Chapter_08_ANN_reg_app.ipynb

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      "------------------------------------------------------------------\n",
      "Date:  2022-02-17 08:20:54\n",
      "Balance: 8990.52 USD, \tEquity: 8990.52 USD, \tProfit: 0.0 USD\n",
      "------------------------------------------------------------------\n",
      "------------------------------------------------------------------\n",
      "Date:  2022-02-17 08:20:55\n",
      "SYMBOL: GBPCAD\n",
      "BUY: False \t  SHORT: True\n",
      "POSITION: None \t ID: None\n",
      "OPEN SHORT TRADE: Request executed\n",
      "------------------------------------------------------------------\n"
     ]
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     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_4960/1491282133.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    153\u001b[0m                      \u001b[1;34mf\"Buy: {buy}\\t\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                      f\"Sell: {sell}\")\n\u001b[1;32m--> 155\u001b[1;33m     \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
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   "source": [
    "from MT5 import *\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import time\n",
    "import pickle\n",
    "import ta\n",
    "from joblib import dump, load\n",
    "import os\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import tensorflow\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "\n",
    "\n",
    "path = \"\" # Ex: C:/Desktop/Python_for_finance_and_algorithmic_trading/ChapterN/Models\n",
    "\n",
    "\n",
    "\n",
    "def feature_engineering(df):\n",
    "    \"\"\" DON'T PUT THE SHIFT HERE\"\"\"\n",
    "    \n",
    "    # We copy the dataframe to avoid interferences in the data\n",
    "    df_copy = df.copy()\n",
    "    df_copy[[\"returns\"]] = df_copy[[\"close\"]].pct_change(1)\n",
    "\n",
    "    # Create the SMAs\n",
    "    df_copy[\"SMA 15\"] = df_copy[[\"close\"]].rolling(15).mean()\n",
    "    df_copy[\"SMA 60\"] = df_copy[[\"close\"]].rolling(60).mean()\n",
    "    \n",
    "    # Create the volatilities\n",
    "    df_copy[\"MSD 10\"] = df_copy[[\"returns\"]].rolling(10).std()\n",
    "    df_copy[\"MSD 30\"] = df_copy[[\"returns\"]].rolling(30).std()\n",
    "    \n",
    "    # Create the Rsi\n",
    "    RSI = ta.momentum.RSIIndicator(df_copy[\"close\"], window= 14, fillna = False)\n",
    "    df_copy[\"rsi\"] = RSI.rsi()\n",
    "    \n",
    "    # STANDARDIZATION\n",
    "\n",
    "    sc = StandardScaler()\n",
    "    df_copy = df_copy[[\"SMA 15\", \"SMA 60\", \"MSD 10\", \"MSD 30\", \"rsi\"]].dropna()\n",
    "    df_copy_sc = sc.fit_transform(df_copy)\n",
    "\n",
    "    return df_copy_sc\n",
    "\n",
    "\n",
    "def ANN_weights():\n",
    "    nb_hidden_layer=2\n",
    "    # INTIALIZATION SEQUENTIAL MODEL\n",
    "    regressor = Sequential()\n",
    "\n",
    "    # FIRST LAYER\n",
    "    regressor.add(Dense(25, input_shape=(5,), activation=\"relu\"))\n",
    "\n",
    "    # ADD HIDDEN LAYER\n",
    "    for i in range(1,nb_hidden_layer):\n",
    "        regressor.add(Dense(int(25/i), activation=\"relu\"))\n",
    "\n",
    "    # OUTPUT LAYER DENSE\n",
    "    regressor.add(Dense(1, activation=\"linear\"))\n",
    "\n",
    "    # COMPILE THE MODEL\n",
    "    regressor.compile(loss=\"mse\", optimizer=\"adam\")\n",
    "\n",
    "    return regressor\n",
    "\n",
    "   \n",
    "    \n",
    "def ANN_reg_sig(symbol):\n",
    "    \"\"\" Function for predict the value of tommorow using DNN model\"\"\"\n",
    "    \n",
    "    # Create the weights if there is not in the folder\n",
    "    ann_1 = ANN_weights()\n",
    "    ann_2 = ANN_weights()\n",
    "    ann_3 = ANN_weights()\n",
    "\n",
    "    # Import trained weights\n",
    "    ann_1.load_weights(f\"ANN_weights/ANN n17\")\n",
    "    ann_2.load_weights(f\"ANN_weights/ANN n38\")\n",
    "    ann_3.load_weights(f\"ANN_weights/ANN n66\")\n",
    "    \n",
    "    # Take the lastest percentage of change \n",
    "    df = MT5.get_data(symbol, 70)\n",
    "    \n",
    "    # Features engeeniring\n",
    "    data = feature_engineering(df)\n",
    "\n",
    "    X = data[-1:,:]\n",
    "    \n",
    "    # Bagging\n",
    "    pr1 = np.sign(ann_1.predict(X))\n",
    "    pr2 = np.sign(ann_2.predict(X))\n",
    "    pr3 = np.sign(ann_3.predict(X))\n",
    "    \n",
    "    # Find the signal\n",
    "    buy = (pr1 + pr2 + pr3)[0][0] >=1\n",
    "    sell = not buy\n",
    "    \n",
    "    \n",
    "    return buy, sell\n",
    "\n",
    "\n",
    "\n",
    "# True = Live Trading and False = Screener\n",
    "live = True\n",
    "\n",
    "if live:\n",
    "    current_account_info = mt5.account_info()\n",
    "    print(\"------------------------------------------------------------------\")\n",
    "    print(\"Date: \", datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n",
    "    print(f\"Balance: {current_account_info.balance} USD, \\t\"\n",
    "          f\"Equity: {current_account_info.equity} USD, \\t\"\n",
    "          f\"Profit: {current_account_info.profit} USD\")\n",
    "    print(\"------------------------------------------------------------------\")\n",
    "\n",
    "\n",
    "\n",
    "info_order = {\n",
    "    \"CADGBP\": [\"GBPCAD\", 1.00]\n",
    "}\n",
    "\n",
    "\n",
    "start = datetime.now().strftime(\"%H:%M:%S\")\n",
    "while True:\n",
    "    # Verfication for launch\n",
    "    if datetime.now().weekday() not in (5,1):\n",
    "        is_time = datetime.now().strftime(\"%H:%M:%S\") == start #\"23:59:59\"\n",
    "    else:\n",
    "        is_time = False\n",
    "\n",
    "    \n",
    "    # Launch the algorithm\n",
    "    if is_time:\n",
    "\n",
    "        # Open the trades\n",
    "        for asset in info_order.keys():\n",
    "\n",
    "            # Initialize the inputs\n",
    "            symbol = info_order[asset][0]\n",
    "            lot = info_order[asset][1]\n",
    "\n",
    "            # Create the signals\n",
    "            buy, sell = ANN_reg_sig(symbol)\n",
    "\n",
    "             # Run the algorithm\n",
    "            if live:\n",
    "                MT5.run(symbol, buy, sell,lot)\n",
    "\n",
    "            else:\n",
    "                print(f\"Symbol: {symbol}\\t\"\n",
    "                     f\"Buy: {buy}\\t\"\n",
    "                     f\"Sell: {sell}\")\n",
    "    time.sleep(1)"
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