DatasetCreator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "67f35910-75ab-4930-a12e-5a601582df23",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " DatasetCreator.ipynb  'Volatility 100 Index_M1_115823311.csv'\n",
      " env\t\t       'Volatility 100 Index_M1_-1994977658.csv'\n",
      " M1M5Charts\t       'Volatility 100 Index_M5_159533415.csv'\n",
      " M1M5Charts.7z\t       'Volatility 100 Index_M5_2052042313.csv'\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c8bace7-b277-41fa-8902-091636cb3693",
   "metadata": {},
   "outputs": [],
   "source": [
    "'Volatility 100 Index_M1_115823311.csv' m1short\n",
    "'Volatility 100 Index_M1_-1994977658.csv' m1 long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "221ab9e2-4e94-4544-b88e-1d301fffc34d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os, csv, codecs, shutil\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from IPython.display import display, clear_output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "65b2dd06-fb00-4cae-a64a-3ce260320f56",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import time\n",
    "class DatasetCreator:\n",
    "    def __init__(self, m1shortfilename, m1longfilename, m5shortfilename, m5longfilename):\n",
    "        self.m1short = self.create(m1shortfilename)\n",
    "        self.m1long = self.create(m1longfilename)\n",
    "        self.m5long = self.create(m5longfilename)\n",
    "        self.m5short = self.create(m5shortfilename)\n",
    "        \n",
    "        \n",
    "        \n",
    "    def create(self, csvname):\n",
    "        with codecs.open(csvname, mode='r', encoding='utf-16-le', errors='ignore') as f:\n",
    "            reader = csv.reader(f, delimiter=\",\")\n",
    "            data = []\n",
    "            for row in reader:\n",
    "                row[0] = row[0].lstrip('\\ufeff')\n",
    "                data.append(row)\n",
    "        columns = ['TimeStamp', 'image_path', 'Open', 'Close', 'High', 'Low', 'Ask', 'Bid',\n",
    "               'MA 5', 'MA 8', 'MA 10', 'MA 14', 'MA 18', 'MA 24', 'MA 30', 'MA 50',\n",
    "               'MA 100', 'MA 200', 'Stochastic %K', 'Stochastic %D', 'RSI', 'MACD',\n",
    "               'MACD Signal', 'Volume', 'Spread', 'Account Balance', 'Profit',\n",
    "               'Take Profit', 'Stop Loss', 'Trailing Stop', 'Lot Size','unknown']\n",
    "        forex_data = pd.DataFrame(data, columns = columns)\n",
    "        data = []\n",
    "        reader = []\n",
    "        return self.clean_up_files(forex_data)\n",
    "    \n",
    "    def clean_up_files(self, data):\n",
    "        data[\"image_path\"] = data[\"image_path\"].apply(self.rename_file)\n",
    "        return data\n",
    "    def rename_file(self,filename):\n",
    "        new_filename = filename.replace(' ', '_')\n",
    "        # os.rename(filename, new_filename)\n",
    "        return new_filename\n",
    "    def display_row_images(self,q,t,s,c):\n",
    "        imageq = Image.open(f\"M1M5Charts/{q}\")\n",
    "        imaget = Image.open(f\"M1M5Charts/{t}\")\n",
    "        images = Image.open(f\"M1M5Charts/{s}\")\n",
    "        imagec = Image.open(f\"M1M5Charts/{c}\")\n",
    "        # Calculate the required dimensions for the new image\n",
    "        new_width = imageq.width * 4\n",
    "        new_height = imageq.height\n",
    "        # Create a new image with the required dimensions\n",
    "        new_image = Image.new(\"RGB\", (new_width, new_height))\n",
    "        # Paste the individual images side by side\n",
    "        new_image.paste(imageq, (0, 0))\n",
    "        new_image.paste(imaget, (imageq.width, 0))\n",
    "        new_image.paste(images, (imageq.width + imaget.width, 0))\n",
    "        new_image.paste(imagec, (imageq.width + imaget.width + images.width, 0))\n",
    "        # Display the final image\n",
    "        display(new_image) \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "6abcfc88-9f77-40e5-ad79-3f33077a677f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datasetCreator = DatasetCreator('Volatility 100 Index_M1_115823311.csv','Volatility 100 Index_M1_-1994977658.csv', 'Volatility 100 Index_M5_2052042313.csv','Volatility 100 Index_M5_159533415.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "9bde99ff-0443-4fc1-87f2-6c8c319c1bca",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ccf321b5-2c3a-4502-b925-c033282415f8",
   "metadata": {},
   "source": [
    "# M5long description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b2fa1b19-5e8d-48a0-ac3b-670d86527652",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:30</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7321.3100</td>\n",
       "      <td>7330.4100</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7320.9700</td>\n",
       "      <td>7332.0800</td>\n",
       "      <td>7330.4100</td>\n",
       "      <td>7326.0440</td>\n",
       "      <td>7319.2562</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.8216</td>\n",
       "      <td>16</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path   \n",
       "0  2023.04.11-04:48:30  Volatility_100_Index_M1_-19949776582023.04.11-...  \\\n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid   \n",
       "0  7321.3100  7330.4100  7333.6200  7320.9700  7332.0800  7330.4100  \\\n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance   \n",
       "0  7326.0440  7319.2562  ...     -4.8216     16  167.0000      10151.8000  \\\n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      27  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m1long.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "86e29e67-d33f-4e2c-afff-d526249a9302",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7321.3100</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7320.9700</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7325.6420</td>\n",
       "      <td>7319.0050</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.8395</td>\n",
       "      <td>20</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:38  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7321.3100  7328.4000  7333.6200  7320.9700  7330.0700  7328.4000   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7325.6420  7319.0050  ...     -4.8395     20  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      32  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m1short.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "7d93f86e-59e0-4919-ac61-e2f4ba36bfa0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:22</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7335.2900</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7334.2700</td>\n",
       "      <td>7332.0225</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1490</td>\n",
       "      <td>101</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:22  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7305.4000  7333.6200  7343.1200  7305.4000  7335.2900  7333.6200   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7334.2700  7332.0225  ...    -47.1490    101  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      19  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m5short.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "cfc316bd-ee67-483e-a93b-525f3c64582a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:13</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7326.2800</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7327.9500</td>\n",
       "      <td>7326.2800</td>\n",
       "      <td>7332.8020</td>\n",
       "      <td>7331.1050</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.2140</td>\n",
       "      <td>96</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:13  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7305.4000  7326.2800  7343.1200  7305.4000  7327.9500  7326.2800   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7332.8020  7331.1050  ...    -47.2140     96  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      35  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m5long.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6548a99-2241-4c0b-b0f6-89a3a9da235a",
   "metadata": {},
   "source": [
    "# M5 long Start index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "bfb17670-f539-4009-9599-8fa4bf21bab4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volatility_100_Index_M1_1158233112023.04.11-04:49:08_7323_7321.png'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m5long.iloc[15][\"image_path\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b59da33-612c-4c91-925a-359b0c47d27e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m5long.iloc[15][\"image_path\"],datasetCreator.m5long.iloc[15+32][\"image_path\"],datasetCreator.m5long.iloc[15+64][\"image_path\"],datasetCreator.m5long.iloc[15+96][\"image_path\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f5255ff-b62b-4b76-98b2-99c419e66f9c",
   "metadata": {},
   "source": [
    "# Display images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "cf63984d-ba58-4fd3-bc60-78e83616632e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=896x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m5short.iloc[15][\"image_path\"],datasetCreator.m1short.iloc[15][\"image_path\"],datasetCreator.m1long.iloc[15][\"image_path\"],datasetCreator.m5long.iloc[15][\"image_path\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c3bfa7-005f-4873-b5bd-89959f64f1be",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "199f8ced-12a9-4504-b312-2ba457edf742",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volatility_100_Index_M5_1595334152023.04.11-04:48:38_7330_7328.png'"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m5long.iloc[15][\"image_path\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "020ab54e-2fb5-493d-b0c4-5e784598f671",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volatility_100_Index_M5_20520423132023.04.11-04:48:38_7330_7328.png'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m5short.iloc[8][\"image_path\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "bf78f5d1-fc84-41da-a486-83213781ae09",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volatility_100_Index_M1_-19949776582023.04.11-04:48:38_7330_7328.png'"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m1long.iloc[5][\"image_path\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "d6041a88-e6bf-4df1-be26-ed7d53104d12",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volatility_100_Index_M1_1158233112023.04.11-04:48:38_7330_7328.png'"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasetCreator.m1short.iloc[0][\"image_path\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb4d7646-ac51-49c0-ba7d-86467b75399d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "97a8cfa5-843c-4be5-a3ff-4a1ea7b28c47",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=896x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m5long.iloc[15][\"image_path\"],datasetCreator.m5long.iloc[15+32][\"image_path\"],datasetCreator.m5long.iloc[15+64][\"image_path\"],datasetCreator.m5long.iloc[15+96][\"image_path\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "1ec7f073-d183-4419-8965-404cde920db7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=896x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m5short.iloc[8][\"image_path\"],datasetCreator.m5short.iloc[8+32][\"image_path\"],datasetCreator.m5short.iloc[8+64][\"image_path\"],datasetCreator.m5short.iloc[8+96][\"image_path\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "b879d3ef-8d9d-442f-b1e3-0c40e254ce01",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=896x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m1long.iloc[5][\"image_path\"],datasetCreator.m1long.iloc[5+32][\"image_path\"],datasetCreator.m1long.iloc[5+64][\"image_path\"],datasetCreator.m1long.iloc[5+96][\"image_path\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "3c099f2d-c702-4325-b7d1-f608f8a1f10b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=896x224>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datasetCreator.display_row_images(datasetCreator.m1short.iloc[0][\"image_path\"],datasetCreator.m1short.iloc[32][\"image_path\"],datasetCreator.m1short.iloc[64][\"image_path\"],datasetCreator.m1short.iloc[96][\"image_path\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e07d994e-50b3-4bc8-b827-d60491632cef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a1c4361c-6387-41ed-98df-df581221a659",
   "metadata": {},
   "source": [
    "Slicing sections\n",
    "M5 section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "12d1ce5b-6137-48c2-b27b-f8cba39b1784",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "0d275cfd-78e7-41ea-adcf-0f7fc3fd8c7f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.2260</td>\n",
       "      <td>7331.3700</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1952</td>\n",
       "      <td>109</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              TimeStamp                                         image_path  \\\n",
       "15  2023.04.11-04:48:38  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "\n",
       "         Open      Close       High        Low        Ask        Bid  \\\n",
       "15  7305.4000  7328.4000  7343.1200  7305.4000  7330.0700  7328.4000   \n",
       "\n",
       "         MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "15  7333.2260  7331.3700  ...    -47.1952    109  167.0000      10151.8000   \n",
       "\n",
       "    Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "15  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "\n",
       "[1 rows x 32 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m5long.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a8a5c09c-af47-4a22-b839-0b3a5fefca02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "m5long = datasetCreator.m5long.iloc[15:][:]\n",
    "m5short = datasetCreator.m5short.iloc[8:][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8856cff8-b84b-4bae-b176-c4761e30310d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "m1long = datasetCreator.m1long.iloc[5:-2][:]\n",
    "m1short = datasetCreator.m1short.iloc[0:-1][:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83fdce2e-5af2-4e68-aa7e-db1388f9567b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "m5 long len 39260 m5 short 39260\n",
      "m1 long len 39260 m1 short 39260\n"
     ]
    }
   ],
   "source": [
    "print(f\"m5 long len {len(m5long)} m5 short {len(m5short)}\")\n",
