LSTM_CodeTrading_updated.ipynb

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     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "Open         1500\n",
      "High         1500\n",
      "Low          1500\n",
      "Close        1500\n",
      "Adj Close    1500\n",
      "Volume       1500\n",
      "dtype: int64\n"
     ]
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       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
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       "      <th>2018-09-23 00:00:00+00:00</th>\n",
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      "text/plain": [
       "                               Open      High       Low     Close  Adj Close  \\\n",
       "Date                                                                           \n",
       "2018-09-23 00:00:00+00:00  0.023198  0.024604  0.023094  0.023791   0.023791   \n",
       "2018-09-24 00:00:00+00:00  0.023739  0.024093  0.021965  0.022089   0.022089   \n",
       "2018-09-25 00:00:00+00:00  0.022029  0.022029  0.020275  0.021280   0.021280   \n",
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       "2018-09-27 00:00:00+00:00  0.021097  0.022797  0.020747  0.022507   0.022507   \n",
       "\n",
       "                              Volume  \n",
       "Date                                  \n",
       "2018-09-23 00:00:00+00:00  138225000  \n",
       "2018-09-24 00:00:00+00:00  116603000  \n",
       "2018-09-25 00:00:00+00:00  126284000  \n",
       "2018-09-26 00:00:00+00:00  110081000  \n",
       "2018-09-27 00:00:00+00:00  124196000  "
      ]
     },
     "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 yfinance as yf\n",
    "import pandas_ta as ta\n",
    "#data = yf.download(tickers = '^RUI', start = '2012-03-11',end = '2022-07-10')\n",
    "data  = yf.download('TRX-USD', period = '1500d', interval = '1d')\n",
    "#data = yf.download('^RUI', period = '10d', interval = '1h')\n",
    "print(data.count())\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68e700e1",
   "metadata": {},
   "outputs": [
    {
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       "      <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>RSI</th>\n",
       "      <th>EMAF</th>\n",
       "      <th>EMAM</th>\n",
       "      <th>EMAS</th>\n",
       "      <th>MFII</th>\n",
       "      <th>VPT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
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       "      <th>2022-10-28 00:00:00+00:00</th>\n",
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       "      <th>2022-10-29 00:00:00+00:00</th>\n",
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       "      <td>0.062455</td>\n",
       "      <td>0.063776</td>\n",
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       "      <th>2022-10-30 00:00:00+00:00</th>\n",
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       "      <td>0.062520</td>\n",
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       "      <th>2022-10-31 00:00:00+00:00</th>\n",
       "      <td>0.063096</td>\n",
       "      <td>0.063305</td>\n",
       "      <td>0.062506</td>\n",
       "      <td>0.062658</td>\n",
       "      <td>0.062658</td>\n",
       "      <td>348529504</td>\n",
       "      <td>51.454597</td>\n",
       "      <td>0.062533</td>\n",
       "      <td>0.063741</td>\n",
       "      <td>0.064999</td>\n",
       "      <td>44.030166</td>\n",
       "      <td>-0.065581</td>\n",
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      "text/plain": [
       "                               Open      High       Low     Close  Adj Close  \\\n",
       "Date                                                                           \n",
       "2022-10-28 00:00:00+00:00  0.062979  0.063725  0.062703  0.063562   0.063562   \n",
       "2022-10-29 00:00:00+00:00  0.063563  0.064425  0.063517  0.064180   0.064180   \n",
       "2022-10-30 00:00:00+00:00  0.064177  0.064725  0.063020  0.063136   0.063136   \n",
       "2022-10-31 00:00:00+00:00  0.063096  0.063305  0.062506  0.062658   0.062658   \n",
       "\n",
       "                              Volume        RSI      EMAF      EMAM      EMAS  \\\n",
       "Date                                                                            \n",
       "2022-10-28 00:00:00+00:00  320154857  57.502960  0.062274  0.063768  0.065068   \n",
       "2022-10-29 00:00:00+00:00  362144152  60.172663  0.062455  0.063776  0.065056   \n",
       "2022-10-30 00:00:00+00:00  324343312  54.029269  0.062520  0.063763  0.065031   \n",
       "2022-10-31 00:00:00+00:00  348529504  51.454597  0.062533  0.063741  0.064999   \n",
       "\n",
       "                                MFII       VPT  \n",
       "Date                                            \n",
       "2022-10-28 00:00:00+00:00  42.880648 -0.091011  \n",
       "2022-10-29 00:00:00+00:00  52.058736 -0.072138  \n",
       "2022-10-30 00:00:00+00:00  51.678099 -0.064925  \n",
       "2022-10-31 00:00:00+00:00  44.030166 -0.065581  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Be aware that some of these counters have long initializing periods.\n",
    "# Its something thats not recommended (or use extreme data set size)\n",
    "\n",
    "\n",
    "# Adding indicators\n",
    "data['RSI']=ta.momentum.rsi(data.Close, length=15)\n",
