prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "9925ad2f-9c50-45e5-8533-eebf1a5fd7d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "130eb0f5-a3ab-4b7a-a3aa-bb1c8437d6f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_ticker = yf.Ticker(\"BTC-USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d71ca889-3406-4139-9da6-792dfc844e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "if os.path.exists(\"btc.csv\"):\n",
    "    btc = pd.read_csv(\"btc.csv\", index_col=0)\n",
    "else:\n",
    "    btc = btc_ticker.history(period=\"max\")\n",
    "    btc.to_csv(\"btc.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "fae85f03-d041-4cdd-832b-5a769e998236",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</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>2014-09-17</th>\n",
       "      <td>465.864014</td>\n",
       "      <td>468.174011</td>\n",
       "      <td>452.421997</td>\n",
       "      <td>457.334015</td>\n",
       "      <td>21056800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-18</th>\n",
       "      <td>456.859985</td>\n",
       "      <td>456.859985</td>\n",
       "      <td>413.104004</td>\n",
       "      <td>424.440002</td>\n",
       "      <td>34483200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-19</th>\n",
       "      <td>424.102997</td>\n",
       "      <td>427.834991</td>\n",
       "      <td>384.532013</td>\n",
       "      <td>394.795990</td>\n",
       "      <td>37919700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-20</th>\n",
       "      <td>394.673004</td>\n",
       "      <td>423.295990</td>\n",
       "      <td>389.882996</td>\n",
       "      <td>408.903992</td>\n",
       "      <td>36863600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-09-21</th>\n",
       "      <td>408.084991</td>\n",
       "      <td>412.425995</td>\n",
       "      <td>393.181000</td>\n",
       "      <td>398.821014</td>\n",
       "      <td>26580100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-04</th>\n",
       "      <td>19832.470703</td>\n",
       "      <td>19999.689453</td>\n",
       "      <td>19636.816406</td>\n",
       "      <td>19986.712891</td>\n",
       "      <td>25245861652</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-05</th>\n",
       "      <td>19988.789062</td>\n",
       "      <td>20031.160156</td>\n",
       "      <td>19673.046875</td>\n",
       "      <td>19812.371094</td>\n",
       "      <td>28813460025</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-06</th>\n",
       "      <td>19817.724609</td>\n",
       "      <td>20155.269531</td>\n",
       "      <td>18800.171875</td>\n",
       "      <td>18837.667969</td>\n",
       "      <td>43403978910</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-07</th>\n",
       "      <td>18837.683594</td>\n",
       "      <td>19427.171875</td>\n",
       "      <td>18644.466797</td>\n",
       "      <td>19290.324219</td>\n",
       "      <td>35239757134</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-08</th>\n",
       "      <td>19309.482422</td>\n",
       "      <td>19410.619141</td>\n",
       "      <td>19089.548828</td>\n",
       "      <td>19337.371094</td>\n",
       "      <td>33201930240</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2914 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2014-09-17    465.864014    468.174011    452.421997    457.334015   \n",
       "2014-09-18    456.859985    456.859985    413.104004    424.440002   \n",
       "2014-09-19    424.102997    427.834991    384.532013    394.795990   \n",
       "2014-09-20    394.673004    423.295990    389.882996    408.903992   \n",
       "2014-09-21    408.084991    412.425995    393.181000    398.821014   \n",
       "...                  ...           ...           ...           ...   \n",
       "2022-09-04  19832.470703  19999.689453  19636.816406  19986.712891   \n",
       "2022-09-05  19988.789062  20031.160156  19673.046875  19812.371094   \n",
       "2022-09-06  19817.724609  20155.269531  18800.171875  18837.667969   \n",
       "2022-09-07  18837.683594  19427.171875  18644.466797  19290.324219   \n",
       "2022-09-08  19309.482422  19410.619141  19089.548828  19337.371094   \n",
       "\n",
       "                 Volume  Dividends  Stock Splits  \n",
       "Date                                              \n",
       "2014-09-17     21056800          0             0  \n",
       "2014-09-18     34483200          0             0  \n",
       "2014-09-19     37919700          0             0  \n",
       "2014-09-20     36863600          0             0  \n",
       "2014-09-21     26580100          0             0  \n",
       "...                 ...        ...           ...  \n",
       "2022-09-04  25245861652          0             0  \n",
       "2022-09-05  28813460025          0             0  \n",
       "2022-09-06  43403978910          0             0  \n",
       "2022-09-07  35239757134          0             0  \n",
       "2022-09-08  33201930240          0             0  \n",
       "\n",
       "[2914 rows x 7 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "086d0aef-f2e4-42eb-9e41-4e4bad1f13d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc.index = pd.to_datetime(btc.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1067f63d-91fd-425a-80a1-fccd1eba3612",
   "metadata": {},
   "outputs": [],
   "source": [
    "del btc[\"Dividends\"]\n",
    "del btc[\"Stock Splits\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "1fd1a503-412e-419f-b3e0-e32bf829ed0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc.columns = [c.lower() for c in btc.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "0f3d1839-bc67-45b7-b1a0-8d8f60c62b50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Date'>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "btc.plot.line(y=\"close\", use_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "17bb4150-7dc3-4dcb-92ac-5b2149899563",
   "metadata": {},
   "outputs": [],
   "source": [
