02_Strategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "25182ab0-89fd-48f4-a2c2-df3fc451fcf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "from statsmodels.tsa.vector_ar.vecm import coint_johansen"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35599241-136f-449e-9b68-112b61eb56bd",
   "metadata": {},
   "source": [
    "## Helper functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b482f29f-49c0-4511-9453-0ffa081aa996",
   "metadata": {},
   "outputs": [],
   "source": [
    "def time_plotter(data, title=None, ax=None):\n",
    "    fig = data.plot(figsize=(16, 5), title=title, ax=ax)\n",
    "    fig.minorticks_on()\n",
    "    fig.grid(b=True, which='major', color='#666666', linestyle='-', alpha=0.6)\n",
    "    fig.grid(b=True, which='minor', color='#999999', linestyle='--', alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "717c6a61-d340-4f32-bf39-902e97817c9e",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Mean reverting strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d4f3bf65-1161-44aa-866d-81f5b9eb718e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sym</th>\n",
       "      <th>avtusd</th>\n",
       "      <th>btceur</th>\n",
       "      <th>btcusd</th>\n",
       "      <th>btgbtc</th>\n",
       "      <th>btgusd</th>\n",
       "      <th>datbtc</th>\n",
       "      <th>datusd</th>\n",
       "      <th>dshbtc</th>\n",
       "      <th>dshusd</th>\n",
       "      <th>edobtc</th>\n",
       "      <th>...</th>\n",
       "      <th>sanbtc</th>\n",
       "      <th>saneth</th>\n",
       "      <th>sanusd</th>\n",
       "      <th>xmrbtc</th>\n",
       "      <th>xmrusd</th>\n",
       "      <th>xrpbtc</th>\n",
       "      <th>xrpusd</th>\n",
       "      <th>yywusd</th>\n",
       "      <th>zecbtc</th>\n",
       "      <th>zecusd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2018-01-01</th>\n",
       "      <td>4.3501</td>\n",
       "      <td>11133.000000</td>\n",
       "      <td>13376.0</td>\n",
       "      <td>0.017510</td>\n",
       "      <td>233.91</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.21599</td>\n",
       "      <td>0.075880</td>\n",
       "      <td>1018.4</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.007218</td>\n",
       "      <td>5.4528</td>\n",
       "      <td>0.025201</td>\n",
       "      <td>337.35</td>\n",
       "      <td>0.000152</td>\n",
       "      <td>2.0395</td>\n",
       "      <td>0.29672</td>\n",
       "      <td>0.038430</td>\n",
       "      <td>513.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>4.8223</td>\n",
       "      <td>12190.000000</td>\n",
       "      <td>14722.0</td>\n",
       "      <td>0.017272</td>\n",
       "      <td>253.45</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.23990</td>\n",
       "      <td>0.078710</td>\n",
       "      <td>1162.9</td>\n",
       "      <td>0.000296</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000426</td>\n",
       "      <td>0.007499</td>\n",
       "      <td>6.3281</td>\n",
       "      <td>0.024751</td>\n",
       "      <td>364.23</td>\n",
       "      <td>0.000149</td>\n",
       "      <td>2.1930</td>\n",
       "      <td>0.32414</td>\n",
       "      <td>0.037153</td>\n",
       "      <td>547.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>4.9444</td>\n",
       "      <td>12611.930380</td>\n",
       "      <td>15150.0</td>\n",
       "      <td>0.016210</td>\n",
       "      <td>245.63</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.31060</td>\n",
       "      <td>0.074679</td>\n",
       "      <td>1131.4</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000437</td>\n",
       "      <td>0.007047</td>\n",
       "      <td>6.6440</td>\n",
       "      <td>0.025456</td>\n",
       "      <td>386.00</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>2.7317</td>\n",