    "data['EMAF']=ta.ema(data.Close, length=20)\n",
    "data['EMAM']=ta.ema(data.Close, length=100)\n",
    "data['EMAS']=ta.ema(data.Close, length=150)\n",
    "\n",
    "data['EMAS']=ta.ema(data.Close, length=150)\n",
    "\n",
    "# Adding volume indicators\n",
    "data[\"MFII\"] = ta.volume.mfi(high =data.High, low =data.Low, close =data.Close, volume =data.Volume, windows =15 )\n",
    "#data['EOM']=ta.volume.cmf(data.High,data.Low,data.Close,data.Volume,15)\n",
    "#data['VWAP']=ta.volume.vwap(data.High,data.Low,data.Close,data.Volume,15)\n",
    "data['VPT']=ta.volume.cmf(high = data.High, low = data.Low, close = data.Close,volume = data.Volume)\n",
    "\n",
    "# Adding Votality indicators\n",
    "#data['ADX']=ta.adx(high = data.High, low = data.Low,close=data.Close,window =30, fillna=False)\n",
    "#data['DPO']=ta.volatility.dpo(data.Close,data.Volume,15)\n",
    "#data['ATR']=ta.volatility.atr(data.High, data.Low, data.Close, data.Volume,15,False)\n",
    "data.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "79170fa2",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Target'] = data['Adj Close']-data.Open\n",
    "data['Target'] = data['Target'].shift(-1)\n",
    "\n",
    "data['TargetClass'] = [1 if data.Target[i]>0 else 0 for i in range(len(data))]\n",
    "\n",
    "data['TargetNextClose'] = data['Adj Close'].shift(-1)\n",
    "\n",
    "data.dropna(inplace=True)\n",
    "data.reset_index(inplace = True)\n",
    "data.drop(['Volume', 'Close', 'Date'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a2b0e972",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>RSI</th>\n",
       "      <th>EMAF</th>\n",
       "      <th>EMAM</th>\n",
       "      <th>EMAS</th>\n",
       "      <th>MFII</th>\n",
       "      <th>VPT</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.025008</td>\n",
       "      <td>0.025988</td>\n",
       "      <td>0.024993</td>\n",
       "      <td>0.024993</td>\n",
       "      <td>50.073511</td>\n",
       "      <td>0.025037</td>\n",
       "      <td>0.023181</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>40.029975</td>\n",
       "      <td>-0.255817</td>\n",
       "      <td>0.000247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.024981</td>\n",
       "      <td>0.025428</td>\n",
       "      <td>0.024452</td>\n",
       "      <td>0.025228</td>\n",
       "      <td>51.323723</td>\n",
       "      <td>0.025055</td>\n",
       "      <td>0.023221</td>\n",
       "      <td>0.021530</td>\n",
       "      <td>41.289628</td>\n",
       "      <td>-0.168132</td>\n",
       "      <td>-0.000397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.025201</td>\n",
       "      <td>0.025364</td>\n",
       "      <td>0.024643</td>\n",
       "      <td>0.024804</td>\n",
       "      <td>48.953968</td>\n",
       "      <td>0.025031</td>\n",
       "      <td>0.023253</td>\n",
       "      <td>0.021573</td>\n",
       "      <td>41.495872</td>\n",
       "      <td>-0.244921</td>\n",
       "      <td>0.000368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.024790</td>\n",
       "      <td>0.025160</td>\n",
       "      <td>0.024727</td>\n",
       "      <td>0.025158</td>\n",
       "      <td>50.978713</td>\n",
       "      <td>0.025043</td>\n",
       "      <td>0.023290</td>\n",
       "      <td>0.021621</td>\n",
       "      <td>38.552255</td>\n",
       "      <td>-0.223900</td>\n",
       "      <td>0.000324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.025131</td>\n",
       "      <td>0.025539</td>\n",
       "      <td>0.024925</td>\n",
       "      <td>0.025455</td>\n",
       "      <td>52.666416</td>\n",
       "      <td>0.025082</td>\n",
       "      <td>0.023333</td>\n",
       "      <td>0.021671</td>\n",
       "      <td>37.052471</td>\n",
       "      <td>-0.163469</td>\n",
       "      <td>-0.001656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.025465</td>\n",
       "      <td>0.027183</td>\n",
       "      <td>0.022805</td>\n",
       "      <td>0.023809</td>\n",
       "      <td>43.727220</td>\n",
       "      <td>0.024961</td>\n",
       "      <td>0.023343</td>\n",
       "      <td>0.021700</td>\n",
       "      <td>35.133246</td>\n",
       "      <td>-0.223794</td>\n",
       "      <td>0.000736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.023714</td>\n",
       "      <td>0.024661</td>\n",
       "      <td>0.023714</td>\n",
       "      <td>0.024450</td>\n",
       "      <td>47.448890</td>\n",
       "      <td>0.024912</td>\n",
       "      <td>0.023365</td>\n",
       "      <td>0.021736</td>\n",
       "      <td>35.701672</td>\n",
       "      <td>-0.137054</td>\n",
       "      <td>0.000068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.024344</td>\n",
       "      <td>0.024680</td>\n",
       "      <td>0.023958</td>\n",
       "      <td>0.024412</td>\n",
       "      <td>47.250403</td>\n",
       "      <td>0.024865</td>\n",
       "      <td>0.023385</td>\n",
       "      <td>0.021772</td>\n",
       "      <td>42.370123</td>\n",
       "      <td>-0.084352</td>\n",
       "      <td>-0.000254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.024385</td>\n",
       "      <td>0.024467</td>\n",
       "      <td>0.023639</td>\n",
       "      <td>0.024131</td>\n",
       "      <td>45.734611</td>\n",
       "      <td>0.024795</td>\n",
       "      <td>0.023400</td>\n",
