马尔科夫模型推算股票

/**
 股票 隐马尔可夫模型
 共3种状态 B M E 大 中 小
 AMap 为状态转移概率矩阵 3*3,表示从{B M E}到{B M E}的概率
 BMap 为当前字属于某种状态{B M E}的概率
 * */
//有限状态
const S=['B','M','E'];

const AMap={
  B:0,
  M:0,
  E:0,

  BB:1,
  BM:1,
  BE:1,

  MB:1,
  MM:1,
  ME:1,

  EB:1,
  EM:1,
  EE:1,
}

const BMap={
  B:0,
  M:1,
  E:0,
};

const mekf={
  add(numArr){
    for(let i=0;i<numArr.length;i++){
      this.push(numArr[i])
      if(i>0){
        this.pushState(numArr[i-1],numArr[i])
      }else{
        this.pushState(0,numArr[i])
      }
    }
  },
  getM(num){
    let key='M'
    if(num>0){
      key='B'
    }else if(num<0){
      key='E'
    }
    return key;
  },
  pushState(n0,n1){
    const t0=this.getM(n0)
    const t1=this.getM(n1)
    const key=t0+t1;
    AMap[key]++;
    AMap[t0]++
  },
  push(num){
    const key=this.getM(num)
    if(!BMap[num]){
      BMap[num]=0;
    }
    BMap[num]++;
    BMap[key]++;
  },

  getT1(n0){
    const t0=this.getM(n0)
    //马尔可夫链条
    let agl=0;
    let bgl=0;
    const obj={}
    const arr=[]
    for(let i=-10;i<=10;i++){
      const t1=this.getM(i);
      const key=t0+t1;
      if(!BMap[i]){
        BMap[i]=0;
      }
      const gl=(BMap[i]/BMap[t1])*(AMap[key]/AMap[t0])
      if(gl>0){
        agl=agl+gl;
        arr.push({i:i,gl:gl})
      }
    }
    arr.forEach(function (item) {
      item.gl=Math.round(item.gl*100/agl);
      if(item.i>0){
        bgl=bgl+item.gl
      }
    })
    arr.sort(function (item1,item2) {
      return item2.gl-item1.gl
    })
    console.log(bgl)
    return arr;
  }
}
const data=[0,-2,0,-1,0,0,0,-3,1,2,-2,1,2,1,-1,1,0,2,1,1,7,4,10,10,-2,1,0,-4,-4,3,10,-6,1,3,6,1,-2,-1,10,-2,7,9,10,-2,2,-7,3,2,2,1,-7,-3,-1]
mekf.add(data)
const t1=mekf.getT1(-1)
console.log(t1)