lstm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "79c89591",
   "metadata": {
    "papermill": {
     "duration": 0.014422,
     "end_time": "2021-11-04T05:41:46.295492",
     "exception": false,
     "start_time": "2021-11-04T05:41:46.281070",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f5c11d07",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-11-04T05:41:46.363507Z",
     "iopub.status.busy": "2021-11-04T05:41:46.361924Z",
     "iopub.status.idle": "2021-11-04T05:41:47.098760Z",
     "shell.execute_reply": "2021-11-04T05:41:47.098184Z",
     "shell.execute_reply.started": "2021-11-04T05:19:06.254073Z"
    },
    "papermill": {
     "duration": 0.75689,
     "end_time": "2021-11-04T05:41:47.098940",
     "exception": false,
     "start_time": "2021-11-04T05:41:46.342050",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import io\n",
    "import json\n",
    "import requests\n",
    "import functools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "import math\n",
    "from sklearn.metrics import accuracy_score\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "from torch.utils import data\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torch.nn import functional as F\n",
    "from torchvision import datasets, models, transforms\n",
    "\n",
    "import pytorch_lightning as pl\n",
    "from sklearn.preprocessing import LabelEncoder, normalize\n",
    "from torchmetrics.functional import accuracy\n",
    "\n",
    "from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from pytorch_lightning.loggers import TensorBoardLogger\n",
    "from torchmetrics.functional import accuracy\n",
    "from torch.autograd import Variable\n",
    "\n",
    "from tqdm import tqdm\n",
    "from time import sleep\n",
    "\n",
    "from pytorch_lightning.callbacks import TQDMProgressBar\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "441a23bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transfer to accelerator\n",
    "use_cuda = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda:0\" if use_cuda else \"cpu\")\n",
    "\n",
    "torch.manual_seed(0)\n",
    "torch.cuda.manual_seed(0)\n",
    "np.random.seed(0)\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b18bbce",
   "metadata": {},
   "source": [
    "## Готовый df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6ecc3b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "N_EPOCHS = 500\n",
    "BATCH_SIZE = 500\n",
    "seq_length = 250\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8fc502a0",
   "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>timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1655337600</td>\n",
       "      <td>22583.72</td>\n",
       "      <td>22622.47</td>\n",
       "      <td>22555.33</td>\n",
       "      <td>22568.17</td>\n",
       "      <td>104.35613</td>\n",
       "      <td>1934</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1655337660</td>\n",
       "      <td>22568.17</td>\n",
       "      <td>22606.09</td>\n",
       "      <td>22553.43</td>\n",
       "      <td>22570.41</td>\n",
       "      <td>105.16669</td>\n",
       "      <td>1831</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1655337720</td>\n",
       "      <td>22570.42</td>\n",
       "      <td>22653.85</td>\n",
       "      <td>22570.00</td>\n",
       "      <td>22631.85</td>\n",
       "      <td>133.63109</td>\n",
       "      <td>2018</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1655337780</td>\n",
       "      <td>22633.99</td>\n",
       "      <td>22714.34</td>\n",
       "      <td>22631.84</td>\n",
       "      <td>22667.71</td>\n",
       "      <td>142.14442</td>\n",
       "      <td>2500</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1655337840</td>\n",
       "      <td>22667.70</td>\n",
       "      <td>22746.25</td>\n",
       "      <td>22660.96</td>\n",
       "      <td>22740.04</td>\n",
       "      <td>118.55492</td>\n",
       "      <td>1818</td>\n",
       "      <td>1.0</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",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>285126</th>\n",
       "      <td>1672445160</td>\n",
       "      <td>16599.34</td>\n",
       "      <td>16600.32</td>\n",
       "      <td>16594.37</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>68.09939</td>\n",
       "      <td>2465</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285127</th>\n",
       "      <td>1672445220</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>16600.91</td>\n",
       "      <td>16593.56</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>88.83927</td>\n",
       "      <td>2709</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285128</th>\n",
       "      <td>1672445280</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>16601.74</td>\n",
       "      <td>16594.96</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>83.76568</td>\n",
       "      <td>2463</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285129</th>\n",
       "      <td>1672445340</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>16600.21</td>\n",
       "      <td>16593.64</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>44.73432</td>\n",
       "      <td>1797</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>285130</th>\n",
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       "      <td>16595.43</td>\n",
       "      <td>16595.68</td>\n",
       "      <td>16591.45</td>\n",
       "      <td>16593.16</td>\n",
       "      <td>80.06386</td>\n",
       "      <td>2371</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>285131 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp      Open      High       Low     Close     Volume  Count  \\\n",
       "0       1655337600  22583.72  22622.47  22555.33  22568.17  104.35613   1934   \n",
       "1       1655337660  22568.17  22606.09  22553.43  22570.41  105.16669   1831   \n",
       "2       1655337720  22570.42  22653.85  22570.00  22631.85  133.63109   2018   \n",
       "3       1655337780  22633.99  22714.34  22631.84  22667.71  142.14442   2500   \n",
       "4       1655337840  22667.70  22746.25  22660.96  22740.04  118.55492   1818   \n",
       "...            ...       ...       ...       ...       ...        ...    ...   \n",
       "285126  1672445160  16599.34  16600.32  16594.37  16594.44   68.09939   2465   \n",
       "285127  1672445220  16594.44  16600.91  16593.56  16599.84   88.83927   2709   \n",
       "285128  1672445280  16599.84  16601.74  16594.96  16599.30   83.76568   2463   \n",
       "285129  1672445340  16599.30  16600.21  16593.64  16595.43   44.73432   1797   \n",
       "285130  1672445400  16595.43  16595.68  16591.45  16593.16   80.06386   2371   \n",
       "\n",
       "        Target  \n",
       "0          1.0  \n",
       "1          1.0  \n",
       "2          1.0  \n",
       "3          1.0  \n",
       "4          1.0  \n",
       "...        ...  \n",
       "285126     0.0  \n",
       "285127     0.0  \n",
       "285128     0.0  \n",
       "285129     0.0  \n",
       "285130     0.0  \n",
       "\n",
       "[285131 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('train_2022_Jun.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad965f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    145694\n",
       "1.0    139437\n",
       "Name: Target, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "644111eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['timestamp', 'Open', 'High', 'Low', 'Close', 'Volume', 'Count',\n",
       "       'Target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "80f28245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" from pandas_datareader import data as pdr\\nimport matplotlib.pyplot as plt\\nimport yfinance\\nimport pandas as pd\\n\\n# Commodity Channel Index \\ndef CCI(data, ndays): \\n    TP = (data['High'] + data['Low'] + data['Close']) / 3 \\n    CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()), name = 'CCI') \\n    data = data.join(CCI) \\n    return data\\n\\n# Retrieve the Nifty data from Yahoo finance:\\n\\ndata = pd.DataFrame(df)\\n\\n# Compute the Commodity Channel Index(CCI) for NIFTY based on the 20-day Moving average\\nn = 60*3\\nNIFTY_CCI = CCI(data, n)\\nCCI = NIFTY_CCI['CCI']\\n\\n# Plotting the Price Series chart and the Commodity Channel index below\\nfig = plt.figure(figsize=(7,5))\\nax = fig.add_subplot(2, 1, 1)\\nax.set_xticklabels([])\\nplt.plot(data['Close'],lw=1)\\nplt.title('NSE Price Chart')\\nplt.ylabel('Close Price')\\nplt.grid(True)\\nbx = fig.add_subplot(2, 1, 2)\\nplt.plot(CCI,'k',lw=0.75,linestyle='-',label='CCI')\\nplt.legend(loc=2,prop={'size':9.5}) \""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\" from pandas_datareader import data as pdr\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance\n",
    "import pandas as pd\n",
    "\n",
    "# Commodity Channel Index \n",
    "def CCI(data, ndays): \n",
    "    TP = (data['High'] + data['Low'] + data['Close']) / 3 \n",
    "    CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()), name = 'CCI') \n",
    "    data = data.join(CCI) \n",
    "    return data\n",
    "\n",
    "# Retrieve the Nifty data from Yahoo finance:\n",
    "\n",
    "data = pd.DataFrame(df)\n",
    "\n",
    "# Compute the Commodity Channel Index(CCI) for NIFTY based on the 20-day Moving average\n",
    "n = 60*3\n",
    "NIFTY_CCI = CCI(data, n)\n",
    "CCI = NIFTY_CCI['CCI']\n",
    "\n",
    "# Plotting the Price Series chart and the Commodity Channel index below\n",
    "fig = plt.figure(figsize=(7,5))\n",
    "ax = fig.add_subplot(2, 1, 1)\n",
    "ax.set_xticklabels([])\n",
    "plt.plot(data['Close'],lw=1)\n",
    "plt.title('NSE Price Chart')\n",
    "plt.ylabel('Close Price')\n",
    "plt.grid(True)\n",
    "bx = fig.add_subplot(2, 1, 2)\n",
    "plt.plot(CCI,'k',lw=0.75,linestyle='-',label='CCI')\n",
    "plt.legend(loc=2,prop={'size':9.5}) \"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f5557fe7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ease Of Movement (EVM) Code\n",
    "\n",
    "# Load the necessary packages and modules\n",
    "from pandas_datareader import data as pdr\n",
    "import matplotlib.pyplot as plt\n",
    "import yfinance\n",
    "import pandas as pd\n",
    " \n",
    "# Commodity Channel Index \n",
    "def CCI(data, ndays): \n",
    "    TP = (data['High'] + data['Low'] + data['Close']) / 3 \n",
    "    CCI = pd.Series((TP - TP.rolling(ndays).mean()) / (0.015 * TP.rolling(ndays).std()), name = 'CCI') \n",
    "    data = data.join(CCI) \n",
    "    return data\n",
    "\n",
    "# Ease of Movement \n",
    "def EVM(data, ndays): \n",
    "    dm = ((data['High'] + data['Low'])/2) - ((data['High'].shift(1) + data['Low'].shift(1))/2)\n",
    "    br = (data['Volume'] / 100000000) / ((data['High'] - data['Low']))\n",
    "    EVM = dm / br \n",
    "    EVM_MA = pd.Series(EVM.rolling(ndays).mean(), name = 'EVM') \n",
    "    data = data.join(EVM_MA) \n",
    "    return data \n",
    "\n",
    "# Simple Moving Average \n",
    "def SMA(data, ndays): \n",
    "    SMA = pd.Series(data['Close'].rolling(ndays).mean(), name = 'SMA') \n",
    "    data = data.join(SMA) \n",
    "    return data\n",
    "\n",
    "# Exponentially-weighted Moving Average \n",
    "def EWMA(data, ndays): \n",
    "    EMA = pd.Series(data['Close'].ewm(span = ndays, min_periods = ndays - 1).mean(), \n",
    "                    name = 'EWMA_' + str(ndays)) \n",
    "    data = data.join(EMA) \n",
    "    return data\n",
    "\n",
    "def BBANDS(data, window):\n",
    "    MA = data.Close.rolling(window).mean()\n",
    "    SD = data.Close.rolling(window).std()\n",
    "    data['UpperBB'] = MA + (2 * SD) \n",
    "    data['LowerBB'] = MA - (2 * SD)\n",
    "    return data\n",
    "\n",
    "# Force Index \n",
    "def ForceIndex(data, ndays): \n",
    "    FI = pd.Series(data['Close'].diff(ndays) * data['Volume'], name = 'ForceIndex') \n",
    "    data = data.join(FI) \n",
    "    return data \n",
    "\n",
    "# Rate of Change (ROC)\n",
    "def ROC(data,n):\n",
    "    N = data['Close'].diff(n)\n",
    "    D = data['Close'].shift(n)\n",
    "    ROC = pd.Series(N/D,name='Rate of Change')\n",
    "    data = data.join(ROC)\n",
    "    return data \n",
    "\n",
    "data = pd.DataFrame(df)\n",
    "\n",
    "n = 20\n",
    "NIFTY_ROC = ROC(data,n)\n",
    "ROC = NIFTY_ROC['Rate of Change']\n",
    "data = NIFTY_ROC\n",
    "\n",
    "n = 50\n",
    "NIFTY_BBANDS = BBANDS(data, n)\n",
    "data = NIFTY_BBANDS\n",
    "\n",
    "\n",
    "n = 20\n",
    "AAPL_ForceIndex = ForceIndex(data,n)\n",
    "ForceIndex = AAPL_ForceIndex['ForceIndex']\n",
    "data = AAPL_ForceIndex\n",
    "\n",
    "# Compute the 14-day Ease of Movement for AAPL\n",
    "n = 14\n",
    "AAPL_EVM = EVM(data, n)\n",
    "AAPL_EVM  = AAPL_EVM.dropna()\n",
    "EVM = AAPL_EVM['EVM']\n",
    "data = AAPL_EVM\n",
    "\n",
    "\n",
    "n = 60*3\n",
    "NIFTY_CCI = CCI(data, n)\n",
    "NIFTY_CCI = NIFTY_CCI.dropna()\n",
    "CCI = NIFTY_CCI['CCI']\n",
    "data = NIFTY_CCI\n",
    "\n",
    "\n",
    "n = 9\n",
    "SMA_NIFTY = SMA(data,n)\n",
    "SMA_NIFTY = SMA_NIFTY.dropna()\n",
    "SMA = SMA_NIFTY['SMA']\n",
    "data = SMA_NIFTY\n",