       "      <td>0.40344</td>\n",
       "      <td>0.037096</td>\n",
       "      <td>559.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>4.5806</td>\n",
       "      <td>12553.152741</td>\n",
       "      <td>15159.0</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>234.33</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>0.30030</td>\n",
       "      <td>0.073563</td>\n",
       "      <td>1118.0</td>\n",
       "      <td>0.000340</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.007583</td>\n",
       "      <td>7.1570</td>\n",
       "      <td>0.024498</td>\n",
       "      <td>372.50</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>2.7367</td>\n",
       "      <td>0.40173</td>\n",
       "      <td>0.035959</td>\n",
       "      <td>545.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>4.7399</td>\n",
       "      <td>14061.196800</td>\n",
       "      <td>16911.0</td>\n",
       "      <td>0.013692</td>\n",
       "      <td>232.08</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.27160</td>\n",
       "      <td>0.063586</td>\n",
       "      <td>1079.8</td>\n",
       "      <td>0.000300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>0.007658</td>\n",
       "      <td>7.3922</td>\n",
       "      <td>0.021088</td>\n",
       "      <td>357.62</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>2.4961</td>\n",
       "      <td>0.50470</td>\n",
       "      <td>0.031951</td>\n",
       "      <td>541.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sym         avtusd        btceur   btcusd    btgbtc  btgusd    datbtc  \\\n",
       "time                                                                    \n",
       "2018-01-01  4.3501  11133.000000  13376.0  0.017510  233.91  0.000016   \n",
       "2018-01-02  4.8223  12190.000000  14722.0  0.017272  253.45  0.000016   \n",
       "2018-01-03  4.9444  12611.930380  15150.0  0.016210  245.63  0.000021   \n",
       "2018-01-04  4.5806  12553.152741  15159.0  0.015500  234.33  0.000020   \n",
       "2018-01-05  4.7399  14061.196800  16911.0  0.013692  232.08  0.000016   \n",
       "\n",
       "sym          datusd    dshbtc  dshusd    edobtc  ...    sanbtc    saneth  \\\n",
       "time                                             ...                       \n",
       "2018-01-01  0.21599  0.075880  1018.4  0.000315  ...  0.000407  0.007218   \n",
       "2018-01-02  0.23990  0.078710  1162.9  0.000296  ...  0.000426  0.007499   \n",
       "2018-01-03  0.31060  0.074679  1131.4  0.000318  ...  0.000437  0.007047   \n",
       "2018-01-04  0.30030  0.073563  1118.0  0.000340  ...  0.000447  0.007583   \n",
       "2018-01-05  0.27160  0.063586  1079.8  0.000300  ...  0.000433  0.007658   \n",
       "\n",
       "sym         sanusd    xmrbtc  xmrusd    xrpbtc  xrpusd   yywusd    zecbtc  \\\n",
       "time                                                                        \n",
       "2018-01-01  5.4528  0.025201  337.35  0.000152  2.0395  0.29672  0.038430   \n",
       "2018-01-02  6.3281  0.024751  364.23  0.000149  2.1930  0.32414  0.037153   \n",
       "2018-01-03  6.6440  0.025456  386.00  0.000180  2.7317  0.40344  0.037096   \n",
       "2018-01-04  7.1570  0.024498  372.50  0.000181  2.7367  0.40173  0.035959   \n",
       "2018-01-05  7.3922  0.021088  357.62  0.000147  2.4961  0.50470  0.031951   \n",
       "\n",
       "sym         zecusd  \n",
       "time                \n",
       "2018-01-01  513.51  \n",
       "2018-01-02  547.88  \n",
       "2018-01-03  559.70  \n",
       "2018-01-04  545.11  \n",
       "2018-01-05  541.98  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data = pd.read_pickle(\"train_data.pkl\")\n",
    "train_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3bcb92a3-3371-4fd7-b83f-28e67dd02ca6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>sym1</th>\n",
       "      <th>sym2</th>\n",
