Efficient_Portfolio_Recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import fedor_levin_toolkit as fltk\n",
    "import macro_indicators_toolkit as mitk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data load and cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = ['BTC',#Bitcoin\n",
    "    'DOGE',#Dogecoin\n",
    "    'LTC', #Litecoin\n",
    "    'ETH'  #Ethereum\n",
    "          ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = fltk.crypto_hist_returns_robin(my_list)\n",
    "returns = returns['2020-11-02':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>LTC</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-13 00:00:00+00:00</th>\n",
       "      <td>0.00764</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.01813</td>\n",
       "      <td>0.04804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-14 00:00:00+00:00</th>\n",
       "      <td>-0.00173</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02036</td>\n",
       "      <td>-0.04116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-15 00:00:00+00:00</th>\n",
       "      <td>0.00289</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.01247</td>\n",
       "      <td>-0.01854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-16 00:00:00+00:00</th>\n",
       "      <td>-0.00777</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.01232</td>\n",
       "      <td>0.13022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-17 00:00:00+00:00</th>\n",
       "      <td>0.00057</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-0.00748</td>\n",
       "      <td>0.07564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               BTC    DOGE      LTC      ETH\n",
       "Date                                                        \n",
       "2016-05-13 00:00:00+00:00  0.00764 0.00000  0.01813  0.04804\n",
       "2016-05-14 00:00:00+00:00 -0.00173 0.00000  0.02036 -0.04116\n",
       "2016-05-15 00:00:00+00:00  0.00289 0.00000  0.01247 -0.01854\n",
       "2016-05-16 00:00:00+00:00 -0.00777 0.00000 -0.01232  0.13022\n",
       "2016-05-17 00:00:00+00:00  0.00057 0.00000 -0.00748  0.07564"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [],
   "source": [
    "returns = returns['2020-11-02':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BTC</th>\n",
       "      <th>DOGE</th>\n",
       "      <th>LTC</th>\n",
       "      <th>ETH</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-11-02 00:00:00+00:00</th>\n",
       "      <td>-0.01573</td>\n",
       "      <td>-0.02289</td>\n",
       "      <td>-0.02787</td>\n",
       "      <td>-0.03287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-03 00:00:00+00:00</th>\n",
       "      <td>0.03376</td>\n",
       "      <td>0.00119</td>\n",
       "      <td>0.00380</td>\n",
       "      <td>0.00983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-04 00:00:00+00:00</th>\n",
       "      <td>0.00926</td>\n",
       "      <td>0.01031</td>\n",
       "      <td>0.01322</td>\n",
       "      <td>0.04140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-05 00:00:00+00:00</th>\n",
       "      <td>0.10575</td>\n",
       "      <td>0.03612</td>\n",
       "      <td>0.07580</td>\n",
       "      <td>0.03382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-11-06 00:00:00+00:00</th>\n",
       "      <td>-0.00244</td>\n",
       "      <td>0.04433</td>\n",
       "      <td>0.07444</td>\n",
       "      <td>0.09068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               BTC     DOGE      LTC      ETH\n",
       "Date                                                         \n",
       "2020-11-02 00:00:00+00:00 -0.01573 -0.02289 -0.02787 -0.03287\n",
       "2020-11-03 00:00:00+00:00  0.03376  0.00119  0.00380  0.00983\n",
       "2020-11-04 00:00:00+00:00  0.00926  0.01031  0.01322  0.04140\n",
       "2020-11-05 00:00:00+00:00  0.10575  0.03612  0.07580  0.03382\n",
       "2020-11-06 00:00:00+00:00 -0.00244  0.04433  0.07444  0.09068"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#returns = fltk.hist_returns(my_list)\n",
    "q = int(len(my_list))\n",
    "er = annualize_rets(returns, 252)\n",
    "cov = returns.cov()\n",
    "n = q #number of assets\n",
    "riskfree_rate = .02\n",
    "\n",
