FI_tests.ipynb

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    "Notebook for hacking and development on the Fispy project."
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   ],
   "source": [
    "import numpy as np\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "from bokeh.charts import Bar, output_file, show\n",
    "from bokeh.charts.attributes import cat, color\n",
    "from bokeh.charts.operations import blend\n",
    "from bokeh.charts.utils import df_from_json\n",
    "from bokeh.plotting import output_notebook, Figure\n",
    "from bokeh.models import ColumnDataSource, HoverTool, TapTool, Quad\n",
    "\n",
    "#from fispy.fispy import Asset, Portfolio\n",
    "\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Testing for adding real stock data functions to investment assets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
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   "source": [
    "from pandas_datareader import data, wb\n",
    "\n",
    "    \n",
    "class Investment(object):\n",
    "    def __init__(self, d):\n",
    "        self.value = None\n",
    "        self.roi = None\n",
    "        self.dividend = None\n",
    "        self.month_index = 0\n",
    "        self.period = d['period']\n",
    "        self.symbols = d['symbols']\n",
    "        self.historical = []\n",
    "    \n",
    "    def buyStock(self, addQuantity):\n",
    "        self.value += addQuantity\n",
    "    \n",
    "    def dividend_reinvest(self):\n",
    "        self.value += self.dividend\n",
    "    \n",
    "    def appreciate(self):\n",
    "        self.dividend = self.value * self.roi\n",
    "\n",
    "    def add_historical_growth(self):\n",
    "        delta = self.historical.values[self.month_index + 1]/self.historical.values[self.month_index]\n",
    "        self.value = self.value * delta\n",
    "        self.month_index += 1\n",
    "    \n",
    "    def fetch_real_stock_data(self):\n",
    "        ls_key = 'Adj Close' \n",
    "        end = dt.datetime(year=dt.datetime.now().year,\n",
    "                            month=dt.datetime.now().month, day=1)\n",
    "        start = end - relativedelta(months=self.period + 2) \n",
    "        f = data.DataReader(self.symbols, 'yahoo', start, end)\n",
    "        cleanData = f.ix[ls_key]\n",
    "        df = pd.DataFrame(cleanData)\n",
    "        self.historical = df.asfreq('m', method='pad',how='end')  "
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "t = Investment(d=d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t.fetch_real_stock_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t.add_historical_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t.value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fac = t.historical.values[1]/t.historical.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fac * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "fac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t.historical.values[1], t.historical.values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a1 = Asset(**{'kind':'job','monthly_income':1.5, 'monthly_expenses':0.7})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a1 = Asset(**{'kind':'job','monthly_income':1.5, 'monthly_expenses':0.7})    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Asset and Portfolio classes for hacking/testing ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class Asset(object):\n",
    "    def __init__(self, **kwargs):\n",
    "        self.kind = kwargs.get('kind')\n",
    "        assert self.kind.lower() in [None, 'real estate', 'stocks', 'job', 'cash']\n",
    "        self.monthly_income = kwargs.get('monthly_income')\n",
    "        self.monthly_expenses = kwargs.get('monthly_expenses')\n",
    "        self.start_date = kwargs.get('start_date', dt.datetime.now().date())\n",
    "        self.end_date = kwargs.get('end_date')\n",
    "        self.debt = kwargs.get('debt')\n",
    "        self.value = kwargs.get('value')\n",
    "        self.max_cash = kwargs.get('max_cash') \n",
    "        self.monthly_repayment = kwargs.get('monthly_repayment')\n",
    "        self.pay_debt_asap = kwargs.get('pay_debt_asap')\n",
    "    \n",
    "    def __str__(self):\n",
    "        return str(\"Asset = {0}\".format(self.kind))\n",
    "\n",
    "    def __iter__(self):\n",
    "        for key, value in sorted(a1.__dict__.items()):\n",
    "            yield key, value\n",
    "    \n",
    "\n",
    "class Portfolio(object):\n",
    "    \"\"\" Operates on lists of assets. Can add assets individually\n",
    "    with addNewAsset() function, or can initilize object with a\n",
