.html | 16 +-
static/review_center.html | 64 ++++
static/strategy.html | 38 ++-
.../test_personalization_strategy_stage2_3.py | 56 +++-
tests/test_review_center.py | 82 +++++
11 files changed, 647 insertions(+), 95 deletions(-)
create mode 100644 app/db/review_center.py
create mode 100644 app/web/routes_review_center.py
create mode 100644 static/review_center.html
create mode 100644 tests/test_review_center.py
diff --git a/app/db/altcoin_db.py b/app/db/altcoin_db.py
index f789653..0a384c4 100644
--- a/app/db/altcoin_db.py
+++ b/app/db/altcoin_db.py
@@ -2359,63 +2359,80 @@ def _safe_dict_json(value):
def get_strategy_insights():
- """阶段3：策略可信度看板数据 — 总体表现、因子归因、市场环境归因。
+ """Strategy attribution based on opportunity and paper-trading conversion.
- 只统计已出结果的样本，避免策略页把仍在实时看板里的 active 浮亏/回撤
- 与历史推荐页的已完成样本混在一起，造成口径不一致。
+ Recommendation rows are opportunities/signals, not an execution ledger.
+ Therefore this read model does not use recommendation.pnl_pct as strategy
+ PnL. Paper-trading PnL is exposed only as an execution-conversion metric.
"""
conn = get_conn()
- rows = conn.execute("SELECT * FROM recommendation ORDER BY rec_time DESC").fetchall()
+ rows = conn.execute(
+ """
+ SELECT
+ r.*,
+ pt.id AS paper_trade_id,
+ pt.status AS paper_status,
+ pt.realized_pnl_pct AS paper_realized_pnl_pct,
+ pt.realized_pnl_usdt AS paper_realized_pnl_usdt,
+ pt.pnl_pct AS paper_pnl_pct,
+ pt.exit_reason AS paper_exit_reason
+ FROM recommendation r
+ LEFT JOIN paper_trades pt ON pt.recommendation_id = r.id
+ ORDER BY r.rec_time DESC, r.id DESC
+ """
+ ).fetchall()
conn.close()
- raw_items = [dict(r) for r in rows]
-
- def outcome(item):
- status = item.get("status") or ""
- if status in ("hit_tp1", "hit_tp2"):
- return "success"
- if status == "stopped_out":
- return "failed"
- if (item.get("max_pnl_pct") or 0) >= 5:
- return "success"
- if (item.get("pnl_pct") or 0) <= -3 or (item.get("max_drawdown_pct") or 0) <= -5:
- return "failed"
- return "pending"
-
- items = [x for x in raw_items if outcome(x) in ("success", "failed")]
+ items = [dict(r) for r in rows]
+ actionable_statuses = {"buy_now", "wait_pullback"}
total = len(items)
- success = sum(1 for x in items if outcome(x) == "success")
- failed = sum(1 for x in items if outcome(x) == "failed")
- resolved = success + failed
- pnl_values = [float(x.get("pnl_pct") or 0) for x in items]
- gains = [p for p in pnl_values if p > 0]
- losses = [p for p in pnl_values if p < 0]
+ actionable = [x for x in items if (x.get("execution_status") or "") in actionable_statuses]
+ buy_now = [x for x in items if (x.get("execution_status") or "") == "buy_now"]
+ paper_items = [x for x in items if x.get("paper_trade_id")]
+ closed_paper = [x for x in paper_items if x.get("paper_status") == "closed"]
+ paper_wins = [x for x in closed_paper if float(x.get("paper_realized_pnl_pct") or 0) > 0]
+ paper_realized_usdt = round(sum(float(x.get("paper_realized_pnl_usdt") or 0) for x in closed_paper), 4)
overview = {
- "total_signals": total,
- "resolved_count": resolved,
- "success_count": success,
- "failed_count": failed,
- "pending_count": total - resolved,
- "win_rate_pct": round(success / resolved * 100, 1) if resolved else 0,
- "avg_pnl_pct": round(sum(pnl_values) / len(pnl_values), 2) if pnl_values else 0,
- "avg_gain_pct": round(sum(gains) / len(gains), 2) if gains else 0,
- "avg_loss_pct": round(sum(losses) / len(losses), 2) if losses else 0,
- "max_gain_pct": round(max([float(x.get("max_pnl_pct") or x.get("pnl_pct") or 0) for x in items] or [0]), 2),
- "max_drawdown_pct": round(min([float(x.get("max_drawdown_pct") or 0) for x in items] or [0]), 2),