    "\n",
    "# Compute the 200-day EWMA for NIFTY\n",
    "ew = 15\n",
    "EWMA_NIFTY = EWMA(data,ew)\n",
    "EWMA_NIFTY = EWMA_NIFTY.dropna()\n",
    "EWMA = EWMA_NIFTY['EWMA_'  + str(ew)]\n",
    "data = EWMA_NIFTY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "db6cc18f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\3579223359.py:4: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(data['Close'][:200], label='NSE Prices')\n",
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\3579223359.py:5: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(SMA[:200],'g', label='9 SMA (green)')\n",
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\3579223359.py:6: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(EWMA[:200],'r', label='14-day EWMA (red)')\n",
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\3579223359.py:11: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(EVM[:200],'k',linestyle='-',label='EVM(14)')\n",
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\3579223359.py:17: FutureWarning: The behavior of `series[i:j]` with an integer-dtype index is deprecated. In a future version, this will be treated as *label-based* indexing, consistent with e.g. `series[i]` lookups. To retain the old behavior, use `series.iloc[i:j]`. To get the future behavior, use `series.loc[i:j]`.\n",
      "  plt.plot(CCI[:200],'r',linestyle='-',label='CCI')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x1080 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,15))\n",
    "\n",
    "ax = fig.add_subplot(5, 1, 1)\n",
    "plt.plot(data['Close'][:200], label='NSE Prices')\n",
    "plt.plot(SMA[:200],'g', label='9 SMA (green)')\n",
    "plt.plot(EWMA[:200],'r', label='14-day EWMA (red)')\n",
    "plt.legend(loc=2,prop={'size':11})\n",
    "plt.grid(True)\n",
    "\n",
    "bx = fig.add_subplot(5, 1, 2)\n",
    "plt.plot(EVM[:200],'k',linestyle='-',label='EVM(14)')\n",
    "plt.legend(loc=2,prop={'size':9})\n",
    "plt.ylabel('EVM values')\n",
    "plt.grid(True)\n",
    "\n",
    "сx = fig.add_subplot(5, 1, 3)\n",
    "plt.plot(CCI[:200],'r',linestyle='-',label='CCI')\n",
    "plt.legend(loc=2,prop={'size':9.5})\n",
    "plt.grid(True)\n",
    "\n",
    "ex = fig.add_subplot(5, 1, 4)\n",
    "plt.plot(ROC[:200],'b',linestyle='-',label='ROC')\n",
    "plt.legend(loc=2,prop={'size':9})\n",
    "plt.ylabel('ROC values')\n",
    "plt.grid(True)\n",
    "\n",
    "ex = fig.add_subplot(5, 1, 5)\n",
    "plt.plot(ForceIndex[:200],'g',linestyle='-',label='ForceIndex')\n",
    "plt.legend(loc=2,prop={'size':9})\n",
    "plt.ylabel('ForceIndex values')\n",
    "plt.grid(True)\n",
    "\n",
    "pd.concat([NIFTY_BBANDS.Close,NIFTY_BBANDS.UpperBB,NIFTY_BBANDS.LowerBB],axis=1).plot(figsize=(9,5),grid=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "59165fd0",
   "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>timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Target</th>\n",
       "      <th>Rate of Change</th>\n",
       "      <th>UpperBB</th>\n",
       "      <th>LowerBB</th>\n",
       "      <th>ForceIndex</th>\n",
       "      <th>EVM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>SMA</th>\n",
       "      <th>EWMA_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1655352540</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22272.01</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>45.64405</td>\n",
       "      <td>979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>22371.600013</td>\n",
       "      <td>22179.761987</td>\n",
       "      <td>1986.885496</td>\n",
       "      <td>1.708244e+07</td>\n",
       "      <td>-80.554032</td>\n",
       "      <td>22244.693333</td>\n",
       "      <td>22242.554903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1655352600</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>22280.40</td>\n",
       "      <td>22256.40</td>\n",
       "      <td>22258.00</td>\n",
       "      <td>31.05384</td>\n",
       "      <td>736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>22371.609477</td>\n",
       "      <td>22180.109723</td>\n",
       "      <td>450.901757</td>\n",
       "      <td>5.686032e+07</td>\n",
       "      <td>-77.728728</td>\n",
       "      <td>22244.491111</td>\n",
       "      <td>22244.786682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1655352660</td>\n",
       "      <td>22258.01</td>\n",
       "      <td>22265.10</td>\n",
       "      <td>22240.99</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>26.36036</td>\n",
       "      <td>569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>22371.548674</td>\n",
       "      <td>22179.166926</td>\n",
       "      <td>279.156212</td>\n",
       "      <td>-2.414865e+08</td>\n",
       "      <td>-83.542529</td>\n",
       "      <td>22243.604444</td>\n",
       "      <td>22244.448408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1655352720</td>\n",
       "      <td>22242.41</td>\n",
       "      <td>22291.32</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>33.19814</td>\n",
       "      <td>853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>22370.493590</td>\n",
       "      <td>22179.116810</td>\n",
       "      <td>934.195660</td>\n",
       "      <td>-1.713638e+08</td>\n",
       "      <td>-73.464364</td>\n",
       "      <td>22248.343333</td>\n",
       "      <td>22249.772368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1655352780</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>22302.94</td>\n",
       "      <td>22266.34</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>51.71966</td>\n",
       "      <td>864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>22370.187252</td>\n",
       "      <td>22179.105548</td>\n",
       "      <td>1564.519715</td>\n",
       "      <td>2.081586e+08</td>\n",
       "      <td>-66.825663</td>\n",
       "      <td>22258.068889</td>\n",
       "      <td>22255.646847</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",
       "      <td>...</td>\n",
       "      <td>...</td>\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>285126</th>\n",
       "      <td>1672445160</td>\n",
       "      <td>16599.34</td>\n",
       "      <td>16600.32</td>\n",
       "      <td>16594.37</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>68.09939</td>\n",
       "      <td>2465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>16617.670956</td>\n",
       "      <td>16587.158644</td>\n",
       "      <td>-963.606369</td>\n",
       "      <td>-2.586268e+06</td>\n",
       "      <td>3.166541</td>\n",
       "      <td>16605.447778</td>\n",
       "      <td>16604.250322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285127</th>\n",
       "      <td>1672445220</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>16600.91</td>\n",
       "      <td>16593.56</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>88.83927</td>\n",
       "      <td>2709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000547</td>\n",
       "      <td>16617.671827</td>\n",
       "      <td>16587.242573</td>\n",
       "      <td>-807.548964</td>\n",
       "      <td>-2.758590e+06</td>\n",
       "      <td>15.626449</td>\n",
       "      <td>16604.554444</td>\n",
       "      <td>16603.699032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285128</th>\n",
       "      <td>1672445280</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>16601.74</td>\n",
       "      <td>16594.96</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>83.76568</td>\n",
       "      <td>2463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>16617.640824</td>\n",
       "      <td>16587.431576</td>\n",
       "      <td>-540.288636</td>\n",
       "      <td>-1.677894e+06</td>\n",
       "      <td>20.168746</td>\n",
       "      <td>16603.645556</td>\n",
       "      <td>16603.149153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285129</th>\n",
       "      <td>1672445340</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>16600.21</td>\n",
       "      <td>16593.64</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>44.73432</td>\n",
       "      <td>1797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>16617.593324</td>\n",
       "      <td>16587.558276</td>\n",
       "      <td>-409.319028</td>\n",
       "      <td>-3.723131e+06</td>\n",
       "      <td>4.814757</td>\n",
       "      <td>16601.672222</td>\n",
       "      <td>16602.184259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285130</th>\n",
       "      <td>1672445400</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>16595.68</td>\n",
       "      <td>16591.45</td>\n",
       "      <td>16593.16</td>\n",
       "      <td>80.06386</td>\n",
       "      <td>2371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>16617.608485</td>\n",
       "      <td>16587.522715</td>\n",
       "      <td>-698.156859</td>\n",
       "      <td>-4.062804e+06</td>\n",
       "      <td>-16.390376</td>\n",
       "      <td>16599.776667</td>\n",
       "      <td>16601.056227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284882 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp      Open      High       Low     Close    Volume  Count  \\\n",
       "249     1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979   \n",
       "250     1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736   \n",
       "251     1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569   \n",
       "252     1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853   \n",
       "253     1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864   \n",
       "...            ...       ...       ...       ...       ...       ...    ...   \n",
       "285126  1672445160  16599.34  16600.32  16594.37  16594.44  68.09939   2465   \n",
       "285127  1672445220  16594.44  16600.91  16593.56  16599.84  88.83927   2709   \n",
       "285128  1672445280  16599.84  16601.74  16594.96  16599.30  83.76568   2463   \n",
       "285129  1672445340  16599.30  16600.21  16593.64  16595.43  44.73432   1797   \n",
       "285130  1672445400  16595.43  16595.68  16591.45  16593.16  80.06386   2371   \n",
       "\n",
       "        Target  Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "249        1.0        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "250        1.0        0.000653  22371.609477  22180.109723   450.901757   \n",
       "251        1.0        0.000476  22371.548674  22179.166926   279.156212   \n",
       "252        1.0        0.001264  22370.493590  22179.116810   934.195660   \n",
       "253        1.0        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "...        ...             ...           ...           ...          ...   \n",
       "285126     0.0       -0.000852  16617.670956  16587.158644  -963.606369   \n",
       "285127     0.0       -0.000547  16617.671827  16587.242573  -807.548964   \n",
       "285128     0.0       -0.000388  16617.640824  16587.431576  -540.288636   \n",
       "285129     0.0       -0.000551  16617.593324  16587.558276  -409.319028   \n",
       "285130     0.0       -0.000525  16617.608485  16587.522715  -698.156859   \n",
       "\n",
       "                 EVM        CCI           SMA       EWMA_15  \n",
       "249     1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "250     5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "251    -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "252    -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "253     2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "...              ...        ...           ...           ...  \n",
       "285126 -2.586268e+06   3.166541  16605.447778  16604.250322  \n",
       "285127 -2.758590e+06  15.626449  16604.554444  16603.699032  \n",
       "285128 -1.677894e+06  20.168746  16603.645556  16603.149153  \n",
       "285129 -3.723131e+06   4.814757  16601.672222  16602.184259  \n",
       "285130 -4.062804e+06 -16.390376  16599.776667  16601.056227  \n",
       "\n",
       "[284882 rows x 16 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7c0acd0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Target</th>\n",
       "      <th>Rate of Change</th>\n",
       "      <th>UpperBB</th>\n",
       "      <th>LowerBB</th>\n",
       "      <th>ForceIndex</th>\n",
       "      <th>EVM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>SMA</th>\n",
       "      <th>EWMA_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1655352540</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22272.01</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>45.64405</td>\n",
       "      <td>979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>22371.600013</td>\n",
       "      <td>22179.761987</td>\n",
       "      <td>1986.885496</td>\n",
       "      <td>1.708244e+07</td>\n",
       "      <td>-80.554032</td>\n",
       "      <td>22244.693333</td>\n",
       "      <td>22242.554903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1655352600</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>22280.40</td>\n",
       "      <td>22256.40</td>\n",
       "      <td>22258.00</td>\n",
       "      <td>31.05384</td>\n",