    "returns = returns.dropna()\n",
    "returns.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "((1+returns['2020-11-02':]).cumprod()).plot(figsize=(15,6)) #returns since 2020-11-02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "def annualize_rets(r, periods_per_year):\n",
    "    \"\"\"\n",
    "    Annualizes a set of returns\n",
    "    \"\"\"\n",
    "    compounded_growth = (1+r).prod()\n",
    "    n_periods = r.shape[0]\n",
    "    return compounded_growth**(periods_per_year/n_periods)-1\n",
    "\n",
    "\n",
    "def annualize_vol(r, periods_per_year):\n",
    "    \"\"\"\n",
    "    Annualizes the vol of a set of returns\n",
    "    \"\"\"\n",
    "    return r.std()*(periods_per_year**0.5)\n",
    "\n",
    "def portfolio_return(weights, returns):\n",
    "    \"\"\"\n",
    "    Computes the return on a portfolio from constituent returns and weights\n",
    "    weights are a numpy array or Nx1 matrix and returns are a numpy array or Nx1 matrix\n",
    "    \"\"\"\n",
    "    return weights.T @ returns\n",
    "\n",
    "\n",
    "def portfolio_vol(weights, covmat):\n",
    "    \"\"\"\n",
    "    Computes the vol of a portfolio from a covariance matrix and constituent weights\n",
    "    weights are a numpy array or N x 1 maxtrix and covmat is an N x N matrix\n",
    "    \"\"\"\n",
    "    return (weights.T @ covmat @ weights)**0.5\n",
    "\n",
    "def minimize_vol(target_return, er, cov):\n",
    "    \"\"\"\n",
    "    Returns the optimal weights that achieve the target return\n",
    "    given a set of expected returns and a covariance matrix\n",
    "    \"\"\"\n",
    "    n = er.shape[0]\n",
    "    init_guess = np.repeat(1/n, n)\n",
    "    bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!\n",
    "    # construct the constraints\n",
    "    weights_sum_to_1 = {'type': 'eq',\n",
    "                        'fun': lambda weights: np.sum(weights) - 1\n",
    "    }\n",
    "    return_is_target = {'type': 'eq',\n",
    "                        'args': (er,),\n",
    "                        'fun': lambda weights, er: target_return - portfolio_return(weights,er)\n",
    "    }\n",
    "    weights = minimize(portfolio_vol, init_guess,\n",
    "                       args=(cov,), method='SLSQP',\n",
    "                       options={'disp': False},\n",
    "                       constraints=(weights_sum_to_1,return_is_target),\n",
    "                       bounds=bounds)\n",
    "    return weights.x\n",
    "\n",
    "def msr(riskfree_rate, er, cov):\n",
    "    \"\"\"\n",
    "    Returns the weights of the portfolio that gives you the maximum sharpe ratio\n",
    "    given the riskfree rate and expected returns and a covariance matrix\n",
    "    \"\"\"\n",
    "    n = er.shape[0]\n",
    "    init_guess = np.repeat(1/n, n)\n",
    "    bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!\n",
    "    # construct the constraints\n",
    "    weights_sum_to_1 = {'type': 'eq',\n",
    "                        'fun': lambda weights: np.sum(weights) - 1\n",
    "    }\n",
    "    def neg_sharpe(weights, riskfree_rate, er, cov):\n",
    "        \"\"\"\n",
    "        Returns the negative of the sharpe ratio\n",
    "        of the given portfolio\n",
    "        \"\"\"\n",
    "        r = portfolio_return(weights, er)\n",
    "        vol = portfolio_vol(weights, cov)\n",
    "        return -(r - riskfree_rate)/vol\n",
    "    \n",
    "    weights = minimize(neg_sharpe, init_guess,\n",
    "                       args=(riskfree_rate, er, cov), method='SLSQP',\n",
    "                       options={'disp': False},\n",
    "                       constraints=(weights_sum_to_1,),\n",
    "                       bounds=bounds)\n",
    "    return weights.x\n",
    "\n",
    "def gmv(cov):\n",
    "    \"\"\"\n",
    "    Returns the weights of the Global Minimum Volatility portfolio\n",
    "    given a covariance matrix\n",
    "    \"\"\"\n",
    "    n = cov.shape[0]\n",
    "    return msr(0, np.repeat(1, n), cov)\n",
    "\n",
    "def optimal_weights(n_points, er, cov):\n",
    "    \"\"\"\n",
    "    Returns a list of weights that represent a grid of n_points on the efficient frontier\n",
    "    \"\"\"\n",
    "    target_rs = np.linspace(er.min(), er.max(), n_points)\n",
