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factorzoo: add comments for strategy
factorzoo: add comments for strategy
This commit is contained in:
austin362667
parent
d282568614
commit
bb4db871b2
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@ -2,6 +2,7 @@ package factorzoo
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import (
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"context"
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"github.com/c9s/bbgo/pkg/bbgo"
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"github.com/c9s/bbgo/pkg/fixedpoint"
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"github.com/c9s/bbgo/pkg/indicator"
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@ -22,12 +23,11 @@ type Linear struct {
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StopEMA *types.IntervalWindow `json:"stopEMA"`
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// Xs (input), factors & indicators
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divergence *factorzoo.AVD // amplitude volume divergence
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divergence *factorzoo.PVD // price volume divergence
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reversion *factorzoo.PMR // price mean reversion
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momentum *factorzoo.MOM // price momentum from WorldQuant's paper, alpha 101
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drift *indicator.Drift
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momentum *factorzoo.MOM // price momentum from paper, alpha 101
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drift *indicator.Drift // GBM
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volume *factorzoo.VMOM // quarterly volume momentum
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bars *factorzoo.LSBAR // long short bar accumulation
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// Y (output), internal rate of return
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irr *factorzoo.RR
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@ -51,28 +51,22 @@ func (s *Linear) Bind(session *bbgo.ExchangeSession, orderExecutor *bbgo.General
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symbol := position.Symbol
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store, _ := session.MarketDataStore(symbol)
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s.divergence = &factorzoo.AVD{IntervalWindow: types.IntervalWindow{Window: s.Window, Interval: s.Interval}}
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// initialize factor indicators
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s.divergence = &factorzoo.PVD{IntervalWindow: types.IntervalWindow{Window: 60, Interval: s.Interval}}
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s.divergence.Bind(store)
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preloadDivergence(s.divergence, store)
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s.reversion = &factorzoo.PMR{IntervalWindow: types.IntervalWindow{Window: s.Window, Interval: s.Interval}}
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s.reversion = &factorzoo.PMR{IntervalWindow: types.IntervalWindow{Window: 60, Interval: s.Interval}}
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s.reversion.Bind(store)
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preloadReversion(s.reversion, store)
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s.drift = &indicator.Drift{IntervalWindow: types.IntervalWindow{Window: 5, Interval: s.Interval}}
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s.drift = &indicator.Drift{IntervalWindow: types.IntervalWindow{Window: 7, Interval: s.Interval}}
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s.drift.Bind(store)
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preloadDrift(s.drift, store)
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s.momentum = &factorzoo.MOM{IntervalWindow: types.IntervalWindow{Window: 1, Interval: s.Interval}}
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s.momentum.Bind(store)
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preloadMomentum(s.momentum, store)
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s.volume = &factorzoo.VMOM{IntervalWindow: types.IntervalWindow{Window: 90, Interval: s.Interval}}
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s.volume.Bind(store)
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preloadVolume(s.volume, store)
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s.bars = &factorzoo.LSBAR{IntervalWindow: types.IntervalWindow{Window: 360, Interval: s.Interval}}
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s.bars.Bind(store)
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preloadBars(s.bars, store)
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s.irr = &factorzoo.RR{IntervalWindow: types.IntervalWindow{Window: 2, Interval: s.Interval}}
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s.irr.Bind(store)
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preloadIRR(s.irr, store)
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predLst := types.NewQueue(s.Window)
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session.MarketDataStream.OnKLineClosed(types.KLineWith(symbol, s.Interval, func(kline types.KLine) {
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ctx := context.Background()
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@ -80,14 +74,20 @@ func (s *Linear) Bind(session *bbgo.ExchangeSession, orderExecutor *bbgo.General
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// graceful cancel all active orders
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_ = orderExecutor.GracefulCancel(ctx)
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a := []types.Float64Slice{s.divergence.Values[len(s.divergence.Values)-s.Window : len(s.divergence.Values)-2],
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s.reversion.Values[len(s.reversion.Values)-s.Window : len(s.reversion.Values)-2],
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s.drift.Values[len(s.drift.Values)-s.Window : len(s.drift.Values)-2],
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s.momentum.Values[len(s.momentum.Values)-s.Window : len(s.momentum.Values)-2],
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s.volume.Values[len(s.volume.Values)-s.Window : len(s.volume.Values)-2],
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//s.bars.Values[len(s.bars.Values)-s.Window : len(s.bars.Values)-2],
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// take past window days' values to predict future return
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// (e.g., 5 here in default configuration file)
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a := []types.Float64Slice{
