example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b31be689",
   "metadata": {},
   "source": [
    "# Backtest Usage Example"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdeba24b",
   "metadata": {},
   "source": [
    "## Step 1. Get StockData from Market"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e34efe73",
   "metadata": {},
   "source": [
    "### 1-1. Get StockData from Upbit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "62c9fd5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2023-05-01 09:00:00</th>\n",
       "      <td>39149000.0</td>\n",
       "      <td>39303000.0</td>\n",
       "      <td>37372000.0</td>\n",
       "      <td>37769000.0</td>\n",
       "      <td>5466.444454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-02 09:00:00</th>\n",
       "      <td>37768000.0</td>\n",
       "      <td>38680000.0</td>\n",
       "      <td>37428000.0</td>\n",
       "      <td>38405000.0</td>\n",
       "      <td>4771.654370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-03 09:00:00</th>\n",
       "      <td>38406000.0</td>\n",
       "      <td>39050000.0</td>\n",
       "      <td>37800000.0</td>\n",
       "      <td>38703000.0</td>\n",
       "      <td>5239.850975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-04 09:00:00</th>\n",
       "      <td>38733000.0</td>\n",
       "      <td>39112000.0</td>\n",
       "      <td>38423000.0</td>\n",
       "      <td>38580000.0</td>\n",
       "      <td>4027.302864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 09:00:00</th>\n",
       "      <td>38599000.0</td>\n",
       "      <td>39359000.0</td>\n",
       "      <td>38520000.0</td>\n",
       "      <td>39053000.0</td>\n",
       "      <td>5614.210355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-23 09:00:00</th>\n",
       "      <td>35964000.0</td>\n",
       "      <td>36019000.0</td>\n",
       "      <td>35901000.0</td>\n",
       "      <td>35960000.0</td>\n",
       "      <td>999.553996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24 09:00:00</th>\n",
       "      <td>35951000.0</td>\n",
       "      <td>36100000.0</td>\n",
       "      <td>35500000.0</td>\n",
       "      <td>35534000.0</td>\n",
       "      <td>1635.893640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-25 09:00:00</th>\n",
       "      <td>35530000.0</td>\n",
       "      <td>35701000.0</td>\n",
       "      <td>35176000.0</td>\n",
       "      <td>35491000.0</td>\n",
       "      <td>2877.482697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-26 09:00:00</th>\n",
       "      <td>35497000.0</td>\n",
       "      <td>35805000.0</td>\n",
       "      <td>35428000.0</td>\n",
       "      <td>35566000.0</td>\n",
       "      <td>2209.875078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 09:00:00</th>\n",
       "      <td>35566000.0</td>\n",
       "      <td>36005000.0</td>\n",
       "      <td>35560000.0</td>\n",
       "      <td>35983000.0</td>\n",
       "      <td>1211.795219</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open        high         low       close  \\\n",
       "date                                                                  \n",
       "2023-05-01 09:00:00  39149000.0  39303000.0  37372000.0  37769000.0   \n",
       "2023-05-02 09:00:00  37768000.0  38680000.0  37428000.0  38405000.0   \n",
       "2023-05-03 09:00:00  38406000.0  39050000.0  37800000.0  38703000.0   \n",
       "2023-05-04 09:00:00  38733000.0  39112000.0  38423000.0  38580000.0   \n",
       "2023-05-05 09:00:00  38599000.0  39359000.0  38520000.0  39053000.0   \n",
       "...                         ...         ...         ...         ...   \n",
       "2023-09-23 09:00:00  35964000.0  36019000.0  35901000.0  35960000.0   \n",
       "2023-09-24 09:00:00  35951000.0  36100000.0  35500000.0  35534000.0   \n",
       "2023-09-25 09:00:00  35530000.0  35701000.0  35176000.0  35491000.0   \n",
       "2023-09-26 09:00:00  35497000.0  35805000.0  35428000.0  35566000.0   \n",
       "2023-09-27 09:00:00  35566000.0  36005000.0  35560000.0  35983000.0   \n",
       "\n",
       "                          volume  \n",
       "date                              \n",
