walkthrough.ipynb

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   "source": [
    "*If you're unfamiliar with Jupyter Notebooks, this one is pre-loaded such that all you need to do is scroll, read, and enjoy.*\n",
    "\n",
    "*To interact with the file, click on cells with `[ ]:` to their left and run them one by one by pressing ▶️ (\"Run\") in the menu above or `Shift`+`Enter` on your keyboard. Go from top to bottom to keep things working properly.*"
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   "cell_type": "markdown",
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    "# A how-to for creating portfolios and simulating strategies with `backstroke`.\n",
    "\n",
    "`backstroke` is a package for **writing custom investment `Strategy` classes** and **backtesting the simualted portfolios** based on them.\n",
    "\n",
    "In practice, you define your desired assets and allocations in a `PortfolioMaker` instance, then pass the result to a `Strategy` to run the simulation. `backstroke` comes pre-equipped with a few `Strategy` classes; **in this walkthrough, we'll take a closer look at `SMAStrategy`**, which makes decisions on how to invest based on how a tracked asset's simple moving average (SMA) compares to its current price.\n",
    "\n",
    "_While you do need some domain knowledge to run the package yourself, **no financial background is needed to follow this tutorial**, so read on to learn more about `backstroke` and I'll try to fill in the blanks where it's pertinent._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## Experimenting with `PortfolioMaker`. 💧\n",
    "\n",
    "Before we use `SMAStrategy`, we have to build a `PortfolioMaker` instance with information about the assets we'll include in the portfolio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from portfolio_maker import PortfolioMaker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`PortfiolioMaker` instances follow a core/satellite model** where core assets' target portfolio fractions remain fixed over time but satellite assets' allocations are allowed to vary based on market conditions. To get things started, **we'll use a 60/40 split**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf = PortfolioMaker(sat_frac=.4, relative_core_frac=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We **divide the core portion** of the portfolio as follows:\n",
    "- **50% in VUG** (large, US-based companies with explosive growth potential)\n",
    "- **15% in VO** (mid-sized, US-based companies)\n",
    "- **15% in EFG** (large, non-US companies with explosive growth potential)\n",
    "- **15% in VBIIX** (intermediate term, highly graded bonds)\n",
    "- **5% in ISHIX** (intermediate term, riskier bonds)\n",
    "\n",
    "_(Again, don't worry if you're not familiar with these assets/terms.)_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.add_ticker('VUG', .5, label='core')\n",
    "pf.add_ticker('VO', .15, label='core')\n",
    "pf.add_ticker('EFG', .15, label='core')\n",
    "pf.add_ticker('VBIIX', .15, label='core')\n",
    "pf.add_ticker('ISHIX', .05, label='core')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Note: You can edit a core asset's fraction after adding it to `PortfolioMaker` with the `edit_ticker_fraction()` method._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Satellite assets take an `'in_market'` argument instead of a fraction. It's typically `True` for riskier assets and `False` for safer ones.\n",
    "\n",
    "Here, the **satellite portion** will contain:\n",
    "- **SSO as the in-market asset** (riskier: leveraged 2x to the S&P 500's daily returns)\n",
    "- **TLT as the out-market asset** (safer: long term US Treasury bonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.add_ticker('SSO', label='satellite', in_market=True)\n",
    "pf.add_ticker('TLT', label='satellite', in_market=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Note: You can edit a satellite asset's market status after adding it to `PortfolioMaker` with the `edit_ticker_mkt_status()` method. Remove tickers of any label with `remove_ticker()`._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Strategy` classes can also take a **benchmark portfolio** to compare to the core/satellite. Besides their different labels, benchmark and core assets function similarly in `PortfolioMaker`. We'll choose a benchmark that matches standard beginner advice in investing.\n",
    "\n",
    "**Important for `SMAStrategy`: one of the assets must have a 'track' attribute**, so we'll place it on SPY since it's often used as a gauge for how the market is doing as a whole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf.add_ticker('SPY', .6, label='benchmark', track=True) # tracks the S&P 500\n",
    "pf.add_ticker('AGG', .4, label='benchmark') # the total US bond market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, you can **check that `PortfolioMaker` objects are ready for use in a `Strategy`**. If no error is raised, we can move on to the next section."
