rustalib 1.0.9

//! # Long-Term Trading Indicators
//!
//! This module provides indicators optimized for long-term trading and investing,
//! focusing on weekly to monthly timeframes and longer-term market cycles.
//!
//! ## Types of Indicators
//!
//! - Secular trend identification
//! - Cyclical market analysis
//! - Long-term sentiment and valuation metrics
//! - Multi-month to multi-year pattern recognition

use polars::prelude::*;

/// Calculate secular trend strength
///
/// Identifies the strength and duration of long-term secular
/// market trends for position trading and investing.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `short_ma` - Short-term moving average period (e.g., 50 weeks)
/// * `long_ma` - Long-term moving average period (e.g., 200 weeks)
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with trend strength values
pub fn secular_trend_strength(
    df: &DataFrame,
    _short_ma: usize,
    _long_ma: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("secular_trend".into(), values))
}

/// Detect market cycles and phases
///
/// Identifies the current position within broader market cycles
/// (accumulation, markup, distribution, markdown).
///
/// # Arguments
///
/// * `df` - DataFrame with price, volume, and possibly fundamental data
/// * `cycle_lookback_periods` - Number of periods to analyze for cycle detection
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with market cycle phase indicators
pub fn market_cycle_phase_detector(
    df: &DataFrame,
    _cycle_lookback_periods: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation - in reality would use complex cycle analysis
    let values = vec![0i32; df.height()]; // 0=accumulation, 1=markup, 2=distribution, 3=markdown
    Ok(Series::new("cycle_phase".into(), values))
}

/// Calculate long-term valuation metrics
///
/// Combines technical and fundamental data to create valuation
/// metrics appropriate for long-term investing decisions.
///
/// # Arguments
///
/// * `price_df` - DataFrame with price data
/// * `fundamental_df` - DataFrame with fundamental data
/// * `metrics` - List of valuation metrics to calculate
///
/// # Returns
///
/// * `Result<DataFrame, PolarsError>` - DataFrame with calculated valuation metrics
pub fn long_term_valuation_metrics(
    price_df: &DataFrame,
    _fundamental_df: &DataFrame,
    _metrics: &[String],
) -> Result<DataFrame, PolarsError> {
    // Placeholder implementation - create a simple DataFrame with empty metrics

    // Use the df! macro for easier DataFrame creation
    df! {
        "date" => (0..price_df.height()).map(|_| chrono::Utc::now().timestamp()).collect::<Vec<i64>>(),
        "close" => vec![0.0; price_df.height()]
    }
}

/// Detect long-term divergences
///
/// Identifies divergences between price and various indicators
/// over long timeframes for potential trend reversals.
///
/// # Arguments
///
/// * `df` - DataFrame with price and indicator data
/// * `price_col` - Name of the price column
/// * `indicator_col` - Name of the indicator column to compare with price
/// * `min_divergence_periods` - Minimum periods for a valid divergence
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with divergence signals
pub fn long_term_divergence_detector(
    df: &DataFrame,
    _price_col: &str,
    _indicator_col: &str,
    _min_divergence_periods: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![0i32; df.height()]; // -1 = bearish divergence, 0 = none, 1 = bullish divergence
    Ok(Series::new("lt_divergence".into(), signals))
}

/// Calculate long-term support and resistance zones
///
/// Identifies significant multi-month or multi-year support and
/// resistance zones for strategic entry and exit points.
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC price data
/// * `min_touches` - Minimum number of touches to consider a valid zone
/// * `price_buffer_pct` - Percentage buffer around price for zone width
///
/// # Returns
///
/// * `Result<DataFrame, PolarsError>` - DataFrame with support and resistance zones
pub fn long_term_support_resistance(
    _df: &DataFrame,
    _min_touches: usize,
    _price_buffer_pct: f64,
) -> Result<DataFrame, PolarsError> {
    // Placeholder implementation - create a simple DataFrame with zones
    df! {
        "zone_type" => vec!["support", "resistance", "support", "resistance", "support"],
        "zone_price" => vec![100.0, 120.0, 90.0, 150.0, 80.0],
        "zone_strength" => vec![0.8, 0.7, 0.9, 0.6, 0.5]
    }
}

/// Detect long term trend change using moving average crossovers
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `short_ma` - Period for short moving average
/// * `long_ma` - Period for long moving average
pub fn detect_trend_change(
    df: &DataFrame,
    _short_ma: usize,
    _long_ma: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![0i32; df.height()]; // 0 = no change, 1 = bullish change, -1 = bearish change
    Ok(Series::new("trend_change".into(), signals))
}

/// Identify secular market cycles
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `cycle_lookback_periods` - Number of periods to analyze for cycle detection
pub fn identify_market_cycles(
    df: &DataFrame,
    _cycle_lookback_periods: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation - in reality would use complex cycle analysis
    let values = vec![0i32; df.height()]; // 0=accumulation, 1=markup, 2=distribution, 3=markdown
    Ok(Series::new("cycle_phase".into(), values))
}

/// Correlate fundamental changes with price trends
///
/// # Arguments
///
/// * `price_df` - DataFrame with price data
/// * `fundamental_df` - DataFrame with fundamental data
pub fn fundamental_trend_correlation(
    price_df: &DataFrame,
    _fundamental_df: &DataFrame,
) -> Result<Series, PolarsError> {
    // Placeholder implementation - create a simple Series with correlation values
    let values = vec![0.0; price_df.height()];
    Ok(Series::new("fund_corr".into(), values))
}

/// Find long term price - indicator divergences
///
/// # Arguments
///
/// * `df` - DataFrame with indicator data
/// * `price_col` - Column name for price
/// * `indicator_col` - Column name for indicator
/// * `min_divergence_periods` - Minimum periods to confirm divergence
pub fn find_price_indicator_divergence(
    df: &DataFrame,
    _price_col: &str,
    _indicator_col: &str,
    _min_divergence_periods: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![0i32; df.height()]; // -1 = bearish divergence, 0 = none, 1 = bullish divergence
    Ok(Series::new("price_indicator_divergence".into(), signals))
}

/// Identify significant support/resistance levels
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `min_touches` - Minimum touches to confirm a level
/// * `price_buffer_pct` - Buffer percentage around price levels
pub fn identify_support_resistance(
    _df: &DataFrame,
    _min_touches: usize,
    _price_buffer_pct: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation - create a simple Series with support/resistance levels
    let values = vec![0.0; 5]; // Assuming 5 levels
    Ok(Series::new("support_resistance".into(), values))
}