    "    list of asset objects.\n",
    "\n",
    "    :param Asset: n number of Asset objects\n",
    "    :param prd: integer indicating number of months to run  \n",
    "    :returns: A Portfolio instance\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, *assets, prd=60):\n",
    "        self.assets = []\n",
    "        print(\"{0} items passed\".format(len(assets)))\n",
    "        if assets:\n",
    "            for asset in assets:\n",
    "                self.assets.append(asset)\n",
    "            self.date = min([asset.start_date for asset in self.assets])\n",
    "        self.monthly_income = 0\n",
    "        self.monthly_expenses = 0\n",
    "        self.net_investments = 0\n",
    "        self.debt = 0\n",
    "        self.prd = prd\n",
    "        self.cash = 0\n",
    "        self._temporary_capitol = None # \n",
    "        self.networth = 0\n",
    "        self.fi = False\n",
    "        self.buy_property_threshold = 80\n",
    "        self.passive_income = 0\n",
    "    \n",
    "    def buy_property(self, asset):\n",
    "        \"\"\"Subtract value of asset from investments\"\"\"\n",
    "        print('placeholder function Portfolio.buy_property()')\n",
    "        self.sell_shares(amount=asset.value - asset.debt)\n",
    "        self.add_new_asset(new_asset=asset)\n",
    "        \n",
    "    def sell_shares(self, amount):\n",
    "        \"\"\"Need a method to handel liquidating a share portfolio\"\"\"\n",
    "        # gather up all stock assets\n",
    "        # remove required value from stocks and place in self._temporary_capitol\n",
    "        print(\"placeholder function in Portfolio.sell_shares()\")\n",
    "        self._temporary_capitol = amount\n",
    "        for asset in self.assets:\n",
    "            if asset.kind.lower() == 'stocks':\n",
    "                if asset.value > amount:\n",
    "                    asset.value -= amount\n",
    "                    self._temporary_capitol = amount\n",
    "                else:\n",
    "                    # if one asset cant meet all requirements look for more\n",
    "                    amount -= asset.value\n",
    "                    asset.value = 0\n",
    "    \n",
    "    def check_fi(self):\n",
    "        \"\"\"Check if FI has been reached. Point at which monthly incoming\n",
    "        is greater than monthly outgoings before counting wages from jobs.\n",
    "        \"\"\"\n",
    "        negative = 0\n",
    "        positive = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.monthly_expenses:\n",
    "                negative += asset.monthly_expenses\n",
    "            if asset.monthly_repayment:\n",
    "                negative += asset.monthly_repayment\n",
    "            if asset.kind != 'job' and asset.monthly_income:\n",
    "                positive += asset.monthly_income\n",
    "        self.passive_income = positive\n",
    "        if positive > negative:\n",
    "            self.fi = True\n",
    "        else:\n",
    "            self.fi = False\n",
    "    \n",
    "    def summary(self):\n",
    "        \"\"\"Print a summary of the Portfolio instance values of Income, Cash\n",
    "        Debt, Investments and Net worth.\n",
    "\n",
    "        :param self:\n",
    "        :returns: A series of string items\n",
    "        \"\"\"\n",
    "        print(\"--Summary--\\nIncome: {0:3.2f} \\nCash: {1:3.2f}\".format(\n",
    "                self.monthly_income, self.cash))\n",
    "        print(\"Debt: {0:3.2f}\".format(self.debt))\n",
    "        print(\"Investments: {0:3.2f}\".format(self.net_investments))\n",
    "        print(\"Net worth: {0:3.2f}\".format(self.networth))\n",
    "        print(\"Passive income: {0:3.2f}\".format(self.passive_income))\n",
    "\n",
    "    def add_new_asset(self, new_asset):\n",
    "        \"\"\"Add a new Asset() obect to an existing Portfolio instance.\n",
    "        This will be useful for modifying established portfolios once this is\n",
    "        running as a web app.\n",
    "\n",
    "        :param Asset(): Instance of Asset() object\n",
    "        :returns: Updates Portfolio object\n",
    "        \"\"\"\n",
    "        self.assets.append(new_asset)\n",
    "\n",
    "    def monthly_ingres(self):\n",
    "        \"\"\"Calculate avaiable monthly cash based on monthly_income from all\n",
    "        Asset() objects of a portfolio.\n",
    "\n",
    "        :param self.assets: Sums asset.monthly_income across asset objects\n",
    "        :returns self.monthly_income: float\n",
    "        \"\"\"\n",
