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>

rdagent/components/coder/factor_coder/evolving_strategy.py

@@ -14,6 +14,7 @@ from rdagent.components.coder.CoSTEER.knowledge_management import (
 )
 from rdagent.components.coder.factor_coder.config import FACTOR_COSTEER_SETTINGS
 from rdagent.components.coder.factor_coder.factor import FactorFBWorkspace, FactorTask
+from rdagent.components.coder.factor_coder.auto_fixer import auto_fix_factor_code
 from rdagent.core.experiment import FBWorkspace
 from rdagent.oai.llm_conf import LLM_SETTINGS
 from rdagent.oai.llm_utils import APIBackend
@@ -156,6 +157,9 @@ class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
                     else:
                         raise  # continue to retry
 
+                # === AUTO-FIX: Apply known fixes before returning code ===
+                code = auto_fix_factor_code(code, target_factor_task_information)
+
                 return code
 
             except (json.decoder.JSONDecodeError, KeyError):
@@ -172,7 +176,17 @@ class FactorMultiProcessEvolvingStrategy(MultiProcessEvolvingStrategy):
             # Since the `implement_one_task` method is not standardized and the `code_list` has both `str` and `dict` data types,
             # we ended up getting an `TypeError` here, so we chose to fix the problem temporarily with this dirty method.
             if isinstance(code_list[index], dict):
-                evo.sub_workspace_list[index].inject_files(**code_list[index])
+                # Auto-fix each file in the dict
+                fixed_dict = {}
+                for filename, file_code in code_list[index].items():
+                    if filename.endswith('.py'):
+                        task_info = evo.sub_tasks[index].get_task_information()
+                        fixed_dict[filename] = auto_fix_factor_code(file_code, task_info)
+                    else:
+                        fixed_dict[filename] = file_code
+                evo.sub_workspace_list[index].inject_files(**fixed_dict)
             else:
-                evo.sub_workspace_list[index].inject_files(**{"factor.py": code_list[index]})
+                task_info = evo.sub_tasks[index].get_task_information()
+                fixed_code = auto_fix_factor_code(code_list[index], task_info)
+                evo.sub_workspace_list[index].inject_files(**{"factor.py": fixed_code})
         return evo