1

app/db/analytics.py

@@ -171,6 +171,13 @@ def get_all_recommendations(limit=50, decision_only=False, version="", offset=0,
     total = None
     summary = None
     version_counts = []
+    success_case = "status IN ('hit_tp1','hit_tp2') OR (status NOT IN ('stopped_out','expired','invalid','archived') AND COALESCE(max_pnl_pct,0) >= 5)"
+    failure_case = "status='stopped_out' OR COALESCE(pnl_pct,0) <= -3 OR COALESCE(max_drawdown_pct,0) <= -5"
+    realized_pnl_case = (
+        f"CASE WHEN {failure_case} THEN COALESCE(pnl_pct,0) "
+        f"WHEN {success_case} THEN COALESCE(NULLIF(max_pnl_pct,0), pnl_pct, 0) "
+        "ELSE 0 END"
+    )
 
     if decision_only:
         if with_meta:
@@ -193,15 +200,21 @@ def get_all_recommendations(limit=50, decision_only=False, version="", offset=0,
                 """
                 SELECT
                     COUNT(*) AS total,
-                    SUM(CASE WHEN status IN ('hit_tp1','hit_tp2') OR COALESCE(max_pnl_pct,0) >= 5 THEN 1 ELSE 0 END) AS success_count,
-                    SUM(CASE WHEN status='stopped_out' OR COALESCE(pnl_pct,0) <= -3 OR COALESCE(max_drawdown_pct,0) <= -5 THEN 1 ELSE 0 END) AS failure_count,
-                    SUM(CASE WHEN status IN ('hit_tp1','hit_tp2') OR COALESCE(max_pnl_pct,0) >= 5 THEN COALESCE(NULLIF(max_pnl_pct,0), pnl_pct, 0)
-                             WHEN status='stopped_out' OR COALESCE(pnl_pct,0) <= -3 OR COALESCE(max_drawdown_pct,0) <= -5 THEN COALESCE(pnl_pct,0)
-                             ELSE 0 END) AS total_pnl,
-                    MAX(CASE WHEN status IN ('hit_tp1','hit_tp2') OR COALESCE(max_pnl_pct,0) >= 5 THEN COALESCE(NULLIF(max_pnl_pct,0), pnl_pct, 0)
-                             WHEN status='stopped_out' OR COALESCE(pnl_pct,0) <= -3 OR COALESCE(max_drawdown_pct,0) <= -5 THEN COALESCE(pnl_pct,0)
-                             ELSE 0 END) AS best_pnl,
-                    AVG(CASE WHEN status='stopped_out' OR COALESCE(pnl_pct,0) <= -3 OR COALESCE(max_drawdown_pct,0) <= -5 THEN COALESCE(pnl_pct,0) END) AS avg_failure_pnl
+                    SUM(CASE WHEN """
+                + success_case
+                + """ THEN 1 ELSE 0 END) AS success_count,
+                    SUM(CASE WHEN """
+                + failure_case
+                + """ THEN 1 ELSE 0 END) AS failure_count,
+                    SUM("""
+                + realized_pnl_case
+                + """) AS total_pnl,
+                    MAX("""
+                + realized_pnl_case
+                + """) AS best_pnl,
+                    AVG(CASE WHEN """
+                + failure_case
+                + """ THEN COALESCE(pnl_pct,0) END) AS avg_failure_pnl
                 FROM (
                     SELECT r.*
                     FROM recommendation r

app/db/scheduler_db.py

@@ -1,9 +1,31 @@
 """SQLite-backed scheduler configuration and runtime state."""
 
 import json
+import os
+import sqlite3
 from datetime import datetime
+from pathlib import Path
 
