rustalib 1.0.9

//! # Implied Volatility Indicators
//!
//! This module provides indicators based on implied volatility analysis for options trading.

use polars::prelude::*;

/// Implied Volatility Surface for analyzing IV patterns across strikes and expirations
pub struct IVSurface {
    /// Minimum number of strikes required to construct a valid IV skew
    pub min_strikes_for_skew: usize,

    /// Minimum number of expirations required to construct a valid term structure
    pub min_expirations_for_term: usize,

    /// Historical percentile window for IV rank calculation
    pub iv_rank_window_days: usize,
}

impl Default for IVSurface {
    fn default() -> Self {
        Self {
            min_strikes_for_skew: 5,
            min_expirations_for_term: 3,
            iv_rank_window_days: 252, // One trading year
        }
    }
}

/// Calculate implied volatility skew
///
/// Measures the difference in IV between OTM puts and OTM calls
/// to identify potential market sentiment and tail risk expectations.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `options_chain` - DataFrame with options data
/// * `current_price` - Current price of the underlying
/// * `delta_range` - Range of delta values to include
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - IV skew value (positive: put skew, negative: call skew)
pub fn calculate_iv_skew(
    _df: &DataFrame,
    _options_chain: &DataFrame,
    _current_price: f64,
    _delta_range: (f64, f64),
) -> Result<Series, PolarsError> {
    // In a real implementation, we would:
    // 1. Filter options to the specified delta range
    // 2. Group by puts vs calls
    // 3. Calculate average IV for each group
    // 4. Return put_iv - call_iv

    // Placeholder implementation
    Ok(Series::new("iv_skew".into(), vec![0.15]))
}

/// Calculate implied volatility term structure
///
/// Analyzes the relationship between IV and time to expiration
/// to identify potential volatility expectations across different timeframes.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `options_chain` - DataFrame with options data
/// * `atm_delta` - Delta value for at-the-money options
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series of IV values for different expirations
pub fn term_structure_analysis(
    _df: &DataFrame,
    _options_chain: &DataFrame,
    _atm_delta: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let term_structure = vec![
        0.25, // 30 DTE: 25% IV
        0.23, // 60 DTE: 23% IV
        0.22, // 90 DTE: 22% IV
    ];

    Ok(Series::new("iv_term_structure".into(), term_structure))
}

/// Calculate IV rank and percentile
///
/// Determines where current IV stands in relation to its historical range.
///
/// # Arguments
///
/// * `current_iv` - Current implied volatility value
/// * `historical_iv` - Series of historical IV values
///
/// # Returns
///
/// * `(f64, f64)` - Tuple of (IV rank, IV percentile)
pub fn calculate_iv_rank_percentile(current_iv: f64, historical_iv: &Series) -> (f64, f64) {
    // Collect valid float values into a Vec to simplify processing
    let mut values = Vec::new();
    if let Ok(f64_chunked) = historical_iv.f64() {
        for i in 0..f64_chunked.len() {
            if let Some(val) = f64_chunked.get(i) {
                if !val.is_nan() {
                    values.push(val);
                }
            }
        }
    }

    // Initialize calculation variables
    let mut min_iv = f64::MAX;
    let mut max_iv = f64::MIN;
    let mut count_below = 0;
    let total_values = values.len();

    // Process all values to find min, max, and count values below current_iv
    for &val in &values {
        min_iv = min_iv.min(val);
        max_iv = max_iv.max(val);

        if val < current_iv {
            count_below += 1;
        }
    }

    // Calculate IV rank and percentile
    let iv_rank = if max_iv > min_iv {
        (current_iv - min_iv) / (max_iv - min_iv)
    } else {
        0.5 // Default if there's no range
    };

    let iv_percentile = if total_values > 0 {
        count_below as f64 / total_values as f64
    } else {
        0.5 // Default if no historical data
    };

    (iv_rank, iv_percentile)
}

/// Generate trading signals based on IV behavior
///
/// Creates buy/sell signals for volatility-based trading strategies
/// using implied volatility patterns.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `iv_series` - Series with historical implied volatility
/// * `iv_percentile_threshold` - Threshold for high and low IV percentile
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series of buy/sell signals
pub fn implied_volatility_regime(
    df: &DataFrame,
    _iv_series: &Series,
    _iv_percentile_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let rows = df.height();
    let signals = vec![false; rows];

    Ok(Series::new("iv_signals".into(), signals))
}