    "    weights = [minimize_vol(target_return, er, cov) for target_return in target_rs]\n",
    "    return weights\n",
    "\n",
    "def target_risk_contributions(target_risk, cov):\n",
    "    \"\"\"\n",
    "    Returns the weights of the portfolio that gives you the weights such\n",
    "    that the contributions to portfolio risk are as close as possible to\n",
    "    the target_risk, given the covariance matrix\n",
    "    \"\"\"\n",
    "    n = cov.shape[0]\n",
    "    init_guess = np.repeat(1/n, n)\n",
    "    bounds = ((0.0, 1.0),) * n # an N-tuple of 2-tuples!\n",
    "    # construct the constraints\n",
    "    weights_sum_to_1 = {'type': 'eq',\n",
    "                        'fun': lambda weights: np.sum(weights) - 1\n",
    "    }\n",
    "    def msd_risk(weights, target_risk, cov):\n",
    "        \"\"\"\n",
    "        Returns the Mean Squared Difference in risk contributions\n",
    "        between weights and target_risk\n",
    "        \"\"\"\n",
    "        w_contribs = risk_contribution(weights, cov)\n",
    "        return ((w_contribs-target_risk)**2).sum()\n",
    "    \n",
    "    weights = minimize(msd_risk, init_guess,\n",
    "                       args=(target_risk, cov), method='SLSQP',\n",
    "                       options={'disp': False},\n",
    "                       constraints=(weights_sum_to_1,),\n",
    "                       bounds=bounds)\n",
    "    return weights.x\n",
    "\n",
    "def equal_risk_contributions(cov):\n",
    "    \"\"\"\n",
    "    Returns the weights of the portfolio that equalizes the contributions\n",
    "    of the constituents based on the given covariance matrix\n",
    "    \"\"\"\n",
    "    n = cov.shape[0]\n",
    "    return target_risk_contributions(target_risk=np.repeat(1/n,n), cov=cov)\n",
    "\n",
    "def risk_contribution(w,cov):\n",
    "    \"\"\"\n",
    "    Compute the contributions to risk of the constituents of a portfolio, \n",
    "    given a set of portfolio weights and a covariance matrix\n",
    "    \"\"\"\n",
    "    total_portfolio_var = portfolio_vol(w,cov)**2\n",
    "    # Marginal contribution of each constituent\n",
    "    marginal_contrib = cov@w\n",
    "    risk_contrib = np.multiply(marginal_contrib,w.T)/total_portfolio_var\n",
    "    return risk_contrib\n",
    "\n",
    "def plot_ef(n_points, er, cov, style='.-', legend=False, show_cml=False, riskfree_rate=0, show_ew=False, show_gmv=False, show_erc=False):\n",
    "    \"\"\"\n",
    "    Plots the efficient frontier with recommended portfolios\n",
    "    \"\"\"\n",
    "    weights = optimal_weights(n_points, er, cov)\n",
    "    rets = [portfolio_return(w, er) for w in weights]\n",
    "    vols = [portfolio_vol(w, cov) for w in weights]\n",
    "    ef = pd.DataFrame({\n",
    "        \"Efficient frontier\": rets, \n",
    "        \"Volatility\": vols\n",
    "    })\n",
    "    ax = ef.plot.line(x=\"Volatility\", y=\"Efficient frontier\", style=style, legend=legend, figsize=(15,6))\n",
    "    plt.style.use('ggplot')\n",
    "    if show_cml:\n",
    "        ax.set_xlim(left = 0)\n",
    "        # get MSR\n",
    "        w_msr = msr(riskfree_rate, er, cov)\n",
    "        r_msr = portfolio_return(w_msr, er)\n",
    "        vol_msr = portfolio_vol(w_msr, cov)\n",
    "        # add CML\n",
    "        cml_x = [0, vol_msr]\n",
    "        cml_y = [riskfree_rate, r_msr]\n",
    "        ax.plot(cml_x, cml_y, color='green', marker='o', linestyle='dashed', linewidth=2, markersize=10, label='Max Sharpe Ratio')\n",
    "    if show_ew:\n",
    "        n = er.shape[0]\n",
    "        w_ew = np.repeat(1/n, n)\n",
    "        r_ew = portfolio_return(w_ew, er)\n",
    "        vol_ew = portfolio_vol(w_ew, cov)\n",
    "        # add EW\n",
    "        ax.plot([vol_ew], [r_ew], color='goldenrod', marker='o', markersize=10, label='Equal-Weighted')\n",
    "    if show_gmv:\n",
    "        w_gmv = gmv(cov)\n",
    "        r_gmv = portfolio_return(w_gmv, er)\n",
    "        vol_gmv = portfolio_vol(w_gmv, cov)\n",
    "        # add GMV\n",
    "        ax.plot([vol_gmv], [r_gmv], color='midnightblue', marker='o', markersize=10, label='Global Minimum Variance')\n",
    "    if show_erc:\n",
    "        w_erc = equal_risk_contributions(cov)\n",