    "        tmp_income = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.monthly_income:\n",
    "                tmp_income += asset.monthly_income\n",
    "        self.monthly_income = tmp_income\n",
    "\n",
    "    def monthly_egres(self):\n",
    "        \"\"\"Calculate and update the monthly outgoing money by summing\n",
    "        monthly_expenses from asset items.\n",
    "\n",
    "        :param self.assets.monthly_expenses: Sums list of asset.monthly_expense\n",
    "        :returns self.monthly_expenses: float\n",
    "        \"\"\"\n",
    "        tmp_expenses = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.monthly_expenses:\n",
    "                tmp_expenses += asset.monthly_expenses\n",
    "        self.monthly_expenses = tmp_expenses\n",
    "        assert tmp_expenses < self.monthly_income, \"Error: spending too much\"\n",
    "        self.monthly_income -= tmp_expenses\n",
    "\n",
    "    def investment_portfolio(self):\n",
    "        \"\"\"Start of portflio methods.\"\"\"\n",
    "        tmp_net = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.kind.lower() == 'stocks':\n",
    "                tmp_net += asset.value\n",
    "                asset.value *= 1.00333\n",
    "                if self.monthly_income > 0:\n",
    "                    asset.value += self.monthly_income\n",
    "                    self.monthly_income = 0\n",
    "        self.net_investments = tmp_net\n",
    "\n",
    "    def count_cash(self):\n",
    "        \"\"\"Examines assets for 'cash' type asset and does various actions.\n",
    "        nb. this function only expects to find one cash asset\n",
    "\n",
    "        :param self.asset.value: float\n",
    "        :results self.cash: float\n",
    "        \"\"\"\n",
    "        max_cash = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.kind.lower() == 'cash':\n",
    "                self.cash = asset.value\n",
    "                max_cash = asset.max_cash\n",
    "                if self.cash < max_cash and self.monthly_income > 0:\n",
    "                    asset.value += self.monthly_income\n",
    "                    self.monthly_income = 0.0\n",
    "                    self.cash = asset.value\n",
    "\n",
    "    def monthly_repay(self):\n",
    "        \"\"\"Examines a flag (asset.pay_debt_asap) from asset objects with debt.\n",
    "        If an asset has the flag set to True, it will take as much as possible\n",
    "        from the montly avaiable money (after expenses account for), and spend\n",
    "        it on minimizing the debt. The minimum repayments should be covered\n",
    "        for all assets before additional payments are considered.\n",
    "\n",
    "        :param self.asset:\n",
    "        :returns self.asset.debt: float\n",
    "        \"\"\"\n",
    "        error1 = \"Error: cant meet monthly repayment :(\"\n",
    "        for asset in self.assets:\n",
    "            if asset.debt and asset.monthly_repayment:\n",
    "                assert self.monthly_income > asset.monthly_repayment, error1\n",
    "                # Pay all minimum debts for the month\n",
    "                if (asset.debt - asset.monthly_repayment) <= 0.0:\n",
    "                    # if this is the last payment, remove the debt\n",
    "                    self.monthly_income -= asset.debt\n",
    "                    asset.debt = None\n",
    "                    asset.monthly_repayment = None\n",
    "                else:\n",
    "                    # otherwise, just take the minimum and dont worry\n",
    "                    asset.debt -= asset.monthly_repayment\n",
    "                    self.monthly_income -= asset.monthly_repayment\n",
    "        for asset in self.assets:\n",
    "            if asset.debt and asset.pay_debt_asap and self.monthly_income > 0.0:\n",
    "                asset.debt -= self.monthly_income\n",
    "                self.monthly_income = 0.0\n",
    "\n",
    "    def monthly_debt(self):\n",
    "        \"\"\"Calculate debt each month. Examines the Assets held in the Portfolio\n",
    "        object, and sums the debt. Assigns this value to self.debt.\n",
    "\n",
    "        :param self: Examines Portfolio objects assets.debt floats\n",
    "        :returns self.debt: float\n",
    "        \"\"\"\n",
    "        tmp_debt = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.debt:\n",
    "                tmp_debt += asset.debt\n",
    "        self.debt = tmp_debt\n",
    "\n",
    "    def calc_net_worth(self):\n",
    "        \"\"\"Calculates Net worth by summing value of assets and subtracting debt.\n",
    "\n",
