rustalib 1.0.9

A library of technical indicators for financial analysis, similar to TA-Lib
Documentation 
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//! # Day Trading Indicators
//!
//! This module provides indicators optimized for intraday trading and
//! short-term price movements within a single trading day.
//!
//! ## Types of Indicators
//!
//! - Intraday momentum indicators with faster response times
//! - Volume-price relationship indicators for short timeframes
//! - Market microstructure indicators for order flow analysis
//! - Volatility indicators calibrated for intraday movements

use polars::prelude::*;

/// Calculate intraday momentum oscillator
///
/// A faster-responding version of RSI optimized for intraday trading.
///
/// # Arguments
///
/// * `df` - DataFrame with price data (typically minute or tick data)
/// * `period` - Calculation period (typically 7-14 periods for intraday)
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with oscillator values
pub fn intraday_momentum_oscillator(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![50.0; df.height()];
    Ok(Series::new("intraday_momentum".into(), values))
}

/// Calculate order flow imbalance
///
/// Measures the imbalance between buying and selling pressure
/// based on tick-by-tick data and trade direction.
///
/// # Arguments
///
/// * `df` - DataFrame with tick data including trade direction
/// * `volume_weighted` - Whether to weight the imbalance by volume
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with imbalance values
pub fn order_flow_imbalance(df: &DataFrame, _volume_weighted: bool) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("order_flow_imbalance".into(), values))
}

/// Detect intraday breakout patterns
///
/// Identifies potential intraday breakout patterns based on
/// price action and volume confirmation.
///
/// # Arguments
///
/// * `df` - DataFrame with price and volume data
/// * `consolidation_periods` - Minimum periods of consolidation before breakout
/// * `volume_threshold` - Volume increase threshold for confirmation
///
/// # Returns
///
/// * `Result<Series, PolarsError>` - Series with breakout signals
pub fn intraday_breakout_detector(
    df: &DataFrame,
    _consolidation_periods: usize,
    _volume_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![false; df.height()];
    Ok(Series::new("intraday_breakouts".into(), signals))
}

/// Calculate price velocities at different timeframes
///
/// Calculates the rate of price change at different intraday timeframes
/// to identify short-term momentum.
///
/// # Arguments
///
/// * `df` - DataFrame with price data
/// * `periods` - Vector of different timeframes to calculate velocity
///
/// # Returns
///
/// * `Result<Vec<Series>, PolarsError>` - Vector of Series with velocities at different timeframes
pub fn price_velocities(df: &DataFrame, periods: &[usize]) -> Result<Vec<Series>, PolarsError> {
    // Placeholder implementation
    let mut result = Vec::with_capacity(periods.len());

    for &period in periods {
        let values = vec![0.0; df.height()];
        let name = format!("velocity_{}", period);
        result.push(Series::new(name.into(), values));
    }

    Ok(result)
}

/// Calculate intraday price levels based on pivot points
///
/// # Arguments
///
/// * `df` - DataFrame with OHLC data
/// * `period` - Lookback period for pivot calculation
pub fn calculate_pivot_levels(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("pivot_levels".into(), values))
}

/// Calculate VWAP and standard deviation bands
///
/// # Arguments
///
/// * `df` - DataFrame with OHLCV data
/// * `volume_weighted` - Whether to use volume weighting for bands
pub fn calculate_vwap_bands(df: &DataFrame, _volume_weighted: bool) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let values = vec![0.0; df.height()];
    Ok(Series::new("vwap_bands".into(), values))
}

/// Identify breakout areas in intraday charts
///
/// # Arguments
///
/// * `df` - DataFrame with OHLCV data
/// * `consolidation_periods` - Number of periods to identify consolidation
/// * `volume_threshold` - Volume threshold to confirm a breakout
pub fn identify_intraday_breakouts(
    df: &DataFrame,
    _consolidation_periods: usize,
    _volume_threshold: f64,
) -> Result<Series, PolarsError> {
    // Placeholder implementation
    let signals = vec![false; df.height()];
    Ok(Series::new("intraday_breakouts".into(), signals))
}