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s.divergence.Values[len(s.divergence.Values)-s.Window-2 : len(s.divergence.Values)-2],
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s.reversion.Values[len(s.reversion.Values)-s.Window-2 : len(s.reversion.Values)-2],
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s.drift.Values[len(s.drift.Values)-s.Window-2 : len(s.drift.Values)-2],
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s.momentum.Values[len(s.momentum.Values)-s.Window-2 : len(s.momentum.Values)-2],
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s.volume.Values[len(s.volume.Values)-s.Window-2 : len(s.volume.Values)-2],
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}
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b := []types.Float64Slice{filter(s.irr.Values[len(s.irr.Values)-(s.Window-1):len(s.irr.Values)-(2-1)], binary)}
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// e.g., s.window is 5
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// 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], ...]
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// the binary(+/-) daily return rate from day -3 to day 1, [0, 1, 1, 0, 0]
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// then we take the latest available factors array into linear regression model
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b := []types.Float64Slice{filter(s.irr.Values[len(s.irr.Values)-s.Window-1:len(s.irr.Values)-1], binary)}
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var x []types.Series
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var y []types.Series
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@ -99,37 +99,53 @@ func (s *Linear) Bind(session *bbgo.ExchangeSession, orderExecutor *bbgo.General
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//x = append(x, &a[5])
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y = append(y, &b[0])
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//log.Infof("actual: %f", y[0])
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model := types.LogisticRegression(x, y[0], s.Window, 8000, 0.0001)
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// use the last value from indicators, or the SeriesExtends' predict function. (e.g., look back: 5)
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input := []float64{
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s.divergence.Predict(5, 20),
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s.reversion.Predict(5, 20),
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s.drift.Predict(5, 20),
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s.momentum.Predict(5, 20),
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s.volume.Predict(5, 20),
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//s.bars.Predict(5, 20),
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s.divergence.Last(),
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s.reversion.Last(),
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s.drift.Last(),
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s.momentum.Last(),
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s.volume.Last(),
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}
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//log.Info(input)
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pred := model.Predict(input)
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//log.Infof("prediction: %f", pred)
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//qty := s.QuantityOrAmount.CalculateQuantity(kline.Close)
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if pred > 0.5 {
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if position.IsShort() && !position.IsDust(kline.Close) {
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predLst.Update(pred)
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qty := s.Quantity //s.QuantityOrAmount.CalculateQuantity(kline.Close)
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// the scale of pred is from 0.0 to 1.0
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// 0.5 can be used as the threshold
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// we use the time-series rolling prediction values here
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if pred > predLst.Mean() {
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if position.IsShort() {
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s.ClosePosition(ctx, one)
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s.placeMarketOrder(ctx, types.SideTypeBuy, s.Quantity, symbol)
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} else if position.IsClosed() || position.IsDust(kline.Close) {
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s.placeMarketOrder(ctx, types.SideTypeBuy, s.Quantity, symbol)
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s.placeMarketOrder(ctx, types.SideTypeBuy, qty, symbol)
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} else if position.IsClosed() {
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s.placeMarketOrder(ctx, types.SideTypeBuy, qty, symbol)
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}
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} else if pred < 0.5 {
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if position.IsLong() && !position.IsDust(kline.Close) {
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} else if pred < predLst.Mean() {
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if position.IsLong() {
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s.ClosePosition(ctx, one)
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s.placeMarketOrder(ctx, types.SideTypeSell, s.Quantity, symbol)
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} else if position.IsClosed() || position.IsDust(kline.Close) {
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s.placeMarketOrder(ctx, types.SideTypeSell, s.Quantity, symbol)
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s.placeMarketOrder(ctx, types.SideTypeSell, qty, symbol)
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} else if position.IsClosed() {
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s.placeMarketOrder(ctx, types.SideTypeSell, qty, symbol)
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}
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}
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// pass if position is opened and not dust, and remain the same direction with alpha signal
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// alpha-weighted inventory and cash
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//alpha := fixedpoint.NewFromFloat(s.r1.Last())
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//targetBase := s.QuantityOrAmount.CalculateQuantity(kline.Close).Mul(alpha)
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////s.ClosePosition(ctx, one)