       "2023-05-01 09:00:00  5466.444454  \n",
       "2023-05-02 09:00:00  4771.654370  \n",
       "2023-05-03 09:00:00  5239.850975  \n",
       "2023-05-04 09:00:00  4027.302864  \n",
       "2023-05-05 09:00:00  5614.210355  \n",
       "...                          ...  \n",
       "2023-09-23 09:00:00   999.553996  \n",
       "2023-09-24 09:00:00  1635.893640  \n",
       "2023-09-25 09:00:00  2877.482697  \n",
       "2023-09-26 09:00:00  2209.875078  \n",
       "2023-09-27 09:00:00  1211.795219  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtest.repository.webrepo.crypto.upbit_repo import UpbitRepo  # Stockdata From Upbit\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.response import ResponseSuccess, ResponseFailure\n",
    "\n",
    "symbol = 'BTC'\n",
    "from_date = '2023-05-01'\n",
    "\n",
    "request = build_stock_data_from_repo_request(filters={'order__eq': symbol, 'from__eq': from_date})\n",
    "upbit_result = stockdata_from_repo(UpbitRepo(), request=request, cache=True)\n",
    "\n",
    "if isinstance(upbit_result,ResponseSuccess):\n",
    "    stockdata = upbit_result.value  # value data is StockData\n",
    "    upbitstock_df = stockdata.data\n",
    "\n",
    "upbitstock_df # result 1d candle chart DataFrame in upbit market."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "922ae748",
   "metadata": {},
   "source": [
    "### 1-2. Get StockData from Bithumb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "dca59913",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-01 00:00:00</th>\n",
       "      <td>39219000.0</td>\n",
       "      <td>39830000.0</td>\n",
       "      <td>38058000.0</td>\n",
       "      <td>38208000.0</td>\n",
       "      <td>1719.653808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-02 00:00:00</th>\n",
       "      <td>38208000.0</td>\n",
       "      <td>38483000.0</td>\n",
       "      <td>37320000.0</td>\n",
       "      <td>38071000.0</td>\n",
       "      <td>1673.640644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-03 00:00:00</th>\n",
       "      <td>38076000.0</td>\n",
       "      <td>38728000.0</td>\n",
       "      <td>37780000.0</td>\n",
       "      <td>37857000.0</td>\n",
       "      <td>1282.164815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-04 00:00:00</th>\n",
       "      <td>37857000.0</td>\n",
       "      <td>39226000.0</td>\n",
       "      <td>37769000.0</td>\n",
       "      <td>38541000.0</td>\n",
       "      <td>1682.555929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 00:00:00</th>\n",
       "      <td>38538000.0</td>\n",
       "      <td>39250000.0</td>\n",
       "      <td>38458000.0</td>\n",
       "      <td>38979000.0</td>\n",
       "      <td>1099.421766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-23 00:00:00</th>\n",
       "      <td>36059000.0</td>\n",
       "      <td>36073000.0</td>\n",
       "      <td>35845000.0</td>\n",
       "      <td>35890000.0</td>\n",
       "      <td>547.048730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24 00:00:00</th>\n",
       "      <td>35890000.0</td>\n",
       "      <td>36006000.0</td>\n",
       "      <td>35876000.0</td>\n",
       "      <td>35896000.0</td>\n",
       "      <td>367.551361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-25 00:00:00</th>\n",
       "      <td>35896000.0</td>\n",
       "      <td>36054000.0</td>\n",
       "      <td>35103000.0</td>\n",
       "      <td>35330000.0</td>\n",
       "      <td>1143.035554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-26 00:00:00</th>\n",
       "      <td>35330000.0</td>\n",
       "      <td>35760000.0</td>\n",
       "      <td>35322000.0</td>\n",
       "      <td>35533000.0</td>\n",
       "      <td>777.435702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 19:00:00</th>\n",
       "      <td>35537000.0</td>\n",
       "      <td>35941000.0</td>\n",
       "      <td>35342000.0</td>\n",
       "      <td>35912000.0</td>\n",
       "      <td>756.612926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open        high         low       close  \\\n",
       "date                                                                  \n",