       "      <td>736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>22371.609477</td>\n",
       "      <td>22180.109723</td>\n",
       "      <td>450.901757</td>\n",
       "      <td>5.686032e+07</td>\n",
       "      <td>-77.728728</td>\n",
       "      <td>22244.491111</td>\n",
       "      <td>22244.786682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1655352660</td>\n",
       "      <td>22258.01</td>\n",
       "      <td>22265.10</td>\n",
       "      <td>22240.99</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>26.36036</td>\n",
       "      <td>569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>22371.548674</td>\n",
       "      <td>22179.166926</td>\n",
       "      <td>279.156212</td>\n",
       "      <td>-2.414865e+08</td>\n",
       "      <td>-83.542529</td>\n",
       "      <td>22243.604444</td>\n",
       "      <td>22244.448408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1655352720</td>\n",
       "      <td>22242.41</td>\n",
       "      <td>22291.32</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>33.19814</td>\n",
       "      <td>853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>22370.493590</td>\n",
       "      <td>22179.116810</td>\n",
       "      <td>934.195660</td>\n",
       "      <td>-1.713638e+08</td>\n",
       "      <td>-73.464364</td>\n",
       "      <td>22248.343333</td>\n",
       "      <td>22249.772368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1655352780</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>22302.94</td>\n",
       "      <td>22266.34</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>51.71966</td>\n",
       "      <td>864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>22370.187252</td>\n",
       "      <td>22179.105548</td>\n",
       "      <td>1564.519715</td>\n",
       "      <td>2.081586e+08</td>\n",
       "      <td>-66.825663</td>\n",
       "      <td>22258.068889</td>\n",
       "      <td>22255.646847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>1655352840</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>22318.30</td>\n",
       "      <td>22291.58</td>\n",
       "      <td>22296.68</td>\n",
       "      <td>32.77570</td>\n",
       "      <td>938</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002377</td>\n",
       "      <td>22369.197938</td>\n",
       "      <td>22179.268862</td>\n",
       "      <td>1733.179016</td>\n",
       "      <td>3.394835e+08</td>\n",
       "      <td>-60.420476</td>\n",
       "      <td>22263.951111</td>\n",
       "      <td>22261.216529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>1655352900</td>\n",
       "      <td>22296.68</td>\n",
       "      <td>22314.70</td>\n",
       "      <td>22295.54</td>\n",
       "      <td>22307.77</td>\n",
       "      <td>22.35770</td>\n",
       "      <td>714</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004372</td>\n",
       "      <td>22368.128343</td>\n",
       "      <td>22179.590057</td>\n",
       "      <td>2171.156247</td>\n",
       "      <td>1.106016e+08</td>\n",
       "      <td>-58.831260</td>\n",
       "      <td>22268.266667</td>\n",
       "      <td>22267.468397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>1655352960</td>\n",
       "      <td>22307.78</td>\n",
       "      <td>22345.36</td>\n",
       "      <td>22307.77</td>\n",
       "      <td>22326.81</td>\n",
       "      <td>44.60896</td>\n",
       "      <td>949</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.004208</td>\n",
       "      <td>22369.888686</td>\n",
       "      <td>22179.232514</td>\n",
       "      <td>4173.168208</td>\n",
       "      <td>1.486788e+08</td>\n",
       "      <td>-49.199817</td>\n",
       "      <td>22279.383333</td>\n",
       "      <td>22275.364252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>1655353020</td>\n",
       "      <td>22326.81</td>\n",
       "      <td>22330.49</td>\n",
       "      <td>22300.63</td>\n",
       "      <td>22302.23</td>\n",
       "      <td>35.90714</td>\n",
       "      <td>966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001662</td>\n",
       "      <td>22369.828448</td>\n",
       "      <td>22179.238352</td>\n",
       "      <td>1328.564180</td>\n",
       "      <td>2.113356e+08</td>\n",
       "      <td>-56.374958</td>\n",
       "      <td>22286.110000</td>\n",
       "      <td>22278.910372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>1655353080</td>\n",
       "      <td>22302.24</td>\n",
       "      <td>22361.98</td>\n",
       "      <td>22290.31</td>\n",
       "      <td>22355.69</td>\n",
       "      <td>47.40024</td>\n",
       "      <td>968</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005614</td>\n",
       "      <td>22372.344626</td>\n",
       "      <td>22178.078974</td>\n",
       "      <td>5916.023954</td>\n",
       "      <td>3.700283e+08</td>\n",
       "      <td>-44.044099</td>\n",
       "      <td>22296.082222</td>\n",
       "      <td>22288.974440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>1655353140</td>\n",
       "      <td>22355.69</td>\n",
       "      <td>22355.70</td>\n",
       "      <td>22312.39</td>\n",
       "      <td>22313.36</td>\n",
       "      <td>33.86162</td>\n",
       "      <td>779</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004580</td>\n",
       "      <td>22372.237692</td>\n",
       "      <td>22178.105108</td>\n",
       "      <td>3445.081219</td>\n",
       "      <td>5.831194e+08</td>\n",
       "      <td>-48.079601</td>\n",
       "      <td>22302.233333</td>\n",
       "      <td>22292.151526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>1655353200</td>\n",
       "      <td>22313.36</td>\n",
       "      <td>22328.60</td>\n",
       "      <td>22299.53</td>\n",
       "      <td>22327.00</td>\n",
       "      <td>39.92673</td>\n",
       "      <td>746</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.005238</td>\n",
       "      <td>22373.765492</td>\n",
       "      <td>22177.788508</td>\n",
       "      <td>4645.075768</td>\n",
       "      <td>2.733682e+08</td>\n",
       "      <td>-52.199344</td>\n",
       "      <td>22311.633333</td>\n",
       "      <td>22296.667907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>1655353260</td>\n",
       "      <td>22326.99</td>\n",
       "      <td>22328.58</td>\n",
       "      <td>22313.61</td>\n",
       "      <td>22328.58</td>\n",
       "      <td>19.35084</td>\n",
       "      <td>399</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003089</td>\n",
       "      <td>22374.306879</td>\n",
       "      <td>22177.607521</td>\n",
       "      <td>1330.563758</td>\n",
       "      <td>5.084513e+08</td>\n",
       "      <td>-49.202620</td>\n",
       "      <td>22316.737778</td>\n",
       "      <td>22300.784791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>1655353320</td>\n",
       "      <td>22328.58</td>\n",
       "      <td>22340.39</td>\n",
       "      <td>22313.90</td>\n",
       "      <td>22325.76</td>\n",
       "      <td>20.43628</td>\n",
       "      <td>506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003388</td>\n",
       "      <td>22374.300741</td>\n",
       "      <td>22177.609659</td>\n",
       "      <td>1540.486786</td>\n",
       "      <td>7.094856e+08</td>\n",
       "      <td>-47.334841</td>\n",
       "      <td>22320.431111</td>\n",
       "      <td>22303.993910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>1655353380</td>\n",
       "      <td>22325.76</td>\n",
       "      <td>22332.05</td>\n",
       "      <td>22310.00</td>\n",
       "      <td>22326.85</td>\n",
       "      <td>30.96968</td>\n",
       "      <td>666</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>22371.766875</td>\n",
       "      <td>22178.823125</td>\n",
       "      <td>2690.026405</td>\n",
       "      <td>5.280704e+08</td>\n",
       "      <td>-49.086093</td>\n",
       "      <td>22323.783333</td>\n",
       "      <td>22306.920518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>1655353440</td>\n",
       "      <td>22326.64</td>\n",
       "      <td>22341.98</td>\n",
       "      <td>22319.21</td>\n",
       "      <td>22319.43</td>\n",
       "      <td>17.64578</td>\n",
       "      <td>539</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.005154</td>\n",
       "      <td>22369.577560</td>\n",
       "      <td>22179.780040</td>\n",
       "      <td>2019.383063</td>\n",
       "      <td>5.525178e+08</td>\n",
       "      <td>-46.571661</td>\n",
       "      <td>22325.078889</td>\n",
       "      <td>22308.517432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>1655353500</td>\n",
       "      <td>22319.43</td>\n",
       "      <td>22334.29</td>\n",
       "      <td>22313.15</td>\n",
       "      <td>22329.69</td>\n",
       "      <td>24.05977</td>\n",
       "      <td>517</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003864</td>\n",
       "      <td>22368.764392</td>\n",
       "      <td>22180.144008</td>\n",
       "      <td>2067.937231</td>\n",
       "      <td>6.096854e+08</td>\n",
       "      <td>-46.796902</td>\n",
       "      <td>22325.398889</td>\n",
       "      <td>22311.213083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>1655353560</td>\n",
       "      <td>22329.68</td>\n",
       "      <td>22349.55</td>\n",
       "      <td>22329.68</td>\n",
       "      <td>22345.44</td>\n",
       "      <td>25.74349</td>\n",
       "      <td>614</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003436</td>\n",
       "      <td>22369.102991</td>\n",
       "      <td>22179.975809</td>\n",
       "      <td>1969.634420</td>\n",
       "      <td>5.519070e+08</td>\n",
       "      <td>-37.304451</td>\n",
       "      <td>22330.200000</td>\n",
       "      <td>22315.560711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>1655353620</td>\n",
       "      <td>22345.45</td>\n",
       "      <td>22345.45</td>\n",
       "      <td>22330.00</td>\n",
       "      <td>22333.06</td>\n",
       "      <td>21.43036</td>\n",
       "      <td>557</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004783</td>\n",
       "      <td>22369.482395</td>\n",
       "      <td>22179.819605</td>\n",
       "      <td>2278.047268</td>\n",
       "      <td>4.523014e+08</td>\n",
       "      <td>-39.921605</td>\n",
       "      <td>22327.685556</td>\n",
       "      <td>22317.779045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>1655353680</td>\n",
       "      <td>22333.05</td>\n",
       "      <td>22340.23</td>\n",
       "      <td>22320.01</td>\n",
       "      <td>22320.91</td>\n",
       "      <td>23.48227</td>\n",
       "      <td>604</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003562</td>\n",
       "      <td>22369.508035</td>\n",
       "      <td>22179.811165</td>\n",
       "      <td>1860.265429</td>\n",
       "      <td>2.873170e+08</td>\n",
       "      <td>-44.827141</td>\n",
       "      <td>22328.524444</td>\n",
       "      <td>22318.175247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>269</th>\n",
       "      <td>1655353740</td>\n",
       "      <td>22320.92</td>\n",
       "      <td>22335.11</td>\n",
       "      <td>22317.73</td>\n",
       "      <td>22321.93</td>\n",
       "      <td>15.84720</td>\n",
       "      <td>428</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.002515</td>\n",
       "      <td>22367.006914</td>\n",
       "      <td>22180.991086</td>\n",
       "      <td>887.284728</td>\n",
       "      <td>2.572303e+08</td>\n",
       "      <td>-45.700442</td>\n",
       "      <td>22327.961111</td>\n",
       "      <td>22318.649655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>1655353800</td>\n",
       "      <td>22321.94</td>\n",
       "      <td>22340.98</td>\n",
       "      <td>22320.28</td>\n",
       "      <td>22330.92</td>\n",
       "      <td>20.80527</td>\n",
       "      <td>534</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.003276</td>\n",
       "      <td>22363.411354</td>\n",
       "      <td>22182.971846</td>\n",
       "      <td>1517.120288</td>\n",
       "      <td>1.580728e+08</td>\n",
       "      <td>-41.706571</td>\n",
       "      <td>22328.221111</td>\n",
       "      <td>22320.197906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>1655353860</td>\n",
       "      <td>22330.91</td>\n",
       "      <td>22336.41</td>\n",
       "      <td>22323.25</td>\n",
       "      <td>22327.19</td>\n",
       "      <td>21.87890</td>\n",
       "      <td>507</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003812</td>\n",
       "      <td>22362.199575</td>\n",
       "      <td>22183.549625</td>\n",
       "      <td>1855.111931</td>\n",
       "      <td>2.200046e+08</td>\n",
       "      <td>-42.294512</td>\n",
       "      <td>22328.380000</td>\n",
       "      <td>22321.079119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>1655353920</td>\n",
       "      <td>22327.19</td>\n",
       "      <td>22343.68</td>\n",
       "      <td>22327.18</td>\n",
       "      <td>22341.15</td>\n",
       "      <td>33.71023</td>\n",
       "      <td>612</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.002626</td>\n",
       "      <td>22365.424778</td>\n",