    "print(f\"m1 long len {len(m1long)} m1 short {len(m1short)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e07c678f-7241-400c-a4fd-ccbeab776629",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "list_m1long = []\n",
    "list_m1short = []\n",
    "list_m5long = []\n",
    "list_m5short = []\n",
    "\n",
    "for key, value in enumerate(list(range(0, 39260, 4))):\n",
    "    # print(key, value)\n",
    "    list_m5long.append(m5long.iloc[value].values.tolist())\n",
    "    list_m5short.append(m5short.iloc[value].values.tolist())\n",
    "    list_m1long.append(m1long.iloc[value].values.tolist())\n",
    "    list_m1short.append(m1short.iloc[value].values.tolist())\n",
    "    \n",
    "    # if key == 20: break\n",
    "columns = ['TimeStamp', 'image_path', 'Open', 'Close', 'High', 'Low', 'Ask', 'Bid',\n",
    "               'MA 5', 'MA 8', 'MA 10', 'MA 14', 'MA 18', 'MA 24', 'MA 30', 'MA 50',\n",
    "               'MA 100', 'MA 200', 'Stochastic %K', 'Stochastic %D', 'RSI', 'MACD',\n",
    "               'MACD Signal', 'Volume', 'Spread', 'Account Balance', 'Profit',\n",
    "               'Take Profit', 'Stop Loss', 'Trailing Stop', 'Lot Size','unknown']\n",
    "final_m5long = pd.DataFrame(data=list_m5long, columns=columns)\n",
    "final_m5short = pd.DataFrame(data=list_m5short, columns=columns)\n",
    "final_m1long = pd.DataFrame(data=list_m1long, columns=columns)\n",
    "final_m1short = pd.DataFrame(data=list_m1short, columns=columns)\n",
    "\n",
    "list_m1long = []\n",
    "list_m1short = []\n",
    "list_m5long = []\n",
    "list_m5short = []\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12630378-5da3-463b-8967-22859717bd42",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_m1short.to_csv(\"final_complete_m1short_timestep_4.csv\", index=False)\n",
    "final_m5short.to_csv(\"final_complete_m5short_timestep_4.csv\", index=False)\n",
    "\n",
    "final_m1long.to_csv(\"final_complete_m1long_timestep_4.csv\", index=False)\n",
    "final_m5long.to_csv(\"final_complete_m5long_timestep_4.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "e0bc6607-226a-4bee-8bec-1bfb863df62f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.2260</td>\n",
       "      <td>7331.3700</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1952</td>\n",
       "      <td>109</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023.04.11-04:49:42</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7336.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7334.4920</td>\n",
       "      <td>7332.1613</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1391</td>\n",
       "      <td>141</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023.04.11-04:50:46</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7333.5400</td>\n",
       "      <td>7337.5800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7332.1500</td>\n",
       "      <td>7334.0613</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.2762</td>\n",
       "      <td>24</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023.04.11-04:51:50</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7330.2100</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7330.6760</td>\n",
       "      <td>7333.1400</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.3415</td>\n",
       "      <td>56</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023.04.11-04:52:54</td>\n",
       "      <td>Volatility_100_Index_M5_1595334152023.04.11-04...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7345.6200</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7333.7580</td>\n",
       "      <td>7335.0663</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.2049</td>\n",
       "      <td>88</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:38  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "1  2023.04.11-04:49:42  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "2  2023.04.11-04:50:46  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "3  2023.04.11-04:51:50  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "4  2023.04.11-04:52:54  Volatility_100_Index_M5_1595334152023.04.11-04...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7305.4000  7328.4000  7343.1200  7305.4000  7330.0700  7328.4000   \n",
       "1  7305.4000  7334.7300  7343.1200  7305.4000  7336.4000  7334.7300   \n",
       "2  7336.1800  7335.9100  7339.1500  7333.5400  7337.5800  7335.9100   \n",
       "3  7336.1800  7328.5400  7339.1500  7327.2200  7330.2100  7328.5400   \n",
       "4  7336.1800  7343.9500  7345.0100  7327.2200  7345.6200  7343.9500   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7333.2260  7331.3700  ...    -47.1952    109  167.0000      10151.8000   \n",
       "1  7334.4920  7332.1613  ...    -47.1391    141  167.0000      10151.8000   \n",
       "2  7332.1500  7334.0613  ...    -46.2762     24  167.0000      10151.8000   \n",
       "3  7330.6760  7333.1400  ...    -46.3415     56  167.0000      10151.8000   \n",
       "4  7333.7580  7335.0663  ...    -46.2049     88  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "1  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "2  0.0000     20.1111   20.1111       20.1111   0.2000      10  \n",
       "3  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "4  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_m5long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "4eb10ac4-6b49-47c8-b222-a9ae1dab419c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.2260</td>\n",
       "      <td>7331.3700</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1952</td>\n",
       "      <td>109</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023.04.11-04:49:42</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7343.1200</td>\n",
       "      <td>7305.4000</td>\n",
       "      <td>7336.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7334.4920</td>\n",
       "      <td>7332.1613</td>\n",
       "      <td>...</td>\n",
       "      <td>-47.1391</td>\n",
       "      <td>141</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023.04.11-04:50:46</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7333.5400</td>\n",
       "      <td>7337.5800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7332.1500</td>\n",
       "      <td>7334.0613</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.2762</td>\n",
       "      <td>24</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023.04.11-04:51:50</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7330.2100</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7330.6760</td>\n",
       "      <td>7333.1400</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.3415</td>\n",
       "      <td>56</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023.04.11-04:52:54</td>\n",
       "      <td>Volatility_100_Index_M5_20520423132023.04.11-0...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7345.6200</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7333.7580</td>\n",
       "      <td>7335.0663</td>\n",
       "      <td>...</td>\n",
       "      <td>-46.2049</td>\n",
       "      <td>88</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:38  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "1  2023.04.11-04:49:42  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "2  2023.04.11-04:50:46  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "3  2023.04.11-04:51:50  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "4  2023.04.11-04:52:54  Volatility_100_Index_M5_20520423132023.04.11-0...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7305.4000  7328.4000  7343.1200  7305.4000  7330.0700  7328.4000   \n",
       "1  7305.4000  7334.7300  7343.1200  7305.4000  7336.4000  7334.7300   \n",
       "2  7336.1800  7335.9100  7339.1500  7333.5400  7337.5800  7335.9100   \n",
       "3  7336.1800  7328.5400  7339.1500  7327.2200  7330.2100  7328.5400   \n",
       "4  7336.1800  7343.9500  7345.0100  7327.2200  7345.6200  7343.9500   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7333.2260  7331.3700  ...    -47.1952    109  167.0000      10151.8000   \n",
       "1  7334.4920  7332.1613  ...    -47.1391    141  167.0000      10151.8000   \n",
       "2  7332.1500  7334.0613  ...    -46.2762     24  167.0000      10151.8000   \n",
       "3  7330.6760  7333.1400  ...    -46.3415     56  167.0000      10151.8000   \n",
       "4  7333.7580  7335.0663  ...    -46.2049     88  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      32  \n",
       "1  0.0000     20.1111   20.1111       20.1111   0.2000      25  \n",
       "2  0.0000     20.1111   20.1111       20.1111   0.2000      31  \n",
       "3  0.0000     20.1111   20.1111       20.1111   0.2000      30  \n",
       "4  0.0000     20.1111   20.1111       20.1111   0.2000      25  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_m5short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "0b0cabe1-fb03-42c4-9eaa-f1fadaf9d7b2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7321.3100</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7320.9700</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7325.6420</td>\n",
       "      <td>7319.0050</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.8395</td>\n",
       "      <td>20</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023.04.11-04:49:40</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7323.4000</td>\n",
       "      <td>7334.8500</td>\n",
       "      <td>7335.3700</td>\n",
       "      <td>7321.4700</td>\n",
       "      <td>7336.5200</td>\n",
       "      <td>7334.8500</td>\n",
       "      <td>7329.9420</td>\n",
       "      <td>7320.3362</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.2315</td>\n",
       "      <td>21</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023.04.11-04:50:44</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7334.7100</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7333.5400</td>\n",
       "      <td>7336.3800</td>\n",
       "      <td>7334.7100</td>\n",
       "      <td>7330.6580</td>\n",
       "      <td>7324.0450</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.3168</td>\n",
       "      <td>23</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023.04.11-04:51:48</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7334.2400</td>\n",
       "      <td>7328.2900</td>\n",
       "      <td>7337.7500</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7329.9600</td>\n",
       "      <td>7328.2900</td>\n",
       "      <td>7328.8320</td>\n",
       "      <td>7327.5850</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.1409</td>\n",
       "      <td>25</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023.04.11-04:52:52</td>\n",
       "      <td>Volatility_100_Index_M1_-19949776582023.04.11-...</td>\n",
       "      <td>7335.4900</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7331.2800</td>\n",
       "      <td>7346.6800</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7334.7100</td>\n",
       "      <td>7333.0262</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.4035</td>\n",
       "      <td>27</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:38  Volatility_100_Index_M1_-19949776582023.04.11-...   \n",
       "1  2023.04.11-04:49:40  Volatility_100_Index_M1_-19949776582023.04.11-...   \n",
       "2  2023.04.11-04:50:44  Volatility_100_Index_M1_-19949776582023.04.11-...   \n",
       "3  2023.04.11-04:51:48  Volatility_100_Index_M1_-19949776582023.04.11-...   \n",
       "4  2023.04.11-04:52:52  Volatility_100_Index_M1_-19949776582023.04.11-...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7321.3100  7328.4000  7333.6200  7320.9700  7330.0700  7328.4000   \n",
       "1  7323.4000  7334.8500  7335.3700  7321.4700  7336.5200  7334.8500   \n",
       "2  7336.1800  7334.7100  7339.1500  7333.5400  7336.3800  7334.7100   \n",
       "3  7334.2400  7328.2900  7337.7500  7327.2200  7329.9600  7328.2900   \n",
       "4  7335.4900  7345.0100  7345.0100  7331.2800  7346.6800  7345.0100   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7325.6420  7319.0050  ...     -4.8395     20  167.0000      10151.8000   \n",
       "1  7329.9420  7320.3362  ...     -5.2315     21  167.0000      10151.8000   \n",
       "2  7330.6580  7324.0450  ...     -5.3168     23  167.0000      10151.8000   \n",
       "3  7328.8320  7327.5850  ...     -5.1409     25  167.0000      10151.8000   \n",
       "4  7334.7100  7333.0262  ...     -4.4035     27  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000       4  \n",
       "1  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "2  0.0000     20.1111   20.1111       20.1111   0.2000       8  \n",
       "3  0.0000     20.1111   20.1111       20.1111   0.2000       7  \n",
       "4  0.0000     20.1111   20.1111       20.1111   0.2000       9  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_m1long.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "75d4f12c-a968-43bb-a195-e6511f9c0c4d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TimeStamp</th>\n",
       "      <th>image_path</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Ask</th>\n",
       "      <th>Bid</th>\n",
       "      <th>MA 5</th>\n",
       "      <th>MA 8</th>\n",
       "      <th>...</th>\n",
       "      <th>MACD Signal</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Spread</th>\n",
       "      <th>Account Balance</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Take Profit</th>\n",
       "      <th>Stop Loss</th>\n",
       "      <th>Trailing Stop</th>\n",
       "      <th>Lot Size</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023.04.11-04:48:38</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7321.3100</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7333.6200</td>\n",
       "      <td>7320.9700</td>\n",
       "      <td>7330.0700</td>\n",
       "      <td>7328.4000</td>\n",
       "      <td>7325.6420</td>\n",
       "      <td>7319.0050</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.8395</td>\n",
       "      <td>20</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023.04.11-04:49:42</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7323.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7335.3700</td>\n",
       "      <td>7321.4700</td>\n",
       "      <td>7336.4000</td>\n",
       "      <td>7334.7300</td>\n",
       "      <td>7329.9180</td>\n",
       "      <td>7320.3212</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.2326</td>\n",