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "core assets and target holding fraction(s):\n",
      "30.00000% in VUG\n",
      "9.00000% in VO\n",
      "9.00000% in EFG\n",
      "9.00000% in VBIIX\n",
      "3.00000% in ISHIX\n",
      "*** 60.00000% in core overall ***\n",
      "----------------\n",
      "satellite assets and target holding fraction(s):\n",
      "in-market asset:     SSO\n",
      "out-of-market asset: TLT\n",
      "*** 40.00000% in satellite overall ***\n",
      "----------------\n",
      "-----passed-----\n",
      "----------------\n",
      "benchmark assets and target holding fraction(s):\n",
      "60.00000% in SPY\n",
      "40.00000% in AGG\n",
      "*** 100.00000% in benchmark overall ***\n",
      "----------------\n",
      "-----passed-----\n",
      "\n"
     ]
    }
   ],
   "source": [
    "pf.check_assets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulating a `Strategy`. 💧\n",
    "\n",
    "**Once the portfolio is ready to go, we can create an instance of `SMAStrategy`.**\n",
    "\n",
    "As a reminder, this `Strategy` places all of its portfolio's satellite portion into either the in-market or out-market asset depending on how a tracked asset's current price (in our case, SPY) compares to its simple moving average.\n",
    "\n",
    "_For more information on arguments and why this is a sensible approach, read the docstrings for `SMAStrategy` and `HistoricalSimulator` after importing them below by clicking either name in a code cell and pressing `Shift`+`Tab`._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from simulator import HistoricalSimulator\n",
    "from strategies import SMAStrategy\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We include `pf` as the first argument**. With the others, we specify:\n",
    "- ...that the `Strategy` will make decisions based on its tracked asset's **200 day SMA**;\n",
    "- ...that we'll start the portfolio off with **$10,000 in cash**;\n",
    "- ...that the simulation will cover **May 30, 2007 through June 1, 2015**.\n",
    "- ...that **the satellite portion will be rebalanced six times a year**, or every other month;\n",
    "- ...that **the total portfolio will be rebalanced three times a year**, or every four months;\n",
    "- ...that rebalances should happen on the **second-to-last market day** of eligible months.\n",
    "- ...and that **any dividends are reinvested** back into the assets from which they came."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sim = SMAStrategy(pf, window=200, cash=1e4,\n",
    "                  start_date=pd.Timestamp(2007, 5, 30),\n",
    "                  end_date=pd.Timestamp(2015, 6, 1),\n",
    "                  sat_rb_freq=6, tot_rb_freq=3, target_rb_day=-2,\n",
    "                  reinvest_dividends=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The main attribute to note is `assets`**, a nested dictionary organized by asset name. Individual asset dictionaries contain the number of shares currently held, downloaded price data, and the attributes we specified in `PortfolioMaker`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['VUG', 'VO', 'EFG', 'VBIIX', 'ISHIX', 'SSO', 'TLT', 'SPY', 'AGG']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(sim.assets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volume</th>\n",
       "      <th>adjClose</th>\n",
       "      <th>adjHigh</th>\n",
       "      <th>adjLow</th>\n",
       "      <th>adjOpen</th>\n",
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       "    <tr>\n",
       "      <th>date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2006-08-09</th>\n",
       "      <td>51.27</td>\n",
       "      <td>52.05</td>\n",
       "      <td>51.25</td>\n",
       "      <td>51.79</td>\n",
       "      <td>62700</td>\n",
       "      <td>43.157788</td>\n",
       "      <td>43.814372</td>\n",
       "      <td>43.140952</td>\n",
       "      <td>43.595511</td>\n",
       "      <td>62700</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-08-10</th>\n",
       "      <td>51.58</td>\n",
       "      <td>51.67</td>\n",
       "      <td>51.15</td>\n",
       "      <td>51.15</td>\n",
       "      <td>53400</td>\n",
       "      <td>43.418738</td>\n",
       "      <td>43.494498</td>\n",
       "      <td>43.056775</td>\n",
       "      <td>43.056775</td>\n",
