ta_lib_in_rust/indicators/short_term/
mod.rs

1//! # Short-Term Trading Indicators
2//!
3//! This module provides indicators optimized for short-term trading with
4//! a timeframe of days to weeks, suitable for swing trading approaches.
5//!
6//! ## Types of Indicators
7//!
8//! - Swing trading momentum indicators
9//! - Short-term trend identification tools
10//! - Pattern recognition for multi-day setups
11//! - Market regime detection for daily timeframes
12
13use polars::prelude::*;
14
15/// Calculate swing strength index
16///
17/// Measures the strength of price swings to identify
18/// potential reversals in short-term trends.
19///
20/// # Arguments
21///
22/// * `df` - DataFrame with OHLC price data
23/// * `period` - Lookback period for swing calculation
24///
25/// # Returns
26///
27/// * `Result<Series, PolarsError>` - Series with swing strength values
28pub fn swing_strength_index(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
29    // Placeholder implementation
30    let values = vec![0.0; df.height()];
31    Ok(Series::new("swing_strength".into(), values))
32}
33
34/// Detect short-term market regimes
35///
36/// Identifies whether the market is in a trending, ranging,
37/// or transitional regime for short-term trading.
38///
39/// # Arguments
40///
41/// * `df` - DataFrame with price data
42/// * `atr_period` - Period for ATR calculation (volatility)
43/// * `trend_period` - Period for trend calculation
44///
45/// # Returns
46///
47/// * `Result<Series, PolarsError>` - Series with regime values (1 = trending, 0 = ranging, -1 = transitional)
48pub fn short_term_regime_detector(
49    df: &DataFrame,
50    _atr_period: usize,
51    _trend_period: usize,
52) -> Result<Series, PolarsError> {
53    // Placeholder implementation
54    let values = vec![0i32; df.height()];
55    Ok(Series::new("market_regime".into(), values))
56}
57
58/// Calculate dip-buying opportunity score
59///
60/// Creates a scoring system to identify potential dip-buying
61/// opportunities in short-term uptrends.
62///
63/// # Arguments
64///
65/// * `df` - DataFrame with price and volume data
66/// * `trend_period` - Period for trend identification
67/// * `oversold_threshold` - RSI threshold to consider oversold
68///
69/// # Returns
70///
71/// * `Result<Series, PolarsError>` - Series with dip-buying scores (0-100)
72pub fn dip_buying_score(
73    df: &DataFrame,
74    _trend_period: usize,
75    _oversold_threshold: f64,
76) -> Result<Series, PolarsError> {
77    // Placeholder implementation
78    let values = vec![50.0; df.height()];
79    Ok(Series::new("dip_buy_score".into(), values))
80}
81
82/// Detect multi-day chart patterns
83///
84/// Identifies common multi-day chart patterns like flags,
85/// pennants, and wedges for short-term trading opportunities.
86///
87/// # Arguments
88///
89/// * `df` - DataFrame with OHLC price data
90/// * `max_pattern_length` - Maximum length of patterns to detect
91/// * `min_pattern_quality` - Minimum quality threshold for pattern detection
92///
93/// # Returns
94///
95/// * `Result<DataFrame, PolarsError>` - DataFrame with detected patterns and attributes
96pub fn multi_day_pattern_detector(
97    _df: &DataFrame,
98    _max_pattern_length: usize,
99    _min_pattern_quality: f64,
100) -> Result<DataFrame, PolarsError> {
101    // Placeholder implementation - create a simple DataFrame with pattern data
102    df! {
103        "pattern_type" => vec!["flag", "pennant", "wedge", "triangle", "none"],
104        "pattern_start" => vec![10, 25, 40, 60, 80],
105        "pattern_quality" => vec![0.85, 0.76, 0.92, 0.68, 0.0]
106    }
107}
108
109/// Calculate average range for swing trading
110///
111/// # Arguments
112///
113/// * `df` - DataFrame with OHLC data
114/// * `period` - Lookback period for range calculation
115pub fn calculate_average_range(df: &DataFrame, _period: usize) -> Result<Series, PolarsError> {
116    // Placeholder implementation
117    let values = vec![0.0; df.height()];
118    Ok(Series::new("average_range".into(), values))
119}
120
121/// Find potential swing points using ATR and trend  
122///
123/// # Arguments
124///
125/// * `df` - DataFrame with OHLC data
126/// * `atr_period` - Period for ATR calculation
127/// * `trend_period` - Period for trend identification
128pub fn find_swing_points(
129    df: &DataFrame,
130    _atr_period: usize,
131    _trend_period: usize,
132) -> Result<Series, PolarsError> {
133    // Placeholder implementation
134    let values = vec![0.0; df.height()];
135    Ok(Series::new("swing_points".into(), values))
136}
137
138/// Generate mean reversion signals based on oversold/overbought conditions
139///
140/// # Arguments
141///
142/// * `df` - DataFrame with OHLC data
143/// * `trend_period` - Period for trend identification
144/// * `oversold_threshold` - Threshold to identify oversold conditions
145pub fn mean_reversion_signals(
146    df: &DataFrame,
147    _trend_period: usize,
148    _oversold_threshold: f64,
149) -> Result<Series, PolarsError> {
150    // Placeholder implementation
151    let values = vec![0.0; df.height()];
152    Ok(Series::new("mean_reversion_signals".into(), values))
153}
154
155/// Detect chart patterns for swing trading
156///
157/// # Arguments
158///
159/// * `df` - DataFrame with OHLC data
160/// * `max_pattern_length` - Maximum length of patterns to detect
161/// * `min_pattern_quality` - Minimum quality threshold for pattern detection
162pub fn detect_chart_patterns(
163    price_df: &DataFrame,
164    _max_pattern_length: usize,
165    _min_pattern_quality: f64,
166) -> Result<Series, PolarsError> {
167    // Placeholder implementation
168    let values = vec![0.0; price_df.height()];
169    Ok(Series::new("chart_patterns".into(), values))
170}
ta_lib_in_rust/indicators/short_term/mod.rs

ta_lib_in_rust/indicators/short_term/
mod.rs
