{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b29811c3",
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4608</th>\n",
       "      <td>39.80</td>\n",
       "      <td>40.22</td>\n",
       "      <td>39.77</td>\n",
       "      <td>40.09</td>\n",
       "      <td>40.09</td>\n",
       "      <td>27456000</td>\n",
       "      <td>24-09-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4609</th>\n",
       "      <td>40.00</td>\n",
       "      <td>40.23</td>\n",
       "      <td>39.58</td>\n",
       "      <td>39.89</td>\n",
       "      <td>39.89</td>\n",
       "      <td>12162500</td>\n",
       "      <td>24-09-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4610</th>\n",
       "      <td>39.70</td>\n",
       "      <td>40.08</td>\n",
       "      <td>39.31</td>\n",
       "      <td>39.49</td>\n",
       "      <td>39.49</td>\n",
       "      <td>17906900</td>\n",
       "      <td>24-09-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4611</th>\n",
       "      <td>39.76</td>\n",
       "      <td>40.99</td>\n",
       "      <td>39.02</td>\n",
       "      <td>39.06</td>\n",
       "      <td>39.06</td>\n",
       "      <td>41241400</td>\n",
       "      <td>24-09-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4612</th>\n",
       "      <td>40.10</td>\n",
       "      <td>40.25</td>\n",
       "      <td>39.26</td>\n",
       "      <td>39.72</td>\n",
       "      <td>39.72</td>\n",
       "      <td>22277500</td>\n",
       "      <td>24-09-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Open   High    Low  Close  Adj Close    Volume      Date\n",
       "4608  39.80  40.22  39.77  40.09      40.09  27456000  24-09-13\n",
       "4609  40.00  40.23  39.58  39.89      39.89  12162500  24-09-16\n",
       "4610  39.70  40.08  39.31  39.49      39.49  17906900  24-09-17\n",
       "4611  39.76  40.99  39.02  39.06      39.06  41241400  24-09-18\n",
       "4612  40.10  40.25  39.26  39.72      39.72  22277500  24-09-19"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import pandas_ta as ta\n",
    "import IPython\n",
    "\n",
    "# Uncomment for Interactive Graphs\n",
    "#%matplotlib widget\n",
    "\n",
    "name = \"GDX\"\n",
    "data = pd.read_csv(\"data/\"+name + \".csv\")\n",
    "data.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68e700e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Date</th>\n",
       "      <th>Diff_Close</th>\n",
       "      <th>RSI</th>\n",
       "      <th>EMAF</th>\n",
       "      <th>EMAM</th>\n",
       "      <th>EMAS</th>\n",
       "      <th>BBL_5_2.0</th>\n",
       "      <th>BBM_5_2.0</th>\n",
       "      <th>BBU_5_2.0</th>\n",
       "      <th>BBB_5_2.0</th>\n",
       "      <th>BBP_5_2.0</th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "      <th>Target1</th>\n",
       "      <th>Target2</th>\n",
       "      <th>Target3</th>\n",
       "      <th>Target4</th>\n",
       "      <th>Target5</th>\n",
       "      <th>Target6</th>\n",
       "      <th>Target7</th>\n",
       "      <th>Target8</th>\n",
       "      <th>Target9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>4602</td>\n",
       "      <td>37.66</td>\n",
       "      <td>37.83</td>\n",
       "      <td>37.28</td>\n",
       "      <td>37.33</td>\n",
       "      <td>37.33</td>\n",
       "      <td>16260000</td>\n",
       "      <td>24-09-05</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>47.133189</td>\n",
       "      <td>37.965111</td>\n",
       "      <td>37.071605</td>\n",
       "      <td>33.759355</td>\n",
       "      <td>36.218919</td>\n",
       "      <td>37.802</td>\n",
       "      <td>39.385081</td>\n",
       "      <td>8.375644</td>\n",
       "      <td>0.350924</td>\n",
       "      <td>15.729597</td>\n",
       "      <td>32.100843</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4404</th>\n",
       "      <td>4603</td>\n",
       "      <td>37.17</td>\n",
