from dotenv import load_dotenv
import os

load_dotenv()

class Settings:
    alpaca_key = os.getenv("ALPACA_API_KEY", "")
    alpaca_secret = os.getenv("ALPACA_API_SECRET", "")
    alpaca_base = os.getenv("ALPACA_BASE_URL", "https://paper-api.alpaca.markets")
    paper_mode = os.getenv("PAPER_MODE", "true").lower() == "true"

    # Capital/risk profile
    starting_capital_usd = float(os.getenv("STARTING_CAPITAL_USD", "100"))
    max_order_usd = float(os.getenv("MAX_ORDER_USD", "5"))
    max_daily_notional = float(os.getenv("MAX_DAILY_NOTIONAL", "40"))
    max_open_positions = int(os.getenv("MAX_OPEN_POSITIONS", "8"))
    min_confidence = float(os.getenv("MIN_CONFIDENCE", "0.60"))

    # Approx fee model for small-size optimization
    fee_per_trade_usd = float(os.getenv("FEE_PER_TRADE_USD", "0.00"))
    slippage_bps = float(os.getenv("SLIPPAGE_BPS", "5"))

    # Scheduling
    trade_interval_hours = int(os.getenv("TRADE_INTERVAL_HOURS", "2"))
    curate_interval_minutes = int(os.getenv("CURATE_INTERVAL_MINUTES", "30"))
    timezone = os.getenv("TIMEZONE", "America/Los_Angeles")

    # LLM stack (small for curation, larger for final decision)
    ollama_url = os.getenv("OLLAMA_URL", "http://10.30.20.110:11434")
    ollama_curator_model = os.getenv("OLLAMA_CURATOR_MODEL", "gemma3:latest")
    ollama_decision_model = os.getenv("OLLAMA_DECISION_MODEL", "agent-oss:latest")

    # Data sources
    searx_url = os.getenv("SEARX_URL", "http://10.30.20.35:6969/search")
    scraper_api = os.getenv("SCRAPER_API_URL", "http://10.30.20.115:24125")

    # App
    db_path = os.getenv("DB_PATH", "sqlite:///./bot.db")
    host = os.getenv("APP_HOST", "0.0.0.0")
    port = int(os.getenv("APP_PORT", "8089"))
    symbols = [s.strip().upper() for s in os.getenv("SYMBOLS", "SPY,QQQ").split(",") if s.strip()]

settings = Settings()