    "        :param self: Examines attributes of Portfolio object\n",
    "        :returns: self.networth in Portfolio object\n",
    "        \"\"\"\n",
    "        net_value = 0\n",
    "        for asset in self.assets:\n",
    "            if asset.value:\n",
    "                net_value += asset.value\n",
    "        self.networth = net_value - self.debt\n",
    "\n",
    "    def update_monthly(self):\n",
    "        self.date = self.date + relativedelta(months=1)\n",
    "        if self.net_investments > self.buy_property_threshold:\n",
    "            print('triggered buy property')\n",
    "            self.buy_property(asset=Asset(**{'kind': 'real estate',\n",
    "                                       'debt': 70,\n",
    "                                       'value':150,\n",
    "                                       'monthly_repayment':0.5,\n",
    "                                       'monthly_income':0.8,\n",
    "                                       'start_date': self.date,\n",
    "                                       'pay_debt_asap':True}))\n",
    "        self.monthly_ingres()\n",
    "        self.monthly_egres()\n",
    "        self.monthly_repay()\n",
    "        self.monthly_debt()\n",
    "        self.count_cash()\n",
    "        self.investment_portfolio()\n",
    "        self.calc_net_worth()\n",
    "        self.check_fi()\n",
    "\n",
    "    def quad_positions(self, left, right, bottom, top, color):\n",
    "        \"\"\"Assign values to the left, right, bottom, and top position\n",
    "        labels and a color value for the Bokeh Quad glyph. Uses two\n",
    "        diffrent colors depending on if FI has been reached.\n",
    "\n",
    "        :param self, left, right, bottom, top, color:\n",
    "        :returns: Assigns values to the list items.\n",
    "        \"\"\"\n",
    "        # Add debt marker\n",
    "        left.append(self.date)\n",
    "        right.append(self.date + relativedelta(months=1))\n",
    "        top.append(0.0)\n",
    "        bottom.append(self.debt * -1)\n",
    "        if self.fi:\n",
    "            color.append('#e65c00')\n",
    "        else:\n",
    "            color.append('#ff944d')\n",
    "        # Add cash marker\n",
    "        left.append(self.date)\n",
    "        right.append(self.date + relativedelta(months=1))\n",
    "        bottom.append(0.0)\n",
    "        top.append(self.cash)\n",
    "        if self.fi:\n",
    "            color.append('#008000')\n",
    "        else:\n",
    "            color.append('#00e600')\n",
    "        # Add stock marker\n",
    "        left.append(self.date)\n",
    "        right.append(self.date + relativedelta(months=1))\n",
    "        bottom.append(self.cash)\n",
    "        top.append(self.cash + self.net_investments)\n",
    "        if self.fi:\n",
    "            color.append('#800080')\n",
    "        else:\n",
    "            color.append('#e600e6')\n",
    "        # Add net worth marker (including  primary property)\n",
    "        left.append(self.date)\n",
    "        right.append(self.date + relativedelta(months=1))\n",
    "        bottom.append(self.cash + self.net_investments)\n",
    "        top.append(self.networth)\n",
    "        if self.fi:\n",
    "            color.append('#75a3a3')\n",
    "        else:\n",
    "            color.append('#b3cccc')\n",
    "        return\n",
    "\n",
    "    def gen_quads(self):\n",
    "        \"\"\"For the current state of Portfolio properties generate quad plot\n",
    "        dataframe object to be consumed by Bokeh plots.\n",
    "\n",
    "        :param self: Examines attributes of Portfolio object\n",
    "        :returns: a pd.DataFrame object\n",
    "        \"\"\"\n",
    "        left = []\n",
    "        right = []\n",
    "        top = []\n",
    "        bottom = []\n",
    "        color = []\n",
    "        for i in range(self.prd):\n",
    "            self.update_monthly()\n",
    "            self.quad_positions(left=left, top=top, bottom=bottom,\n",
    "                                right=right, color=color)\n",
    "        return pd.DataFrame({'left': left, 'right': right, 'top': top,\n",
    "                            'bottom': bottom, 'color': color})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 items passed\n",
      "triggered buy property\n",
      "placeholder function Portfolio.buy_property()\n",
      "placeholder function in Portfolio.sell_shares()\n",
      "triggered buy property\n",
      "placeholder function Portfolio.buy_property()\n",
      "placeholder function in Portfolio.sell_shares()\n",
      "triggered buy property\n",