    "        r_erc = portfolio_return(w_erc, er)\n",
    "        vol_erc = portfolio_vol(w_erc, cov)\n",
    "        # add ERC\n",
    "        ax.plot([vol_erc], [r_erc], color='lime', marker='o', markersize=10, label='Equal Risk Contribution')\n",
    "        plt.legend(loc='upper left', fontsize=10)\n",
    "        plt.title('Efficient Frontier')\n",
    "        \n",
    "        return ax\n",
    "\n",
    "def sharpe_ratio(r, riskfree_rate, periods_per_year):\n",
    "    \"\"\"\n",
    "    Computes the annualized sharpe ratio of a set of returns\n",
    "    \"\"\"\n",
    "    # convert the annual riskfree rate to per period\n",
    "    rf_per_period = (1+riskfree_rate)**(1/periods_per_year)-1\n",
    "    excess_ret = r - rf_per_period\n",
    "    ann_ex_ret = annualize_rets(excess_ret, periods_per_year)\n",
    "    ann_vol = annualize_vol(r, periods_per_year)\n",
    "    return ann_ex_ret/ann_vol\n",
    "\n",
    "def summary(returns, rf, periods):\n",
    "    '''\n",
    "    Returns a DataFrame that contains aggregated summary stats for the returns \n",
    "    '''\n",
    "    \n",
    "    ann_r = returns.aggregate(annualize_rets, periods_per_year=periods)\n",
    "    ann_vol = returns.aggregate(annualize_vol, periods_per_year=periods)\n",
    "    max_dd = returns.min()\n",
    "    ann_sr = returns.aggregate(sharpe_ratio, riskfree_rate=rf, periods_per_year=periods)\n",
    "    stat = pd.DataFrame({'Annualized Return': ann_r, \n",
    "                         'Annualized Vol': ann_vol, \n",
    "                         'Sharpe Ratio': ann_sr, \n",
    "                         'Max Drawdown': max_dd})\n",
    "    return stat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EW, ERC and GMV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Efficient Frontier'}, xlabel='Volatility'>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Equally Weighted %%%%%%%%%%%%%%%%%%%%%%\n",
    "def ew(n):\n",
    "    '''\n",
    "    Returns equal weights for a number of assets\n",
    "    '''\n",
    "    w_ew = np.repeat(1/n, n)\n",
    "    return w_ew\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "result_aggressive = pd.DataFrame({'ticker': er.index.to_list(),\n",
    "                       'weight': ew(n)})\n",
    "\n",
    "\n",
    "# Equal Risk Contribution %%%%%%%%%%%%%%%%%%%\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "result_balanced = pd.DataFrame({'ticker': er.index.to_list(),\n",
    "                       'weight': equal_risk_contributions(cov)})\n",
    "\n",
    "\n",
    "# Global Minimum Variance %%%%%%%%%%%%%%%%%%%\n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "result_conservative = pd.DataFrame({'ticker': er.index.to_list(),\n",
    "                       'weight': gmv(cov)})\n",
    "\n",
    "\n",
    "# final \n",
    "pd.set_option('display.float_format', lambda x: '%.5f' % x)\n",
    "final_weights = pd.DataFrame({'ticker': er.index.to_list(), \n",
    "                              'EW': ew(n), \n",
    "                              'ERC': equal_risk_contributions(cov),\n",
    "                              'GMV': gmv(cov), \n",
    "                              'MSR': msr(riskfree_rate, er, cov)})\n",
    "\n",
    "# 3 portfolios with MSR\n",
    "plot_ef(40, er, cov, style='.-', legend=True, show_cml=True, riskfree_rate=riskfree_rate, show_ew=True, show_gmv=True, show_erc=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>EW</th>\n",
       "      <th>ERC</th>\n",
       "      <th>GMV</th>\n",
       "      <th>MSR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BTC</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.37433</td>\n",
       "      <td>0.84213</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DOGE</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.06621</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LTC</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.25833</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETH</td>\n",
       "      <td>0.25000</td>\n",
       "      <td>0.30113</td>\n",
       "      <td>0.15787</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker      EW     ERC     GMV     MSR\n",
       "0    BTC 0.25000 0.37433 0.84213 0.00000\n",