       "2023-05-01 00:00:00  39219000.0  39830000.0  38058000.0  38208000.0   \n",
       "2023-05-02 00:00:00  38208000.0  38483000.0  37320000.0  38071000.0   \n",
       "2023-05-03 00:00:00  38076000.0  38728000.0  37780000.0  37857000.0   \n",
       "2023-05-04 00:00:00  37857000.0  39226000.0  37769000.0  38541000.0   \n",
       "2023-05-05 00:00:00  38538000.0  39250000.0  38458000.0  38979000.0   \n",
       "...                         ...         ...         ...         ...   \n",
       "2023-09-23 00:00:00  36059000.0  36073000.0  35845000.0  35890000.0   \n",
       "2023-09-24 00:00:00  35890000.0  36006000.0  35876000.0  35896000.0   \n",
       "2023-09-25 00:00:00  35896000.0  36054000.0  35103000.0  35330000.0   \n",
       "2023-09-26 00:00:00  35330000.0  35760000.0  35322000.0  35533000.0   \n",
       "2023-09-27 19:00:00  35537000.0  35941000.0  35342000.0  35912000.0   \n",
       "\n",
       "                          volume  \n",
       "date                              \n",
       "2023-05-01 00:00:00  1719.653808  \n",
       "2023-05-02 00:00:00  1673.640644  \n",
       "2023-05-03 00:00:00  1282.164815  \n",
       "2023-05-04 00:00:00  1682.555929  \n",
       "2023-05-05 00:00:00  1099.421766  \n",
       "...                          ...  \n",
       "2023-09-23 00:00:00   547.048730  \n",
       "2023-09-24 00:00:00   367.551361  \n",
       "2023-09-25 00:00:00  1143.035554  \n",
       "2023-09-26 00:00:00   777.435702  \n",
       "2023-09-27 19:00:00   756.612926  \n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtest.repository.webrepo.crypto.bithumb_repo import BithumbRepo  # stockdata from Bithumb\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.response import ResponseSuccess, ResponseFailure\n",
    "\n",
    "symbol = 'BTC'\n",
    "from_date = '2023-05-01'\n",
    "\n",
    "request = build_stock_data_from_repo_request(filters={'order__eq': symbol, 'from__eq': from_date})\n",
    "bithumb_result = stockdata_from_repo(BithumbRepo(), request=request, cache=True)\n",
    "\n",
    "if isinstance(bithumb_result,ResponseSuccess):\n",
    "    stockdata = bithumb_result.value  # value data is StockData\n",
    "    bithumbstock_df = stockdata.data\n",
    "\n",
    "bithumbstock_df # result 1d candle chart DataFrame in bithumb market."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ef35c90",
   "metadata": {},
   "source": [
    "### 1-3. Get StockData from Binance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ebf5e36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-01-01\n"
     ]
    },
    {
     "data": {
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       "  <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-02 09:00:00</th>\n",
       "      <td>28068.26</td>\n",
       "      <td>28879.88</td>\n",
       "      <td>27872.00</td>\n",
       "      <td>28669.86</td>\n",
       "      <td>50824.52240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-03 09:00:00</th>\n",
       "      <td>28669.85</td>\n",
       "      <td>29266.66</td>\n",
       "      <td>28113.69</td>\n",
       "      <td>29026.16</td>\n",
       "      <td>64615.79213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-04 09:00:00</th>\n",
       "      <td>29026.16</td>\n",
       "      <td>29379.83</td>\n",
       "      <td>28663.64</td>\n",
       "      <td>28838.16</td>\n",
       "      <td>42575.47501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 09:00:00</th>\n",
       "      <td>28838.16</td>\n",
       "      <td>29677.00</td>\n",
       "      <td>28800.00</td>\n",
       "      <td>29505.61</td>\n",
       "      <td>58415.83048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-06 09:00:00</th>\n",
       "      <td>29505.60</td>\n",
       "      <td>29820.00</td>\n",
       "      <td>28300.00</td>\n",
       "      <td>28848.20</td>\n",
       "      <td>49249.28459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-23 09:00:00</th>\n",
       "      <td>26580.14</td>\n",
       "      <td>26632.81</td>\n",
       "      <td>26509.00</td>\n",
       "      <td>26575.96</td>\n",
       "      <td>9440.70260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24 09:00:00</th>\n",
