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DataFrame from 'dataframe-js'\n\nexport const buildTenorsData = function(data) {\n  const months = {\n    jan: 'January',\n    feb: 'February',\n    mar: 'March',\n    apr: 'April',\n    may: 'May',\n    jun: 'June',\n    jul: 'July',\n    aug: 'August',\n    sep: 'September',\n    oct: 'October',\n    nov: 'November',\n    dec: 'December'\n  }\n  let df = new DataFrame(\n    data,\n    ['name', 'start', 'end', 'settlementStart',\n     'settlementDate', 'settlementDays']\n  )\n  df = df.withColumn('month', row => months[row.get('name').slice(0, 3)])\n  return df\n}\n\nexport const getTenorForDateAndMonth = function(tenors, date, month) {\n  return tenors.filter(row => row.get('end') >= date).slice(month, month+1).toCollection()[0]\n}\n\n","import {currencyFormatter} from '@/mixins';\n\nexport const buildRecommendationsTableData = function(recommendations) {\n  const items = recommendations.reduce((acc, rec) => {\n    const action = rec.signal_action.replace('Action.', '')\n    const buy = action == \"BUY\"\n    const availableCapital = buy ? rec.buy_capital : rec.sell_capital\n    const shares = Math.round(availableCapital / rec.signal_price)\n    const netShares = shares * (buy ? 1 : -1)\n    const condition = `price ${buy ? 'below' : 'above'} ${currencyFormatter.format(rec.order_limit)}`\n    if (shares != 0) {\n      acc.push({\n        equity: rec.ticker,\n        action,\n        availableCapital: currencyFormatter.format(availableCapital),\n        netShares,\n        condition,\n        _cellVariants: {\n          action: netShares < 0 ? \"red\" : \"green\",\n        }\n      })\n    }\n    return acc\n  }, []).sort((x, y) => x.equity > y.equity)\n  const fields = [\n    {key: 'equity'},\n    {key: 'action'},\n    {key: 'availableCapital', thClass: 'text-right', tdClass: 'text-right'},\n    {key: 'netShares', thClass: 'text-right', tdClass: 'text-right'},\n    {key: 'condition', thClass: 'text-right', tdClass: 'text-right'}\n  ]\n  return {items, fields}\n}\n\nexport const buildExitIndicationsTableData = function(exit_indications) {\n  const items = exit_indications.reduce((acc, ei) => {\n    acc.push({\n      equity: ei.ticker,\n      action: ei.delta < 0 ? \"SELL\" : \"BUY\",\n      netShares: Math.round(ei.delta),\n      _cellVariants: {\n        action: ei.delta < 0 ? \"red\" : \"green\",\n      }\n    })\n    return acc\n  }, []).sort((x, y) => x.equity > y.equity)\n  const fields = [\n    {key: 'equity'},\n    {key: 'action'},\n    {key: 'netShares', thClass: 'text-right', tdClass: 'text-right'}\n  ]\n  return {items, fields}\n}\n\nconst extractEquities = function(current, previous) {\n  const equities = new Set()\n  for (let target of [current, previous]) {\n    for (let key of Object.keys(target)) {\n      if (key.startsWith('m2m_') && !key.includes('wte')) {\n        const equity = key.replace('m2m_', '')\n        equities.add(equity)\n      }\n    }\n  }\n  return equities\n}\n\nconst colorClass = x => x < 0 ? \"red\" : \"green\"\n\nexport const buildM2MTableData = function(current, previous) {\n  const equities = extractEquities(current, previous)\n  const items = []\n  const fields = [{key: 'equity'}]\n  const footer_values = {}\n  for (let wte of [0, 1, 2, 3, 4]) {\n    const key = `m2m_wte${wte}`\n    footer_values[key] = 0\n  }\n  for (let equity of Array.from(equities).sort()) {\n    const item = {\n      equity,\n      '_cellVariants': {}\n    }\n    for (let wte of [0, 1, 2, 3, 4]) {\n      const key = `m2m_wte${wte}`\n      const m2m = current[`${key}_${equity}`]\n      item[key] = currencyFormatter.format(m2m)\n      item._cellVariants[key] = colorClass(m2m)\n      footer_values[key] += m2m\n    }\n    items.push(item)\n  }\n  for (let wte of [0, 1, 2, 3, 4]) {\n    const key = `m2m_wte${wte}`\n    const count = wte + 1\n    const week = count > 1 ? \"weeks\" : \"week\"\n    const label = `M2M for exits in ${count} ${week}`\n    fields.push({\n      key, label, thClass: 'text-right', tdClass: 'text-right',\n      footer: footer_values[key],\n      footerClass: `text-right table-${colorClass(footer_values[key])}`\n    })\n  }\n  return {\n    items,\n    fields\n  }\n}\n\nexport const buildPortfolioTableData = function(current, previous) {\n  const equities = extractEquities(current, previous)\n  const items = []\n  const footer_values = {\n    net_shares: 0, m2m: 0, m2m_diff: 0, cum_net_profit: 0, pnl_plus_m2m: 0\n  }\n  for (let equity of Array.from(equities).sort()) {\n    const m2m_key = `m2m_${equity}`\n    const m2m = current[m2m_key]\n    footer_values['m2m'] += m2m\n    const prev_m2m = previous[m2m_key]\n    const m2m_diff = m2m - prev_m2m\n    footer_values['m2m_diff'] += m2m_diff\n    const m2m_pct_change = prev_m2m == 0 ? 