       "      <td>0.021803</td>\n",
       "      <td>42.263106</td>\n",
       "      <td>-0.060572</td>\n",
       "      <td>-0.000265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.024132</td>\n",
       "      <td>0.024339</td>\n",
       "      <td>0.023737</td>\n",
       "      <td>0.023867</td>\n",
       "      <td>44.303948</td>\n",
       "      <td>0.024707</td>\n",
       "      <td>0.023409</td>\n",
       "      <td>0.021830</td>\n",
       "      <td>41.953105</td>\n",
       "      <td>-0.114800</td>\n",
       "      <td>-0.000152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.023878</td>\n",
       "      <td>0.024011</td>\n",
       "      <td>0.023666</td>\n",
       "      <td>0.023726</td>\n",
       "      <td>43.524821</td>\n",
       "      <td>0.024613</td>\n",
       "      <td>0.023416</td>\n",
       "      <td>0.021855</td>\n",
       "      <td>36.602306</td>\n",
       "      <td>-0.088151</td>\n",
       "      <td>-0.000734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.023671</td>\n",
       "      <td>0.023700</td>\n",
       "      <td>0.022514</td>\n",
       "      <td>0.022937</td>\n",
       "      <td>39.373470</td>\n",
       "      <td>0.024454</td>\n",
       "      <td>0.023406</td>\n",
       "      <td>0.021870</td>\n",
       "      <td>35.572305</td>\n",
       "      <td>-0.115669</td>\n",
       "      <td>-0.000211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.022934</td>\n",
       "      <td>0.023188</td>\n",
       "      <td>0.022599</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>38.311576</td>\n",
       "      <td>0.024289</td>\n",
       "      <td>0.023393</td>\n",
       "      <td>0.021881</td>\n",
       "      <td>35.853346</td>\n",
       "      <td>-0.093734</td>\n",
       "      <td>0.000263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.022674</td>\n",
       "      <td>0.023244</td>\n",
       "      <td>0.021043</td>\n",
       "      <td>0.022937</td>\n",
       "      <td>40.044075</td>\n",
       "      <td>0.024160</td>\n",
       "      <td>0.023384</td>\n",
       "      <td>0.021895</td>\n",
       "      <td>26.916729</td>\n",
       "      <td>-0.039886</td>\n",
       "      <td>0.000929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.023068</td>\n",
       "      <td>0.024047</td>\n",
       "      <td>0.022270</td>\n",
       "      <td>0.023997</td>\n",
       "      <td>47.821212</td>\n",
       "      <td>0.024144</td>\n",
       "      <td>0.023396</td>\n",
       "      <td>0.021923</td>\n",
       "      <td>27.409582</td>\n",
       "      <td>0.052281</td>\n",
       "      <td>-0.000538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.023969</td>\n",
       "      <td>0.023988</td>\n",
       "      <td>0.023225</td>\n",
       "      <td>0.023431</td>\n",
       "      <td>44.517569</td>\n",
       "      <td>0.024077</td>\n",
       "      <td>0.023397</td>\n",
       "      <td>0.021943</td>\n",
       "      <td>33.652915</td>\n",
       "      <td>0.045922</td>\n",
       "      <td>-0.000238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.023426</td>\n",
       "      <td>0.023717</td>\n",
       "      <td>0.023120</td>\n",
       "      <td>0.023188</td>\n",
       "      <td>43.146470</td>\n",
       "      <td>0.023992</td>\n",
       "      <td>0.023392</td>\n",
       "      <td>0.021959</td>\n",
       "      <td>33.894596</td>\n",
       "      <td>0.025333</td>\n",
       "      <td>-0.000642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.023206</td>\n",
       "      <td>0.023212</td>\n",
       "      <td>0.022533</td>\n",
       "      <td>0.022564</td>\n",
       "      <td>39.775913</td>\n",
       "      <td>0.023856</td>\n",
       "      <td>0.023376</td>\n",
       "      <td>0.021967</td>\n",
       "      <td>28.356059</td>\n",
       "      <td>-0.016615</td>\n",
       "      <td>0.000327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.022548</td>\n",
       "      <td>0.023019</td>\n",
       "      <td>0.022230</td>\n",
       "      <td>0.022875</td>\n",
       "      <td>42.187574</td>\n",
       "      <td>0.023762</td>\n",
       "      <td>0.023366</td>\n",
       "      <td>0.021979</td>\n",
       "      <td>22.047369</td>\n",
       "      <td>0.013798</td>\n",
       "      <td>0.000123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.022948</td>\n",
       "      <td>0.023207</td>\n",
       "      <td>0.022556</td>\n",
       "      <td>0.023071</td>\n",
       "      <td>43.709662</td>\n",
       "      <td>0.023697</td>\n",
       "      <td>0.023360</td>\n",
       "      <td>0.021994</td>\n",
       "      <td>30.425250</td>\n",
       "      <td>-0.006113</td>\n",
       "      <td>-0.000647</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open      High       Low  Adj Close        RSI      EMAF      EMAM  \\\n",
       "0   0.025008  0.025988  0.024993   0.024993  50.073511  0.025037  0.023181   \n",
       "1   0.024981  0.025428  0.024452   0.025228  51.323723  0.025055  0.023221   \n",
       "2   0.025201  0.025364  0.024643   0.024804  48.953968  0.025031  0.023253   \n",
       "3   0.024790  0.025160  0.024727   0.025158  50.978713  0.025043  0.023290   \n",
       "4   0.025131  0.025539  0.024925   0.025455  52.666416  0.025082  0.023333   \n",