+ "total_opportunities": total,
+ "actionable_count": len(actionable),
+ "buy_now_count": len(buy_now),
+ "paper_trade_count": len(paper_items),
+ "closed_paper_trade_count": len(closed_paper),
+ "paper_win_count": len(paper_wins),
+ "paper_win_rate_pct": round(len(paper_wins) / len(closed_paper) * 100, 1) if closed_paper else 0,
+ "paper_realized_pnl_usdt": paper_realized_usdt,
+ "actionable_conversion_pct": round(len(actionable) / total * 100, 1) if total else 0,
+ "paper_conversion_pct": round(len(paper_items) / len(buy_now) * 100, 1) if buy_now else 0,
+ "definition": "策略归因只看机会转化和模拟交易转化；收益只来自 paper_trades，不读取 recommendation.pnl_pct。",
}
def add_bucket(bucket_map, key, item):
if not key:
return
- b = bucket_map.setdefault(key, {"total_count": 0, "success_count": 0, "failed_count": 0, "pending_count": 0, "pnl_values": [], "max_gains": [], "drawdowns": []})
- b["total_count"] += 1
- oc = outcome(item)
- if oc == "success": b["success_count"] += 1
- elif oc == "failed": b["failed_count"] += 1
- else: b["pending_count"] += 1
- b["pnl_values"].append(float(item.get("pnl_pct") or 0))
- b["max_gains"].append(float(item.get("max_pnl_pct") or item.get("pnl_pct") or 0))
- b["drawdowns"].append(float(item.get("max_drawdown_pct") or 0))
+ b = bucket_map.setdefault(key, {
+ "opportunity_count": 0,
+ "actionable_count": 0,
+ "buy_now_count": 0,
+ "paper_trade_count": 0,
+ "closed_paper_trade_count": 0,
+ "paper_win_count": 0,
+ "paper_realized_pnl_usdt": 0.0,
+ })
+ execution_status = item.get("execution_status") or ""
+ paper_status = item.get("paper_status") or ""
+ b["opportunity_count"] += 1
+ if execution_status in actionable_statuses:
+ b["actionable_count"] += 1
+ if execution_status == "buy_now":
+ b["buy_now_count"] += 1
+ if item.get("paper_trade_id"):
+ b["paper_trade_count"] += 1
+ if paper_status == "closed":
+ b["closed_paper_trade_count"] += 1
+ pnl_pct = float(item.get("paper_realized_pnl_pct") or 0)
+ if pnl_pct > 0:
+ b["paper_win_count"] += 1
+ b["paper_realized_pnl_usdt"] += float(item.get("paper_realized_pnl_usdt") or 0)
def env_buckets_from_market_context(mc):
"""把当前实际存在的 market_context_json 数值字段转成可归因桶。
@@ -2472,18 +2489,24 @@ def get_strategy_insights():
factor_map = {}
env_map = {}
version_map = {}
+ evidence_map = {}
for item in items:
- for factor in _safe_list_json(item.get("signals")):
+ labels = _safe_list_json(item.get("signal_labels_json")) or _safe_list_json(item.get("signals"))
+ codes = _safe_list_json(item.get("signal_codes_json"))
+ for factor in labels:
add_bucket(factor_map, str(factor).strip(), item)
+ for code in codes:
+ text = str(code or "").strip()
+ if text.startswith(("sentiment_", "listing_", "ecosystem_")):
+ add_bucket(evidence_map, "舆情:" + text, item)
+ elif text.startswith(("dex_", "liquidity_", "exchange_", "whale_", "smart_money", "holder_")):
+ add_bucket(evidence_map, "链上:" + text, item)
mc = _safe_dict_json(item.get("market_context_json"))
- added_env = False
for key in ("btc_trend", "market_regime", "altcoin_regime", "sentiment"):
if mc.get(key):
add_bucket(env_map, f"{key}:{mc.get(key)}", item)
- added_env = True
for bucket in env_buckets_from_market_context(mc):
add_bucket(env_map, bucket, item)
- added_env = True
if item.get("strategy_version"):
add_bucket(version_map, str(item.get("strategy_version")).strip(), item)
@@ -2506,27 +2529,35 @@ def get_strategy_insights():
def serialize(name_key, bucket_map, sort_by_version=False):
rows = []
for key, b in bucket_map.items():
- resolved_count = b["success_count"] + b["failed_count"]
rows.append({
name_key: key,
- "total_count": b["total_count"],
- "success_count": b["success_count"],
