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    "# Essential portfolio math\n",
    "\n",
    "This brief chapter will cover the type of statistics that we often see when looking at **portfolios**. Parts of the material, such as skewness and kurtosis, are also covered in **Chapter 13** of [Python for Finance, 2e](https://www.oreilly.com/library/view/python-for-finance/9781492024323/)\n",
    "\n",
    "This material will have us thinking about **portfolio construction**. This type of portfolio construction is, typically, most helpful in a quantitative, or \"quant\", strategy. If you're picking 20 stocks to buy, you aren't going to run an optimizer. You'll probably just 1/n, or equally-weight, the positions. \n",
    "\n",
    "But, what if you have a lot of positions, probably chosen by some kind of quant or algorithmic strategy? You will be choosing and sizing the various trades (or, assets, more generally) to achieve a good trade-off between risk and expected return. What should you consider?\n",
    "\n",
    "1. Diversification! Quant strategies might have 1000's of positions.\n",
    "2. Position limits and risk limits: At the level of securities, asset classes, and overall portfolio. \n",
    "3. Larger bets on higher conviction trade. You can weight a position based on the strength of your signal.\n",
    "4. Size bets in terms of risk.\n",
    "5. Correlations matter: For a long position, correlation with other longs is bad, corr. with shorts is good. Powerful to go long/short within each industry, diversify across industries. Can use ETFs and futures to “hedge” out sector and market exposure.\n",
    "6. Resize positions according to forward-looking risk and conviction.\n",
    "\n",
    "We won't start with full-on quantitative-based strategies though. Let's look at a few, basic return series. \n",
    "   \n",
    "We'll start by bringing in some **monthly hedge fund return data**. We'll calculate some portfolio returns, using assumed weights. We'll then move on to portfolio-level **variance and standard deviation**. We'll see how to **annualize returns and volatility**. We'll look at other risk measures, like **skewness** and **kurtosis**. We'll see **Sharpe** and **Sortino** ratios. Finally, we'll make a graph showing the **drawdown**, or worse loss, of our portfolio.\n",
    "\n",
    "These ideas will get us thinking about portfolios, trading strategies, risk management, and portfolio optimization. We'll go into more detail in later chapters.\n",
    "\n",
    "The formulas are **must knows** for finance folks! For example, you need these on CFA Level 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rf</th>\n",
       "      <th>mkt_rf</th>\n",
       "      <th>smb</th>\n",
       "      <th>hml</th>\n",
       "      <th>umd</th>\n",
       "      <th>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <th>ded_sh_bs_hedge_fund_usd</th>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
       "      <th>emg_mkts_hedge_fund_usd</th>\n",
       "      <th>evnt_drvn_hedge_fund_usd</th>\n",
       "      <th>_cnvrt_arb_hedge_fund_usd</th>\n",
       "      <th>fx_inc_arb_hedge_fund_usd</th>\n",
       "      <th>djcs_hedge_fund_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>222.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.002536</td>\n",
       "      <td>0.005089</td>\n",
       "      <td>0.002121</td>\n",
       "      <td>0.002133</td>\n",
       "      <td>0.004886</td>\n",
       "      <td>0.007535</td>\n",
       "      <td>0.004410</td>\n",
       "      <td>-0.002217</td>\n",
       "      <td>0.009594</td>\n",
       "      <td>0.005108</td>\n",
       "      <td>0.006656</td>\n",
       "      <td>0.007360</td>\n",
       "      <td>0.006129</td>\n",
       "      <td>0.004366</td>\n",
       "      <td>0.007011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.001742</td>\n",
       "      <td>0.047007</td>\n",
       "      <td>0.035581</td>\n",
       "      <td>0.033738</td>\n",
       "      <td>0.054827</td>\n",
       "      <td>0.028745</td>\n",
       "      <td>0.029770</td>\n",
       "      <td>0.049144</td>\n",
       "      <td>0.028000</td>\n",
       "      <td>0.033738</td>\n",
       "      <td>0.042929</td>\n",
       "      <td>0.018291</td>\n",
       "      <td>0.019981</td>\n",
       "      <td>0.016485</td>\n",
       "      <td>0.021820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.185500</td>\n",
       "      <td>-0.166200</td>\n",
       "      <td>-0.128700</td>\n",
       "      <td>-0.347500</td>\n",
       "      <td>-0.114806</td>\n",
       "      <td>-0.404590</td>\n",
       "      <td>-0.112825</td>\n",
       "      <td>-0.115652</td>\n",
       "      <td>-0.093702</td>\n",
       "      <td>-0.230716</td>\n",
       "      <td>-0.118201</td>\n",
       "      <td>-0.126007</td>\n",
       "      <td>-0.140442</td>\n",
       "      <td>-0.075948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000900</td>\n",
       "      <td>-0.023225</td>\n",
       "      <td>-0.019750</td>\n",
       "      <td>-0.015275</td>\n",
       "      <td>-0.011225</td>\n",
       "      <td>-0.008805</td>\n",
       "      <td>0.001250</td>\n",
       "      <td>-0.033677</td>\n",
       "      <td>-0.001949</td>\n",
       "      <td>-0.018947</td>\n",
       "      <td>-0.012924</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>-0.000482</td>\n",
       "      <td>0.001716</td>\n",
       "      <td>-0.002376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.011550</td>\n",
       "      <td>-0.001450</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>0.006300</td>\n",
       "      <td>0.007663</td>\n",
       "      <td>0.006715</td>\n",
       "      <td>-0.007478</td>\n",
       "      <td>0.010404</td>\n",
       "      <td>0.003235</td>\n",
       "      <td>0.012111</td>\n",
       "      <td>0.009848</td>\n",