       "5   0.025465  0.027183  0.022805   0.023809  43.727220  0.024961  0.023343   \n",
       "6   0.023714  0.024661  0.023714   0.024450  47.448890  0.024912  0.023365   \n",
       "7   0.024344  0.024680  0.023958   0.024412  47.250403  0.024865  0.023385   \n",
       "8   0.024385  0.024467  0.023639   0.024131  45.734611  0.024795  0.023400   \n",
       "9   0.024132  0.024339  0.023737   0.023867  44.303948  0.024707  0.023409   \n",
       "10  0.023878  0.024011  0.023666   0.023726  43.524821  0.024613  0.023416   \n",
       "11  0.023671  0.023700  0.022514   0.022937  39.373470  0.024454  0.023406   \n",
       "12  0.022934  0.023188  0.022599   0.022723  38.311576  0.024289  0.023393   \n",
       "13  0.022674  0.023244  0.021043   0.022937  40.044075  0.024160  0.023384   \n",
       "14  0.023068  0.024047  0.022270   0.023997  47.821212  0.024144  0.023396   \n",
       "15  0.023969  0.023988  0.023225   0.023431  44.517569  0.024077  0.023397   \n",
       "16  0.023426  0.023717  0.023120   0.023188  43.146470  0.023992  0.023392   \n",
       "17  0.023206  0.023212  0.022533   0.022564  39.775913  0.023856  0.023376   \n",
       "18  0.022548  0.023019  0.022230   0.022875  42.187574  0.023762  0.023366   \n",
       "19  0.022948  0.023207  0.022556   0.023071  43.709662  0.023697  0.023360   \n",
       "\n",
       "        EMAS       MFII       VPT    Target  \n",
       "0   0.021480  40.029975 -0.255817  0.000247  \n",
       "1   0.021530  41.289628 -0.168132 -0.000397  \n",
       "2   0.021573  41.495872 -0.244921  0.000368  \n",
       "3   0.021621  38.552255 -0.223900  0.000324  \n",
       "4   0.021671  37.052471 -0.163469 -0.001656  \n",
       "5   0.021700  35.133246 -0.223794  0.000736  \n",
       "6   0.021736  35.701672 -0.137054  0.000068  \n",
       "7   0.021772  42.370123 -0.084352 -0.000254  \n",
       "8   0.021803  42.263106 -0.060572 -0.000265  \n",
       "9   0.021830  41.953105 -0.114800 -0.000152  \n",
       "10  0.021855  36.602306 -0.088151 -0.000734  \n",
       "11  0.021870  35.572305 -0.115669 -0.000211  \n",
       "12  0.021881  35.853346 -0.093734  0.000263  \n",
       "13  0.021895  26.916729 -0.039886  0.000929  \n",
       "14  0.021923  27.409582  0.052281 -0.000538  \n",
       "15  0.021943  33.652915  0.045922 -0.000238  \n",
       "16  0.021959  33.894596  0.025333 -0.000642  \n",
       "17  0.021967  28.356059 -0.016615  0.000327  \n",
       "18  0.021979  22.047369  0.013798  0.000123  \n",
       "19  0.021994  30.425250 -0.006113 -0.000647  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_set = data.iloc[:, 0:11]#.values\n",
    "pd.set_option('display.max_columns', None)\n",
    "data_set.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8f1058be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1350, 13)\n"
     ]
    }
   ],
   "source": [
    "data_set2 = data_set.diff(periods=-1)\n",
    "#over write with original target.\n",
    "data_set2['Target'] = data_set['Target']\n",
    "\n",
    "data_set2.head(5)\n",
    "data_set = data_set2\n",
    "#print(data_set.shape)\n",
    "print(data.shape)\n",
    "#print(type(data_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb3a2c0c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Target column Categories\n",
    "#y =[1 if data.Open[i]>data.Close[i] else 0 for i in range(0, len(data))]\n",
    "#yi = [data.Open[i]-data.Close[i] for i in range(0, len(data))]\n",
    "#print(yi)\n",
    "#print(len(yi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9d38e4c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.42189103 0.55882302 0.37666375 ... 0.48514157 0.18049458 0.58058505]\n",
      " [0.41807892 0.54977476 0.36695026 ... 0.51372149 0.769144   0.57059048]\n",
      " [0.42781749 0.55232872 0.36837012 ... 0.59918013 0.41908404 0.5824629 ]\n",
      " ...\n",
      " [0.41246115 0.53583753 0.35868318 ... 0.27030713 0.42677083 0.58632735]\n",
      " [0.41199809 0.54313452 0.37607986 ... 0.52964414 0.46850252 0.56059588]\n",
      " [       nan        nan        nan ...        nan        nan 0.56996764]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range=(0,1))\n",
    "data_set_scaled = sc.fit_transform(data_set)\n",
    "print(data_set_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99ca74fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1350\n",
      "[[[0.42189103 0.55882302 0.37666375 ... 0.56004849 0.73138092 0.76198435]\n",
      "  [0.41807892 0.54977476 0.36695026 ... 0.56413746 0.73485575 0.76540213]\n",
      "  [0.42781749 0.55232872 0.36837012 ... 0.56064305 0.73244217 0.76316062]\n",
      "  ...\n",
      "  [0.42328003 0.5536969  0.36968383 ... 0.56323606 0.74810781 0.78170903]\n",
      "  [0.42061002 0.54933694 0.36976344 ... 0.56319329 0.7481546  0.78187813]\n",
      "  [0.42170581 0.5490998  0.3754562  ... 0.56869342 0.75268113 0.78636738]]\n",
      "\n",
      " [[0.41807892 0.54977476 0.36695026 ... 0.56413746 0.73485575 0.76540213]\n",
      "  [0.42781749 0.55232872 0.36837012 ... 0.56064305 0.73244217 0.76316062]\n",
      "  [0.41621147 0.54169332 0.36685737 ... 0.5580085  0.73050275 0.7613601 ]\n",
      "  ...\n",
      "  [0.42061002 0.54933694 0.36976344 ... 0.56319329 0.7481546  0.78187813]\n",
      "  [0.42170581 0.5490998  0.3754562  ... 0.56869342 0.75268113 0.78636738]\n",
      "  [0.43132091 0.55181791 0.36783932 ... 0.56547711 0.75049055 0.78440382]]\n",