       "      <td>37.43</td>\n",
       "      <td>36.22</td>\n",
       "      <td>36.32</td>\n",
       "      <td>36.32</td>\n",
       "      <td>19939200</td>\n",
       "      <td>24-09-06</td>\n",
       "      <td>-0.85</td>\n",
       "      <td>41.326931</td>\n",
       "      <td>37.791941</td>\n",
       "      <td>37.042130</td>\n",
       "      <td>33.784834</td>\n",
       "      <td>35.778392</td>\n",
       "      <td>37.290</td>\n",
       "      <td>38.801608</td>\n",
       "      <td>8.107312</td>\n",
       "      <td>0.179150</td>\n",
       "      <td>9.755741</td>\n",
       "      <td>18.352188</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.49</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.29</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>-0.70</td>\n",
       "      <td>-0.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index   Open   High    Low  Close  Adj Close    Volume      Date  \\\n",
       "4403   4602  37.66  37.83  37.28  37.33      37.33  16260000  24-09-05   \n",
       "4404   4603  37.17  37.43  36.22  36.32      36.32  19939200  24-09-06   \n",
       "\n",
       "      Diff_Close        RSI       EMAF       EMAM       EMAS  BBL_5_2.0  \\\n",
       "4403       -0.33  47.133189  37.965111  37.071605  33.759355  36.218919   \n",
       "4404       -0.85  41.326931  37.791941  37.042130  33.784834  35.778392   \n",
       "\n",
       "      BBM_5_2.0  BBU_5_2.0  BBB_5_2.0  BBP_5_2.0  STOCHk_14_3_3  \\\n",
       "4403     37.802  39.385081   8.375644   0.350924      15.729597   \n",
       "4404     37.290  38.801608   8.107312   0.179150       9.755741   \n",
       "\n",
       "      STOCHd_14_3_3  Target1  Target2  Target3  Target4  Target5  Target6  \\\n",
       "4403      32.100843    -0.85     0.15     0.46     0.49     1.15     0.29   \n",
       "4404      18.352188     0.15     0.46     0.49     1.15     0.29    -0.11   \n",
       "\n",
       "      Target7  Target8  Target9  \n",
       "4403    -0.11    -0.21    -0.70  \n",
       "4404    -0.21    -0.70    -0.38  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding indicators\n",
    "data['Diff_Close'] = data['Adj Close']-data.Open\n",
    "data['RSI']=ta.rsi(data.Close, length=14)\n",
    "data['EMAF']=ta.ema(data.Close, length=18)\n",
    "data['EMAM']=ta.ema(data.Close, length=50)\n",
    "data['EMAS']=ta.ema(data.Close, length=200)\n",
    "data.ta.bbands(append=True)\n",
    "data.ta.stoch(append=True)\n",
    "\n",
    "\n",
    "data['Target1'] = data['Diff_Close'].shift(-1)\n",
    "data['Target2'] = data['Diff_Close'].shift(-2)\n",
    "data['Target3'] = data['Diff_Close'].shift(-3)\n",
    "data['Target4'] = data['Diff_Close'].shift(-4)\n",
    "data['Target5'] = data['Diff_Close'].shift(-5)\n",
    "data['Target6'] = data['Diff_Close'].shift(-6)\n",
    "data['Target7'] = data['Diff_Close'].shift(-7)\n",
    "data['Target8'] = data['Diff_Close'].shift(-8)\n",
    "data['Target9'] = data['Diff_Close'].shift(-9)\n",
    "\n",
    "\n",
    "data.dropna(inplace=True)\n",
    "data.reset_index(inplace = True)\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "data.tail(2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a2b0e972",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Diff_Close</th>\n",
       "      <th>RSI</th>\n",
       "      <th>EMAF</th>\n",
       "      <th>EMAM</th>\n",
       "      <th>EMAS</th>\n",
       "      <th>BBL_5_2.0</th>\n",
       "      <th>BBM_5_2.0</th>\n",
       "      <th>BBU_5_2.0</th>\n",
       "      <th>BBB_5_2.0</th>\n",
       "      <th>BBP_5_2.0</th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "      <th>Target1</th>\n",
       "      <th>Target2</th>\n",
       "      <th>Target3</th>\n",
       "      <th>Target4</th>\n",
       "      <th>Target5</th>\n",
       "      <th>Target6</th>\n",
       "      <th>Target7</th>\n",
       "      <th>Target8</th>\n",
       "      <th>Target9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>199</td>\n",
