Stock_Price_Prediction_Model.ipynb

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      "authorship_tag": "ABX9TyOikno/VGLUDZC5MEZ15ONi",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Alpha-mon/AI-RoboAdvisor/blob/main/Stock_Price_Prediction_Model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "YvYWThVbuADp"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime, timedelta"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 야후 파이낸스 주식 정보 가져오기\n",
        "# 주식 종목 지정 필수\n",
        "\n",
        "ticker = 'NVDA'\n",
        "end_date = datetime.now()\n",
        "start_date = end_date - timedelta(days=3650)\n",
        "data = yf.download(ticker, start=start_date, end=end_date)\n",
        "\n",
        "closing_prices = data['Close']\n",
        "volume = data['Volume']\n",
        "moving_average = closing_prices.rolling(window=120).mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1KfkLOGvSol",
        "outputId": "18bd0004-09a1-470c-f1bd-623267e77112"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 6개 알고리즘 (Bollinger, MACD, MOK, RSI, STCK, WR) 전체 코드\n",
        "\n",
        "\n",
        "# Bollinger\n",
        "\n",
        "def calculate_bollinger_bands(closing_prices, window=20, num_std=2):\n",
        "    rolling_mean = closing_prices.rolling(window=window).mean()\n",
        "    rolling_std = closing_prices.rolling(window=window).std()\n",
        "\n",
        "    upper_band = rolling_mean + num_std * rolling_std\n",
        "    lower_band = rolling_mean - num_std * rolling_std\n",
        "    return upper_band, lower_band\n",
        "\n",
        "upper_band, lower_band = calculate_bollinger_bands(closing_prices)\n",
        "bollinger_bands_data = pd.DataFrame({'Upper Band': upper_band, 'Lower Band': lower_band})\n",
        "\n",
        "bollinger_bands_data.dropna(inplace=True)\n",
        "result = np.where(closing_prices > upper_band, (closing_prices - upper_band) / (upper_band - lower_band), np.where(closing_prices < lower_band, (closing_prices - lower_band) / (lower_band - upper_band), np.nan))\n",
        "bollinger_bands_data['Result'] = pd.Series(result, index=closing_prices.index)\n",
        "\n",
        "bollinger_bands_data['Result'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "bollinger_bands_data['Prediction'] = bollinger_bands_data['Result'].shift(-1)\n",
        "bollinger_bands_data.dropna(inplace=True)\n",
        "bollinger_bands_data['Trend'] = bollinger_bands_data['Prediction'].diff()\n",
        "bollinger_bands_data['Trend'] = (bollinger_bands_data['Trend'] + 1) / 2\n",
        "\n",
        "min_pred = bollinger_bands_data['Prediction'].min()\n",
        "max_pred = bollinger_bands_data['Prediction'].max()\n",
        "bollinger_bands_data['Prediction'] = (bollinger_bands_data['Prediction'] - min_pred) / (max_pred - min_pred)\n",
        "\n",
        "min_trend = bollinger_bands_data['Trend'].min()\n",
        "max_trend = bollinger_bands_data['Trend'].max()\n",
        "bollinger_bands_data['Trend'] = (bollinger_bands_data['Trend'] - min_trend) / (max_trend - min_trend)*100\n",
        "\n",
        "pd.set_option('display.float_format', '{:.4f}'.format)\n",
        "\n",
        "\n",
        "# MACD\n",
        "\n",
        "def calculate_macd(closing_prices):\n",
        "\n",
        "    short_ema = closing_prices.ewm(span=26, adjust=False).mean()\n",
        "    long_ema = closing_prices.ewm(span=12, adjust=False).mean()\n",
        "\n",
        "    dif = short_ema - long_ema\n",
