var ƒ = d3.f
var width = 960
var height = 500

console.clear()
d3.loadData(['ratings-good.csv'], function(err, res){
  data = res[0]


  byState = d3.nestBy(data, ƒ('state'))
    .filter(d => d.key.length == 2)

  byState.forEach(function(d){
    d.byDate = d3.nestBy(d, ƒ('date'))
    
    d.byDate.forEach(function(d, i){
      d.kos = +_.find(d, {slug: 'dailykos'}).dem_prob
      d.fte = +_.find(d, {slug: 'fte'}).dem_prob
      d.pdw = +_.find(d, {slug: 'predictwise'}).dem_prob
      d.nyt = +_.find(d, {slug: 'nyt'}).dem_prob
      d.pec = +_.find(d, {slug: 'pec'}).dem_prob
      d.huf = +_.find(d, {slug: 'huffpost'}).dem_prob

      d.m = d.kos         // market
      d.f = d.nyt         // forecast
    
      //uncomment to invert
      // var temp = d.m
      // d.m = d.f
      // d.f = temp
    })

    scoreState(d)

  })

  byState = byState.filter(function(d){
    return d.byDate.every(d => d.m > .005 && d.m < .995)
  })

  maxChange = d3.max(_.flatten(byState.map(ƒ('byDate'))), d => Math.abs(d.logChange))


  var sel = d3.select('body').html('')

  var stateSel = sel.appendMany(byState, 'div')
    .st({display: 'inline-block', textAlign: 'center', marginBottom: 20})
  stateSel.append('div').text(d => d.key.toUpperCase())

  stateSel.each(function(d){
    var c = d3.conventions({
      totalWidth: 150,
      totalHeight: 150,
      margin: {top: 0, left: 20, right: 20, bottom: 15},
      parentSel: d3.select(this)
    })

    c.xAxis.ticks(3)
    c.yAxis.ticks(3)
    c.x.domain([0, d.byDate.length])
    c.drawAxis()

    var line = d3.line()
      .x((d, i) => c.x(i))
      .y(c.y)
      .curve(d3.curveStep)

    var color = d3.scaleOrdinal(['orange', 'grey']);

    c.svg.appendMany(['f', 'm'], 'path')
      .at({
        d: function(e){ return line(d.byDate.map(ƒ(e))) },
        fill: 'none',
        stroke: color,
        strokeWidth: 1
      })    

    var y = d3.scaleLinear()
      .domain([-maxChange, maxChange])
      .range([c.height, 0])

    line.y(ƒ('logChange', y))

    // throw 'up'

    c.svg.append('path')
      .at({
        d: line(d.byDate),
        stroke: 'black',
        fill: 'none',
        strokeWidth: 2
      })
  })

  function scoreState(state){
    var dollars = 1000000
    var dContracts = 0
    var rContracts = 0 
    state.byDate.forEach(function(d, i){
      // d.initNetworth = d.f*dContracts + (1 - d.f)*rContracts + dollars
      d.initNetworth = d.m*dContracts + (1 - d.m)*rContracts + dollars
      d.change = d.initNetworth/1000000
      d.logChange = Math.log(d.change)

      d.dollars = dollars
      d.dContracts = dContracts
      d.rContracts = rContracts

      var p = d.f > d.m ? d.f : 1 - d.f
      var m = d.f > d.m ? d.m : 1 - d.m

      var q = 1 - p
      var b = 1/m - 1
      d.k = (b*p - q)/b

      // if (state.key == 'al') throw 'up'

      if (d.m < d.f){
        // forecaster favors clinton more than market

        //liquidate r contracts
        dollars = dollars + rContracts*(1 - d.m)
        rContracts = 0

        //adjust ratio of dollars to contracts
        d.curRatio = dContracts*d.m/d.initNetworth
        d.dif = d.k - d.curRatio

        dContracts = dollars*d.dif/d.m + dContracts
        dollars = dollars*(1 - d.dif)

      } else{
        // forecaster favors trump more than market

        // liquidate d contracts
        dollars = dollars + dContracts*d.m
        dContracts = 0

        //adjust ratio of dollars to contracts
        d.curRatio = rContracts*(1 - d.m)/d.initNetworth
        d.dif = d.k - d.curRatio

        rContracts = dollars*d.dif/(1 - d.m) + rContracts
        dollars = dollars*(1 - d.dif)
      }
    })

    state.networth = _.last(state.byDate).initNetworth    
    state.change = state.networth/1000000
    state.logChange = Math.log(state.change)
  }

})