- "failed_count": b["failed_count"],
- "pending_count": b["pending_count"],
- "win_rate_pct": round(b["success_count"] / resolved_count * 100, 1) if resolved_count else 0,
- "avg_pnl_pct": round(sum(b["pnl_values"]) / len(b["pnl_values"]), 2) if b["pnl_values"] else 0,
- "max_gain_pct": round(max(b["max_gains"] or [0]), 2),
- "max_drawdown_pct": round(min(b["drawdowns"] or [0]), 2),
+ "opportunity_count": b["opportunity_count"],
+ "actionable_count": b["actionable_count"],
+ "buy_now_count": b["buy_now_count"],
+ "paper_trade_count": b["paper_trade_count"],
+ "closed_paper_trade_count": b["closed_paper_trade_count"],
+ "paper_win_count": b["paper_win_count"],
+ "actionable_conversion_pct": round(b["actionable_count"] / b["opportunity_count"] * 100, 1) if b["opportunity_count"] else 0,
+ "paper_conversion_pct": round(b["paper_trade_count"] / b["buy_now_count"] * 100, 1) if b["buy_now_count"] else 0,
+ "paper_win_rate_pct": round(b["paper_win_count"] / b["closed_paper_trade_count"] * 100, 1) if b["closed_paper_trade_count"] else 0,
+ "paper_realized_pnl_usdt": round(b["paper_realized_pnl_usdt"], 4),
})
if sort_by_version:
- rows.sort(key=lambda x: (version_sort_key(x[name_key]), x["total_count"], x["win_rate_pct"]), reverse=True)
+ rows.sort(key=lambda x: (version_sort_key(x[name_key]), x["opportunity_count"], x["actionable_conversion_pct"]), reverse=True)
else:
- rows.sort(key=lambda x: (-x["total_count"], -x["win_rate_pct"], x[name_key]))
+ rows.sort(key=lambda x: (-x["opportunity_count"], -x["actionable_conversion_pct"], x[name_key]))
return rows
return {
"overview": overview,
+ "metric_definition": {
+ "opportunity_count": "进入 opportunity/recommendation 表的机会样本数，不代表交易。",
+ "actionable_count": "确认层输出 buy_now 或 wait_pullback 的样本数。",
+ "paper_trade_count": "已经被模拟交易账本执行的样本数。",
+ "paper_realized_pnl_usdt": "仅来自 paper_trades 的已平仓模拟收益。",
+ },
"factor_attribution": serialize("factor", factor_map)[:30],
"market_environment": serialize("environment", env_map)[:20],
+ "evidence_attribution": serialize("evidence", evidence_map)[:20],
"version_performance": serialize("strategy_version", version_map, sort_by_version=True)[:20],
}
diff --git a/app/db/review_center.py b/app/db/review_center.py
new file mode 100644
index 0000000..f226e65
--- /dev/null
+++ b/app/db/review_center.py
@@ -0,0 +1,304 @@
+"""Review center read models.
+
+This module keeps the new review/iteration semantics explicit:
+- opportunity review describes whether the system found useful opportunities;
+- paper trading review is the only place where PnL is treated as execution PnL;
+- evidence attribution describes whether onchain/sentiment/LLM evidence helped.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timedelta
+
+from app.db.paper_trading import get_paper_trading_summary
+from app.db.schema import get_conn
+
+
+def _safe_int(value, default=0):
+ try:
+ return int(value or 0)
+ except Exception:
+ return default
+
+
+def _safe_float(value, default=0.0):
+ try:
+ return float(value or 0)
+ except Exception:
+ return default
+
+
+def _loads(value, default=None):
+ try:
+ if isinstance(value, (dict, list)):
+ return value
+ if isinstance(value, str) and value.strip():
+ return json.loads(value)
+ except Exception:
+ pass
+ return default if default is not None else {}
+
+
+def _since(days):
+ return (datetime.now() - timedelta(days=max(1, min(_safe_int(days, 30), 365)))).isoformat()
+
+
+def _bucket_count(rows, key, fallback="unknown"):
+ counts = {}
+ for row in rows:
+ value = (row.get(key) or fallback) if isinstance(row, dict) else fallback
+ counts[value] = counts.get(value, 0) + 1