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//diffQty := targetBase.Sub(position.Base)
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//log.Info(alpha.Float64(), position.Base, diffQty.Float64())
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//
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//if diffQty.Sign() > 0 {
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// s.placeMarketOrder(ctx, types.SideTypeBuy, diffQty.Abs(), symbol)
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//} else if diffQty.Sign() < 0 {
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// s.placeMarketOrder(ctx, types.SideTypeSell, diffQty.Abs(), symbol)
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//}
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}))
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if !bbgo.IsBackTesting {
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@ -158,103 +174,6 @@ func (s *Linear) placeMarketOrder(ctx context.Context, side types.SideType, quan
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}
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}
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func (s *Linear) placeLimitOrder(ctx context.Context, side types.SideType, quantity fixedpoint.Value, price fixedpoint.Value, symbol string) {
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market, _ := s.session.Market(symbol)
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_, err := s.orderExecutor.SubmitOrders(ctx, types.SubmitOrder{
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Symbol: symbol,
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Market: market,
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Side: side,
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Type: types.OrderTypeLimitMaker,
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Quantity: quantity,
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Price: price,
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//TimeInForce: types.TimeInForceGTC,
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Tag: "linearLimit",
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})
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if err != nil {
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log.WithError(err).Errorf("can not place market order")
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}
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}
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func preloadDivergence(divergence *factorzoo.AVD, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(divergence.Interval)
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log.Debugf("updating divergence indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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divergence.Update(*klines)
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}
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}
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func preloadReversion(reversion *factorzoo.PMR, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(reversion.Interval)
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log.Debugf("updating reversion indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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reversion.Update(*klines)
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}
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}
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func preloadDrift(drift *indicator.Drift, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(drift.Interval)
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log.Debugf("updating drift indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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drift.CalculateAndUpdate(*klines)
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}
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}
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func preloadMomentum(momentum *factorzoo.MOM, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(momentum.Interval)
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log.Debugf("updating momentum indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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momentum.Update(*klines)
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}
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}
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func preloadMomentum2(momentum *factorzoo.MOM2, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(momentum.Interval)
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log.Debugf("updating momentum2 indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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momentum.Update(*klines)
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}
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}
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func preloadVolume(momentum *factorzoo.VMOM, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(momentum.Interval)
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log.Debugf("updating volume momentum indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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momentum.Update(*klines)
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}
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}
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func preloadBars(bars *factorzoo.LSBAR, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(bars.Interval)
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log.Debugf("updating long short bars indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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bars.Update(*klines)
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}
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}
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func preloadIRR(irr *factorzoo.RR, store *bbgo.MarketDataStore) {
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klines, _ := store.KLinesOfInterval(irr.Interval)
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log.Debugf("updating irr indicator: %d klines", len(*klines))
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for i := 0; i < len(*klines); i++ {
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irr.CalculateAndUpdate(*klines)
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}
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}
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func binary(val float64) float64 {
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if val > 0. {
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return 1.
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