       "      <th>pvalue</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>ethusd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>xmrusd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>etcusd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>ltcusd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>qtmusd</td>\n",
       "      <td>ethusd</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       sym1    sym2  pvalue\n",
       "104  btgusd  ethusd     0.0\n",
       "103  btgusd  xmrusd     0.0\n",
       "100  btgusd  etcusd     0.0\n",
       "105  btgusd  ltcusd     0.0\n",
       "96   qtmusd  ethusd     0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr_pairs = pd.read_pickle(\"mean_reverting_pairs.pkl\")\n",
    "mr_pairs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cc15af4-11f8-4d7f-bded-3d3eae15d039",
   "metadata": {},
   "source": [
    "## Finding the optimal ratio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5f191c5f-fd23-44ce-ab9f-749e0e0b8b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|█████████████████████████████████████████████████████████████████████████████████| 57/57 [00:00<00:00, 258.91it/s]\n"
     ]
    }
   ],
   "source": [
    "hratios = []\n",
    "\n",
    "for i, row in tqdm(list(mr_pairs.iterrows())):\n",
    "    syms = list(row[[\"sym1\", \"sym2\"]])\n",
    "    test = coint_johansen(train_data[syms].dropna(), det_order=0, k_ar_diff=1)\n",
    "    hratio = test.evec[:, 0]\n",
    "    hratios.append(hratio)\n",
    "    \n",
    "mr_pairs[\"hratio\"] = hratios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ed4659b-c4d0-4acc-986a-b4d2857b1f95",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sym1</th>\n",
       "      <th>sym2</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>hratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>ethusd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.046955652162842004, -0.005909207286486495]</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>xmrusd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.04459535710413538, -0.01634131625385791]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>etcusd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.03733968609519922, -0.11673349603827508]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>btgusd</td>\n",
       "      <td>ltcusd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.03238266616972063, -0.013405791625567872]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>qtmusd</td>\n",
       "      <td>ethusd</td>\n",
       "      <td>0.0</td>\n",
       "      <td>[0.300696518434622, -0.01092250787095261]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "       sym1    sym2  pvalue                                         hratio\n",
       "104  btgusd  ethusd     0.0  [0.046955652162842004, -0.005909207286486495]\n",
       "103  btgusd  xmrusd     0.0    [0.04459535710413538, -0.01634131625385791]\n",
       "100  btgusd  etcusd     0.0    [0.03733968609519922, -0.11673349603827508]\n",
       "105  btgusd  ltcusd     0.0   [0.03238266616972063, -0.013405791625567872]\n",
       "96   qtmusd  ethusd     0.0      [0.300696518434622, -0.01092250787095261]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mr_pairs.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb668be-7253-4194-9e65-7f8024cb259d",
   "metadata": {},
   "source": [
    "## Forming the stationary spread time series "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ea487622-a64e-443e-a9f2-ec586ac842db",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data = pd.read_pickle(\"test_data.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "07695c17-d740-4035-b4e0-81260008bee3",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sym</th>\n",
       "      <th>avtusd</th>\n",
       "      <th>btceur</th>\n",
       "      <th>btcusd</th>\n",
       "      <th>btgbtc</th>\n",