       "      <td>38.70</td>\n",
       "      <td>38.70</td>\n",
       "      <td>37.97</td>\n",
       "      <td>32.99</td>\n",
       "      <td>-5.71</td>\n",
       "      <td>42.823849</td>\n",
       "      <td>39.130266</td>\n",
       "      <td>39.242898</td>\n",
       "      <td>38.151400</td>\n",
       "      <td>36.894752</td>\n",
       "      <td>37.894</td>\n",
       "      <td>38.893248</td>\n",
       "      <td>5.273910</td>\n",
       "      <td>0.568051</td>\n",
       "      <td>26.968411</td>\n",
       "      <td>24.878202</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>-6.22</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>200</td>\n",
       "      <td>38.02</td>\n",
       "      <td>38.73</td>\n",
       "      <td>37.93</td>\n",
       "      <td>33.52</td>\n",
       "      <td>-4.50</td>\n",
       "      <td>46.633750</td>\n",
       "      <td>39.079712</td>\n",
       "      <td>39.219647</td>\n",
       "      <td>38.156361</td>\n",
       "      <td>37.773181</td>\n",
       "      <td>38.240</td>\n",
       "      <td>38.706819</td>\n",
       "      <td>2.441522</td>\n",
       "      <td>0.939142</td>\n",
       "      <td>29.608675</td>\n",
       "      <td>27.927078</td>\n",
       "      <td>-6.22</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201</td>\n",
       "      <td>38.44</td>\n",
       "      <td>38.75</td>\n",
       "      <td>37.06</td>\n",
       "      <td>32.22</td>\n",
       "      <td>-6.22</td>\n",
       "      <td>39.735192</td>\n",
       "      <td>38.876584</td>\n",
       "      <td>39.138484</td>\n",
       "      <td>38.146348</td>\n",
       "      <td>37.049243</td>\n",
       "      <td>38.054</td>\n",
       "      <td>39.058757</td>\n",
       "      <td>5.280689</td>\n",
       "      <td>0.050140</td>\n",
       "      <td>23.950967</td>\n",
       "      <td>26.842684</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>-5.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>202</td>\n",
       "      <td>37.01</td>\n",
       "      <td>37.48</td>\n",
       "      <td>36.46</td>\n",
       "      <td>32.40</td>\n",
       "      <td>-4.61</td>\n",
       "      <td>41.049977</td>\n",
       "      <td>38.716944</td>\n",
       "      <td>39.068740</td>\n",
       "      <td>38.138523</td>\n",
       "      <td>36.764084</td>\n",
       "      <td>37.908</td>\n",
       "      <td>39.051916</td>\n",
       "      <td>6.035223</td>\n",
       "      <td>0.260472</td>\n",
       "      <td>20.792079</td>\n",
       "      <td>24.783907</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>203</td>\n",
       "      <td>37.68</td>\n",
       "      <td>38.44</td>\n",
       "      <td>37.68</td>\n",
       "      <td>33.17</td>\n",
       "      <td>-4.51</td>\n",
       "      <td>46.333706</td>\n",
       "      <td>38.666739</td>\n",
       "      <td>39.036240</td>\n",
       "      <td>38.139533</td>\n",
       "      <td>36.773123</td>\n",
       "      <td>37.886</td>\n",
       "      <td>38.998877</td>\n",
       "      <td>5.874874</td>\n",
       "      <td>0.659047</td>\n",
       "      <td>20.107004</td>\n",
       "      <td>21.616683</td>\n",
       "      <td>-5.38</td>\n",
       "      <td>-4.97</td>\n",
       "      <td>-5.43</td>\n",
       "      <td>-4.69</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.36</td>\n",
       "      <td>-5.46</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-5.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   Open   High    Low  Adj Close  Diff_Close        RSI       EMAF  \\\n",
       "0    199  38.70  38.70  37.97      32.99       -5.71  42.823849  39.130266   \n",
       "1    200  38.02  38.73  37.93      33.52       -4.50  46.633750  39.079712   \n",
       "2    201  38.44  38.75  37.06      32.22       -6.22  39.735192  38.876584   \n",
       "3    202  37.01  37.48  36.46      32.40       -4.61  41.049977  38.716944   \n",
