{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2acce44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5ba70af7",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "                        Timestamp     Open     High      Low    Close  \\\n",
       "7317754 2025-11-30 23:55:00+00:00  90405.0  90452.0  90403.0  90452.0   \n",
       "7317755 2025-11-30 23:56:00+00:00  90452.0  90481.0  90420.0  90420.0   \n",
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       "7317758 2025-11-30 23:59:00+00:00  90363.0  90386.0  90362.0  90382.0   \n",
       "\n",
       "           Volume  \n",
       "7317754  0.531700  \n",
       "7317755  0.055547  \n",
       "7317756  0.301931  \n",
       "7317757  4.591653  \n",
       "7317758  0.410369  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s', utc=True)\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3b320537",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                        Timestamp     Open     High      Low    Close  \\\n",
       "7317754 2025-11-30 23:55:00+00:00  90405.0  90452.0  90403.0  90452.0   \n",
       "7317755 2025-11-30 23:56:00+00:00  90452.0  90481.0  90420.0  90420.0   \n",
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       "7317758 2025-11-30 23:59:00+00:00  90363.0  90386.0  90362.0  90382.0   \n",
       "\n",
       "           Volume      Avg  \n",
       "7317754  0.531700  90427.5  \n",
       "7317755  0.055547  90450.5  \n",
       "7317756  0.301931  90427.0  \n",
       "7317757  4.591653  90395.0  \n",
       "7317758  0.410369  90374.0  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Avg'] = (df['Low'] + df['High']) / 2\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4b1cd63c",
   "metadata": {},
   "outputs": [
    {
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       "      <th>5080</th>\n",
       "      <td>2025-11-28</td>\n",
       "      <td>91324.308681</td>\n",
       "      <td>90255.0</td>\n",
       "      <td>92970.0</td>\n",
       "      <td>90283.0</td>\n",
       "      <td>92966.0</td>\n",
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       "      <th>5081</th>\n",
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       "      <td>90746.479514</td>\n",
       "      <td>90265.0</td>\n",
       "      <td>91158.0</td>\n",
       "      <td>90279.0</td>\n",
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       "      <th>5082</th>\n",
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       "      <td>91187.356250</td>\n",
       "      <td>90363.0</td>\n",
       "      <td>91940.0</td>\n",
       "      <td>90362.0</td>\n",
       "      <td>91940.0</td>\n",
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      "text/plain": [
       "       Timestamp           Avg  OpenMin  OpenMax  CloseMin  CloseMax\n",
       "5078  2025-11-26  88057.301736  86312.0  90574.0   86323.0   90574.0\n",
       "5079  2025-11-27  91245.092708  90126.0  91888.0   90126.0   91925.0\n",
       "5080  2025-11-28  91324.308681  90255.0  92970.0   90283.0   92966.0\n",
       "5081  2025-11-29  90746.479514  90265.0  91158.0   90279.0   91179.0\n",
       "5082  2025-11-30  91187.356250  90363.0  91940.0   90362.0   91940.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_days = (\n",
    "    df.groupby(df[\"Timestamp\"].dt.date)\n",
    "    .agg(\n",
    "        Avg=(\"Avg\", \"mean\"),\n",
    "        OpenMin=(\"Open\", \"min\"),\n",
    "        OpenMax=(\"Open\", \"max\"),\n",
    "        CloseMin=(\"Close\", \"min\"),\n",
    "        CloseMax=(\"Close\", \"max\"),\n",
    "    )\n",
    "    .reset_index()\n",
    ")\n",
    "df_days.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9a7b3310",
   "metadata": {},
   "outputs": [
    {
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       "      <th>316</th>\n",
       "      <td>2025-02-27</td>\n",
       "      <td>2025-04-25</td>\n",
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       "      <td>95801.0</td>\n",
       "      <td>74515.0</td>\n",
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       "      <th>317</th>\n",
       "      <td>2025-04-26</td>\n",
       "      <td>2025-05-11</td>\n",
       "      <td>92877.0</td>\n",
       "      <td>104971.0</td>\n",
       "      <td>92872.0</td>\n",
       "      <td>104965.0</td>\n",
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       "      <th>318</th>\n",
       "      <td>2025-05-12</td>\n",
       "      <td>2025-07-11</td>\n",
       "      <td>98384.0</td>\n",
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       "      <th>319</th>\n",
       "      <td>2025-07-12</td>\n",
       "      <td>2025-11-04</td>\n",
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       "      <td>126202.0</td>\n",
       "      <td>98943.0</td>\n",
       "      <td>126202.0</td>\n",
       "      <td>117640.026389</td>\n",
       "      <td>103712.985764</td>\n",
       "      <td>0.118387</td>\n",
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       "      <th>320</th>\n",
       "      <td>2025-11-05</td>\n",
       "      <td>2025-11-18</td>\n",
       "      <td>89291.0</td>\n",
       "      <td>107343.0</td>\n",
       "      <td>89286.0</td>\n",
       "      <td>107343.0</td>\n",
       "      <td>102514.621181</td>\n",
       "      <td>91705.833333</td>\n",
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       "     start_date    end_date  min_open  max_open  min_close  max_close  \\\n",
       "316  2025-02-27  2025-04-25   74509.0   95801.0    74515.0    95800.0   \n",
       "317  2025-04-26  2025-05-11   92877.0  104971.0    92872.0   104965.0   \n",
       "318  2025-05-12  2025-07-11   98384.0  118833.0    98382.0   118839.0   \n",
       "319  2025-07-12  2025-11-04   98944.0  126202.0    98943.0   126202.0   \n",
       "320  2025-11-05  2025-11-18   89291.0  107343.0    89286.0   107343.0   \n",
       "\n",
       "         start_avg        end_avg    change  \n",
       "316   85166.063889   94303.907292  0.107294  \n",
       "317   94500.950347  104182.167708  0.102446  \n",
       "318  103569.791319  117463.666667  0.134150  \n",
       "319  117640.026389  103712.985764  0.118387  \n",
       "320  102514.621181   91705.833333  0.105437  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "intervals = []\n",
    "start_idx = 0\n",
    "price_base = df_days.loc[start_idx, \"Avg\"]\n",
    "\n",
    "for i in range(1, len(df_days)):\n",
    "    price_now = df_days.loc[i, \"Avg\"]\n",
    "    change = abs(price_now - price_base) / price_base\n",
    "\n",
    "    if change >= 0.10:\n",
    "        interval = df_days.loc[start_idx:i]\n",
    "        \n",
    "        intervals.append({\n",
    "            \"start_date\": df_days.loc[start_idx, \"Timestamp\"],\n",
    "            \"end_date\": df_days.loc[i, \"Timestamp\"],\n",
    "            \"min_open\": interval[\"OpenMin\"].min(),\n",
    "            \"max_open\": interval[\"OpenMax\"].max(),\n",
    "            \"min_close\": interval[\"CloseMin\"].min(),\n",
    "            \"max_close\": interval[\"CloseMax\"].max(),\n",
    "            \"start_avg\": price_base,\n",
    "            \"end_avg\": price_now,\n",
    "            \"change\": change,\n",
    "        })\n",
    "\n",
    "        start_idx = i + 1\n",
    "        if start_idx >= len(df_days):\n",
    "            break\n",
    "        price_base = df_days.loc[start_idx, \"Avg\"]\n",
    "\n",
    "df_intervals = pd.DataFrame(intervals)\n",
    "df_intervals.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07f1cd58",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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