       "      <td>0.009817</td>\n",
       "      <td>0.007131</td>\n",
       "      <td>0.007447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.004100</td>\n",
       "      <td>0.035600</td>\n",
       "      <td>0.023025</td>\n",
       "      <td>0.017750</td>\n",
       "      <td>0.030425</td>\n",
       "      <td>0.023168</td>\n",
       "      <td>0.012581</td>\n",
       "      <td>0.029215</td>\n",
       "      <td>0.021420</td>\n",
       "      <td>0.028790</td>\n",
       "      <td>0.028144</td>\n",
       "      <td>0.018590</td>\n",
       "      <td>0.014712</td>\n",
       "      <td>0.011529</td>\n",
       "      <td>0.018074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.005600</td>\n",
       "      <td>0.115300</td>\n",
       "      <td>0.220600</td>\n",
       "      <td>0.138800</td>\n",
       "      <td>0.184000</td>\n",
       "      <td>0.129913</td>\n",
       "      <td>0.036573</td>\n",
       "      <td>0.226661</td>\n",
       "      <td>0.105808</td>\n",
       "      <td>0.099063</td>\n",
       "      <td>0.164018</td>\n",
       "      <td>0.042168</td>\n",
       "      <td>0.058055</td>\n",
       "      <td>0.043331</td>\n",
       "      <td>0.085117</td>\n",
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       "               rf      mkt_rf         smb         hml         umd  \\\n",
       "count  222.000000  222.000000  222.000000  222.000000  222.000000   \n",
       "mean     0.002536    0.005089    0.002121    0.002133    0.004886   \n",
       "std      0.001742    0.047007    0.035581    0.033738    0.054827   \n",
       "min      0.000000   -0.185500   -0.166200   -0.128700   -0.347500   \n",
       "25%      0.000900   -0.023225   -0.019750   -0.015275   -0.011225   \n",
       "50%      0.003000    0.011550   -0.001450    0.002150    0.006300   \n",
       "75%      0.004100    0.035600    0.023025    0.017750    0.030425   \n",
       "max      0.005600    0.115300    0.220600    0.138800    0.184000   \n",
       "\n",
       "       ln_sh_eq_hedge_fund_usd  eq_mkt_ntr_hedge_fund_usd  \\\n",
       "count               222.000000                 222.000000   \n",
       "mean                  0.007535                   0.004410   \n",
       "std                   0.028745                   0.029770   \n",
       "min                  -0.114806                  -0.404590   \n",
       "25%                  -0.008805                   0.001250   \n",
       "50%                   0.007663                   0.006715   \n",
       "75%                   0.023168                   0.012581   \n",
       "max                   0.129913                   0.036573   \n",
       "\n",
       "       ded_sh_bs_hedge_fund_usd  global_mac_hedge_fund_usd  \\\n",
       "count                222.000000                 222.000000   \n",
       "mean                  -0.002217                   0.009594   \n",
       "std                    0.049144                   0.028000   \n",
       "min                   -0.112825                  -0.115652   \n",
       "25%                   -0.033677                  -0.001949   \n",
       "50%                   -0.007478                   0.010404   \n",
       "75%                    0.029215                   0.021420   \n",
       "max                    0.226661                   0.105808   \n",
       "\n",
       "       mngd_fut_hedge_fund_usd  emg_mkts_hedge_fund_usd  \\\n",
       "count               222.000000               222.000000   \n",
       "mean                  0.005108                 0.006656   \n",
       "std                   0.033738                 0.042929   \n",
       "min                  -0.093702                -0.230716   \n",
       "25%                  -0.018947                -0.012924   \n",
       "50%                   0.003235                 0.012111   \n",
       "75%                   0.028790                 0.028144   \n",
       "max                   0.099063                 0.164018   \n",
       "\n",
       "       evnt_drvn_hedge_fund_usd  _cnvrt_arb_hedge_fund_usd  \\\n",
       "count                222.000000                 222.000000   \n",
       "mean                   0.007360                   0.006129   \n",
       "std                    0.018291                   0.019981   \n",
       "min                   -0.118201                  -0.126007   \n",
       "25%                    0.000110                  -0.000482   \n",
       "50%                    0.009848                   0.009817   \n",
       "75%                    0.018590                   0.014712   \n",
       "max                    0.042168                   0.058055   \n",
       "\n",
       "       fx_inc_arb_hedge_fund_usd  djcs_hedge_fund_usd  \n",
       "count                 222.000000           222.000000  \n",
       "mean                    0.004366             0.007011  \n",
       "std                     0.016485             0.021820  \n",
       "min                    -0.140442            -0.075948  \n",
       "25%                     0.001716            -0.002376  \n",
       "50%                     0.007131             0.007447  \n",
       "75%                     0.011529             0.018074  \n",
       "max                     0.043331             0.085117  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import janitor\n",
    "from janitor import clean_names\n",
    "\n",
    "# This brings in all of matplotlib\n",
    "import matplotlib as mpl \n",
    "\n",
    "# This lets us refer to the pyplot part of matplot lib more easily. Just use plt!\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Include this to have plots show up in your Jupyter notebook.\n",
    "%matplotlib inline \n",
    "\n",
    "hf = pd.read_excel('https://raw.githubusercontent.com/aaiken1/fin-data-analysis-python/main/data/hf_rets.xlsx',\n",
    "                  index_col=0, parse_dates=True)  \n",
