Say hello to RusTI, the clawed crusader of candlesticks and the battle-hardened cousin of Ferris! 🦀📈
Forged from rusted metal and born in the depths of the financial abyss, RusTI doesn't just ride sideways markets — he lives for them. With a stack of notebooks, a thousand-yard stare, and more indicators on his screen than legs on his body, RusTI is the ultimate trading bro. He reads charts, calculates MACD in his sleep, and isn’t afraid to pinch your code into shape.
Welcome to RustTI — powered by precision, performance, and one extremely serious crustacean.
A highly configurable and high-performance technical indicators library written in pure Rust.
Designed for flexibility, speed, and advanced use cases in quantitative and algorithmic trading.
Looking for the Python version? PyTechnicalIndicators is what you're looking for.
The fastest way to get up and running with RustTI.
1. Add RustTI to your project:
cargo add rust_tiOr, manually in your Cargo.toml:
rust_ti = "2.1.4"2. Calculate your first indicator:
use rust_ti;
let prices = vec![100.2, 100.46, 100.53, 100.38, 100.19];
let ma = rust_ti::moving_average::single::moving_average(
&prices,
rust_ti::MovingAverageType::Simple
);
println!("Simple Moving Average: {}", ma);Expected output:
Simple Moving Average: 100.352
3. Explore more tutorials
- Getting started tutorial
- Choosing the right model
- Building your first strategy
- Backtesting tutorial
- Visualization tutorial
Task-oriented guides for common problems and advanced scenarios.
- Load CSV Price Data: Parse OHLC prices from a file and calculate RSI
- When to use a bulk vs single module: Understand when to use bulk functions or single functions
- Choosing the right constant model type: Programatically determine the best
ConstantModelType - Choosing the right deviation model: Programatically determine the best deviation model
- Choosing the right period: Programatically determine the best period
- How to use the Personalised Moving Average: Programatically determine the alpha of the moving average
- How to use the McGinley dynamic variation of functions: Quick guide on how to use the McGinley Dynamic functions
(Contributions welcome! Submit your favorite how-to guide as a PR.)
For complete API details, see docs.rs/rust_ti.
A reference of how to call each function can be found
Clone and run:
cargo build
cargo run --example reference- Modules based on their analysis areas (
moving_average,momentum_indicators,strength_indicators...) bulk&singlesubmodulesbulk: Compute indicator over rolling periods, returns a vector.single: Compute indicator for the entire vector, returns a single value.
- Types used to personalise the technical indicators (
MovingAverageType,DeviationModel,Position...)
- Performance: Pure Rust implementation for maximal speed, safety, and zero dependencies.
- Configurability: Most indicators are highly customizable—tweak calculation methods, periods, or even use medians instead of means.
- Breadth: Covers a wide range of technical indicators out of the box.
- Advanced Use: Designed for users who understand technical analysis and want deep control.
Note: Some features may require background in technical analysis. See Investopedia: Technical Analysis for a primer.
All indicators are grouped and split into modules based on their analysis area.
Each module has bulk (vector output) and single (scalar output) submodules.
- Simple, Smoothed, Exponential Moving Average, Bollinger Bands, MACD, RSI
- Absolute Deviation, Log, Mean, Median, Mode, Std. Deviation, Variance, Max/Min
- Ichimoku Cloud, Moving Constant Bands/Envelopes, Donchian Channels, Keltner, Supertrend
- Trend break down, overall trends, peak/valley trends
- Correlate asset prices
- Chaikin Oscillator, CCI, MACD, Money Flow Index, On Balance Volume, ROC, RSI, Williams %R
- McGinley Dynamic, Moving Average
- ROI, True Range, ATR, Internal Bar Strength
- Accumulation/Distribution, PVI, NVI, RVI
- Aroon (Up/Down/Oscillator), Parabolic, DM, Volume-Price Trend, TSI
- Ulcer Index, Volatility System
Want to know how fast RustTI runs in real-world scenarios?
We provide detailed, reproducible benchmarks using realistic OHLCV data and a variety of indicators.
| Function | Time per Operation |
|---|---|
relative_strength_index |
573.86 µs |
stochastic_oscillator |
784.13 µs |
slow_stochastic |
28.866 µs |
slowest_stochastic |
28.866 µs |
williams_percent_r |
76.256 µs |
money_flow_index |
150.69 µs |
rate_of_change |
5.3984 µs |
on_balance_volume |
17.405 µs |
commodity_channel_index |
103.19 µs |
mcginley_dynamic_commodity_channel_index |
66.044 µs |
macd_line |
51.482 µs |
mcginley_dynamic_macd_line |
44.461 µs |
chaikin_oscillator |
258.33 µs |
percentage_price_oscillator |
58.060 µs |
chande_momentum_oscillator |
370.14 µs |
| Function | Time per Operation |
|---|---|
moving_constant_envelopes |
37.572 µs |
mcginley_dynamic_envelopes |
39.264 µs |
moving_constant_bands |
119.70 µs |
mcginley_dynamic_bands |
43.219 µs |
ichimoku_cloud |
192.93 µs |
donchian_channel |
28.481 µs |
keltner_channel |
318.05 µs |
supertrend |
148.80 µs |
| Function | Time per Operation |
|---|---|
aroon_up |
16.531 µs |
aroon_down |
16.592 µs |
aroon_indicator |
66.468 µs |
parabolic_time_price_system |
43.939 µs |
directional_movement_system |
88.965 µs |
volume_price_trend |
6.2801 µs |
true_strength_indx |
705.25 µs |
| Function | Time per Operation |
|---|---|
accumulation_distribution |
8.2935 µs |
positive_volume_index |
7.6977 µs |
negative_volume_index |
7.6167 µs |
relative_vigor_index |
505.34 µs |
| Function | Time per Operation |
|---|---|
return_on_investment |
40.962 µs |
true_range |
3.4663 µs |
average_true_range |
122.08 µs |
internal_bar_strength |
5.3943 µs |
positivity_indicator |
20.683 µs |
| Function | Time per Operation |
|---|---|
mean |
5.7432 µs |
median |
333.68 µs |
mode |
931.09 µs |
log |
20.335 µs |
log_difference |
42.223 µs |
variance |
20.921 µs |
standard_deviation |
24.095 µs |
absolute_deviation(Mean) |
26.991 µs |
absolute_deviation(Median) |
345.14 µs |
absoluite_deviation(Mode) |
956.83 µs |
| Function | Time per Operation |
|---|---|
peaks |
93.094 µs |
valleys |
92.119 µs |
peak_trend |
188.14 µs |
valley_trend |
188.81 µs |
overall_trend |
10.337 µs |
break_down_trends |
14.655 ms |
| Function | Time per Operation |
|---|---|
correlate_asset_prices |
231.14 µs |
| Function | Time per Operation |
|---|---|
moving_average(Simple) |
17.575 µs |
moving_average(Smoothed) |
76.601 µs |
moving_average(Exponential) |
78.505 µs |
mcginley_dynamic |
39.653 µs |
| Function | Time per Operation |
|---|---|
ulcer_index |
65.959 µs |
volatility_system |
137.25 µs |
These results are from a Raspberry Pi 5 8GB, your machine will likely be faster!
👉 See all benchmarks and how to run your own
Contributions, bug reports, and feature requests are welcome!
- Open an issue
- Submit a pull request
- See CONTRIBUTING.md for guidelines
- Start a discussion
- File issues
- Add your project to the Showcase
Latest (v2.1.4):
- Minor document updates
MIT License. See LICENSE.