bbgo_origin/pkg/strategy/factorzoo/linear_regression.go

package factorzoo

import (
	"context"

	"github.com/c9s/bbgo/pkg/bbgo"
	"github.com/c9s/bbgo/pkg/datatype/floats"
	"github.com/c9s/bbgo/pkg/fixedpoint"
	"github.com/c9s/bbgo/pkg/indicator"
	"github.com/c9s/bbgo/pkg/strategy/factorzoo/factors"
	"github.com/c9s/bbgo/pkg/types"
)

type Linear struct {
	Symbol string
	Market types.Market `json:"-"`
	types.IntervalWindow

	// MarketOrder is the option to enable market order short.
	MarketOrder bool `json:"marketOrder"`

	Quantity     fixedpoint.Value      `json:"quantity"`
	StopEMARange fixedpoint.Value      `json:"stopEMARange"`
	StopEMA      *types.IntervalWindow `json:"stopEMA"`

	// Xs (input), factors & indicators
	divergence *factorzoo.PVD   // price volume divergence
	reversion  *factorzoo.PMR   // price mean reversion
	momentum   *factorzoo.MOM   // price momentum from paper, alpha 101
	drift      *indicator.Drift // GBM
	volume     *factorzoo.VMOM  // quarterly volume momentum

	// Y (output), internal rate of return
	irr *factorzoo.RR

	orderExecutor *bbgo.GeneralOrderExecutor
	session       *bbgo.ExchangeSession
	activeOrders  *bbgo.ActiveOrderBook

	bbgo.QuantityOrAmount
}

func (s *Linear) Subscribe(session *bbgo.ExchangeSession) {
	session.Subscribe(types.KLineChannel, s.Symbol, types.SubscribeOptions{Interval: s.Interval})
}

func (s *Linear) Bind(session *bbgo.ExchangeSession, orderExecutor *bbgo.GeneralOrderExecutor) {
	s.session = session
	s.orderExecutor = orderExecutor

	position := orderExecutor.Position()
	symbol := position.Symbol
	store, _ := session.MarketDataStore(symbol)

	// initialize factor indicators
	s.divergence = &factorzoo.PVD{IntervalWindow: types.IntervalWindow{Window: 60, Interval: s.Interval}}
	s.divergence.Bind(store)
	s.reversion = &factorzoo.PMR{IntervalWindow: types.IntervalWindow{Window: 60, Interval: s.Interval}}
	s.reversion.Bind(store)
	s.drift = &indicator.Drift{IntervalWindow: types.IntervalWindow{Window: 7, Interval: s.Interval}}
	s.drift.Bind(store)
	s.momentum = &factorzoo.MOM{IntervalWindow: types.IntervalWindow{Window: 1, Interval: s.Interval}}
	s.momentum.Bind(store)
	s.volume = &factorzoo.VMOM{IntervalWindow: types.IntervalWindow{Window: 90, Interval: s.Interval}}
	s.volume.Bind(store)

	s.irr = &factorzoo.RR{IntervalWindow: types.IntervalWindow{Window: 2, Interval: s.Interval}}
	s.irr.Bind(store)

	predLst := types.NewQueue(s.Window)
	session.MarketDataStream.OnKLineClosed(types.KLineWith(symbol, s.Interval, func(kline types.KLine) {

		ctx := context.Background()

		// graceful cancel all active orders
		_ = orderExecutor.GracefulCancel(ctx)

		// take past window days' values to predict future return
		// (e.g., 5 here in default configuration file)
		a := []floats.Slice{
			s.divergence.Values[len(s.divergence.Values)-s.Window-2 : len(s.divergence.Values)-2],
			s.reversion.Values[len(s.reversion.Values)-s.Window-2 : len(s.reversion.Values)-2],
			s.drift.Values[len(s.drift.Values)-s.Window-2 : len(s.drift.Values)-2],
			s.momentum.Values[len(s.momentum.Values)-s.Window-2 : len(s.momentum.Values)-2],
			s.volume.Values[len(s.volume.Values)-s.Window-2 : len(s.volume.Values)-2],
		}
		// e.g., s.window is 5
		// factors array from day -4 to day 0, [[0.1, 0.2, 0.35, 0.3 , 0.25], [1.1, -0.2, 1.35, -0.3 , -0.25], ...]
		// the binary(+/-) daily return rate from day -3 to day 1, [0, 1, 1, 0, 0]
		// then we take the latest available factors array into linear regression model
		b := []floats.Slice{filter(s.irr.Values[len(s.irr.Values)-s.Window-1:len(s.irr.Values)-1], binary)}
		var x []types.Series
		var y []types.Series