    "\n",
    "hf = hf.clean_names()\n",
    "\n",
    "hf.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a lot there, so let's just keep four different columns. These columns represent the average monthly return for that hedge fund type. I'm keeping long/short equity, equity market neutral, global macro, managed futures.\n",
    "\n",
    "Long/short equity funds do what the name says -- they go long and short stocks, generally keeping their market beta less than one. But not that different from traditional mutual funds.\n",
    "\n",
    "Equity market neutral might aim for a beta of zero, but usually have more exposure than that. They are trying to get rid of their risk exposures and just earn alpha from security mispricings. Easier said than done.\n",
    "\n",
    "Global macro funds make trades based on broad market trends, currencies, and other markets affected by macro events, like inflation or economic growth. They don't buy or sell individual stocks and often use futures contracts to get their exposures. \n",
    "\n",
    "Finally, managed futures are similar, but tend to follow trend-following strategies. They buy stuff that's been going up and sell stuff that's been going down. Some investors use them as diversifiers in their portfolios.\n",
    "\n",
    "You'll see more on these ideas in the factor model notes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
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       "    <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>1994-01-31</th>\n",
       "      <td>0.011733</td>\n",
       "      <td>-0.005467</td>\n",
       "      <td>0.001433</td>\n",
       "      <td>0.001933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-02-28</th>\n",
       "      <td>-0.025011</td>\n",
       "      <td>0.002106</td>\n",
       "      <td>-0.056811</td>\n",
       "      <td>0.011667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-03-31</th>\n",
       "      <td>-0.039114</td>\n",
       "      <td>-0.002514</td>\n",
       "      <td>-0.042854</td>\n",
       "      <td>0.025923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-04-29</th>\n",
       "      <td>-0.015720</td>\n",
       "      <td>0.002397</td>\n",
       "      <td>-0.016033</td>\n",
       "      <td>0.008437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994-05-31</th>\n",
       "      <td>0.005550</td>\n",
       "      <td>-0.001229</td>\n",
       "      <td>0.037839</td>\n",
       "      <td>0.007497</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-29</th>\n",
       "      <td>0.026418</td>\n",
       "      <td>0.013335</td>\n",
       "      <td>0.007852</td>\n",
       "      <td>0.012039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-30</th>\n",
       "      <td>0.004998</td>\n",
       "      <td>-0.001553</td>\n",
       "      <td>-0.004271</td>\n",
       "      <td>-0.029648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-30</th>\n",
       "      <td>-0.004680</td>\n",
       "      <td>-0.004139</td>\n",
       "      <td>-0.002522</td>\n",
       "      <td>0.004299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-31</th>\n",
       "      <td>-0.045274</td>\n",
       "      <td>-0.031806</td>\n",
       "      <td>0.001936</td>\n",
       "      <td>0.023829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-29</th>\n",
       "      <td>0.003438</td>\n",
       "      <td>0.009249</td>\n",
       "      <td>-0.013726</td>\n",
       "      <td>-0.035612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            ln_sh_eq_hedge_fund_usd  eq_mkt_ntr_hedge_fund_usd  \\\n",
       "date                                                             \n",
       "1994-01-31                 0.011733                  -0.005467   \n",
       "1994-02-28                -0.025011                   0.002106   \n",
       "1994-03-31                -0.039114                  -0.002514   \n",
       "1994-04-29                -0.015720                   0.002397   \n",
       "1994-05-31                 0.005550                  -0.001229   \n",
       "...                             ...                        ...   \n",
       "2012-02-29                 0.026418                   0.013335   \n",
       "2012-03-30                 0.004998                  -0.001553   \n",
       "2012-04-30                -0.004680                  -0.004139   \n",
       "2012-05-31                -0.045274                  -0.031806   \n",
       "2012-06-29                 0.003438                   0.009249   \n",
       "\n",
       "            global_mac_hedge_fund_usd  mngd_fut_hedge_fund_usd  \n",
       "date                                                            \n",
       "1994-01-31                   0.001433                 0.001933  \n",
       "1994-02-28                  -0.056811                 0.011667  \n",
       "1994-03-31                  -0.042854                 0.025923  \n",
       "1994-04-29                  -0.016033                 0.008437  \n",
       "1994-05-31                   0.037839                 0.007497  \n",
       "...                               ...                      ...  \n",
       "2012-02-29                   0.007852                 0.012039  \n",
       "2012-03-30                  -0.004271                -0.029648  \n",
       "2012-04-30                  -0.002522                 0.004299  \n",
       "2012-05-31                   0.001936                 0.023829  \n",
       "2012-06-29                  -0.013726                -0.035612  \n",
       "\n",
       "[222 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = hf.iloc[:,[5, 6, 8, 9]]\n",
    "rets"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I used `.iloc` to keep the columns I wanted. Easier to use a column number reference than type out these long names."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometric vs. arithmetic averages\n",