       "      <td>53400</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2006-08-11</th>\n",
       "      <td>51.33</td>\n",
       "      <td>51.56</td>\n",
       "      <td>51.16</td>\n",
       "      <td>51.56</td>\n",
       "      <td>90900</td>\n",
       "      <td>43.208294</td>\n",
       "      <td>43.401903</td>\n",
       "      <td>43.065193</td>\n",
       "      <td>43.401903</td>\n",
       "      <td>90900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2006-08-14</th>\n",
       "      <td>51.40</td>\n",
       "      <td>51.91</td>\n",
       "      <td>51.40</td>\n",
       "      <td>51.75</td>\n",
       "      <td>53900</td>\n",
       "      <td>43.267219</td>\n",
       "      <td>43.696524</td>\n",
       "      <td>43.267219</td>\n",
       "      <td>43.561840</td>\n",
       "      <td>53900</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-08-15</th>\n",
       "      <td>52.28</td>\n",
       "      <td>52.28</td>\n",
       "      <td>51.80</td>\n",
       "      <td>52.10</td>\n",
       "      <td>81300</td>\n",
       "      <td>44.007980</td>\n",
       "      <td>44.007980</td>\n",
       "      <td>43.603928</td>\n",
       "      <td>43.856461</td>\n",
       "      <td>81300</td>\n",
       "      <td>0.0</td>\n",
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       "            close   high    low   open  volume   adjClose    adjHigh  \\\n",
       "date                                                                   \n",
       "2006-08-09  51.27  52.05  51.25  51.79   62700  43.157788  43.814372   \n",
       "2006-08-10  51.58  51.67  51.15  51.15   53400  43.418738  43.494498   \n",
       "2006-08-11  51.33  51.56  51.16  51.56   90900  43.208294  43.401903   \n",
       "2006-08-14  51.40  51.91  51.40  51.75   53900  43.267219  43.696524   \n",
       "2006-08-15  52.28  52.28  51.80  52.10   81300  44.007980  44.007980   \n",
       "\n",
       "               adjLow    adjOpen  adjVolume  divCash  splitFactor  \n",
       "date                                                               \n",
       "2006-08-09  43.140952  43.595511      62700      0.0          1.0  \n",
       "2006-08-10  43.056775  43.056775      53400      0.0          1.0  \n",
       "2006-08-11  43.065193  43.401903      90900      0.0          1.0  \n",
       "2006-08-14  43.267219  43.561840      53900      0.0          1.0  \n",
       "2006-08-15  43.603928  43.856461      81300      0.0          1.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sim.assets['VUG']['df'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now **run the simulation in one line**.\n",
    "\n",
    "_Note: i) to see a log of transactions, add `verbose=True` as an argument. ii) Keep in mind that each class instance is only good for one simulation._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sim.begin_time_loop()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, we can **plot and compare our portfolio's results against the benchmark**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "end date main portfolio value: $29,647.75\n",
      "end date main portfolio shares: \n",
      "VUG: 86.5, VO: 21.4, EFG: 40.1, VBIIX: 269.5, ISHIX: 116.7, SSO: 179.7, TLT: 0.0\n",
      "\n",
      "end date benchmark portfolio value: $21,063.16\n",
      "end date benchmark portfolio shares: \n",
      "SPY: 65.5, AGG: 87.6\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim.plot_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "## The idea from here...\n",
    "...is to try out different `Strategy` instances and vary assets, core/satellite allocations, dates, rebalance frequencies, and so on to **perhaps translate what you've learned to your real-life investments**. As long as they inherit from `HistoricalSimulator`, you may also **create `Strategy` classes that follow your own logic** and see how they perform.\n",
    "\n",
    "The package is under active development [on GitHub](https://github.com/ojustino/backstroke), so any constructive critiques (or compliments) are welcome there. If you're interested in seeing how another, simpler strategy works, [take a look at `buy_and_hold.ipynb`](buy_and_hold.ipynb). In the meantime, **thank you for reading.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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