       "      <td>22182.698422</td>\n",
       "      <td>1972.385557</td>\n",
       "      <td>1.252639e+08</td>\n",
       "      <td>-36.485735</td>\n",
       "      <td>22329.968889</td>\n",
       "      <td>22323.606046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>1655353980</td>\n",
       "      <td>22341.16</td>\n",
       "      <td>22341.16</td>\n",
       "      <td>22330.36</td>\n",
       "      <td>22333.90</td>\n",
       "      <td>18.09917</td>\n",
       "      <td>494</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>22367.474604</td>\n",
       "      <td>22182.093796</td>\n",
       "      <td>748.943655</td>\n",
       "      <td>5.449661e+07</td>\n",
       "      <td>-37.316458</td>\n",
       "      <td>22331.576667</td>\n",
       "      <td>22324.900891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>1655354040</td>\n",
       "      <td>22333.89</td>\n",
       "      <td>22349.99</td>\n",
       "      <td>22331.64</td>\n",
       "      <td>22331.66</td>\n",
       "      <td>23.92373</td>\n",
       "      <td>582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>22369.594404</td>\n",
       "      <td>22184.574396</td>\n",
       "      <td>836.852075</td>\n",
       "      <td>1.860995e+08</td>\n",
       "      <td>-35.063247</td>\n",
       "      <td>22331.795556</td>\n",
       "      <td>22325.750430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>1655354100</td>\n",
       "      <td>22331.65</td>\n",
       "      <td>22333.00</td>\n",
       "      <td>22296.00</td>\n",
       "      <td>22296.00</td>\n",
       "      <td>48.26413</td>\n",
       "      <td>746</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000528</td>\n",
       "      <td>22370.030731</td>\n",
       "      <td>22186.813669</td>\n",
       "      <td>-568.068810</td>\n",
       "      <td>3.157004e+06</td>\n",
       "      <td>-53.637898</td>\n",
       "      <td>22326.302222</td>\n",
       "      <td>22322.013728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276</th>\n",
       "      <td>1655354160</td>\n",
       "      <td>22296.01</td>\n",
       "      <td>22296.01</td>\n",
       "      <td>22262.87</td>\n",
       "      <td>22265.89</td>\n",
       "      <td>33.98031</td>\n",
       "      <td>859</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.002729</td>\n",
       "      <td>22368.566246</td>\n",
       "      <td>22190.937754</td>\n",
       "      <td>-2070.080485</td>\n",
       "      <td>-2.970940e+08</td>\n",
       "      <td>-75.035217</td>\n",
       "      <td>22318.838889</td>\n",
       "      <td>22314.968736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>1655354220</td>\n",
       "      <td>22265.88</td>\n",
       "      <td>22273.88</td>\n",
       "      <td>22263.19</td>\n",
       "      <td>22267.34</td>\n",
       "      <td>27.12675</td>\n",
       "      <td>732</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.001564</td>\n",
       "      <td>22368.551335</td>\n",
       "      <td>22191.745865</td>\n",
       "      <td>-946.452307</td>\n",
       "      <td>-2.966658e+08</td>\n",
       "      <td>-79.075165</td>\n",
       "      <td>22312.886667</td>\n",
       "      <td>22308.993230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>1655354280</td>\n",
       "      <td>22267.34</td>\n",
       "      <td>22273.88</td>\n",
       "      <td>22260.53</td>\n",
       "      <td>22263.10</td>\n",
       "      <td>26.31766</td>\n",
       "      <td>550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.004142</td>\n",
       "      <td>22368.267724</td>\n",
       "      <td>22193.201076</td>\n",
       "      <td>-2436.752139</td>\n",
       "      <td>-3.896924e+08</td>\n",
       "      <td>-79.944554</td>\n",
       "      <td>22306.350000</td>\n",
       "      <td>22303.238109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      timestamp      Open      High       Low     Close    Volume  Count  \\\n",
       "249  1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979   \n",
       "250  1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736   \n",
       "251  1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569   \n",
       "252  1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853   \n",
       "253  1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864   \n",
       "254  1655352840  22292.52  22318.30  22291.58  22296.68  32.77570    938   \n",
       "255  1655352900  22296.68  22314.70  22295.54  22307.77  22.35770    714   \n",
       "256  1655352960  22307.78  22345.36  22307.77  22326.81  44.60896    949   \n",
       "257  1655353020  22326.81  22330.49  22300.63  22302.23  35.90714    966   \n",
       "258  1655353080  22302.24  22361.98  22290.31  22355.69  47.40024    968   \n",
       "259  1655353140  22355.69  22355.70  22312.39  22313.36  33.86162    779   \n",
       "260  1655353200  22313.36  22328.60  22299.53  22327.00  39.92673    746   \n",
       "261  1655353260  22326.99  22328.58  22313.61  22328.58  19.35084    399   \n",
       "262  1655353320  22328.58  22340.39  22313.90  22325.76  20.43628    506   \n",
       "263  1655353380  22325.76  22332.05  22310.00  22326.85  30.96968    666   \n",
       "264  1655353440  22326.64  22341.98  22319.21  22319.43  17.64578    539   \n",
       "265  1655353500  22319.43  22334.29  22313.15  22329.69  24.05977    517   \n",
       "266  1655353560  22329.68  22349.55  22329.68  22345.44  25.74349    614   \n",
       "267  1655353620  22345.45  22345.45  22330.00  22333.06  21.43036    557   \n",
       "268  1655353680  22333.05  22340.23  22320.01  22320.91  23.48227    604   \n",
       "269  1655353740  22320.92  22335.11  22317.73  22321.93  15.84720    428   \n",
       "270  1655353800  22321.94  22340.98  22320.28  22330.92  20.80527    534   \n",
       "271  1655353860  22330.91  22336.41  22323.25  22327.19  21.87890    507   \n",
       "272  1655353920  22327.19  22343.68  22327.18  22341.15  33.71023    612   \n",
       "273  1655353980  22341.16  22341.16  22330.36  22333.90  18.09917    494   \n",
       "274  1655354040  22333.89  22349.99  22331.64  22331.66  23.92373    582   \n",
       "275  1655354100  22331.65  22333.00  22296.00  22296.00  48.26413    746   \n",
       "276  1655354160  22296.01  22296.01  22262.87  22265.89  33.98031    859   \n",
       "277  1655354220  22265.88  22273.88  22263.19  22267.34  27.12675    732   \n",
       "278  1655354280  22267.34  22273.88  22260.53  22263.10  26.31766    550   \n",
       "\n",
       "     Target  Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "249     1.0        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "250     1.0        0.000653  22371.609477  22180.109723   450.901757   \n",
       "251     1.0        0.000476  22371.548674  22179.166926   279.156212   \n",
       "252     1.0        0.001264  22370.493590  22179.116810   934.195660   \n",
       "253     1.0        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "254     1.0        0.002377  22369.197938  22179.268862  1733.179016   \n",
       "255     1.0        0.004372  22368.128343  22179.590057  2171.156247   \n",
       "256     1.0        0.004208  22369.888686  22179.232514  4173.168208   \n",
       "257     1.0        0.001662  22369.828448  22179.238352  1328.564180   \n",
       "258     1.0        0.005614  22372.344626  22178.078974  5916.023954   \n",
       "259     0.0        0.004580  22372.237692  22178.105108  3445.081219   \n",
       "260     1.0        0.005238  22373.765492  22177.788508  4645.075768   \n",
       "261     0.0        0.003089  22374.306879  22177.607521  1330.563758   \n",
       "262     0.0        0.003388  22374.300741  22177.609659  1540.486786   \n",
       "263     0.0        0.003906  22371.766875  22178.823125  2690.026405   \n",
       "264     0.0        0.005154  22369.577560  22179.780040  2019.383063   \n",
       "265     1.0        0.003864  22368.764392  22180.144008  2067.937231   \n",
       "266     0.0        0.003436  22369.102991  22179.975809  1969.634420   \n",
       "267     0.0        0.004783  22369.482395  22179.819605  2278.047268   \n",
       "268     0.0        0.003562  22369.508035  22179.811165  1860.265429   \n",
       "269     1.0        0.002515  22367.006914  22180.991086   887.284728   \n",
       "270     1.0        0.003276  22363.411354  22182.971846  1517.120288   \n",
       "271     0.0        0.003812  22362.199575  22183.549625  1855.111931   \n",
       "272     0.0        0.002626  22365.424778  22182.698422  1972.385557   \n",
       "273     0.0        0.001856  22367.474604  22182.093796   748.943655   \n",
       "274     0.0        0.001569  22369.594404  22184.574396   836.852075   \n",
       "275     0.0       -0.000528  22370.030731  22186.813669  -568.068810   \n",
       "276     0.0       -0.002729  22368.566246  22190.937754 -2070.080485   \n",
       "277     0.0       -0.001564  22368.551335  22191.745865  -946.452307   \n",
       "278     0.0       -0.004142  22368.267724  22193.201076 -2436.752139   \n",
       "\n",
       "              EVM        CCI           SMA       EWMA_15  \n",
       "249  1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "250  5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "251 -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "252 -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "253  2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "254  3.394835e+08 -60.420476  22263.951111  22261.216529  \n",
       "255  1.106016e+08 -58.831260  22268.266667  22267.468397  \n",
       "256  1.486788e+08 -49.199817  22279.383333  22275.364252  \n",
       "257  2.113356e+08 -56.374958  22286.110000  22278.910372  \n",
       "258  3.700283e+08 -44.044099  22296.082222  22288.974440  \n",
       "259  5.831194e+08 -48.079601  22302.233333  22292.151526  \n",
       "260  2.733682e+08 -52.199344  22311.633333  22296.667907  \n",
       "261  5.084513e+08 -49.202620  22316.737778  22300.784791  \n",
       "262  7.094856e+08 -47.334841  22320.431111  22303.993910  \n",
       "263  5.280704e+08 -49.086093  22323.783333  22306.920518  \n",
       "264  5.525178e+08 -46.571661  22325.078889  22308.517432  \n",
       "265  6.096854e+08 -46.796902  22325.398889  22311.213083  \n",
       "266  5.519070e+08 -37.304451  22330.200000  22315.560711  \n",
       "267  4.523014e+08 -39.921605  22327.685556  22317.779045  \n",
       "268  2.873170e+08 -44.827141  22328.524444  22318.175247  \n",
       "269  2.572303e+08 -45.700442  22327.961111  22318.649655  \n",
       "270  1.580728e+08 -41.706571  22328.221111  22320.197906  \n",
       "271  2.200046e+08 -42.294512  22328.380000  22321.079119  \n",
       "272  1.252639e+08 -36.485735  22329.968889  22323.606046  \n",
       "273  5.449661e+07 -37.316458  22331.576667  22324.900891  \n",
       "274  1.860995e+08 -35.063247  22331.795556  22325.750430  \n",
       "275  3.157004e+06 -53.637898  22326.302222  22322.013728  \n",
       "276 -2.970940e+08 -75.035217  22318.838889  22314.968736  \n",
       "277 -2.966658e+08 -79.075165  22312.886667  22308.993230  \n",
       "278 -3.896924e+08 -79.944554  22306.350000  22303.238109  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cb28aaa",
   "metadata": {},
   "source": [
    "### Выделим немного теста"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eeffbc86",
   "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>timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Target</th>\n",
       "      <th>Rate of Change</th>\n",
       "      <th>UpperBB</th>\n",
       "      <th>LowerBB</th>\n",
       "      <th>ForceIndex</th>\n",
       "      <th>EVM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>SMA</th>\n",
       "      <th>EWMA_15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1655352540</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22272.01</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>45.64405</td>\n",
       "      <td>979</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>22371.600013</td>\n",
       "      <td>22179.761987</td>\n",
       "      <td>1986.885496</td>\n",
       "      <td>1.708244e+07</td>\n",
       "      <td>-80.554032</td>\n",
       "      <td>22244.693333</td>\n",
       "      <td>22242.554903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1655352600</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>22280.40</td>\n",
       "      <td>22256.40</td>\n",
       "      <td>22258.00</td>\n",
       "      <td>31.05384</td>\n",
       "      <td>736</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>22371.609477</td>\n",
       "      <td>22180.109723</td>\n",
       "      <td>450.901757</td>\n",
       "      <td>5.686032e+07</td>\n",
       "      <td>-77.728728</td>\n",
       "      <td>22244.491111</td>\n",
       "      <td>22244.786682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>1655352660</td>\n",
       "      <td>22258.01</td>\n",
       "      <td>22265.10</td>\n",
       "      <td>22240.99</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>26.36036</td>\n",
       "      <td>569</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>22371.548674</td>\n",