+ return [{"name": k, "count": v} for k, v in sorted(counts.items(), key=lambda x: (-x[1], x[0]))]
+
+
+def _opportunity_review(conn, since):
+ rec_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT id, symbol, rec_time, status, display_bucket, execution_status, action_status,
+ entry_triggered, strategy_version, signal_codes_json, signal_labels_json,
+ market_context_json, derivatives_context_json, sector_context_json
+ FROM recommendation
+ WHERE rec_time >= %s
+ ORDER BY rec_time DESC, id DESC
+ """,
+ (since,),
+ ).fetchall()]
+ review_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM review_log
+ WHERE review_time >= %s
+ ORDER BY review_time DESC, id DESC
+ """,
+ (since,),
+ ).fetchall()]
+ missed_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM missed_explosions
+ WHERE detect_time >= %s
+ ORDER BY gain_pct DESC, detect_time DESC
+ LIMIT 20
+ """,
+ (since,),
+ ).fetchall()]
+
+ total = len(rec_rows)
+ executed_ids = {
+ int(r["recommendation_id"])
+ for r in conn.execute("SELECT recommendation_id FROM paper_trades WHERE opened_at >= %s", (since,)).fetchall()
+ if r.get("recommendation_id")
+ }
+ buy_now = [r for r in rec_rows if r.get("execution_status") == "buy_now"]
+ wait_pullback = [r for r in rec_rows if r.get("execution_status") == "wait_pullback"]
+ observe = [r for r in rec_rows if r.get("execution_status") == "observe" or r.get("display_bucket") == "watch_pool"]
+ invalid = [r for r in rec_rows if r.get("execution_status") == "invalid" or r.get("status") in ("expired", "invalid", "archived")]
+ executed = [r for r in rec_rows if int(r.get("id") or 0) in executed_ids]
+
+ outcomes = _bucket_count(review_rows, "outcome", "未复盘")
+ reviewed_effective = [r for r in review_rows if r.get("outcome") in ("爆发", "失败", "横盘")]
+ hit_count = sum(1 for r in reviewed_effective if r.get("outcome") == "爆发")
+
+ return {
+ "definition": "机会复盘只评价机会发现、确认和漏选，不代表交易收益。",
+ "summary": {
+ "total_opportunities": total,
+ "buy_now_count": len(buy_now),
+ "wait_pullback_count": len(wait_pullback),
+ "observe_count": len(observe),
+ "invalid_count": len(invalid),
+ "paper_executed_count": len(executed),
+ "reviewed_count": len(review_rows),
+ "effective_review_count": len(reviewed_effective),
+ "opportunity_hit_rate": round(hit_count / len(reviewed_effective) * 100, 2) if reviewed_effective else 0,
+ "missed_explosion_count": len(missed_rows),
+ },
+ "status_distribution": _bucket_count(rec_rows, "execution_status", "unknown"),
+ "outcome_distribution": outcomes,
+ "missed_explosions": missed_rows[:10],
+ "recent_reviews": review_rows[:12],
+ }
+
+
+def _paper_review(conn, since, days):
+ summary = get_paper_trading_summary(days=days)
+ trades = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM paper_trades
+ WHERE opened_at >= %s
+ ORDER BY opened_at DESC, id DESC
+ LIMIT 20
+ """,
+ (since,),
+ ).fetchall()]
+ events = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM paper_trade_events
+ WHERE event_time >= %s
+ ORDER BY event_time DESC, id DESC
+ LIMIT 30
+ """,
+ (since,),
+ ).fetchall()]
+ exit_reasons = _bucket_count([t for t in trades if t.get("status") == "closed"], "exit_reason", "unknown")
+ event_types = _bucket_count(events, "event_type", "unknown")
+ return {
+ "definition": "模拟交易复盘是唯一收益口径，基于 paper_trades 的开仓、平仓、移动止盈事件。",
+ "summary": summary,
+ "exit_reasons": exit_reasons,
+ "event_types": event_types,
+ "recent_trades": trades,
+ "recent_events": events,
+ }
+
+
+def _evidence_review(conn, since):
+ news_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT source, symbol, importance, event_type, decision, tech_score, processed, detected_at, title
+ FROM event_news