    "\n",
    "In order to think about portfolio returns, we also need to understand the relationship between an arithmetic average and the geometric average, and when to use one vs. another.\n",
    "\n",
    "This article, [by the financial planner Michael Kitches](https://www.kitces.com/blog/volatility-drag-variance-drain-mean-arithmetic-vs-geometric-average-investment-returns/), is a great explanation of why the difference matters and how **volatility drag** affects your total return. From his summary:\n",
    "\n",
    "> In the investment world, it’s common to discuss average rates of return, both in a backward-looking fashion (e.g., to report investment results), and in a more forward-looking manner (e.g., to project the average growth rate of investments for funding future goals in retirement planning software). However, the reality is that because returns are linked to each other – the return in one year increases or decreases the available wealth to compound in the subsequent year – it’s not sufficient to simply determine an “average” return by adding up all the historical returns and dividing by how many there are.\n",
    "\n",
    "> Instead of this traditional “arithmetic mean” approach to calculating an average, in the case of investment returns, the proper way to calculate average returns is with a geometric mean, that takes into account the compounding effects of a series of volatile returns over time. Which is important, because in practice the geometric average return is never as high as its arithmetic mean counterpart, due to the fact that volatility always produces some level of “volatility drag”, which can be estimated by subtracting ½ of the investment’s variance (standard deviation squared) from its arithmetic return.\n",
    "\n",
    "> Fortunately, the reality is that most investment returns, as commonly discussed by financial advisors, are already reported as geometric returns, typically stated as either a Compound Average Growth Rate (CAGR), an annualized return, or some similar label. Which means, intended or not, most financial advisors already project future wealth values in a retirement plan using the (proper) geometric return assumption.\n",
    "\n",
    "> However, the variance drain on a sequence of volatile returns still matters when financial advisors use Monte Carlo analysis, which by design actually projects sequences of random volatile returns (based on the probability that they will occur) to determine the outcome of particular retirement strategies. Because the fact that volatility drag is already part of a Monte Carlo analysis means that the return assumption plugged into a Monte Carlo projection should actually be the (higher) arithmetic return, and not the investment’s long-term compound average growth rate. Otherwise, the impact of volatility drag is effectively counted twice, which can understate long-term returns and overstate the actual risk of the prospective retirement plan!\n",
    "\n",
    "We can put that reasoning into a simple, but important formula.\n",
    "\n",
    "\\begin{align}\n",
    "\\text{Compound Return}_i = E[R_p] - \\frac{1}{2}\\sigma^2_p\n",
    "\\end{align}\n",
    "\n",
    "where $E[R_i]$ is the arithmetic mean of the portfolio (i.e. the expected return) and $\\sigma^2_p$ is the variance of the portfolio. Note that this math works for single assets, too. It isn't portfolio specific.\n",
    "\n",
    "```{hint}\n",
    "This is yet another formula that appears on the CFA and CFP exams.\n",
    "```\n",
    "\n",
    "Believe it or not, just figuring out how to calculate returns in order to create projections (e.g. for a client portfolio) is a controversial, non-trivial task. Two of the authors of your FIN 4120 investments textbook suggest that you should forecast the value of a portfolio using an average [between the arithmetic and geometric](https://www.tandfonline.com/doi/abs/10.2469/faj.v59.n6.2574?src=recsys).\n",
    "\n",
    "I like this discussion, too, by Kris Abdelmessih, an options trader who also writes the [Moontower Substack](https://moontower.substack.com/p/well-what-did-you-expect):\n",
    "\n",
    "> What you expect to happen in the colloquial sense of the term is the geometric mean. The arithmetic average is a measure of centrality when you sum the results and divide by the number of results. (In our examples you are summing results weighted by their probabilities, but you are still summing). The geometric mean corresponds to the median result of a compounding process. Compounding means “multiplying not summing”. The median is the measure that maps to our colloquial use of “expected” because it’s the 50/50 point of the distribution. That’s the number you plan life around.\n",
    "\n",
    "> The theoretical arithmetic mean result of playing the lotto might be losing 50% of your $2 Powerball ticket (which is another way of saying you are paying 2x what the ticket is mathematically worth). The median result is you lit your cash on fire. You plan your life around the median, especially when it’s far away from the mean. We’ll come back to that.\n",
    "\n",
    "\n",
    "Here's [a link](https://www.dropbox.com/s/ph4xmjt5wsobeuv/Well%20What%20Did%20You%20%22Expect%22%3F%20-%20Party%20at%20the%20Moontower.pdf?dl=0) to a PDF of that Substack post. If you're interested in trading, options, math puzzles, etc., check out his work. I don't agree with every conclusion, but I like the way he breaks things down into the step-by-step logic of underlying math."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Portfolio-level returns\n",
    "\n",