-from app.db.schema import get_conn
+from app.db import altcoin_db
+
+
+REPO_ROOT = Path(__file__).resolve().parents[2]
+SCHEDULER_DB_PATH = os.getenv("ALPHAX_SCHEDULER_DB_PATH", str(REPO_ROOT / "data" / "scheduler_state.db"))
+
+
+def get_scheduler_conn():
+    path = Path(SCHEDULER_DB_PATH)
+    path.parent.mkdir(parents=True, exist_ok=True)
+    conn = sqlite3.connect(str(path), timeout=30, isolation_level=None)
+    conn.row_factory = sqlite3.Row
+    conn.execute("PRAGMA busy_timeout=30000")
+    conn.execute("PRAGMA journal_mode=WAL")
+    conn.execute("PRAGMA synchronous=NORMAL")
+    return conn
+
+
+def get_main_conn():
+    return altcoin_db.get_conn()
 
 
 DEFAULT_JOBS = [
@@ -212,7 +234,7 @@ def _seed_scheduler_tables(conn):
 
 
 def init_scheduler_tables():
-    conn = get_conn()
+    conn = get_scheduler_conn()
     conn.execute(
         """
         CREATE TABLE IF NOT EXISTS scheduler_job_config (
@@ -284,7 +306,7 @@ def init_scheduler_tables():
 
 def get_job_configs():
     init_scheduler_tables()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     rows = conn.execute("SELECT * FROM scheduler_job_config ORDER BY sort_order ASC, job_name ASC").fetchall()
     conn.close()
     jobs = []
@@ -298,7 +320,7 @@ def get_job_configs():
 
 def get_job_config(job_name):
     init_scheduler_tables()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     row = conn.execute("SELECT * FROM scheduler_job_config WHERE job_name=?", (job_name,)).fetchone()
     conn.close()
     if not row:
@@ -312,7 +334,7 @@ def get_job_config(job_name):
 def set_job_enabled(job_name, enabled):
     init_scheduler_tables()
     now = _now()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     cur = conn.execute(
         "UPDATE scheduler_job_config SET enabled=?, updated_at=? WHERE job_name=?",
         (1 if enabled else 0, now, job_name),
@@ -326,7 +348,7 @@ def set_job_interval(job_name, every_seconds):
     seconds = max(30, int(every_seconds or 0))
     init_scheduler_tables()
     now = _now()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     cur = conn.execute(
         "UPDATE scheduler_job_config SET every_seconds=?, updated_at=? WHERE job_name=?",
         (seconds, now, job_name),
@@ -345,7 +367,7 @@ def update_runtime(job_name, **fields):
     }
     values = {k: v for k, v in fields.items() if k in allowed}
     values["updated_at"] = _now()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     try:
         conn.execute(
             "INSERT INTO scheduler_runtime_status (job_name, updated_at) VALUES (?, ?) ON CONFLICT(job_name) DO NOTHING",
@@ -375,7 +397,7 @@ def enqueue_manual_trigger(job_name, force=False, requested_by=""):
     init_scheduler_tables()
     if not get_job_config(job_name):
         return None
-    conn = get_conn()
+    conn = get_scheduler_conn()
     cur = conn.execute(
         """
         INSERT INTO scheduler_manual_trigger (job_name, force, status, requested_by, requested_at)
@@ -391,7 +413,7 @@ def enqueue_manual_trigger(job_name, force=False, requested_by=""):
 
 def claim_manual_triggers(limit=10):
     init_scheduler_tables()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     rows = conn.execute(
         """
         SELECT * FROM scheduler_manual_trigger
@@ -411,7 +433,7 @@ def update_manual_trigger(trigger_id, **fields):
     values = {k: v for k, v in fields.items() if k in allowed}
     if not values:
         return
-    conn = get_conn()
+    conn = get_scheduler_conn()
     assignments = ", ".join([f"{k}=?" for k in values])
     conn.execute(
         f"UPDATE scheduler_manual_trigger SET {assignments} WHERE id=?",
@@ -424,7 +446,7 @@ def update_manual_trigger(trigger_id, **fields):
 def list_manual_triggers(limit=30):
     init_scheduler_tables()
     limit = max(1, min(int(limit or 30), 100))
-    conn = get_conn()
+    conn = get_scheduler_conn()
     rows = conn.execute(
         "SELECT * FROM scheduler_manual_trigger ORDER BY requested_at DESC, id DESC LIMIT ?",
         (limit,),
@@ -435,21 +457,26 @@ def list_manual_triggers(limit=30):
 