       "      <td>22</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023.04.11-04:50:46</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7336.1800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7339.1500</td>\n",
       "      <td>7333.5400</td>\n",
       "      <td>7337.5800</td>\n",
       "      <td>7335.9100</td>\n",
       "      <td>7330.8980</td>\n",
       "      <td>7324.1950</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.3062</td>\n",
       "      <td>24</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023.04.11-04:51:50</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7334.2400</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7337.7500</td>\n",
       "      <td>7327.2200</td>\n",
       "      <td>7330.2100</td>\n",
       "      <td>7328.5400</td>\n",
       "      <td>7328.8820</td>\n",
       "      <td>7327.6162</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.1387</td>\n",
       "      <td>26</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023.04.11-04:52:54</td>\n",
       "      <td>Volatility_100_Index_M1_1158233112023.04.11-04...</td>\n",
       "      <td>7335.4900</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7345.0100</td>\n",
       "      <td>7331.2800</td>\n",
       "      <td>7345.6200</td>\n",
       "      <td>7343.9500</td>\n",
       "      <td>7334.4980</td>\n",
       "      <td>7332.8937</td>\n",
       "      <td>...</td>\n",
       "      <td>-4.4129</td>\n",
       "      <td>28</td>\n",
       "      <td>167.0000</td>\n",
       "      <td>10151.8000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>20.1111</td>\n",
       "      <td>0.2000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             TimeStamp                                         image_path  \\\n",
       "0  2023.04.11-04:48:38  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "1  2023.04.11-04:49:42  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "2  2023.04.11-04:50:46  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "3  2023.04.11-04:51:50  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "4  2023.04.11-04:52:54  Volatility_100_Index_M1_1158233112023.04.11-04...   \n",
       "\n",
       "        Open      Close       High        Low        Ask        Bid  \\\n",
       "0  7321.3100  7328.4000  7333.6200  7320.9700  7330.0700  7328.4000   \n",
       "1  7323.4000  7334.7300  7335.3700  7321.4700  7336.4000  7334.7300   \n",
       "2  7336.1800  7335.9100  7339.1500  7333.5400  7337.5800  7335.9100   \n",
       "3  7334.2400  7328.5400  7337.7500  7327.2200  7330.2100  7328.5400   \n",
       "4  7335.4900  7343.9500  7345.0100  7331.2800  7345.6200  7343.9500   \n",
       "\n",
       "        MA 5       MA 8  ... MACD Signal Volume    Spread Account Balance  \\\n",
       "0  7325.6420  7319.0050  ...     -4.8395     20  167.0000      10151.8000   \n",
       "1  7329.9180  7320.3212  ...     -5.2326     22  167.0000      10151.8000   \n",
       "2  7330.8980  7324.1950  ...     -5.3062     24  167.0000      10151.8000   \n",
       "3  7328.8820  7327.6162  ...     -5.1387     26  167.0000      10151.8000   \n",
       "4  7334.4980  7332.8937  ...     -4.4129     28  167.0000      10151.8000   \n",
       "\n",
       "   Profit Take Profit Stop Loss Trailing Stop Lot Size unknown  \n",
       "0  0.0000     20.1111   20.1111       20.1111   0.2000      32  \n",
       "1  0.0000     20.1111   20.1111       20.1111   0.2000       4  \n",
       "2  0.0000     20.1111   20.1111       20.1111   0.2000       4  \n",
       "3  0.0000     20.1111   20.1111       20.1111   0.2000       4  \n",
       "4  0.0000     20.1111   20.1111       20.1111   0.2000       4  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_m1short.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "3dcdf4f3-76d2-4d56-bc04-ce9c920e3a0a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1227"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(final_m1short)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81b3abb4-aae7-4d25-8a3e-57070c008e85",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for key, value in enumerate(range(3, 1228)):\n",
    "    print(key,value)\n",
    "    datasetCreator.display_row_images(final_m1short.iloc[value-3][\"image_path\"],final_m1short.iloc[value-2][\"image_path\"],final_m1short.iloc[value-1][\"image_path\"],final_m1short.iloc[value][\"image_path\"])\n",
    "    datasetCreator.display_row_images(final_m1long.iloc[value-3][\"image_path\"],final_m1long.iloc[value-2][\"image_path\"],final_m1long.iloc[value-1][\"image_path\"],final_m1long.iloc[value][\"image_path\"])\n",
    "    datasetCreator.display_row_images(final_m5long.iloc[value-3][\"image_path\"],final_m5long.iloc[value-2][\"image_path\"],final_m5long.iloc[value-1][\"image_path\"],final_m5long.iloc[value][\"image_path\"])\n",
    "    datasetCreator.display_row_images(final_m5short.iloc[value-3][\"image_path\"],final_m5short.iloc[value-2][\"image_path\"],final_m5short.iloc[value-1][\"image_path\"],final_m5short.iloc[value][\"image_path\"])\n",
    "    \n",
    "    print(f\"\\n\\nValue {key}:{value} \\n\\n\")\n",
    "    if key == 10:break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a65477dc-0e64-40c0-a042-a2f5b6a2aa63",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (54199690.py, line 417)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [187], line 417\u001b[0;36m\u001b[0m\n\u001b[0;31m    elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "class ForexEnv():\n",
    "    def __init__(self, m5long, m5short, m1long, m1short):\n",
    "        self.forex_m5long       = m5long\n",
    "        self.forex_m5short      = m5short\n",
    "        self.forex_m1long       = m1long\n",
    "        self.forex_m1short      = m1short\n",
    "        self.current_trade      = {}\n",
    "        self.position           = None\n",
    "        self.profit             = 0.0\n",
    "        self.holding            = 0\n",
    "        self.take_profit        = 50.0\n",
    "        self.stop_loss          = 50.0\n",
    "        self.stop_price         = 0.0\n",
    "        self.trailing_loss      = 0.0\n",
    "        self.lot_size           = 0.1\n",
    "        self.pip_value          = 1.5\n",
    "        self.current_step       = 126\n",
    "        self.current_end        = len(self.forex_m1short)\n",
    "        self.account_balance    = 1000.0\n",
    "        self.balance_limit      = 900\n",
    "        self.profit_limit       = -50\n",
    "        self.done               = 0\n",
    "        self.target_lookahead   = 1\n",
    "        self.current_trade      = {\n",
    "                                    \"current_price\"   : 0.0,\n",
    "                                    \"entry_price\"   : 0.0,\n",
    "                                    \"stop_loss\"     : 0.0,\n",
    "                                    \"take_profit\"   : 0.0,\n",
    "                                    \"stop_price\"    : 0.0,\n",
    "                                    \"profit\"        : 0.0,                                    \n",
    "                                    \"pnl\"           : 0.0 \n",
    "        }\n",
    "        self.account_balance_norm = MinMaxScaler(feature_range=(0, 1))\n",
    "        self.holding_norm =     MinMaxScaler(feature_range=(0, 1))\n",
    "        self.profit_norm =      MinMaxScaler(feature_range=(0, 1))\n",
    "        self.action_norm=   MinMaxScaler(feature_range=(0, 1))\n",
    "        self.total_pips = 100\n",
    "        self.init_scalers()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_trade = {\n",
    "            \"entry_price\" : 5000.0,\n",
    "            \"current_price\": 5000.0,\n",
    "            \"stop_loss\":5000.0,\n",
    "            \"take_profit\" : 100.0,\n",
    "            \"stop_price\": 5000.0,\n",
    "            \"profit\": 0.0,\n",
    "            \"pnl\":0.0\n",
    "        }\n",
    "        self.position = None\n",
    "        self.holding = 0\n",
    "        self.profit = 0.0\n",
    "        self.current_step = 4\n",
    "        self.account_balance = 1000.0\n",
    "        self.done = 0\n",
    "        \n",
    "        return self.get_observation(self.current_step - 1 , 0, 0.0,0.0,0.0)\n",
    "\n",
    "    def read_image(self, image_path):\n",
    "        directory = \"colab/dataset/episode1/\"\n",
    "        return np.asarray(Image.open(directory+image_path))/255\n",
    "    \n",
    "\n",
    "    def step(self, action, test=False):\n",
    "        reward = 0\n",
    "        self.stop_price = 0.0\n",
    "        \n",
    "        if self.position == None:\n",
    "            if action == 0:\n",
    "                self.position = \"long\"\n",
    "                self.holding = 0\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price - self.stop_loss,\n",
    "                    \"take_profit\": current_price + self.take_profit,\n",
    "                    \"stop_price\": current_price - self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0 \n",
    "                }\n",
    "                self.stop_price = 0.0\n",
    "            elif action == 1:\n",
    "                self.holding = 1\n",
    "                self.position = \"short\"\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price + self.stop_loss,\n",
    "                    \"take_profit\": current_price - self.take_profit,\n",
    "                    \"stop_price\": current_price + self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0\n",
    "                }\n",
    "                self.stop_price = current_price - self.take_profit\n",
    "            # elif action == 0:\n",
    "\n",
    "            #     self.current_trade = {\n",
    "            #         \"entry_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"current_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_loss\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"take_profit\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"profit\": 0.0,\n",
    "            #         \"pnl\": 0.0\n",
    "            #     }\n",
    "            #     self.profit = 0\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "        elif self.position == \"long\":\n",
    "            if action == 0:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0\n",
    "                    \n",
    "              \n",
    "            elif action == 1:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                try:              \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.account_balance += self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price  \n",
    "\n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )             \n",
    "            #     except:\n",
    "            #         reward = 0                 \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] =  (current_price - self.current_trade[\"entry_price\"]) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"]                 \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                               \n",
    "        elif self.position == \"short\":\n",
    "            if action == 0:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                try:               \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.account_balance += self.current_trade[\"pnl\"] \n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price\n",
    "\n",
    "            elif action == 1:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]  \n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "            #     except:\n",
    "            #         reward = 0                \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"] \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                \n",
    "        self.current_step += 1\n",
    "        if self.current_step >= self.current_end + 1:\n",
    "            self.done = 1\n",
    "        if  int(self.account_balance) < self.balance_limit or self.current_trade[\"profit\"]< self.profit_limit:\n",
    "            reward = -1\n",
    "            self.done = 1\n",
    "            \n",
    "        info = {\n",
    "            \"_step\": self.current_step,\n",
    "            \"pos\": self.position,\n",
    "            \"done\":self.done,\n",
    "            \"c_trade\": self.current_trade,\n",
    "            \"balance\": \"{:.2f}\".format(self.account_balance),\n",
    "            \"reward\": reward,\n",
    "            \"c_price\": float(self.forex_data.iloc[self.current_step][\"Bid\"]) if self.position == \"long\" else float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "        }\n",
    "        if test:\n",
    "            next_state, long_short_dict = self.observation_generator(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        else:\n",
    "            next_state,long_short_dict = self.get_observation(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        long_short_dict[\"reward\"] = reward\n",
    "        long_short_dict[\"done\"] = self.done\n",
    "        long_short_dict[\"info\"] = info\n",
    "        \n",
    "        return next_state, look_short_dict\n",
    "\n",
    "    def get_observation(self, time, action, account_balance, profit, holding):\n",
    "        #m5long\n",
    "        long_short_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        m5long_last_image         =       self.read_image(m5long_last_image_path)\n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        m5long_third_image        =       self.read_image(m5long_third_image_path)\n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        m5long_second_image       =       self.read_image(m5long_second_image_path)\n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        m5long_current_image      =       self.read_image(m5long_current_image_path)\n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        m5short_last_image         =       self.read_image(m5short_last_image_path)\n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        m5short_third_image        =       self.read_image(m5short_third_image_path)\n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        m5short_second_image       =       self.read_image(m5short_second_image_path)\n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        m5short_current_image      =       self.read_image(m5short_current_image_path)        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        m1long_last_image         =       self.read_image(m1long_last_image_path)\n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        m1long_third_image        =       self.read_image(m1long_third_image_path)\n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        m1long_second_image       =       self.read_image(m1long_second_image_path)\n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        m1long_current_image      =       self.read_image(m1long_current_image_path)\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        m1short_last_image         =       self.read_image(m1short_last_image_path)\n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        m1short_third_image        =       self.read_image(m1short_third_image_path)\n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        m1short_second_image       =       self.read_image(m1short_second_image_path)\n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        m1short_current_image      =       self.read_image(m1short_current_image_path)         \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        #create image representation for this\n",