    "Let's define an array of weights. We'll put 25% of our money into each style category. This isn't actually an investable strategy, since you can't invest in the average hedge fund!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "wgts = np.array([0.25, 0.25, 0.25, 0.25])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can find the monthly means for each strategy. I'll multiply each by 100, name the column, and then turn the series into a DataFrame. You need the table to be a DataFrame in order to style it. I then use a **dictionary** to change the row names. Remember, a Python dictionary matches one item to another. I finally style the number format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_522cc\">\n",
       "  <caption>Hedge Fund Style Returns</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_522cc_level0_col0\" class=\"col_heading level0 col0\" >Monthly Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_522cc_level0_row0\" class=\"row_heading level0 row0\" >L/S Equity</th>\n",
       "      <td id=\"T_522cc_row0_col0\" class=\"data row0 col0\" >0.754%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_522cc_level0_row1\" class=\"row_heading level0 row1\" >Equity Market Neutral</th>\n",
       "      <td id=\"T_522cc_row1_col0\" class=\"data row1 col0\" >0.441%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_522cc_level0_row2\" class=\"row_heading level0 row2\" >Global Macro</th>\n",
       "      <td id=\"T_522cc_row2_col0\" class=\"data row2 col0\" >0.959%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_522cc_level0_row3\" class=\"row_heading level0 row3\" >Managed Futures</th>\n",
       "      <td id=\"T_522cc_row3_col0\" class=\"data row3 col0\" >0.511%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2aca606a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_rets_avg = rets.mean().mul(100)\n",
    "\n",
    "monthly_rets_avg = monthly_rets_avg.rename('Monthly Return')\n",
    "\n",
    "monthly_rets_avg = monthly_rets_avg.to_frame()\n",
    "\n",
    "monthly_rets_avg = monthly_rets_avg.rename({'ln_sh_eq_hedge_fund_usd':'L/S Equity','eq_mkt_ntr_hedge_fund_usd':'Equity Market Neutral','global_mac_hedge_fund_usd':'Global Macro','mngd_fut_hedge_fund_usd':'Managed Futures'}, axis='rows')\n",
    "\n",
    "monthly_rets_avg = monthly_rets_avg.style.format('{:.3f}%').set_caption('Hedge Fund Style Returns') \n",
    "\n",
    "monthly_rets_avg"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **method chaining** to writing the code step-by-step to make the table works too. I find this easier to read. Note that I'm **not** saving the table as a DataFrame anywhere, though I could. I do one action and then the output gets passed on to the next action."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_aee6c\">\n",
       "  <caption>Hedge Fund Style Returns</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_aee6c_level0_col0\" class=\"col_heading level0 col0\" >Monthly Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_aee6c_level0_row0\" class=\"row_heading level0 row0\" >L/S Equity</th>\n",
       "      <td id=\"T_aee6c_row0_col0\" class=\"data row0 col0\" >0.754%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aee6c_level0_row1\" class=\"row_heading level0 row1\" >Equity Market Neutral</th>\n",
       "      <td id=\"T_aee6c_row1_col0\" class=\"data row1 col0\" >0.441%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aee6c_level0_row2\" class=\"row_heading level0 row2\" >Global Macro</th>\n",
       "      <td id=\"T_aee6c_row2_col0\" class=\"data row2 col0\" >0.959%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_aee6c_level0_row3\" class=\"row_heading level0 row3\" >Managed Futures</th>\n",
       "      <td id=\"T_aee6c_row3_col0\" class=\"data row3 col0\" >0.511%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x28744c730>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    rets.mean()\n",
    "    .mul(100)\n",
    "    .rename('Monthly Return')\n",
    "    .to_frame()\n",
    "    .rename({'ln_sh_eq_hedge_fund_usd':'L/S Equity','eq_mkt_ntr_hedge_fund_usd':'Equity Market Neutral','global_mac_hedge_fund_usd':'Global Macro','mngd_fut_hedge_fund_usd':'Managed Futures'}, axis='rows')\n",
    "    .style\n",
    "        .format('{:.3f}%')\n",
    "        .set_caption('Hedge Fund Style Returns')   \n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also see what our equally-weighted portfolio would have done per month over that time period. I'll remake the monthly average returns, since you can't use it for math now that it's been styled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0066618444208333735"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_rets_avg = rets.mean()\n",
    "hf_port_ret = np.sum(monthly_rets_avg * wgts)\n",
    "hf_port_ret"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's weight each month's returns by our weights so that we can then calculate monthly portfolio returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "weighted_returns = (wgts * rets)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm calculating monthly returns by summing up across each row. That's the `axis=1` argument. I'm then naming this series *port_ret*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "port_ret = weighted_returns.sum(axis=1).rename('port_ret')\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll join my portfolio returns to the monthly fund returns. There should be the same number of portfolio returns as individual hedge fund returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "rets_with_port = rets.join(port_ret)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And, I can calculate cumulative returns and then graph them. As we've seen using a function like `.cumprod()` like this is an example of **broadcasting** and **vectorization**. I am adding 1 to every monthly return and then applying the `.cumprod()` function to each observation. Take a look at the result to see what happened. \n",
    "\n",