+ WHERE detected_at >= %s
+ ORDER BY detected_at DESC, id DESC
+ LIMIT 80
+ """,
+ (since,),
+ ).fetchall()]
+ onchain_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT source, chain, symbol, signal_code, signal_label, direction, value_usd,
+ confidence, severity, detected_at
+ FROM onchain_events
+ WHERE detected_at >= %s
+ ORDER BY detected_at DESC, id DESC
+ LIMIT 80
+ """,
+ (since,),
+ ).fetchall()]
+ raw_onchain_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT source, chain, event_type, symbol_guess, mapped_symbol, mapping_status,
+ importance, detected_at, title
+ FROM onchain_raw_events
+ WHERE detected_at >= %s
+ ORDER BY importance DESC, detected_at DESC, id DESC
+ LIMIT 80
+ """,
+ (since,),
+ ).fetchall()]
+ llm_rows = [dict(r) for r in conn.execute(
+ """
+ SELECT target_type, insight_type, status, model, prompt_version, updated_at
+ FROM llm_insights
+ WHERE updated_at >= %s
+ ORDER BY updated_at DESC, id DESC
+ LIMIT 80
+ """,
+ (since,),
+ ).fetchall()]
+
+ mapped_raw = [r for r in raw_onchain_rows if r.get("mapping_status") == "mapped" or r.get("mapped_symbol")]
+ high_onchain = [r for r in onchain_rows if _safe_int(r.get("confidence")) >= 70 or str(r.get("severity") or "").upper() in ("A", "S")]
+ actionable_news = [r for r in news_rows if r.get("decision") in ("recommend", "observe", "risk") or str(r.get("importance") or "").upper() in ("A", "S")]
+ llm_success = [r for r in llm_rows if r.get("status") == "success"]
+
+ return {
+ "definition": "多源归因只判断证据贡献：舆情、链上、LLM 是否帮助发现/解释机会，不直接生成交易收益。",
+ "summary": {
+ "news_count": len(news_rows),
+ "actionable_news_count": len(actionable_news),
+ "onchain_signal_count": len(onchain_rows),
+ "high_confidence_onchain_count": len(high_onchain),
+ "raw_onchain_count": len(raw_onchain_rows),
+ "mapped_raw_onchain_count": len(mapped_raw),
+ "llm_runs": len(llm_rows),
+ "llm_success_count": len(llm_success),
+ },
+ "news_sources": _bucket_count(news_rows, "source", "unknown"),
+ "news_decisions": _bucket_count(news_rows, "decision", "unprocessed"),
+ "onchain_sources": _bucket_count(onchain_rows, "source", "unknown"),
+ "onchain_signals": _bucket_count(onchain_rows, "signal_code", "unknown")[:12],
+ "raw_mapping": _bucket_count(raw_onchain_rows, "mapping_status", "unknown"),
+ "llm_status": _bucket_count(llm_rows, "status", "unknown"),
+ "recent_news": news_rows[:8],
+ "recent_onchain": onchain_rows[:8],
+ "recent_llm": llm_rows[:8],
+ }
+
+
+def _iteration_review(conn, since):
+ logs = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM strategy_iteration_log
+ WHERE created_at >= %s
+ ORDER BY created_at DESC, id DESC
+ LIMIT 12
+ """,
+ (since,),
+ ).fetchall()]
+ candidates = [dict(r) for r in conn.execute(
+ """
+ SELECT *
+ FROM strategy_rule_candidate
+ ORDER BY created_at DESC, id DESC
+ LIMIT 30
+ """
+ ).fetchall()]
+ for item in logs:
+ for field, fallback in (
+ ("metrics_json", {}),
+ ("findings_json", []),
+ ("problems_json", []),
+ ("actions_json", []),
+ ("candidate_rules_json", []),
+ ):
+ item[field.replace("_json", "")] = _loads(item.get(field), fallback)
+ return {
+ "definition": "策略迭代只产生候选假设和发布闸门结论，不直接等于收益提升。",
+ "summary": {
+ "iteration_count": len(logs),
+ "candidate_count": len(candidates),
+ "gray_count": sum(1 for c in candidates if c.get("status") == "gray"),
+ "active_count": sum(1 for c in candidates if c.get("status") == "active"),
+ "latest_release_decision": (logs[0].get("release_decision") if logs else "") or "hold",
+ "latest_release_reason": (logs[0].get("release_reason") if logs else "") or "",
+ },
+ "release_decisions": _bucket_count(logs, "release_decision", "unknown"),