       "      <td>22179.166926</td>\n",
       "      <td>279.156212</td>\n",
       "      <td>-2.414865e+08</td>\n",
       "      <td>-83.542529</td>\n",
       "      <td>22243.604444</td>\n",
       "      <td>22244.448408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1655352720</td>\n",
       "      <td>22242.41</td>\n",
       "      <td>22291.32</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>33.19814</td>\n",
       "      <td>853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>22370.493590</td>\n",
       "      <td>22179.116810</td>\n",
       "      <td>934.195660</td>\n",
       "      <td>-1.713638e+08</td>\n",
       "      <td>-73.464364</td>\n",
       "      <td>22248.343333</td>\n",
       "      <td>22249.772368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1655352780</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>22302.94</td>\n",
       "      <td>22266.34</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>51.71966</td>\n",
       "      <td>864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>22370.187252</td>\n",
       "      <td>22179.105548</td>\n",
       "      <td>1564.519715</td>\n",
       "      <td>2.081586e+08</td>\n",
       "      <td>-66.825663</td>\n",
       "      <td>22258.068889</td>\n",
       "      <td>22255.646847</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>285126</th>\n",
       "      <td>1672445160</td>\n",
       "      <td>16599.34</td>\n",
       "      <td>16600.32</td>\n",
       "      <td>16594.37</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>68.09939</td>\n",
       "      <td>2465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>16617.670956</td>\n",
       "      <td>16587.158644</td>\n",
       "      <td>-963.606369</td>\n",
       "      <td>-2.586268e+06</td>\n",
       "      <td>3.166541</td>\n",
       "      <td>16605.447778</td>\n",
       "      <td>16604.250322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285127</th>\n",
       "      <td>1672445220</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>16600.91</td>\n",
       "      <td>16593.56</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>88.83927</td>\n",
       "      <td>2709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000547</td>\n",
       "      <td>16617.671827</td>\n",
       "      <td>16587.242573</td>\n",
       "      <td>-807.548964</td>\n",
       "      <td>-2.758590e+06</td>\n",
       "      <td>15.626449</td>\n",
       "      <td>16604.554444</td>\n",
       "      <td>16603.699032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285128</th>\n",
       "      <td>1672445280</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>16601.74</td>\n",
       "      <td>16594.96</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>83.76568</td>\n",
       "      <td>2463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>16617.640824</td>\n",
       "      <td>16587.431576</td>\n",
       "      <td>-540.288636</td>\n",
       "      <td>-1.677894e+06</td>\n",
       "      <td>20.168746</td>\n",
       "      <td>16603.645556</td>\n",
       "      <td>16603.149153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285129</th>\n",
       "      <td>1672445340</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>16600.21</td>\n",
       "      <td>16593.64</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>44.73432</td>\n",
       "      <td>1797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>16617.593324</td>\n",
       "      <td>16587.558276</td>\n",
       "      <td>-409.319028</td>\n",
       "      <td>-3.723131e+06</td>\n",
       "      <td>4.814757</td>\n",
       "      <td>16601.672222</td>\n",
       "      <td>16602.184259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285130</th>\n",
       "      <td>1672445400</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>16595.68</td>\n",
       "      <td>16591.45</td>\n",
       "      <td>16593.16</td>\n",
       "      <td>80.06386</td>\n",
       "      <td>2371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>16617.608485</td>\n",
       "      <td>16587.522715</td>\n",
       "      <td>-698.156859</td>\n",
       "      <td>-4.062804e+06</td>\n",
       "      <td>-16.390376</td>\n",
       "      <td>16599.776667</td>\n",
       "      <td>16601.056227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284882 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp      Open      High       Low     Close    Volume  Count  \\\n",
       "249     1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979   \n",
       "250     1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736   \n",
       "251     1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569   \n",
       "252     1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853   \n",
       "253     1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864   \n",
       "...            ...       ...       ...       ...       ...       ...    ...   \n",
       "285126  1672445160  16599.34  16600.32  16594.37  16594.44  68.09939   2465   \n",
       "285127  1672445220  16594.44  16600.91  16593.56  16599.84  88.83927   2709   \n",
       "285128  1672445280  16599.84  16601.74  16594.96  16599.30  83.76568   2463   \n",
       "285129  1672445340  16599.30  16600.21  16593.64  16595.43  44.73432   1797   \n",
       "285130  1672445400  16595.43  16595.68  16591.45  16593.16  80.06386   2371   \n",
       "\n",
       "        Target  Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "249        1.0        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "250        1.0        0.000653  22371.609477  22180.109723   450.901757   \n",
       "251        1.0        0.000476  22371.548674  22179.166926   279.156212   \n",
       "252        1.0        0.001264  22370.493590  22179.116810   934.195660   \n",
       "253        1.0        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "...        ...             ...           ...           ...          ...   \n",
       "285126     0.0       -0.000852  16617.670956  16587.158644  -963.606369   \n",
       "285127     0.0       -0.000547  16617.671827  16587.242573  -807.548964   \n",
       "285128     0.0       -0.000388  16617.640824  16587.431576  -540.288636   \n",
       "285129     0.0       -0.000551  16617.593324  16587.558276  -409.319028   \n",
       "285130     0.0       -0.000525  16617.608485  16587.522715  -698.156859   \n",
       "\n",
       "                 EVM        CCI           SMA       EWMA_15  \n",
       "249     1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "250     5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "251    -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "252    -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "253     2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "...              ...        ...           ...           ...  \n",
       "285126 -2.586268e+06   3.166541  16605.447778  16604.250322  \n",
       "285127 -2.758590e+06  15.626449  16604.554444  16603.699032  \n",
       "285128 -1.677894e+06  20.168746  16603.645556  16603.149153  \n",
       "285129 -3.723131e+06   4.814757  16601.672222  16602.184259  \n",
       "285130 -4.062804e+06 -16.390376  16599.776667  16601.056227  \n",
       "\n",
       "[284882 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_len = 0\n",
    "df_train = df[:len(df)-test_len ]\n",
    "df_test = df[len(df)-test_len:]\n",
    "\n",
    "df_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fac18c34",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train.set_index(\"timestamp\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "191f6b75",
   "metadata": {},
   "source": [
    "#### Смотрим, есть ли разрыввы по дате."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14c045e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60    284881\n",
       "Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_train.index[1:]-df_train.index[:-1]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "df6c48d1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>1655352720</th>\n",
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       "<p>284882 rows × 15 columns</p>\n",
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      ],
      "text/plain": [
       "                Open      High       Low     Close    Volume  Count  Target  \\\n",
       "timestamp                                                                     \n",
       "1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979     1.0   \n",
       "1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736     1.0   \n",
       "1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569     1.0   \n",
       "1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853     1.0   \n",
       "1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864     1.0   \n",
       "...              ...       ...       ...       ...       ...    ...     ...   \n",
       "1672445160  16599.34  16600.32  16594.37  16594.44  68.09939   2465     0.0   \n",
       "1672445220  16594.44  16600.91  16593.56  16599.84  88.83927   2709     0.0   \n",
       "1672445280  16599.84  16601.74  16594.96  16599.30  83.76568   2463     0.0   \n",
       "1672445340  16599.30  16600.21  16593.64  16595.43  44.73432   1797     0.0   \n",
       "1672445400  16595.43  16595.68  16591.45  16593.16  80.06386   2371     0.0   \n",
       "\n",
       "            Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "timestamp                                                             \n",
       "1655352540        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "1655352600        0.000653  22371.609477  22180.109723   450.901757   \n",
       "1655352660        0.000476  22371.548674  22179.166926   279.156212   \n",
       "1655352720        0.001264  22370.493590  22179.116810   934.195660   \n",
       "1655352780        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "...                    ...           ...           ...          ...   \n",
       "1672445160       -0.000852  16617.670956  16587.158644  -963.606369   \n",
       "1672445220       -0.000547  16617.671827  16587.242573  -807.548964   \n",
       "1672445280       -0.000388  16617.640824  16587.431576  -540.288636   \n",
       "1672445340       -0.000551  16617.593324  16587.558276  -409.319028   \n",
       "1672445400       -0.000525  16617.608485  16587.522715  -698.156859   \n",
       "\n",
       "                     EVM        CCI           SMA       EWMA_15  \n",
       "timestamp                                                        \n",
       "1655352540  1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "1655352600  5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "1655352660 -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "1655352720 -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "1655352780  2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "...                  ...        ...           ...           ...  \n",
       "1672445160 -2.586268e+06   3.166541  16605.447778  16604.250322  \n",
       "1672445220 -2.758590e+06  15.626449  16604.554444  16603.699032  \n",
       "1672445280 -1.677894e+06  20.168746  16603.645556  16603.149153  \n",
       "1672445340 -3.723131e+06   4.814757  16601.672222  16602.184259  \n",
       "1672445400 -4.062804e+06 -16.390376  16599.776667  16601.056227  \n",
       "\n",
       "[284882 rows x 15 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f734c21",
   "metadata": {},
   "source": [
    "#### Разрывов мало, так что просто reindex с методом pad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b9d80cc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60    284881\n",
       "Name: timestamp, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = df_train.reindex(range(df_train.index[0],df_train.index[-1]+60,60),method='pad')\n",
    "(df_train.index[1:]-df_train.index[:-1]).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c128088e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: timestamp, dtype: int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = df_test.set_index(\"timestamp\")\n",
    "(df_test.index[1:]-df_test.index[:-1]).value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a2ad30d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 284882 entries, 1655352540 to 1672445400\n",
      "Data columns (total 15 columns):\n",
      " #   Column          Non-Null Count   Dtype  \n",
      "---  ------          --------------   -----  \n",
      " 0   Open            284882 non-null  float64\n",
      " 1   High            284882 non-null  float64\n",
      " 2   Low             284882 non-null  float64\n",
      " 3   Close           284882 non-null  float64\n",
      " 4   Volume          284882 non-null  float64\n",
      " 5   Count           284882 non-null  int64  \n",
      " 6   Target          284882 non-null  float64\n",
      " 7   Rate of Change  284882 non-null  float64\n",
      " 8   UpperBB         284882 non-null  float64\n",
      " 9   LowerBB         284882 non-null  float64\n",
      " 10  ForceIndex      284882 non-null  float64\n",
      " 11  EVM             284882 non-null  float64\n",
