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strategy:harmonic: fix

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This commit is contained in:
Austin Liu 2022-11-02 17:16:32 +08:00
parent c8aa4ae400
commit 7d03c69406
1 changed files with 33 additions and 17 deletions
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50
pkg/strategy/harmonic/strategy.go
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@ -13,7 +13,6 @@ import (
"github.com/c9s/bbgo/pkg/indicator" "github.com/c9s/bbgo/pkg/indicator"
"github.com/c9s/bbgo/pkg/types" "github.com/c9s/bbgo/pkg/types"
"github.com/sirupsen/logrus" "github.com/sirupsen/logrus"
floats2 "gonum.org/v1/gonum/floats"
) )
const ID = "harmonic" const ID = "harmonic"
@ -342,9 +341,9 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
states.Update(0) states.Update(0)
s.session.MarketDataStream.OnKLineClosed(types.KLineWith(s.Symbol, s.Interval, func(kline types.KLine) { s.session.MarketDataStream.OnKLineClosed(types.KLineWith(s.Symbol, s.Interval, func(kline types.KLine) {
log.Infof("Shark Score: %f, Current Price: %f", s.shark.Last(), kline.Close.Float64()) log.Infof("shark score: %f, current price: %f", s.shark.Last(), kline.Close.Float64())
nextState := alpha(s.shark.Array(s.Window), states.Array(s.Window), s.Window) nextState := hmm(s.shark.Array(s.Window), states.Array(s.Window), s.Window)
states.Update(nextState) states.Update(nextState)
log.Infof("Denoised signal via HMM: %f", states.Last()) log.Infof("Denoised signal via HMM: %f", states.Last())
@ -367,7 +366,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
Side: types.SideTypeBuy, Side: types.SideTypeBuy,
Quantity: s.Quantity, Quantity: s.Quantity,
Type: types.OrderTypeMarket, Type: types.OrderTypeMarket,
Tag: "shark long", Tag: "sharkLong",
}) })
} else if states.Mean(5) == -1 && direction != -1 { } else if states.Mean(5) == -1 && direction != -1 {
_, _ = s.orderExecutor.SubmitOrders(ctx, types.SubmitOrder{ _, _ = s.orderExecutor.SubmitOrders(ctx, types.SubmitOrder{
@ -375,7 +374,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
Side: types.SideTypeSell, Side: types.SideTypeSell,
Quantity: s.Quantity, Quantity: s.Quantity,
Type: types.OrderTypeMarket, Type: types.OrderTypeMarket,
Tag: "shark short", Tag: "sharkShort",
}) })
} }
})) }))
@ -402,7 +401,7 @@ func (s *Strategy) Run(ctx context.Context, orderExecutor bbgo.OrderExecutor, se
} }
// TODO: dirichlet distribution is a too naive solution // TODO: dirichlet distribution is a too naive solution
func observationDistribution(y_t, x_t float64) float64 { func observeDistribution(y_t, x_t float64) float64 {
if x_t == 0. && y_t == 0 { if x_t == 0. && y_t == 0 {
// observed zero value from indicator when in neutral state // observed zero value from indicator when in neutral state
return 1. return 1.
@ -417,7 +416,7 @@ func observationDistribution(y_t, x_t float64) float64 {
} }
} }
func transitionProbability(x_t0, x_t1 int) float64 { func transitProbability(x_t0, x_t1 int) float64 {
// stick to the same sate // stick to the same sate
if x_t0 == x_t1 { if x_t0 == x_t1 {
return 0.99 return 0.99
@ -426,7 +425,21 @@ func transitionProbability(x_t0, x_t1 int) float64 {
return 1 - 0.99 return 1 - 0.99
} }
func alpha(y_t []float64, x_t []float64, l int) float64 { // HMM main function, ref: https://tr8dr.github.io/HMMFiltering/
/*
# initialize time step 0 using state priors and observation dist p(y | x = s)
for si in states:
alpha[t = 0, state = si] = pi[si] * p(y[0] | x = si)
# determine alpha for t = 1 .. n
for t in 1 .. n:
for sj in states:
alpha[t,sj] = max([alpha[t-1,si] * M[si,sj] for si in states]) * p(y[t] | x = sj)
# determine current state at time t
return argmax(alpha[t,si] over si)
*/
func hmm(y_t []float64, x_t []float64, l int) float64 {
al := make([]float64, l) al := make([]float64, l)
an := make([]float64, l) an := make([]float64, l)
as := make([]float64, l) as := make([]float64, l)
@ -440,26 +453,29 @@ func alpha(y_t []float64, x_t []float64, l int) float64 {
sin := make([]float64, 3) sin := make([]float64, 3)
sis := make([]float64, 3) sis := make([]float64, 3)
for i := -1; i <= 1; i++ { for i := -1; i <= 1; i++ {
sil = append(sil, x_t[n-1-1]*transitionProbability(i, j)) sil = append(sil, x_t[n-1-1]*transitProbability(i, j))
sin = append(sin, x_t[n-1-1]*transitionProbability(i, j)) sin = append(sin, x_t[n-1-1]*transitProbability(i, j))
sis = append(sis, x_t[n-1-1]*transitionProbability(i, j)) sis = append(sis, x_t[n-1-1]*transitProbability(i, j))
} }
if j > 0 { if j > 0 {
long = floats2.Max(sil) * observationDistribution(y_t[n-1], float64(j)) _, longArr := floats.MinMax(sil, 3)
long = longArr[0] * observeDistribution(y_t[n-1], float64(j))
al = append(al, long) al = append(al, long)
} else if j == 0 { } else if j == 0 {
neut = floats2.Max(sin) * observationDistribution(y_t[n-1], float64(j)) _, neutArr := floats.MinMax(sin, 3)
neut = neutArr[0] * observeDistribution(y_t[n-1], float64(j))
an = append(an, neut) an = append(an, neut)
} else if j < 0 { } else if j < 0 {
short = floats2.Max(sis) * observationDistribution(y_t[n-1], float64(j)) _, shortArr := floats.MinMax(sis, 3)
short = shortArr[0] * observeDistribution(y_t[n-1], float64(j))
as = append(as, short) as = append(as, short)
} }
} }
} }
maximum := floats2.Max([]float64{long, neut, short}) _, maximum := floats.MinMax([]float64{long, neut, short}, 3)
if maximum == long { if maximum[0] == long {
return 1 return 1
} else if maximum == short { } else if maximum[0] == short {
return -1 return -1
} }
return 0 return 0