rustalib 1.0.9

//! # Stock Fundamental Indicators
//!
//! This module provides indicators that combine fundamental data with technical analysis
//! for stock/equity markets.

use polars::prelude::*;

/// Fundamental indicators for stock analysis
pub struct FundamentalIndicators {
    /// Lookback period for earnings analysis
    pub earnings_lookback_quarters: usize,

    /// Minimum earnings growth rate considered positive
    pub earnings_growth_threshold: f64,

    /// Lookback period for PEG ratio calculation
    pub peg_calc_years: usize,
}

impl Default for FundamentalIndicators {
    fn default() -> Self {
        Self {
            earnings_lookback_quarters: 4,
            earnings_growth_threshold: 0.1, // 10%
            peg_calc_years: 5,
        }
    }
}

/// Calculate PEG ratio (Price/Earnings to Growth) with technical trigger
///
/// Combines fundamental PEG ratio with technical indicators to generate
/// potential entry signals for growth at reasonable price strategies.
///
/// # Arguments
///
/// * `price_df` - DataFrame with OHLCV data
/// * `fundamental_df` - DataFrame with fundamental data including earnings and growth rates
/// * `max_peg` - Maximum PEG ratio to consider for entry
/// * `min_uptrend_days` - Minimum number of days the stock should be in uptrend
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Boolean series indicating buy signals
pub fn peg_ratio_with_technical_trigger(
    price_df: &DataFrame,
    _fundamental_df: &DataFrame,
    _max_peg: f64,
    _min_uptrend_days: usize,
) -> Result<Series, PolarsError> {
    // In a real implementation, we would:
    // 1. Calculate PEG ratios from fundamental_df
    // 2. Detect uptrends from price_df
    // 3. Generate signals where PEG < max_peg && in_uptrend_for >= min_uptrend_days

    // For now, we'll return a placeholder series
    let signals = vec![false; price_df.height()];
    Ok(Series::new("peg_buy_signals".into(), signals))
}

/// Calculate earnings surprise momentum
///
/// Identifies stocks with positive earnings surprises and combines this
/// with price momentum to find potential momentum candidates.
///
/// # Arguments
///
/// * `price_df` - DataFrame with OHLCV data
/// * `earnings_df` - DataFrame with earnings estimates and actual results
/// * `surprise_threshold` - Minimum positive surprise percentage to consider
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with surprise momentum scores
pub fn earnings_surprise_momentum(
    price_df: &DataFrame,
    _earnings_df: &DataFrame,
    _surprise_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let momentum_scores = vec![0.0; price_df.height()];
    Ok(Series::new("surprise_momentum".into(), momentum_scores))
}

/// Calculate relative valuation score
///
/// Compares a stock's valuation metrics to its sector/industry peers
/// and assigns a relative score that can be used for stock screening.
///
/// # Arguments
///
/// * `price_df` - DataFrame with OHLCV data
/// * `valuation_df` - DataFrame with valuation metrics for the stock and its peers
/// * `metrics` - Vector of metrics to include in the score (e.g., P/E, P/S, P/B)
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with relative valuation scores
pub fn relative_valuation_score(
    price_df: &DataFrame,
    _valuation_df: &DataFrame,
    _metrics: &[String],
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let valuation_scores = vec![0.0; price_df.height()];
    Ok(Series::new("relative_valuation".into(), valuation_scores))
}

/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `fundamental_df` - DataFrame with fundamental data
/// * `max_peg` - Maximum PEG ratio for a growth stock
/// * `min_uptrend_days` - Minimum number of days in uptrend
pub fn find_growth_stocks(
    df: &DataFrame,
    _fundamental_df: &DataFrame,
    _max_peg: f64,
    _min_uptrend_days: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![false; df.height()];
    Ok(Series::new("growth_stocks".into(), signals))
}

/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `earnings_df` - DataFrame with earnings data
/// * `surprise_threshold` - Threshold for earnings surprise percentage
pub fn earnings_surprise_impact(
    df: &DataFrame,
    _earnings_df: &DataFrame,
    _surprise_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let impact_scores = vec![0.0; df.height()];
    Ok(Series::new(
        "earnings_surprise_impact".into(),
        impact_scores,
    ))
}

/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `valuation_df` - DataFrame with valuation metrics
/// * `metrics` - List of valuation metrics to consider
pub fn value_stock_screening(
    df: &DataFrame,
    _valuation_df: &DataFrame,
    _metrics: &[String],
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let screening_scores = vec![0.0; df.height()];
    Ok(Series::new(
        "value_stock_screening".into(),
        screening_scores,
    ))
}