      " 12  CCI             284882 non-null  float64\n",
      " 13  SMA             284882 non-null  float64\n",
      " 14  EWMA_15         284882 non-null  float64\n",
      "dtypes: float64(14), int64(1)\n",
      "memory usage: 32.6 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f49cdc",
   "metadata": {
    "papermill": {
     "duration": 0.027162,
     "end_time": "2021-11-04T05:41:49.099908",
     "exception": false,
     "start_time": "2021-11-04T05:41:49.072746",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1dc530f3",
   "metadata": {},
   "outputs": [
    {
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       "      <td>22243.604444</td>\n",
       "      <td>22244.448408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>1655352720</td>\n",
       "      <td>22242.41</td>\n",
       "      <td>22291.32</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>33.19814</td>\n",
       "      <td>853</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>22370.493590</td>\n",
       "      <td>22179.116810</td>\n",
       "      <td>934.195660</td>\n",
       "      <td>-1.713638e+08</td>\n",
       "      <td>-73.464364</td>\n",
       "      <td>22248.343333</td>\n",
       "      <td>22249.772368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>1655352780</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>22302.94</td>\n",
       "      <td>22266.34</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>51.71966</td>\n",
       "      <td>864</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>22370.187252</td>\n",
       "      <td>22179.105548</td>\n",
       "      <td>1564.519715</td>\n",
       "      <td>2.081586e+08</td>\n",
       "      <td>-66.825663</td>\n",
       "      <td>22258.068889</td>\n",
       "      <td>22255.646847</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",
       "      <td>...</td>\n",
       "      <td>...</td>\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>285126</th>\n",
       "      <td>1672445160</td>\n",
       "      <td>16599.34</td>\n",
       "      <td>16600.32</td>\n",
       "      <td>16594.37</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>68.09939</td>\n",
       "      <td>2465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>16617.670956</td>\n",
       "      <td>16587.158644</td>\n",
       "      <td>-963.606369</td>\n",
       "      <td>-2.586268e+06</td>\n",
       "      <td>3.166541</td>\n",
       "      <td>16605.447778</td>\n",
       "      <td>16604.250322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285127</th>\n",
       "      <td>1672445220</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>16600.91</td>\n",
       "      <td>16593.56</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>88.83927</td>\n",
       "      <td>2709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000547</td>\n",
       "      <td>16617.671827</td>\n",
       "      <td>16587.242573</td>\n",
       "      <td>-807.548964</td>\n",
       "      <td>-2.758590e+06</td>\n",
       "      <td>15.626449</td>\n",
       "      <td>16604.554444</td>\n",
       "      <td>16603.699032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285128</th>\n",
       "      <td>1672445280</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>16601.74</td>\n",
       "      <td>16594.96</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>83.76568</td>\n",
       "      <td>2463</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>16617.640824</td>\n",
       "      <td>16587.431576</td>\n",
       "      <td>-540.288636</td>\n",
       "      <td>-1.677894e+06</td>\n",
       "      <td>20.168746</td>\n",
       "      <td>16603.645556</td>\n",
       "      <td>16603.149153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285129</th>\n",
       "      <td>1672445340</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>16600.21</td>\n",
       "      <td>16593.64</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>44.73432</td>\n",
       "      <td>1797</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>16617.593324</td>\n",
       "      <td>16587.558276</td>\n",
       "      <td>-409.319028</td>\n",
       "      <td>-3.723131e+06</td>\n",
       "      <td>4.814757</td>\n",
       "      <td>16601.672222</td>\n",
       "      <td>16602.184259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285130</th>\n",
       "      <td>1672445400</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>16595.68</td>\n",
       "      <td>16591.45</td>\n",
       "      <td>16593.16</td>\n",
       "      <td>80.06386</td>\n",
       "      <td>2371</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>16617.608485</td>\n",
       "      <td>16587.522715</td>\n",
       "      <td>-698.156859</td>\n",
       "      <td>-4.062804e+06</td>\n",
       "      <td>-16.390376</td>\n",
       "      <td>16599.776667</td>\n",
       "      <td>16601.056227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284882 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         timestamp      Open      High       Low     Close    Volume  Count  \\\n",
       "249     1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979   \n",
       "250     1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736   \n",
       "251     1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569   \n",
       "252     1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853   \n",
       "253     1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864   \n",
       "...            ...       ...       ...       ...       ...       ...    ...   \n",
       "285126  1672445160  16599.34  16600.32  16594.37  16594.44  68.09939   2465   \n",
       "285127  1672445220  16594.44  16600.91  16593.56  16599.84  88.83927   2709   \n",
       "285128  1672445280  16599.84  16601.74  16594.96  16599.30  83.76568   2463   \n",
       "285129  1672445340  16599.30  16600.21  16593.64  16595.43  44.73432   1797   \n",
       "285130  1672445400  16595.43  16595.68  16591.45  16593.16  80.06386   2371   \n",
       "\n",
       "        Target  Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "249        1.0        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "250        1.0        0.000653  22371.609477  22180.109723   450.901757   \n",
       "251        1.0        0.000476  22371.548674  22179.166926   279.156212   \n",
       "252        1.0        0.001264  22370.493590  22179.116810   934.195660   \n",
       "253        1.0        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "...        ...             ...           ...           ...          ...   \n",
       "285126     0.0       -0.000852  16617.670956  16587.158644  -963.606369   \n",
       "285127     0.0       -0.000547  16617.671827  16587.242573  -807.548964   \n",
       "285128     0.0       -0.000388  16617.640824  16587.431576  -540.288636   \n",
       "285129     0.0       -0.000551  16617.593324  16587.558276  -409.319028   \n",
       "285130     0.0       -0.000525  16617.608485  16587.522715  -698.156859   \n",
       "\n",
       "                 EVM        CCI           SMA       EWMA_15  \n",
       "249     1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "250     5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "251    -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "252    -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "253     2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "...              ...        ...           ...           ...  \n",
       "285126 -2.586268e+06   3.166541  16605.447778  16604.250322  \n",
       "285127 -2.758590e+06  15.626449  16604.554444  16603.699032  \n",
       "285128 -1.677894e+06  20.168746  16603.645556  16603.149153  \n",
       "285129 -3.723131e+06   4.814757  16601.672222  16602.184259  \n",
       "285130 -4.062804e+06 -16.390376  16599.776667  16601.056227  \n",
       "\n",
       "[284882 rows x 16 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "df2e7350",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284882, 15)\n",
      "14 features: ['Open', 'High', 'Low', 'Close', 'Volume', 'Count', 'Rate of Change', 'UpperBB', 'LowerBB', 'ForceIndex', 'EVM', 'CCI', 'SMA', 'EWMA_15']\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# feature engineering\n",
    "#df_train = get_row_feats(df_train)\n",
    "\n",
    "print(df_train.shape)\n",
    "\n",
    "df_train = df_train.fillna(df_train.mean())\n",
    "\n",
    "target = 'Target'\n",
    "drops = ['timestamp']\n",
    "features = [f for f in df_train.columns if f not in drops + [target]]\n",
    "\n",
    "print('{:,} features: {}'.format(len(features), features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fcc94f1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = df_train[features]\n",
    "y_train = df_train[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "7773ba7e",
   "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",
       "      <th>Count</th>\n",
       "      <th>Rate of Change</th>\n",
       "      <th>UpperBB</th>\n",
       "      <th>LowerBB</th>\n",
       "      <th>ForceIndex</th>\n",
       "      <th>EVM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>SMA</th>\n",
       "      <th>EWMA_15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</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",
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       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1655352540</th>\n",
       "      <td>22241.69</td>\n",
       "      <td>22272.01</td>\n",
       "      <td>22241.69</td>\n",
       "      <td>22265.94</td>\n",
       "      <td>45.64405</td>\n",
       "      <td>979</td>\n",
       "      <td>0.001959</td>\n",
       "      <td>22371.600013</td>\n",
       "      <td>22179.761987</td>\n",
       "      <td>1986.885496</td>\n",
       "      <td>1.708244e+07</td>\n",
       "      <td>-80.554032</td>\n",
       "      <td>22244.693333</td>\n",
       "      <td>22242.554903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655352600</th>\n",
       "      <td>22265.94</td>\n",
       "      <td>22280.40</td>\n",
       "      <td>22256.40</td>\n",
       "      <td>22258.00</td>\n",
       "      <td>31.05384</td>\n",
       "      <td>736</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>22371.609477</td>\n",
       "      <td>22180.109723</td>\n",
       "      <td>450.901757</td>\n",
       "      <td>5.686032e+07</td>\n",
       "      <td>-77.728728</td>\n",
       "      <td>22244.491111</td>\n",
       "      <td>22244.786682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655352660</th>\n",
       "      <td>22258.01</td>\n",
       "      <td>22265.10</td>\n",
       "      <td>22240.99</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>26.36036</td>\n",
       "      <td>569</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>22371.548674</td>\n",
       "      <td>22179.166926</td>\n",
       "      <td>279.156212</td>\n",
       "      <td>-2.414865e+08</td>\n",
       "      <td>-83.542529</td>\n",
       "      <td>22243.604444</td>\n",
       "      <td>22244.448408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655352720</th>\n",
       "      <td>22242.41</td>\n",
       "      <td>22291.32</td>\n",
       "      <td>22242.40</td>\n",
       "      <td>22282.64</td>\n",
       "      <td>33.19814</td>\n",
       "      <td>853</td>\n",
       "      <td>0.001264</td>\n",
       "      <td>22370.493590</td>\n",
       "      <td>22179.116810</td>\n",
       "      <td>934.195660</td>\n",
       "      <td>-1.713638e+08</td>\n",
       "      <td>-73.464364</td>\n",
       "      <td>22248.343333</td>\n",
       "      <td>22249.772368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1655352780</th>\n",
       "      <td>22282.64</td>\n",
       "      <td>22302.94</td>\n",
       "      <td>22266.34</td>\n",
       "      <td>22292.52</td>\n",
       "      <td>51.71966</td>\n",
       "      <td>864</td>\n",
       "      <td>0.001359</td>\n",
       "      <td>22370.187252</td>\n",
       "      <td>22179.105548</td>\n",
       "      <td>1564.519715</td>\n",
       "      <td>2.081586e+08</td>\n",
       "      <td>-66.825663</td>\n",
       "      <td>22258.068889</td>\n",
       "      <td>22255.646847</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",
       "      <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>1672445160</th>\n",
       "      <td>16599.34</td>\n",
       "      <td>16600.32</td>\n",
       "      <td>16594.37</td>\n",
       "      <td>16594.44</td>\n",
       "      <td>68.09939</td>\n",
       "      <td>2465</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>16617.670956</td>\n",
       "      <td>16587.158644</td>\n",
       "      <td>-963.606369</td>\n",
       "      <td>-2.586268e+06</td>\n",
       "      <td>3.166541</td>\n",
       "      <td>16605.447778</td>\n",
       "      <td>16604.250322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672445220</th>\n",
       "      <td>16594.44</td>\n",
       "      <td>16600.91</td>\n",
       "      <td>16593.56</td>\n",
       "      <td>16599.84</td>\n",
       "      <td>88.83927</td>\n",
       "      <td>2709</td>\n",
       "      <td>-0.000547</td>\n",
