trading-agent/config/settings.py

"""Central configuration. Single source of truth for keys, budgets, timezones, paths.

Reads from the repo-root ``.env`` (see ``.env.example``). Env var names map to the
snake_case fields below case-insensitively (pydantic-settings default).
"""

from __future__ import annotations

from functools import lru_cache
from pathlib import Path

from pydantic_settings import BaseSettings, SettingsConfigDict

from .models import ModelProfile, resolve_profile

REPO_ROOT = Path(__file__).resolve().parent.parent


class Settings(BaseSettings):
    model_config = SettingsConfigDict(
        env_file=REPO_ROOT / ".env",
        env_file_encoding="utf-8",
        extra="ignore",
        case_sensitive=False,
    )

    # --- Alpaca: one paper account per persona (single-account fields are a fallback) ---
    alpaca_paper: bool = True
    alpaca_api_key: str = ""
    alpaca_secret_key: str = ""
    alpaca_nova_key: str = ""
    alpaca_nova_secret: str = ""
    alpaca_warren_lite_key: str = ""
    alpaca_warren_lite_secret: str = ""
    alpaca_atlas_key: str = ""
    alpaca_atlas_secret: str = ""

    # --- Finnhub ---
    finnhub_api_key: str = ""

    # --- oMLX local server (Apple MLX; OpenAI-compatible; auto-starts on login) ---
    llm_host: str = "127.0.0.1"
    llm_port: int = 8090
    # oMLX enforces an API key. Blank → auto-read from ~/.omlx/settings.json (auth.api_key).
    omlx_api_key: str = ""
    # Model toggle (presets in config/models.py): gpt_oss | qwen35_27b
    model_profile: str = "gpt_oss"
    # Optional explicit served model id; blank = use the profile + resolve against /v1/models.
    llm_model: str = ""
    llm_max_tokens: int = 2048

    # --- Budgets ---
    per_agent_wallclock_min: int = 130  # hard wall-clock cap per agent
    per_agent_min_minutes: int = 120  # TIME FLOOR: keep researching until at least this long
    per_agent_step_budget: int = 400  # high safety cap so steps don't cap before the time floor
    per_agent_min_steps: int = 25  # secondary floor (steps)
    hard_stop: str = "07:00"  # local wall-clock HH:MM; nightly run must finish by this
    sandbox_timeout_s: int = 90  # run_analysis script wall-clock limit
    # Compact when the prompt approaches this many tokens. This is the MEMORY-safe KV ceiling
    # for a 24GB Mac (gpt-oss hits oMLX's prefill guard ~47k tokens / ~16GB) — NOT the model's
    # 131k context window. Keeping context well under the guard is what makes long runs survive.
    compaction_token_budget: int = 26000

    # --- Email (daily report; use a Gmail App Password, never your real password) ---
    email_to: str = ""
    email_from: str = ""
    smtp_host: str = "smtp.gmail.com"
    smtp_port: int = 587
    email_app_password: str = ""

    # --- Timezones ---
    market_tz: str = "America/New_York"  # authoritative market clock
    local_tz: str = "America/Chicago"  # this Mac

    # --- Derived paths ---
    @property
    def state_dir(self) -> Path:
        return REPO_ROOT / "state"

    @property
    def output_dir(self) -> Path:
        return REPO_ROOT / "state" / "output"

    @property
    def log_dir(self) -> Path:
        return REPO_ROOT / "state" / "logs"

    @property
    def db_path(self) -> Path:
        return REPO_ROOT / "state" / "trading.db"

    @property
    def reports_dir(self) -> Path:
        return REPO_ROOT / "state" / "reports"

    @property
    def sandbox_dir(self) -> Path:
        return REPO_ROOT / "state" / "sandbox"

    @property
    def llm_base_url(self) -> str:
        return f"http://{self.llm_host}:{self.llm_port}/v1"

    @property
    def llm_api_key(self) -> str:
        """oMLX API key: explicit env value, else auto-discovered from oMLX's settings.json."""
        if self.omlx_api_key:
            return self.omlx_api_key
        try:
            import json

            data = json.loads((Path.home() / ".omlx" / "settings.json").read_text())
            return data["auth"]["api_key"]
        except Exception:
            return "not-needed"

    @property
    def active_profile(self) -> ModelProfile:
        return resolve_profile(self.model_profile)

    @property
    def effective_model_id(self) -> str:
        """Explicit override if set, else the active profile's served id.
        The client further resolves this against oMLX's /v1/models."""
        return self.llm_model or self.active_profile.served_model_id

    @property
    def llm_context_length(self) -> int:
        return self.active_profile.context_length

    @property
    def llm_temperature(self) -> float:
        return self.active_profile.temperature

    def alpaca_creds(self, persona_id: str) -> tuple[str, str]:
        """(key, secret) for a persona's paper account, falling back to the single-account keys."""
        key = getattr(self, f"alpaca_{persona_id}_key", "") or ""
        secret = getattr(self, f"alpaca_{persona_id}_secret", "") or ""
        if key and secret:
            return key, secret
        return self.alpaca_api_key, self.alpaca_secret_key

    def alpaca_data_creds(self) -> tuple[str, str]:
        """Any valid creds — Alpaca market data is account-agnostic."""
        for pid in ("nova", "warren_lite", "atlas"):
            key, secret = self.alpaca_creds(pid)
            if key and secret:
                return key, secret
        return self.alpaca_api_key, self.alpaca_secret_key

    def ensure_dirs(self) -> None:
        for d in (self.output_dir, self.log_dir, self.reports_dir, self.sandbox_dir):
            d.mkdir(parents=True, exist_ok=True)


@lru_cache
def get_settings() -> Settings:
    return Settings()