{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import vectorbtpro as vbt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "vbt.settings.set_theme('dark')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task / Learning ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this exercise, we want to create and understand a 3-dim-array to make a Volume Plot in VBT.\n",
    "\n",
    "Cf. https://stackoverflow.com/a/63748235\n",
    "Cf. https://vectorbt.pro/api/generic/plotting/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "x = np.zeros((2,3,4)) \n",
    "Simply Means:\n",
    "\n",
    "2 Sets, 3 Rows per Set, 4 Columns\n",
    "Example:\n",
    "\n",
    "Input\n",
    "\n",
    "x = np.zeros((2,3,4))\n",
    "Output\n",
    "\n",
    "Set # 1 ---- [[[ 0.,  0.,  0.,  0.],  ---- Row 1\n",
    "               [ 0.,  0.,  0.,  0.],  ---- Row 2\n",
    "               [ 0.,  0.,  0.,  0.]], ---- Row 3 \n",
    "    \n",
    "Set # 2 ----  [[ 0.,  0.,  0.,  0.],  ---- Row 1\n",
    "               [ 0.,  0.,  0.,  0.],  ---- Row 2\n",
    "               [ 0.,  0.,  0.,  0.]]] ---- Row 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Execution ##"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare sample backtest ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import Some real data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3044c306f4984c36b256a30e16ff861b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "start = '2021-01-01 UTC'  # crypto is in UTC\n",
    "end = '2021-12-31 UTC'\n",
    "timeframe = '1h'\n",
    "cols = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "\n",
    "ohlcv = vbt.BinanceData.fetch('BTCUSDT', start=start, end=end, timeframe=timeframe, limit=100000).get(cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basic Function to test our parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_entry(close=ohlcv['Close'], exit_shift=1, fast_window=9, slow_window=50, wait=0):\n",
    "    fast_ma = vbt.MA.run(close=ohlcv['Close'], window=fast_window)\n",
    "    slow_ma = vbt.MA.run(close=ohlcv['Close'], window=slow_window)\n",
    "    entries = fast_ma.ma_crossed_above(slow_ma, wait=wait)\n",
    "    exits = entries.shift(exit_shift).astype(bool)\n",
    "    pf = vbt.Portfolio.from_signals(\n",
    "        close=close, \n",
    "        entries=entries, \n",
    "        exits=exits,\n",
    "        size=100,\n",
    "        size_type='value',\n",
    "        init_cash='auto')\n",
    "    return pf.stats([\n",
    "        'total_return', \n",
    "        'win_rate', \n",
    "        'profit_factor',\n",
    "        'max_dd',\n",
    "        'total_trades'\n",
    "        ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the ranges of our three variables (shift, slow ma, fast ma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "exit_shift = range(5,20)            # in our 3d plot, this will equal the x axis (15 values)\n",
    "slow_ma = range(40,60)              # in our 3d plot, this will equal the y axis (20 values)\n",
    "fast_ma = range(7,27)               # in our 3d plot, this will equal the z axis (20 values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. BTW: see above, cell 5 reg. the exit_shift variable. \n",
    "\n",
    "- All it does, is \"shifting\" the entry signals x timestamps forward to generate the signal for exiting the trade. \n",
    "\n",
    "Such technique is especially helpful to inspect the reliability and robustness of your entry logic __without__ confusing it with exit indicators (want to know whether your profit comes from the entry or the exit?). That is to say, it gives you an idea, if and how frequently a move in the anticipated direction occurs after the entry signal has happened."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets run our backtest now!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "th_combs = list(product(exit_shift, slow_ma, fast_ma))\n",
    "\n",
    "comb_stats = [\n",
    "    test_entry(exit_shift=exit_shift, slow_window=slow_ma, fast_window=fast_ma)\n",
    "    for exit_shift, slow_ma, fast_ma in th_combs\n",
    "    ]  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Dataframe from our results and label it accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "comb_stats_df = pd.DataFrame(comb_stats)\n",
    "\n",
    "comb_stats_df.index = pd.MultiIndex.from_tuples(\n",
    "    th_combs, \n",
    "    names=['exit_shift', 'slow_ma', 'fast_ma'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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></th>\n",
       "      <th></th>\n",
       "      <th>Total Return [%]</th>\n",
       "      <th>Win Rate [%]</th>\n",
       "      <th>Profit Factor</th>\n",
       "      <th>Max Drawdown [%]</th>\n",
       "      <th>Total Trades</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exit_shift</th>\n",
       "      <th>slow_ma</th>\n",
       "      <th>fast_ma</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">40</th>\n",
       "      <th>7</th>\n",
       "      <td>28.620706</td>\n",
       "      <td>52.727273</td>\n",
       "      <td>1.289403</td>\n",
       "      <td>12.039517</td>\n",
       "      <td>165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12.865810</td>\n",
       "      <td>51.592357</td>\n",
       "      <td>1.128907</td>\n",