      "\n",
      " [[0.42781749 0.55232872 0.36837012 ... 0.56064305 0.73244217 0.76316062]\n",
      "  [0.41621147 0.54169332 0.36685737 ... 0.5580085  0.73050275 0.7613601 ]\n",
      "  [0.41631951 0.51861653 0.39761678 ... 0.57359126 0.7431358  0.77360406]\n",
      "  ...\n",
      "  [0.42170581 0.5490998  0.3754562  ... 0.56869342 0.75268113 0.78636738]\n",
      "  [0.43132091 0.55181791 0.36783932 ... 0.56547711 0.75049055 0.78440382]\n",
      "  [0.41701401 0.51987526 0.36647255 ... 0.54982509 0.73803559 0.77252265]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.41844936 0.51319853 0.35796658 ... 0.56006335 0.77725365 0.82866642]\n",
      "  [0.39929631 0.54074469 0.35634767 ... 0.56039674 0.77678315 0.82827131]\n",
      "  [0.42187562 0.56402216 0.37225817 ... 0.56551593 0.78021917 0.83164096]\n",
      "  ...\n",
      "  [0.40539256 0.53271809 0.35476855 ... 0.54768709 0.74940945 0.80134758]\n",
      "  [0.40556228 0.54149264 0.36304893 ... 0.55322152 0.75274423 0.80445328]\n",
      "  [0.42851208 0.5552839  0.37089137 ... 0.54864922 0.74826345 0.80004207]]\n",
      "\n",
      " [[0.39929631 0.54074469 0.35634767 ... 0.56039674 0.77678315 0.82827131]\n",
      "  [0.42187562 0.56402216 0.37225817 ... 0.56551593 0.78021917 0.83164096]\n",
      "  [0.4296695  0.54019742 0.36579578 ... 0.56142789 0.77653329 0.82816132]\n",
      "  ...\n",
      "  [0.40556228 0.54149264 0.36304893 ... 0.55322152 0.75274423 0.80445328]\n",
      "  [0.42851208 0.5552839  0.37089137 ... 0.54864922 0.74826345 0.80004207]\n",
      "  [0.41246115 0.53583753 0.35868318 ... 0.5441887  0.74360956 0.79543671]]\n",
      "\n",
      " [[0.42187562 0.56402216 0.37225817 ... 0.56551593 0.78021917 0.83164096]\n",
      "  [0.4296695  0.54019742 0.36579578 ... 0.56142789 0.77653329 0.82816132]\n",
      "  [0.41520828 0.55564891 0.36446882 ... 0.55670282 0.7720901  0.82392679]\n",
      "  ...\n",
      "  [0.42851208 0.5552839  0.37089137 ... 0.54864922 0.74826345 0.80004207]\n",
      "  [0.41246115 0.53583753 0.35868318 ... 0.5441887  0.74360956 0.79543671]\n",
      "  [0.41199809 0.54313452 0.37607986 ... 0.55552116 0.75148003 0.80288535]]]\n",
      "(1320, 30, 8)\n",
      "[[0.58143863]\n",
      " [0.6026383 ]\n",
      " [0.56008379]\n",
      " ...\n",
      " [0.58632735]\n",
      " [0.56059588]\n",
      " [0.56996764]]\n",
      "(1320, 1)\n"
     ]
    }
   ],
   "source": [
    "# multiple feature from data provided to the model\n",
    "X = []\n",
    "#print(data_set_scaled[0].size)\n",
    "#data_set_scaled=data_set.values\n",
    "backcandles = 30\n",
    "print(data_set_scaled.shape[0])\n",
    "for j in range(8):#data_set_scaled[0].size):#2 columns are target not X\n",
    "    X.append([])\n",
    "    for i in range(backcandles, data_set_scaled.shape[0]):#backcandles+2\n",
    "        X[j].append(data_set_scaled[i-backcandles:i, j])\n",
    "\n",
    "#move axis from 0 to position 2\n",
    "X=np.moveaxis(X, [0], [2])\n",
    "\n",
    "#Erase first elements of y because of backcandles to match X length\n",
    "#del(yi[0:backcandles])\n",
    "#X, yi = np.array(X), np.array(yi)\n",
    "# Choose -1 for last column, classification else -2...\n",
    "X, yi =np.array(X), np.array(data_set_scaled[backcandles:,-1])\n",
    "y=np.reshape(yi,(len(yi),1))\n",
    "#y=sc.fit_transform(yi)\n",
    "#X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "print(X)\n",
    "print(X.shape)\n",
    "print(y)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c01b699a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#also comprehensions for X\n",
    "#X = np.array([data_set_scaled[i-backcandles:i,:4].copy() for i in range(backcandles,len(data_set_scaled))])\n",
    "#print(X)\n",
    "#print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a2a87918",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1056\n",
      "(1056, 30, 8)\n",
      "(264, 30, 8)\n",
      "(1056, 1)\n",
      "(264, 1)\n",
      "[[0.58143863]\n",
      " [0.6026383 ]\n",
      " [0.56008379]\n",
      " ...\n",
      " [0.61333122]\n",
      " [0.57010941]\n",
      " [0.60294873]]\n"
     ]
    }
   ],
   "source": [
    "# split data into train test sets\n",
    "splitlimit = int(len(X)*0.8)\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",
    "print(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9867161a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "64/64 [==============================] - 2s 18ms/step - loss: 0.0194 - val_loss: 0.0039\n",
      "Epoch 2/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0040 - val_loss: 0.0032\n",
      "Epoch 3/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0037 - val_loss: 0.0030\n",
      "Epoch 4/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0036 - val_loss: 0.0035\n",
      "Epoch 5/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0035 - val_loss: 0.0046\n",
      "Epoch 6/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0036 - val_loss: 0.0031\n",
      "Epoch 7/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0038 - val_loss: 0.0029\n",
      "Epoch 8/500\n",
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      "64/64 [==============================] - 1s 14ms/step - loss: 7.7277e-04 - val_loss: 0.0032\n",
      "Epoch 360/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.9358e-04 - val_loss: 0.0033\n",