       "      <td>26575.97</td>\n",
       "      <td>26738.54</td>\n",
       "      <td>26122.08</td>\n",
       "      <td>26248.38</td>\n",
       "      <td>15706.65771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-25 09:00:00</th>\n",
       "      <td>26248.39</td>\n",
       "      <td>26446.15</td>\n",
       "      <td>25990.46</td>\n",
       "      <td>26304.81</td>\n",
       "      <td>26266.20390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-26 09:00:00</th>\n",
       "      <td>26304.80</td>\n",
       "      <td>26397.46</td>\n",
       "      <td>26088.34</td>\n",
       "      <td>26221.67</td>\n",
       "      <td>18495.35066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 09:00:00</th>\n",
       "      <td>26221.68</td>\n",
       "      <td>26442.67</td>\n",
       "      <td>26202.96</td>\n",
       "      <td>26395.62</td>\n",
       "      <td>7129.08161</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         open      high       low     close       volume\n",
       "date                                                                    \n",
       "2023-05-02 09:00:00  28068.26  28879.88  27872.00  28669.86  50824.52240\n",
       "2023-05-03 09:00:00  28669.85  29266.66  28113.69  29026.16  64615.79213\n",
       "2023-05-04 09:00:00  29026.16  29379.83  28663.64  28838.16  42575.47501\n",
       "2023-05-05 09:00:00  28838.16  29677.00  28800.00  29505.61  58415.83048\n",
       "2023-05-06 09:00:00  29505.60  29820.00  28300.00  28848.20  49249.28459\n",
       "...                       ...       ...       ...       ...          ...\n",
       "2023-09-23 09:00:00  26580.14  26632.81  26509.00  26575.96   9440.70260\n",
       "2023-09-24 09:00:00  26575.97  26738.54  26122.08  26248.38  15706.65771\n",
       "2023-09-25 09:00:00  26248.39  26446.15  25990.46  26304.81  26266.20390\n",
       "2023-09-26 09:00:00  26304.80  26397.46  26088.34  26221.67  18495.35066\n",
       "2023-09-27 09:00:00  26221.68  26442.67  26202.96  26395.62   7129.08161\n",
       "\n",
       "[149 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtest.repository.webrepo.crypto.binance_repo import BinanceRepo  # stockdata from Binance\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.response import ResponseSuccess, ResponseFailure\n",
    "\n",
    "symbol = 'BTC'\n",
    "from_date = '2023-05-01'\n",
    "\n",
    "request = build_stock_data_from_repo_request(filters={'order__eq': symbol, 'from__eq': from_date})\n",
    "binance_result = stockdata_from_repo(BinanceRepo(), request=request, cache=True)\n",
    "\n",
    "if isinstance(binance_result,ResponseSuccess):\n",
    "    stockdata = binance_result.value  # value data is StockData\n",
    "    binancestock_df = stockdata.data\n",
    "\n",
    "binancestock_df # result 1d candle chart DataFrame in binance market"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7380601d",
   "metadata": {},
   "source": [
    "### 1-4 set chart interval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c4044a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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>2023-05-01 09:00:00</th>\n",
       "      <td>39149000.0</td>\n",
       "      <td>39303000.0</td>\n",
       "      <td>38104000.0</td>\n",
       "      <td>38328000.0</td>\n",
       "      <td>1957.818772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 13:00:00</th>\n",
       "      <td>38329000.0</td>\n",
       "      <td>38586000.0</td>\n",
       "      <td>38066000.0</td>\n",
       "      <td>38434000.0</td>\n",
       "      <td>641.905257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 17:00:00</th>\n",
       "      <td>38434000.0</td>\n",
       "      <td>38483000.0</td>\n",
       "      <td>38255000.0</td>\n",
       "      <td>38282000.0</td>\n",
       "      <td>419.183368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-01 21:00:00</th>\n",
       "      <td>38283000.0</td>\n",
       "      <td>38402000.0</td>\n",
       "      <td>37777000.0</td>\n",
       "      <td>37889000.0</td>\n",
       "      <td>1074.935545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-02 01:00:00</th>\n",