 def get_scheduler_overview():
     init_scheduler_tables()
-    conn = get_conn()
+    conn = get_scheduler_conn()
     configs = conn.execute("SELECT * FROM scheduler_job_config ORDER BY sort_order ASC, job_name ASC").fetchall()
     runtime_rows = conn.execute("SELECT * FROM scheduler_runtime_status").fetchall()
-    latest_rows = conn.execute(
-        """
-        SELECT c.*
-        FROM cron_run_log c
-        JOIN (
-            SELECT job_name, MAX(id) AS max_id
-            FROM cron_run_log
-            GROUP BY job_name
-        ) x ON x.max_id = c.id
-        """
-    ).fetchall()
     conn.close()
+    try:
+        main_conn = get_main_conn()
+        latest_rows = main_conn.execute(
+            """
+            SELECT c.*
+            FROM cron_run_log c
+            JOIN (
+                SELECT job_name, MAX(id) AS max_id
+                FROM cron_run_log
+                GROUP BY job_name
+            ) x ON x.max_id = c.id
+            """
+        ).fetchall()
+        main_conn.close()
+    except Exception:
+        latest_rows = []
     runtime = {row["job_name"]: dict(row) for row in runtime_rows}
     latest = {row["job_name"]: dict(row) for row in latest_rows}
     jobs = []
@@ -482,6 +509,7 @@ __all__ = [
     "get_job_config",
     "get_job_configs",
     "get_scheduler_overview",
+    "get_scheduler_conn",
     "init_scheduler_tables",
     "list_manual_triggers",
     "set_job_enabled",

app/integrations/feishu_push.py

@@ -193,10 +193,33 @@ def push_altcoin_tp_sl_alert(symbol, current_price, entry_price, pnl_pct, action
 
 def build_trade_action_card(symbol, current_price, entry_price, pnl_pct, action_status, signals, stop_loss=0, tp1=0, tp2=0):
     """只构建交易执行卡片，不做冷却判断或落库。"""
-    if action_status not in ("可即刻买入", "跟踪止盈"):
+    if action_status not in ("可即刻买入", "跟踪止盈", "移动止盈保护"):
         print(f"[飞书跳过] {symbol} {action_status} — 用户要求止盈/止损/衰减不推送，只在网站展示")
         return True, {"skipped": True, "reason": "only_buy_now_and_trailing_stop_push_enabled"}
 
+    if action_status == "移动止盈保护":
+        coin = symbol.replace("/USDT", "")
+        signal_lines = "\n".join([f"  • {s}" for s in signals]) or "  • 移动止盈保护已启动"
+        trail_info = f"入场${entry_price:.4f} → 当前${current_price:.4f}"
+        if pnl_pct > 0:
+            trail_info += f"\n**当前浮盈: +{pnl_pct:.2f}%**"
+        return {
+            "config": {"wide_screen_mode": True},
+            "header": {
+                "template": "yellow",
+                "title": {"tag": "plain_text", "content": f"🛡️ 移动止盈保护启动 — {coin}"},
+            },
+            "elements": [
+                {
+                    "tag": "div",
+                    "text": {
+                        "tag": "lark_md",
+                        "content": f"{trail_info}\n\n**保护详情**:\n{signal_lines}\n\n💡 已进入利润保护阶段，后续跌破保护位会触发跟踪止盈。",
+                    },
+                }
+            ],
+        }
+
     # v1.7.8: 跟踪止盈用独立的醒目卡片
     if action_status == "跟踪止盈":
         coin = symbol.replace("/USDT", "")

app/services/price_tracker.py

@@ -155,6 +155,8 @@ def analyze_tracking_signals(symbol, rec, current_price):
     rules = load_rules()
     trail_cfg = rules.get("tracker", {}).get("trailing_stop", {})
     trailing_stop_level = entry_plan.get("trailing_stop_level", 0)
+    trailing_stop_activated = False
+    trailing_stop_moved = False
     