+ "candidate_status": _bucket_count(candidates, "status", "candidate"),
+ "recent_logs": logs,
+ "recent_candidates": candidates[:12],
+ }
+
+
+def get_review_center_dashboard(days=30):
+ days = max(1, min(_safe_int(days, 30), 365))
+ since = _since(days)
+ conn = get_conn()
+ try:
+ opportunity = _opportunity_review(conn, since)
+ paper = _paper_review(conn, since, days)
+ evidence = _evidence_review(conn, since)
+ iteration = _iteration_review(conn, since)
+ finally:
+ conn.close()
+
+ return {
+ "days": days,
+ "generated_at": datetime.now().isoformat(timespec="seconds"),
+ "principles": [
+ "机会归档不计算交易收益，只记录发现、确认、失效和漏选。",
+ "真实收益口径只来自模拟交易或未来真实交易账本。",
+ "链上、舆情、LLM 属于证据层，只做发现和解释，不直接改变推荐状态。",
+ "策略迭代只发布经过样本约束和灰度闸门验证的规则。",
+ ],
+ "opportunity": opportunity,
+ "paper_trading": paper,
+ "evidence": evidence,
+ "iteration": iteration,
+ }
+
+
+__all__ = ["get_review_center_dashboard"]
diff --git a/app/web/routes_pages.py b/app/web/routes_pages.py
index 4cff7c3..caadcd0 100644
--- a/app/web/routes_pages.py
+++ b/app/web/routes_pages.py
@@ -118,6 +118,17 @@ def build_router(templates, repo_root: Path, stock_report_template: str):
return HTMLResponse(content=f"需要管理员权限 {exc.detail}
返回看板 ", status_code=exc.status_code)
return render_page("paper_trading.html", request, active_nav="paper_trading")
+ @router.get("/review-center", response_class=HTMLResponse)
+ async def review_center_page(request: Request):
+ user, redirect = require_page_user(request)
+ if redirect:
+ return redirect
+ try:
+ require_admin(request.cookies.get("altcoin_session", ""))
+ except HTTPException as exc:
+ return HTMLResponse(content=f"需要管理员权限 {exc.detail}
返回看板 ", status_code=exc.status_code)
+ return render_page("review_center.html", request, active_nav="review_center")
+
@router.get("/strategy", response_class=HTMLResponse)
async def strategy_page(request: Request):
user, redirect = require_page_user(request)
diff --git a/app/web/routes_review_center.py b/app/web/routes_review_center.py
new file mode 100644
index 0000000..77e57b7
--- /dev/null
+++ b/app/web/routes_review_center.py
@@ -0,0 +1,13 @@
+from fastapi import APIRouter, Cookie
+
+from app.db.review_center import get_review_center_dashboard
+from app.web.shared import require_admin
+
+
+router = APIRouter()
+
+
+@router.get("/api/review-center/dashboard")
+async def api_review_center_dashboard(days: int = 30, altcoin_session: str = Cookie(default="")):
+ require_admin(altcoin_session)
+ return get_review_center_dashboard(days=days)
diff --git a/app/web/web_server.py b/app/web/web_server.py
index 179fca7..f4c074a 100644
--- a/app/web/web_server.py
+++ b/app/web/web_server.py
@@ -23,6 +23,7 @@ from app.web.routes_onchain import router as onchain_router
from app.web.routes_paper_trading import router as paper_trading_router
from app.web.routes_pages import build_router as build_pages_router
from app.web.routes_recommendations import router as recommendations_router
+from app.web.routes_review_center import router as review_center_router
from app.web.routes_strategy import router as strategy_router
from app.web.shared import current_request
from app.web.shared import require_active_subscription as _require_active_subscription
@@ -47,6 +48,7 @@ templates = Jinja2Templates(directory=str(REPO_ROOT / "static"))
app.include_router(auth_router)
app.include_router(chat_router)
app.include_router(recommendations_router)
+app.include_router(review_center_router)
app.include_router(strategy_router)
app.include_router(onchain_router)
app.include_router(paper_trading_router)
diff --git a/static/base.html b/static/base.html
index cbc513d..a374b85 100644