       "      <td>37889000.0</td>\n",
       "      <td>38208000.0</td>\n",
       "      <td>37525000.0</td>\n",
       "      <td>37570000.0</td>\n",
       "      <td>582.018874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 01:00:00</th>\n",
       "      <td>35486000.0</td>\n",
       "      <td>35680000.0</td>\n",
       "      <td>35480000.0</td>\n",
       "      <td>35619000.0</td>\n",
       "      <td>134.054487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 05:00:00</th>\n",
       "      <td>35619000.0</td>\n",
       "      <td>35667000.0</td>\n",
       "      <td>35497000.0</td>\n",
       "      <td>35566000.0</td>\n",
       "      <td>152.886879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 09:00:00</th>\n",
       "      <td>35566000.0</td>\n",
       "      <td>35723000.0</td>\n",
       "      <td>35560000.0</td>\n",
       "      <td>35695000.0</td>\n",
       "      <td>299.538546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 13:00:00</th>\n",
       "      <td>35695000.0</td>\n",
       "      <td>35831000.0</td>\n",
       "      <td>35619000.0</td>\n",
       "      <td>35803000.0</td>\n",
       "      <td>633.582304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 17:00:00</th>\n",
       "      <td>35809000.0</td>\n",
       "      <td>35984000.0</td>\n",
       "      <td>35718000.0</td>\n",
       "      <td>35984000.0</td>\n",
       "      <td>229.457300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>884 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           open        high         low       close  \\\n",
       "date                                                                  \n",
       "2023-05-01 09:00:00  39149000.0  39303000.0  38104000.0  38328000.0   \n",
       "2023-05-01 13:00:00  38329000.0  38586000.0  38066000.0  38434000.0   \n",
       "2023-05-01 17:00:00  38434000.0  38483000.0  38255000.0  38282000.0   \n",
       "2023-05-01 21:00:00  38283000.0  38402000.0  37777000.0  37889000.0   \n",
       "2023-05-02 01:00:00  37889000.0  38208000.0  37525000.0  37570000.0   \n",
       "...                         ...         ...         ...         ...   \n",
       "2023-09-27 01:00:00  35486000.0  35680000.0  35480000.0  35619000.0   \n",
       "2023-09-27 05:00:00  35619000.0  35667000.0  35497000.0  35566000.0   \n",
       "2023-09-27 09:00:00  35566000.0  35723000.0  35560000.0  35695000.0   \n",
       "2023-09-27 13:00:00  35695000.0  35831000.0  35619000.0  35803000.0   \n",
       "2023-09-27 17:00:00  35809000.0  35984000.0  35718000.0  35984000.0   \n",
       "\n",
       "                          volume  \n",
       "date                              \n",
       "2023-05-01 09:00:00  1957.818772  \n",
       "2023-05-01 13:00:00   641.905257  \n",
       "2023-05-01 17:00:00   419.183368  \n",
       "2023-05-01 21:00:00  1074.935545  \n",
       "2023-05-02 01:00:00   582.018874  \n",
       "...                          ...  \n",
       "2023-09-27 01:00:00   134.054487  \n",
       "2023-09-27 05:00:00   152.886879  \n",
       "2023-09-27 09:00:00   299.538546  \n",
       "2023-09-27 13:00:00   633.582304  \n",
       "2023-09-27 17:00:00   229.457300  \n",
       "\n",
       "[884 rows x 5 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from backtest.repository.webrepo.crypto.upbit_repo import UpbitRepo  # stockdata from Binance\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.response import ResponseSuccess, ResponseFailure\n",
    "\n",
    "symbol = 'BTC'\n",
    "from_date = '2023-05-01'\n",
    "\n",
    "# you can set chart interval using \"chart_interval__eq\" option\n",
    "request = build_stock_data_from_repo_request(filters={'order__eq': symbol, 'from__eq': from_date,\"chart_interval__eq\": \"240m\"})\n",
    "upbit_result = stockdata_from_repo(UpbitRepo(), request=request, cache=True)\n",
    "\n",
    "if isinstance(upbit_result,ResponseSuccess):\n",
    "    stockdata = upbit_result.value \n",
    "    upbitstock_df = stockdata.data\n",
    "\n",
    "upbitstock_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aa5ce3e",
   "metadata": {},
   "source": [