     if trail_cfg.get("enabled", True) and atr_1h > 0 and entry_price > 0:
         activate_pct = trail_cfg.get("activate_pnl_pct", 3)
@@ -176,15 +178,27 @@ def analyze_tracking_signals(symbol, rec, current_price):
         # 激活条件: pnl_pct ≥ activate_pct (3%)
         if pnl_pct >= activate_pct:
             new_trail = current_price - trail_atr_mult * atr_1h
+            min_lock_pct = float(trail_cfg.get("min_lock_profit_pct", 0.5))
+            breakeven_buffer_pct = float(trail_cfg.get("breakeven_buffer_pct", min_lock_pct))
+            # ATR 对高波动山寨币会很宽。利润保护一旦激活，止盈线至少要高于入场价，
+            # 且不能低于原硬止损；否则“移动止盈已激活”实际没有任何保护效果。
+            protection_floor = max(
+                stop_loss or 0,
+                entry_price * (1 + max(min_lock_pct, breakeven_buffer_pct) / 100),
+            )
+            new_trail = max(new_trail, protection_floor)
             
             if trailing_stop_level > 0:
                 # 已有跟踪位 → 只上移不下移
+                old_trail = trailing_stop_level
                 trailing_stop_level = max(trailing_stop_level, new_trail)
+                trailing_stop_moved = trailing_stop_level > old_trail + 1e-12
             else:
                 # 首次激活
                 trailing_stop_level = new_trail
+                trailing_stop_activated = True
                 tier_info = f" [{tier_label}·{trail_atr_mult}×ATR]" if tier_label else ""
-                sell_signals.append(f"🎯 跟踪止盈激活(盈+{pnl_pct:.1f}%≥{activate_pct}%{tier_info}, 回撤{trail_atr_mult}×ATR触发)")
+                sell_signals.append(f"🎯 跟踪止盈激活(盈+{pnl_pct:.1f}%≥{activate_pct}%{tier_info}, 保护位${trailing_stop_level:.4f})")
             
             # === 触发检查：当前价跌破跟踪止盈位 → 止盈 ===
             # 🔴 v1.7.8: 跟踪止盈触发时无条件覆盖(利润保护优先级最高)
@@ -330,6 +344,8 @@ def analyze_tracking_signals(symbol, rec, current_price):
         "entry_update": entry_update,
         "pnl_pct": round(pnl_pct, 2),
         "trailing_stop_level": trailing_stop_level,
+        "trailing_stop_activated": trailing_stop_activated,
+        "trailing_stop_moved": trailing_stop_moved,
     }
 
 
@@ -395,6 +411,17 @@ def track_prices():
             )
             final_action = state_decision.get("action_status", requested_action)
             push_trade_action_update(symbol, rec["id"], state_decision, final_action, push_type="entry")
+            if tracking_signals.get("trailing_stop_activated"):
+                activation_decision = dict(state_decision)
+                activation_decision["push_required"] = True
+                activation_decision["push_signals"] = tracking_signals.get("sell_signals", [])
+                push_trade_action_update(
+                    symbol,
+                    rec["id"],
+                    activation_decision,
+                    "移动止盈保护",
+                    push_type="profit_protection",
+                )
 
             results.append({
                 "symbol": symbol,

rules.yaml

@@ -180,6 +180,8 @@ tracker:
   trailing_stop:
     enabled: true
     activate_pnl_pct: 3
+    min_lock_profit_pct: 0.5
+    breakeven_buffer_pct: 0.5
     step_ratchet: true
     tiers:
     - min_pnl_pct: 0
@@ -405,11 +407,11 @@ event_driven:
         note: Solana meme主题扩散
 meta:
   version: 1
-  last_review: '2026-05-14T17:09:45.630655'
-  last_reverse_analysis: '2026-05-14T17:10:41.080069'
-  total_reviews: 36
+  last_review: '2026-05-15T00:15:38.149520'
+  last_reverse_analysis: '2026-05-15T00:16:18.257946'
+  total_reviews: 38
   total_rules_learned: 37
-  iteration_count: 41
+  iteration_count: 43
   strategy_version: v1.7.11
   strategy_revision_started_at: '2026-05-09T01:20:00'
   strategy_revision_note: 'v1.7.11: 触发时效治理，旧形态只作背景，消息触发显式标记'