--- a/static/base.html
+++ b/static/base.html
@@ -183,10 +183,11 @@ a { color: inherit; text-decoration: none; }
+
-
-
+
+
diff --git a/static/iteration.html b/static/iteration.html
index 7a6b7b2..642972b 100644
--- a/static/iteration.html
+++ b/static/iteration.html
@@ -114,14 +114,14 @@ h2 { font-size:26px; font-weight:900; margin:0 0 8px; color:var(--ink); }
发现的规律
发布预演
错误复盘
- 版本表现
+ 版本机会表现
- 发现的规律 未达标不发布
这些是系统复盘后发现的可能规律。只有样本、成功率、收益和稳定性都达标，才会进入线上策略。
发现的规律 未达标不发布
这些是系统复盘后发现的可能规律。只有样本、机会表现和稳定性都达标，才会进入线上策略；交易收益以模拟交易页为准。
'+esc(name)+' '+qtxt+' 样本 '+esc(c.sample_size||0)+' · 置信 '+esc(c.confidence_score||0)+' · 平均表现 '+esc(c.avg_pnl||0)+'
';
+ return ''+esc(name)+' '+qtxt+' 样本 '+esc(c.sample_size||0)+' · 置信 '+esc(c.confidence_score||0)+' · 机会表现 '+esc(c.avg_pnl||0)+'
';
}).join(''):'暂无新规律 当前没有足够证据支持策略改动。
';
var failHtml=failures.length?failures.map(function(f){return ''+esc(f.type||'失败模式')+' 出现 '+esc(f.count||0)+' 次，后续复盘会重点观察是否重复发生。
';}).join(''):'暂无集中失败模式 当前失败样本不足，先继续观察。
';
var aiHtml = aiMemo ? 'AI 复盘摘要
'+esc(aiMemo.summary || aiMemo.memo || aiMemo.why_it_matters || '暂无摘要')+'
'+label+'
'+items.slice(0,10).map(function(x){ var t=typeof x==='string'?x:(x.label||x.type||x.signal||x.description||JSON.stringify(x)); var c=x.count?(' · '+x.count):''; return '
'+esc(t+c)+'
'; }).join('')+'
本轮候选规则
'+items.slice(0,8).map(function(x){return '
'+esc(x.description||x.signal||'候选规则')+' · 置信 '+esc(x.confidence_score||0)+' · 样本 '+esc(x.sample_size||0)+' · '+esc(x.status||'candidate')+'
';}).join('')+'
暂无待验证规律
';return;} var dryMap={}; (dry.evaluated_candidates||[]).forEach(function(x){dryMap[x.id]=x;}); $('candidates').innerHTML='来源 当前阶段 预演结论 规律 样本 成功/失败 可信度 平均表现 为什么还没发布 '+items.map(function(c){var d=dryMap[c.id]||{};return ''+esc(sourceLabel(c))+' '+badge(c.status||'candidate')+' '+badge(d.dry_run_status||c.status||'candidate')+' '+esc(c.rule_description||c.signal_name||'--')+' '+esc(d.sample_size!=null?d.sample_size:(c.sample_size||0))+' '+esc(d.success_count!=null?d.success_count:(c.success_count||0))+' / '+esc(d.fail_count!=null?d.fail_count:(c.fail_count||0))+' '+esc(d.confidence_score!=null?d.confidence_score:(c.confidence_score||0))+' '+esc(d.avg_pnl!=null?d.avg_pnl:(c.avg_pnl||0))+' '+esc(d.gate_reason||'等待样本验证')+'
'; }
-function renderDryRun(dry){ var items=dry.evaluated_candidates||[]; if(!items.length){$('dryrun').innerHTML='暂无待验证规律可评估
';return;} $('dryrun').innerHTML='当前版本 '+esc(dry.current_version||'--')+'；干净样本起点 '+esc(dry.clean_started_at||'未设置')+'；干净复盘样本 '+esc(dry.review_sample_count||0)+'；污染历史候选 '+esc(dry.dirty_history_candidate_count||0)+'；可灰度 '+esc(dry.gray_ready_count||0)+'；是否发布：'+(dry.would_bump_version?'是':'否')+'。
灰度标准： '+esc((dry.gate_policy&&dry.gate_policy.gray)||'--')+'
预演结论 规律 样本 成功/失败 可信度 平均表现 原因 '+items.map(function(x){return ''+badge(x.dry_run_status||'candidate')+' '+esc(x.rule_description||x.signal_name||'--')+' '+esc(x.sample_size||0)+' '+esc(x.success_count||0)+' / '+esc(x.fail_count||0)+' '+esc(x.confidence_score||0)+' '+esc(x.avg_pnl||0)+' '+esc(x.gate_reason||'--')+'
'; }
-function renderFailures(d){ var fs=(d.overview&&d.overview.failure_type_counts)||[]; $('failureSummary').innerHTML=fs.length?fs.map(function(f){return ''+esc(f.type)+' · '+esc(f.count)+' ';}).join(''):'暂无失败模式
'; var items=d.failures||[]; $('failures').innerHTML=items.length?items.slice(0,30).map(function(f){return ''+esc(f.symbol||'--')+' · '+esc(f.failure_type||'未分类')+' · '+esc((f.failure_reason||'').slice(0,90))+' · PnL '+esc(f.pnl_pct||0)+'
';}).join(''):'暂无失败样本
'; }
-function renderVersions(items){ if(!items.length){$('versions').innerHTML='暂无版本表现
