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README
¶
go-rsi
The Relative Strength Index (RSI) technical analysis algorithm implemented in Golang.
Usage
For full examples, please see the examples directory.
Preconditions
- Gather test data.
- Decide on the number of periods for the averages for the RSI algorithm.
// Gather some test data.
//
// For production systems, it'd be best to gather test data asynchronously.
avgGains, avgLosses, _ := testData()
// Determine the number of periods for the initial inputs. Defaults to 14.
periods := rsi.DefaultPeriods
Step 1
Create the initial input. This is the average of the gains and losses over a given number of periods.
// Average the gains and losses over the given period.
avgGain := avg(avgGains[0:periods])
avgLoss := avg(avgLosses[0:periods])
initialInput := rsi.Input{
AverageGain: avgGain,
AverageLoss: avgLoss,
}
Step 2
Create the RSI data structure and get the first result.
// Create the RSI data structure and get the first result.
//
// If the first argument, the initial periods is 0, the default value, 14, will be used.
r, result := rsi.New(uint(periods), initialInput)
Step 3
Use the remaining data per period to calculate the RSI result at that period.
// Use the remaining data to generate the RSI for each period.
for i := periods; i < len(avgGains); i++ {
avgGain = avgGains[i]
avgLoss = avgLosses[i]
result = r.Calculate(rsi.Input{
AverageGain: avgGain,
AverageLoss: avgLoss,
})
}
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-rsi
cpu: Intel(R) Core(TM) i5-9600K CPU @ 3.70GHz
BenchmarkBigRSI_Calculate-6 1000000000 0.0001274 ns/op
BenchmarkRSI_Calculate-6 1000000000 0.0000007 ns/op
PASS
ok github.com/MicahParks/go-rsi 0.006s
Resources
I built and tested this package using these resources:
Documentation
¶
Index ¶
Examples ¶
Constants ¶
const DefaultPeriods = 14
DefaultPeriods is the default number of periods for the averages for the RSI algorithm.
Variables ¶
This section is empty.
Functions ¶
This section is empty.
Types ¶
type BigInput ¶
BigInput represents the averages for a period that are inputted into the RSI algorithm.
type BigRSI ¶
type BigRSI struct {
// contains filtered or unexported fields
}
BigRSI represents the state of a Relative Strength Index (RSI) algorithm.
type RSI ¶
type RSI struct {
// contains filtered or unexported fields
}
RSI represents the state of a Relative Strength Index (RSI) algorithm.
func (*RSI) Calculate ¶
Calculate produces the next RSI result given the next input.
Example ¶
// Create a logger.
logger := log.New(os.Stdout, "", 0)
// Gather some data.
//
// For production systems, it'd be best to gather test data asynchronously.
avgGains, avgLosses = avgGains, avgLosses
// Determine the number of periods for the initial inputs. Defaults to 14.
periods := rsi.DefaultPeriods
// Average the gains and losses over the given period.
avgGain := avg(avgGains[0:periods])
avgLoss := avg(avgLosses[0:periods])
initialInput := rsi.Input{
AverageGain: avgGain,
AverageLoss: avgLoss,
}
// Create the RSI data structure and get the first result.
//
// If the first argument, the initial periods is 0, the default value, 14, will be used.
r, result := rsi.New(uint(periods), initialInput)
logger.Printf("Period index: %d\nAverage gain: %.2f\nAverage loss: %.2f\nRSI: %.2f", periods-1, avgGain, avgLoss, result)
// Use the remaining data to generate the RSI for each period.
for i := periods; i < len(avgGains); i++ {
avgGain = avgGains[i]
avgLoss = avgLosses[i]
result = r.Calculate(rsi.Input{
AverageGain: avgGain,
AverageLoss: avgLoss,
})
}
logger.Printf("Period index: %d\nAverage gain: %.2f\nAverage loss: %.2f\nRSI: %.2f", len(avgGains)-1, avgGain, avgLoss, result)
Output: Period index: 13 Average gain: -0.11 Average loss: -0.11 RSI: 50.37 Period index: 99 Average gain: 0.35 Average loss: 0.00 RSI: 271.62
Source Files
Directories