feat(backtest): add backtest engine with evaluation pipeline and WebUI (#269)

Add a complete backtest/evaluation system that measures the accuracy of
AI-generated stock analysis recommendations against actual market outcomes.

Backend:
- Backtest engine (src/core/backtest_engine.py) with direction inference,
  stop-loss/take-profit simulation, and outcome classification (win/loss/neutral)
- Repository layer (src/repositories/backtest_repo.py) with SQLite persistence
  for backtest_results and backtest_summaries tables
- Service layer (src/services/backtest_service.py) orchestrating evaluation runs
  with configurable window days, neutral band, and min-age filters
- REST API endpoints: POST /run, GET /results, GET /performance, GET /performance/{code}
- Pydantic schemas for request/response validation

Frontend (apps/dsa-web):
- New Backtest page with performance dashboard sidebar showing direction
  accuracy, win rate, simulated returns, SL/TP trigger rates, and W/L/N counts
- Paginated results table with outcome badges, direction indicators, and
  color-coded return percentages
- Stock code filter and one-click "Run Backtest" trigger
- Full TypeScript types and API client matching backend schemas

Direction mapping fix:
- "Wait/observe" (观望) advice now maps to direction_expected="down" instead
  of "flat", correctly reflecting that "wait" means "stay out due to downside
  risk" rather than predicting a flat market

Tests:
- 21 unit tests covering engine logic, service orchestration, and summary
  aggregation (all passing)

Docs:
- Updated README, full-guide, and translations with backtest feature docs

.gitignore

@@ -6,7 +6,8 @@
 .env.*.local
 
 # 测试文件（可能包含敏感配置）
-test_*.py
+# 仅忽略仓库根目录下的临时 test_*.py 脚本；tests/ 目录下的单元测试需要纳入版本控制。
+/test_*.py
 !test_env.py
 
 # Python