       "4    203  37.68  38.44  37.68      33.17       -4.51  46.333706  38.666739   \n",
       "\n",
       "        EMAM       EMAS  BBL_5_2.0  BBM_5_2.0  BBU_5_2.0  BBB_5_2.0  \\\n",
       "0  39.242898  38.151400  36.894752     37.894  38.893248   5.273910   \n",
       "1  39.219647  38.156361  37.773181     38.240  38.706819   2.441522   \n",
       "2  39.138484  38.146348  37.049243     38.054  39.058757   5.280689   \n",
       "3  39.068740  38.138523  36.764084     37.908  39.051916   6.035223   \n",
       "4  39.036240  38.139533  36.773123     37.886  38.998877   5.874874   \n",
       "\n",
       "   BBP_5_2.0  STOCHk_14_3_3  STOCHd_14_3_3  Target1  Target2  Target3  \\\n",
       "0   0.568051      26.968411      24.878202    -4.50    -6.22    -4.61   \n",
       "1   0.939142      29.608675      27.927078    -6.22    -4.61    -4.51   \n",
       "2   0.050140      23.950967      26.842684    -4.61    -4.51    -5.38   \n",
       "3   0.260472      20.792079      24.783907    -4.51    -5.38    -4.97   \n",
       "4   0.659047      20.107004      21.616683    -5.38    -4.97    -5.43   \n",
       "\n",
       "   Target4  Target5  Target6  Target7  Target8  Target9  \n",
       "0    -4.51    -5.38    -4.97    -5.43    -4.69    -5.66  \n",
       "1    -5.38    -4.97    -5.43    -4.69    -5.66    -5.36  \n",
       "2    -4.97    -5.43    -4.69    -5.66    -5.36    -5.46  \n",
       "3    -5.43    -4.69    -5.66    -5.36    -5.46    -5.66  \n",
       "4    -4.69    -5.66    -5.36    -5.46    -5.66    -5.34  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Volume', 'Close', 'Date'], axis=1, inplace=True)\n",
    "data_set = data\n",
    "data_set.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9d38e4c",
   "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>index</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Diff_Close</th>\n",
       "      <th>RSI</th>\n",
       "      <th>EMAF</th>\n",
       "      <th>EMAM</th>\n",
       "      <th>EMAS</th>\n",
       "      <th>BBL_5_2.0</th>\n",
       "      <th>BBM_5_2.0</th>\n",
       "      <th>BBU_5_2.0</th>\n",
       "      <th>BBB_5_2.0</th>\n",
       "      <th>BBP_5_2.0</th>\n",
       "      <th>STOCHk_14_3_3</th>\n",
       "      <th>STOCHd_14_3_3</th>\n",
       "      <th>Target1</th>\n",
       "      <th>Target2</th>\n",
       "      <th>Target3</th>\n",
       "      <th>Target4</th>\n",
       "      <th>Target5</th>\n",
       "      <th>Target6</th>\n",
       "      <th>Target7</th>\n",
       "      <th>Target8</th>\n",
       "      <th>Target9</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4400</th>\n",
       "      <td>4599</td>\n",
       "      <td>-0.032629</td>\n",
       "      <td>-0.035886</td>\n",
       "      <td>-0.032090</td>\n",
       "      <td>0.134366</td>\n",
       "      <td>0.357534</td>\n",
       "      <td>0.135110</td>\n",
       "      <td>-0.011064</td>\n",
       "      <td>-0.023456</td>\n",
       "      <td>-0.152467</td>\n",
       "      <td>-0.007310</td>\n",
       "      <td>-0.014559</td>\n",
       "      <td>-0.050959</td>\n",
       "      <td>-0.884847</td>\n",
       "      <td>-0.358144</td>\n",
       "      <td>0.330323</td>\n",
       "      <td>0.488259</td>\n",
       "      <td>0.279452</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.335616</td>\n",
       "      <td>0.264384</td>\n",
       "      <td>0.401370</td>\n",
       "      <td>0.443836</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.538356</td>\n",
       "      <td>0.420548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4401</th>\n",
       "      <td>4600</td>\n",
       "      <td>-0.061550</td>\n",
       "      <td>-0.068073</td>\n",
       "      <td>-0.085821</td>\n",
       "      <td>0.077857</td>\n",
       "      <td>0.279452</td>\n",
       "      <td>-0.115170</td>\n",
       "      <td>-0.015294</td>\n",
       "      <td>-0.023108</td>\n",