       "      <th>btgusd</th>\n",
       "      <th>datbtc</th>\n",
       "      <th>datusd</th>\n",
       "      <th>dshbtc</th>\n",
       "      <th>dshusd</th>\n",
       "      <th>edobtc</th>\n",
       "      <th>...</th>\n",
       "      <th>sanbtc</th>\n",
       "      <th>saneth</th>\n",
       "      <th>sanusd</th>\n",
       "      <th>xmrbtc</th>\n",
       "      <th>xmrusd</th>\n",
       "      <th>xrpbtc</th>\n",
       "      <th>xrpusd</th>\n",
       "      <th>yywusd</th>\n",
       "      <th>zecbtc</th>\n",
       "      <th>zecusd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2020-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6429.721274</td>\n",
       "      <td>7212.700000</td>\n",
       "      <td>0.000741</td>\n",
       "      <td>5.3025</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.013999</td>\n",
       "      <td>0.005806</td>\n",
       "      <td>41.845</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.001600</td>\n",
       "      <td>0.20300</td>\n",
       "      <td>0.006365</td>\n",
       "      <td>45.878</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.19333</td>\n",
       "      <td>0.011362</td>\n",
       "      <td>0.003905</td>\n",
       "      <td>28.051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>0.0655</td>\n",
       "      <td>6256.800000</td>\n",
       "      <td>6989.400000</td>\n",
       "      <td>0.000725</td>\n",
       "      <td>5.1014</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.012588</td>\n",
       "      <td>0.005733</td>\n",
       "      <td>40.152</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.001451</td>\n",
       "      <td>0.19578</td>\n",
       "      <td>0.006510</td>\n",
       "      <td>45.643</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>0.18817</td>\n",
       "      <td>0.009701</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>27.157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6605.317587</td>\n",
       "      <td>7371.200000</td>\n",
       "      <td>0.000733</td>\n",
       "      <td>5.4152</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.013616</td>\n",
       "      <td>0.005981</td>\n",
       "      <td>43.897</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001407</td>\n",
       "      <td>0.21550</td>\n",
       "      <td>0.006949</td>\n",
       "      <td>50.996</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.19413</td>\n",
       "      <td>0.009504</td>\n",
       "      <td>0.003886</td>\n",
       "      <td>28.597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6607.800000</td>\n",
       "      <td>7369.831761</td>\n",
       "      <td>0.000724</td>\n",
       "      <td>5.3854</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.016580</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>46.298</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>0.001506</td>\n",
       "      <td>0.18804</td>\n",
       "      <td>0.006830</td>\n",
       "      <td>50.520</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.19352</td>\n",
       "      <td>0.010998</td>\n",
       "      <td>0.004073</td>\n",
       "      <td>29.937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6598.193025</td>\n",
       "      <td>7364.600000</td>\n",
       "      <td>0.000734</td>\n",
       "      <td>5.4270</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.015300</td>\n",
       "      <td>0.006952</td>\n",
       "      <td>51.190</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.19607</td>\n",
       "      <td>0.007300</td>\n",
       "      <td>53.633</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>0.19468</td>\n",
       "      <td>0.009545</td>\n",
       "      <td>0.004171</td>\n",
       "      <td>30.741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "sym         avtusd       btceur       btcusd    btgbtc  btgusd    datbtc  \\\n",
