feat: AI analysis engine refactor, dark theme polish & virtual position management

Core changes:
- Refactor FastAnalysisService: single LLM multi-factor analysis replaces
  7-agent pipeline; add multi-timeframe consensus, threshold calibration,
  confidence calibration, multi-model ensemble voting
- Add RAG memory injection and reflection validation (analysis_memory +
  reflection worker)
- Simplify billing config: remove unused strategy_run/backtest/portfolio_monitor,
  add ai_code_gen separate billing (different token consumption scale)
- Settings hot-reload after save, no backend restart needed

Frontend:
- Global dark theme overhaul: pure black palette replacing blue-tinted colors
  across sidebar/header/dashboard/analysis/K-line/user-manage/profile/settings/billing
- Fix USDT payment modal dark theme (portal rendering broke CSS selectors)
- Refactor position modal: direction + quantity + entry price, remove add/reduce
  logic, show raw DB values on re-open, save exactly what user inputs
- Fix Polymarket prediction market dark text
- i18n for position modal title

Backend:
- Position management: one record per symbol (DELETE+INSERT replacing
  ON CONFLICT with side), fixes PnL showing 0 when switching long/short
- MarketDataCollector data fetching optimization
- portfolio_monitor scheduled monitoring improvements
- env.example reorganized: common config first, advanced config last

Documentation:
- README architecture diagram updated to FastAnalysisService flow
- Add virtual position, AI tuning config, billing items documentation
- Add INDICATOR_DEFINITIONS_CN.md, FRONTEND_FAST_ANALYSIS.md

Made-with: Cursor

backend_api_python/scripts/run_calibration.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3
+"""
+Run AI calibration manually (e.g. via cron).
+
+Usage:
+  python scripts/run_calibration.py
+  AI_CALIBRATION_MARKET=Crypto python scripts/run_calibration.py
+  AI_CALIBRATION_MARKETS=Crypto,USStock python scripts/run_calibration.py
+"""
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
+
+from app.services.ai_calibration import AICalibrationService
+
+
+def main():
+    markets = (os.getenv("AI_CALIBRATION_MARKETS") or os.getenv("AI_CALIBRATION_MARKET") or "Crypto").strip().split(",")
+    for m in markets:
+        m = m.strip()
+        if not m:
+            continue
+        print(f"Calibrating market: {m}")
+        svc = AICalibrationService()
+        r = svc.calibrate_market(market=m, validate_before=True)
+        if r:
+            print(f"  OK: accuracy={r.best_accuracy:.1f}% threshold=±{r.buy_threshold:.1f} samples={r.sample_count}")
+        else:
+            print("  SKIP: not enough validated samples")
+    print("Done.")
+
+
+if __name__ == "__main__":
+    main()

backend_api_python/scripts/run_reflection_task.py

@@ -1,19 +1,31 @@
 import sys
 import os
+from dotenv import load_dotenv
 
-# 添加项目根目录到 Python 路径
-sys.path.append(os.path.join(os.path.dirname(__file__), '..', '..'))
+# 添加后端目录到 Python 路径（使得可以 import app.*）
+# 由于 app 包位于 backend_api_python/app 下，而脚本位于 backend_api_python/scripts
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 
-from app.services.agents.reflection import ReflectionService
+from app.services.reflection import ReflectionService
 
 def main():
+    # Load backend envs for DATABASE_URL, reflection switches, etc.
+    # This script may be run locally, so we must load .env explicitly.
+    backend_env_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '.env'))
+    root_env_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..', '.env'))
+    if os.path.exists(root_env_path):
+        load_dotenv(root_env_path, override=False)
+    if os.path.exists(backend_env_path):
+        load_dotenv(backend_env_path, override=False)
+
     """
     运行自动反思验证任务
     建议通过 cron 或 定时任务调度器 每天运行一次
     """
     print("Running Automated Reflection Verification Task...")
     service = ReflectionService()
-    service.run_verification_cycle()
+    stats = service.run_verification_cycle()
+    print("Reflection stats:", stats)
     print("Task Completed.")
 
 if __name__ == "__main__":