       "      <td>14.661135</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.272885</td>\n",
       "      <td>51.333333</td>\n",
       "      <td>1.082172</td>\n",
       "      <td>15.812769</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13.276195</td>\n",
       "      <td>48.591549</td>\n",
       "      <td>1.124026</td>\n",
       "      <td>17.905736</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9.227458</td>\n",
       "      <td>48.905109</td>\n",
       "      <td>1.099671</td>\n",
       "      <td>22.467860</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">19</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">59</th>\n",
       "      <th>22</th>\n",
       "      <td>17.943975</td>\n",
       "      <td>59.740260</td>\n",
       "      <td>1.195631</td>\n",
       "      <td>35.704668</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-2.045321</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>0.977928</td>\n",
       "      <td>41.559362</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>16.698445</td>\n",
       "      <td>54.545455</td>\n",
       "      <td>1.177064</td>\n",
       "      <td>32.215622</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26.545968</td>\n",
       "      <td>57.142857</td>\n",
       "      <td>1.280304</td>\n",
       "      <td>31.915274</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>41.837836</td>\n",
       "      <td>57.894737</td>\n",
       "      <td>1.456534</td>\n",
       "      <td>29.457537</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Total Return [%]  Win Rate [%]  Profit Factor  \\\n",
       "exit_shift slow_ma fast_ma                                                  \n",
       "5          40      7               28.620706     52.727273       1.289403   \n",
       "                   8               12.865810     51.592357       1.128907   \n",
       "                   9                8.272885     51.333333       1.082172   \n",
       "                   10              13.276195     48.591549       1.124026   \n",
       "                   11               9.227458     48.905109       1.099671   \n",
       "...                                      ...           ...            ...   \n",
       "19         59      22              17.943975     59.740260       1.195631   \n",
       "                   23              -2.045321     54.545455       0.977928   \n",
       "                   24              16.698445     54.545455       1.177064   \n",
       "                   25              26.545968     57.142857       1.280304   \n",
       "                   26              41.837836     57.894737       1.456534   \n",
       "\n",
       "                            Max Drawdown [%]  Total Trades  \n",
       "exit_shift slow_ma fast_ma                                  \n",
       "5          40      7               12.039517           165  \n",
       "                   8               14.661135           157  \n",
       "                   9               15.812769           150  \n",
       "                   10              17.905736           142  \n",
       "                   11              22.467860           138  \n",
       "...                                      ...           ...  \n",
       "19         59      22              35.704668            77  \n",
       "                   23              41.559362            77  \n",
       "                   24              32.215622            77  \n",
       "                   25              31.915274            77  \n",
       "                   26              29.457537            76  \n",
       "\n",
       "[6000 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comb_stats_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare DF for conversion into 3D-array ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we can only display one metric, lets get rid of all columns except for 'Total Return [%]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df = comb_stats_df.drop(['Win Rate [%]', 'Profit Factor', 'Max Drawdown [%]', 'Total Trades'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... the same could be achieved via clean_df = comb_stats_df['Total Return [%]']\n",
    "\n",
    "to get an idea of how to interact with the new DF, try for instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            ...\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26],\n",
       "           dtype='int64', name='fast_ma', length=6000)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.index.names\n",
    "clean_df.index.get_level_values(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. and extract the __pure__ array data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "array_data = clean_df['Total Return [%]']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thats it - we're ready for conversion."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3D Conversion ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create a 3d-array by using .to_numpy. \n",
    "\n",
    "However, we need to further use .reshape(). Reshape() requires us to define, how the array_data should be allocated to x, y, and z. In our setup,\n",
    "- x equals the shift\n",
    "- y equals the slow ma\n",
    "- z equals the fast ma\n",