    "## Step 2. Set Strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c2a4c24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from backtest.domains.strategy import Strategy\n",
    "from backtest.use_cases.strategy_execute import (stocastic_rsi_ema_mix_function,\n",
    "                                                 trendilo_mix_function,\n",
    "                                                 ema_local_min_max_trandline_function,\n",
    "                                                 ema_swing_low_function,\n",
    "                                                 candle_stick_pattern,\n",
    "                                                 ema_swing_another_function,\n",
    "                                                 finally_fib,\n",
    "                                                 swing_search,)\n",
    "\n",
    "# if you want to create your strategy, you can reference the above function in /backtest/use_cases/strategy_execute.py \n",
    "strategy = Strategy(name='finally_fib', function=finally_fib, weight=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cb5b9ab",
   "metadata": {},
   "source": [
    "## Step 3. Backtest stockdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75c39de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>current_money</th>\n",
       "      <th>stock_bucket</th>\n",
       "      <th>total_potential_earn</th>\n",
       "      <th>total_potential_profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-05-02 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-03 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-04 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-05 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-06 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-23 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-24 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-25 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-26 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-27 09:00:00</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>{}</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     current_money stock_bucket  total_potential_earn  \\\n",
       "date                                                                    \n",
       "2023-05-02 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-05-03 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-05-04 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-05-05 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-05-06 09:00:00     10000000.0           {}            10000000.0   \n",
       "...                            ...          ...                   ...   \n",
       "2023-09-23 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-09-24 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-09-25 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-09-26 09:00:00     10000000.0           {}            10000000.0   \n",
       "2023-09-27 09:00:00     10000000.0           {}            10000000.0   \n",
       "\n",
       "                     total_potential_profit  \n",
       "date                                         \n",
       "2023-05-02 09:00:00                     0.0  \n",
       "2023-05-03 09:00:00                     0.0  \n",
       "2023-05-04 09:00:00                     0.0  \n",
       "2023-05-05 09:00:00                     0.0  \n",
       "2023-05-06 09:00:00                     0.0  \n",
       "...                                     ...  \n",
       "2023-09-23 09:00:00                     0.0  \n",
       "2023-09-24 09:00:00                     0.0  \n",
       "2023-09-25 09:00:00                     0.0  \n",
       "2023-09-26 09:00:00                     0.0  \n",
       "2023-09-27 09:00:00                     0.0  \n",
       "\n",
       "[149 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# required step 1.\n",
    "from backtest.repository.webrepo.crypto.binance_repo import BinanceRepo  # stockdata from Binance\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.response import ResponseSuccess, ResponseFailure\n",
    "\n",
    "# required step 2.\n",
    "from backtest.domains.strategy import Strategy\n",
    "from backtest.use_cases.strategy_execute import (stocastic_rsi_ema_mix_function,\n",