0 : (m2m_diff / Math.abs(prev_m2m)) * 100\n    const cum_net_profit = current[`cum_net_profit_${equity}`] || 0\n    footer_values['cum_net_profit'] += cum_net_profit\n    let pl_ratio = current[`pnl_ratio_${equity}`]\n    pl_ratio = pl_ratio ? pl_ratio.toFixed(2) + ':1' : 'N/A'\n    const pnl_plus_m2m = m2m + cum_net_profit\n    footer_values['pnl_plus_m2m'] += pnl_plus_m2m\n    const netShares = Math.round(current[`delta_${equity}`])\n    footer_values['net_shares'] += netShares\n    items.push({\n      equity,\n      netShares,\n      m2m: currencyFormatter.format(m2m),\n      m2m_diff: currencyFormatter.format(m2m_diff),\n      m2m_pct_change: `${m2m_pct_change.toFixed(2)}%`,\n      cum_net_profit: currencyFormatter.format(cum_net_profit),\n      pl_ratio,\n      pnl_plus_m2m: currencyFormatter.format(pnl_plus_m2m),\n      _cellVariants: {\n        m2m: colorClass(m2m),\n        m2m_diff: colorClass(m2m_diff),\n        m2m_pct_change: colorClass(m2m_pct_change),\n        cum_net_profit: colorClass(cum_net_profit),\n        pnl_plus_m2m: colorClass(pnl_plus_m2m),\n      }\n    })\n  }\n  footer_values['m2m_pct_change'] = (\n    footer_values['m2m_diff'] /\n    Math.abs(footer_values['m2m'] - footer_values['m2m_diff'])\n  )\n  const fields = [\n    {key: 'equity'},\n    {\n      key: 'netShares', thClass: 'text-right', tdClass: 'text-right',\n      footer: footer_values['net_shares']\n    },\n    {\n      key: 'm2m', label: 'M2M', thClass: 'text-right', tdClass: 'text-right',\n      footer: footer_values['m2m'],\n      footerClass: `text-right table-${colorClass(footer_values['m2m'])}`\n    },\n    {\n      key: 'm2m_diff', label: 'M2M Daily Change', thClass: 'text-right',\n      tdClass: 'text-right', footer: footer_values['m2m_diff'],\n      footerClass: `text-right table-${colorClass(footer_values['m2m_diff'])}`\n    },\n    {\n      key: 'm2m_pct_change', label: 'M2M Daily % Change',\n      thClass: 'text-right', tdClass: 'text-right',\n      footer: footer_values['m2m_pct_change'],\n      footerClass: `text-right table-${colorClass(footer_values['m2m_pct_change'])}`\n    },\n    {\n      key: 'cum_net_profit', label: 'Cum. Net Profit',\n      thClass: 'text-right', tdClass: 'text-right',\n      footer: footer_values['cum_net_profit'],\n      footerClass: `text-right table-${colorClass(footer_values['cum_net_profit'])}`\n    },\n    {\n      key: 'pl_ratio', label: 'Profit/loss Ratio',\n      thClass: 'text-right', tdClass: 'text-right',\n      footer: current['pnl_ratio'] ? current['pnl_ratio'].toFixed(2) + ':1' : 'N/A',\n      footerClass: 'text-right'\n    },\n    {\n      key: 'pnl_plus_m2m', label: 'P&L + M2M', thClass: 'text-right',\n      tdClass: 'text-right', footer: footer_values['pnl_plus_m2m'],\n      footerClass: `text-right table-${colorClass(footer_values['pnl_plus_m2m'])}`\n    }\n  ]\n  return {\n    items,\n    fields\n  }\n}\n\nexport const buildAccountValuePlotData = function(df) {\n  const dates = df.toArray('date')\n  return [\n    {\n      type: 'scatter',\n      x: dates,\n      y: df.toArray('account_value'),\n      name: 'Account Value'\n    },\n    {\n      type: 'scatter',\n      x: dates,\n      y: df.toArray('net_outcome'),\n      name: 'Cum. Net Profit'\n    },\n    {\n      type: 'scatter',\n      x: dates,\n      y: df.toArray('m2m'),\n      name: 'M2M'\n    }\n  ]\n}\n\nexport const buildClosePricesPlotData = function(df) {\n  const dates = df.toArray('date')\n  const plots = []\n  for (let col of df.listColumns()) {\n    if (col.endsWith('_close')) {\n      const ticker = col.replace('_close', '')\n      plots.push({\n        type: 'scatter',\n        x: dates,\n        y: df.toArray(col),\n        name: ticker\n      })\n    }\n  }\n  return plots.sort((a, b) => a.name < b.name)\n}\n","export const buildRouteD3ForecastData = function(df, route, week, effectiveDate) {\n  const filtered = df.filter(\n    row => (\n      row.get('date') <= effectiveDate &&\n      row.get(`${route}_W${week}_spot_price`)\n    )\n  ).tail(250)\n  const baseKey = route + '_W' + week\n  const spotPrices = filtered.toArray(baseKey + '_spot_price')\n  const spots = []\n  for (let i of [1, 2, 3, 4, 5]) {\n    const s = filtered.toArray(baseKey + '_spot_s' + i).slice(-1)[0]\n    if (s) {\n      spots.push(s)\n    }\n  }\n  let averageSpot = null\n  if (spots.length > 0) {\n    averageSpot = spots.reduce((a, b) => a+b, 0) / spots.length\n  }\n  const values = spotPrices.slice(-4)\n  const forecasts = filtered.toArray(baseKey + '_prediction').slice(-4)\n  const ll_50s = filtered.toArray(baseKey + '_ll_50').slice(-4)\n  const ul_50s = filtered.toArray(baseKey + '_ul_50').slice(-4)\n  const ll_70s = filtered.toArray(baseKey + '_ll_70').slice(-4)\n  const ul_70s = filtered.toArray(baseKey + '_ul_70').slice(-4)\n  return {\n    min_value: Math.min(...spotPrices),\n    max_value: Math.max(...spotPrices),\n    value: values.slice(-1)[0],\n    values: values.slice(0, 3),\n    spots,\n    averageSpot,\n    forecast: forecasts.slice(-1)[0],\n    forecasts: forecasts.slice(0, 3),\n    ll_50: ll_50s.slice(-1)[0],\n    ll_50s: ll_50s.slice(0, 3),\n    ul_50: ul_50s.slice(-1)[0],\n    ul_50s: ul_50s.slice(0, 3),\n    ll_70: ll_70s.slice(-1)[0],\n    ll_70s: ll_70s.slice(0, 3),\n    ul_70: ul_70s.slice(-1)[0],\n    ul_70s: ul_70s.slice(0, 3)\n  }\n}\n\nexport const buildRouteD3FeaturesData = function(df, route, week, effectiveDate) {\n  const baseKey = route + '_W' + week\n  const columns = df.listColumns()\n    .filter(c => c.startsWith(baseKey))\n  const categories = columns.map(c => c.slice(baseKey.length+1))\n  return df\n    .filter(row => row.get('date') <= effectiveDate)\n    .select(...columns)\n    .renameAll(categories)\n    .tail(1)\n    .toCollection()[0]\n}\n\nexport const buildRouteD3PerformanceData = function(df, route, week, effectiveDate) {\n  const baseKey = route + '_W' + week\n  const record = df\n    .filter(row => row.get('date') <= effectiveDate)\n    .tail(1)\n    .toCollection()[0]\n  const relDA3M = record[`${baseKey}_da_roll_pct_60`]\n  const relForecast3M = record[`${baseKey}_error_norm_roll_pct_60`]\n  const relRange3M = record[`${baseKey}_spot_inrange_roll_pct_60_50`]\n  const relAccuracy3M = (\n    relDA3M +\n    relForecast3M +\n    relRange3M\n  ) / 3.0\n  const relDA6M = record[`${baseKey}_da_roll_pct_125`]\n  const relForecast6M = record[`${baseKey}_error_norm_roll_pct_125`]\n  const relRange6M = record[`${baseKey}_spot_inrange_roll_pct_125_50`]\n  const relAccuracy6M = (\n    relDA6M +\n    relForecast6M +\n    relRange6M\n  ) / 3.0\n  const relDA1Y = record[`${baseKey}_da_roll_pct_250`]\n  const relForecast1Y = record[`${baseKey}_error_norm_roll_pct_250`]\n  const relRange1Y = record[`${baseKey}_spot_inrange_roll_pct_250_50`]\n  const relAccuracy1Y = (\n    relDA1Y +\n    relForecast1Y +\n    relRange1Y\n  ) / 3.0\n  const absDA3M = record[`${baseKey}_da_roll_60`]\n  const absForecast3M = record[`${baseKey}_median_abs_pct_error_60`]\n  const absRange3M = record[`${baseKey}_spot_inrange_roll_60_50`]\n  const absAccuracy3M = (\n    absDA3M +\n    absForecast3M +\n    absRange3M\n  ) / 3.0\n  const absDA6M = record[`${baseKey}_da_roll_125`]\n  const absForecast6M = record[`${baseKey}_median_abs_pct_error_125`]\n  const absRange6M = record[`${baseKey}_spot_inrange_roll_125_50`]\n  const absAccuracy6M = (\n    absDA6M +\n    absForecast6M +\n    absRange6M\n  ) / 3.0\n  const absDA1Y = record[`${baseKey}_da_roll_250`]\n  const absForecast1Y = record[`${baseKey}_median_abs_pct_error_250`]\n  const absRange1Y = record[`${baseKey}_spot_inrange_roll_250_50`]\n  const absAccuracy1Y = (\n    absDA1Y +\n    absForecast1Y +\n    absRange1Y\n  ) / 3.0\n  const out = {\n    relative: {\n      past_three_months: {\n        accuracy: relAccuracy3M,\n        directionalAccuracy: relDA3M,\n        forecastAccuracy: relForecast3M,\n        rangeAccuracy: relRange3M\n      },\n      past_six_months: {\n        accuracy: relAccuracy6M,\n        directionalAccuracy: relDA6M,\n        forecastAccuracy: relForecast6M,\n        rangeAccuracy: relRange6M\n      },\n      past_year: {\n        accuracy: relAccuracy1Y,\n        directionalAccuracy: relDA1Y,\n        forecastAccuracy: relForecast1Y,\n        rangeAccuracy: relRange1Y\n      }\n    },\n    absolute: {\n      past_three_months: {\n        accuracy: absAccuracy3M,\n        directionalAccuracy: absDA3M,\n        forecastAccuracy: absForecast3M,\n        rangeAccuracy: absRange3M\n      },\n      past_six_months: {\n        accuracy: absAccuracy6M,\n        directionalAccuracy: absDA6M,\n        forecastAccuracy: absForecast6M,\n        rangeAccuracy: absRange6M\n      },\n      past_year: {\n        accuracy: absAccuracy1Y,\n        directionalAccuracy: absDA1Y,\n        forecastAccuracy: absForecast1Y,\n        rangeAccuracy: absRange1Y\n      }\n    },\n  }\n  return out\n}\n","//import moment from 'moment'\n\nexport const viterraLabels = {\n  'pmx_M1_roll': 'Pmx M1',\n  'pmx_M2_roll': 'Pmx M2',\n  'pmx_M3_roll': 'Pmx M3',\n  'capes_M1_roll': 'Capes M1',\n  'capes_M2_roll': 'Capes M2',\n  'capes_M3_roll': 'Capes M3',\n  'pmx_Q2': 'Pmx Q2',\n  'pmx_Q3': 'Pmx Q3',\n  'pmx_Q4': 'Pmx Q4',\n  'capes_Q2': 'Capes Q2',\n  'capes_Q3': 'Capes Q3',\n  'capes_Q4': 'Capes Q4',\n  'P1A_82_proxy_W4': 'P1A',\n  'P1A_82_proxy_W5': 'P1A',\n  'P1A_82_proxy_W6': 'P1A',\n  'P2A_82_proxy_W4': 'P2A',\n  'P2A_82_proxy_W5': 'P2A',\n  'P2A_82_proxy_W6': 'P2A',\n  'P3A_82_proxy_W4': 'P3A',\n  'P3A_82_proxy_W5': 'P3A',\n  'P3A_82_proxy_W6': 'P3A',\n  'P6_82_W4': 'P6',\n  'P6_82_W5': 'P6',\n  'P6_82_W6': 'P6'\n}\n\nexport const buildViterraD3ForecastData = function(df, forecast, week, effectiveDate) {\n  let baseKey = forecast\n  if (forecast.includes('roll')) {\n    baseKey = forecast.replace('_W4', '').replace('_W6', '')\n  }\n  const filtered = df.filter(\n    row => (\n      row.get('date') <= effectiveDate &&\n      row.get(`${baseKey}_spot_price`)\n    )\n  ).tail(250)\n  const spotPrices = filtered.toArray(baseKey + '_spot_price')\n  const spots = []\n  for (let i of [1, 2, 3, 4, 5]) {\n    const s = filtered.toArray(baseKey + '_spot_s' + i).slice(-1)[0]\n    if (s) {\n      spots.push(s)\n    }\n  }\n  let averageSpot = null\n  if (spots.length > 0) {\n    averageSpot = spots.reduce((a, b) => a+b, 0) / spots.length\n  }\n  const values = spotPrices.slice(-4)\n  const forecasts = filtered.toArray(baseKey + '_prediction').slice(-4)\n  const ll_50s = filtered.toArray(baseKey + '_ll_50').slice(-4)\n  const ul_50s = filtered.toArray(baseKey + '_ul_50').slice(-4)\n  const ll_70s = filtered.toArray(baseKey + '_ll_70').slice(-4)\n  const ul_70s = filtered.toArray(baseKey + '_ul_70').slice(-4)\n  return {\n    min_value: Math.min(...spotPrices),\n    max_value: Math.max(...spotPrices),\n    value: values.slice(-1)[0],\n    values: values.slice(0, 3),\n    spots,\n    averageSpot,\n    forecast: forecasts.slice(-1)[0],\n    forecasts: forecasts.slice(0, 3),\n    ll_50: ll_50s.slice(-1)[0],\n    ll_50s: ll_50s.slice(0, 3),\n    ul_50: ul_50s.slice(-1)[0],\n    ul_50s: ul_50s.slice(0, 3),\n    ll_70: ll_70s.slice(-1)[0],\n    ll_70s: ll_70s.slice(0, 3),\n    ul_70: ul_70s.slice(-1)[0],\n    ul_70s: ul_70s.slice(0, 3)\n  }\n}\n\nexport const buildViterraD3FeaturesData = function(df, forecast, week, effectiveDate) {\n  let baseKey = forecast\n  if (forecast.includes('roll')) {\n    baseKey = forecast.replace('_W4', '').replace('_W6', '')\n  }\n  const columns = df.listColumns()\n    .filter(c => c.startsWith(baseKey))\n  const categories = columns.map(c => c.slice(baseKey.length+1))\n  return df\n    .filter(row => row.get('date') <= effectiveDate)\n    .select(...columns)\n    .renameAll(categories)\n    .tail(1)\n    .toCollection()[0]\n}\n\nexport const buildViterraD3PerformanceData = function(df, forecast, week, effectiveDate) {\n  let baseKey = forecast\n  const record = df\n    .filter(row => row.get('date') <= effectiveDate)\n    .tail(1)\n    .toCollection()[0]\n  const relDA6M = record[`${baseKey}_da_roll_pct_125`]\n  const relForecast6M = record[`${baseKey}_error_norm_roll_pct_125`]\n  const relRange6M = record[`${baseKey}_target_inrange_roll_pct_125_50`]\n  const relAccuracy6M = (\n    relDA6M +\n    relForecast6M +\n    relRange6M\n  ) / 3.0\n  const relDA1Y = record[`${baseKey}_da_roll_pct_250`]\n  const relForecast1Y = record[`${baseKey}_error_norm_roll_pct_250`]\n  const relRange1Y = record[`${baseKey}_target_inrange_roll_pct_250_50`]\n  const relAccuracy1Y = (\n    relDA1Y +\n    relForecast1Y +\n    relRange1Y\n  ) / 3.0\n  const absDA6M = record[`${baseKey}_da_roll_125`]\n  const absForecast6M = record[`${baseKey}_median_abs_pct_error_125`]\n  const absRange6M = record[`${baseKey}_target_inrange_roll_125_50`]\n  const absAccuracy6M = (\n    absDA6M +\n    absForecast6M +\n    absRange6M\n  ) / 3.0\n  const absDA1Y = record[`${baseKey}_da_roll_250`]\n  const absForecast1Y = record[`${baseKey}_median_abs_pct_error_250`]\n  const absRange1Y = record[`${baseKey}_target_inrange_roll_250_50`]\n  const absAccuracy1Y = (\n    absDA1Y +\n    absForecast1Y +\n    absRange1Y\n  ) / 3.0\n  const out = {\n    relative: {\n      past_six_months: {\n        accuracy: relAccuracy6M,\n        directionalAccuracy: relDA6M,\n        forecastAccuracy: relForecast6M,\n        rangeAccuracy: relRange6M\n      },\n      past_year: {\n        accuracy: relAccuracy1Y,\n        directionalAccuracy: relDA1Y,\n        forecastAccuracy: relForecast1Y,\n        rangeAccuracy: relRange1Y\n      }\n    },\n    absolute: {\n      past_six_months: {\n        accuracy: absAccuracy6M,\n        directionalAccuracy: absDA6M,\n        forecastAccuracy: absForecast6M,\n        rangeAccuracy: absRange6M\n      },\n      past_year: {\n        accuracy: absAccuracy1Y,\n        directionalAccuracy: absDA1Y,\n        forecastAccuracy: absForecast1Y,\n        rangeAccuracy: absRange1Y\n      }\n    },\n  }\n  return out\n}\n","export const buildCommodities = function(data) {\n  if (!data) return []\n  if (data.type != 'commodities') return []\n  if (!data.configuration.commodities) return []\n  const displayNames = {\n    capesize: 'Capesize',\n    panamax: 'Panamax',\n    iron_ore: 'Iron Ore'\n  }\n  const commodities = Object.keys(data.configuration.commodities).reduce(\n    (acc, commodity) =>  acc.concat([{\n        ...data.configuration.commodities[commodity],\n        name: commodity,\n        // TODO: this should belong to the config we get from the backend\n        contracts: ['M1', 'M2', 'M3'],\n        displayName: displayNames[commodity]\n    }]),\n    []\n  )\n  return commodities\n}\n","import DataFrame from 'dataframe-js'\n\n\nexport const buildPositionsData = function(rawData) {\n  const { open_summary, realized_summary, open_trades, realized_trades } = rawData\n  return {\n    openSummary: new DataFrame(open_summary.rows, open_summary.columns),\n    realizedSummary: new DataFrame(realized_summary.rows, realized_summary.columns),\n    openTrades: new DataFrame(open_trades.rows, open_trades.columns),\n    realizedTrades: new DataFrame(realized_trades.rows, realized_trades.columns)\n  }\n}\n","import DataFrame from 'dataframe-js'\nimport * as Moment from 'moment'\nimport { extendMoment } from 'moment-range'\nimport { buildTenorsData, getTenorForDateAndMonth } from './tenors'\nimport {\n  buildRecommendationsTableData,\n  buildExitIndicationsTableData,\n  buildM2MTableData,\n  buildPortfolioTableData,\n  buildAccountValuePlotData,\n  buildClosePricesPlotData\n} from './equities'\nimport {\n  buildRouteD3ForecastData,\n  buildRouteD3FeaturesData,\n  buildRouteD3PerformanceData,\n} from './routes'\nimport {\n  viterraLabels,\n  buildViterraD3ForecastData,\n  buildViterraD3FeaturesData,\n  buildViterraD3PerformanceData\n} from './viterra'\nexport * from './ais'\nexport * from './commodities'\nexport * from './equities'\nexport * from './positions'\nexport * from './tenors'\n\nconst moment = extendMoment(Moment)\n\nconst buildOpenTradesCardsData = function(dayData, commodities) {\n  return commodities.reduce(\n    (d, commodity) => ({\n      ...d,\n      [commodity]: {\n        markToMarket: dayData[commodity + '_m2m'],\n        partiallyRealized: dayData[commodity + '_partially_realized'],\n        marginExposure: dayData[commodity + '_margin_exposure'],\n        premiumExposure: dayData[commodity + '_premium_exposure'],\n        delta: dayData[commodity + '_delta'],\n        count: dayData[commodity + '_open_trades']\n      }\n    }),\n    {\n      portfolio: {\n        valueAtRisk: dayData.value_at_risk,\n        expectedShortfall: dayData.expected_shortfall,\n        markToMarket: dayData.mark_to_market,\n        partiallyRealized: dayData.partially_realized,\n        marginExposure: dayData.margin_exposure,\n        premiumExposure: dayData.premium_exposure,\n        delta: dayData.delta,\n        count: dayData.open_trades\n      }\n    }\n  )\n}\n\nconst buildRealizedCardData = function(dayData, firstDate) {\n  return ['ytd', 'tmr'].reduce(\n    (d, key) => ({\n      ...d,\n      [key]: {\n        cumulativeNetProfit: dayData[key + '_cum_net_profit'],\n        profitLossRatio: dayData[key + '_profit_loss_ratio'],\n        averageNetProfitPerTrade: dayData[key + '_avg_net_profit_per_trade'],\n        averageTradeDuration: dayData[key +  '_avg_trade_duration'],\n        count: dayData[key + '_realized_trades'],\n        averageVaR: dayData[key + '_avg_var'],\n        maxVaR: dayData[key + '_max_var'],\n        sharpeRatio: null,\n        sortinoRatio: null\n      }\n    }),\n    {\n      overall: {\n        title: moment(firstDate).format('MMM YY') + ' - Present',\n        cumulativeNetProfit: dayData.cum_net_profit,\n        profitLossRatio: dayData.profit_loss_ratio,\n        averageNetProfitPerTrade: dayData.avg_net_profit_per_trade,\n        averageTradeDuration: dayData.avg_trade_duration,\n        count: dayData.realized_trades,\n        averageVaR: dayData.avg_var,\n        maxVaR: dayData.max_var,\n        sharpeRatio: dayData.sharpe_ratio,\n        sortinoRatio: dayData.sortino_ratio\n      }\n    }\n  )\n}\n\nconst diffToClass = x => ({[(x[0] ==  '+' ? 'text-success' : 'text-danger')]: true})\n\nconst diff = function(key, day, previousDay) {\n  const value = Number(day[key]) - Number(previousDay[key])\n  return (value < 0 ? '' : '+') + String(value)\n}\n\nconst recommendationToClass = function(recommendation) {\n  const cls = {'float-right': true}\n  if (recommendation == 'B') {\n    cls['text-success'] = true\n  } else if (recommendation == 'S') {\n    cls['text-danger'] = true\n  }\n  return cls\n}\n\nconst closestDate = (df, date) => {\n  const allDates = df.toArray('date')\n  if (!date) return allDates[allDates.length-1]\n  return allDates.filter(d => d <= date).slice(-1)[0]\n}\n\nconst buildForecastGraphs = function(date, df, dayData, previousDayData, tenors, commodities) {\n  const [mm3, mm2, mm1] = tenors.filter(row => row.get('end') < date).tail(3).toCollection()\n  const [m0, m1, m2, m3] = tenors.filter(row => row.get('end') >= date).head(4).toCollection()\n\n  return commodities.reduce((acc, commodity) => {\n    const pastData = [mm3, mm2, mm1, m0].map(t => {\n      const startDate = t.settlementStart\n      const endDate = t.settlementDate > date ? date : t.settlementDate\n\n      const slice = df.filter(row => row.get('date') >= startDate && row.get('date') <= endDate)\n      const spotDates = slice.toArray('date')\n      const spotKey = commodity + '_Spot_price'\n      const spotPrices = slice.toArray(spotKey)\n      const settlement = slice.stat.mean(spotKey)\n      return {\n        spotDates,\n        spotPrices,\n        settlements: Array(spotPrices.length).fill(settlement)\n      }\n    })\n\n    const futureData = [m1, m2, m3].map((t, ix) => {\n      const forecastKey = (contract, engine=1) => (\n        commodity + '_forecast_engine_' + engine + '_M' + contract\n      )\n      const FFAPriceKey = contract => (\n        commodity + '_FFA_price_M'+ contract\n      )\n\n      const dates = Array.from(\n        moment.range(\n          t.settlementStart,\n          t.settlementDate\n        ).by('day')\n      ).map(x => x.format('YYYY-MM-DD'))\n\n      const forecasts = Array(dates.length).fill(\n        Math.round(dayData[forecastKey(ix+1)])\n      )\n      const previousForecasts = Array(dates.length).fill(\n        Math.round(previousDayData[forecastKey(ix+1)])\n      )\n      const FFAPrices = Array(dates.length).fill(\n        Math.round(dayData[FFAPriceKey(ix+1)])\n      )\n      const previousFFAPrices = Array(dates.length).fill(\n        Math.round(previousDayData[FFAPriceKey(ix+1)])\n      )\n      const forecasts2E = Array(dates.length).fill(null)\n      const previousForecasts2E = Array(dates.length).fill(null)\n      const has2E = !!dayData[forecastKey(ix+1, 2)]\n      if (has2E) {\n        forecasts2E.fill(\n          Math.round(dayData[forecastKey(ix+1, 2)])\n        )\n        previousForecasts2E.fill(\n          Math.round(previousDayData[forecastKey(ix+1, 2)])\n        )\n      }\n      return {\n        dates,\n        forecasts,\n        previousForecasts,\n        forecasts2E,\n        previousForecasts2E,\n        FFAPrices,\n        previousFFAPrices\n      }\n    })\n    const spotDates = [].concat(...pastData.map(x => x.spotDates))\n    const forecastDates = [].concat(...futureData.map(x => x.dates))\n\n    acc[commodity] = [\n      {\n        type: 'scatter',\n        x: spotDates,\n        y: [].concat(...pastData.map(x => x.spotPrices)),\n        mode: 'lines',\n        line: {color: 'rgba(243, 92, 16, 0.75)'},\n        name: 'Spot