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feature: add sharpe function implementation
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commit
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@ -1,14 +1,34 @@
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package statistics
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import (
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"math"
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"github.com/c9s/bbgo/pkg/types"
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)
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// Sharpe: Calcluates the sharpe ratio of access returns
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//
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// @param rf (float): Risk-free rate expressed as a yearly (annualized) return
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// @param periods (int): Freq. of returns (252/365 for daily, 12 for monthy)
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// @param annualize (bool): return annualize sharpe?
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// @param smart (bool): return smart sharpe ratio
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func Sharpe(returns types.Series, rf float64, periods int, annualize bool, smart bool) {
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func Sharpe(returns types.Series, periods int, annualize bool, smart bool) float64 {
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data := returns
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num := data.Length()
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if types.Lowest(data, num) >= 0 && types.Highest(data, num) > 1 {
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data = types.PercentageChange(returns)
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}
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divisor := types.Stdev(data, data.Length(), 1)
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if smart {
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sum := 0.
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coef := math.Abs(types.Correlation(data, types.Shift(data, 1), num-1))
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for i := 1; i < num; i++ {
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sum += float64(num-i) / float64(num) * math.Pow(coef, float64(i))
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}
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divisor = divisor * math.Sqrt(1.+2.*sum)
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}
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result := types.Mean(data) / divisor
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if annualize {
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return result * math.Sqrt(float64(periods))
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}
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return result
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}
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27
pkg/statistics/sharp_test.go
Normal file
27
pkg/statistics/sharp_test.go
Normal file
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@ -0,0 +1,27 @@
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package statistics
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import (
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"github.com/c9s/bbgo/pkg/types"
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"github.com/stretchr/testify/assert"
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"testing"
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)
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/*
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python
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import quantstats as qx
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import pandas as pd
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print(qx.stats.sharpe(pd.Series([0.01, 0.1, 0.001]), 0, 0, False, False))
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print(qx.stats.sharpe(pd.Series([0.01, 0.1, 0.001]), 0, 252, False, False))
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print(qx.stats.sharpe(pd.Series([0.01, 0.1, 0.001]), 0, 252, True, False))
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*/
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func TestSharpe(t *testing.T) {
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var a types.Series = &types.Float64Slice{0.01, 0.1, 0.001}
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output := Sharpe(a, 0, false, false)
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assert.InDelta(t, output, 0.67586, 0.0001)
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output = Sharpe(a, 252, false, false)
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assert.InDelta(t, output, 0.67586, 0.0001)
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output = Sharpe(a, 252, true, false)
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assert.InDelta(t, output, 10.7289, 0.0001)
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}
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@ -633,14 +633,24 @@ func PercentageChange(a Series, offset ...int) Series {
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return &PercentageChangeResult{a, o}
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}
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func Stdev(a Series, length int) float64 {
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func Stdev(a Series, params ...int) float64 {
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length := a.Length()
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if len(params) > 0 {
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if params[0] < length {
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length = params[0]
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}
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}
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ddof := 0
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if len(params) > 1 {
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ddof = params[1]
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}
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avg := Mean(a, length)
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s := .0
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for i := 0; i < length; i++ {
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diff := a.Index(i) - avg
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s += diff * diff
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}
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return math.Sqrt(s / float64(length))
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return math.Sqrt(s / float64(length - ddof))
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}
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type CorrFunc func(Series, Series, int) float64
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@ -780,4 +790,86 @@ func Skew(a Series, length int) float64 {
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return l * math.Sqrt(l-1) / (l - 2) * sum3 / math.Pow(sum2, 1.5)
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}
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type ShiftResult struct {
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a Series
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offset int
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}
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func (inc *ShiftResult) Last() float64 {
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if inc.offset < 0 {
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return 0
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}
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if inc.offset > inc.a.Length() {
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return 0
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}
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return inc.a.Index(inc.offset)
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}
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func (inc *ShiftResult) Index(i int) float64 {
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if inc.offset + i < 0 {
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return 0
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}
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if inc.offset + i > inc.a.Length() {
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return 0
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}
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return inc.a.Index(inc.offset + i)
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}
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func (inc *ShiftResult) Length() int {
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return inc.a.Length() - inc.offset
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}
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func Shift(a Series, offset int) Series {
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return &ShiftResult{a, offset}
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}
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type RollingResult struct {
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a Series
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window int
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}
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type SliceView struct {
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a Series
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start int
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length int
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}
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func (s *SliceView) Last() float64 {
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return s.a.Index(s.start)
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}
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func (s *SliceView) Index(i int) float64 {
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if i >= s.length {
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return 0
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}
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return s.a.Index(i+s.start)
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}
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func (s *SliceView) Length() int {
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return s.length
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}
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var _ Series = &SliceView{}
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func (r *RollingResult) Last() Series {
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return &SliceView{r.a, 0, r.window}
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}
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func (r *RollingResult) Index(i int) Series {
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if i * r.window > r.a.Length() {
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return nil
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}
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return &SliceView{r.a, i*r.window, r.window}
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}
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func (r *RollingResult) Length() int {
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mod := r.a.Length() % r.window
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if mod > 0 {
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return r.a.Length() / r.window + 1
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} else {
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return r.a.Length() / r.window
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}
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}
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func Rolling(a Series, window int) *RollingResult {
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return &RollingResult{a, window}
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}
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// TODO: ta.linreg
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