		x = append(x, &a[0])
		x = append(x, &a[1])
		x = append(x, &a[2])
		x = append(x, &a[3])
		x = append(x, &a[4])
		//x = append(x, &a[5])

		y = append(y, &b[0])
		model := types.LogisticRegression(x, y[0], s.Window, 8000, 0.0001)

		// use the last value from indicators, or the SeriesExtends' predict function. (e.g., look back: 5)
		input := []float64{
			s.divergence.Last(0),
			s.reversion.Last(0),
			s.drift.Last(0),
			s.momentum.Last(0),
			s.volume.Last(0),
		}
		pred := model.Predict(input)
		predLst.Update(pred)

		qty := s.Quantity //s.QuantityOrAmount.CalculateQuantity(kline.Close)

		// the scale of pred is from 0.0 to 1.0
		// 0.5 can be used as the threshold
		// we use the time-series rolling prediction values here
		if pred > predLst.Mean() {
			if position.IsShort() {
				s.ClosePosition(ctx, one)
				s.placeMarketOrder(ctx, types.SideTypeBuy, qty, symbol)
			} else if position.IsClosed() {
				s.placeMarketOrder(ctx, types.SideTypeBuy, qty, symbol)
			}
		} else if pred < predLst.Mean() {
			if position.IsLong() {
				s.ClosePosition(ctx, one)
				s.placeMarketOrder(ctx, types.SideTypeSell, qty, symbol)
			} else if position.IsClosed() {
				s.placeMarketOrder(ctx, types.SideTypeSell, qty, symbol)
			}
		}
		// pass if position is opened and not dust, and remain the same direction with alpha signal

		// alpha-weighted inventory and cash
		//alpha := fixedpoint.NewFromFloat(s.r1.Last())
		//targetBase := s.QuantityOrAmount.CalculateQuantity(kline.Close).Mul(alpha)
		////s.ClosePosition(ctx, one)
		//diffQty := targetBase.Sub(position.Base)
		//log.Info(alpha.Float64(), position.Base, diffQty.Float64())
		//
		//if diffQty.Sign() > 0 {
		//	s.placeMarketOrder(ctx, types.SideTypeBuy, diffQty.Abs(), symbol)
		//} else if diffQty.Sign() < 0 {
		//	s.placeMarketOrder(ctx, types.SideTypeSell, diffQty.Abs(), symbol)
		//}
	}))

	if !bbgo.IsBackTesting {
		session.MarketDataStream.OnMarketTrade(func(trade types.Trade) {
		})
	}
}

func (s *Linear) ClosePosition(ctx context.Context, percentage fixedpoint.Value) error {
	return s.orderExecutor.ClosePosition(ctx, percentage)
}

func (s *Linear) placeMarketOrder(ctx context.Context, side types.SideType, quantity fixedpoint.Value, symbol string) {
	market, _ := s.session.Market(symbol)
	_, err := s.orderExecutor.SubmitOrders(ctx, types.SubmitOrder{
		Symbol:   symbol,
		Market:   market,
		Side:     side,
		Type:     types.OrderTypeMarket,
		Quantity: quantity,
		//TimeInForce: types.TimeInForceGTC,
		Tag: "linear",
	})
	if err != nil {
		log.WithError(err).Errorf("can not place market order")
	}
}

func binary(val float64) float64 {
	if val > 0. {
		return 1.
	} else {
		return 0.
	}
}

func filter(data []float64, f func(float64) float64) []float64 {
	fltd := make([]float64, 0)
	for _, e := range data {
		//if f(e) >= 0. {
		fltd = append(fltd, f(e))
		//}
	}
	return fltd
}