    "        \n",
    "        \n",
    "        holding =           self.get_scaled_values(\"Holding\", holding)\n",
    "        \n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        array8 = np.broadcast_to(np.array([account_balance, profit, holding]).reshape(1,-1).reshape((1,1,-1)),(224,224,3))\n",
    "        stacked_image   =       np.stack([\n",
    "                                        m5long_last_image, m5long_third_image, m5long_second_image, m5long_current_image,\n",
    "                                        m5short_last_image, m5short_third_image, m5short_second_image, m5short_current_image,\n",
    "                                        m1long_last_image, m1long_third_image, m1long_second_image, m1long_current_image,\n",
    "                                        m1short_last_image, m1short_third_image, m1short_second_image, m1short_current_imag, earray8], axis=2)\n",
    "        \n",
    "        \n",
    "        stacked_image   =       stacked_image.transpose((2,0,1,3))\n",
    "        stacked_image   =       np.expand_dims(stacked_image, axis=0)\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        state_features = [account_balance, profit, holding, action]\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        long_short_dict[\"now\"] = time\n",
    "        long_short_dict[\"next\"] = time + 1\n",
    "       \n",
    "        return stacked_image, long_short_dict\n",
    "\n",
    "      \n",
    "\n",
    "    def observation_generator(self, time, action, account_balance, profit, holding):\n",
    "        ##Add \n",
    "        longshort_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        account_balance =   self.get_scaled_values(\"Account Balance\", self.account_balance)\n",
    "        holding =           self.get_scaled_values(\"Holding\", self.holding)\n",
    "        profit =            self.get_scaled_values(\"Profit\", profit)\n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        \n",
    "        \n",
    "        \n",
    "        long_short_dict[\"state_features\"] = [account_balance, profit, holding, action]\n",
    "\n",
    "        return [], long_short_dict\n",
    "\n",
    "    def get_scaled_values(self, key, value):\n",
    "\n",
    "        elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Holding\":        return self.holding_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Profit\":         return self.profit_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Action\":         return self.action_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]        \n",
    "   \n",
    "\n",
    "    \n",
    "    def init_scalers(self):\n",
    "        self.account_balance_norm.fit(np.array([-1000,0,10000]).reshape(-1,1))\n",
    "        self.holding_norm.fit(np.array([0,2]).reshape(-1,1))\n",
    "        self.profit_norm.fit(np.array([-300,0,300]).reshape(-1,1))\n",
    "        self.action_norm.fit(np.array([0,1]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "a41e636d-8cfd-429f-82c6-151faa2f0fde",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (54199690.py, line 417)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [187], line 417\u001b[0;36m\u001b[0m\n\u001b[0;31m    elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "class ForexEnv():\n",
    "    def __init__(self, m5long, m5short, m1long, m1short):\n",
    "        self.forex_m5long       = m5long\n",
    "        self.forex_m5short      = m5short\n",
    "        self.forex_m1long       = m1long\n",
    "        self.forex_m1short      = m1short\n",
    "        self.current_trade      = {}\n",
    "        self.position           = None\n",
    "        self.profit             = 0.0\n",
    "        self.holding            = 0\n",
    "        self.take_profit        = 50.0\n",
    "        self.stop_loss          = 50.0\n",
    "        self.stop_price         = 0.0\n",
    "        self.trailing_loss      = 0.0\n",
    "        self.lot_size           = 0.1\n",
    "        self.pip_value          = 1.5\n",
    "        self.current_step       = 126\n",
    "        self.current_end        = len(self.forex_m1short)\n",
    "        self.account_balance    = 1000.0\n",
    "        self.balance_limit      = 900\n",
    "        self.profit_limit       = -50\n",
    "        self.done               = 0\n",
    "        self.target_lookahead   = 1\n",
    "        self.current_trade      = {\n",
    "                                    \"current_price\"   : 0.0,\n",
    "                                    \"entry_price\"   : 0.0,\n",
    "                                    \"stop_loss\"     : 0.0,\n",
    "                                    \"take_profit\"   : 0.0,\n",
    "                                    \"stop_price\"    : 0.0,\n",
    "                                    \"profit\"        : 0.0,                                    \n",
    "                                    \"pnl\"           : 0.0 \n",
    "        }\n",
    "        self.account_balance_norm = MinMaxScaler(feature_range=(0, 1))\n",
    "        self.holding_norm =     MinMaxScaler(feature_range=(0, 1))\n",
    "        self.profit_norm =      MinMaxScaler(feature_range=(0, 1))\n",
    "        self.action_norm=   MinMaxScaler(feature_range=(0, 1))\n",
    "        self.total_pips = 100\n",
    "        self.init_scalers()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_trade = {\n",
    "            \"entry_price\" : 5000.0,\n",
    "            \"current_price\": 5000.0,\n",
    "            \"stop_loss\":5000.0,\n",
    "            \"take_profit\" : 100.0,\n",
    "            \"stop_price\": 5000.0,\n",
    "            \"profit\": 0.0,\n",
    "            \"pnl\":0.0\n",
    "        }\n",
    "        self.position = None\n",
    "        self.holding = 0\n",
    "        self.profit = 0.0\n",
    "        self.current_step = 4\n",
    "        self.account_balance = 1000.0\n",
    "        self.done = 0\n",
    "        \n",
    "        return self.get_observation(self.current_step - 1 , 0, 0.0,0.0,0.0)\n",
    "\n",
    "    def read_image(self, image_path):\n",
    "        directory = \"colab/dataset/episode1/\"\n",
    "        return np.asarray(Image.open(directory+image_path))/255\n",
    "    \n",
    "\n",
    "    def step(self, action, test=False):\n",
    "        reward = 0\n",
    "        self.stop_price = 0.0\n",
    "        \n",
    "        if self.position == None:\n",
    "            if action == 0:\n",
    "                self.position = \"long\"\n",
    "                self.holding = 0\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price - self.stop_loss,\n",
    "                    \"take_profit\": current_price + self.take_profit,\n",
    "                    \"stop_price\": current_price - self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0 \n",
    "                }\n",
    "                self.stop_price = 0.0\n",
    "            elif action == 1:\n",
    "                self.holding = 1\n",
    "                self.position = \"short\"\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price + self.stop_loss,\n",
    "                    \"take_profit\": current_price - self.take_profit,\n",
    "                    \"stop_price\": current_price + self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0\n",
    "                }\n",
    "                self.stop_price = current_price - self.take_profit\n",
    "            # elif action == 0:\n",
    "\n",
    "            #     self.current_trade = {\n",
    "            #         \"entry_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"current_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_loss\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"take_profit\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"profit\": 0.0,\n",
    "            #         \"pnl\": 0.0\n",
    "            #     }\n",
    "            #     self.profit = 0\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "        elif self.position == \"long\":\n",
    "            if action == 0:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0\n",
    "                    \n",
    "              \n",
    "            elif action == 1:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                try:              \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.account_balance += self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price  \n",
    "\n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )             \n",
    "            #     except:\n",
    "            #         reward = 0                 \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] =  (current_price - self.current_trade[\"entry_price\"]) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"]                 \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                               \n",
    "        elif self.position == \"short\":\n",
    "            if action == 0:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                try:               \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.account_balance += self.current_trade[\"pnl\"] \n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price\n",
    "\n",
    "            elif action == 1:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]  \n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "            #     except:\n",
    "            #         reward = 0                \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"] \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                \n",
    "        self.current_step += 1\n",
    "        if self.current_step >= self.current_end + 1:\n",
    "            self.done = 1\n",
    "        if  int(self.account_balance) < self.balance_limit or self.current_trade[\"profit\"]< self.profit_limit:\n",
    "            reward = -1\n",
    "            self.done = 1\n",
    "            \n",
    "        info = {\n",
    "            \"_step\": self.current_step,\n",
    "            \"pos\": self.position,\n",
    "            \"done\":self.done,\n",
    "            \"c_trade\": self.current_trade,\n",
    "            \"balance\": \"{:.2f}\".format(self.account_balance),\n",
    "            \"reward\": reward,\n",
    "            \"c_price\": float(self.forex_data.iloc[self.current_step][\"Bid\"]) if self.position == \"long\" else float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "        }\n",
    "        if test:\n",
    "            next_state, long_short_dict = self.observation_generator(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        else:\n",
    "            next_state,long_short_dict = self.get_observation(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        long_short_dict[\"reward\"] = reward\n",
    "        long_short_dict[\"done\"] = self.done\n",
    "        long_short_dict[\"info\"] = info\n",
    "        \n",
    "        return next_state, look_short_dict\n",
    "\n",
    "    def get_observation(self, time, action, account_balance, profit, holding):\n",
    "        #m5long\n",
    "        long_short_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        m5long_last_image         =       self.read_image(m5long_last_image_path)\n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        m5long_third_image        =       self.read_image(m5long_third_image_path)\n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        m5long_second_image       =       self.read_image(m5long_second_image_path)\n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        m5long_current_image      =       self.read_image(m5long_current_image_path)\n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        m5short_last_image         =       self.read_image(m5short_last_image_path)\n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        m5short_third_image        =       self.read_image(m5short_third_image_path)\n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        m5short_second_image       =       self.read_image(m5short_second_image_path)\n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        m5short_current_image      =       self.read_image(m5short_current_image_path)        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        m1long_last_image         =       self.read_image(m1long_last_image_path)\n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        m1long_third_image        =       self.read_image(m1long_third_image_path)\n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        m1long_second_image       =       self.read_image(m1long_second_image_path)\n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        m1long_current_image      =       self.read_image(m1long_current_image_path)\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        m1short_last_image         =       self.read_image(m1short_last_image_path)\n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        m1short_third_image        =       self.read_image(m1short_third_image_path)\n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        m1short_second_image       =       self.read_image(m1short_second_image_path)\n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        m1short_current_image      =       self.read_image(m1short_current_image_path)         \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        #create image representation for this\n",
    "        \n",
    "        \n",
    "        holding =           self.get_scaled_values(\"Holding\", holding)\n",
