Tevec

Introduction

A crate to provide financial quantitative analysis functions across different backends (currently Vec & Ndarray & Polars). It uses the Rust trait system to facilitate the support of more backends in the future. Currently, it is mainly divided into three categories of functions:

• Rolling functions
• Mapping functions
• Aggregation functions

Installation

add tevec = "0.2" to your Cargo.toml

Aggregation Functions

Most aggregation functions are implemented for structs that satisfy the IntoIterator + Sized traits.

use tevec::prelude::*;
let data = vec![1, 2, 3, 4, 5];
data.titer().mean();  // not consume data, return Some(3)
data.mean();  // consume data, return Some(3)
let data = vec![1., f64::NAN, 3.];
data.titer().vmean();  // valid mean, this will ignore nan, return 2.
// valid function can also be used for Option<T> dtype
let data = vec![Some(1), None, Some(3)];
data.vmean(); // return 2.

Using titer returns an Iterator that satisfies TrustedLen, allowing for further method calls. The titer method comes from the Titer trait, which has been implemented for all backends.

Rolling Functions

use tevec::prelude::*;
let data = vec![1, 2, 3, 4, 5];
let mean: Vec<f64> = data.ts_mean(3, Some(1)); // params: window, min_periods
#[cfg(feature = "ndarray")]
{   
use tevec::ndarray::Array1;  // reexported from ndarray crate
let mean2: Array1<f32> = data.ts_vmean(4, None); // rolling_mean function ignore none values
}

Mapping Functions

use tevec::prelude::*;
let v = vec![1., 2., 3., 4., 5.];
let shift_v: Vec<_> = v.titer().vshift(2, None).collect_trusted_vec1();
let shfit_abs_v: Vec<_> = v.titer().abs().vshift(2, None).collect_trusted_vec1();

Some mapping functions return an Iterator, allowing for chained calls without reallocating memory, and only collecting the iterator when needed.

Feature Flags

pl: For Polars backend ndarray: For Ndarray backend agg: Aggregate Functions map: Mapping Functions rolling: Rolling Functions stat: Statistic Functions time: DateTime and TimeDelta structs