       "time                                                                       \n",
       "2020-01-01     NaN  6429.721274  7212.700000  0.000741  5.3025  0.000002   \n",
       "2020-01-02  0.0655  6256.800000  6989.400000  0.000725  5.1014  0.000002   \n",
       "2020-01-03     NaN  6605.317587  7371.200000  0.000733  5.4152  0.000002   \n",
       "2020-01-04     NaN  6607.800000  7369.831761  0.000724  5.3854  0.000002   \n",
       "2020-01-05     NaN  6598.193025  7364.600000  0.000734  5.4270  0.000002   \n",
       "\n",
       "sym           datusd    dshbtc  dshusd    edobtc  ...    sanbtc    saneth  \\\n",
       "time                                              ...                       \n",
       "2020-01-01  0.013999  0.005806  41.845  0.000027  ...  0.000029  0.001600   \n",
       "2020-01-02  0.012588  0.005733  40.152  0.000027  ...  0.000026  0.001451   \n",
       "2020-01-03  0.013616  0.005981  43.897  0.000026  ...       NaN  0.001407   \n",
       "2020-01-04  0.016580  0.006282  46.298  0.000027  ...  0.000028  0.001506   \n",
       "2020-01-05  0.015300  0.006952  51.190  0.000027  ...  0.000029       NaN   \n",
       "\n",
       "sym          sanusd    xmrbtc  xmrusd    xrpbtc   xrpusd    yywusd    zecbtc  \\\n",
       "time                                                                           \n",
       "2020-01-01  0.20300  0.006365  45.878  0.000027  0.19333  0.011362  0.003905   \n",
       "2020-01-02  0.19578  0.006510  45.643  0.000027  0.18817  0.009701  0.003890   \n",
       "2020-01-03  0.21550  0.006949  50.996  0.000026  0.19413  0.009504  0.003886   \n",
       "2020-01-04  0.18804  0.006830  50.520  0.000026  0.19352  0.010998  0.004073   \n",
       "2020-01-05  0.19607  0.007300  53.633  0.000026  0.19468  0.009545  0.004171   \n",
       "\n",
       "sym         zecusd  \n",
       "time                \n",
       "2020-01-01  28.051  \n",
       "2020-01-02  27.157  \n",
       "2020-01-03  28.597  \n",
       "2020-01-04  29.937  \n",
       "2020-01-05  30.741  \n",
       "\n",
       "[5 rows x 49 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "11c03101-048d-4def-9df2-967f66bd3f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "spread = pd.DataFrame(test_data[[\"btgusd\", \"ethusd\"]].values * mr_pairs[\"hratio\"].iloc[0], index=test_data.index, columns=[\"btgusd\", \"ethusd\"])\n",
    "spread[\"spd\"] = spread.sum(axis=1)\n",
    "spread[\"rolling_mean\"] = spread[\"spd\"].rolling(30).mean()\n",
    "spread[\"rolling_std\"] = spread[\"spd\"].rolling(30).std()\n",
    "spread[\"zscore\"] = (spread[\"spd\"] - spread[\"rolling_mean\"]) / spread[\"rolling_std\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3093c462-11bb-4c6a-b01d-9678d5085ec1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "time_plotter(spread[\"zscore\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a0f9f845-1be8-4218-8959-645e1826168a",
   "metadata": {},
   "outputs": [],
   "source": [
    "spread[\"longpos\"] = np.nan\n",
    "spread[\"shortpos\"] = np.nan\n",
    "\n",
    "spread.loc[spread[\"zscore\"]<=-2, \"longpos\"] = 1\n",
    "spread.loc[spread[\"zscore\"]>=0, \"longpos\"] = 0\n",
    "\n",
    "spread.loc[spread[\"zscore\"]>=2, \"shortpos\"] = -1\n",
    "spread.loc[spread[\"zscore\"]<=0, \"shortpos\"] = 0\n",
    "\n",
    "spread[\"pos\"] = spread[\"longpos\"] + spread[\"shortpos\"]\n",
    "spread[\"pos\"] = spread[\"pos\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60e8ab3d-507a-4148-b777-b803c8422df2",
   "metadata": {},
   "outputs": [],
   "source": [
    "spread[\"change\"] = spread[\"spd\"].pct_change()\n",
    "spread[\"ret\"] = spread[\"pos\"] * spread[\"change\"]\n",
    "spread[\"ret\"] = spread[\"ret\"].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5a6caf3a-a255-49ce-b38a-e2cd3ba945db",
   "metadata": {},
   "outputs": [],