    "        \n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        array8 = np.broadcast_to(np.array([account_balance, profit, holding]).reshape(1,-1).reshape((1,1,-1)),(224,224,3))\n",
    "        stacked_image   =       np.stack([\n",
    "                                        m5long_last_image, m5long_third_image, m5long_second_image, m5long_current_image,\n",
    "                                        m5short_last_image, m5short_third_image, m5short_second_image, m5short_current_image,\n",
    "                                        m1long_last_image, m1long_third_image, m1long_second_image, m1long_current_image,\n",
    "                                        m1short_last_image, m1short_third_image, m1short_second_image, m1short_current_imag, earray8], axis=2)\n",
    "        \n",
    "        \n",
    "        stacked_image   =       stacked_image.transpose((2,0,1,3))\n",
    "        stacked_image   =       np.expand_dims(stacked_image, axis=0)\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        state_features = [account_balance, profit, holding, action]\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        long_short_dict[\"now\"] = time\n",
    "        long_short_dict[\"next\"] = time + 1\n",
    "       \n",
    "        return stacked_image, long_short_dict\n",
    "\n",
    "      \n",
    "\n",
    "    def observation_generator(self, time, action, account_balance, profit, holding):\n",
    "        ##Add \n",
    "        longshort_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        account_balance =   self.get_scaled_values(\"Account Balance\", self.account_balance)\n",
    "        holding =           self.get_scaled_values(\"Holding\", self.holding)\n",
    "        profit =            self.get_scaled_values(\"Profit\", profit)\n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        \n",
    "        \n",
    "        \n",
    "        long_short_dict[\"state_features\"] = [account_balance, profit, holding, action]\n",
    "\n",
    "        return [], long_short_dict\n",
    "\n",
    "    def get_scaled_values(self, key, value):\n",
    "\n",
    "        elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Holding\":        return self.holding_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Profit\":         return self.profit_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Action\":         return self.action_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]        \n",
    "   \n",
    "\n",
    "    \n",
    "    def init_scalers(self):\n",
    "        self.account_balance_norm.fit(np.array([-1000,0,10000]).reshape(-1,1))\n",
    "        self.holding_norm.fit(np.array([0,2]).reshape(-1,1))\n",
    "        self.profit_norm.fit(np.array([-300,0,300]).reshape(-1,1))\n",
    "        self.action_norm.fit(np.array([0,1]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "4ca51bc3-36c4-4843-bf2b-b982e6b4653f",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (54199690.py, line 417)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [187], line 417\u001b[0;36m\u001b[0m\n\u001b[0;31m    elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "class ForexEnv():\n",
    "    def __init__(self, m5long, m5short, m1long, m1short):\n",
    "        self.forex_m5long       = m5long\n",
    "        self.forex_m5short      = m5short\n",
    "        self.forex_m1long       = m1long\n",
    "        self.forex_m1short      = m1short\n",
    "        self.current_trade      = {}\n",
    "        self.position           = None\n",
    "        self.profit             = 0.0\n",
    "        self.holding            = 0\n",
    "        self.take_profit        = 50.0\n",
    "        self.stop_loss          = 50.0\n",
    "        self.stop_price         = 0.0\n",
    "        self.trailing_loss      = 0.0\n",
    "        self.lot_size           = 0.1\n",
    "        self.pip_value          = 1.5\n",
    "        self.current_step       = 126\n",
    "        self.current_end        = len(self.forex_m1short)\n",
    "        self.account_balance    = 1000.0\n",
    "        self.balance_limit      = 900\n",
    "        self.profit_limit       = -50\n",
    "        self.done               = 0\n",
    "        self.target_lookahead   = 1\n",
    "        self.current_trade      = {\n",
    "                                    \"current_price\"   : 0.0,\n",
    "                                    \"entry_price\"   : 0.0,\n",
    "                                    \"stop_loss\"     : 0.0,\n",
    "                                    \"take_profit\"   : 0.0,\n",
    "                                    \"stop_price\"    : 0.0,\n",
    "                                    \"profit\"        : 0.0,                                    \n",
    "                                    \"pnl\"           : 0.0 \n",
    "        }\n",
    "        self.account_balance_norm = MinMaxScaler(feature_range=(0, 1))\n",
    "        self.holding_norm =     MinMaxScaler(feature_range=(0, 1))\n",
    "        self.profit_norm =      MinMaxScaler(feature_range=(0, 1))\n",
    "        self.action_norm=   MinMaxScaler(feature_range=(0, 1))\n",
    "        self.total_pips = 100\n",
    "        self.init_scalers()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_trade = {\n",
    "            \"entry_price\" : 5000.0,\n",
    "            \"current_price\": 5000.0,\n",
    "            \"stop_loss\":5000.0,\n",
    "            \"take_profit\" : 100.0,\n",
    "            \"stop_price\": 5000.0,\n",
    "            \"profit\": 0.0,\n",
    "            \"pnl\":0.0\n",
    "        }\n",
    "        self.position = None\n",
    "        self.holding = 0\n",
    "        self.profit = 0.0\n",
    "        self.current_step = 4\n",
    "        self.account_balance = 1000.0\n",
    "        self.done = 0\n",
    "        \n",
    "        return self.get_observation(self.current_step - 1 , 0, 0.0,0.0,0.0)\n",
    "\n",
    "    def read_image(self, image_path):\n",
    "        directory = \"colab/dataset/episode1/\"\n",
    "        return np.asarray(Image.open(directory+image_path))/255\n",
    "    \n",
    "\n",
    "    def step(self, action, test=False):\n",
    "        reward = 0\n",
    "        self.stop_price = 0.0\n",
    "        \n",
    "        if self.position == None:\n",
    "            if action == 0:\n",
    "                self.position = \"long\"\n",
    "                self.holding = 0\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price - self.stop_loss,\n",
    "                    \"take_profit\": current_price + self.take_profit,\n",
    "                    \"stop_price\": current_price - self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0 \n",
    "                }\n",
    "                self.stop_price = 0.0\n",
    "            elif action == 1:\n",
    "                self.holding = 1\n",
    "                self.position = \"short\"\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price + self.stop_loss,\n",
    "                    \"take_profit\": current_price - self.take_profit,\n",
    "                    \"stop_price\": current_price + self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0\n",
    "                }\n",
    "                self.stop_price = current_price - self.take_profit\n",
    "            # elif action == 0:\n",
    "\n",
    "            #     self.current_trade = {\n",
    "            #         \"entry_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"current_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_loss\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"take_profit\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"profit\": 0.0,\n",
    "            #         \"pnl\": 0.0\n",
    "            #     }\n",
    "            #     self.profit = 0\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "        elif self.position == \"long\":\n",
    "            if action == 0:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0\n",
    "                    \n",
    "              \n",
    "            elif action == 1:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                try:              \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.account_balance += self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price  \n",
    "\n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )             \n",
    "            #     except:\n",
    "            #         reward = 0                 \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] =  (current_price - self.current_trade[\"entry_price\"]) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"]                 \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                               \n",
    "        elif self.position == \"short\":\n",
    "            if action == 0:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                try:               \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.account_balance += self.current_trade[\"pnl\"] \n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price\n",
    "\n",
    "            elif action == 1:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]  \n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "            #     except:\n",
    "            #         reward = 0                \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"] \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                \n",
    "        self.current_step += 1\n",
    "        if self.current_step >= self.current_end + 1:\n",
    "            self.done = 1\n",
    "        if  int(self.account_balance) < self.balance_limit or self.current_trade[\"profit\"]< self.profit_limit:\n",
    "            reward = -1\n",
    "            self.done = 1\n",
    "            \n",
    "        info = {\n",
    "            \"_step\": self.current_step,\n",
    "            \"pos\": self.position,\n",
    "            \"done\":self.done,\n",
    "            \"c_trade\": self.current_trade,\n",
    "            \"balance\": \"{:.2f}\".format(self.account_balance),\n",
    "            \"reward\": reward,\n",
    "            \"c_price\": float(self.forex_data.iloc[self.current_step][\"Bid\"]) if self.position == \"long\" else float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "        }\n",
    "        if test:\n",
    "            next_state, long_short_dict = self.observation_generator(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        else:\n",
    "            next_state,long_short_dict = self.get_observation(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        long_short_dict[\"reward\"] = reward\n",
    "        long_short_dict[\"done\"] = self.done\n",
    "        long_short_dict[\"info\"] = info\n",
    "        \n",
    "        return next_state, look_short_dict\n",
    "\n",
    "    def get_observation(self, time, action, account_balance, profit, holding):\n",
    "        #m5long\n",
    "        long_short_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        m5long_last_image         =       self.read_image(m5long_last_image_path)\n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        m5long_third_image        =       self.read_image(m5long_third_image_path)\n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        m5long_second_image       =       self.read_image(m5long_second_image_path)\n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        m5long_current_image      =       self.read_image(m5long_current_image_path)\n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        m5short_last_image         =       self.read_image(m5short_last_image_path)\n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        m5short_third_image        =       self.read_image(m5short_third_image_path)\n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        m5short_second_image       =       self.read_image(m5short_second_image_path)\n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        m5short_current_image      =       self.read_image(m5short_current_image_path)        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        m1long_last_image         =       self.read_image(m1long_last_image_path)\n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        m1long_third_image        =       self.read_image(m1long_third_image_path)\n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        m1long_second_image       =       self.read_image(m1long_second_image_path)\n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        m1long_current_image      =       self.read_image(m1long_current_image_path)\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        m1short_last_image         =       self.read_image(m1short_last_image_path)\n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        m1short_third_image        =       self.read_image(m1short_third_image_path)\n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        m1short_second_image       =       self.read_image(m1short_second_image_path)\n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        m1short_current_image      =       self.read_image(m1short_current_image_path)         \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        #create image representation for this\n",
    "        \n",
    "        \n",
    "        holding =           self.get_scaled_values(\"Holding\", holding)\n",
    "        \n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        array8 = np.broadcast_to(np.array([account_balance, profit, holding]).reshape(1,-1).reshape((1,1,-1)),(224,224,3))\n",
    "        stacked_image   =       np.stack([\n",
    "                                        m5long_last_image, m5long_third_image, m5long_second_image, m5long_current_image,\n",
    "                                        m5short_last_image, m5short_third_image, m5short_second_image, m5short_current_image,\n",
    "                                        m1long_last_image, m1long_third_image, m1long_second_image, m1long_current_image,\n",
    "                                        m1short_last_image, m1short_third_image, m1short_second_image, m1short_current_imag, earray8], axis=2)\n",
    "        \n",
    "        \n",
    "        stacked_image   =       stacked_image.transpose((2,0,1,3))\n",
    "        stacked_image   =       np.expand_dims(stacked_image, axis=0)\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        state_features = [account_balance, profit, holding, action]\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        long_short_dict[\"now\"] = time\n",