       "      <td>16617.671827</td>\n",
       "      <td>16587.242573</td>\n",
       "      <td>-807.548964</td>\n",
       "      <td>-2.758590e+06</td>\n",
       "      <td>15.626449</td>\n",
       "      <td>16604.554444</td>\n",
       "      <td>16603.699032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672445280</th>\n",
       "      <td>16599.84</td>\n",
       "      <td>16601.74</td>\n",
       "      <td>16594.96</td>\n",
       "      <td>16599.30</td>\n",
       "      <td>83.76568</td>\n",
       "      <td>2463</td>\n",
       "      <td>-0.000388</td>\n",
       "      <td>16617.640824</td>\n",
       "      <td>16587.431576</td>\n",
       "      <td>-540.288636</td>\n",
       "      <td>-1.677894e+06</td>\n",
       "      <td>20.168746</td>\n",
       "      <td>16603.645556</td>\n",
       "      <td>16603.149153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672445340</th>\n",
       "      <td>16599.30</td>\n",
       "      <td>16600.21</td>\n",
       "      <td>16593.64</td>\n",
       "      <td>16595.43</td>\n",
       "      <td>44.73432</td>\n",
       "      <td>1797</td>\n",
       "      <td>-0.000551</td>\n",
       "      <td>16617.593324</td>\n",
       "      <td>16587.558276</td>\n",
       "      <td>-409.319028</td>\n",
       "      <td>-3.723131e+06</td>\n",
       "      <td>4.814757</td>\n",
       "      <td>16601.672222</td>\n",
       "      <td>16602.184259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672445400</th>\n",
       "      <td>16595.43</td>\n",
       "      <td>16595.68</td>\n",
       "      <td>16591.45</td>\n",
       "      <td>16593.16</td>\n",
       "      <td>80.06386</td>\n",
       "      <td>2371</td>\n",
       "      <td>-0.000525</td>\n",
       "      <td>16617.608485</td>\n",
       "      <td>16587.522715</td>\n",
       "      <td>-698.156859</td>\n",
       "      <td>-4.062804e+06</td>\n",
       "      <td>-16.390376</td>\n",
       "      <td>16599.776667</td>\n",
       "      <td>16601.056227</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284882 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Open      High       Low     Close    Volume  Count  \\\n",
       "timestamp                                                             \n",
       "1655352540  22241.69  22272.01  22241.69  22265.94  45.64405    979   \n",
       "1655352600  22265.94  22280.40  22256.40  22258.00  31.05384    736   \n",
       "1655352660  22258.01  22265.10  22240.99  22242.40  26.36036    569   \n",
       "1655352720  22242.41  22291.32  22242.40  22282.64  33.19814    853   \n",
       "1655352780  22282.64  22302.94  22266.34  22292.52  51.71966    864   \n",
       "...              ...       ...       ...       ...       ...    ...   \n",
       "1672445160  16599.34  16600.32  16594.37  16594.44  68.09939   2465   \n",
       "1672445220  16594.44  16600.91  16593.56  16599.84  88.83927   2709   \n",
       "1672445280  16599.84  16601.74  16594.96  16599.30  83.76568   2463   \n",
       "1672445340  16599.30  16600.21  16593.64  16595.43  44.73432   1797   \n",
       "1672445400  16595.43  16595.68  16591.45  16593.16  80.06386   2371   \n",
       "\n",
       "            Rate of Change       UpperBB       LowerBB   ForceIndex  \\\n",
       "timestamp                                                             \n",
       "1655352540        0.001959  22371.600013  22179.761987  1986.885496   \n",
       "1655352600        0.000653  22371.609477  22180.109723   450.901757   \n",
       "1655352660        0.000476  22371.548674  22179.166926   279.156212   \n",
       "1655352720        0.001264  22370.493590  22179.116810   934.195660   \n",
       "1655352780        0.001359  22370.187252  22179.105548  1564.519715   \n",
       "...                    ...           ...           ...          ...   \n",
       "1672445160       -0.000852  16617.670956  16587.158644  -963.606369   \n",
       "1672445220       -0.000547  16617.671827  16587.242573  -807.548964   \n",
       "1672445280       -0.000388  16617.640824  16587.431576  -540.288636   \n",
       "1672445340       -0.000551  16617.593324  16587.558276  -409.319028   \n",
       "1672445400       -0.000525  16617.608485  16587.522715  -698.156859   \n",
       "\n",
       "                     EVM        CCI           SMA       EWMA_15  \n",
       "timestamp                                                        \n",
       "1655352540  1.708244e+07 -80.554032  22244.693333  22242.554903  \n",
       "1655352600  5.686032e+07 -77.728728  22244.491111  22244.786682  \n",
       "1655352660 -2.414865e+08 -83.542529  22243.604444  22244.448408  \n",
       "1655352720 -1.713638e+08 -73.464364  22248.343333  22249.772368  \n",
       "1655352780  2.081586e+08 -66.825663  22258.068889  22255.646847  \n",
       "...                  ...        ...           ...           ...  \n",
       "1672445160 -2.586268e+06   3.166541  16605.447778  16604.250322  \n",
       "1672445220 -2.758590e+06  15.626449  16604.554444  16603.699032  \n",
       "1672445280 -1.677894e+06  20.168746  16603.645556  16603.149153  \n",
       "1672445340 -3.723131e+06   4.814757  16601.672222  16602.184259  \n",
       "1672445400 -4.062804e+06 -16.390376  16599.776667  16601.056227  \n",
       "\n",
       "[284882 rows x 14 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1427a8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "d = preprocessing.normalize(X_train, axis=0)\n",
    "X_train = pd.DataFrame(d, columns=X_train.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4a9c753b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>Count</th>\n",
       "      <th>Rate of Change</th>\n",
       "      <th>UpperBB</th>\n",
       "      <th>LowerBB</th>\n",
       "      <th>ForceIndex</th>\n",
       "      <th>EVM</th>\n",
       "      <th>CCI</th>\n",
       "      <th>SMA</th>\n",
       "      <th>EWMA_15</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.002096</td>\n",
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       "      <td>0.002097</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002099</td>\n",
       "      <td>0.002099</td>\n",
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       "      <td>0.000256</td>\n",
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       "      <td>0.002097</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.002097</td>\n",
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       "      <td>0.000243</td>\n",
       "      <td>0.000245</td>\n",
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       "      <td>0.002097</td>\n",
       "      <td>0.002097</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.002097</td>\n",
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       "      <td>0.002100</td>\n",
       "      <td>0.000274</td>\n",
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       "      <td>-0.001468</td>\n",
       "      <td>0.002097</td>\n",
       "      <td>0.002097</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>0.002100</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>0.000427</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>0.002101</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>0.003037</td>\n",
       "      <td>-0.001335</td>\n",
       "      <td>0.002098</td>\n",
       "      <td>0.002098</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>0.000562</td>\n",
       "      <td>0.001053</td>\n",
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       "      <td>-0.000038</td>\n",
       "      <td>0.000063</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001565</td>\n",
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       "      <td>-0.000016</td>\n",
       "      <td>-0.000054</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001565</td>\n",
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       "    <tr>\n",
       "      <th>284881</th>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.001563</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001564</td>\n",
       "      <td>0.000661</td>\n",
       "      <td>0.001012</td>\n",
       "      <td>-0.000270</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>0.001569</td>\n",
       "      <td>-0.000027</td>\n",
       "      <td>-0.000059</td>\n",
       "      <td>-0.000327</td>\n",
       "      <td>0.001565</td>\n",
       "      <td>0.001565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>284882 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open      High       Low     Close    Volume     Count  \\\n",
       "0       0.002096  0.002098  0.002098  0.002099  0.000377  0.000418   \n",
       "1       0.002099  0.002099  0.002099  0.002098  0.000256  0.000314   \n",
       "2       0.002098  0.002098  0.002097  0.002097  0.000218  0.000243   \n",
       "3       0.002097  0.002100  0.002098  0.002100  0.000274  0.000364   \n",
       "4       0.002100  0.002101  0.002100  0.002101  0.000427  0.000369   \n",
       "...          ...       ...       ...       ...       ...       ...   \n",
       "284877  0.001565  0.001564  0.001565  0.001564  0.000562  0.001053   \n",
       "284878  0.001564  0.001564  0.001565  0.001565  0.000734  0.001157   \n",
       "284879  0.001565  0.001564  0.001565  0.001565  0.000692  0.001052   \n",
       "284880  0.001565  0.001564  0.001565  0.001564  0.000369  0.000767   \n",
       "284881  0.001564  0.001563  0.001565  0.001564  0.000661  0.001012   \n",
       "\n",
       "        Rate of Change   UpperBB   LowerBB  ForceIndex       EVM       CCI  \\\n",
       "0             0.001008  0.002101  0.002098    0.000076  0.000249 -0.001609   \n",
       "1             0.000336  0.002101  0.002098    0.000017  0.000830 -0.001553   \n",
       "2             0.000245  0.002101  0.002098    0.000011 -0.003523 -0.001669   \n",
       "3             0.000651  0.002101  0.002098    0.000036 -0.002500 -0.001468   \n",
       "4             0.000699  0.002101  0.002098    0.000060  0.003037 -0.001335   \n",
       "...                ...       ...       ...         ...       ...       ...   \n",
       "284877       -0.000439  0.001561  0.001569   -0.000037 -0.000038  0.000063   \n",
       "284878       -0.000282  0.001561  0.001569   -0.000031 -0.000040  0.000312   \n",
       "284879       -0.000200  0.001561  0.001569   -0.000021 -0.000024  0.000403   \n",
       "284880       -0.000284  0.001561  0.001569   -0.000016 -0.000054  0.000096   \n",
       "284881       -0.000270  0.001561  0.001569   -0.000027 -0.000059 -0.000327   \n",
       "\n",
       "             SMA   EWMA_15  \n",
       "0       0.002097  0.002097  \n",
       "1       0.002097  0.002097  \n",
       "2       0.002097  0.002097  \n",
       "3       0.002097  0.002097  \n",
       "4       0.002098  0.002098  \n",
       "...          ...       ...  \n",
       "284877  0.001565  0.001565  \n",
       "284878  0.001565  0.001565  \n",
       "284879  0.001565  0.001565  \n",
       "284880  0.001565  0.001565  \n",
       "284881  0.001565  0.001565  \n",
       "\n",
       "[284882 rows x 14 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7b294066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 1 1 1 1 1 1 1 1 1]\n",
      "Label Classes:  [0. 1.]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ilya\\AppData\\Local\\Temp\\ipykernel_2904\\2633290842.py:6: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  y_train[\"label\"] = encoded_labels\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "timestamp\n",
       "1655352540                                                  1.0\n",
       "1655352600                                                  1.0\n",
       "1655352660                                                  1.0\n",
       "1655352720                                                  1.0\n",
       "1655352780                                                  1.0\n",
       "                                    ...                        \n",
       "1672445220                                                  0.0\n",
       "1672445280                                                  0.0\n",
       "1672445340                                                  0.0\n",
       "1672445400                                                  0.0\n",
       "label         [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, ...\n",
       "Name: Target, Length: 284883, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label_encoder = LabelEncoder()\n",
    "encoded_labels = label_encoder.fit_transform(y_train)\n",
    "print(encoded_labels[:10])\n",
    "print(\"Label Classes: \", label_encoder.classes_)\n",
    "\n",
    "y_train[\"label\"] = encoded_labels\n",
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "83f0ba76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "284882"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "f29e772f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corr = X_train.corr()\n",
    "mask = np.zeros_like(corr)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "#with sns.axes_style(\"darkgrid\"):\n",
    "f, ax = plt.subplots(figsize=(15, 8))\n",
    "ax = sns.heatmap(corr, annot = True, cmap = 'PiYG')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2df4c84b",