       "1   DOGE 0.25000 0.06621 0.00000 1.00000\n",
       "2    LTC 0.25000 0.25833 0.00000 0.00000\n",
       "3    ETH 0.25000 0.30113 0.15787 0.00000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Backtesting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agr=[]\n",
    "bal=[]\n",
    "cons=[]\n",
    "theo=[]\n",
    "eq_r_w = equal_risk_contributions(cov)\n",
    "gmv_w = gmv(cov)\n",
    "msr_w = msr(riskfree_rate, er, cov)\n",
    "for i in range(len(returns)):\n",
    "    ret = portfolio_return(ew(n), returns.iloc[i])\n",
    "    agr.append(ret)\n",
    "    \n",
    "for i in range(len(returns)):\n",
    "    ret = portfolio_return(eq_r_w, returns.iloc[i])\n",
    "    bal.append(ret)\n",
    "\n",
    "for i in range(len(returns)):\n",
    "    ret = portfolio_return(gmv_w, returns.iloc[i])\n",
    "    cons.append(ret)\n",
    "    \n",
    "for i in range(len(returns)):\n",
    "    ret = portfolio_return(msr_w, returns.iloc[i])\n",
    "    theo.append(ret)\n",
    "\n",
    "recom = pd.DataFrame({'EW': agr, 'ERC': bal, 'GMV': cons, 'MSR': theo}, index=returns.index)\n",
    "((1+recom['2020-11-02':]).cumprod()).plot(figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annualized Return</th>\n",
       "      <th>Annualized Vol</th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "      <th>Max Drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EW</th>\n",
       "      <td>80.39235</td>\n",
       "      <td>1.55133</td>\n",
       "      <td>50.80763</td>\n",
       "      <td>-0.14022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ERC</th>\n",
       "      <td>21.29537</td>\n",
       "      <td>0.83385</td>\n",
       "      <td>25.02010</td>\n",
       "      <td>-0.14949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GMV</th>\n",
       "      <td>7.09049</td>\n",
       "      <td>0.67401</td>\n",
       "      <td>10.28621</td>\n",
       "      <td>-0.14371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSR</th>\n",
       "      <td>1028.29092</td>\n",
       "      <td>5.35077</td>\n",
       "      <td>188.46271</td>\n",
       "      <td>-0.38532</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Annualized Return  Annualized Vol  Sharpe Ratio  Max Drawdown\n",
       "EW            80.39235         1.55133      50.80763      -0.14022\n",
       "ERC           21.29537         0.83385      25.02010      -0.14949\n",
       "GMV            7.09049         0.67401      10.28621      -0.14371\n",
       "MSR         1028.29092         5.35077     188.46271      -0.38532"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(recom['2020-11-02':], rf=riskfree_rate, periods=252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Annualized Return</th>\n",
       "      <th>Annualized Vol</th>\n",
       "      <th>Sharpe Ratio</th>\n",
       "      <th>Max Drawdown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>BTC</th>\n",
       "      <td>5.47304</td>\n",
       "      <td>0.68224</td>\n",
       "      <td>7.83728</td>\n",
       "      <td>-0.13431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DOGE</th>\n",
       "      <td>1028.29092</td>\n",
       "      <td>5.35077</td>\n",
       "      <td>188.46271</td>\n",
       "      <td>-0.38532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTC</th>\n",
       "      <td>11.60755</td>\n",
       "      <td>0.99450</td>\n",
       "      <td>11.42519</td>\n",
       "      <td>-0.18297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETH</th>\n",
       "      <td>21.27597</td>\n",
       "      <td>0.87836</td>\n",
       "      <td>23.73030</td>\n",
       "      <td>-0.19383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Annualized Return  Annualized Vol  Sharpe Ratio  Max Drawdown\n",
       "BTC             5.47304         0.68224       7.83728      -0.13431\n",
       "DOGE         1028.29092         5.35077     188.46271      -0.38532\n",
       "LTC            11.60755         0.99450      11.42519      -0.18297\n",
       "ETH            21.27597         0.87836      23.73030      -0.19383"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(returns['2020-11-02':], rf=riskfree_rate, periods=252)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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