Price',\n      },\n      {\n        type: 'scatter',\n        x: spotDates,\n        y: [].concat(...pastData.map(x => x.settlements)),\n        mode: 'lines',\n        line: {color: 'rgba(243, 92, 16, 0.75)', dash: 'dot'},\n        name: 'Settlement',\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.forecasts)),\n        mode: 'lines',\n        line: {color: 'rgba(16, 54, 243, 0.5)'},\n        name: \"Forecast\",\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.previousForecasts)),\n        mode: 'lines',\n        line: {color: 'rgba(16, 54, 243, 0.5)', dash: 'dot'},\n        name: \"Forecast (Previous)\",\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.forecasts2E)),\n        mode: 'lines',\n        line: {color: 'rgba(16, 54, 243, 0.25)'},\n        name: \"2nd Engine Forecast\",\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.previousForecasts2E)),\n        mode: 'lines',\n        line: {color: 'rgba(16, 54, 243, 0.25)', dash: 'dot'},\n        name: \"2nd Engine Forecast (Previous)\",\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.FFAPrices)),\n        mode: 'lines',\n        line: {color: 'rgba(66, 248, 6, 0.8)'},\n        name: \"FFA Price\",\n      },\n      {\n        type: 'scatter',\n        x: forecastDates,\n        y: [].concat(...futureData.map(x => x.previousFFAPrices)),\n        mode: 'lines',\n        line: {color: 'rgba(66, 248, 6, 0.8)', dash: 'dot'},\n        name: \"FFA Price (Previous)\",\n      }\n    ]\n    return acc\n  }, {})\n}\n\nconst buildContractsData = function(date, tenors, dayData, previousDayData, graphData, commodities) {\n  const data = {}\n  for (let i = 0; i < commodities.length; i++) {\n    for (let contractMonth = 1; contractMonth <= 3; contractMonth++) {\n      const commodity = commodities[i]\n      const marketPriceKey = commodity + '_FFA_price_M' + contractMonth\n      const marketPrice = dayData[marketPriceKey]\n      const marketPriceDiff = diff(marketPriceKey, dayData, previousDayData)\n      const marketPriceDiffClass = diffToClass(marketPriceDiff)\n      const spotPriceKey = commodity + '_Spot_price'\n      const spotPrice = dayData[spotPriceKey]\n      const spotPriceDiff = diff(spotPriceKey, dayData, previousDayData)\n      const spotPriceDiffClass = diffToClass(spotPriceDiff)\n      const recommendation = dayData[commodity + '_Recommendation_M' + contractMonth]\n      const blocked = dayData[commodity + '_Blocked_M' + contractMonth]\n      const initialMargin = dayData[commodity + '_initial_margin_M' + contractMonth]\n      const maintenanceMargin = dayData[commodity + '_maint_margin_M' + contractMonth]\n      const gap = dayData[commodity + '_Gap_M' + contractMonth]\n      const forecast = dayData[commodity + '_forecast_engine_1_M' + contractMonth]\n      const callPut = recommendation == 'B' ? 'call' : 'put'\n      const U1StrikePrice = dayData[commodity + '_U1_Strike_price_' + callPut + '_M' + contractMonth]\n      const U2StrikePrice = dayData[commodity + '_U2_Strike_price_' + callPut + '_M' + contractMonth]\n      const U3StrikePrice = dayData[commodity + '_U3_Strike_price_' + callPut + '_M' + contractMonth]\n      const U1RawPremium = dayData[commodity + '_U1_Premium_M' + contractMonth]\n      const U2RawPremium = dayData[commodity + '_U2_Premium_M' + contractMonth]\n      const U3RawPremium = dayData[commodity + '_U3_Premium_M' + contractMonth]\n      const U1Premium = Math.round(U1RawPremium / 30.0)\n      const U2Premium = Math.round(U2RawPremium / 30.0)\n      const U3Premium = Math.round(U3RawPremium / 30.0)\n      const CV = dayData[commodity + '_CV_filter_M' + contractMonth]\n      const impliedVolatility = dayData[commodity + '_Imp_Vol_M' + contractMonth]\n      const makeGraph = (col, name) => ({\n          type: 'scatter',\n          x: graphData.toArray('date'),\n          y: graphData.toArray(col),\n          name\n      })\n      const marketPricesGraph = [\n        makeGraph(commodity + '_FFA_price_M' + contractMonth, 'Market Price'),\n        makeGraph(commodity + '_Spot_price', 'Spot Price')\n      ]\n      const CVGraph = [\n        makeGraph(commodity + '_CV_filter_M' + contractMonth, 'CV Value')\n      ]\n      const makePayoffGraph = function(strikeLevel, premium, strikePrice, width=4000) {\n        const x = []\n        for (let i = marketPrice - width; i < marketPrice + width; i += 250) {\n          x.push(i)\n        }\n        const compute = function(x) {\n          let value = premium\n          if ((x > strikePrice && recommendation == \"B\") ||\n              (x < strikePrice && recommendation == \"S\")) {\n            value = Math.abs(x - strikePrice) + premium\n          }\n          return value\n        }\n        const y = x.map(compute)\n        const name = strikeLevel + ' Strike'\n        return {type: 'scatter', x, y, name}\n      }\n      const payoffGraph = [\n        makePayoffGraph(\"U1\", U1Premium, U1StrikePrice),\n        makePayoffGraph(\"U2\", U2Premium, U2StrikePrice),\n        makePayoffGraph(\"U3\", U3Premium, U3StrikePrice)\n      ]\n      let enginesData = []\n      const engineNames = ['Main Engine', '2nd Engine', '3rd Engine']\n      for (let i = 1; i <= 3; i++) {\n        const rec = dayData[commodity + '_recommendation_engine_' + i + '_M' + contractMonth]\n        