    "                                                 trendilo_mix_function,\n",
    "                                                 ema_local_min_max_trandline_function,\n",
    "                                                 ema_swing_low_function,\n",
    "                                                 candle_stick_pattern,\n",
    "                                                 ema_swing_another_function,\n",
    "                                                 finally_fib,\n",
    "                                                 swing_search,)\n",
    "\n",
    "# required step 3.\n",
    "from backtest.domains.backtest import Backtest\n",
    "from backtest.use_cases.backtest_execute import backtest_execute\n",
    "\n",
    "## Step 1. Get Stockdata From Repository\n",
    "symbol = 'ETH'\n",
    "from_date = '2022-05-01'\n",
    "\n",
    "request = build_stock_data_from_repo_request(filters={'order__eq': symbol, 'from__eq': from_date})\n",
    "binance_result = stockdata_from_repo(BinanceRepo(), request=request, cache=True)\n",
    "\n",
    "if isinstance(binance_result,ResponseSuccess):\n",
    "    stockdata = binance_result.value  # value data is StockData\n",
    "    binancestock_df = stockdata.data\n",
    "\n",
    "\n",
    "## Step 2. Set Strategy\n",
    "strategy = Strategy(name='finally_fib', function=finally_fib, weight=100)\n",
    "\n",
    "\n",
    "## Step 3. backtest stockdata with strategy\n",
    "backtest = Backtest(strategy_list=[strategy], stockdata_list=[stockdata])\n",
    "result = backtest_execute(backtest, verbose=False)\n",
    "\n",
    "if isinstance(binance_result,ResponseSuccess):\n",
    "    backtest_result = result.value\n",
    "\n",
    "\n",
    "backtest_result.value"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1882b47",
   "metadata": {},
   "source": [
    "## Advance Example (backtest using vectorbt module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3fb5d627",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "function result : finally_fib\n",
      "symbol:  BTC win rate : 45.45454545454545 backtest profit : 5.6999138564200535\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import vectorbt as vbt\n",
    "\n",
    "from backtest.use_cases.backtest_execute import backtest_execute\n",
    "from backtest.use_cases.strategy_execute import (stocastic_rsi_ema_mix_function,\n",
    "                                                 trendilo_mix_function,\n",
    "                                                 ema_local_min_max_trandline_function,\n",
    "                                                 ema_swing_low_function,\n",
    "                                                 candle_stick_pattern,\n",
    "                                                 ema_swing_another_function,\n",
    "                                                 finally_fib,\n",
    "                                                 swing_search,)\n",
    "from backtest.domains.strategy_result import StrategyResultColumnType\n",
    "from backtest.request.stockdata_from_repo import build_stock_data_from_repo_request\n",
    "from backtest.use_cases.stockdata_from_repo import stockdata_from_repo\n",
    "from backtest.repository.webrepo.crypto.upbit_repo import UpbitRepo\n",
    "from backtest.domains.backtest_plot_package import BacktestPlotPackage\n",
    "from backtest.domains.backtest import Backtest\n",
    "from backtest.domains.strategy import Strategy\n",
    "from backtest.domains.stockdata import StockData\n",
    "from backtest.use_cases.standardize_stock import standardize_stock\n",
    "from backtest.use_cases.make_crypto_backtest import get_upbit_symbol\n",
    "from datetime import datetime, timedelta\n",
    "import time\n",
    "import pyupbit\n",
    "from typing import List\n",
    "\n",
    "def build_support_stockdata(stockdata_list : List[StockData]) -> pd.DataFrame:\n",
    "    standardize_stock(stockdata_list)\n",
    "    stockdata_support_df = pd.DataFrame(index=stockdata_list[0].data.index)\n",
    "    for stockdata in stockdata_list:\n",
    "        stockdata_df = stockdata.data\n",
    "        symbol = stockdata.symbol\n",