        "    signal_line = dif.ewm(span=9, adjust=False).mean()\n",
        "    histogram = dif - signal_line\n",
        "\n",
        "    return dif, signal_line, histogram\n",
        "\n",
        "dif, signal_line, histogram = calculate_macd(closing_prices)\n",
        "macd_data = pd.DataFrame({'DIF': dif, 'Signal Line': signal_line, 'Histogram': histogram})\n",
        "macd_data['Result'] = np.where(macd_data['DIF'] > macd_data['Signal Line'], 1, 0)\n",
        "\n",
        "macd_data['Result'] = (macd_data['Result'].rolling(window=20, min_periods=1).mean())*100\n",
        "macd_data['Prediction'] = macd_data['Result'].shift(-1)\n",
        "macd_data.dropna(inplace=True)\n",
        "\n",
        "# MOK\n",
        "\n",
        "def calculate_mok(closing_prices, period=14, ma_period=20):\n",
        "    returns = closing_prices.pct_change()\n",
        "\n",
        "    moving_average = closing_prices.rolling(window=ma_period).mean()\n",
        "    momentum = closing_prices.diff(period)\n",
        "    normalized_momentum = 100 * (momentum - np.min(momentum)) / (np.max(momentum) - np.min(momentum))\n",
        "\n",
        "    return normalized_momentum\n",
        "\n",
        "mok_values = calculate_mok(closing_prices)\n",
        "\n",
        "# RSI\n",
        "\n",
        "def calculate_rsi(closing_prices, window=20):\n",
        "    price_changes = closing_prices.diff()\n",
        "    up_changes = price_changes.where(price_changes > 0, 0)\n",
        "    down_changes = -price_changes.where(price_changes < 0, 0)\n",
        "\n",
        "    avg_up_changes = up_changes.rolling(window=window, min_periods=1).mean()\n",
        "    avg_down_changes = down_changes.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_up_changes / avg_down_changes\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "rsi_result = calculate_rsi(closing_prices, window=len(closing_prices)//10)  # 기간을 데이터의 1/10로 동적으로 설정\n",
        "rsi_result.dropna(inplace=True)\n",
        "\n",
        "# STCK\n",
        "\n",
        "def calculate_stck(closing_prices, window=20):\n",
        "    lowest_low = closing_prices.rolling(window=window).min()\n",
        "    highest_high = closing_prices.rolling(window=window).max()\n",
        "\n",
        "    stck = 100 * (closing_prices - lowest_low) / (highest_high - lowest_low)\n",
        "\n",
        "    stck_ma = stck.rolling(window=20).mean()\n",
        "\n",
        "    return stck_ma\n",
        "\n",
        "stck_result = calculate_stck(closing_prices)\n",
        "stck_result.dropna(inplace=True)\n",
        "\n",
        "# WR\n",
        "\n",
        "def calculate_williams_r(closing_prices, period=14):\n",
        "    high_prices = data['High']\n",
        "    low_prices = data['Low']\n",
        "    moving_average = closing_prices.rolling(window=20).mean()\n",
        "\n",
        "    highest_high = high_prices.rolling(window=period).max()\n",
        "    lowest_low = low_prices.rolling(window=period).min()\n",
        "\n",
        "    williams_r = (highest_high - moving_average) / (highest_high - lowest_low) * -100\n",
        "\n",
        "    normalized_williams_r = 100 * (williams_r + 100) / 100\n",
        "\n",
        "    return normalized_williams_r\n",
        "\n",
        "wr_result = calculate_williams_r(closing_prices)"
      ],
      "metadata": {
        "id": "oCfnmb4bvYxb"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 6개 지표들을 하나의 데이터 프레임에 합치기\n",
        "\n",
        "final_df = pd.DataFrame({\n",