    "wiki = pd.read_csv(\"wikipedia_edits.csv\", index_col=0, parse_dates=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "10685b74-a2fb-4399-9a50-83d1fb0a986a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>edit_count</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>neg_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-04-06</th>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.018351</td>\n",
       "      <td>0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-07</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2009-04-08</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>2009-04-09</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-04-10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-04</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.108480</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-05</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>-0.108480</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-06</th>\n",
       "      <td>0.433333</td>\n",
       "      <td>-0.081502</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-07</th>\n",
       "      <td>0.433333</td>\n",
       "      <td>-0.081502</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-08</th>\n",
       "      <td>0.466667</td>\n",
       "      <td>-0.114104</td>\n",
       "      <td>0.183333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4904 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            edit_count  sentiment  neg_sentiment\n",
       "2009-04-06    0.133333  -0.018351       0.025000\n",
       "2009-04-07    0.000000   0.000000       0.000000\n",
       "2009-04-08    0.000000   0.000000       0.000000\n",
       "2009-04-09    0.000000   0.000000       0.000000\n",
       "2009-04-10    0.000000   0.000000       0.000000\n",
       "...                ...        ...            ...\n",
       "2022-09-04    0.400000  -0.108480       0.150000\n",
       "2022-09-05    0.400000  -0.108480       0.150000\n",
       "2022-09-06    0.433333  -0.081502       0.150000\n",
       "2022-09-07    0.433333  -0.081502       0.150000\n",
       "2022-09-08    0.466667  -0.114104       0.183333\n",
       "\n",
       "[4904 rows x 3 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wiki"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "dbb1b8f3-2058-4e67-9b86-0aef8cfde65c",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc = btc.merge(wiki, left_index=True, right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "8767cdef-7b18-4902-ab92-e2f9d802f174",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc[\"tomorrow\"] = btc[\"close\"].shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "bef1484f-353d-4bdf-afbf-9274b396d8a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc[\"target\"] = (btc[\"tomorrow\"] > btc[\"close\"]).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "c696cca8-ff0b-4735-b87d-5d41e6d8eccf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1563\n",
       "0    1351\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc[\"target\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "641e7710-24e0-408a-83e4-9c651fac29a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2022-09-04</th>\n",
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       "      <th>2022-09-05</th>\n",
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       "      <td>NaN</td>\n",
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       "  </tbody>\n",
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       "<p>2914 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    open          high           low         close  \\\n",
       "2014-09-17    465.864014    468.174011    452.421997    457.334015   \n",
       "2014-09-18    456.859985    456.859985    413.104004    424.440002   \n",
       "2014-09-19    424.102997    427.834991    384.532013    394.795990   \n",
       "2014-09-20    394.673004    423.295990    389.882996    408.903992   \n",
       "2014-09-21    408.084991    412.425995    393.181000    398.821014   \n",
       "...                  ...           ...           ...           ...   \n",
       "2022-09-04  19832.470703  19999.689453  19636.816406  19986.712891   \n",
       "2022-09-05  19988.789062  20031.160156  19673.046875  19812.371094   \n",
       "2022-09-06  19817.724609  20155.269531  18800.171875  18837.667969   \n",
       "2022-09-07  18837.683594  19427.171875  18644.466797  19290.324219   \n",
       "2022-09-08  19309.482422  19410.619141  19089.548828  19337.371094   \n",
       "\n",
       "                 volume  edit_count  sentiment  neg_sentiment      tomorrow  \\\n",
       "2014-09-17     21056800    5.033333  -0.232191       0.532718    424.440002   \n",
       "2014-09-18     34483200    5.066667  -0.232759       0.532718    394.795990   \n",
       "2014-09-19     37919700    5.200000  -0.235415       0.549385    408.903992   \n",
       "2014-09-20     36863600    5.200000  -0.233185       0.549385    398.821014   \n",
       "2014-09-21     26580100    5.233333  -0.204017       0.532718    402.152008   \n",
       "...                 ...         ...        ...            ...           ...   \n",
       "2022-09-04  25245861652    0.400000  -0.108480       0.150000  19812.371094   \n",
       "2022-09-05  28813460025    0.400000  -0.108480       0.150000  18837.667969   \n",
       "2022-09-06  43403978910    0.433333  -0.081502       0.150000  19290.324219   \n",
       "2022-09-07  35239757134    0.433333  -0.081502       0.150000  19337.371094   \n",
       "2022-09-08  33201930240    0.466667  -0.114104       0.183333           NaN   \n",
       "\n",
       "            target  \n",
       "2014-09-17       0  \n",
       "2014-09-18       0  \n",
       "2014-09-19       1  \n",
       "2014-09-20       0  \n",
       "2014-09-21       1  \n",
       "...            ...  \n",
       "2022-09-04       0  \n",
       "2022-09-05       0  \n",
       "2022-09-06       1  \n",
       "2022-09-07       1  \n",
       "2022-09-08       0  \n",
       "\n",