    "        long_short_dict[\"next\"] = time + 1\n",
    "       \n",
    "        return stacked_image, long_short_dict\n",
    "\n",
    "      \n",
    "\n",
    "    def observation_generator(self, time, action, account_balance, profit, holding):\n",
    "        ##Add \n",
    "        longshort_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        account_balance =   self.get_scaled_values(\"Account Balance\", self.account_balance)\n",
    "        holding =           self.get_scaled_values(\"Holding\", self.holding)\n",
    "        profit =            self.get_scaled_values(\"Profit\", profit)\n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        \n",
    "        \n",
    "        \n",
    "        long_short_dict[\"state_features\"] = [account_balance, profit, holding, action]\n",
    "\n",
    "        return [], long_short_dict\n",
    "\n",
    "    def get_scaled_values(self, key, value):\n",
    "\n",
    "        elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Holding\":        return self.holding_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Profit\":         return self.profit_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Action\":         return self.action_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]        \n",
    "   \n",
    "\n",
    "    \n",
    "    def init_scalers(self):\n",
    "        self.account_balance_norm.fit(np.array([-1000,0,10000]).reshape(-1,1))\n",
    "        self.holding_norm.fit(np.array([0,2]).reshape(-1,1))\n",
    "        self.profit_norm.fit(np.array([-300,0,300]).reshape(-1,1))\n",
    "        self.action_norm.fit(np.array([0,1]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "ce6ea1b8-e5ab-43a3-9b0c-3ff270888c1b",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (54199690.py, line 417)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn [187], line 417\u001b[0;36m\u001b[0m\n\u001b[0;31m    elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "#@title\n",
    "class ForexEnv():\n",
    "    def __init__(self, m5long, m5short, m1long, m1short):\n",
    "        self.forex_m5long       = m5long\n",
    "        self.forex_m5short      = m5short\n",
    "        self.forex_m1long       = m1long\n",
    "        self.forex_m1short      = m1short\n",
    "        self.current_trade      = {}\n",
    "        self.position           = None\n",
    "        self.profit             = 0.0\n",
    "        self.holding            = 0\n",
    "        self.take_profit        = 50.0\n",
    "        self.stop_loss          = 50.0\n",
    "        self.stop_price         = 0.0\n",
    "        self.trailing_loss      = 0.0\n",
    "        self.lot_size           = 0.1\n",
    "        self.pip_value          = 1.5\n",
    "        self.current_step       = 126\n",
    "        self.current_end        = len(self.forex_m1short)\n",
    "        self.account_balance    = 1000.0\n",
    "        self.balance_limit      = 900\n",
    "        self.profit_limit       = -50\n",
    "        self.done               = 0\n",
    "        self.target_lookahead   = 1\n",
    "        self.current_trade      = {\n",
    "                                    \"current_price\"   : 0.0,\n",
    "                                    \"entry_price\"   : 0.0,\n",
    "                                    \"stop_loss\"     : 0.0,\n",
    "                                    \"take_profit\"   : 0.0,\n",
    "                                    \"stop_price\"    : 0.0,\n",
    "                                    \"profit\"        : 0.0,                                    \n",
    "                                    \"pnl\"           : 0.0 \n",
    "        }\n",
    "        self.account_balance_norm = MinMaxScaler(feature_range=(0, 1))\n",
    "        self.holding_norm =     MinMaxScaler(feature_range=(0, 1))\n",
    "        self.profit_norm =      MinMaxScaler(feature_range=(0, 1))\n",
    "        self.action_norm=   MinMaxScaler(feature_range=(0, 1))\n",
    "        self.total_pips = 100\n",
    "        self.init_scalers()\n",
    "        \n",
    "    def reset(self):\n",
    "        self.current_trade = {\n",
    "            \"entry_price\" : 5000.0,\n",
    "            \"current_price\": 5000.0,\n",
    "            \"stop_loss\":5000.0,\n",
    "            \"take_profit\" : 100.0,\n",
    "            \"stop_price\": 5000.0,\n",
    "            \"profit\": 0.0,\n",
    "            \"pnl\":0.0\n",
    "        }\n",
    "        self.position = None\n",
    "        self.holding = 0\n",
    "        self.profit = 0.0\n",
    "        self.current_step = 4\n",
    "        self.account_balance = 1000.0\n",
    "        self.done = 0\n",
    "        \n",
    "        return self.get_observation(self.current_step - 1 , 0, 0.0,0.0,0.0)\n",
    "\n",
    "    def read_image(self, image_path):\n",
    "        directory = \"colab/dataset/episode1/\"\n",
    "        return np.asarray(Image.open(directory+image_path))/255\n",
    "    \n",
    "\n",
    "    def step(self, action, test=False):\n",
    "        reward = 0\n",
    "        self.stop_price = 0.0\n",
    "        \n",
    "        if self.position == None:\n",
    "            if action == 0:\n",
    "                self.position = \"long\"\n",
    "                self.holding = 0\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price - self.stop_loss,\n",
    "                    \"take_profit\": current_price + self.take_profit,\n",
    "                    \"stop_price\": current_price - self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0 \n",
    "                }\n",
    "                self.stop_price = 0.0\n",
    "            elif action == 1:\n",
    "                self.holding = 1\n",
    "                self.position = \"short\"\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade = {\n",
    "                    \"entry_price\": current_price,\n",
    "                    \"current_price\": current_price,\n",
    "                    \"stop_loss\": current_price + self.stop_loss,\n",
    "                    \"take_profit\": current_price - self.take_profit,\n",
    "                    \"stop_price\": current_price + self.stop_loss,\n",
    "                    \"profit\": 0.0,\n",
    "                    \"pnl\": 0\n",
    "                }\n",
    "                self.stop_price = current_price - self.take_profit\n",
    "            # elif action == 0:\n",
    "\n",
    "            #     self.current_trade = {\n",
    "            #         \"entry_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"current_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_loss\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"take_profit\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"stop_price\": float(self.forex_data.iloc[self.current_step][\"Ask\"]),\n",
    "            #         \"profit\": 0.0,\n",
    "            #         \"pnl\": 0.0\n",
    "            #     }\n",
    "            #     self.profit = 0\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "        elif self.position == \"long\":\n",
    "            if action == 0:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0\n",
    "                    \n",
    "              \n",
    "            elif action == 1:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "                try:              \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"pnl\"] = (current_price - self.current_trade[\"entry_price\"]) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.account_balance += self.current_trade[\"pnl\"]\n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price  \n",
    "\n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Bid\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = (current_price - self.current_trade[\"entry_price\"]) / (self.current_trade[\"take_profit\"] - self.current_trade[\"entry_price\"])\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = -((self.current_trade[\"take_profit\"] - current_price )/(self.current_trade[\"take_profit\"] - self.current_trade[\"stop_loss\"]) )             \n",
    "            #     except:\n",
    "            #         reward = 0                 \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] =  (current_price - self.current_trade[\"entry_price\"]) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"]                 \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                               \n",
    "        elif self.position == \"short\":\n",
    "            if action == 0:\n",
    "                self.position = None\n",
    "                self.holding = 2\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                try:               \n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0                \n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "                self.account_balance += self.current_trade[\"pnl\"] \n",
    "                self.current_trade[\"stop_price\"] = current_price\n",
    "                self.current_trade[\"stop_loss\"] = current_price \n",
    "                self.current_trade[\"take_profit\"] = current_price\n",
    "\n",
    "            elif action == 1:\n",
    "                current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "                self.current_trade[\"current_price\"] = current_price\n",
    "                try:\n",
    "                    if current_price > self.current_trade[\"entry_price\"]:\n",
    "                        reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "                    elif self.current_trade[\"entry_price\"] > current_price:\n",
    "                        reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "                except:\n",
    "                    reward = 0\n",
    "                self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "                self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]  \n",
    "\n",
    "            # elif action == 0:\n",
    "            #     self.position = None\n",
    "            #     self.holding = 0\n",
    "            #     current_price = float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "            #     try:\n",
    "            #         if current_price > self.current_trade[\"entry_price\"]:\n",
    "            #             reward = - (abs(current_price - self.current_trade[\"take_profit\"]) / abs(self.current_trade[\"stop_loss\"]-self.current_trade[\"take_profit\"]))\n",
    "            #         elif self.current_trade[\"entry_price\"] > current_price:\n",
    "            #             reward = (current_price-self.current_trade[\"entry_price\"]) / (self.current_trade[\"entry_price\"]-self.current_trade[\"stop_loss\"])\n",
    "            #     except:\n",
    "            #         reward = 0                \n",
    "            #     self.current_trade[\"current_price\"] = current_price\n",
    "            #     self.current_trade[\"pnl\"] = (self.current_trade[\"entry_price\"] - current_price) \n",
    "            #     self.current_trade[\"profit\"] = self.current_trade[\"pnl\"]\n",
    "            #     self.account_balance += self.current_trade[\"pnl\"] \n",
    "\n",
    "            #     self.current_trade[\"stop_loss\"] = current_price \n",
    "            #     self.current_trade[\"take_profit\"] = current_price\n",
    "            #     self.current_trade[\"stop_price\"] = current_price                \n",
    "        self.current_step += 1\n",
    "        if self.current_step >= self.current_end + 1:\n",
    "            self.done = 1\n",
    "        if  int(self.account_balance) < self.balance_limit or self.current_trade[\"profit\"]< self.profit_limit:\n",
    "            reward = -1\n",
    "            self.done = 1\n",
    "            \n",
    "        info = {\n",
    "            \"_step\": self.current_step,\n",
    "            \"pos\": self.position,\n",
    "            \"done\":self.done,\n",
    "            \"c_trade\": self.current_trade,\n",
    "            \"balance\": \"{:.2f}\".format(self.account_balance),\n",
    "            \"reward\": reward,\n",
    "            \"c_price\": float(self.forex_data.iloc[self.current_step][\"Bid\"]) if self.position == \"long\" else float(self.forex_data.iloc[self.current_step][\"Ask\"])\n",
    "        }\n",
    "        if test:\n",
    "            next_state, long_short_dict = self.observation_generator(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        else:\n",
    "            next_state,long_short_dict = self.get_observation(self.current_step + self.target_lookahead, self.get_scaled_values(\"Action\", action),self.get_scaled_values(\"Account Balance\", self.account_balance), self.get_scaled_values(\"Profit\", self.current_trade[\"profit\"]),self.get_scaled_values(\"Holding\", self.holding))\n",
    "        long_short_dict[\"reward\"] = reward\n",
    "        long_short_dict[\"done\"] = self.done\n",
    "        long_short_dict[\"info\"] = info\n",
    "        \n",
    "        return next_state, look_short_dict\n",
    "\n",
    "    def get_observation(self, time, action, account_balance, profit, holding):\n",
    "        #m5long\n",
    "        long_short_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        m5long_last_image         =       self.read_image(m5long_last_image_path)\n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        m5long_third_image        =       self.read_image(m5long_third_image_path)\n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        m5long_second_image       =       self.read_image(m5long_second_image_path)\n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        m5long_current_image      =       self.read_image(m5long_current_image_path)\n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        m5short_last_image         =       self.read_image(m5short_last_image_path)\n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        m5short_third_image        =       self.read_image(m5short_third_image_path)\n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        m5short_second_image       =       self.read_image(m5short_second_image_path)\n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        m5short_current_image      =       self.read_image(m5short_current_image_path)        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        m1long_last_image         =       self.read_image(m1long_last_image_path)\n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        m1long_third_image        =       self.read_image(m1long_third_image_path)\n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        m1long_second_image       =       self.read_image(m1long_second_image_path)\n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        m1long_current_image      =       self.read_image(m1long_current_image_path)\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        m1short_last_image         =       self.read_image(m1short_last_image_path)\n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        m1short_third_image        =       self.read_image(m1short_third_image_path)\n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        m1short_second_image       =       self.read_image(m1short_second_image_path)\n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        m1short_current_image      =       self.read_image(m1short_current_image_path)         \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        #create image representation for this\n",