static/app.html

@@ -943,14 +943,14 @@ function historyOutcome(r) {
   var pnl = Number((r && r.pnl_pct) || 0);
   var maxPnl = Number((r && r.max_pnl_pct) || 0);
   var maxDd = Number((r && r.max_drawdown_pct) || 0);
-  var hitSuccess = status === 'hit_tp1' || status === 'hit_tp2' || maxPnl >= 5;
   var hitFailure = status === 'stopped_out' || pnl <= -3 || maxDd <= -5;
-  if (hitSuccess) {
-    return { resolved: true, type: 'success', pnl: maxPnl || pnl, label: '阶段兑现' };
-  }
   if (hitFailure) {
     return { resolved: true, type: 'failure', pnl: pnl, label: '风险边界' };
   }
+  var hitSuccess = status === 'hit_tp1' || status === 'hit_tp2' || maxPnl >= 5;
+  if (hitSuccess) {
+    return { resolved: true, type: 'success', pnl: maxPnl || pnl, label: '阶段兑现' };
+  }
   return { resolved: false, type: 'pending', pnl: pnl, label: '跟踪中' };
 }
 function isResolvedHistory(r) {
@@ -1012,7 +1012,7 @@ async function loadHistoryRecommendations(reset) {
       var isRiskExit = outcome.type === 'failure';
       var hEntryTime = r.rec_time||'', hTpTime = (!isRiskExit && (r.status==='hit_tp1'||r.status==='hit_tp2'||Number(r.max_pnl_pct||0)>=5))?(r.hit_tp1_time||r.last_track_time||''):'';
       var hSlTime = isRiskExit ? (r.stopped_out_time||r.last_track_time||r.expired_time||'') : '';
-      var hEntryPrice = r.entry_price||0, hSl = isRiskExit ? exitP : (r.stop_loss||0), hTp = r.tp1||0, hid = 'hkline'+idx;
+      var hEntryPrice = r.entry_price||0, hSl = isRiskExit ? exitP : (r.stop_loss||0), hTp = isRiskExit ? 0 : (r.tp1||0), hid = 'hkline'+idx;
       var score = r.rec_score||0;
       function scoreTier(s) {
         if(s>=80) return{label:'强势异动',cls:'tier-strong'}; if(s>=65) return{label:'值得关注',cls:'tier-good'};

tests/test_history_grouping.py

@@ -97,6 +97,7 @@ class RecommendationHistoryGroupingTests(RecommendationHistoryBase):
             symbol='BBB/USDT',
             action_status='等回踩',
             status='active',
+            current_price=105.0,
             entry_plan_json=json.dumps({'entry_action': '等回踩', 'entry_price': 100.0}, ensure_ascii=False),
         )
         self._insert_rec(
@@ -124,6 +125,7 @@ class RecommendationHistoryGroupingTests(RecommendationHistoryBase):
             symbol='CCC/USDT',
             action_status='等回踩',
             status='active',
+            current_price=105.0,
             entry_plan_json=json.dumps({'entry_action': '等回踩', 'entry_price': 100.0}, ensure_ascii=False),
         )
         self._insert_rec(
@@ -157,6 +159,7 @@ class DecisionModeHistoryTests(RecommendationHistoryBase):
             symbol='WAIT/USDT',
             action_status='等回踩',
             status='active',
+            current_price=105.0,
             entry_plan_json=json.dumps({'entry_action': '等回踩', 'entry_price': 100.0}, ensure_ascii=False),
         )
         self._insert_rec(
@@ -176,6 +179,45 @@ class DecisionModeHistoryTests(RecommendationHistoryBase):
         self.assertEqual(mapping['TP/USDT'], 'completed')
         self.assertEqual(mapping['STOP/USDT'], 'invalid')
 
