import os

# ─────────────────────────────────────────────
# UI & TERMINAL FORMATTING
# ─────────────────────────────────────────────
class Color:
    PURPLE = '\033[95m'
    CYAN = '\033[96m'
    DARKCYAN = '\033[36m'
    BLUE = '\033[94m'
    GREEN = '\033[92m'
    YELLOW = '\033[93m'
    RED = '\033[91m'
    MAGENTA = '\033[35m'
    BOLD = '\033[1m'
    UNDERLINE = '\033[4m'
    DIM = '\033[2m'
    RESET = '\033[0m'

def hr(char: str = "—", length: int = 65, color: str = Color.DIM) -> None:
    """Helper to print horizontal lines in the terminal."""
    print(f"{color}{char * length}{Color.RESET}")

# ─────────────────────────────────────────────
# CORE QUANTITATIVE CONSTANTS
# ─────────────────────────────────────────────
DEFAULT_SEED = 42
DEFAULT_ADV = 50_000_000.0
LONG_RUN_ERP = 0.05  # Long-run Equity Risk Premium (5%)

OUTPUT_DIR = "output"
os.makedirs(OUTPUT_DIR, exist_ok=True)

COST_BASIS_FILE = os.path.join(OUTPUT_DIR, "portfolio_state.json")
CONFIG_FILE = os.path.join(OUTPUT_DIR, "portfolio_config.json")
KEYS_FILE = os.path.join(OUTPUT_DIR, "access_keys.json")

MASTER_KEY = os.getenv("MASTER_KEY", "QUANT-ALPHA-99")

MODEL_NAMES = {
    1: "CAPM (Capital Asset Pricing Model)",
    2: "Black-Litterman (Market Equilibrium Prior)",
    3: "Bayesian Shrinkage (James-Stein)",
    4: "Fama-French 3-Factor + Momentum",
    5: "Machine Learning Stacking Ensemble",
    6: "End-to-End Differentiable Optimization (SPO+)",
    7: "Regime-Adaptive Factor Blend",
}

SPREAD_BY_SECTOR = {
    "Index": 0.0003,      # 3 bps for highly liquid ETFs
    "Tech": 0.0005,       # 5 bps for mega-cap tech
    "Bonds": 0.0004,      # 4 bps for treasury ETFs
    "Defensive": 0.0006,  # 6 bps for large-cap value
    "Commodity": 0.0008,  # 8 bps for commodity/gold ETFs
    "International": 0.0008, # 8 bps for international ETFs
    "Crypto": 0.0025,     # 25 bps for crypto proxies
    "Other": 0.0008       # 8 bps default fallback
}