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences =[]\n",
    "\n",
    "for idx in range(len(X_train) - seq_length):\n",
    "    x = X_train[idx:idx + seq_length]\n",
    "    y = y_train[idx:idx + seq_length].iloc[0]\n",
    "    sequences.append((x, y))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "00458064",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" train_sequences, test_sequences = train_test_split(sequences, test_size=0.2)\n",
    "print(\"Number of Training Sequences: \", len(train_sequences))\n",
    "print(\"Number of Testing Sequences: \", len(test_sequences)) \"\"\"\n",
    "\n",
    "train_sequences = sequences\n",
    "test_sequences = sequences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2d40e90c",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurfaceDataset(Dataset):\n",
    "    def __init__(self, sequences):\n",
    "        super().__init__()\n",
    "        self.sequences = sequences\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.sequences)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        sequence, label = self.sequences[idx]\n",
    "        return dict(\n",
    "            sequence = torch.Tensor(sequence.values),\n",
    "            label = torch.tensor(label).long()\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5c478984",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurfaceDataModule(pl.LightningDataModule):\n",
    "    def __init__(self, train_sequences, test_sequences, batch_size):\n",
    "        super().__init__()\n",
    "        self.train_sequences = train_sequences\n",
    "        self.test_sequences = test_sequences\n",
    "        self.batch_size = batch_size\n",
    "    \n",
    "    def setup(self, stage=None):\n",
    "        self.train_dataset = SurfaceDataset(self.train_sequences)\n",
    "        self.test_dataset = SurfaceDataset(self.test_sequences)\n",
    "    \n",
    "    def train_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.train_dataset,\n",
    "            batch_size = self.batch_size,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )\n",
    "\n",
    "    def val_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.test_dataset,\n",
    "            batch_size = self.batch_size,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )\n",
    "    \n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.test_dataset,\n",
    "            batch_size = self.batch_size,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )\n",
    "\n",
    "    def predict_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.test_dataset,\n",
    "            batch_size = self.batch_size,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9b3f4d40",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_module = SurfaceDataModule(train_sequences, test_sequences, BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "dfbfbaa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = df_train[features][1:250]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a2e2945",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Test(pl.LightningDataModule):\n",
    "    def __init__(self, test_sequences):\n",
    "        super().__init__()\n",
    "        self.test_sequences = test_sequences\n",
    "\n",
    "    def len(self):\n",
    "        return len(test_sequences)\n",
    "    \n",
    "    def setup(self, stage=None):\n",
    "        self.test_dataset = SurfaceDataset(self.test_sequences)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.test_dataset,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )\n",
    "    \n",
    "    def predict_dataloader(self):\n",
    "        return DataLoader(\n",
    "            self.test_dataset,\n",
    "            shuffle = False,\n",
    "            drop_last = True\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ca9ae943",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = Test(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e626e571",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SequenceModel(pl.LightningModule):\n",
    "  def __init__(self, n_features, n_classes, n_hidden=400, n_layers=2):\n",
    "    super().__init__()\n",
    "\n",
    "    self.lstm = nn.LSTM(\n",
    "        input_size = n_features,\n",
    "        hidden_size = n_hidden,\n",
    "        batch_first = True,\n",
    "        num_layers = n_layers, # Stack LSTMs\n",
    "        dropout = 0.3\n",
    "    )\n",
    "\n",
    "    self.liner1 = nn.Linear(n_hidden, n_classes)\n",
    "    #self.batchnorm1 = nn.BatchNorm1d(n_hidden)\n",
    "\n",
    "\n",
    "  def forward(self, x):\n",
    "\n",
    "\n",
    "    _, (hidden, _) = self.lstm(x)\n",
    "    # We want the output from the last layer to go into the final\n",
    "    # regressor linear layer\n",
    "    out = hidden[-1] \n",
    "    #print(f'befor sfotmax: {out}')\n",
    "    #print(f'after sfotmax: {out}')\n",
    "\n",
    "    return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ed35dc6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class SurfacePredictor(pl.LightningModule):\n",
    "    def __init__(self, n_features, n_classes):\n",
    "        super().__init__()\n",
    "        self.n_features = n_features\n",
    "        self.n_classes = n_classes\n",
    "        self.model = SequenceModel(n_features, n_classes)\n",
    "        self.criterion = nn.CrossEntropyLoss()\n",
    "    \n",
    "    def to_torchscript(self):\n",
    "        return self.model.to_torchscript()\n",
    "    \n",
    "    def forward(self, x, labels=None):\n",
    "        output = self.model(x)\n",
    "        loss = 0\n",
    " \n",
    "        if labels is not None:\n",
    "            output = Variable(torch.randn(BATCH_SIZE, len(label_encoder.classes_)).float(), requires_grad=True).to(device)\n",
    "            labels = Variable(torch.FloatTensor(BATCH_SIZE).uniform_(0, len(label_encoder.classes_)).long()).to(device)\n",
    "            loss = self.criterion(output, labels)\n",
    "        return loss, output\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "\n",
    "        sequences = batch[\"sequence\"]\n",
    "        labels = batch[\"label\"]\n",
    " \n",
    "        \n",
    "        loss, outputs = self.forward(sequences, labels)\n",
    "\n",
    "        #print(outputs.shape, labels.shape)\n",
    "        #print('1')\n",
    "\n",
    "        predictions = torch.argmax(outputs, dim=1)\n",
    "        #print('2')\n",
    "        step_accuracy = accuracy(predictions, labels)\n",
    "        #print('3')\n",
    "\n",
    "\n",
    "        self.log(\"train_loss\", loss, prog_bar=True, logger=True)\n",
    "        self.log(\"train_accuracy\", step_accuracy, prog_bar=True, logger=True)\n",
    "\n",
    "        \"\"\" plt.figure(figsize=(40,5)) \n",
    "\n",
    "\n",
    "        plt.plot(predictions.cpu(),'--r')\n",
    "        plt.plot(labels.cpu()) \n",
    "        plt.show()\n",
    " \"\"\"\n",
    "        #print({\"training_loss\": loss, \"training_accuracy\": step_accuracy})\n",
    "        \n",
    "        return {\"loss\": loss, \"accuracy\": step_accuracy}\n",
    "    \n",
    "    def validation_step(self, batch, batch_idx):\n",
    "        sequences = batch[\"sequence\"]\n",
    "        labels = batch[\"label\"]\n",
    "\n",
    "        loss, outputs = self.forward(sequences, labels)\n",
    "\n",
    "        predictions = torch.argmax(outputs, dim=1)\n",
    "        step_accuracy = accuracy(predictions, labels)\n",
    "        #print('outputs = ', outputs.size(), outputs)\n",
    "        #print('predictions = ', predictions.size(), predictions)\n",
    "        #print('labels = ',labels)\n",
    "        \n",
    "\n",
    "        self.log(\"val_loss\", loss, prog_bar=True, logger=True)\n",
    "        self.log(\"val_accuracy\", step_accuracy, prog_bar=True, logger=True)\n",
    "\n",
    "        #print({\"val_loss\": loss, \"val_accuracy\": step_accuracy})\n",
    "\n",
    "        return {\"loss\": loss, \"accuracy\": step_accuracy}\n",
    "\n",
    "    def test_step(self, batch, batch_idx):\n",
    "        print(batch_idx)\n",
    "        sequences = batch[\"sequence\"]\n",
    "        labels = batch[\"label\"]\n",
    "        loss, outputs = self.forward(sequences, labels)\n",
    "        predictions = torch.argmax(outputs, dim=1)\n",
    "        step_accuracy = accuracy(predictions, labels)\n",
    "  \n",
    "        self.log(\"test_loss\", loss, prog_bar=True, logger=True)\n",
    "        self.log(\"test_accuracy\", step_accuracy, prog_bar=True, logger=True)\n",
    "        f = open('final_predict.txt', 'w')\n",
    "        print(predictions.cpu().numpy())\n",
    "        f.write(str(predictions.cpu().numpy()))\n",
    "\n",
    "      \n",
    "\n",
    "        return {\"loss\": loss, \"accuracy\": step_accuracy}\n",
    "\n",
    "    \n",
    "    def configure_optimizers(self):\n",
    "        return optim.Adam(self.model.parameters(), lr=0.001)\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "9915d4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SurfacePredictor(\n",
    "    n_features=len(features),\n",
    "    n_classes=len(label_encoder.classes_)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d8fc318f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-b41cfb2412f29798\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-b41cfb2412f29798\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir ./lightning_logs \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1193418a",
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint_callback = ModelCheckpoint(\n",
    "    dirpath=\"checkpoints\",\n",
    "    filename=\"best-checkpoint\",\n",
    "    save_top_k = 1,\n",
    "    verbose = True,\n",
    "    monitor = \"val_loss\",\n",
    "    mode = \"min\"\n",
    ")\n",
    "\n",
    "logger = TensorBoardLogger(\"lightning_logs\", name = \"surface_predictor\")\n",
    "\n",
    "#early_stopping_callback = EarlyStopping(monitor = \"val_loss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "94c991fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (cuda), used: True\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n"
     ]
    }
   ],
   "source": [
    "# Trainer\n",
    "trainer = pl.Trainer(\n",
    "    enable_progress_bar=True,\n",
    "    #log_every_n_steps=2,\n",
    "    check_val_every_n_epoch = 10,\n",
    "    logger = logger,\n",
    "    callbacks=[checkpoint_callback],\n",
    "    enable_checkpointing=True,\n",
    "    max_epochs = 10,\n",
    "    accelerator='gpu',\n",
    "    devices=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "037c7160",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "a:\\python\\lib\\site-packages\\pytorch_lightning\\callbacks\\model_checkpoint.py:616: UserWarning: Checkpoint directory A:\\Proga\\DP\\Kaggle\\G-Research_Crypto_Competition\\project\\checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
      "\n",
      "  | Name      | Type             | Params\n",
      "-----------------------------------------------\n",
      "0 | model     | SequenceModel    | 1.9 M \n",
      "1 | criterion | CrossEntropyLoss | 0     \n",
      "-----------------------------------------------\n",
      "1.9 M     Trainable params\n",
      "0         Non-trainable params\n",
      "1.9 M     Total params\n",
      "7.798     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf26afbfb210419885ea2491d8d7c718",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "a:\\python\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:236: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "a:\\python\\lib\\site-packages\\pytorch_lightning\\trainer\\connectors\\data_connector.py:236: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fba03d0d48a642799c5bd7a94243f5d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01f61589529e4332a578a78d4f26fca4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Validation: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch 9, global step 5690: 'val_loss' reached 0.90275 (best 0.90275), saving model to 'A:\\\\Proga\\\\DP\\\\Kaggle\\\\G-Research_Crypto_Competition\\\\project\\\\checkpoints\\\\best-checkpoint-v80.ckpt' as top 1\n",
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    }
   ],
   "source": [
    "trainer.fit(model, data_module)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d95203e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model.state_dict(), 'model_weights_epochs_500.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ea77ca66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SurfacePredictor(\n",
       "  (model): SequenceModel(\n",
       "    (lstm): LSTM(14, 400, num_layers=2, batch_first=True, dropout=0.3)\n",
       "    (liner1): Linear(in_features=400, out_features=2, bias=True)\n",
       "  )\n",
       "  (criterion): CrossEntropyLoss()\n",
       ")"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1 = SurfacePredictor(\n",
    "    n_features=len(features),\n",
    "    n_classes=len(label_encoder.classes_)\n",
    ")\n",
    "model1.load_state_dict(torch.load('model_weights_epochs_500.pth'))\n",
    "model1.eval()"
   ]
  }
 ],
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