    "        stockdata_df['krw'] = stockdata_df['close'] * stockdata_df['volume']\n",
    "        stockdata_support_df[symbol] = stockdata_df['krw'].rolling(48).sum()\n",
    "    result_support_df = stockdata_support_df.rank(axis=1,ascending = 0)    \n",
    "    \n",
    "    return result_support_df\n",
    "\n",
    "def build_stockdata(symbol: str, from_date: str, cache: bool = False,check_rank: bool = False, big_stock: bool = False):\n",
    "    request = build_stock_data_from_repo_request(\n",
    "        filters={'order__eq': symbol, 'from__eq': from_date})\n",
    "    response = stockdata_from_repo(UpbitRepo(), request=request, cache=cache)\n",
    "    stockdata = response.value\n",
    "    support_df = None\n",
    "    support_symbols=[ symbol.replace(\"KRW-\",\"\") for symbol in pyupbit.get_tickers(fiat=\"KRW\")]\n",
    "    support_stockdata = []\n",
    "    if check_rank:\n",
    "        for idx,support_symbol in enumerate(support_symbols,1):\n",
    "            # print(\"REQUEST SYMBOL : {} [{}/{}]\".format(support_symbol,idx,len(support_symbols)))\n",
    "            request2 = build_stock_data_from_repo_request(\n",
    "            filters={'order__eq': support_symbol, 'from__eq': from_date})\n",
    "            response2 = stockdata_from_repo(UpbitRepo(), request=request2, cache=cache)\n",
    "            stockdata2 =response2.value\n",
    "            support_stockdata.append(stockdata2)\n",
    "            support_df = build_support_stockdata(support_stockdata)\n",
    "    big_stockdata = None\n",
    "    if big_stock:\n",
    "        big_stock_from_date = datetime.strptime(from_date,'%Y-%m-%d') - timedelta(days=900)\n",
    "        big_request = build_stock_data_from_repo_request(\n",
    "            filters={'order__eq': symbol, 'from__eq': big_stock_from_date.strftime('%Y-%m-%d')})\n",
    "        big_response = stockdata_from_repo(UpbitRepo(), request=big_request, cache=cache)\n",
    "        big_stockdata = big_response.value\n",
    "\n",
    "    return stockdata , support_df , big_stockdata\n",
    "\n",
    "symbols=[symbol.replace(\"KRW-\",\"\") for symbol in pyupbit.get_tickers(fiat=\"KRW\")]\n",
    "symbols=['BTC']\n",
    "for symbol in symbols:\n",
    "    from_date = '2022-05-01'\n",
    "    try:\n",
    "        stockdata,support_df,big_stockdata = build_stockdata(symbol=symbol, from_date=from_date, cache=False, big_stock=False, check_rank=False)\n",
    "        function_name_list = ['finally_fib',]\n",
    "        strategy1 = Strategy(name='finally_fib',function=finally_fib,weight=100,options={'support_df':support_df,'vp_range':250})\n",
    "        backtest = Backtest(strategy_list=[strategy1], stockdata_list=[stockdata])\n",
    "\n",
    "        plot_package = BacktestPlotPackage() # for use vectorbt\n",
    "        backtest_execute(\n",
    "            backtest, verbose=False, plot_package=plot_package).value\n",
    "        for function_name,strategy_result in zip(function_name_list, plot_package.package_data_bucket[symbol]):\n",
    "            strategy_result = strategy_result[function_name][function_name]\n",
    "            buy_signal = strategy_result.apply(lambda r : True if r[0] == StrategyResultColumnType.BUY else False) # get buy signal. result is True of False\n",
    "            sell_signal = strategy_result.apply(lambda r : True if r[0] == StrategyResultColumnType.SELL else False) # get sell signal. result is True of False\n",
    "            print('function result :',function_name)\n",
    "            pf = vbt.Portfolio.from_signals(stockdata.data.close,buy_signal,sell_signal,fees=0.0005)\n",
    "            result=pf.stats()\n",
    "            print('symbol: ',symbol,'win rate :',result['Win Rate [%]'],'backtest profit :',result['Total Return [%]'])\n",
    "        time.sleep(5)\n",
    "    except Exception as e:\n",
    "        print('error',symbol)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fae7034",
   "metadata": {},
   "source": [
    "### plus: Plot Backtest Result Using vectorbt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e2905e5e",
   "metadata": {},
   "outputs": [
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