@showr/core
Version:
Core library for Showr
96 lines (70 loc) • 4.09 kB
Markdown
# @showr/core
[](https://coveralls.io/github/pratikgaloria/showr-core?branch=master)
[](https://www.npmjs.com/package/@showr/core)
@showr/core is a library built with Typescript that allows you to define efficient trading strategy to gain profits during a live market. You can use and combine hundreds of technical indicators to define your strategy or you can create your own technical indicator.
Its powerful API also allows you to backtest your strategy over a historical data before putting it in action. The strongly typed Classes and API allows you to focus more on your trading strategy and worry less about the technical setup and boilerplate.
# Features
- [x] Use hundreds of in-built technical indicators or create your own.
- [x] Define trading strategies using combination of indicators, rules and configuration.
- [x] Backtest your trading strategy over any historical dataset.
- [ ] Test your strategy over a live market. (In progress)
# Installation
```bash
npm i --save @showr/core
```
or
```bash
yarn add @showr/core
```
# Usage
You can import available classes and helpers from the main library.
```javascript
import { Dataset, Indicator } from '@showr/core';
// or
const { Dataset, Indicator } = require('@showr/core');
```
See complete API [documentation](https://pratikgaloria.github.io/showr-core) for all the available classes and methods.
# API
## [Dataset](https://pratikgaloria.github.io/showr-core/classes/dataset.html)
Dataset can be created out of any data which should in a form of an array. Dataset allows you to apply indicators over each data point and you can backtest your trading strategy over any Dataset.
```javascript
import { Dataset } from '@showr/core';
const ds = new Dataset([10, 12.5, 11]);
console.log(ds.value); // [{ close: 10 }, { close: 12.5 }, { close: 11 }]
```
You can provide an array containing any kind of objects or numbers and that will be converted into a Dataset. Learn more about [Dataset](https://pratikgaloria.github.io/showr-core/classes/dataset.html) in API documentation.
## [Indicator](https://pratikgaloria.github.io/showr-core/classes/indicator.html)
The outstanding advantage of this library is that you can create your own technical indicator and then use it for defining trading strategies.
All you need is to define a `calculate` function for the indicator you are creating and then you can apply it over your Dataset.
For example, you can create an indicator, let's say `goldenR`, that multiplies the quote value with golden ratio.
```Typescript
import { Dataset, Indicator } from '@showr/core';
const goldenR = new Indicator('goldenR', (ds: Dataset) => {
const lastQuote = ds.at(-1);
return lastQuote.value * 1.61;
});
```
Now create a dataset and apply the above indicator to it.
```JavaScript
// ...
const ds = new Dataset([10, 20, 30]);
ds.apply(goldenR);
console.log(ds.quotes); // [{ close: 10, indicators: { goldenR: 16.1 }}, ... ]
// Get indicator value for the first quote
console.log(ds.at(0).getIndicator('goldenR')); // 16.1
```
Learn more about creating [Indicators](https://pratikgaloria.github.io/showr-core/classes/indicator.html) with parameters in API documentation.
Many well known indicators are provided in-built with a stand-alone library [@showr/indicators](https://www.npmjs.com/package/@showr/indicators) and you can directly import them inside your project.
For example,
```JavaScript
import { Dataset } from '@showr/core';
import { SMA } from '@showr/indicators';
const ds = new Dataset([10, 20, 30]);
const sma2 = new SMA('sma2', { period: 2 });
ds.apply(sma2);
// Get sma2 value for the last quote
console.log(ds.at(-1).getIndicator('sma2')); // 25
```