108 lines
3.2 KiB
Go
108 lines
3.2 KiB
Go
package rpc_fea
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import (
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"github.com/banbox/banbot/biz"
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"github.com/banbox/banbot/btime"
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"github.com/banbox/banbot/core"
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"github.com/banbox/banbot/orm"
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"github.com/banbox/banexg"
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"github.com/banbox/banexg/errs"
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"github.com/banbox/banexg/log"
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utils2 "github.com/banbox/banexg/utils"
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"github.com/banbox/banta"
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"go.uber.org/zap"
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"gonum.org/v1/gonum/mat"
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)
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type AiFea2State struct {
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cg *banta.CGraph
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feasBig *mat.Dense
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avgCostBigs []float64 //大周期的成本价
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}
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func pubAiFea2(exsList []*orm.ExSymbol, req *biz.SubReq, rsp biz.FeaFeeder_SubFeaturesServer) error {
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var sample = int(req.Sample)
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if sample <= 0 {
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sample = 10
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}
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startDt := btime.ToDateStr(req.Start, core.DefaultDateFmt)
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endDt := btime.ToDateStr(req.End, core.DefaultDateFmt)
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log.Info("start ai fea2", zap.Int("pair", len(exsList)), zap.String("start", startDt),
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zap.String("end", endDt), zap.Int("sample", sample))
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var seqNum = 150 // 特征序列长度
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var penNum = 80 // 笔矩阵长度
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var depth = 100 // 笔矩阵深度：100个深度
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tfSml, tfBig := "5m", "1h"
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tfMSecs := int64(utils2.TFToSecs(tfSml) * 1000)
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tfMSecsBig := int64(utils2.TFToSecs(tfBig) * 1000)
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task := &GenFeaTask{
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TfSml: tfSml,
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TfBig: tfBig,
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WarmSml: 2000,
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WarmBig: 300,
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TfMSecs: tfMSecs,
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TfMSecsBig: tfMSecsBig,
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NextObsNum: 30,
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}
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states := make(map[string]*AiFea2State)
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for _, exs := range exsList {
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states[exs.Symbol] = &AiFea2State{
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cg: &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2},
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}
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}
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batch := NewBatchMap(len(exsList))
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task.OnBar = func(e *banta.BarEnv, b *banexg.Kline, futs []*banexg.Kline) *errs.Error {
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state := states[e.Symbol]
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state.cg.AddBar(e)
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state.cg.Parse()
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feas := onAiFeatures2(e, 30)
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if e.BarNum%sample > 0 || len(state.cg.Pens) < penNum || len(futs) == 0 || state.feasBig == nil {
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// 预热时futs为nil
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return nil
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}
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// 收集特征，发送到监听端
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minChgVal := banta.ATR(e.High, e.Low, e.Close, 30).Get(0) * 5
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futProbs := genFutProbsV1(futs, minChgVal, 0.4)
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mats, _ := GenPenMats2(state.cg, penNum, depth, false)
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avgCosts := avgCostN(e, 10, 20, 40, 80, 160, 320)
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info := make([]float64, 0, 25)
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info = append(info, avgCosts...)
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info = append(info, state.avgCostBigs...)
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return batch.Add(rsp, e, b.Time, map[string]*biz.NumArr{
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"one": matToNumArr(mats[0]),
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"two": matToNumArr(mats[1]),
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"feas": matToNumArr(feas),
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"feas2": matToNumArr(state.feasBig),
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"bar": {
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Data: []float64{float64(b.Time / 1000), b.Open, b.High, b.Low, b.Close, b.Volume},
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Shape: []int32{6},
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},
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"y": {
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Data: []float64{futProbs[0], futProbs[1], futProbs[2]},
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Shape: []int32{3},
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},
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"info": {
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Data: info, Shape: []int32{int32(len(info))},
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},
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})
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}
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task.OnInfoBar = func(e *banta.BarEnv, b *banexg.Kline) *errs.Error {
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state := states[e.Symbol]
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state.feasBig = onAiFeatures2(e, seqNum)
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state.avgCostBigs = avgCostN(e, 10, 20, 40, 80, 160, 320)
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return nil
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}
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task.OnEnvEnd = func(bar *banexg.PairTFKline, adj *orm.AdjInfo) {
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if bar == nil {
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return
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}
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state := states[bar.Symbol]
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state.cg = &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2}
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}
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return pubFeaBase(exsList, req, task)
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}
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