   "source": [
    "spread[\"portret\"] = spread[\"ret\"].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "44a86536-1712-4cf7-8c90-b8dccfa08797",
   "metadata": {},
   "outputs": [],
   "source": [
    "pair_rets = spread[[\"portret\"]].copy(deep=True)\n",
    "pair_rets[\"pair\"] = \"a_b\"\n",
    "pair_rets = pair_rets.set_index(\"pair\", append=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9de9377-7181-4f03-b93f-7ebbb1ab2311",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 57/57 [00:00<00:00, 91.90it/s]\n"
     ]
    }
   ],
   "source": [
    "pairs_port = []\n",
    "\n",
    "for i, row in tqdm(list(mr_pairs.iterrows())):\n",
    "    syms = list(row[[\"sym1\", \"sym2\"]])\n",
    "    \n",
    "    spread = pd.DataFrame(test_data[syms].values * row[\"hratio\"], index=test_data.index, columns=syms)\n",
    "    spread[\"spd\"] = spread.sum(axis=1)\n",
    "    spread[\"rolling_mean\"] = spread[\"spd\"].rolling(30).mean()\n",
    "    spread[\"rolling_std\"] = spread[\"spd\"].rolling(30).std()\n",
    "    spread[\"zscore\"] = (spread[\"spd\"] - spread[\"rolling_mean\"]) / spread[\"rolling_std\"]\n",
    "\n",
    "    spread[\"longpos\"] = np.nan\n",
    "    spread[\"shortpos\"] = np.nan\n",
    "\n",
    "    spread.loc[spread[\"zscore\"]<=-2, \"longpos\"] = 1\n",
    "    spread.loc[spread[\"zscore\"]>=0, \"longpos\"] = 0\n",
    "\n",
    "    spread.loc[spread[\"zscore\"]>=2, \"shortpos\"] = -1\n",
    "    spread.loc[spread[\"zscore\"]<=0, \"shortpos\"] = 0\n",
    "\n",
    "    spread[\"pos\"] = spread[\"longpos\"] + spread[\"shortpos\"]\n",
    "    spread[\"pos\"] = spread[\"pos\"].fillna(0)\n",
    "\n",
    "    spread[\"change\"] = spread[\"spd\"].pct_change()\n",
    "    spread[\"ret\"] = spread[\"pos\"] * spread[\"change\"]\n",
    "    spread[\"ret\"] = spread[\"ret\"].fillna(0)\n",
    "\n",
    "    spread[\"portret\"] = spread[\"ret\"].cumsum()\n",
    "    \n",
    "    pair_ret = spread[[\"portret\"]].copy(deep=True)\n",
    "    pair_ret[\"pair\"] = f\"{row['sym1']}_{row['sym2']}\"\n",
    "    pair_ret = pair_ret.set_index(\"pair\", append=True)\n",
    "    pair_ret\n",
    "    \n",
    "    pairs_port.append(pair_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5131026-a4bc-4b35-a760-1f22cd933e65",
   "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>pair</th>\n",
       "      <th>btgusd_etcusd</th>\n",
       "      <th>btgusd_ethusd</th>\n",
       "      <th>btgusd_ltcusd</th>\n",
       "      <th>btgusd_neousd</th>\n",
       "      <th>btgusd_omgusd</th>\n",
       "      <th>btgusd_xmrusd</th>\n",
       "      <th>btgusd_zecusd</th>\n",
       "      <th>dshusd_ethusd</th>\n",
       "      <th>dshusd_ltcusd</th>\n",
       "      <th>dshusd_omgusd</th>\n",
       "      <th>...</th>\n",
       "      <th>xrpusd_ethusd</th>\n",
       "      <th>xrpusd_ioteur</th>\n",
       "      <th>xrpusd_iotusd</th>\n",
       "      <th>xrpusd_ltcusd</th>\n",
       "      <th>xrpusd_omgusd</th>\n",
       "      <th>xrpusd_xmrusd</th>\n",
       "      <th>zecusd_ethusd</th>\n",
       "      <th>zecusd_ltcusd</th>\n",
       "      <th>zecusd_omgusd</th>\n",
       "      <th>zecusd_xmrusd</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>2021-12-27</th>\n",
       "      <td>11.034308</td>\n",
       "      <td>6.150120</td>\n",
       "      <td>10.608175</td>\n",
       "      <td>110.072824</td>\n",
       "      <td>19.726023</td>\n",
       "      <td>8.566829</td>\n",
       "      <td>7.805443</td>\n",
       "      <td>8.041593</td>\n",
       "      <td>-145.755301</td>\n",
       "      <td>-15.267111</td>\n",
       "      <td>...</td>\n",
       "      <td>-605.466449</td>\n",
       "      <td>-10.046439</td>\n",
       "      <td>-9.847517</td>\n",
       "      <td>-11.612311</td>\n",
       "      <td>-11.74729</td>\n",
       "      <td>-5.767696</td>\n",