       "[2914 rows x 10 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "4f781df2-f1a6-4499-ad69-8749daadc142",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(min_samples_split=50, random_state=1)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(n_estimators=100, min_samples_split=50, random_state=1)\n",
    "\n",
    "train = btc.iloc[:-200]\n",
    "test = btc.iloc[-200:]\n",
    "\n",
    "predictors = [\"close\", \"volume\", \"open\", \"high\", \"low\", \"edit_count\", \"sentiment\", \"neg_sentiment\"]\n",
    "model.fit(train[predictors], train[\"target\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5db58bd9-009f-400c-9692-1d0787d40de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48484848484848486"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_score\n",
    "\n",
    "preds = model.predict(test[predictors])\n",
    "preds = pd.Series(preds, index=test.index)\n",
    "precision_score(test[\"target\"], preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bce7d7dd-4f0d-4405-ad17-42516045941f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(train, test, predictors, model):\n",
    "    model.fit(train[predictors], train[\"target\"])\n",
    "    preds = model.predict(test[predictors])\n",
    "    preds = pd.Series(preds, index=test.index, name=\"predictions\")\n",
    "    combined = pd.concat([test[\"target\"], preds], axis=1)\n",
    "    return combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "bec7bbe6-7c57-4ae9-82ca-404182d8218d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def backtest(data, model, predictors, start=1095, step=150):\n",
    "    all_predictions = []\n",
    "\n",
    "    for i in range(start, data.shape[0], step):\n",
    "        train = data.iloc[0:i].copy()\n",
    "        test = data.iloc[i:(i+step)].copy()\n",
    "        predictions = predict(train, test, predictors, model)\n",
    "        all_predictions.append(predictions)\n",
    "    \n",
    "    return pd.concat(all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "0d70183c-1ee0-48e8-b012-16804463e6d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "\n",
    "model = XGBClassifier(random_state=1, learning_rate=.1, n_estimators=200)\n",
    "predictions = backtest(btc, model, predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "c363f4ad-68ae-4733-8cfa-fc61b1a15e6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1084\n",
       "0     735\n",
       "Name: predictions, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions[\"predictions\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ed54e2de-089d-450b-8a5e-88d4a2dbdfcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5009225092250923"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(predictions[\"target\"], predictions[\"predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "ef3a6fd5-4e97-4011-be28-9fd115c8b004",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_rolling(btc):\n",
    "    horizons = [2,7,60,365]\n",
    "    new_predictors = [\"close\", \"sentiment\", \"neg_sentiment\"]\n",
    "\n",
    "    for horizon in horizons:\n",
    "        rolling_averages = btc.rolling(horizon, min_periods=1).mean()\n",
    "\n",
    "        ratio_column = f\"close_ratio_{horizon}\"\n",
    "        btc[ratio_column] = btc[\"close\"] / rolling_averages[\"close\"]\n",
    "        \n",
    "        edit_column = f\"edit_{horizon}\"\n",
    "        btc[edit_column] = rolling_averages[\"edit_count\"]\n",
    "\n",
    "        rolling = btc.rolling(horizon, closed='left', min_periods=1).mean()\n",
    "        trend_column = f\"trend_{horizon}\"\n",
    "        btc[trend_column] = rolling[\"target\"]\n",
    "\n",
    "        new_predictors+= [ratio_column, trend_column, edit_column]\n",
    "    return btc, new_predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "bb127d2d-5bef-42d1-89ad-759bdbb45a7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "btc, new_predictors = compute_rolling(btc.copy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "159bf89c-e329-4d1c-a21d-fed642d2dad2",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = backtest(btc, model, new_predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ab6dbc06-6137-4f5b-8276-731c94541c29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5386289445048966"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(predictions[\"target\"], predictions[\"predictions\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "4187bd69-6af6-4e18-a6a6-069a36dc841e",
   "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>target</th>\n",
       "      <th>predictions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-09-16</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-17</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-18</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-19</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-09-20</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-04</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-05</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-06</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-07</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-08</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1819 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            target  predictions\n",
       "2017-09-16       0            1\n",
       "2017-09-17       1            0\n",
       "2017-09-18       0            0\n",
       "2017-09-19       0            1\n",
       "2017-09-20       0            1\n",
       "...            ...          ...\n",
       "2022-09-04       0            1\n",
       "2022-09-05       0            1\n",
       "2022-09-06       1            1\n",
       "2022-09-07       1            1\n",
       "2022-09-08       0            1\n",
       "\n",
       "[1819 rows x 2 columns]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bc631c2-0677-40dc-b57b-89eaf2182be6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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