feat: add daily log rotation, llama health wait, factor auto-fixer, and README updates

- Add rdagent/log/daily_log.py: daily-rotating structured logs per command
  (fin_quant, strategies, evaluate, parallel) with loguru; all.log combined sink
- predix.py: route TeeWriter output to logs/YYYY-MM-DD/ instead of root dir;
  wrap quant() and evaluate() in daily_log.session() for start/stop/duration tracking
- rdagent/app/cli.py: fin_quant_cli waits for llama.cpp /health endpoint before
  starting pipeline (up to 300 s); daily_log integration for fin_quant,
  generate_strategies, eval_all, parallel commands
- scripts/predix_gen_strategies_real_bt.py: daily_log integration with
  per-strategy ACCEPTED/REJECTED entries and summary on completion
- rdagent/components/coder/factor_coder/auto_fixer.py: new module that patches
  common LLM-generated factor issues (min_periods, inf/NaN, groupby.transform,
  MultiIndex corrections)
- rdagent/components/coder/factor_coder/prompts.yaml: add critical rules for
  EURUSD 1-min intraday factors (min_periods, inf handling, groupby, date range)
- README.md: document --reasoning off and --n-gpu-layers 28 for llama-server;
  explain VRAM constraints when Ollama is running alongside llama.cpp
- .bandit.yml: suppress B615 (HuggingFace unsafe download) for RL benchmark files

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

scripts/predix_gen_strategies_real_bt.py

@@ -331,7 +331,17 @@ print(json.dumps(result))
 # ============================================================================
 def main(target_count=10):
     """Generate strategies in parallel with real backtesting."""
-    
+    import sys as _sys
+    _sys.path.insert(0, str(Path(__file__).parent.parent))
+    from rdagent.log import daily_log as _dlog
+    _log = _dlog.setup(
+        "strategies_bt",
+        style=TRADING_STYLE,
+        forward_bars=FORWARD_BARS,
+        target=target_count,
+        workers=N_WORKERS,
+    )
+
     console.print(f"\n[bold cyan]{STYLE_EMOJI} Parallel Strategy Generation[/bold cyan]")
     console.print(f"   Style: {STYLE_DESC}")
     console.print(f"   Forward bars: {FORWARD_BARS}")
@@ -446,18 +456,25 @@ def main(target_count=10):
                         pass
                     
                     accepted.append(strategy)
+                    _log.success(f"ACCEPTED  {strategy['strategy_name']}  IC={ic:.4f}  Sharpe={sharpe:.3f}  Trades={trades}  DD={dd:.1%}")
                     feedback_history.append(f"Excellent! IC={ic:.4f}, Sharpe={sharpe:.2f}, Trades={trades}. Try to improve further.")
-                    
+
                     progress.console.print(f"[green]✓ Strategy #{len(accepted)}:[/green] {strategy['strategy_name']} "
                                           f"IC={ic:.4f}, Sharpe={sharpe:.3f}, Trades={trades}, DD={dd:.1%}")
                 else:
+                    _log.info(f"REJECTED  IC={ic:.4f}  Sharpe={sharpe:.2f}  Trades={trades}  DD={dd:.1%}")
                     feedback_history.append(f"Failed: IC={ic:.4f}, Sharpe={sharpe:.2f}, Trades={trades}, DD={dd:.1%}. Need |IC|>{MIN_IC}, Sharpe>{MIN_SHARPE}, Trades>{MIN_TRADES}")
             
             progress.update(task, advance=1)
     
     # Summary
+    _log.info(f"DONE  accepted={len(accepted)}  target={target_count}")
+    for i, s in enumerate(sorted(accepted, key=lambda x: x['real_backtest'].get('ic', 0), reverse=True), 1):
+        bt = s['real_backtest']
+        _log.info(f"  #{i}  {s['strategy_name']}  IC={bt.get('ic',0):.4f}  Sharpe={bt.get('sharpe',0):.3f}  Monthly={bt.get('monthly_return_pct',0):.2f}%")
+
     console.print(f"\n[bold green]✓ Generated {len(accepted)}/{target_count} accepted strategies[/bold green]\n")
-    
+
     if accepted:
         accepted.sort(key=lambda x: x['real_backtest'].get('ic', 0), reverse=True)
         console.print("[bold]Results:[/bold]")