       "      <td>-0.150777</td>\n",
       "      <td>-0.047258</td>\n",
       "      <td>-0.029421</td>\n",
       "      <td>-0.041646</td>\n",
       "      <td>-0.762153</td>\n",
       "      <td>-0.905362</td>\n",
       "      <td>0.020945</td>\n",
       "      <td>0.291154</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>0.335616</td>\n",
       "      <td>0.264384</td>\n",
       "      <td>0.401370</td>\n",
       "      <td>0.443836</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.538356</td>\n",
       "      <td>0.420548</td>\n",
       "      <td>0.365753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4402</th>\n",
       "      <td>4601</td>\n",
       "      <td>-0.103077</td>\n",
       "      <td>-0.095819</td>\n",
       "      <td>-0.092537</td>\n",
       "      <td>0.062788</td>\n",
       "      <td>0.383562</td>\n",
       "      <td>-0.172337</td>\n",
       "      <td>-0.020589</td>\n",
       "      <td>-0.023368</td>\n",
       "      <td>-0.149273</td>\n",
       "      <td>-0.073525</td>\n",
       "      <td>-0.046937</td>\n",
       "      <td>-0.050252</td>\n",
       "      <td>-0.716935</td>\n",
       "      <td>-0.709322</td>\n",
       "      <td>-0.415359</td>\n",
       "      <td>-0.022089</td>\n",
       "      <td>0.335616</td>\n",
       "      <td>0.264384</td>\n",
       "      <td>0.401370</td>\n",
       "      <td>0.443836</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.538356</td>\n",
       "      <td>0.420548</td>\n",
       "      <td>0.365753</td>\n",
       "      <td>0.352055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4403</th>\n",
       "      <td>4602</td>\n",
       "      <td>-0.074527</td>\n",
       "      <td>-0.078431</td>\n",
       "      <td>-0.071642</td>\n",
       "      <td>0.080368</td>\n",
       "      <td>0.335616</td>\n",
       "      <td>-0.090165</td>\n",
       "      <td>-0.023565</td>\n",
       "      <td>-0.022924</td>\n",
       "      <td>-0.147587</td>\n",
       "      <td>-0.082629</td>\n",
       "      <td>-0.054747</td>\n",
       "      <td>-0.056542</td>\n",
       "      <td>-0.709344</td>\n",
       "      <td>-0.299248</td>\n",
       "      <td>-0.697161</td>\n",
       "      <td>-0.372758</td>\n",
       "      <td>0.264384</td>\n",
       "      <td>0.401370</td>\n",
       "      <td>0.443836</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.538356</td>\n",
       "      <td>0.420548</td>\n",
       "      <td>0.365753</td>\n",
       "      <td>0.352055</td>\n",
       "      <td>0.284932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4404</th>\n",
       "      <td>4603</td>\n",
       "      <td>-0.092696</td>\n",
       "      <td>-0.093230</td>\n",
       "      <td>-0.111194</td>\n",
       "      <td>0.038091</td>\n",
       "      <td>0.264384</td>\n",
       "      <td>-0.247492</td>\n",
       "      <td>-0.030463</td>\n",
       "      <td>-0.024164</td>\n",
       "      <td>-0.146390</td>\n",
       "      <td>-0.099513</td>\n",
       "      <td>-0.074158</td>\n",
       "      <td>-0.077581</td>\n",
       "      <td>-0.718980</td>\n",
       "      <td>-0.643250</td>\n",
       "      <td>-0.818783</td>\n",
       "      <td>-0.659348</td>\n",
       "      <td>0.401370</td>\n",
       "      <td>0.443836</td>\n",
       "      <td>0.447945</td>\n",
       "      <td>0.538356</td>\n",
       "      <td>0.420548</td>\n",
       "      <td>0.365753</td>\n",
       "      <td>0.352055</td>\n",
       "      <td>0.284932</td>\n",
       "      <td>0.328767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      index      Open      High       Low  Adj Close  Diff_Close       RSI  \\\n",
       "4400   4599 -0.032629 -0.035886 -0.032090   0.134366    0.357534  0.135110   \n",
       "4401   4600 -0.061550 -0.068073 -0.085821   0.077857    0.279452 -0.115170   \n",
       "4402   4601 -0.103077 -0.095819 -0.092537   0.062788    0.383562 -0.172337   \n",