      "Epoch 361/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 8.3814e-04 - val_loss: 0.0037\n",
      "Epoch 362/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 9.0754e-04 - val_loss: 0.0037\n",
      "Epoch 363/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.9385e-04 - val_loss: 0.0033\n",
      "Epoch 364/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.6337e-04 - val_loss: 0.0034\n",
      "Epoch 365/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 8.0055e-04 - val_loss: 0.0033\n",
      "Epoch 366/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.3952e-04 - val_loss: 0.0032\n",
      "Epoch 367/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.0573e-04 - val_loss: 0.0034\n",
      "Epoch 368/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.6037e-04 - val_loss: 0.0032\n",
      "Epoch 369/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.9109e-04 - val_loss: 0.0031\n",
      "Epoch 370/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.3353e-04 - val_loss: 0.0032\n",
      "Epoch 371/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.2762e-04 - val_loss: 0.0032\n",
      "Epoch 372/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 8.0637e-04 - val_loss: 0.0034\n",
      "Epoch 373/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5605e-04 - val_loss: 0.0033\n",
      "Epoch 374/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.8358e-04 - val_loss: 0.0034\n",
      "Epoch 375/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.1376e-04 - val_loss: 0.0031\n",
      "Epoch 376/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.0222e-04 - val_loss: 0.0030\n",
      "Epoch 377/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 8.0955e-04 - val_loss: 0.0031\n",
      "Epoch 378/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.0482e-04 - val_loss: 0.0033\n",
      "Epoch 379/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5804e-04 - val_loss: 0.0033\n",
      "Epoch 380/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.6979e-04 - val_loss: 0.0041\n",
      "Epoch 381/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.7773e-04 - val_loss: 0.0031\n",
      "Epoch 382/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.8437e-04 - val_loss: 0.0031\n",
      "Epoch 383/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 6.8219e-04 - val_loss: 0.0034\n",
      "Epoch 384/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.5415e-04 - val_loss: 0.0033\n",
      "Epoch 385/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 8.3193e-04 - val_loss: 0.0034\n",
      "Epoch 386/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.5830e-04 - val_loss: 0.0031\n",
      "Epoch 387/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.6543e-04 - val_loss: 0.0032\n",
      "Epoch 388/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.4681e-04 - val_loss: 0.0035\n",
      "Epoch 389/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 7.6423e-04 - val_loss: 0.0037\n",
      "Epoch 390/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 6.5292e-04 - val_loss: 0.0036\n",
      "Epoch 391/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5030e-04 - val_loss: 0.0035\n",
      "Epoch 392/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5118e-04 - val_loss: 0.0035\n",
      "Epoch 393/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.9284e-04 - val_loss: 0.0042\n",
      "Epoch 394/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.3998e-04 - val_loss: 0.0033\n",
      "Epoch 395/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.8233e-04 - val_loss: 0.0037\n",
      "Epoch 396/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.9994e-04 - val_loss: 0.0036\n",
      "Epoch 397/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.7511e-04 - val_loss: 0.0039\n",
      "Epoch 398/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.3461e-04 - val_loss: 0.0040\n",
      "Epoch 399/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.4101e-04 - val_loss: 0.0037\n",
      "Epoch 400/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.0652e-04 - val_loss: 0.0038\n",
      "Epoch 401/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.9942e-04 - val_loss: 0.0032\n",
      "Epoch 402/500\n",
      "64/64 [==============================] - 1s 13ms/step - loss: 5.8270e-04 - val_loss: 0.0035\n",
      "Epoch 403/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.0915e-04 - val_loss: 0.0035\n",
      "Epoch 404/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5276e-04 - val_loss: 0.0040\n",
      "Epoch 405/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.0969e-04 - val_loss: 0.0035\n",
      "Epoch 406/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.7124e-04 - val_loss: 0.0032\n",
      "Epoch 407/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.4452e-04 - val_loss: 0.0040\n",
      "Epoch 408/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.4696e-04 - val_loss: 0.0043\n",
      "Epoch 409/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.3011e-04 - val_loss: 0.0037\n",
      "Epoch 410/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.8451e-04 - val_loss: 0.0038\n",
      "Epoch 411/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.6423e-04 - val_loss: 0.0034\n",
      "Epoch 412/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4598e-04 - val_loss: 0.0035\n",