    "        \n",
    "        \n",
    "        holding =           self.get_scaled_values(\"Holding\", holding)\n",
    "        \n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        array8 = np.broadcast_to(np.array([account_balance, profit, holding]).reshape(1,-1).reshape((1,1,-1)),(224,224,3))\n",
    "        stacked_image   =       np.stack([\n",
    "                                        m5long_last_image, m5long_third_image, m5long_second_image, m5long_current_image,\n",
    "                                        m5short_last_image, m5short_third_image, m5short_second_image, m5short_current_image,\n",
    "                                        m1long_last_image, m1long_third_image, m1long_second_image, m1long_current_image,\n",
    "                                        m1short_last_image, m1short_third_image, m1short_second_image, m1short_current_imag, earray8], axis=2)\n",
    "        \n",
    "        \n",
    "        stacked_image   =       stacked_image.transpose((2,0,1,3))\n",
    "        stacked_image   =       np.expand_dims(stacked_image, axis=0)\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        state_features = [account_balance, profit, holding, action]\n",
    "        long_short_dict[\"state_features\"] = state_features\n",
    "        long_short_dict[\"now\"] = time\n",
    "        long_short_dict[\"next\"] = time + 1\n",
    "       \n",
    "        return stacked_image, long_short_dict\n",
    "\n",
    "      \n",
    "\n",
    "    def observation_generator(self, time, action, account_balance, profit, holding):\n",
    "        ##Add \n",
    "        longshort_dict = {}\n",
    "        \n",
    "        m5long_last_image_path    =       self.forex_m5long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5long_third_image_path   =       self.forex_m5long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5long_second_image_path  =       self.forex_m5long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5long_current_image_path =       self.forex_m5long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5long_last_image_path\"] = m5long_last_image_path\n",
    "        long_short_dict[\"m5long_third_image_path\"] = m5long_third_image_path\n",
    "        long_short_dict[\"m5long_second_image_path\"] = m5long_second_image_path\n",
    "        long_short_dict[\"m5long_current_image_path\"] = m5long_current_image_path\n",
    "        \n",
    "        \n",
    "        #m5short\n",
    "        m5short_last_image_path    =       self.forex_m5short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m5short_third_image_path   =       self.forex_m5short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m5short_second_image_path  =       self.forex_m5short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m5short_current_image_path =       self.forex_m5short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m5short_last_image_path\"] = m5short_last_image_path\n",
    "        long_short_dict[\"m5short_third_image_path\"] = m5short_third_image_path\n",
    "        long_short_dict[\"m5short_second_image_path\"] = m5short_second_image_path\n",
    "        long_short_dict[\"m5short_current_image_path\"] = m5short_current_image_path\n",
    "        \n",
    "        \n",
    "        #m1long\n",
    "        m1long_last_image_path    =       self.forex_m1long.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1long_third_image_path   =       self.forex_m1long.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1long_second_image_path  =       self.forex_m1long.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1long_current_image_path =       self.forex_m1long.iloc[time][\"image_path\"]\n",
    "        \n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1long_last_image_path\"] = m1long_last_image_path\n",
    "        long_short_dict[\"m1long_third_image_path\"] = m1long_third_image_path\n",
    "        long_short_dict[\"m1long_second_image_path\"] = m1long_second_image_path\n",
    "        long_short_dict[\"m1long_current_image_path\"] = m1long_current_image_path        \n",
    "        \n",
    "        #m1short\n",
    "        m1short_last_image_path    =       self.forex_m1short.iloc[time-3][\"image_path\"]\n",
    "        \n",
    "        m1short_third_image_path   =       self.forex_m1short.iloc[time-2][\"image_path\"]\n",
    "        \n",
    "        m1short_second_image_path  =       self.forex_m1short.iloc[time-1][\"image_path\"]\n",
    "        \n",
    "        m1short_current_image_path =       self.forex_m1short.iloc[time][\"image_path\"]\n",
    "        \n",
    "        #save\n",
    "        long_short_dict[\"m1short_last_image_path\"] = m1short_last_image_path\n",
    "        long_short_dict[\"m1short_third_image_path\"] = m1short_third_image_path\n",
    "        long_short_dict[\"m1short_second_image_path\"] = m1short_second_image_path\n",
    "        long_short_dict[\"m1short_current_image_path\"] = m1short_current_image_path   \n",
    "        \n",
    "        account_balance =   self.get_scaled_values(\"Account Balance\", self.account_balance)\n",
    "        holding =           self.get_scaled_values(\"Holding\", self.holding)\n",
    "        profit =            self.get_scaled_values(\"Profit\", profit)\n",
    "        action =            self.get_scaled_values(\"Action\", action)\n",
    "        \n",
    "        \n",
    "        \n",
    "        long_short_dict[\"state_features\"] = [account_balance, profit, holding, action]\n",
    "\n",
    "        return [], long_short_dict\n",
    "\n",
    "    def get_scaled_values(self, key, value):\n",
    "\n",
    "        elif key == \"Account Balance\":return self.account_balance_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Holding\":        return self.holding_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Profit\":         return self.profit_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]\n",
    "        elif key == \"Action\":         return self.action_norm.transform(np.array(float(value)).reshape(-1,1))[0][0]        \n",
    "   \n",
    "\n",
    "    \n",
    "    def init_scalers(self):\n",
    "        self.account_balance_norm.fit(np.array([-1000,0,10000]).reshape(-1,1))\n",
    "        self.holding_norm.fit(np.array([0,2]).reshape(-1,1))\n",
    "        self.profit_norm.fit(np.array([-300,0,300]).reshape(-1,1))\n",
    "        self.action_norm.fit(np.array([0,1]).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "8c0fbf8d-560d-422e-b773-35d96d6bf08d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " DatasetCreator.ipynb   test\n",
      " env\t\t       'Volatility 100 Index_M1_115823311.csv'\n",
      " filerenamer.sh        'Volatility 100 Index_M1_-1994977658.csv'\n",
      " M1M5Charts\t       'Volatility 100 Index_M5_159533415.csv'\n",
      " M1M5Charts.7z\t       'Volatility 100 Index_M5_2052042313.csv'\n",
      " spacefilecreator.sh\n"
     ]
    }
   ],
   "source": [
    "env = ForexEvn(final_m5long, final_m5short, final_m1long, final_m1short)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7c01dba-9d0d-4c12-b478-b749d27b33a2",
   "metadata": {},
   "source": [
    "# AGENT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e936cbeb-6701-4e2e-958c-b014bb1becc9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class Agent:\n",
    "    def __init__(self, state_size, action_size, env):\n",
    "        self.model_name = \"v19Stacked3DModel_2states_v1.0\"\n",
    "        self.database_normal = \"v19Stacked3Database_v1.0\"\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.gamma = 0.99\n",
    "        self.alpha = 0.001\n",
    "        self.epsilon = 1.0\n",
    "        self.epsilon_min = 0.1\n",
    "        self.epsilon_decay = 0.995\n",
    "        self.update_rate = 1000\n",
    "        self.timesteps = 1\n",
    "        self.target_update_rate = 0.06\n",
    "        \n",
    "        # self.actor_model = self._build_actor_model()\n",
    "        # self.critic_model = self._build_critic_model()\n",
    "        # self.memory_indexer = 0\n",
    "        \n",
    "        # #Initialize the target models with sam\n",
    "        # self.target_actor_model = self._build_actor_model()\n",
    "        # self.target_critic_model = self._build_critic_model()\n",
    "        # self.target_critic_model.set_weights(self.critic_model.get_weights())\n",
    "        self.env = env\n",
    "        self.remote = False\n",
    "        self.__setup__()\n",
    "    \n",
    "    def __setup__(self, tables = [\"memory\", \"specialmemory\",]):\n",
    "        for t in tables:\n",
    "            self.connect()\n",
    "            self.create_tables(t)\n",
    "            print(f\"Created {t} table\")\n",
    "        self.shutdown()\n",
    "        print(f\"Closing database\")\n",
    "        time.sleep(5)\n",
    "\n",
    "    def create_tables(self, table_name):\n",
    "        #to do add primary key to this table\n",
    "        if table_name == \"history\":\n",
    "            self.cursor.execute(f'''CREATE TABLE IF NOT EXISTS {table_name} (\n",
    "                id PRIMARY KEY AUTOINCREMENT,\n",
    "                date DATE,\n",
    "                name TEXT,\n",
    "                number INTEGER\n",
    "                )''')\n",
    "        else:\n",
    "            self.cursor.execute(f'''CREATE TABLE IF NOT EXISTS {table_name} (\n",
    "                id PRIMARY KEY AUTOINCREMENT,\n",
    "                long_short_dict TETX,\n",
    "                now INTEGER,\n",
    "                next INTEGER,            \n",
    "                count INTEGER,\n",
    "                priority INTEGER\n",
    "                )''')\n",
    "        self.connection.commit()\n",
    "\n",
    "\n",
    "    def connect(self, ):\n",
    "        #remote\n",
    "        if self.remote:\n",
    "            self.connection = sqlite3.connect(f\"/content/drive/MyDrive/MLDatasetsVGG/colab/{self.database}.db\")\n",
    "        #local\n",
    "        else:\n",
    "            self.connection = sqlite3.connect(f\"colab/{self.database}.db\")\n",
    "        self.cursor = self.connection.cursor()\n",
    "    def shutdown(self):\n",
    "        self.cursor.close()\n",
    "        self.connection.close()\n",
    "\n",
    "    def _build_base_model(self):\n",
    "        model = Sequential()\n",
    "        model.add(tf.keras.layers.Conv3D(32, (3,3,3), strides=(1,4,4), padding=\"same\", input_shape=self.state_size))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(tf.keras.layers.Conv3D(64, (3,3,3), strides=(1,2,2), padding=\"same\"))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(tf.keras.layers.Conv3D(64, (3,3,3), strides=(1,1,,1), padding=\"same\"))\n",
    "        model.add(Activation('relu'))\n",
    "        model.add(Flatten())\n",
    "        model.add(Dense(256, activation='relu'))\n",
    "        model.add(Dense(128, activation='relu'))\n",
    "        model.add(Dense(64, activation='relu'))\n",
    "        model.add(Dense(32, activation='relu'))\n",
    "        \n",
    "        return model\n",
    "    # Build the actor model responsible for predicting\n",
    "    \n",
    "    def _build_actor_model(self):\n",
    "        model = _build_base_model()\n",
    "        model.add(Dense(self.action_size, activation='softmax'))\n",
    "        model.compile(loss='categorical_crossentropy', optimizer=RMSprop(learning_rate=self.alpha, rho=0.95, epsilon=None))\n",
    "\n",
    "        return model\n",
    "\n",
    "    \n",
    "    #Build the critic model responsible for criticising and calculating the risk of the reward\n",
    "    def _build_critic_model(self):\n",
    "        model = _build_base_model()\n",
    "        model.add(Dense(1, activation='linear'))\n",
    "        model.compile(loss='mse', optimizer=RMSprop(learning_rate=self.alpha, rho=0.95, epsilon=None))\n",
    "        \n",
    "        return model\n",
    "\n",
    "    def replay_v3(self, batch_size = 128, memory_type = \"normal\" ):\n",
    "        update_weights = 0\n",
    "        break_while = True\n",
    "        while(break_while):\n",
    "            # try:\n",
    "            print(\"dataset Generator\", self.memory_indexer)\n",
    "            self.dataset_generator(save=True,show_images=False, clear_output = True)\n",
    "            if self.memory_indexer % 2 == 0  :#and self.memory_indexer % 2 == 0:                    \n",
    "                self.connect()\n",
    "                self.cursor.execute(f\"SELECT * FROM memory WHERE action = 0 ORDER BY RANDOM() LIMIT {batch_size*2}\")\n",
    "                rows = self.cursor.fetchall()\n",
    "                self.cursor.execute(f\"SELECT * FROM memory WHERE action = 1 ORDER BY RANDOM() LIMIT {batch_size*2}\")\n",
    "                rows += self.cursor.fetchall()\n",
    "                self.cursor.execute(f\"SELECT * FROM specialmemory WHERE action = 1 ORDER BY RANDOM() LIMIT {batch_size*2}\")\n",
    "                rows += self.cursor.fetchall()\n",
    "                self.cursor.execute(f\"SELECT * FROM specialmemory WHERE action = 0 ORDER BY RANDOM() LIMIT {batch_size*2}\")\n",
    "                rows += self.cursor.fetchall()\n",
    "                self.shutdown()\n",
    "\n",
    "            random.shuffle(rows)\n",
    "            for i,row in enumerate(rows):            \n",
    "                import json\n",
