122 lines
3.8 KiB
Go
122 lines
3.8 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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"math"
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)
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type AIFeaState struct {
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cg *banta.CGraph
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cgBig *banta.CGraph
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matBig [3]*mat.Dense
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feasBig *mat.Dense
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rateBig float64
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avgCostBigs []float64 //大周期的成本价
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}
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func pubAiFea(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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log.Info("start ai fea2", zap.Int("pair", len(exsList)), zap.String("start", startDt),
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zap.Int("sample", sample))
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var seqNum = 150 // 特征序列长度
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var penNum = 120 // 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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states := make(map[string]*AIFeaState)
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for _, exs := range exsList {
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states[exs.Symbol] = &AIFeaState{
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cg: &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2},
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cgBig: &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2},
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}
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}
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task := &GenFeaTask{
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TfSml: tfSml,
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TfBig: tfBig,
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WarmSml: 3000,
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WarmBig: 3000,
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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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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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//if !core.IsWarmUp {
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// log.Info("OnBar", zap.Int64("time", e.TimeStop))
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//}
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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 := onAiFeatures(e, seqNum)
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if e.BarNum%sample > 0 || len(state.cg.Pens) < penNum || state.rateBig == 0 || len(futs) == 0 {
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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, rate := GenFeaMats(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, float64(b.Time/1000), b.Open, b.High, b.Low, b.Close, b.Volume,
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math.Log1p(rate), math.Log1p(state.rateBig))
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info = append(info, avgCosts...)
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info = append(info, state.avgCostBigs...)
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info = append(info, futProbs[0], futProbs[1], futProbs[2])
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return batch.Add(rsp, e, b.Time, map[string]*biz.NumArr{
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"time": matToNumArr(mats[0]),
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"buy": matToNumArr(mats[1]),
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"sell": matToNumArr(mats[2]),
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"feas": matToNumArr(feas),
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"time2": matToNumArr(state.matBig[0]),
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"buy2": matToNumArr(state.matBig[1]),
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"sell2": matToNumArr(state.matBig[2]),
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"feas2": matToNumArr(state.feasBig),
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"info": {Data: info, Shape: []int32{int32(len(info))}},
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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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//if !core.IsWarmUp {
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// log.Info("OnInfoBar", zap.Int64("time", e.TimeStop))
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//}
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state := states[e.Symbol]
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state.cgBig.AddBar(e)
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state.cgBig.Parse()
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state.feasBig = onAiFeatures(e, seqNum)
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state.avgCostBigs = avgCostN(e, 10, 20, 40, 80, 160, 320)
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if len(state.cgBig.Pens) >= penNum {
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state.matBig, state.rateBig = GenFeaMats(state.cgBig, penNum, depth, false)
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}
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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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state.cgBig = &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2}
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state.rateBig = 0
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}
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return pubFeaBase(exsList, req, task)
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}
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