      "Epoch 413/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.5303e-04 - val_loss: 0.0038\n",
      "Epoch 414/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4709e-04 - val_loss: 0.0034\n",
      "Epoch 415/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.5410e-04 - val_loss: 0.0033\n",
      "Epoch 416/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.5288e-04 - val_loss: 0.0035\n",
      "Epoch 417/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3297e-04 - val_loss: 0.0038\n",
      "Epoch 418/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.8469e-04 - val_loss: 0.0033\n",
      "Epoch 419/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.5625e-04 - val_loss: 0.0031\n",
      "Epoch 420/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.1225e-04 - val_loss: 0.0036\n",
      "Epoch 421/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 0.0012 - val_loss: 0.0033\n",
      "Epoch 422/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 7.4225e-04 - val_loss: 0.0036\n",
      "Epoch 423/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.1890e-04 - val_loss: 0.0035\n",
      "Epoch 424/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.6833e-04 - val_loss: 0.0037\n",
      "Epoch 425/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4858e-04 - val_loss: 0.0036\n",
      "Epoch 426/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4195e-04 - val_loss: 0.0030\n",
      "Epoch 427/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.6948e-04 - val_loss: 0.0034\n",
      "Epoch 428/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.0448e-04 - val_loss: 0.0029\n",
      "Epoch 429/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 8.3933e-04 - val_loss: 0.0039\n",
      "Epoch 430/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.8674e-04 - val_loss: 0.0034\n",
      "Epoch 431/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.8106e-04 - val_loss: 0.0035\n",
      "Epoch 432/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.2552e-04 - val_loss: 0.0037\n",
      "Epoch 433/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4813e-04 - val_loss: 0.0034\n",
      "Epoch 434/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.1890e-04 - val_loss: 0.0034\n",
      "Epoch 435/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.1214e-04 - val_loss: 0.0036\n",
      "Epoch 436/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9497e-04 - val_loss: 0.0036\n",
      "Epoch 437/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9198e-04 - val_loss: 0.0037\n",
      "Epoch 438/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.0005e-04 - val_loss: 0.0034\n",
      "Epoch 439/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9063e-04 - val_loss: 0.0035\n",
      "Epoch 440/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.0073e-04 - val_loss: 0.0034\n",
      "Epoch 441/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.6892e-04 - val_loss: 0.0036\n",
      "Epoch 442/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9694e-04 - val_loss: 0.0041\n",
      "Epoch 443/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3741e-04 - val_loss: 0.0035\n",
      "Epoch 444/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.8392e-04 - val_loss: 0.0042\n",
      "Epoch 445/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.2056e-04 - val_loss: 0.0035\n",
      "Epoch 446/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.8521e-04 - val_loss: 0.0032\n",
      "Epoch 447/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.2828e-04 - val_loss: 0.0036\n",
      "Epoch 448/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9447e-04 - val_loss: 0.0033\n",
      "Epoch 449/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7288e-04 - val_loss: 0.0039\n",
      "Epoch 450/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.8717e-04 - val_loss: 0.0040\n",
      "Epoch 451/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7839e-04 - val_loss: 0.0039\n",
      "Epoch 452/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.5677e-04 - val_loss: 0.0045\n",
      "Epoch 453/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9722e-04 - val_loss: 0.0038\n",
      "Epoch 454/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.7723e-04 - val_loss: 0.0042\n",
      "Epoch 455/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3825e-04 - val_loss: 0.0035\n",
      "Epoch 456/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3290e-04 - val_loss: 0.0040\n",
      "Epoch 457/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9100e-04 - val_loss: 0.0041\n",
      "Epoch 458/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3347e-04 - val_loss: 0.0035\n",
      "Epoch 459/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.0203e-04 - val_loss: 0.0038\n",
      "Epoch 460/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.2233e-04 - val_loss: 0.0038\n",
      "Epoch 461/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4779e-04 - val_loss: 0.0035\n",
      "Epoch 462/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7845e-04 - val_loss: 0.0041\n",
      "Epoch 463/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7245e-04 - val_loss: 0.0037\n",
      "Epoch 464/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.5077e-04 - val_loss: 0.0037\n",
      "Epoch 465/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.5136e-04 - val_loss: 0.0037\n",
