{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2acce44b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5ba70af7",
   "metadata": {},
   "outputs": [
    {
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       "      <th>7317758</th>\n",
       "      <td>2025-11-30 23:59:00+00:00</td>\n",
       "      <td>90363.0</td>\n",
       "      <td>90386.0</td>\n",
       "      <td>90362.0</td>\n",
       "      <td>90382.0</td>\n",
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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": 14,
     "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": 19,
   "id": "d4b22f3b",
   "metadata": {},
   "outputs": [
    {
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       "      <td>2025-11-29</td>\n",
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       "       Timestamp      Low     High     Open    Close\n",
       "5078  2025-11-26  86304.0  90646.0  87331.0  90477.0\n",
       "5079  2025-11-27  90091.0  91926.0  90476.0  91325.0\n",
       "5080  2025-11-28  90233.0  93091.0  91326.0  90913.0\n",
       "5081  2025-11-29  90216.0  91179.0  90913.0  90832.0\n",
       "5082  2025-11-30  90362.0  91969.0  90832.0  90382.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_days = (\n",
    "    df.groupby(df[\"Timestamp\"].dt.date)\n",
    "    .agg({\"Low\": \"min\", \"High\": \"max\", \"Open\": \"first\", \"Close\": \"last\"})\n",
    "    .reset_index()\n",
    ")\n",
    "df_days.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "91823496",
   "metadata": {},
   "outputs": [
    {
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       "      <td>91969.0</td>\n",
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       "      <td>90382.0</td>\n",
       "      <td>91165.5</td>\n",
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      ],
      "text/plain": [
       "       Timestamp      Low     High     Open    Close      Avg\n",
       "5078  2025-11-26  86304.0  90646.0  87331.0  90477.0  88475.0\n",
       "5079  2025-11-27  90091.0  91926.0  90476.0  91325.0  91008.5\n",
       "5080  2025-11-28  90233.0  93091.0  91326.0  90913.0  91662.0\n",
       "5081  2025-11-29  90216.0  91179.0  90913.0  90832.0  90697.5\n",
       "5082  2025-11-30  90362.0  91969.0  90832.0  90382.0  91165.5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_days[\"Avg\"] = (df_days[\"Low\"] + df_days[\"High\"]) / 2\n",
    "df_days.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9a7b3310",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>max_close</th>\n",
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       "      <th>335</th>\n",
       "      <td>2025-02-27</td>\n",
       "      <td>2025-04-23</td>\n",
       "      <td>76252.0</td>\n",
       "      <td>94273.0</td>\n",
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       "      <th>336</th>\n",
       "      <td>2025-04-24</td>\n",
       "      <td>2025-05-09</td>\n",
       "      <td>93730.0</td>\n",
       "      <td>103261.0</td>\n",
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       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2025-07-12</td>\n",
       "      <td>2025-11-04</td>\n",
       "      <td>106470.0</td>\n",
       "      <td>124728.0</td>\n",
       "      <td>117599.0</td>\n",
       "      <td>103079.0</td>\n",
       "      <td>0.123470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>2025-11-05</td>\n",
       "      <td>2025-11-18</td>\n",
       "      <td>92112.0</td>\n",
       "      <td>105972.0</td>\n",
       "      <td>101737.5</td>\n",
       "      <td>91471.0</td>\n",
       "      <td>0.100912</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     start_date    end_date  min_open  max_close  start_avg   end_avg  \\\n",
       "335  2025-02-27  2025-04-23   76252.0    94273.0    84801.5   93335.0   \n",
       "336  2025-04-24  2025-05-09   93730.0   103261.0    92867.5  103341.0   \n",
       "337  2025-05-10  2025-07-11  100990.0   117579.0   103915.0  117032.5   \n",
       "338  2025-07-12  2025-11-04  106470.0   124728.0   117599.0  103079.0   \n",
       "339  2025-11-05  2025-11-18   92112.0   105972.0   101737.5   91471.0   \n",
       "\n",
       "       change  \n",
       "335  0.100629  \n",
       "336  0.112779  \n",
       "337  0.126233  \n",
       "338  0.123470  \n",
       "339  0.100912  "
      ]
     },
     "execution_count": 25,
     "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[\"Open\"].min(),\n",
    "            \"max_close\": interval[\"Close\"].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()"
   ]
  }
 ],
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