    "I'll also rename the columns a slightly different way. Notice that I'm using `inplace=True` and have the column names inside of a **dictionary**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "monthly_cum_rets = (1+rets_with_port).cumprod()\n",
    "monthly_cum_rets.rename(columns={'ln_sh_eq_hedge_fund_usd':'L/S Equity', 'eq_mkt_ntr_hedge_fund_usd':'Equity Market Neutral', 'global_mac_hedge_fund_usd':'Global Macro', 'mngd_fut_hedge_fund_usd':'Managed Futures', 'port_ret':'EW Port Ret' }, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "monthly_cum_rets.plot();"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes! Check out the orange line - equity market neutral funds aren't necessarily very market neutral!"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Risk measures at the portfolio-level\n",
    "\n",
    "As you know from your investments class, the variance of a portfolio is **not** the average variance of the assets in the portfolio. We need to take into account the effects of **diversification**. For example, here's the formula for the variance of a portfolio with two assets. We need weights, variances, and the covariance between the two.\n",
    "\n",
    "\\begin{align}\n",
    "\\sigma^2_p = w^2_1 \\sigma^2_1 + w^2_2 \\sigma^2_2 + 2 w_1 w_2 \\sigma^2_{1,2}\n",
    "\\end{align}\n",
    "\n",
    "That last sigma, $\\sigma^2_{1,2}$, is the **covariance** between the two assets. \n",
    "\n",
    "This formula generalizes for **any number of assets**. \n",
    "\n",
    "\\begin{align}\n",
    "\\sigma^2_p = w^T \\Sigma w\n",
    "\\end{align}\n",
    "\n",
    "where $w$ is a **vector** containing all of our portfolio weights and sigma is the variance-covariance matrix for our assets. The \"T\" means the matrix operation **transpose**. This is necessary for the matrix multiplication. \n",
    "\n",
    "Let's use Python to find the variance-covariance matrix. There are variances along with diagonal of the matrix and covariance terms **between assets** in the off-diagonals. Note that upper-right and lower-left of the matrix are identical. This is because the covariance between Asset 1 and Asset 2 is the same as the covariance between Asset 2 and Asset 1. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <td>0.000826</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000886</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <td>0.000362</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.000784</td>\n",
       "      <td>0.000283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.001138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ln_sh_eq_hedge_fund_usd  eq_mkt_ntr_hedge_fund_usd  \\\n",
       "ln_sh_eq_hedge_fund_usd                   0.000826                   0.000192   \n",
       "eq_mkt_ntr_hedge_fund_usd                 0.000192                   0.000886   \n",
       "global_mac_hedge_fund_usd                 0.000362                   0.000069   \n",
       "mngd_fut_hedge_fund_usd                   0.000066                   0.000005   \n",
       "\n",
       "                           global_mac_hedge_fund_usd  mngd_fut_hedge_fund_usd  \n",
       "ln_sh_eq_hedge_fund_usd                     0.000362                 0.000066  \n",
       "eq_mkt_ntr_hedge_fund_usd                   0.000069                 0.000005  \n",
       "global_mac_hedge_fund_usd                   0.000784                 0.000283  \n",
       "mngd_fut_hedge_fund_usd                     0.000283                 0.001138  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cov_matrix = rets.cov()\n",
    "cov_matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also find the correlation matrix. Correlation ($\\rho$) is the **covariance between two assets divided by the standard deviation of each asset multiplied together**: $\\rho_{1,2} = \\frac{\\sigma_{1,2}}{\\sigma_1 \\sigma_2}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        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>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ln_sh_eq_hedge_fund_usd</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.224745</td>\n",
       "      <td>0.450291</td>\n",
       "      <td>0.068435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eq_mkt_ntr_hedge_fund_usd</th>\n",
       "      <td>0.224745</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.083255</td>\n",
       "      <td>0.005156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>global_mac_hedge_fund_usd</th>\n",
       "      <td>0.450291</td>\n",
       "      <td>0.083255</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.299970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mngd_fut_hedge_fund_usd</th>\n",
       "      <td>0.068435</td>\n",
       "      <td>0.005156</td>\n",
       "      <td>0.299970</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           ln_sh_eq_hedge_fund_usd  eq_mkt_ntr_hedge_fund_usd  \\\n",
       "ln_sh_eq_hedge_fund_usd                   1.000000                   0.224745   \n",
       "eq_mkt_ntr_hedge_fund_usd                 0.224745                   1.000000   \n",
       "global_mac_hedge_fund_usd                 0.450291                   0.083255   \n",
       "mngd_fut_hedge_fund_usd                   0.068435                   0.005156   \n",
       "\n",
       "                           global_mac_hedge_fund_usd  mngd_fut_hedge_fund_usd  \n",
       "ln_sh_eq_hedge_fund_usd                     0.450291                 0.068435  \n",
       "eq_mkt_ntr_hedge_fund_usd                   0.083255                 0.005156  \n",
       "global_mac_hedge_fund_usd                   1.000000                 0.299970  \n",
       "mngd_fut_hedge_fund_usd                     0.299970                 1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = rets.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correlation among these general strategies is pretty low! That's good for portfolio formation. In fact, strategies like managed futures are often considered a diversifier in a traditional equity and bond portfolio. \n",