       "4403   4602 -0.074527 -0.078431 -0.071642   0.080368    0.335616 -0.090165   \n",
       "4404   4603 -0.092696 -0.093230 -0.111194   0.038091    0.264384 -0.247492   \n",
       "\n",
       "          EMAF      EMAM      EMAS  BBL_5_2.0  BBM_5_2.0  BBU_5_2.0  \\\n",
       "4400 -0.011064 -0.023456 -0.152467  -0.007310  -0.014559  -0.050959   \n",
       "4401 -0.015294 -0.023108 -0.150777  -0.047258  -0.029421  -0.041646   \n",
       "4402 -0.020589 -0.023368 -0.149273  -0.073525  -0.046937  -0.050252   \n",
       "4403 -0.023565 -0.022924 -0.147587  -0.082629  -0.054747  -0.056542   \n",
       "4404 -0.030463 -0.024164 -0.146390  -0.099513  -0.074158  -0.077581   \n",
       "\n",
       "      BBB_5_2.0  BBP_5_2.0  STOCHk_14_3_3  STOCHd_14_3_3   Target1   Target2  \\\n",
       "4400  -0.884847  -0.358144       0.330323       0.488259  0.279452  0.383562   \n",
       "4401  -0.762153  -0.905362       0.020945       0.291154  0.383562  0.335616   \n",
       "4402  -0.716935  -0.709322      -0.415359      -0.022089  0.335616  0.264384   \n",
       "4403  -0.709344  -0.299248      -0.697161      -0.372758  0.264384  0.401370   \n",
       "4404  -0.718980  -0.643250      -0.818783      -0.659348  0.401370  0.443836   \n",
       "\n",
       "       Target3   Target4   Target5   Target6   Target7   Target8   Target9  \n",
       "4400  0.335616  0.264384  0.401370  0.443836  0.447945  0.538356  0.420548  \n",
       "4401  0.264384  0.401370  0.443836  0.447945  0.538356  0.420548  0.365753  \n",
       "4402  0.401370  0.443836  0.447945  0.538356  0.420548  0.365753  0.352055  \n",
       "4403  0.443836  0.447945  0.538356  0.420548  0.365753  0.352055  0.284932  \n",
       "4404  0.447945  0.538356  0.420548  0.365753  0.352055  0.284932  0.328767  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range=(-1,1))\n",
    "\n",
    "df_scaled = sc.fit_transform(data_set.to_numpy())\n",
    "data_set_scaled_pd = pd.DataFrame(df_scaled, columns=data_set.columns.tolist())\n",
    "\n",
    "\n",
    "\n",
    "data_set_scaled_pd['index'] = data_set['index']\n",
    "\n",
    "data_set_scaled_pd.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "99ca74fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of Data 4405\n",
      "X Shape: (4385, 20, 16)\n",
      "Y Shape: (4385, 9)\n"
     ]
    }
   ],
   "source": [
    "X = []\n",
    "backcandles = 20\n",
    "\n",
    "\n",
    "data_set_scaled = data_set_scaled_pd.to_numpy()\n",
    "\n",
    "print(\"Length of Data\", data_set_scaled.shape[0])\n",
    "\n",
    "features = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16]\n",
    "#features = [5]\n",
    "feature_count = len(features)\n",
    "\n",
    "it = 0\n",
    "for j in features:\n",
    "    X.append([])\n",
    "    for i in range(backcandles, data_set_scaled.shape[0]):\n",
    "        X[it].append(data_set_scaled[i-backcandles:i, j])\n",
    "    it += 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#move axis from 0 to position 2\n",
    "X=np.moveaxis(X, [0], [2])\n",
    "\n",
    "X = np.array(X)\n",
    "\n",
    "yi = np.array(data_set_scaled[backcandles:, -9:])\n",
    "y=yi\n",
    "\n",
    "print(\"X Shape:\", X.shape)\n",
    "print(\"Y Shape:\", y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a2a87918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4385\n",
      "(4385, 20, 16)\n",
      "(0, 20, 16)\n",
      "(4385, 9)\n",
      "(0, 9)\n"
     ]
    }
   ],
   "source": [
    "# split data into train test sets\n",
    "splitlimit = int(len(X)*1)\n",
    "print(splitlimit)\n",
    "X_train, X_test = X[:splitlimit], X[splitlimit:]\n",