        "    'Bollinger': bollinger_bands_data['Trend'],\n",
        "    'MACD': macd_data['Prediction'],\n",
        "    'MOK': mok_values,\n",
        "    'RSI': rsi_result,\n",
        "    'STCK': stck_result,\n",
        "    'WR': wr_result\n",
        "})\n",
        "\n",
        "# NaN 값 제거\n",
        "final_df.dropna(inplace=True)\n",
        "\n",
        "# 최종 데이터 프레임 출력\n",
        "print(final_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rCoy0jTJvoH5",
        "outputId": "ba66f7c1-5cfd-44fc-d35a-3f4bb58f51f4"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "            Bollinger    MACD     MOK     RSI    STCK      WR\n",
            "Date                                                         \n",
            "2014-02-13    43.8505 55.0000 40.3087 55.0434 50.1006 27.0795\n",
            "2014-02-14    28.7469 55.0000 40.3868 56.6366 51.5429 25.5513\n",
            "2014-02-18    42.4711 55.0000 40.3676 56.6001 53.3726 28.8534\n",
            "2014-02-19    51.9447 55.0000 40.4145 57.2606 54.9226 29.2618\n",
            "2014-02-20    47.6999 55.0000 40.4602 58.9275 56.5726 28.8429\n",
            "...               ...     ...     ...     ...     ...     ...\n",
            "2023-10-10    47.6999 60.0000 57.2054 60.5174 35.5890 51.8735\n",
            "2023-10-11    47.6999 55.0000 67.9281 60.7924 38.7841 45.8897\n",
            "2023-10-12    47.6999 50.0000 65.7451 60.8578 41.9000 41.5991\n",
            "2023-10-13    47.6999 45.0000 55.6667 60.1984 45.1522 38.4900\n",
            "2023-10-16    47.6999 40.0000 60.2106 60.6209 49.3747 40.2701\n",
            "\n",
            "[2435 rows x 6 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM 모델\n",
        "\n",
        "\n",
        "final_df_values = final_df.values\n",
        "data_values = data.values\n",
        "\n",
        "# 입력 시퀀스에 대한 타임 스텝(T)을 정의합니다.\n",
        "\n",
        "T = 10  # 원하는대로 조정할 수 있습니다.\n",
        "\n",
        "# 입력 및 타겟을 위한 데이터 시퀀스 생성\n",
        "final_df_sequences = []\n",
        "data_sequences = []\n",
        "\n",
        "for i in range(len(data_values) - T):\n",
        "    final_df_sequences.append(final_df_values[i:i+T])\n",
        "    data_sequences.append(closing_prices.iloc[i+T])\n",
        "\n",
        "filtered_final_df_sequences = []\n",
        "filtered_data_sequences = []\n",
        "\n",
        "# 시퀀스 길이 맞추기\n",
        "for i, seq in enumerate(final_df_sequences):\n",
        "    if len(seq) == 10:\n",
        "        filtered_final_df_sequences.append(seq)\n",
        "        filtered_data_sequences.append(data_sequences[i])\n",
        "\n",
        "final_df_sequences = filtered_final_df_sequences\n",
        "data_sequences = filtered_data_sequences\n",
        "\n",
        "# 시퀀스를 넘파이 배열로 변환\n",
        "X = np.array(final_df_sequences)\n",
        "y = np.array(data_sequences)\n",
        "\n",
        "\n",
        "# 데이터를 훈련 및 테스트 세트로 분할\n",
        "split_ratio = 0.8  # 분할 비율을 조정할 수 있습니다.\n",
        "split_index = int(split_ratio * len(X))\n",
        "\n",
        "X_train, X_test = X[:split_index], X[split_index:]\n",
        "y_train, y_test = y[:split_index], y[split_index:]\n",
        "\n",
        "# LSTM 모델 구축\n",
        "model = Sequential()\n",
        "\n",
        "# 첫 번째 LSTM 레이어 (시퀀스 출력을 반환하여 다음 레이어로 전달)\n",
        "model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(T, 6)))\n",
        "\n",
        "# 두 번째 LSTM 레이어\n",
        "model.add(LSTM(50, activation='relu', return_sequences=True))\n",