    "\n",
    "Let's use our general formula, our weights, and the cov matrix to find **portfolio variance**. We'll use `np.dot()` from `numpy` to do the dot product. This is like **mmult** in Excel. The `.T` transposes, or flips, the vector of returns so that we can multiply. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0003495578603062863"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_variance = np.dot(wgts.T, np.dot(cov_matrix, wgts))\n",
    "port_variance"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a [refresher on matrix multiplication](https://www.mathsisfun.com/algebra/matrix-multiplying.html) in case you need it."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Portfolio standard deviation is, of course, just the square root of variance. We can use `np.sqrt()`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.9%\n"
     ]
    }
   ],
   "source": [
    "port_stddev = np.sqrt(np.dot(wgts.T, np.dot(cov_matrix, wgts)))\n",
    "print(str(np.round(port_stddev, 3) * 100) + '%')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Skewness and kurtosis\n",
    "\n",
    "We'll look more at the distribution of asset returns when we get to a more formal treatment of risk management. But, for now, we need to know that asset returns, like stocks, and even portfolio returns, like those of a hedge fund, are not typically normally distributed. They \"lean\", or have **skewness**. That skewness can be either positive or negative, depending on the strategy. They also have **kurtosis**, or \"fat tails\". This means that there are more extreme returns than you would expect if they followed a normal distribution.\n",
    "\n",
    "It is easy to find both in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ln_sh_eq_hedge_fund_usd      -0.012623\n",
       "eq_mkt_ntr_hedge_fund_usd   -11.866451\n",
       "global_mac_hedge_fund_usd     0.016477\n",
       "mngd_fut_hedge_fund_usd       0.026489\n",
       "port_ret                     -0.386969\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets_with_port.skew()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's easier to see what skewness and kurtosis mean with pictures.\n",
    "\n",
    "```{figure} ../images/08-skew.png\n",
    "---\n",
    "name: 08-skew.png\n",
    "align: center\n",
    "---\n",
    "Some strategies, like selling options, have negative skewness. You make a small profit much of the time -- until you don't. Other investments have positive skewness. You'll lose money much of the time, but occasionally get a large payoff. Some volatility-protection strategies look like this. Source: DataCamp.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ln_sh_eq_hedge_fund_usd        3.186683\n",
       "eq_mkt_ntr_hedge_fund_usd    162.760371\n",
       "global_mac_hedge_fund_usd      3.888268\n",
       "mngd_fut_hedge_fund_usd       -0.017587\n",
       "port_ret                       3.306429\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets_with_port.kurt()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{figure} ../images/08-kurt.png\n",
    "---\n",
    "name: 08-kurt.png\n",
    "align: center\n",
    "---\n",
    "Many investment strategies have more \"rare events\" or larger gains and losses than you would expect if returns followed the normal distribution. Source: DataCamp.\n",
    "```"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Annualizing and adjusting for risk\n",
    "\n",
    "There are many ways to report returns. We've seen discrete vs. log returns. There are returns for specific period, like a day or a month. We can then take that specific period return and **annualize** it. This takes a return over a shorter time frame and asks what are return would be if we earned that return over a year.\n",
    "\n",
    "We can also annualize a multi-year return and ask what annual return would have gotten us the same multi-year return.\n",
    "\n",
    "Both of these ideas are really the same thing mathematically and take into account **compounding**. They let us compare returns that are different time periods. Fund managers that fall under the SEC '40 Act are required to report calculations like this.\n",
    "\n",
    "There are also many ways to **adjust for risk**. Why do we need to adjust for risk? Simply put, you can go to a casino and put all of your money on black, get lucky, and double your \"investment\". But I don't want you managing my money! Your risk-adjusted return is zero. \n",
    "\n",
    "The same thing is true for trading securities, like stocks, bonds, options, and crypto. You can buy a penny stock, or a Dog coin, or a deep out-of-the-money call option and make a lot of money. And that money is real - you can spend it! But, we need ways to disentangle return, risk, and luck if we're going to assign skill to someone's good forture. \n",
    "\n",
    "Let's combine these two ideas, getting our return and risk measures in the same time units and taking risk into account, to calculate to basic risk-adjustment measures: the **Sharpe Ratio** and the **Sortino Ratio**.\n",
    "\n",
    "We'll look at **factor models** in another chapter. This is a more comprehensive way to adjust our returns for the risks that we're taking in order to see if we have skill. \n",
    "\n",
    "Let's use the monthly portfolio returns that I created above to find our measures of risk/return trade-off. I will first annualize my monthly return. I will then annualize my monthly standard deviation, or volatility as we say in finance. I am \"cheating\" a bit on my volatility annualization. These are discrete returns. Multiplying by the square root of the number of your time periods in a year (12, because we have montly returns) really only works if you have log returns.\n",
    "\n",