       "      <td>6.377879</td>\n",
       "      <td>-6.0701</td>\n",
       "      <td>-29.496337</td>\n",
       "      <td>6.586693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-28</th>\n",
       "      <td>11.034308</td>\n",
       "      <td>6.150120</td>\n",
       "      <td>10.608175</td>\n",
       "      <td>110.072824</td>\n",
       "      <td>19.726023</td>\n",
       "      <td>8.566829</td>\n",
       "      <td>7.805443</td>\n",
       "      <td>8.041593</td>\n",
       "      <td>-145.755301</td>\n",
       "      <td>-15.267111</td>\n",
       "      <td>...</td>\n",
       "      <td>-605.466449</td>\n",
       "      <td>-10.200421</td>\n",
       "      <td>-9.847517</td>\n",
       "      <td>-11.612311</td>\n",
       "      <td>-11.74729</td>\n",
       "      <td>-5.767696</td>\n",
       "      <td>6.377879</td>\n",
       "      <td>-6.0701</td>\n",
       "      <td>-29.496337</td>\n",
       "      <td>6.590032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-29</th>\n",
       "      <td>11.034308</td>\n",
       "      <td>6.194768</td>\n",
       "      <td>10.608175</td>\n",
       "      <td>110.072824</td>\n",
       "      <td>19.726023</td>\n",
       "      <td>8.663423</td>\n",
       "      <td>7.805443</td>\n",
       "      <td>8.087242</td>\n",
       "      <td>-145.755301</td>\n",
       "      <td>-15.267111</td>\n",
       "      <td>...</td>\n",
       "      <td>-605.466449</td>\n",
       "      <td>-10.313509</td>\n",
       "      <td>-9.957022</td>\n",
       "      <td>-11.612311</td>\n",
       "      <td>-11.74729</td>\n",
       "      <td>-5.767696</td>\n",
       "      <td>6.423633</td>\n",
       "      <td>-6.0701</td>\n",
       "      <td>-29.496337</td>\n",
       "      <td>6.649347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-30</th>\n",
       "      <td>11.034308</td>\n",
       "      <td>6.194768</td>\n",
       "      <td>10.608175</td>\n",
       "      <td>110.072824</td>\n",
       "      <td>19.726023</td>\n",
       "      <td>8.756387</td>\n",
       "      <td>7.805443</td>\n",
       "      <td>8.087242</td>\n",
       "      <td>-145.755301</td>\n",
       "      <td>-15.267111</td>\n",
       "      <td>...</td>\n",
       "      <td>-605.466449</td>\n",
       "      <td>-10.313509</td>\n",
       "      <td>-9.957022</td>\n",
       "      <td>-11.612311</td>\n",
       "      <td>-11.74729</td>\n",
       "      <td>-5.767696</td>\n",
       "      <td>6.423633</td>\n",
       "      <td>-6.0701</td>\n",
       "      <td>-29.496337</td>\n",
       "      <td>6.701261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-12-31</th>\n",
       "      <td>11.034308</td>\n",
       "      <td>6.194768</td>\n",
       "      <td>10.608175</td>\n",
       "      <td>110.072824</td>\n",
       "      <td>19.726023</td>\n",
       "      <td>8.803027</td>\n",
       "      <td>7.805443</td>\n",
       "      <td>8.087242</td>\n",
       "      <td>-145.755301</td>\n",
       "      <td>-15.267111</td>\n",
       "      <td>...</td>\n",
       "      <td>-605.466449</td>\n",
       "      <td>-10.313509</td>\n",
       "      <td>-9.957022</td>\n",
       "      <td>-11.612311</td>\n",
       "      <td>-11.74729</td>\n",
       "      <td>-5.767696</td>\n",
       "      <td>6.423633</td>\n",
       "      <td>-6.0701</td>\n",
       "      <td>-29.496337</td>\n",
       "      <td>6.755553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 57 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "pair        btgusd_etcusd  btgusd_ethusd  btgusd_ltcusd  btgusd_neousd  \\\n",
       "time                                                                     \n",
       "2021-12-27      11.034308       6.150120      10.608175     110.072824   \n",
       "2021-12-28      11.034308       6.150120      10.608175     110.072824   \n",
