rustalib 1.0.9

//! # Short-Term Trading Indicators
//!
//! This module provides indicators optimized for short-term trading with
//! a timeframe of days to weeks, suitable for swing trading approaches.
//!
//! ## Types of Indicators
//!
//! - Swing trading momentum indicators
//! - Short-term trend identification tools
//! - Pattern recognition for multi-day setups
//! - Market regime detection for daily timeframes

use polars::prelude::*;

/// Calculate swing strength index
///
/// Measures the strength of price swings to identify
/// potential reversals in short-term trends.
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC price data
/// * `period` - Lookback period for swing calculation
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with swing strength values
pub fn swing_strength_index(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("swing_strength".into(), values))
}

/// Detect short-term market regimes
///
/// Identifies whether the market is in a trending, ranging,
/// or transitional regime for short-term trading.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `atr_period` - Period for ATR calculation (volatility)
/// * `trend_period` - Period for trend calculation
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with regime values (1 = trending, 0 = ranging, -1 = transitional)
pub fn short_term_regime_detector(
    df: &DataFrame,
    _atr_period: usize,
    _trend_period: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0i32; df.height()];
    Ok(Series::new("market_regime".into(), values))
}

/// Calculate dip-buying opportunity score
///
/// Creates a scoring system to identify potential dip-buying
/// opportunities in short-term uptrends.
///
/// # Arguments
///
/// * `df` - DataFrame with price and volume data
/// * `trend_period` - Period for trend identification
/// * `oversold_threshold` - RSI threshold to consider oversold
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with dip-buying scores (0-100)
pub fn dip_buying_score(
    df: &DataFrame,
    _trend_period: usize,
    _oversold_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![50.0; df.height()];
    Ok(Series::new("dip_buy_score".into(), values))
}

/// Detect multi-day chart patterns
///
/// Identifies common multi-day chart patterns like flags,
/// pennants, and wedges for short-term trading opportunities.
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC price data
/// * `max_pattern_length` - Maximum length of patterns to detect
/// * `min_pattern_quality` - Minimum quality threshold for pattern detection
///
/// # Returns
///
/// * `Result<DataFrame, PolarsError>` - DataFrame with detected patterns and attributes
pub fn multi_day_pattern_detector(
    _df: &DataFrame,
    _max_pattern_length: usize,
    _min_pattern_quality: f64,
) -> Result<DataFrame, PolarsError> {
    // Placeholder implementation - create a simple DataFrame with pattern data
    df! {
        "pattern_type" => vec!["flag", "pennant", "wedge", "triangle", "none"],
        "pattern_start" => vec![10, 25, 40, 60, 80],
        "pattern_quality" => vec![0.85, 0.76, 0.92, 0.68, 0.0]
    }
}

/// Calculate average range for swing trading
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `period` - Lookback period for range calculation
pub fn calculate_average_range(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("average_range".into(), values))
}

/// Find potential swing points using ATR and trend  
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `atr_period` - Period for ATR calculation
/// * `trend_period` - Period for trend identification
pub fn find_swing_points(
    df: &DataFrame,
    _atr_period: usize,
    _trend_period: usize,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("swing_points".into(), values))
}

/// Generate mean reversion signals based on oversold/overbought conditions
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `trend_period` - Period for trend identification
/// * `oversold_threshold` - Threshold to identify oversold conditions
pub fn mean_reversion_signals(
    df: &DataFrame,
    _trend_period: usize,
    _oversold_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("mean_reversion_signals".into(), values))
}

/// Detect chart patterns for swing trading
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `max_pattern_length` - Maximum length of patterns to detect
/// * `min_pattern_quality` - Minimum quality threshold for pattern detection
pub fn detect_chart_patterns(
    price_df: &DataFrame,
    _max_pattern_length: usize,
    _min_pattern_quality: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; price_df.height()];
    Ok(Series::new("chart_patterns".into(), values))
}