+    def test_stopped_out_with_prior_float_profit_stays_failed_in_history_summary(self):
+        self._insert_rec(
+            symbol='MLN/USDT',
+            action_status='止损',
+            status='stopped_out',
+            entry_price=3.61,
+            current_price=3.12,
+            max_price=3.80,
+            min_price=3.12,
+            pnl_pct=-13.57,
+            max_pnl_pct=5.26,
+            max_drawdown_pct=-13.57,
+            stopped_out_time='2026-05-14T21:21:07',
+            last_track_time='2026-05-14T21:21:07',
+        )
+
+        result, label = altcoin_db._classify_recommendation_result({
+            'symbol': 'MLN/USDT',
+            'status': 'stopped_out',
+            'action_status': '止损',
+            'entry_price': 3.61,
+            'current_price': 3.12,
+            'pnl_pct': -13.57,
+            'max_pnl_pct': 5.26,
+            'max_drawdown_pct': -13.57,
+        })
+        self.assertEqual(result, 'failed')
+        self.assertIn('止损', label)
+
+        page = altcoin_db.get_all_recommendations(limit=20, decision_only=True, with_meta=True)
+        item = page['items'][0]
+        self.assertEqual(item['symbol'], 'MLN/USDT')
+        self.assertEqual(item['recommendation_result'], 'failed')
+        self.assertEqual(item['execution_status'], 'invalid')
+        self.assertEqual(page['summary']['success_count'], 0)
+        self.assertEqual(page['summary']['failure_count'], 1)
+        self.assertAlmostEqual(page['summary']['total_pnl'], -13.57, places=2)
+        self.assertAlmostEqual(page['summary']['best_pnl'], -13.57, places=2)
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_price_tracker_trailing_stop.py

@@ -0,0 +1,54 @@
+import os
+import sys
+
+import pandas as pd
+
+PROJECT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+if PROJECT_DIR not in sys.path:
+    sys.path.insert(0, PROJECT_DIR)
+
+from app.services import price_tracker
+
+
+def test_trailing_stop_activation_uses_profit_floor_when_atr_is_too_wide(monkeypatch):
+    """MLN-style case: +5% float profit with huge ATR must still protect above entry."""
+    monkeypatch.setattr(price_tracker, "calc_atr", lambda df, period=14: 0.447143)
+    monkeypatch.setattr(price_tracker, "detect_trend_exhaustion", lambda df, atr: {"exhausted": False, "signals": [], "severity": "low"})
+    monkeypatch.setattr(price_tracker, "full_pa_analysis", lambda df, timeframe: {"candles_class": []})
+    monkeypatch.setattr(price_tracker, "load_rules", lambda: {
+        "tracker": {
+            "trailing_stop": {
+                "enabled": True,
+                "activate_pnl_pct": 3,
+                "min_lock_profit_pct": 0.5,
+                "breakeven_buffer_pct": 0.5,
+                "tiers": [
+                    {"min_pnl_pct": 0, "atr_mult": 3.0, "label": "防震"},
+                    {"min_pnl_pct": 5, "atr_mult": 2.0, "label": "锁利"},
+                ],
+            }
+        }
+    })
+    df = pd.DataFrame(
+        [
+            {"open": 3.5, "high": 3.8, "low": 3.4, "close": 3.7, "volume": 100},
+            {"open": 3.7, "high": 3.9, "low": 3.6, "close": 3.8, "volume": 120},
+        ]
+        * 20
+    )
+    monkeypatch.setattr(price_tracker, "fetch_klines", lambda symbol, timeframe, limit=100: df)
+
+    rec = {
+        "entry_price": 3.61,
+        "stop_loss": 3.249,
+        "tp1": 4.822857,
+        "tp2": 5.631429,
+        "entry_plan": {"trailing_stop_level": 0.0},
+    }
+
+    result = price_tracker.analyze_tracking_signals("MLN/USDT", rec, 3.8)
+
+    assert result["trailing_stop_activated"] is True
+    assert result["trailing_stop_level"] == max(3.249, 3.61 * 1.005)
+    assert result["trailing_stop_level"] > rec["entry_price"]
+    assert any("跟踪止盈激活" in signal for signal in result["sell_signals"])