    "                long_short_dict_now = json.loads(row[1])\n",
    "                long_short_dict_next = json.loads(row[2])\n",
    "                state , _ = self.env.get_observation(row[3], long_short_dict_now[\"action\"], long_short_dict_now[\"account_balance\"], long_short_dict_now[\"profit\"], long_short_dict_now[\"holding\"]), \n",
    "                next_state, _ = self.env.get_observation(row[4], long_short_dict_next[\"action\"], long_short_dict_next[\"account_balance\"], long_short_dict_next[\"profit\"], long_short_dict_next[\"holding\"]), #np.expand_dims(current_stacked, axis=0)\n",
    "                \n",
    "                reward = long_short_dict_now[\"reward\"]\n",
    "                done = long_short_dict_now[\"done\"]\n",
    "                target = reward\n",
    "                action = long_short_dict_now[\"action\"]\n",
    "\n",
    "                headers = [\"index\",\"Variations\", \"Probs\" ,\"rewards\", \"Actions\"]\n",
    "                rows_display = []            \n",
    "                if not done:\n",
    "                    target = (reward + self.gamma * self.target_critic_model.predict(next_state, verbose=0)[0][0])\n",
    "                target_f = self.actor_model.predict(state)\n",
    "                row = [i, \"target_f\", \",\".join(str(\"{:.2f}\".format(x)) for x in list(target_f[0])), target]\n",
    "                rows_display.append(row)\n",
    "                target_f[0][action] = target\n",
    "                row = [i, \"suggested\",  \",\".join(str(\"{:.2f}\".format(x)) for x in list(target_f[0])), action]\n",
    "                rows_display.append(row)\n",
    "                \n",
    "                #You need to test after every 64 epochs\n",
    "                #test an episode with a certain number of lists\n",
    "                if i % 64 == 0:\n",
    "                    self.dataset_generator(save=False,show_images=True, clear_output = False)\n",
    "\n",
    "                self.actor_model.fit(state, target_f, epochs=2, verbose=1)\n",
    "                target_critic = np.array([[target]])\n",
    "                self.critic_model.fit(state, target_critic, epochs=2, verbose=1)\n",
    "\n",
    "                row = [i, \"final\", \",\".join(str(\"{:.2f}\".format(x)) for x in list(self.actor_model.predict(state, verbose=0)[0])), self.critic_model.predict(state, verbose=0)[0]]\n",
    "                rows_display.append(row)\n",
    "\n",
    "                print(tabulate(rows_display,headers=headers, tablefmt='grid'))\n",
    "\n",
    "            if update_weights % 900 == 0 and update_weights > 0:\n",
    "                online_critic_weights = self.critic_model.get_weights()\n",
    "                target_critic_weights = self.target_critic_model.get_weights()\n",
    "                \n",
    "                for i in range(len(target_critic_weights)):\n",
    "                    target_critic_weights[i] = self.target_update_rate * online_critic_weights[i] + (1 - self.target_update_rate) * target_critic_weights[i]\n",
    "                self.target_critic_model.set_weights(target_critic_weights)\n",
    "                self.connect()\n",
    "                self.cursor.execute(\"INSERT INTO history VALUES ( ?, ?, ?)\",tuple([datetime('now'), self.model_name, self.memory_indexer]))\n",
    "                self.connection.commit()\n",
    "                self.shutdown()\n",
    "                self.save()\n",
    "                print(\"\\n\\n\\nModel Weights Updated\\n\\n\\n\")\n",
    "\n",
    "            update_weights += 1\n",
    "            self.memory_indexer += 1\n",
    "            clear_output(wait=True)\n",
    "            except KeyboardInterrupt:\n",
    "                self.shutdown()\n",
    "                self.save()\n",
    "                time.sleep(10)\n",
    "                break_while = False\n",
    "                rows=[]\n",
    "                state_values=[]\n",
    "                nextstate_values=[]\n",
    "                state = []\n",
    "                next_state=[]\n",
    "            #     clear_output(wait=True)\n",
    "            except Exception as e:\n",
    "                print(f\"\\nException in Replay : \\n {e} \\n\") \n",
    "                self.shutdown()\n",
    "                self.save()\n",
    "                time.sleep(10)\n",
    "                break_while = False\n",
    "                rows=[]\n",
    "                state_values=[]\n",
    "                nextstate_values=[]\n",
    "                state = []\n",
    "                next_state=[]\n",
    "                # clear_output(wait=True)\n",
    "            \n",
    "                  \n",
    "    def remember_v3(self, long_short_dict_now,long_short_dict_next,count = 0, priority=0):\n",
    "        row = []\n",
    "        row.append(json.dumps(long_short_dict_now))\n",
    "        row.append(json.dumps(long_short_dict_next))\n",
    "        \n",
    "        row.append(long_short_dict[\"now\"])\n",
    "        row.append(long_short_dict[\"next\"])\n",
    "        row.append(count)\n",
    "        row.append(priority)\n",
    "        self.connect()\n",
    "        if reward > 0.01 or (reward > 0.01 and action == 0) :\n",
    "            self.cursor.execute(\"INSERT INTO specialmemory VALUES ( ?, ?, ?, ?, ?, ? )\",tuple(row))\n",
    "            self.connection.commit()\n",
    "        self.cursor.execute(\"INSERT INTO memory VALUES ( ?, ?, ?, ?, ?, ? )\",tuple(row))\n",
    "        self.connection.commit()\n",
    "        self.shutdown()\n",
    "        \n",
    "    def act(self, state):\n",
    "        if np.random.rand() <= self.epsilon:\n",
    "            return secrets.randbelow(self.action_size) if np.random.choice(20) % 2 == 0 else np.random.choice(self.action_size)\n",
    "        else:\n",
    "            return np.argmax(self.actor_model.predict(state, verbose=0)[0])    \n",
    "   \n",
    "    def predict(self, state):\n",
    "        return self.actor_model.predict(state, verbose=0)\n",
    "\n",
    "    # Load the models\n",
    "    def load(self):\n",
    "        self.actor_model.load_weights(\"/content/drive/MyDrive/MLDatasetsVGG/colab/memories/\"+ self.model_name + str(self.memory_indexer) + \"_actor.h5\")\n",
    "        self.critic_model.load_weights(\"/content/drive/MyDrive/MLDatasetsVGG/colab/memories/\"+ self.model_name + str(self.memory_indexer) + \"_critic.h5\")\n",
    "        self.target_critic_model.set_weights(self.critic_model.get_weights())\n",
    "    \n",
    "    # Save the models\n",
    "    def save(self):\n",
    "        # self.actor_model.save_weights(\"colab/memories/\"+ name + \"_actor.h5\")\n",
    "        self.actor_model.save_weights(\"/content/drive/MyDrive/MLDatasetsVGG/colab/memories/\"+ self.model_name + str(self.memory_indexer) + \"_actor.h5\")\n",
    "\n",
    "        # self.critic_model.save_weights(\"colab/memories/\"+ self.model_name + self.memory_indexer + \"_critic.h5\")\n",
    "        self.critic_model.save_weights(\"/content/drive/MyDrive/MLDatasetsVGG/colab/memories/\"+ self.model_name + str(self.memory_indexer)+ \"_critic.h5\")\n",
    "\n",
    "        \n",
    "    def read_image(self, image_path):\n",
    "        return np.asarray(Image.open(image_path))/255\n",
    "    '''\n",
    "        this function generates random environment noise for the ai\n",
    "        it takes save parameter if you want the random data to be saved or not. \n",
    "        also this function can run the environment to see how the ai behaves. It can print out how the learning is progressing\n",
    "    '''\n",
    "    def dataset_generator(self, save = False, show_image=False, show_info=True, clear_output=False, start=30, end=300, step = 30):\n",
    "        print(\"Database generator\")\n",
    "        episode = 0\n",
    "        for epoch in list(range(start, end, step))\n",
    "            state, long_short_dict_now = self.env.reset()\n",
    "            headers = [\"index\",\"Action\",\"epsilon\",\"Pos\",\"done\",\"reward\",\"E Price\",\"C Price\", \"takep\",\"stopLoss\",\"profit\" ,\"balance\",\"pred\"]\n",
    "            break_while = True\n",
    "            memo = 0\n",
    "            while(break_while):\n",
    "                # try:\n",
    "                self.env.current_step = 4\n",
    "                self.env.current_end = 1228\n",
    "                for index in list(range(4,1228, 1)):\n",
    "                    if index % 1 == 0:\n",
    "                        rows = [] \n",
    "                        row = []    \n",
    "                        action, pred = agent.act(state)\n",
    "                        next_state, long_short_dict_next = self.env.step(action)\n",
    "                        if save:\n",
    "                            self.remember_v3(long_short_dict_now , long_short_dict_next)                    \n",
    "                        state = next_state\n",
    "                        if show_image:\n",
    "                            self.display_row_images(long_short_dict_now, long_short_dict_next)\n",
    "                        long_short_dict_now = long_short_dict_next\n",
    "                        row = [index,action,agent.epsilon,long_short_dict_now[\"info\"][\"pos\"], long_short_dict_now[\"done\"], \"{:.2f}\".format(reward), long_short_dict_now[\"c_trade\"][\"entry_price\"],long_short_dict_now[\"c_trade\"][\"current_price\"],long_short_dict_now[\"c_trade\"][\"take_profit\"],long_short_dict_now[\"c_trade\"][\"stop_loss\"], long_short_dict_now[\"c_trade\"][\"profit\"],long_short_dict_now[\"balance\"], \",\".join(str(\"{:.2f}\".format(x)) for x in list(pred[0]))]\n",
    "                        row_totals = [\"_step\",long_short_dict_now[\"_step\"]-1,\"\",\"\",\"\",\"Total\",5 * long_short_dict_now[\"c_trade\"][\"profit\"],\"\",\"\"]\n",
    "                        rows.append(row)\n",
    "                        rows.append(row_totals)\n",
    "                        if show_image:\n",
    "                            self.display_row_images(long_short_dict_now, long_short_dict_next)\n",
    "                        if long_short_dict_now[\"profit\"] < -start:\n",
    "                            print(\"Episode {} \".format(episode))\n",
    "                        if show_info:\n",
    "                            print(tabulate(rows, headers=headers, tablefmt='grid'))\n",
    "                        if long_short_dict_now[\"done\"] == 1 or index > 1227:\n",
    "                            episode += 1\n",
    "                            done=0\n",
    "                            print(\"Episode {} \".format(episode))\n",
    "                            state, long_short_dict_now = env.reset()\n",
    "                            break_while = False\n",
    "                            clear_output(wait=True)\n",
    "                            break\n",
    "                if clear_output:\n",
    "                    clear_output(wait=True)\n",
    "    def display_row_images(self,long_short_dict_now , long_short_dict_next):\n",
    "        \n",
    "        imageq = Image.open(f\"colab/dataset/episode2/{long_short_dict_now[\"m1short_image_path\"]}\")\n",
    "        imaget = Image.open(f\"colab/dataset/episode2/{long_short_dict_now[\"m5short_image_path\"]}\")\n",
    "        images = Image.open(f\"colab/dataset/episode2/{long_short_dict_now[\"m1long_image_path\"]}\")\n",
    "        imagec = Image.open(f\"colab/dataset/episode2/{long_short_dict_now[\"m5short_image_path\"]}\")\n",
    "        # Calculate the required dimensions for the new image\n",
    "        new_width = imageq.width * 4\n",
    "        new_height = imageq.height\n",
    "        # Create a new image with the required dimensions\n",
    "        new_image = Image.new(\"RGB\", (new_width, new_height))\n",
    "        # Paste the individual images side by side\n",
    "        new_image.paste(imageq, (0, 0))\n",
    "        new_image.paste(imaget, (imageq.width, 0))\n",
    "        new_image.paste(images, (imageq.width + imaget.width, 0))\n",
    "        new_image.paste(imagec, (imageq.width + imaget.width + images.width, 0))\n",
    "        # Display the final image\n",
    "        display(new_image) \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "793240e9-fd58-465b-800c-fbcb6594865c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE IF NOT EXISTS memory (\n",
      "                            m5long_last_image_path TEXT, memory)\n"
     ]
    }
   ],
   "source": [
    "#To do date in replay \n",
    "#to do date in history database model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "19736d98-3f1d-46b5-966d-a7cae8e87b21",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "final_m5long.to_csv(\"final_m5long.csv\", index=False)\n",
    "final_m5short.to_csv(\"final_m5short.csv\", index=False)\n",
    "final_m1long.to_csv(\"final_m1long.csv\", index=False)\n",
    "final_m1short.to_csv(\"final_m1short.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "6f5f642e-437f-4c4e-bc3b-1f0c077cee38",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dest_dir = \"colab/episode2/\"\n",
    "for i, row in final_m5long.iterrows():\n",
    "    srcfile = f\"M1M5Charts/{row['image_path']}\"\n",
    "    if not os.path.exists(\"M1M5Charts/\"+ 'Volatility_100_Index_M5_1595334152023.04.11-04:48:38_7330_7328.png'): \n",
    "        print(f\"File {row['image_path']} {i} does not exist\")\n",
    "    # shutil.copy(srcfile, dest_dir)\n",
    "    # print(f\"Copied file {i} : {srcfile}\", end=\"\\r\")\n",
    "    # break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "a7ba5740-293e-4a14-aeac-3bb26adb7a42",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "4ffd223a-849e-4589-87aa-70d595de01a6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 4, 12, 20, 56, 58, 871747)"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime.now()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "5b0466fd-066b-4a40-b712-93ca7aa9d479",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2023, 4, 12, 20, 6, 19, 512052)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.datetime.now()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be1fe095-de7e-4911-9ff0-01e71bd52463",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasetCreator.m1short.to_csv(\"final_complete_m1short.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "401f81cd-dd18-4525-bc1e-86fa3a3d110d",
   "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": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}