        "\n",
        "# 세 번째 LSTM 레이어 (시퀀스 출력을 반환하지 않음)\n",
        "model.add(LSTM(50, activation='relu'))\n",
        "\n",
        "# 출력 레이어\n",
        "model.add(Dense(1))\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "\n",
        "# 모델 훈련\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "\n",
        "# 테스트 데이터에서 모델 평가\n",
        "loss = model.evaluate(X_test, y_test)\n",
        "print(f'Test Loss: {loss}')\n",
        "\n",
        "# 예측 수행\n",
        "predictions = model.predict(X_test)\n",
        "print(predictions)\n",
        "\n",
        "# 예측 결과를 데이터 프레임으로 변환\n",
        "predictions_df = pd.DataFrame(predictions, columns=[\"Predictions\"])\n",
        "print(predictions_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k3hbof7Qvt2K",
        "outputId": "628e7d13-4d40-4528-a7ca-11e20939b6fb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "49/49 [==============================] - 6s 28ms/step - loss: 547.6461 - val_loss: 9884.0986\n",
            "Epoch 2/10\n",
            "49/49 [==============================] - 1s 19ms/step - loss: 397.9840 - val_loss: 10015.0977\n",
            "Epoch 3/10\n",
            "49/49 [==============================] - 1s 26ms/step - loss: 352.1669 - val_loss: 9916.8691\n",
            "Epoch 4/10\n",
            "49/49 [==============================] - 1s 25ms/step - loss: 332.0870 - val_loss: 10228.9287\n",
            "Epoch 5/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 351.9483 - val_loss: 10096.6865\n",
            "Epoch 6/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 332.7534 - val_loss: 9351.4844\n",
            "Epoch 7/10\n",
            "49/49 [==============================] - 1s 17ms/step - loss: 321.3829 - val_loss: 9958.0596\n",
            "Epoch 8/10\n",
            "49/49 [==============================] - 1s 15ms/step - loss: 327.6346 - val_loss: 10500.0000\n",
            "Epoch 9/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 1151.5594 - val_loss: 11156.7100\n",
            "Epoch 10/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 557.3321 - val_loss: 12536.7666\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 45369.6055\n",
            "Test Loss: 45369.60546875\n",
            "16/16 [==============================] - 1s 4ms/step\n",
            "[[ 1.14638653e+01]\n",
            " [ 1.38425789e+01]\n",
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            " [ 2.03315964e+01]\n",
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            " [ 1.80932961e+01]\n",
            " [ 1.71140289e+01]\n",
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            " [ 7.46698809e+00]\n",
            " [ 5.70870638e+00]\n",
            " [ 4.41621065e+00]\n",
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            " [ 2.68941116e+01]\n",
            " [ 3.37856445e+01]\n",
            " [ 6.10817184e+01]\n",
            " [ 5.98053131e+01]\n",
            " [ 5.78916588e+01]\n",
            " [ 4.77382851e+01]\n",
            " [ 3.04630890e+01]\n",
            " [ 1.89373665e+01]\n",
            " [ 1.68954582e+01]\n",
            " [ 2.92234612e+01]\n",
            " [ 2.78431053e+01]\n",
            " [ 2.60962563e+01]\n",
            " [ 1.78896809e+01]\n",
            " [ 1.53280993e+01]\n",
            " [ 9.53063583e+00]\n",
            " [ 9.69610786e+00]\n",
            " [ 1.43894062e+01]\n",
            " [ 1.52166958e+01]\n",
            " [ 1.64285717e+01]\n",
            " [ 1.78482723e+01]\n",
            " [ 1.85548897e+01]\n",
