{
 "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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       "  <thead>\n",
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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",
    "\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",
    "\n",
    "data_set.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9d38e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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",
    "\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",
    "\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",
    "\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",
    "X=np.moveaxis(X, [0], [2])\n",
    "\n",
    "\n",
    "X = np.array(X)\n",
    "\n",
    "yi = np.array(data_set_scaled[backcandles:, -9:])\n",
    "y=yi\n",
    "\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9867161a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-07 19:55:07.650654: 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 19:55:07.665948: 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 19:55:07.670711: 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 19:55:07.682482: 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 19:55:08.294473: 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 00:00:1728345308.788157  164271 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 00:00:1728345308.833335  164271 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 00:00:1728345308.847700  164271 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 00:00:1728345308.857535  164271 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 00:00:1728345308.861197  164271 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 00:00:1728345308.869432  164271 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 00:00:1728345309.008330  164271 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 00:00:1728345309.009957  164271 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 00:00:1728345309.011379  164271 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 19:55:09.013238: I tensorflow/core/common_runtime/gpu/gpu_device.cc:2021] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1212 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3050, pci bus id: 0000:2d:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "data": {
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       "<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"
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       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
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     "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",
       "│ 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\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)        │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,176</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<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\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,256</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (<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\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)         │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">20</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)         │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">640</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</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\">5,769</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",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m128\u001b[0m)        │         \u001b[38;5;34m2,176\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m64\u001b[0m)         │         \u001b[38;5;34m8,256\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m32\u001b[0m)         │         \u001b[38;5;34m2,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m20\u001b[0m, \u001b[38;5;34m32\u001b[0m)         │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m640\u001b[0m)            │             \u001b[38;5;34m0\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;34m5,769\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\">18,281</span> (71.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m18,281\u001b[0m (71.41 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\">18,281</span> (71.41 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m18,281\u001b[0m (71.41 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": [
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "I0000 00:00:1728345310.384833  164347 service.cc:146] XLA service 0x7c45a00043e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "I0000 00:00:1728345310.384862  164347 service.cc:154]   StreamExecutor device (0): NVIDIA GeForce RTX 3050, Compute Capability 8.6\n",
      "2024-10-07 19:55:10.434223: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2024-10-07 19:55:10.569875: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:531] Loaded cuDNN version 8907\n",
