| """ |
| Configuration v3 — all settings in one place. |
| Pydantic v2 compatible (removed deprecated model_post_init). |
| """ |
| from pydantic import BaseModel, Field, model_validator |
| from pathlib import Path |
| import os |
|
|
|
|
| class LLMConfig(BaseModel): |
| microfish_model: str = "Qwen/Qwen2.5-1.5B-Instruct" |
| tinyfish_model: str = "Qwen/Qwen2.5-3B-Instruct" |
| mediumfish_model: str = "Qwen/Qwen2.5-7B-Instruct" |
| bigfish_model: str = "Qwen/Qwen2.5-72B-Instruct" |
| base_url: str = Field(default="http://localhost:8000/v1") |
| api_key: str = "dummy" |
| temperature_generation: float = 0.7 |
| temperature_compilation: float = 0.1 |
| temperature_critique: float = 0.3 |
| max_tokens: int = 4096 |
|
|
|
|
| class BrainConfig(BaseModel): |
| api_url: str = "https://api.worldquantbrain.com" |
| region: str = "USA" |
| universe: str = "TOP3000" |
| delay: int = 1 |
| truncation: float = 0.08 |
| pasteurization: str = "ON" |
| nan_handling: str = "OFF" |
| max_concurrent: int = 4 |
| submit_interval_sec: float = 15.0 |
|
|
|
|
| class FieldSelectionStrategy(BaseModel): |
| """Strategy for picking fields — key lever for alpha novelty.""" |
| tier_priority: list[str] = ["tier1", "tier2", "tier3"] |
| max_alpha_count: int = 50 |
| goldmine_weight_multiplier: float = 10.0 |
| min_coverage: float = 0.55 |
| prefer_novel_groups: bool = True |
| max_group_ac: int = 30 |
| fields_per_batch: int = 5 |
|
|
|
|
| class KillSwitches(BaseModel): |
| daily_brain_submissions_max: int = 200 |
| consecutive_lint_fail_max: int = 10 |
| consecutive_kill_verdict_max: int = 30 |
| daily_llm_token_budget: int = 5_000_000 |
| max_credits_per_family: int = 3 |
|
|
|
|
| class FitnessWeights(BaseModel): |
| sharpe_os: float = 1.0 |
| is_os_gap_penalty: float = 0.5 |
| worst_year_penalty: float = 1.0 |
| crowding_penalty: float = 0.3 |
| turnover_penalty: float = 0.2 |
| turnover_threshold: float = 0.40 |
| drawdown_penalty: float = 0.1 |
| drawdown_threshold: float = 0.05 |
| novelty_bonus: float = 0.4 |
| goldmine_field_bonus: float = 0.3 |
| novel_group_bonus: float = 0.2 |
|
|
|
|
| class SubmissionThresholds(BaseModel): |
| """BRAIN submission pass/fail cutoffs from IQC 2026.""" |
| min_sharpe: float = 1.25 |
| min_fitness: float = 1.0 |
| max_turnover: float = 0.70 |
| min_sub_universe_sharpe: float = 0.78 |
| max_self_correlation: float = 0.65 |
|
|
|
|
| class Paths(BaseModel): |
| root: Path = Path(os.getenv("AF_ROOT", ".")) |
| data: Path | None = None |
| factor_store: Path | None = None |
| prompts: Path | None = None |
| logs: Path | None = None |
|
|
| @model_validator(mode="after") |
| def set_defaults(self): |
| if self.data is None: |
| self.data = self.root / "data" |
| if self.factor_store is None: |
| self.factor_store = self.root / "factor_store" |
| if self.prompts is None: |
| self.prompts = self.root / "prompts" |
| if self.logs is None: |
| self.logs = self.root / "logs" |
| for p in [self.data, self.factor_store, self.factor_store / "alphas", |
| self.prompts, self.prompts / "templates", self.logs]: |
| p.mkdir(parents=True, exist_ok=True) |
| return self |
|
|
|
|
| class Config(BaseModel): |
| llm: LLMConfig = LLMConfig() |
| brain: BrainConfig = BrainConfig() |
| field_strategy: FieldSelectionStrategy = FieldSelectionStrategy() |
| kill: KillSwitches = KillSwitches() |
| fitness: FitnessWeights = FitnessWeights() |
| submission: SubmissionThresholds = SubmissionThresholds() |
| paths: Paths = Paths() |
|
|
| |
| batch_size: int = 10 |
| max_iterations_per_family: int = 3 |
| correlation_threshold: float = 0.65 |
| min_sharpe_local_sim: float = 1.0 |
| min_info_value_sign_sweep: float = 0.3 |
| max_parallel_candidates: int = 3 |
| enable_brain_client: bool = False |
| use_proven_templates: bool = False |
|
|
|
|
| def load_config() -> Config: |
| return Config() |
|
|