    "\n",
    "Lets do it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "three_d_array = clean_df['Total Return [%]'].to_numpy().reshape(15, 20, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See above, cell 57 to get a clue, where this numbers (15, 20,20) come from.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Volume Plotting ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To assign the values to the respective axes, we need to define input lists."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "exit_shift_list = list(exit_shift)\n",
    "slow_ma_list = list(slow_ma)\n",
    "fast_ma_list = list(fast_ma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. and we are ready to go:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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    "volume = vbt.Volume(\n",
    "    data=three_d_array, # our 3dim-array, which we created above\n",
    "    x_labels=exit_shift_list, # the lists that we created from our parameter ranges to label the values\n",
    "    y_labels=slow_ma_list,\n",
    "    z_labels=fast_ma_list,\n",
    "    width=800, \n",
    "    height=800,\n",
    "    trace_kwargs=dict(colorscale=\"icefire\", cmid=0), \n",
    "    ## Cf. https://plotly.com/python/builtin-colorscales/, \n",
    "    ## cmid=0 dives the color scale between posite and negative results\n",
    "    scene=dict(\n",
    "      xaxis=dict(\n",
    "        title='Shift',\n",
    "        showaxeslabels=True,\n",
    "      ),\n",
    "      yaxis=dict(\n",
    "        title='Slow MA',\n",
    "        showaxeslabels=True,\n",
    "      ),\n",
    "      zaxis=dict(\n",
    "        title='Fast MA',\n",
    "        #autorange=True,\n",
    "        showaxeslabels=True,\n",
    "      ),\n",
    "    ),\n",
    ")\n",
    "volume.fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example for further analysis ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The lower region does not look too bad for our simply strategy. But we might want to consider other metrics as well. Lets sort the dataframe by max drawdown."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "drawdown_df = comb_stats_df.sort_values(by=['Max Drawdown [%]'], ascending=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we want to use leverage and are thus interested in strategies that do not exceed a 15% DD?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "rslt_df = drawdown_df.loc[drawdown_df['Max Drawdown [%]'] < 15.0] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. and chose the best performing out of such? According to profit factor?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "rslt_df_pf = rslt_df.sort_values(by=['Profit Factor'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>100</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">50</th>\n",
       "      <th>23</th>\n",
       "      <td>-3.665415</td>\n",
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       "      <td>0.926288</td>\n",
       "      <td>14.002172</td>\n",
       "      <td>98</td>\n",
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       "      <td>-5.637548</td>\n",
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      "text/plain": [
       "                            Total Return [%]  Win Rate [%]  Profit Factor  \\\n",
       "exit_shift slow_ma fast_ma                                                  \n",
       "11         53      11              63.973228     55.000000       1.973216   \n",
       "8          59      9               55.996476     60.396040       1.956220   \n",
       "           53      11              51.522183     59.000000       1.949297   \n",
       "9          54      11              55.580395     53.061224       1.943820   \n",
       "8          58      10              54.401509     59.405941       1.938970   \n",
       "...                                      ...           ...            ...   \n",
       "5          51      13              -3.241266     44.444444       0.944172   \n",
       "           50      23              -3.665415     46.938776       0.926288   \n",
       "                   13              -5.637548     47.000000       0.903764   \n",
       "           51      22              -6.530703     48.979592       0.868143   \n",
       "6          51      22              -6.985558     46.938776       0.866175   \n",
       "\n",
       "                            Max Drawdown [%]  Total Trades  \n",
       "exit_shift slow_ma fast_ma                                  \n",
       "11         53      11               9.656734           101  \n",
       "8          59      9               10.207960           101  \n",
       "           53      11              10.397894           101  \n",
       "9          54      11               9.979441            99  \n",
       "8          58      10               7.701408           101  \n",
       "...                                      ...           ...  \n",
       "5          51      13              13.009315           100  \n",
       "           50      23              14.002172            98  \n",
       "                   13              13.811262           101  \n",
       "           51      22              11.870502            98  \n",
       "6          51      22              14.173559            98  \n",
