ta_lib_in_rust/indicators/day_trading/
mod.rs

1//! # Day Trading Indicators
2//!
3//! This module provides indicators optimized for intraday trading and
4//! short-term price movements within a single trading day.
5//!
6//! ## Types of Indicators
7//!
8//! - Intraday momentum indicators with faster response times
9//! - Volume-price relationship indicators for short timeframes
10//! - Market microstructure indicators for order flow analysis
11//! - Volatility indicators calibrated for intraday movements
12
13use polars::prelude::*;
14
15/// Calculate intraday momentum oscillator
16///
17/// A faster-responding version of RSI optimized for intraday trading.
18///
19/// # Arguments
20///
21/// * `df` - DataFrame with price data (typically minute or tick data)
22/// * `period` - Calculation period (typically 7-14 periods for intraday)
23///
24/// # Returns
25///
26/// * `Result<Series, PolarsError>` - Series with oscillator values
27pub fn intraday_momentum_oscillator(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
28    // Placeholder implementation
29    let values = vec![50.0; df.height()];
30    Ok(Series::new("intraday_momentum".into(), values))
31}
32
33/// Calculate order flow imbalance
34///
35/// Measures the imbalance between buying and selling pressure
36/// based on tick-by-tick data and trade direction.
37///
38/// # Arguments
39///
40/// * `df` - DataFrame with tick data including trade direction
41/// * `volume_weighted` - Whether to weight the imbalance by volume
42///
43/// # Returns
44///
45/// * `Result<Series, PolarsError>` - Series with imbalance values
46pub fn order_flow_imbalance(df: &DataFrame, _volume_weighted: bool) -> Result<Series, PolarsError> {
47    // Placeholder implementation
48    let values = vec![0.0; df.height()];
49    Ok(Series::new("order_flow_imbalance".into(), values))
50}
51
52/// Detect intraday breakout patterns
53///
54/// Identifies potential intraday breakout patterns based on
55/// price action and volume confirmation.
56///
57/// # Arguments
58///
59/// * `df` - DataFrame with price and volume data
60/// * `consolidation_periods` - Minimum periods of consolidation before breakout
61/// * `volume_threshold` - Volume increase threshold for confirmation
62///
63/// # Returns
64///
65/// * `Result<Series, PolarsError>` - Series with breakout signals
66pub fn intraday_breakout_detector(
67    df: &DataFrame,
68    _consolidation_periods: usize,
69    _volume_threshold: f64,
70) -> Result<Series, PolarsError> {
71    // Placeholder implementation
72    let signals = vec![false; df.height()];
73    Ok(Series::new("intraday_breakouts".into(), signals))
74}
75
76/// Calculate price velocities at different timeframes
77///
78/// Calculates the rate of price change at different intraday timeframes
79/// to identify short-term momentum.
80///
81/// # Arguments
82///
83/// * `df` - DataFrame with price data
84/// * `periods` - Vector of different timeframes to calculate velocity
85///
86/// # Returns
87///
88/// * `Result<Vec<Series>, PolarsError>` - Vector of Series with velocities at different timeframes
89pub fn price_velocities(df: &DataFrame, periods: &[usize]) -> Result<Vec<Series>, PolarsError> {
90    // Placeholder implementation
91    let mut result = Vec::with_capacity(periods.len());
92
93    for &period in periods {
94        let values = vec![0.0; df.height()];
95        let name = format!("velocity_{}", period);
96        result.push(Series::new(name.into(), values));
97    }
98
99    Ok(result)
100}
101
102/// Calculate intraday price levels based on pivot points
103///
104/// # Arguments
105///
106/// * `df` - DataFrame with OHLC data
107/// * `period` - Lookback period for pivot calculation
108pub fn calculate_pivot_levels(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
109    // Placeholder implementation
110    let values = vec![0.0; df.height()];
111    Ok(Series::new("pivot_levels".into(), values))
112}
113
114/// Calculate VWAP and standard deviation bands
115///
116/// # Arguments
117///
118/// * `df` - DataFrame with OHLCV data
119/// * `volume_weighted` - Whether to use volume weighting for bands
120pub fn calculate_vwap_bands(df: &DataFrame, _volume_weighted: bool) -> Result<Series, PolarsError> {
121    // Placeholder implementation
122    let values = vec![0.0; df.height()];
123    Ok(Series::new("vwap_bands".into(), values))
124}
125
126/// Identify breakout areas in intraday charts
127///
128/// # Arguments
129///
130/// * `df` - DataFrame with OHLCV data
131/// * `consolidation_periods` - Number of periods to identify consolidation
132/// * `volume_threshold` - Volume threshold to confirm a breakout
133pub fn identify_intraday_breakouts(
134    df: &DataFrame,
135    _consolidation_periods: usize,
136    _volume_threshold: f64,
137) -> Result<Series, PolarsError> {
138    // Placeholder implementation
139    let signals = vec![false; df.height()];
140    Ok(Series::new("intraday_breakouts".into(), signals))
141}
ta_lib_in_rust/indicators/day_trading/mod.rs

ta_lib_in_rust/indicators/day_trading/
mod.rs
