feat(hub): add AI coach page with daily summary and chat

Aggregate four-account trades via hub_ai module and /api/hub/trades/today; store sessions in JSON; default OpenAI config matches instances. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-06 23:51:36 +08:00
parent 4fad5696df
commit cee641ba5d
23 changed files with 1557 additions and 14 deletions
@@ -0,0 +1,57 @@
+"""hub_trades_lib 单元测试。"""
+from __future__ import annotations
+
+import sqlite3
+import unittest
+
+from hub_trades_lib import fetch_trades_for_trading_day, summarize_trades, trading_day_from_dt
+from datetime import datetime
+
+
+class HubTradesLibTest(unittest.TestCase):
+    def test_trading_day_reset(self):
+        dt = datetime(2026, 6, 6, 7, 30, 0)
+        self.assertEqual(trading_day_from_dt(dt, 8), "2026-06-05")
+        dt2 = datetime(2026, 6, 6, 8, 0, 0)
+        self.assertEqual(trading_day_from_dt(dt2, 8), "2026-06-06")
+
+    def test_fetch_and_summarize(self):
+        conn = sqlite3.connect(":memory:")
+        conn.row_factory = sqlite3.Row
+        conn.execute(
+            """CREATE TABLE trade_records (
+                symbol TEXT, exchange_symbol TEXT, direction TEXT, result TEXT,
+                pnl_amount REAL, closed_at TEXT, opened_at TEXT, session_date TEXT,
+                monitor_type TEXT, actual_rr REAL, planned_rr REAL, trade_style TEXT, entry_reason TEXT
+            )"""
+        )
+        conn.execute(
+            "INSERT INTO trade_records VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)",
+            (
+                "ONDO/USDT",
+                "ONDO/USDT:USDT",
+                "short",
+                "止损",
+                -0.5,
+                "2026-06-06 10:00:00",
+                "2026-06-06 09:00:00",
+                "2026-06-06",
+                "趋势回调",
+                None,
+                None,
+                "trend",
+                "",
+            ),
+        )
+        conn.commit()
+        rows = fetch_trades_for_trading_day(conn, "2026-06-06")
+        self.assertEqual(len(rows), 1)
+        stats = summarize_trades(rows)
+        self.assertEqual(stats["closed_count"], 1)
+        self.assertEqual(stats["loss_count"], 1)
+        self.assertAlmostEqual(stats["total_pnl_u"], -0.5)
+        conn.close()
+
+
+if __name__ == "__main__":
+    unittest.main()