    "y_train, y_test = y[:splitlimit], y[splitlimit:]\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9867161a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-07 20:29:09.720660: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-10-07 20:29:09.737019: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-10-07 20:29:09.741871: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2024-10-07 20:29:09.754590: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-10-07 20:29:10.373903: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4385, 20, 16)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1728347350.828378  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347350.874002  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347350.878920  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347350.883919  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347350.887319  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347350.891213  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347351.030925  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347351.032497  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "I0000 00:00:1728347351.033977  332220 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-10-07 20:29:11.035404: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5325 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050, pci bus id: 0000:2d:00.0, compute capability: 8.6\n",
      "/home/brickman/miniconda3/envs/stock/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">150</span>)            │       <span style=\"color: #00af00; text-decoration-color: #00af00\">100,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">4,832</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_layer (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)              │           <span style=\"color: #00af00; text-decoration-color: #00af00\">297</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m)            │       \u001b[38;5;34m100,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)             │         \u001b[38;5;34m4,832\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_layer (\u001b[38;5;33mDense\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)              │           \u001b[38;5;34m297\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">105,329</span> (411.44 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m105,329\u001b[0m (411.44 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">105,329</span> (411.44 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m105,329\u001b[0m (411.44 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-07 20:29:12.885047: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Dense\n",
    "from keras.layers import TimeDistributed\n",
    "\n",
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import optimizers\n",
    "from keras.callbacks import History\n",
    "from keras.models import Model\n",
    "from keras.layers import Dense, Dropout, LSTM, Input, Activation, concatenate\n",
    "import numpy as np\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "np.random.seed(10)\n",
    "print(X_train.shape)\n",
    "\n",
    "model = Sequential([layers.LSTM(150, input_shape=(backcandles, feature_count), activation='tanh'),\n",
    "                    layers.Dense(32, activation='relu'),\n",
    "                    layers.Dense(9, name='dense_layer', activation='tanh')])\n",
    "\n",
    "\n",
    "model.compile(loss='mse', \n",
    "              optimizer=Adam(learning_rate=0.001),\n",
    "              metrics=['mean_absolute_error'])\n",
    "\n",
    "model.summary()\n",
    "\n",
    "epochs=2400\n",
    "history = model.fit(x=X_train, y=y_train, batch_size=15, epochs=epochs, shuffle=True, validation_split = 0.2, verbose=0)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e2b61f1-032f-43b5-a457-d6835076b7f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7cb97803ba30>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1100x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "acc = history.history['mean_absolute_error']\n",
    "val_acc = history.history['val_mean_absolute_error']\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(11, 5))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.ion()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "08324ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m138/138\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n",
