final code

.ipynb_checkpoints/dashboard-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

.ipynb_checkpoints/efficieny_frontier_rch-checkpoint.ipynb

@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'quandl'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-2-076b88a2f767>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# import needed modules\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mquandl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'quandl'"
+     ]
+    }
+   ],
+   "source": [
+    "# import needed modules\n",
+    "import quandl\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'quandl' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-3-2aa8bb83b75a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# get adjusted closing prices of 5 selected companies with Quandl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mquandl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mApiConfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'1tETqu7ZxhRNAZ2sWNVS'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mselected\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'CNP'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'F'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'WMT'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'GE'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TSLA'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m data = quandl.get_table('WIKI/PRICES', ticker = selected,\n\u001b[0;32m      5\u001b[0m                         \u001b[0mqopts\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m \u001b[1;34m'columns'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'ticker'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'adj_close'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'quandl' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# get adjusted closing prices of 5 selected companies with Quandl\n",
+    "quandl.ApiConfig.api_key = '1tETqu7ZxhRNAZ2sWNVS'\n",
+    "selected = ['CNP', 'F', 'WMT', 'GE', 'TSLA']\n",
+    "data = quandl.get_table('WIKI/PRICES', ticker = selected,\n",
+    "                        qopts = { 'columns': ['date', 'ticker', 'adj_close'] },\n",
+    "                        date = { 'gte': '2014-1-1', 'lte': '2016-12-31' }, paginate=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'data' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-4-3853dc989bbf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# reorganise data pulled by setting date as index with\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# columns of tickers and their corresponding adjusted prices\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mclean\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'date'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mtable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mclean\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'ticker'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'data' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "# reorganise data pulled by setting date as index with\n",
+    "# columns of tickers and their corresponding adjusted prices\n",
+    "clean = data.set_index('date')\n",
+    "table = clean.pivot(columns='ticker')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# calculate daily and annual returns of the stocks\n",
+    "returns_daily = table.pct_change()\n",
+    "returns_annual = returns_daily.mean() * 250"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get daily and covariance of returns of the stock\n",
+    "cov_daily = returns_daily.cov()\n",
+    "cov_annual = cov_daily * 250"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# empty lists to store returns, volatility and weights of imiginary portfolios\n",
+    "port_returns = []\n",
+    "port_volatility = []\n",
+    "stock_weights = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the number of combinations for imaginary portfolios\n",
+    "num_assets = len(selected)\n",
+    "num_portfolios = 50000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# populate the empty lists with each portfolios returns,risk and weights\n",
+    "for single_portfolio in range(num_portfolios):\n",
+    "    weights = np.random.random(num_assets)\n",
+    "    weights /= np.sum(weights)\n",
+    "    returns = np.dot(weights, returns_annual)\n",
+    "    volatility = np.sqrt(np.dot(weights.T, np.dot(cov_annual, weights)))\n",
+    "    port_returns.append(returns)\n",
+    "    port_volatility.append(volatility)\n",
+    "    stock_weights.append(weights)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a dictionary for Returns and Risk values of each portfolio\n",
+    "portfolio = {'Returns': port_returns,\n",
+    "             'Volatility': port_volatility}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extend original dictionary to accomodate each ticker and weight in the portfolio\n",
+    "for counter,symbol in enumerate(selected):\n",
+    "    portfolio[symbol+' Weight'] = [Weight[counter] for Weight in stock_weights]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# make a nice dataframe of the extended dictionary\n",
+    "df = pd.DataFrame(portfolio)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get better labels for desired arrangement of columns\n",
+    "column_order = ['Returns', 'Volatility'] + [stock+' Weight' for stock in selected]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reorder dataframe columns\n",
+    "df = df[column_order]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the efficient frontier with a scatter plot\n",
+    "plt.style.use('seaborn')\n",
+    "df.plot.scatter(x='Volatility', y='Returns', figsize=(10, 8), grid=True)\n",
+    "plt.xlabel('Volatility (Std. Deviation)')\n",
+    "plt.ylabel('Expected Returns')\n",
+    "plt.title('Efficient Frontier')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}