2022-03-28 18:01:03 +00:00
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package indicator
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import (
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"math"
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"time"
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2022-08-25 09:31:42 +00:00
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"github.com/c9s/bbgo/pkg/datatype/floats"
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2022-03-28 18:01:03 +00:00
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"github.com/c9s/bbgo/pkg/types"
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)
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2022-12-13 05:02:38 +00:00
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// rsi implements Relative Strength Index (RSI)
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// https://www.investopedia.com/terms/r/rsi.asp
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//
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// The Relative Strength Index (RSI) is a technical analysis indicator that is used to measure the strength of a security's price. It is
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// calculated by taking the average of the gains and losses of the security over a specified period of time, and then dividing the average gain
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// by the average loss. This resulting value is then plotted as a line on the price chart, with values above 70 indicating overbought conditions
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// and values below 30 indicating oversold conditions. The RSI can be used by traders to identify potential entry and exit points for trades,
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// or to confirm other technical analysis signals. It is typically used in conjunction with other indicators to provide a more comprehensive
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// view of the security's price.
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2022-03-28 18:01:03 +00:00
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//go:generate callbackgen -type RSI
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type RSI struct {
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2022-06-29 12:49:02 +00:00
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types.SeriesBase
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2022-03-28 18:01:03 +00:00
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types.IntervalWindow
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2022-08-25 09:31:42 +00:00
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Values floats.Slice
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Prices floats.Slice
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2022-03-28 18:01:03 +00:00
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PreviousAvgLoss float64
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PreviousAvgGain float64
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EndTime time.Time
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2022-07-13 17:18:55 +00:00
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updateCallbacks []func(value float64)
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2022-03-28 18:01:03 +00:00
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}
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2022-04-18 04:08:21 +00:00
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func (inc *RSI) Update(price float64) {
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2022-06-29 12:49:02 +00:00
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if len(inc.Prices) == 0 {
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inc.SeriesBase.Series = inc
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}
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2022-03-28 18:01:03 +00:00
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inc.Prices.Push(price)
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if len(inc.Prices) < inc.Window+1 {
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return
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}
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var avgGain float64
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var avgLoss float64
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if len(inc.Prices) == inc.Window+1 {
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2022-03-28 18:39:35 +00:00
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priceDifferences := inc.Prices.Diff()
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2022-03-28 18:01:03 +00:00
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2022-04-09 14:46:17 +00:00
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avgGain = priceDifferences.PositiveValuesOrZero().Abs().Sum() / float64(inc.Window)
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avgLoss = priceDifferences.NegativeValuesOrZero().Abs().Sum() / float64(inc.Window)
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} else {
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2022-03-28 18:39:35 +00:00
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difference := price - inc.Prices[len(inc.Prices)-2]
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currentGain := math.Max(difference, 0)
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currentLoss := -math.Min(difference, 0)
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2022-03-28 18:01:03 +00:00
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avgGain = (inc.PreviousAvgGain*13 + currentGain) / float64(inc.Window)
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avgLoss = (inc.PreviousAvgLoss*13 + currentLoss) / float64(inc.Window)
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}
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rs := avgGain / avgLoss
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rsi := 100 - (100 / (1 + rs))
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inc.Values.Push(rsi)
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inc.PreviousAvgGain = avgGain
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inc.PreviousAvgLoss = avgLoss
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}
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2023-05-31 11:35:44 +00:00
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func (inc *RSI) Last(i int) float64 {
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return inc.Values.Last(i)
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}
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2022-04-04 04:14:17 +00:00
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func (inc *RSI) Index(i int) float64 {
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return inc.Last(i)
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}
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func (inc *RSI) Length() int {
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return len(inc.Values)
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}
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2022-06-29 12:49:02 +00:00
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var _ types.SeriesExtend = &RSI{}
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2022-04-04 04:14:17 +00:00
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2022-07-13 17:12:36 +00:00
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func (inc *RSI) PushK(k types.KLine) {
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inc.Update(k.Close.Float64())
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}
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func (inc *RSI) CalculateAndUpdate(kLines []types.KLine) {
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for _, k := range kLines {
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2022-04-14 21:43:04 +00:00
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if inc.EndTime != zeroTime && !k.EndTime.After(inc.EndTime) {
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continue
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}
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2022-07-13 17:12:36 +00:00
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inc.PushK(k)
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}
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2023-05-31 11:35:44 +00:00
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inc.EmitUpdate(inc.Last(0))
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2022-03-28 18:01:03 +00:00
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inc.EndTime = kLines[len(kLines)-1].EndTime.Time()
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}
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func (inc *RSI) handleKLineWindowUpdate(interval types.Interval, window types.KLineWindow) {
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if inc.Interval != interval {
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return
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}
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2022-07-13 17:12:36 +00:00
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inc.CalculateAndUpdate(window)
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2022-03-28 18:01:03 +00:00
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}
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func (inc *RSI) Bind(updater KLineWindowUpdater) {
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updater.OnKLineWindowUpdate(inc.handleKLineWindowUpdate)
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}
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