yata 0.7.0

Yet another Technical Analysis library. For rust now.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148

use crate::core::{Error, PeriodType, ValueType};
use crate::core::{Method, MovingAverage};
use crate::helpers::{Buffered, Peekable};
use crate::methods::SMA;

#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

/// Triangular Moving Average of specified `length` for timeseries of type [`ValueType`]
///
/// # Parameters
///
/// Has a single parameter `length`: [`PeriodType`]
///
/// `length` should be > `0`
///
/// # Input type
///
/// Input type is [`ValueType`]
///
/// # Output type
///
/// Output type is [`ValueType`]
///
/// # Examples
///
/// ```
/// use yata::prelude::*;
/// use yata::methods::TRIMA;
///
/// // TRIMA of length=3
/// let mut trima = TRIMA::new(4, &1.0).unwrap();
///
/// trima.next(&1.0);
/// trima.next(&2.0);
///
/// assert_eq!(trima.next(&3.0), 1.25);
/// assert_eq!(trima.next(&4.0), 1.625);
/// ```
///
/// # Performance
///
/// O(1)
///
/// [`ValueType`]: crate::core::ValueType
/// [`PeriodType`]: crate::core::PeriodType

#[derive(Debug, Clone)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub struct TRIMA {
	sma1: SMA,
	sma2: SMA,
}

impl Method for TRIMA {
	type Params = PeriodType;
	type Input = ValueType;
	type Output = Self::Input;

	fn new(length: Self::Params, value: &Self::Input) -> Result<Self, Error> {
		Ok(Self {
			sma1: SMA::new(length, value)?,
			sma2: SMA::new(length, value)?,
		})
	}

	#[inline]
	fn next(&mut self, value: &Self::Input) -> Self::Output {
		self.sma2.next(&self.sma1.next(value))
	}
}

impl MovingAverage for TRIMA {}

impl Peekable<<Self as Method>::Output> for TRIMA {
	fn peek(&self) -> <Self as Method>::Output {
		self.sma2.peek()
	}
}

impl Buffered<<Self as Method>::Output> for TRIMA {
	fn get(&self, index: usize) -> Option<<Self as Method>::Output> {
		self.sma2.get(index)
	}
}

#[cfg(test)]
mod tests {
	use super::{Method, TRIMA as TestingMethod};
	use crate::core::ValueType;
	use crate::helpers::{assert_eq_float, RandomCandles};
	use crate::methods::tests::test_const;

	#[test]
	fn test_trima_const() {
		for i in 1..255 {
			let input = (i as ValueType + 56.0) / 16.3251;
			let mut method = TestingMethod::new(i, &input).unwrap();

			let output = method.next(&input);
			test_const(&mut method, &input, &output);
		}
	}

	#[test]
	fn test_trima1() {
		let mut candles = RandomCandles::default();

		let mut ma = TestingMethod::new(1, &candles.first().close).unwrap();

		candles.take(100).for_each(|x| {
			assert_eq_float(x.close, ma.next(&x.close));
		});
	}

	#[test]
	fn test_trima() {
		let candles = RandomCandles::default();

		let src: Vec<ValueType> = candles.take(300).map(|x| x.close).collect();

		(1..255).for_each(|sma_length| {
			let mut ma = TestingMethod::new(sma_length, &src[0]).unwrap();
			let mut level2 = Vec::new();

			src.iter().enumerate().for_each(|(i, x)| {
				let value = ma.next(x);
				let slice_from = i.saturating_sub((sma_length - 1) as usize);
				let slice_to = i;
				let slice = &src[slice_from..=slice_to];

				let mut sum: ValueType = slice.iter().sum();
				if slice.len() < sma_length as usize {
					sum += (sma_length as usize - slice.len()) as ValueType * src.first().unwrap();
				}

				level2.push(sum / sma_length as ValueType);

				let mut sum: ValueType = level2.iter().rev().take(sma_length as usize).sum();
				if level2.len() < sma_length as usize {
					sum += (sma_length as usize - level2.len()) as ValueType * src.first().unwrap();
				}

				assert_eq_float(sum / sma_length as ValueType, value);
			});
		});
	}
}