package rpc_fea

import (
	"github.com/banbox/banbot/biz"
	"github.com/banbox/banbot/btime"
	"github.com/banbox/banbot/core"
	"github.com/banbox/banbot/orm"
	"github.com/banbox/banexg"
	"github.com/banbox/banexg/errs"
	"github.com/banbox/banexg/log"
	utils2 "github.com/banbox/banexg/utils"
	"github.com/banbox/banta"
	"go.uber.org/zap"
	"gonum.org/v1/gonum/mat"
	"math"
)

type AIFeaState struct {
	cg          *banta.CGraph
	cgBig       *banta.CGraph
	matBig      [3]*mat.Dense
	feasBig     *mat.Dense
	rateBig     float64
	avgCostBigs []float64 //大周期的成本价
}

func pubAiFea(exsList []*orm.ExSymbol, req *biz.SubReq, rsp biz.FeaFeeder_SubFeaturesServer) error {
	var sample = int(req.Sample)
	if sample <= 0 {
		sample = 10
	}
	startDt := btime.ToDateStr(req.Start, core.DefaultDateFmt)
	log.Info("start ai fea2", zap.Int("pair", len(exsList)), zap.String("start", startDt),
		zap.Int("sample", sample))
	var seqNum = 150 // 特征序列长度
	var penNum = 120 // 笔矩阵长度
	var depth = 100  // 笔矩阵深度：100个深度
	tfSml, tfBig := "5m", "1h"
	tfMSecs := int64(utils2.TFToSecs(tfSml) * 1000)
	tfMSecsBig := int64(utils2.TFToSecs(tfBig) * 1000)

	states := make(map[string]*AIFeaState)
	for _, exs := range exsList {
		states[exs.Symbol] = &AIFeaState{
			cg:    &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2},
			cgBig: &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2},
		}
	}

	task := &GenFeaTask{
		TfSml:      tfSml,
		TfBig:      tfBig,
		WarmSml:    3000,
		WarmBig:    3000,
		TfMSecs:    tfMSecs,
		TfMSecsBig: tfMSecsBig,
		NextObsNum: 30,
	}

	batch := NewBatchMap(len(exsList))

	task.OnBar = func(e *banta.BarEnv, b *banexg.Kline, futs []*banexg.Kline) *errs.Error {
		//if !core.IsWarmUp {
		//	log.Info("OnBar", zap.Int64("time", e.TimeStop))
		//}
		state := states[e.Symbol]
		state.cg.AddBar(e)
		state.cg.Parse()
		feas := onAiFeatures(e, seqNum)
		if e.BarNum%sample > 0 || len(state.cg.Pens) < penNum || state.rateBig == 0 || len(futs) == 0 {
			// 预热时futs为nil
			return nil
		}
		// 收集特征，发送到监听端
		minChgVal := banta.ATR(e.High, e.Low, e.Close, 30).Get(0) * 5
		futProbs := genFutProbsV1(futs, minChgVal, 0.4)
		mats, rate := GenFeaMats(state.cg, penNum, depth, false)
		avgCosts := avgCostN(e, 10, 20, 40, 80, 160, 320)
		info := make([]float64, 0, 25)
		info = append(info, float64(b.Time/1000), b.Open, b.High, b.Low, b.Close, b.Volume,
			math.Log1p(rate), math.Log1p(state.rateBig))
		info = append(info, avgCosts...)
		info = append(info, state.avgCostBigs...)
		info = append(info, futProbs[0], futProbs[1], futProbs[2])
		return batch.Add(rsp, e, b.Time, map[string]*biz.NumArr{
			"time":  matToNumArr(mats[0]),
			"buy":   matToNumArr(mats[1]),
			"sell":  matToNumArr(mats[2]),
			"feas":  matToNumArr(feas),
			"time2": matToNumArr(state.matBig[0]),
			"buy2":  matToNumArr(state.matBig[1]),
			"sell2": matToNumArr(state.matBig[2]),
			"feas2": matToNumArr(state.feasBig),
			"info":  {Data: info, Shape: []int32{int32(len(info))}},
		})
	}
	task.OnInfoBar = func(e *banta.BarEnv, b *banexg.Kline) *errs.Error {
		//if !core.IsWarmUp {
		//	log.Info("OnInfoBar", zap.Int64("time", e.TimeStop))
		//}
		state := states[e.Symbol]
		state.cgBig.AddBar(e)
		state.cgBig.Parse()
		state.feasBig = onAiFeatures(e, seqNum)
		state.avgCostBigs = avgCostN(e, 10, 20, 40, 80, 160, 320)
		if len(state.cgBig.Pens) >= penNum {
			state.matBig, state.rateBig = GenFeaMats(state.cgBig, penNum, depth, false)
		}
		return nil
	}
	task.OnEnvEnd = func(bar *banexg.PairTFKline, adj *orm.AdjInfo) {
		if bar == nil {
			return
		}
		state := states[bar.Symbol]
		state.cg = &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2}
		state.cgBig = &banta.CGraph{ParseLevel: banta.ParseLevelPen, MaxPen: penNum * 3 / 2}
		state.rateBig = 0
	}
	return pubFeaBase(exsList, req, task)
}