      "Epoch 466/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.3129e-04 - val_loss: 0.0035\n",
      "Epoch 467/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.2988e-04 - val_loss: 0.0035\n",
      "Epoch 468/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.4377e-04 - val_loss: 0.0039\n",
      "Epoch 469/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.8510e-04 - val_loss: 0.0039\n",
      "Epoch 470/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 6.5461e-04 - val_loss: 0.0043\n",
      "Epoch 471/500\n",
      "64/64 [==============================] - 1s 16ms/step - loss: 5.6945e-04 - val_loss: 0.0036\n",
      "Epoch 472/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.5964e-04 - val_loss: 0.0042\n",
      "Epoch 473/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7910e-04 - val_loss: 0.0037\n",
      "Epoch 474/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 6.0362e-04 - val_loss: 0.0037\n",
      "Epoch 475/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.8274e-04 - val_loss: 0.0035\n",
      "Epoch 476/500\n",
      "64/64 [==============================] - 1s 17ms/step - loss: 4.5013e-04 - val_loss: 0.0041\n",
      "Epoch 477/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 5.0541e-04 - val_loss: 0.0034\n",
      "Epoch 478/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 4.3745e-04 - val_loss: 0.0041\n",
      "Epoch 479/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 4.5469e-04 - val_loss: 0.0036\n",
      "Epoch 480/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.9858e-04 - val_loss: 0.0038\n",
      "Epoch 481/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.0644e-04 - val_loss: 0.0042\n",
      "Epoch 482/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.9573e-04 - val_loss: 0.0038\n",
      "Epoch 483/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.7934e-04 - val_loss: 0.0042\n",
      "Epoch 484/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.2321e-04 - val_loss: 0.0043\n",
      "Epoch 485/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.1068e-04 - val_loss: 0.0037\n",
      "Epoch 486/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.2636e-04 - val_loss: 0.0039\n",
      "Epoch 487/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 4.3911e-04 - val_loss: 0.0042\n",
      "Epoch 488/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.0485e-04 - val_loss: 0.0041\n",
      "Epoch 489/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.9642e-04 - val_loss: 0.0036\n",
      "Epoch 490/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.0159e-04 - val_loss: 0.0043\n",
      "Epoch 491/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.6184e-04 - val_loss: 0.0040\n",
      "Epoch 492/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.1138e-04 - val_loss: 0.0041\n",
      "Epoch 493/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 4.6260e-04 - val_loss: 0.0036\n",
      "Epoch 494/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.9770e-04 - val_loss: 0.0037\n",
      "Epoch 495/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.7697e-04 - val_loss: 0.0038\n",
      "Epoch 496/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 5.1221e-04 - val_loss: 0.0039\n",
      "Epoch 497/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 4.9282e-04 - val_loss: 0.0037\n",
      "Epoch 498/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 4.3563e-04 - val_loss: 0.0038\n",
      "Epoch 499/500\n",
      "64/64 [==============================] - 1s 15ms/step - loss: 3.9903e-04 - val_loss: 0.0041\n",
      "Epoch 500/500\n",
      "64/64 [==============================] - 1s 14ms/step - loss: 3.8155e-04 - val_loss: 0.0042\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fc2d47d1af0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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",
    "#tf.random.set_seed(20)\n",
    "np.random.seed(10)\n",
    "\n",
    "lstm_input = Input(shape=(backcandles, 8), name='lstm_input')\n",
    "inputs = LSTM(150, name='first_layer')(lstm_input)\n",
    "inputs = Dense(1, name='dense_layer')(inputs)\n",
    "output = Activation('linear', name='output')(inputs)\n",
    "model = Model(inputs=lstm_input, outputs=output)\n",
    "adam = optimizers.Adam()\n",
    "model.compile(optimizer=adam, loss='mse')\n",
    "model.fit(x=X_train, y=y_train, batch_size=15, epochs=500, shuffle=True, validation_split = 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "08324ede",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 5ms/step\n",
      "[0.57904994] [0.55868695]\n",
      "[0.59571874] [0.50519124]\n",
      "[0.6042005] [0.56809179]\n",
      "[0.61734676] [0.59208501]\n",
      "[0.62394464] [0.57858307]\n",
      "[0.59643304] [0.61249313]\n",
      "[0.5928029] [0.5708233]\n",
      "[0.60322165] [0.51515474]\n",
      "[0.5692247] [0.58674628]\n",
      "[0.572814] [0.58309921]\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "#y_pred=np.where(y_pred > 0.43, 1,0)\n",
    "for i in range(10):\n",
    "    print(y_pred[i], y_test[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "67f5e31a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(y_test, color = 'black', label = 'Test')\n",
    "plt.plot(y_pred, color = 'green', label = 'pred')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18555402",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}