    "See your Principles of Finance notes for more on these calculations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    222.000000\n",
       "mean       0.006662\n",
       "std        0.018696\n",
       "min       -0.092835\n",
       "25%       -0.004634\n",
       "50%        0.006880\n",
       "75%        0.017815\n",
       "max        0.065128\n",
       "Name: port_ret, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "risk_free = 0.02\n",
    " \n",
    "port_ret = rets_with_port.port_ret\n",
    "port_ret.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "annualized_return=((1 + port_ret.mean())**(12))-1\n",
    "annualized_vol = port_ret.std()*np.sqrt(12)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `**` denotes exponent, while `*` is multiplication in Python. \n",
    "\n",
    "We can then calculate the **Sharpe Ratio**, which is the ratio of returns above the risk-free rate to the volatility (i.e. the risk) in the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9717568897720553"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sharpe_ratio = (annualized_return - risk_free) / annualized_vol\n",
    "sharpe_ratio"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a Sharpe near 1. That's pretty high! Hedge fund strategies often take risks that are not well-defined by standard deviation. For example, if a strategy has a lot of negative skewness, then we would expect to occasionally have large losses. Volatility doesn't capture that type of risk.\n",
    "\n",
    "Returns that are \"too smooth\" can also create artificially low Sharpe Ratios. For example, if a strategy holds illiquid securities that are not marked-to-market (or priced) consistently, then the reported returns won't affect the actual volatility of the positions if they were priced, say, daily. For example, many venture capital and private equity strategies only mark, or change the prices of their investments, quarterly. A mutual fund is going to see their positions market daily. ETFs trade throughout the day. Private equity strategies look less volatile than they really are and, so, have higher Sharpe ratios than they do in reality. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also calculate the **Sortino Ratio**. This looks very similar to the Sharpe Ratio, expect that we only calculate the standard deviation using returns below some target, or threshold. So, we are interesting in volatility during bad times, not just overall volatility. We often use a target of 0 to look at negative returns, but you can choose something else if that's appropriate.\n",
    "\n",
    "Note that I am masking with `.loc` to get just the negative returns from the *port_ret* Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4514485366477672"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target_return = 0\n",
    "\n",
    "negative_returns = port_ret.loc[port_ret < target_return]\n",
    "\n",
    "down_stdev = negative_returns.std()*np.sqrt(12)\n",
    "\n",
    "sortino_ratio = (annualized_return - risk_free)/down_stdev\n",
    "\n",
    "sortino_ratio"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Drawdowns\n",
    "\n",
    "We might also be interested in draw-downs, or what the bad times look like, how long they last, and how long it takes to get our money back.\n",
    "\n",
    "We will calculate the cumulative portfolio return, the previous maximum cumulative return (often called a **high water mark**), and the percentage below the high water mark at any point. \n",
    "\n",
    "I print my index, just to show you that *date* is the index of the *port_drawdown* DataFrame. So, my x-axis in my plots can just be `port_drawdown.index`. \n",
    "\n",
    "I am using `matplotlib` and `plt.plot` to make two stacked line charts. I find this to be the easiest way, even if it is more verbose. You can really see what you're doing in each step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['1994-01-31', '1994-02-28', '1994-03-31', '1994-04-29',\n",
      "               '1994-05-31', '1994-06-30', '1994-07-29', '1994-08-31',\n",
      "               '1994-09-30', '1994-10-31',\n",
      "               ...\n",
      "               '2011-09-30', '2011-10-31', '2011-11-30', '2011-12-30',\n",
      "               '2012-01-31', '2012-02-29', '2012-03-30', '2012-04-30',\n",
      "               '2012-05-31', '2012-06-29'],\n",
      "              dtype='datetime64[ns]', name='date', length=222, freq=None)\n"
     ]
    }
   ],
   "source": [
    "port_ret_cum = (1+port_ret).cumprod()\n",
    "previous_peaks = port_ret_cum.cummax()\n",
    "drawdowns = (port_ret_cum - previous_peaks)/previous_peaks\n",
    "\n",
    "port_drawdown = pd.DataFrame({'Cumulative Return': port_ret_cum, 'Previous Peak': previous_peaks, 'Drawdown': drawdowns})\n",
    "\n",
    "print(port_drawdown.index)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make the graph. You'll see that the Great Financial Crisis was the high water mark, at least in this data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.subplot(211)\n",
    "plt.plot(port_drawdown.index, port_drawdown['Cumulative Return'], lw=1.5, label='Cumulative Portfolio Returns')\n",
    "plt.plot(port_drawdown.index, port_drawdown['Previous Peak'], lw=1.5, label='Previous High Water Mark')\n",
    "plt.legend(loc=0)\n",
    "plt.ylabel('Cumulative Returns')\n",
    "plt.title('Hedge Fund Portfolio Drawdowns')\n",
    "\n",
    "plt.subplot(212)\n",
    "plt.plot(port_drawdown.index, port_drawdown['Drawdown'], lw=1.5, label='Drawdown')\n",
    "plt.legend(loc=0)\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Return');"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "b67b392f8c699abfccff34ffff3abf55b26d6520ea7ac337064f990a3d8607af"
  },
  "kernelspec": {
   "display_name": "Python 3.9.7 ('base')",
   "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": "3.9.13"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}