"""Data fetching and processing utilities for the financial dashboard."""

import pandas as pd
from openbb import sdk


def load_stock_data(symbol: str) -> pd.DataFrame:
    """Load historical stock price data."""
    df = sdk.equity.price.historical(symbol=symbol).to_dataframe()
    return df


def load_company_profile(symbol: str):
    """Load company profile information."""
    profile_response = sdk.equity.profile(symbol=symbol)
    profile_info = profile_response.results[0] if hasattr(profile_response, 'results') and profile_response.results else None
    return profile_info


def load_income_statement(symbol: str) -> pd.DataFrame:
    """Load company income statement data."""
    income_stmt = sdk.equity.fundamental.income(symbol=symbol).to_dataframe()
    return income_stmt


def calculate_technical_indicators(df: pd.DataFrame, period: int) -> pd.DataFrame:
    """Calculate SMA, EMA, and RSI indicators."""
    df["SMA"] = df["close"].rolling(period).mean()
    df["EMA"] = df["close"].ewm(span=period, adjust=False).mean()

    # Calculate RSI
    delta = df["close"].diff()
    gain = delta.clip(lower=0)
    loss = -1 * delta.clip(upper=0)
    avg_gain = gain.rolling(period).mean()
    avg_loss = loss.rolling(period).mean()
    rs = avg_gain / avg_loss
    df["RSI"] = 100 - (100 / (1 + rs))

    return df


def format_financial_value(value) -> str:
    """Format financial values with appropriate units."""
    if pd.isna(value):
        return "N/A"
    if abs(value) >= 1e9:
        return f"${value/1e9:.2f}B"
    elif abs(value) >= 1e6:
        return f"${value/1e6:.2f}M"
    else:
        return f"${value:.2f}"


def get_price_metrics(df: pd.DataFrame) -> dict:
    """Calculate key price metrics."""
    current_price = df["close"].iloc[-1]
    prev_close = df["close"].iloc[-2] if len(df) > 1 else df["close"].iloc[0]
    price_change = current_price - prev_close
    price_change_pct = (price_change / prev_close) * 100 if prev_close != 0 else 0

    return {
        "current_price": current_price,
        "price_change": price_change,
        "price_change_pct": price_change_pct,
        "high_52w": df['high'].max(),
        "low_52w": df['low'].min(),
    }


def get_profitability_metrics(income_data: pd.Series) -> dict:
    """Calculate profitability metrics from income statement."""
    total_rev = income_data.get('total_revenue', 0)
    gross_prof = income_data.get('gross_profit', 0)
    net_inc = income_data.get('net_income', 0)
    operating_inc = income_data.get('operating_income', 0)

    metrics = {}

    if total_rev and total_rev > 0:
        metrics["gross_margin"] = (gross_prof / total_rev) * 100 if pd.notna(gross_prof) else 0
        metrics["net_margin"] = (net_inc / total_rev) * 100 if pd.notna(net_inc) else 0
        if operating_inc:
            metrics["operating_margin"] = (operating_inc / total_rev) * 100
    else:
        metrics = {"gross_margin": 0, "net_margin": 0}

    return metrics