README.md

go-ad

The Accumulation Distribution Line technical analysis algorithm implemented in Golang.

import "github.com/MicahParks/go-ad"

Usage

For full examples, please see the examples directory.

Step 1

Gather the input for the current period.

input := ad.Input{
	Close:  closePrices[i],
	Low:    low[i],
	High:   high[i],
	Volume: volume[i],
}

Step 2

Create the A/D data structure and get the first result

adLine, result := ad.New(input)

Step 3

Use the next period's data to calculate the next point on the A/D line. Repeat for all periods.

result = adLine.Calculate(input)

Somewhat complete example (without data)

package main

import (
	"log"
	"os"

	"github.com/MicahParks/go-ad"
)

func main() {
	// Create a logger.
	logger := log.New(os.Stdout, "", 0)

	// Iterate through the rest of the periods' data and calculate the A/D line's point for the given period.
	var adLine *ad.AD
	var result float64
	for i := range open {
		input := ad.Input{
			Close:  closePrices[i],
			Low:    low[i],
			High:   high[i],
			Volume: volume[i],
		}

		if adLine == nil {
			adLine, result = ad.New(input)
		} else {
			result = adLine.Calculate(input)
		}
		logger.Printf("Index: %d AD: %.4f", i, result)
	}
}

Testing

There is 100% test coverage and benchmarks for this project. Here is an example benchmark result:

$ go test -bench .
goos: linux
goarch: amd64
pkg: github.com/MicahParks/go-ad
cpu: Intel(R) Core(TM) i5-9600K CPU @ 3.70GHz
BenchmarkAD_Calculate-6         1000000000               0.0000010 ns/op
BenchmarkBigAD_Calculate-6      1000000000               0.0000765 ns/op
PASS
ok      github.com/MicahParks/go-ad     0.004s

Other Technical Algorithms

Looking for some other technical analysis algorithms? Here are some other ones I've implemented:

• Accumulation/Distribution (A/D): go-ad
• Chaikin: go-chaikin
• Moving Average Convergence Divergence (MACD), Exponential Moving Average (EMA), Simple Moving Average (SMA): go-ma
• Relative Strength Index (RSI): go-rsi

Resources

I built and tested this package using these resources:

• Investopedia
• Invest Excel