';return;} $('versions').innerHTML='版本 推荐数 成功 失败 待观察 成功率 均值收益 '+items.map(function(v){return ''+esc(v.strategy_version)+' '+esc(v.recommendation_count)+' '+esc(v.success_count)+' '+esc(v.failed_count)+' '+esc(v.pending_count)+' '+esc(v.success_rate_pct)+' '+esc(v.avg_pnl_pct)+'
'; }
+function renderCandidates(items,dry){ if(!items.length){$('candidates').innerHTML='暂无待验证规律
';return;} var dryMap={}; (dry.evaluated_candidates||[]).forEach(function(x){dryMap[x.id]=x;}); $('candidates').innerHTML='来源 当前阶段 预演结论 规律 样本 成功/失败 可信度 机会表现 为什么还没发布 '+items.map(function(c){var d=dryMap[c.id]||{};return ''+esc(sourceLabel(c))+' '+badge(c.status||'candidate')+' '+badge(d.dry_run_status||c.status||'candidate')+' '+esc(c.rule_description||c.signal_name||'--')+' '+esc(d.sample_size!=null?d.sample_size:(c.sample_size||0))+' '+esc(d.success_count!=null?d.success_count:(c.success_count||0))+' / '+esc(d.fail_count!=null?d.fail_count:(c.fail_count||0))+' '+esc(d.confidence_score!=null?d.confidence_score:(c.confidence_score||0))+' '+esc(d.avg_pnl!=null?d.avg_pnl:(c.avg_pnl||0))+' '+esc(d.gate_reason||'等待样本验证')+'
'; }
+function renderDryRun(dry){ var items=dry.evaluated_candidates||[]; if(!items.length){$('dryrun').innerHTML='暂无待验证规律可评估
';return;} $('dryrun').innerHTML='当前版本 '+esc(dry.current_version||'--')+'；干净样本起点 '+esc(dry.clean_started_at||'未设置')+'；干净复盘样本 '+esc(dry.review_sample_count||0)+'；污染历史候选 '+esc(dry.dirty_history_candidate_count||0)+'；可灰度 '+esc(dry.gray_ready_count||0)+'；是否发布：'+(dry.would_bump_version?'是':'否')+'。
灰度标准： '+esc((dry.gate_policy&&dry.gate_policy.gray)||'--')+'
预演结论 规律 样本 成功/失败 可信度 机会表现 原因 '+items.map(function(x){return ''+badge(x.dry_run_status||'candidate')+' '+esc(x.rule_description||x.signal_name||'--')+' '+esc(x.sample_size||0)+' '+esc(x.success_count||0)+' / '+esc(x.fail_count||0)+' '+esc(x.confidence_score||0)+' '+esc(x.avg_pnl||0)+' '+esc(x.gate_reason||'--')+'
'; }
+function renderFailures(d){ var fs=(d.overview&&d.overview.failure_type_counts)||[]; $('failureSummary').innerHTML=fs.length?fs.map(function(f){return ''+esc(f.type)+' · '+esc(f.count)+' ';}).join(''):'暂无失败模式
'; var items=d.failures||[]; $('failures').innerHTML=items.length?items.slice(0,30).map(function(f){return ''+esc(f.symbol||'--')+' · '+esc(f.failure_type||'未分类')+' · '+esc((f.failure_reason||'').slice(0,90))+' · 机会表现 '+esc(f.pnl_pct||0)+'
';}).join(''):'暂无失败样本
'; }
+function renderVersions(items){ if(!items.length){$('versions').innerHTML='暂无版本机会表现
';return;} $('versions').innerHTML='版本 机会数 成功 失败 待观察 机会成功率 机会均值 '+items.map(function(v){return ''+esc(v.strategy_version)+' '+esc(v.recommendation_count)+' '+esc(v.success_count)+' '+esc(v.failed_count)+' '+esc(v.pending_count)+' '+esc(v.success_rate_pct)+' '+esc(v.avg_pnl_pct)+'
'; }
loadAll();
{% endblock %}
diff --git a/static/review_center.html b/static/review_center.html
new file mode 100644
index 0000000..a8be902
--- /dev/null
+++ b/static/review_center.html
@@ -0,0 +1,64 @@
+{% extends "base.html" %}
+{% block title %}复盘中心 — AlphaX Agent{% endblock %}
+{% block extra_head_css %}
+
+{% endblock %}
+{% block content %}
+
+
+
+
复盘中心
+
把机会发现、模拟交易收益、多源证据和策略迭代拆开看。收益只看模拟交易；机会归档只看发现和确认质量。
+
+
+
+ 近 7 天
+ 近 30 天
+ 近 60 天
+ 近 120 天
+
+ 刷新
+
+
+
+
+
+
+
+
+
+ 最近关键样本
方便快速定位是机会问题、执行问题，还是证据源问题。
+
+
-
策略 系统可信度、版本表现、因子归因与市场环境归因。
-
-
数据基于历史信号跟踪，仅用于策略研究与模型评估，不构成收益承诺或投资建议。
+
+
+
策略归因
+
看清哪些因子、环境和证据源更容易把机会推进到可执行，以及是否真的进入模拟交易。这里不把机会价格波动当作交易收益。
+
+
收益只来自模拟交易
+
+
+
策略归因只看机会转化和模拟交易转化；收益只来自 paper_trades。
-
发现 粗筛只进观察/候选，不直接计交易绩效。
-
确认 只有新鲜触发和入场计划达标才进入可交易口径。
-
复盘 只用48h窗口样本更新有效表现。
-
+
机会样本 系统发现并入库的机会，不等于交易。
+
可执行转化 确认层输出现在可买或等回踩。
+
模拟执行 只有 buy_now 被 paper trading 开仓才进入收益账本。
+
证据归因 链上、舆情、技术因子只做贡献分析。
-