npm package discovery and stats viewer.

Discover Tips

  • General search

    [free text search, go nuts!]

  • Package details

    pkg:[package-name]

  • User packages

    @[username]

Sponsor

Optimize Toolset

I’ve always been into building performant and accessible sites, but lately I’ve been taking it extremely seriously. So much so that I’ve been building a tool to help me optimize and monitor the sites that I build to make sure that I’m making an attempt to offer the best experience to those who visit them. If you’re into performant, accessible and SEO friendly sites, you might like it too! You can check it out at Optimize Toolset.

About

Hi, 👋, I’m Ryan Hefner  and I built this site for me, and you! The goal of this site was to provide an easy way for me to check the stats on my npm packages, both for prioritizing issues and updates, and to give me a little kick in the pants to keep up on stuff.

As I was building it, I realized that I was actually using the tool to build the tool, and figured I might as well put this out there and hopefully others will find it to be a fast and useful way to search and browse npm packages as I have.

If you’re interested in other things I’m working on, follow me on Twitter or check out the open source projects I’ve been publishing on GitHub.

I am also working on a Twitter bot for this site to tweet the most popular, newest, random packages from npm. Please follow that account now and it will start sending out packages soon–ish.

Open Software & Tools

This site wouldn’t be possible without the immense generosity and tireless efforts from the people who make contributions to the world and share their work via open source initiatives. Thank you 🙏

© 2024 – Pkg Stats / Ryan Hefner

@fomobro/forex-analytics-fomo

v1.0.13

Published

Performs data analysis on a set of candlesticks and using available indicators constructs the best strategy which worked on a certain period.

Downloads

26

Readme

Forex analytics native module for Node.js

Build Status npm version

Node.js native library performing technical analysis over an OHLC dataset with use of genetic algorithm. The result of technical analysis are two binary trees describing strategies for buy and sell signals which produced profit in a certain period of time specified by the input OHLC data set.

Running the source

First if you don't have it yet. Install node-gyp for comiling the c++ source code;

npm install -g node-gyp

Then run the install npm command to download and install all dependencies. It also compiles the ta-lib dependency and builds the source code

npm install

To do further builds you can use

node-gyp build

You can run embedded examples which are located in the examples folder

cd examples
node example

NPM

You can install forex.analytics via npm:

npm install forex.analytics

Usage

Import the the module

var analytics = require('forex.analytics');

or with ES6 modules syntax

import analytics from 'forex.analytics'

Analytics object will give you several functions to use.

findStrategy(candlesticks, options, progressCallback)

Finds the optimal strategy for a certain period defined by the candlesticks array.

candlesticks parameter should contain an array of objects representing one candlestick in OHLC chart.

{
  open: 1.113990,
  high: 1.113990,
  low: 1.113890,
  close: 1.113890,
  time: 1435701600
}

options parameter lists several properties that influence the genetic algorithm

{
 populationCount: 100,
 generationCount: 300,
 selectionAmount: 10,
 leafValueMutationProbability: 0.5,
 leafSignMutationProbability: 0.3,
 logicalNodeMutationProbability: 0.3,
 leafIndicatorMutationProbability: 0.2,
 crossoverProbability: 0.03,
 indicators: [ 'CCI', 'MACD', 'RSI', 'SMA15_SMA50' ],
 strategy: { buy : {...}, sell : {...} }
}

progressCallback parameter has to be a function. This function is invoked when one generation passes. It contains three arguments: strategy, fitness, generation. Where strategy stands for the currently best strategy found in a certain generation. Fitness is a fitness value of a certain strategy calculated by fitness evaluation algorithm. Generation is the number of generation which was just completed. The strategy parameter describes which strategy it shall use as a referential. This parameter is not mandatory.

function progressCallback(strategy, fitness, generation) {
  console.log('Fitness: ' + fitness + '; Generation: ' + generation);
}

could print

Fitness: 0.00010751596495091384; Generation: 285

The returning value is a promise which when it's resolved passes one argument with the best found strategy.

Full example:

analytics.findStrategy(candlesticks, {
  populationCount: 100,
  generationCount: 300,
  selectionAmount: 10,
  leafValueMutationProbability: 0.5,
  leafSignMutationProbability: 0.3,
  logicalNodeMutationProbability: 0.3,
  leafIndicatorMutationProbability: 0.2,
  crossoverProbability: 0.03,
  indicators: indicators

}, function(strategy, fitness, generation) {
  console.log('Fitness: ' + fitness + '; Generation: ' + generation);
})
.then(function(strategy) {
  console.log('------------Strategy-------------')
  console.log(strategy);
});

This could print something like:

Fitness: 0.0; Generation: 1
....
Fitness: 0.00010751596495091384; Generation: 297
Fitness: 0.00010751596495091384; Generation: 298
Fitness: 0.00010751596495091384; Generation: 299
Fitness: 0.00010751596495091384; Generation: 300
------------Strategy-------------
{
   "buy":{
      "operator":"And",
      "left":{
         "operator":"Or",
         "left":{
           "indicator":"Momentum",
           "sign":">",
           "value":-0.8390790893276773
         },
         "right":{
           "indicator":"RSI",
           "sign":">",
           "value":74.22093161865811
         }
      },
      "right":{
         "operator":"And",
         "left":{
           "indicator":"Momentum",
           "sign":"<",
           "value":-0.6815039536729026
         },
         "right":{
           "indicator":"Momentum",
           "sign":"<",
           "value":-0.32888175664540553
         }
      }
   },
   "sell":{
      "operator":"And",
      "left":{
         "operator":"Or",
         "left":{
           "indicator":"Momentum",
           "sign":">",
           "value":-0.8390790893276773
         },
         "right":{
           "indicator":"RSI",
           "sign":">",
           "value":74.22093161865811
         }
      },
      "right":{
         "operator":"And",
         "left":{
           "indicator":"Momentum",
           "sign":"<",
           "value":-0.6815039536729026
         },
         "right":{
           "indicator":"Momentum",
           "sign":"<",
           "value":-0.32888175664540553
         }
      }
   }
}

convertOHLC(candlesticks, targetTimeframe)

Converts OHLC data set to a larger timeframe (e.g. from 5 minute interval to 30 minute interval)

candlesticks parameter should contain an array of objects representing one candlestick in OHLC chart.

{
  open: 1.113990,
  high: 1.113990,
  low: 1.113890,
  close: 1.113890,
  time: 1435701600
}

targetTimeframe parameter defines the target interval in seconds

Example:

/**
 * Converts candlesticks from lower timeframe to 30 minute timeframe
 */
function convertTo30MOhlc(candlesticks) {
  return analytics.convertOHLC(candlesticks, 1800);
}

This function can be used for converting ticks to OHLC as well with a simple trick.

/**
 * Converts ticks to one minute OHLC
 * Input could be for example: { time : 0, value : 1.225 },  { time : 10, value : 1.226 },
 *  { time : 20, value : 1.227 },  { time : 30, value : 1.228 }, ...
 */
function ticksTo1MOhlc(ticks) {
  return analytics.convertOHLC(candlesticks
    .map(t => {
      open: t.value,
      high: t.value,
      low: t.value,
      close: t.value,
      time: t.time
    }), 60 /* one minute interval */);
}

getMarketStatus(candlesticks, options)

Returns suggestion whether to buy or sell current for the last candlestick in the candlesticks array passed in as a first parameter.

candlesticks parameter should contain an array of objects representing one candlestick in OHLC chart.

{
  open: 1.113990,
  high: 1.113990,
  low: 1.113890,
  close: 1.113890,
  time: 1435701600
}

options parameter lists one property

{
 strategy: { buy : [...], sell : [...] }
}

strategy is the result from the findStrategy function and defines when to buy and when to sell.

Example:

var status = analytics.getMarketStatus(candlesticks, { strategy: strategy });
console.log(status);

Can output:

{ shouldBuy: true, shouldSell: false }

getTrades(candlesticks, options)

Returns an array of trades that were performed on a provided candlestick array with given strategy candlesticks array passed in as a first parameter.

candlesticks parameter should contain an array of objects representing one candlestick in OHLC chart.

{
  open: 1.113990,
  high: 1.113990,
  low: 1.113890,
  close: 1.113890,
  time: 1435701600
}

options parameter lists one property

{
 strategy: { buy : [...], sell : [...] }
}

strategy is the result from the findStrategy function and defines when to buy and when to sell.

Example:

var trades = analytics.getTrades(candlesticks, {
  strategy: strategy
});
console.log(trades);

Can output:

[
   {
      "Buy":true,
      "Revenue":0.0031200000000000117,
      "MaximumLoss":0.0023200000000000998,
      "MaximumProfit":0.005009999999999959,
      "ProfitBeforeLoss" : true,
      "start":{
         "open":1.10604,
         "low":1.10586,
         "high":1.10762,
         "close":1.10711,
         "time":1435782600
      },
      "end":{
         "open":1.10833,
         "low":1.10833,
         "high":1.11044,
         "close":1.11023,
         "time":1435824000
      }
   }
]

Where buy stands for whether the specific trade was made on a profit from a rising or falling market.

Revenue is the the revenue that was obtained at the end of a trade.

MaximumLoss describes how far the price movement went against the wanted direction. MaximumProfit describes how far the price movement went on the wanted direction. start and end are the candlesticks describing the boundaries of a given trade. ProfitBeforeLoss indicates whether the maximum profit was reached before maximum loss

Roadmap

  • [ ] Stabilize the solution / fix bugs
  • [ ] Make the algorithm more abstract (support for different kind of chromosomes and tree nodes)
  • [ ] Delegate indicator generation to a different library (perhaps use node-talib)
  • [ ] Implementing support for generating indicators (math formulas with OHLC as an input) based on successful trades
  • [ ] Think of more stuff to add. :)