       "\n",
       "[909 rows x 5 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 20) ## using display.max_rows you can extend the dataframe display!\n",
    "rslt_df_pf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or better in terms of total return?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "rslt_df_pf_2 = rslt_df_pf.sort_values(by=['Total Return [%]'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"2\" valign=\"top\">50</th>\n",
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       "      <td>14.002172</td>\n",
       "      <td>98</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>13.811262</td>\n",
       "      <td>101</td>\n",
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       "    <tr>\n",
       "      <th>51</th>\n",
       "      <th>22</th>\n",
       "      <td>-6.530703</td>\n",
       "      <td>48.979592</td>\n",
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       "      <td>11.870502</td>\n",
       "      <td>98</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>909 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Total Return [%]  Win Rate [%]  Profit Factor  \\\n",
       "exit_shift slow_ma fast_ma                                                  \n",
       "12         51      7               75.931181     57.627119       1.817858   \n",
       "11         57      8               69.868964     56.310680       1.917144   \n",
       "           51      7               69.634861     53.781513       1.769494   \n",
       "           57      7               67.116657     56.190476       1.860671   \n",
       "           59      7               66.624758     55.339806       1.861782   \n",
       "...                                      ...           ...            ...   \n",
       "5          51      13              -3.241266     44.444444       0.944172   \n",
       "           50      23              -3.665415     46.938776       0.926288   \n",
       "                   13              -5.637548     47.000000       0.903764   \n",
       "           51      22              -6.530703     48.979592       0.868143   \n",
       "6          51      22              -6.985558     46.938776       0.866175   \n",
       "\n",
       "                            Max Drawdown [%]  Total Trades  \n",
       "exit_shift slow_ma fast_ma                                  \n",
       "12         51      7               12.950735           119  \n",
       "11         57      8                9.424025           103  \n",
       "           51      7               11.517904           120  \n",
       "           57      7                9.742177           105  \n",
       "           59      7                7.539600           103  \n",
       "...                                      ...           ...  \n",
       "5          51      13              13.009315           100  \n",
       "           50      23              14.002172            98  \n",
       "                   13              13.811262           101  \n",
       "           51      22              11.870502            98  \n",
       "6          51      22              14.173559            98  \n",
       "\n",
       "[909 rows x 5 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rslt_df_pf_2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first result looks promosing, both in terms of total return and max DD. This is\n",
    "- exit_shift: 10\n",
    "- slow_ma = 59\n",
    "- fast_ma = 7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example Cross-verification ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But is this reliable? Or are we just overfitting? Lets find out!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grab some data for another time frame and add another symbol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b866a6b9e3ac49fb916de829b32a9855",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f04f4b4de1964fe387a8ce1a7cb819b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c971d1426a54cbc8a5c7a845bbbd0e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/data/base.py:538: UserWarning:\n",
      "\n",
      "Symbols have mismatching index. Setting missing data points to NaN.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "start = '2016-01-01 UTC'  # crypto is in UTC\n",
    "end = '2020-12-31 UTC'\n",
    "timeframe = '1h'\n",
    "cols = ['Open', 'High', 'Low', 'Close', 'Volume']\n",
    "\n",
    "data = vbt.BinanceData.fetch(['BTCUSDT', 'ETHUSDT'], start=start, end=end, timeframe=timeframe, limit=1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "BTC_Close = data.data['BTCUSDT']['Close']\n",
    "ETH_Close = data.data['ETHUSDT']['Close']\n",
    "close = data.get('Close') # this applies for both symbols!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets run our best combination!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_ma = vbt.MA.run(close=close, window=7) # value from our best test\n",
    "slow_ma = vbt.MA.run(close=close, window=59) # value from our best test\n",
    "\n",
    "entries = fast_ma.ma_crossed_above(slow_ma, wait=0)\n",
    "\n",
    "exits = entries.shift(10).astype(bool) # value shift from our best test.\n",
    "\n",
    "pf = vbt.Portfolio.from_signals(\n",
    "    close=close, \n",
    "    entries=entries, \n",
    "    exits=exits,\n",