      "Pridicted: [-0.4036709  -0.46461192 -0.3590175  -0.28324017 -0.34823442 -0.43334588\n",
      " -0.42882568 -0.41436017 -0.403928  ] Real value: [-0.40136986 -0.46164384 -0.35342466 -0.27945205 -0.34931507 -0.43013699\n",
      " -0.42739726 -0.41369863 -0.39726027]\n",
      "Pridicted: [-0.46557218 -0.35770795 -0.2813072  -0.3510212  -0.42975435 -0.43653348\n",
      " -0.40678245 -0.4031321  -0.41870612] Real value: [-0.46164384 -0.35342466 -0.27945205 -0.34931507 -0.43013699 -0.42739726\n",
      " -0.41369863 -0.39726027 -0.41780822]\n",
      "Pridicted: [-0.35209465 -0.28051785 -0.34810475 -0.43110624 -0.43116555 -0.41247454\n",
      " -0.39174688 -0.42331287 -0.42454508] Real value: [-0.35342466 -0.27945205 -0.34931507 -0.43013699 -0.42739726 -0.41369863\n",
      " -0.39726027 -0.41780822 -0.42328767]\n",
      "Pridicted: [-0.28172114 -0.34396863 -0.4297893  -0.4320941  -0.41340354 -0.39519808\n",
      " -0.41816622 -0.42309865 -0.3611399 ] Real value: [-0.27945205 -0.34931507 -0.43013699 -0.42739726 -0.41369863 -0.39726027\n",
      " -0.41780822 -0.42328767 -0.36027397]\n",
      "Pridicted: [-0.34929985 -0.42562777 -0.433488   -0.41463438 -0.39713734 -0.42165747\n",
      " -0.42167172 -0.3633375  -0.41996527] Real value: [-0.34931507 -0.43013699 -0.42739726 -0.41369863 -0.39726027 -0.41780822\n",
      " -0.42328767 -0.36027397 -0.42054795]\n",
      "Pridicted: [-0.42858496 -0.4267884  -0.40962404 -0.39073083 -0.42235476 -0.4238721\n",
      " -0.3564185  -0.42203316 -0.3270994 ] Real value: [-0.43013699 -0.42739726 -0.41369863 -0.39726027 -0.41780822 -0.42328767\n",
      " -0.36027397 -0.42054795 -0.32739726]\n",
      "Pridicted: [-0.43434742 -0.40459284 -0.38885665 -0.4140891  -0.42647222 -0.36434662\n",
      " -0.4164863  -0.32749146 -0.4512253 ] Real value: [-0.42739726 -0.41369863 -0.39726027 -0.41780822 -0.42328767 -0.36027397\n",
      " -0.42054795 -0.32739726 -0.45205479]\n",
      "Pridicted: [-0.41408408 -0.3860175  -0.4147621  -0.42086872 -0.36401656 -0.41890422\n",
      " -0.32657167 -0.45092446 -0.3550458 ] Real value: [-0.41369863 -0.39726027 -0.41780822 -0.42328767 -0.36027397 -0.42054795\n",
      " -0.32739726 -0.45205479 -0.35342466]\n",
      "Pridicted: [-0.39207783 -0.4143812  -0.42082974 -0.36102867 -0.42305246 -0.32819805\n",
      " -0.44679096 -0.35819703 -0.18476132] Real value: [-0.39726027 -0.41780822 -0.42328767 -0.36027397 -0.42054795 -0.32739726\n",
      " -0.45205479 -0.35342466 -0.18767123]\n",
      "Pridicted: [-0.41665655 -0.41635478 -0.36310774 -0.41913277 -0.33059776 -0.4508693\n",
      " -0.35423303 -0.18578593 -0.30750462] Real value: [-0.41780822 -0.42328767 -0.36027397 -0.42054795 -0.32739726 -0.45205479\n",
      " -0.35342466 -0.18767123 -0.31643836]\n",
      "\n",
      "[0.40136986 0.44383562 0.44794521 0.53835616 0.42054795 0.36575342\n",
      " 0.35205479 0.28493151 0.32876712]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_train)\n",
    "for i in range(10):\n",
    "    print(\"Pridicted:\", y_pred[i], \"Real value:\", y_train[i])\n",
    "print()\n",
    "print(f\"{y_train[-1]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "67f5e31a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7cb970beb0d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(y_train[-1], color = 'orange', label = 'real')\n",
    "plt.plot(y_pred[-1], color = 'green', label = 'pred')\n",
    "plt.legend()\n",
    "plt.ion()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b76d089d-356a-4f9c-9a94-3d00671e8f4d",
   "metadata": {},
   "source": [
    "# Save Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71f7fd19-5a06-40ae-bc25-fab6e2aa08bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"models/\" + name + \".keras\")"
   ]
  }
 ],
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