const fcast = dayData[commodity + '_forecast_engine_' + i + '_M' + contractMonth]\n        const ng_gap = dayData[commodity + '_gap_engine_' + i + '_M' + contractMonth]\n        const recs = {\n          B: 'Buy',\n          S: 'Sell',\n          N: 'Do Nothing'\n        }\n        if (rec) {\n          enginesData.push({\n            name: engineNames[i-1],\n            recommendation: recs[rec],\n            forecast: Math.round(fcast),\n            gap: ng_gap && ng_gap.toFixed(2)\n          })\n        }\n      }\n      data[commodities[i] + '_M' + contractMonth] = {\n        contract: 'M' + contractMonth,\n        commodity: commodities[i],\n        month: getTenorForDateAndMonth(tenors, date, contractMonth).month,\n        date,\n        recommendation,\n        blocked,\n        initialMargin,\n        maintenanceMargin,\n        gap,\n        forecast,\n        rawRecommendation: recommendation,\n        recommendationClass: recommendationToClass(recommendation),\n        marketPrice,\n        marketPriceDiff,\n        marketPriceDiffClass,\n        spotPrice,\n        spotPriceDiff,\n        spotPriceDiffClass,\n        U1StrikePrice,\n        U2StrikePrice,\n        U3StrikePrice,\n        U1Premium,\n        U2Premium,\n        U3Premium,\n        CV,\n        impliedVolatility,\n        payoffGraph,\n        marketPricesGraph,\n        CVGraph,\n        enginesData\n      }\n    }\n  }\n  return data\n}\n\nexport const buildDailyData = function(date, data) {\n  const columns = [\n    'index', 'date', ...data.dashboard.columns\n  ]\n  let dates = Object.keys(data.dashboard.dates).sort()\n  const rows = data.dashboard.data.map((row, ix) => {\n    return [ix, dates[ix], ...row]\n  })\n  let df = new DataFrame(rows, columns)\n  if (data.type == 'routes') {\n    // Work around broken data on baltic holidays\n    const key = `${data.dashboard.config.routes[0].name}_W1_spot_price`\n    df = df.filter(\n      row => row.get(key)\n    )\n    dates = df.toArray('date')\n  }\n  if (data.type == 'equities') {\n    // expand the columns\n    df = df.withColumn(\n      'account_value',\n      row => (\n        data.configuration.parameters.start_capital +\n        row.get('net_outcome') +\n        row.get('m2m')\n      )\n    ).withColumn(\n      'available_capital',\n      row => (\n        data.configuration.parameters.start_capital +\n        row.get('m2m')\n      )\n    ).withColumn(\n      'pct_avail_cap_used',\n      row => (\n        (row.get('available_capital') - row.get('balance')) /\n        row.get('available_capital')\n      )\n    )\n  }\n  const effectiveDate = closestDate(df, date)\n  const dayData = df.filter(row => row.get('date') == effectiveDate).toCollection()[0]\n  const previousDayData = df.filter(row => row.get('index') == dayData.index-1).toCollection()[0]\n  const firstDate = dates[0]\n\n  const record = {\n    date: effectiveDate,\n    lastDate: dates[dates.length-1],\n    dayData,\n    previousDayData,\n    forecasts: {}\n  }\n  if (data.type == 'routes') {\n    const ftrColumns = [\n      'index', 'date', ...data.dashboard.features.columns\n    ]\n    const ftrDates = Object.keys(data.dashboard.features.dates).sort()\n    const ftrRows = data.dashboard.features.data.map((row, ix) => {\n      return [ix, ftrDates[ix], ...row]\n    })\n    const ftrDf = new DataFrame(ftrRows, ftrColumns).filter(row => row.get('date') <= effectiveDate)\n    const routes = []\n    for (const route of data.dashboard.config.routes) {\n        const routeData = {\n          'name': route.name,\n        }\n        const weeks = []\n        for (const week of route.weeks) {\n          weeks.push({\n            number: week,\n            d3ForecastData: buildRouteD3ForecastData(\n              df,\n              route.name,\n              week,\n              effectiveDate\n            ),\n            d3FeaturesData: buildRouteD3FeaturesData(\n              ftrDf,\n              route.name,\n              week,\n              effectiveDate\n            ),\n            d3PerformanceData: buildRouteD3PerformanceData(\n              df,\n              route.name,\n              week,\n              effectiveDate\n            ),\n        })\n        routeData['weeks'] = weeks\n      }\n      routes.push(routeData)\n    }\n    record['routes'] = routes\n  } else if (data.type == 'viterra') {\n    const ftrColumns = [\n      'index', 'date', ...data.dashboard.features.columns\n    ]\n    const ftrDates = Object.keys(data.dashboard.features.dates).sort()\n    const ftrRows = data.dashboard.features.data.map((row, ix) => {\n      return [ix, ftrDates[ix], ...row]\n    })\n    const ftrDf = new DataFrame(ftrRows, ftrColumns).filter(row => row.get('date') <= effectiveDate)\n    const forecasts = {}\n    for (const forecast of data.dashboard.config.forecasts) {\n      const forecastData = {\n        'name': forecast.name,\n        'label': viterraLabels[forecast.name],\n        'week': forecast.week,\n        'd3ForecastData': buildViterraD3ForecastData(\n          df,\n          forecast.name,\n          forecast.week,\n          effectiveDate\n        ),\n        'd3FeaturesData': buildViterraD3FeaturesData(\n          ftrDf,\n          forecast.name,\n          forecast.week,\n          effectiveDate\n        ),\n        'd3PerformanceData': buildViterraD3PerformanceData(\n          df,\n          forecast.name,\n          forecast.week,\n          effectiveDate\n        ),\n      }\n      forecasts[forecast.name] = forecastData\n    }\n    record['forecasts'] = forecasts\n  } else if (data.type == 'commodities') {\n    const graphData = df.filter(row => row.get('date') <= effectiveDate).tail(90)\n    const commodities = Object.keys(data.configuration.commodities)\n    const tenors = buildTenorsData(data.dashboard.tennors)\n    record['tenor'] = tenors\n    record['forecastGraphs'] = buildForecastGraphs(effectiveDate, df, dayData, previousDayData, tenors, commodities)\n    record['openTradesCardsData'] = buildOpenTradesCardsData(dayData, commodities)\n    record['realizedCardData'] =  buildRealizedCardData(dayData, firstDate)\n    record['contractsData'] = buildContractsData(effectiveDate, tenors, dayData, previousDayData, graphData, commodities)\n  } else if (data.type == 'equities') {\n    const dateIndex = data.dashboard.dates[effectiveDate]\n    record['exitIndications'] = buildExitIndicationsTableData(\n      data.dashboard.exit_indications[dateIndex]\n    )\n    record['recommendations'] = buildRecommendationsTableData(\n      data.dashboard.recommendations[dateIndex]\n    )\n    record['portfolioTableData'] = buildPortfolioTableData(\n      dayData, previousDayData\n    )\n    record['m2mTableData'] = buildM2MTableData(\n      dayData, previousDayData\n    )\n    const filteredDf = df.filter(row => row.get('date') <= effectiveDate)\n    record['accountValuePlotData'] = buildAccountValuePlotData(\n      filteredDf\n    )\n    record['closePricesPlotData'] = buildClosePricesPlotData(\n      filteredDf\n    )\n  }\n  return record\n}\n","import 'mutationobserver-shim'\nimport Vue from 'vue'\nimport './plugins/bootstrap-vue'\nimport App from './App.vue'\nimport './registerServiceWorker'\nimport router from './router'\nimport VueSocketIO from 'vue-socket.io-extended'\nimport { io } from 'socket.io-client'\nimport store from './store'\nimport { sync } from 'vuex-router-sync'\nimport { domain, clientId, audience } from \"../auth_config.json\";\nimport { Auth0Plugin } from \"./auth\";\nimport './assets/tbi.css'\nimport VueMoment from 'vue-moment'\n\nVue.use(VueMoment);\n\n// Hold the router state in the store\nsync(store, router);\n\nVue.use(Auth0Plugin, {\n  domain,\n  clientId,\n  audience,\n  onRedirectCallback: appState => {\n    router.push(\n      appState && appState.targetUrl\n        ? appState.targetUrl\n        : window.location.pathname\n    );\n  }\n});\n\nconst connection = process.env.NODE_ENV === 'development' ? 'http://localhost:8000' : window.location.origin;\nconst socket = io(connection, {\n  reconnection: true,\n  reconnectionDelay: 500,\n  maxReconnectionAttempts: Infinity,\n  path: \"/socket.io\",\n  query: {\n    uuid: store.state.uuid\n  }\n})\nconst debug = process.env.NODE_ENV === 'development';\nif (debug) {\n  localStorage.debug = '*'\n}\n\nVue.use(VueSocketIO, socket, {\n  store,\n  actionPrefix: 'SOCKET_',\n  eventToActionTransformer: (actionName) => actionName\n});\n\nVue.config.productionTip = false\n\nconst vue = new Vue({\n  router,\n  render: h => h(App),\n  store,\n  watch: {\n    '$auth.isAuthenticated': async function(value) {\n      if (value) {\n        this.$store.dispatch('login');\n      }\n    }\n  }\n}).$mount('#app')\nif (process.env.NODE_ENV == 'development') {\n  window._vue = vue;\n}\n","import DataError from '@/components/Error.vue';\nimport Loading from '@/components/Loading.vue';\nimport NotAsked from '@/components/NotAsked.vue';\n\nexport const wrap = component => ({\n  name: 'RemoteData',\n  render: function(createElement) {\n    return this.$store.state.daily_data.cata({\n      'NotAsked': () => createElement(NotAsked, [],),\n      'Loading': () => createElement(Loading, []),\n      'Error': e => createElement(DataError, {props: {error: e}}),\n      'Success': d => createElement(\n        component,\n        {\n          props: {\n            name: this.$store.state.route.params.name,\n            slug: this.$store.state.route.params.slug,\n            data: d,\n            commodities: this.$store.state.commodities\n          }\n        }\n      )\n    })\n  }\n})\n\nexport const wrapPositions = component => ({\n  name: 'RemoteData',\n  render: function(createElement) {\n    return this.$store.state.positions_data.cata({\n      'NotAsked': () => createElement(NotAsked, [],),\n      'Loading': () => createElement(Loading, []),\n      'Error': e => createElement(DataError, {props: {error: e}}),\n      'Success': d => createElement(\n        component,\n        {\n          props: {\n            data: d\n          }\n        }\n      )\n    })\n  }\n})\n\nexport const wrapOceanBoltAIS = component => ({\n  name: 'RemoteData',\n  render: function(createElement) {\n    return this.$store.state.oceanbolt_ais_data.cata({\n      'NotAsked': () => createElement(NotAsked, [],),\n      'Loading': () => createElement(Loading, []),\n      'Error': e => createElement(DataError, {props: {error: e}}),\n      'Success': d => createElement(\n        component,\n        {\n          props: {\n            data: d\n          }\n        }\n      )\n    })\n  }\n})\n"],"sourceRoot":""}