      "placeholder function Portfolio.buy_property()\n",
      "placeholder function in Portfolio.sell_shares()\n",
      "triggered buy property\n",
      "placeholder function Portfolio.buy_property()\n",
      "placeholder function in Portfolio.sell_shares()\n",
      "--Summary--\n",
      "Income: 0.00 \n",
      "Cash: 30.10\n",
      "Debt: 0.00\n",
      "Investments: 35.69\n",
      "Net worth: 821.41\n",
      "Passive income: 3.20\n"
     ]
    }
   ],
   "source": [
    "# Initilise with list of assets...\n",
    "d1 = {'kind': 'job', \n",
    "      'monthly_income': 1.5, \n",
    "      'start_date': dt.date(2016, 6, 1)}\n",
    "\n",
    "d2 = {'kind': 'job',\n",
    "      'monthly_income': 1.5,\n",
    "      'monthly_expenses': 0.7,\n",
    "      'start_date': dt.date(2016, 12, 1)}\n",
    "\n",
    "d3 = {'kind': 'real estate',\n",
    "      'debt': 70,\n",
    "      'value':150,\n",
    "      'monthly_repayment':0.5,\n",
    "      'start_date': dt.date(2016, 6, 1),\n",
    "      'pay_debt_asap':True}\n",
    "\n",
    "d4 = {'kind': 'stocks',\n",
    "      'value': 15,\n",
    "      'symbol': 'NYSE:BRK.B'}\n",
    "\n",
    "d5 = {'kind': 'cash',\n",
    "      'value': 15,\n",
    "      'max_cash':30}\n",
    "\n",
    "test2 = Portfolio(Asset(**d1), Asset(**d2), Asset(**d3), \n",
    "                  Asset(**d4), Asset(**d5), prd=200)\n",
    "\n",
    "#test2.monthlyIncome()\n",
    "#test2.monthlyExpenses()\n",
    "#test2.monthlyDebt()\n",
    "#print(test2.monthly_income, test2.monthly_expenses, test2.debt)\n",
    "#print(test2.date)\n",
    "bdf_quad = test2.gen_quads()\n",
    "test2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 items passed\n",
      "debt None\n",
      "end_date None\n",
      "kind job\n",
      "max_cash None\n",
      "monthly_expenses 0.7\n",
      "monthly_income 1.5\n",
      "monthly_repayment None\n",
      "pay_debt_asap None\n",
      "start_date 2016-06-12\n",
      "value None\n"
     ]
    }
   ],
   "source": [
    "a1 = Asset(**{'kind':'job','monthly_income':1.5, 'monthly_expenses':0.7})\n",
    "p1 = Portfolio(a1)\n",
    "\n",
    "for k,v in a1:\n",
    "    print(k,v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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       "            var render_items = [{\"docid\":\"1403131d-c0f2-4f7e-a6cf-b053c0aa19b6\",\"elementid\":\"2609ac52-b652-44b9-b38c-8d3aaee0b7ba\",\"modelid\":\"f23a5afa-9d5c-4014-a1fe-0c83a58f8542\",\"notebook_comms_target\":\"d19a695b-86d9-4991-8718-c3617773d537\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "        });\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      for (var i = 0; i < inline_js.length; i++) {\n",
       "        inline_js[i](window.Bokeh);\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<p><code>&lt;Bokeh Notebook handle for <strong>In[58]</strong>&gt;</code></p>"
      ],
      "text/plain": [
       "<bokeh.io._CommsHandle at 0x7fd9e2e74048>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "source=ColumnDataSource(bdf_quad)\n",
    "TOOLS=\"crosshair, pan, reset, resize, wheel_zoom\"\n",
    "\n",
    "plot = Figure(tools=TOOLS, x_axis_type='datetime', plot_height=500, \n",
    "              plot_width=700, title=\"FI chart\")\n",
    "\n",
    "#plot.add_tools(TapTool())\n",
    "plot.yaxis.axis_label = \"Val\"\n",
    "plot.xaxis.axis_label = \"Time\"\n",
    "\n",
    "renderer = plot.quad(source=source, left='left', right='right', \n",
    "                     bottom='bottom', top='top', color='color')\n",
    "\n",
    "#selected_q = Quad(fill_alpha=1, fill_color=\"green\", line_color=None)\n",
    "#nonselected_q = Quad(fill_alpha=0.2, fill_color=\"blue\", line_color=\"firebrick\")\n",
    "#renderer.selection_glyph = selected_q\n",
    "#renderer.nonselection_glyph = nonselected_q\n",
    "    \n",
    "show(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "d1 = {'kind': 'job', \n",
    "      'monthly_income': 1.5, \n",
    "      'start_date': dt.date(2016, 6, 1)}\n",
    "\n",
    "d2 = {'kind': 'job', \n",
    "      'monthly_income': 1.5, \n",
    "      'start_date': dt.date(2016, 6, 1)}\n",
    "\n",
    "\n",
    "test2 = Portfolio(Asset(**d1),Asset(**d2) )\n",
    "test2.monthlyIncome()\n",
    "\n",
    "assert test2.monthly_income == 3.0, \"Income incorrect\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tdate = dt.date(2003,2,1)\n",
    "start = dt.date(2000,1,1)\n",
    "end = dt.date(2099,1,1)\n",
    "in_month = tdate >= start and tdate <= end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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