    "    #slippage=0.00055,\n",
    "    sl_stop=0.0075,\n",
    "    #fees=0.0006,\n",
    "    size=1000,\n",
    "    size_type='value',\n",
    "    init_cash='auto'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sharpe_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'calmar_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'omega_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sortino_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/base/wrapping.py:1285: UserWarning:\n",
      "\n",
      "Couldn't parse the frequency of index. Pass it as `freq` or define it globally under `settings.wrapping`.\n",
      "\n",
      "/tmp/ipykernel_116506/3705677322.py:1: UserWarning:\n",
      "\n",
      "Object has multiple columns. Aggregated some metrics using <function mean at 0x7f32e0069c10>. Pass column to select a single column/group.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Start                         2017-08-17 04:00:00+00:00\n",
       "End                           2020-12-30 23:00:00+00:00\n",
       "Period                                            29494\n",
       "Start Value                                 1010.722202\n",
       "Min Value                                    997.435799\n",
       "                                        ...            \n",
       "Avg Losing Trade [%]                          -0.643434\n",
       "Avg Winning Trade Duration                     9.952336\n",
       "Avg Losing Trade Duration                      5.340928\n",
       "Profit Factor                                  1.978824\n",
       "Expectancy                                     3.704043\n",
       "Name: agg_stats, Length: 25, dtype: object"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pf.stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sharpe_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'calmar_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'omega_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sortino_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sharpe_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'calmar_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'omega_ratio' requires frequency to be set\n",
      "\n",
      "/home/pdr0906pwr0407/anaconda3/lib/python3.9/site-packages/vectorbtpro/generic/stats_builder.py:461: UserWarning:\n",
      "\n",
      "Metric 'sortino_ratio' requires frequency to be set\n",
      "\n"
     ]
    }
   ],
   "source": [
    "btc_stats = pf.stats(column=0)\n",
    "eth_stats = pf.stats(column=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start                         2017-08-17 04:00:00+00:00\n",
       "End                           2020-12-30 23:00:00+00:00\n",
       "Period                                            29494\n",
       "Start Value                                 1013.944405\n",
       "Min Value                                    999.281598\n",
       "                                        ...            \n",
       "Avg Losing Trade [%]                           -0.63482\n",
       "Avg Winning Trade Duration                    10.025478\n",
       "Avg Losing Trade Duration                      5.718894\n",
       "Profit Factor                                  1.787723\n",
       "Expectancy                                     2.893693\n",
       "Name: 0, Length: 25, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Doesn't look too bad! We dont beat the benchmark - however, we are only invested around 9.5 percent of the time and, more strikingly, we yielded a positive return on this data sample as well!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Start                         2017-08-17 04:00:00+00:00\n",
       "End                           2020-12-30 23:00:00+00:00\n",
       "Period                                            29494\n",
       "Start Value                                      1007.5\n",
       "Min Value                                        995.59\n",
       "                                        ...            \n",
       "Avg Losing Trade [%]                          -0.652049\n",
       "Avg Winning Trade Duration                     9.879195\n",
       "Avg Losing Trade Duration                      4.962963\n",
       "Profit Factor                                  2.169926\n",
       "Expectancy                                     4.514393\n",
       "Name: 1, Length: 25, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eth_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ".. and look at this! It works for ETH as well. In this case, we even outperformed the benchmark return - while being invested only 8.6 percent of the time.\n",
    "\n",
    ".. and remember: this is just a simple crossover strategy with fixed exit after 10 bars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nice feature: Lets look at a heatmap to see how the returns are distributed and to spot correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cac664b7c0734ab791297903718b4b2d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'colorscale': [[0.0, '#0d0887'], [0.1111111111111111, '#46039f'],\n",
       "               …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf.returns_acc.resample(\"M\").ts_heatmap()"
   ]
  }
 ],
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