       "2021-12-29      11.034308       6.194768      10.608175     110.072824   \n",
       "2021-12-30      11.034308       6.194768      10.608175     110.072824   \n",
       "2021-12-31      11.034308       6.194768      10.608175     110.072824   \n",
       "\n",
       "pair        btgusd_omgusd  btgusd_xmrusd  btgusd_zecusd  dshusd_ethusd  \\\n",
       "time                                                                     \n",
       "2021-12-27      19.726023       8.566829       7.805443       8.041593   \n",
       "2021-12-28      19.726023       8.566829       7.805443       8.041593   \n",
       "2021-12-29      19.726023       8.663423       7.805443       8.087242   \n",
       "2021-12-30      19.726023       8.756387       7.805443       8.087242   \n",
       "2021-12-31      19.726023       8.803027       7.805443       8.087242   \n",
       "\n",
       "pair        dshusd_ltcusd  dshusd_omgusd  ...  xrpusd_ethusd  xrpusd_ioteur  \\\n",
       "time                                      ...                                 \n",
       "2021-12-27    -145.755301     -15.267111  ...    -605.466449     -10.046439   \n",
       "2021-12-28    -145.755301     -15.267111  ...    -605.466449     -10.200421   \n",
       "2021-12-29    -145.755301     -15.267111  ...    -605.466449     -10.313509   \n",
       "2021-12-30    -145.755301     -15.267111  ...    -605.466449     -10.313509   \n",
       "2021-12-31    -145.755301     -15.267111  ...    -605.466449     -10.313509   \n",
       "\n",
       "pair        xrpusd_iotusd  xrpusd_ltcusd  xrpusd_omgusd  xrpusd_xmrusd  \\\n",
       "time                                                                     \n",
       "2021-12-27      -9.847517     -11.612311      -11.74729      -5.767696   \n",
       "2021-12-28      -9.847517     -11.612311      -11.74729      -5.767696   \n",
       "2021-12-29      -9.957022     -11.612311      -11.74729      -5.767696   \n",
       "2021-12-30      -9.957022     -11.612311      -11.74729      -5.767696   \n",
       "2021-12-31      -9.957022     -11.612311      -11.74729      -5.767696   \n",
       "\n",
       "pair        zecusd_ethusd  zecusd_ltcusd  zecusd_omgusd  zecusd_xmrusd  \n",
       "time                                                                    \n",
       "2021-12-27       6.377879        -6.0701     -29.496337       6.586693  \n",
       "2021-12-28       6.377879        -6.0701     -29.496337       6.590032  \n",
       "2021-12-29       6.423633        -6.0701     -29.496337       6.649347  \n",
       "2021-12-30       6.423633        -6.0701     -29.496337       6.701261  \n",
       "2021-12-31       6.423633        -6.0701     -29.496337       6.755553  \n",
       "\n",
       "[5 rows x 57 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basket = pd.concat(pairs_port).unstack(\"pair\")[\"portret\"]\n",
    "basket.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c9f9d26-b71a-4f2b-9307-98f82588a1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "terminal_wealth = basket.iloc[-1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a71325d-fae5-4c16-a14e-4626a074bd4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "terminal_wealth[abs(terminal_wealth) > 20].size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7f4c46e2-4174-40d1-9038-44ee806ea44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "keep_wealth = terminal_wealth[abs(terminal_wealth) <= 20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "bcb5f32d-7e38-48e8-a8da-4c2b0a10f91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(keep_wealth).set_title(\"Terminal Wealth Distribution\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7ea50f46-3932-49a8-9187-c2f79eea6881",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2965735407658925"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep_wealth.mean()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}