      "I0000 00:00:1728345315.348810  164347 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n",
      "2024-10-07 19:55:21.210858: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_41', 96 bytes spill stores, 96 bytes spill loads\n",
      "\n",
      "2024-10-07 19:55:23.138934: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_41', 92 bytes spill stores, 92 bytes spill loads\n",
      "\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",
    "np.random.seed(10)\n",
    "print(X_train.shape)\n",
    "\n",
    "model = Sequential([layers.Input((backcandles, feature_count)),\n",
    "                    layers.Dense(128, activation='relu'),\n",
    "                    layers.Dense(64, activation='relu'),\n",
    "                    layers.Dense(32, activation='relu'),\n",
    "                    layers.Dropout(0.2),\n",
    "                    layers.Flatten(),\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",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7e2b61f1-032f-43b5-a457-d6835076b7f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7c46a9d69960>"
      ]
     },
     "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": [
    "history = model.fit(x=X_train, y=y_train, batch_size=15, epochs=epochs, shuffle=True, validation_split = 0.2, verbose=0)\n",
    "\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": "stderr",
     "output_type": "stream",
     "text": [
      "2024-10-07 20:14:04.453124: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 36 bytes spill stores, 36 bytes spill loads\n",
      "\n",
      "2024-10-07 20:14:04.563263: I external/local_xla/xla/stream_executor/cuda/cuda_asm_compiler.cc:393] ptxas warning : Registers are spilled to local memory in function 'gemm_fusion_dot_33', 360 bytes spill stores, 360 bytes spill loads\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m138/138\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 7ms/step\n",
      "Pridicted: [-0.36162856 -0.43332186 -0.3523488  -0.28136992 -0.3563385  -0.41920266\n",
      " -0.40086812 -0.40910283 -0.38491216] Real value: [-0.40136986 -0.46164384 -0.35342466 -0.27945205 -0.34931507 -0.43013699\n",
      " -0.42739726 -0.41369863 -0.39726027]\n",
      "Pridicted: [-0.4348482  -0.35673153 -0.28561482 -0.349432   -0.43765935 -0.4265165\n",
      " -0.40560463 -0.3999594  -0.3821063 ] Real value: [-0.46164384 -0.35342466 -0.27945205 -0.34931507 -0.43013699 -0.42739726\n",
      " -0.41369863 -0.39726027 -0.41780822]\n",
      "Pridicted: [-0.38370562 -0.2812817  -0.340804   -0.41657993 -0.416808   -0.40895227\n",
      " -0.39119083 -0.39400247 -0.41795033] Real value: [-0.35342466 -0.27945205 -0.34931507 -0.43013699 -0.42739726 -0.41369863\n",
      " -0.39726027 -0.41780822 -0.42328767]\n",
      "Pridicted: [-0.29063264 -0.34499377 -0.4208798  -0.40789008 -0.40698296 -0.4131924\n",
      " -0.38356876 -0.44646472 -0.37091735] Real value: [-0.27945205 -0.34931507 -0.43013699 -0.42739726 -0.41369863 -0.39726027\n",
      " -0.41780822 -0.42328767 -0.36027397]\n",
      "Pridicted: [-0.3494526  -0.4203193  -0.40559536 -0.3937887  -0.41101933 -0.39652374\n",
      " -0.42679134 -0.38282433 -0.39217785] Real value: [-0.34931507 -0.43013699 -0.42739726 -0.41369863 -0.39726027 -0.41780822\n",
      " -0.42328767 -0.36027397 -0.42054795]\n",
      "Pridicted: [-0.42891395 -0.40761665 -0.39522445 -0.3996233  -0.40153465 -0.44026577\n",
      " -0.3772592  -0.40035576 -0.35613042] Real value: [-0.43013699 -0.42739726 -0.41369863 -0.39726027 -0.41780822 -0.42328767\n",
      " -0.36027397 -0.42054795 -0.32739726]\n",
      "Pridicted: [-0.40219325 -0.38756976 -0.39211658 -0.3793638  -0.4407541  -0.38048762\n",
      " -0.3906664  -0.34461808 -0.45740467] Real value: [-0.42739726 -0.41369863 -0.39726027 -0.41780822 -0.42328767 -0.36027397\n",
      " -0.42054795 -0.32739726 -0.45205479]\n",
      "Pridicted: [-0.3997392  -0.4036399  -0.38481608 -0.43141615 -0.39609036 -0.40770343\n",
      " -0.3379205  -0.4757088  -0.30856273] Real value: [-0.41369863 -0.39726027 -0.41780822 -0.42328767 -0.36027397 -0.42054795\n",
      " -0.32739726 -0.45205479 -0.35342466]\n",
      "Pridicted: [-0.39815712 -0.38647228 -0.43177652 -0.372594   -0.4144538  -0.3646657\n",
      " -0.47009906 -0.3289713  -0.21806642] Real value: [-0.39726027 -0.41780822 -0.42328767 -0.36027397 -0.42054795 -0.32739726\n",
      " -0.45205479 -0.35342466 -0.18767123]\n",
      "Pridicted: [-0.3795808  -0.433394   -0.37585306 -0.40730223 -0.35770226 -0.4803442\n",
      " -0.31476918 -0.2233371  -0.30246612] 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": 12,
   "id": "67f5e31a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<contextlib.ExitStack at 0x7c46a81eb1c0>"
      ]
     },
     "execution_count": 12,
     "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[100], color = 'orange', label = 'real')\n",
    "plt.plot(y_pred[100], 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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b953033-d9e2-417d-bf5f-e3c2b401a2b2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}