            " [ 2.39732761e+01]\n",
            " [ 2.95790577e+01]\n",
            " [ 2.87368298e+01]\n",
            " [ 3.18703194e+01]\n",
            " [ 2.42709618e+01]\n",
            " [ 2.95546494e+01]\n",
            " [ 1.00049267e+01]\n",
            " [ 1.06713371e+01]\n",
            " [ 1.46754227e+01]\n",
            " [ 1.84621735e+01]\n",
            " [ 2.04362888e+01]\n",
            " [ 2.29355392e+01]\n",
            " [ 2.35625381e+01]\n",
            " [ 2.30561218e+01]\n",
            " [ 2.27738209e+01]\n",
            " [ 2.18965302e+01]\n",
            " [ 2.52928238e+01]\n",
            " [ 2.83502541e+01]\n",
            " [ 3.15179176e+01]\n",
            " [ 3.50668945e+01]\n",
            " [ 3.54891319e+01]\n",
            " [ 3.34932137e+01]\n",
            " [ 3.14164047e+01]\n",
            " [ 2.92648277e+01]\n",
            " [ 2.68855362e+01]\n",
            " [ 2.34146461e+01]\n",
            " [ 1.94546223e+01]\n",
            " [ 1.73035164e+01]\n",
            " [ 1.68140888e+01]\n",
            " [ 1.49024277e+01]\n",
            " [ 1.23393250e+01]\n",
            " [ 1.24480667e+01]\n",
            " [ 1.25418205e+01]\n",
            " [ 5.37856712e+01]\n",
            " [ 2.91531830e+01]\n",
            " [ 2.38320847e+01]\n",
            " [ 2.69512215e+01]\n",
            " [ 2.85349655e+01]\n",
            " [ 2.73346329e+01]\n",
            " [ 4.61966753e+00]\n",
            " [ 6.99504709e+00]\n",
            " [ 8.12119293e+00]\n",
            " [ 9.87683296e+00]\n",
            " [ 1.28301830e+01]\n",
            " [ 1.58249283e+01]\n",
            " [ 1.83172188e+01]\n",
            " [ 2.01017036e+01]\n",
            " [ 1.94675694e+01]\n",
            " [ 2.02532463e+01]\n",
            " [ 2.03361378e+01]\n",
            " [ 2.03446465e+01]\n",
            " [ 1.80120945e+01]\n",
            " [ 2.45262260e+01]\n",
            " [ 2.45255871e+01]\n",
            " [ 2.80844040e+01]\n",
            " [ 2.53842411e+01]\n",
            " [ 2.70906124e+01]\n",
            " [ 2.96551285e+01]\n",
            " [ 2.91744804e+01]\n",
            " [ 2.88556099e+01]\n",
            " [ 2.85729294e+01]\n",
            " [ 2.83807983e+01]\n",
            " [ 2.87956924e+01]\n",
            " [ 2.92250938e+01]\n",
            " [ 2.99743423e+01]\n",
            " [ 2.97825584e+01]\n",
            " [ 2.92751350e+01]\n",
            " [ 2.82371101e+01]\n",
            " [ 2.63598442e+01]\n",
            " [ 2.47575874e+01]\n",
            " [ 2.28589935e+01]\n",
            " [ 2.10761375e+01]\n",
            " [ 1.71459618e+01]\n",
            " [ 1.48875389e+01]\n",
            " [ 1.20940857e+01]\n",
            " [ 9.56684113e+00]\n",
            " [ 7.63933611e+00]\n",
            " [ 1.01806145e+01]\n",
            " [ 2.80985222e+01]\n",
            " [ 2.65115776e+01]\n",
            " [ 1.62928886e+01]\n",
            " [ 1.98475914e+01]\n",
            " [ 1.56086540e+01]\n",
            " [ 1.23943157e+01]\n",
            " [ 6.17170191e+00]\n",
            " [ 9.39014816e+00]\n",
            " [ 5.01639223e+00]\n",
            " [-3.08964133e+00]\n",
            " [ 8.04567051e+00]\n",
            " [ 2.17835350e+01]\n",
            " [ 3.57536240e+01]\n",
            " [ 4.52991867e+01]\n",
            " [ 3.49052124e+01]\n",
            " [ 2.89902821e+01]\n",
            " [ 1.35115213e+01]\n",
            " [ 3.25292854e+01]\n",
            " [ 2.74849930e+01]\n",
            " [ 2.44033146e+01]\n",
            " [ 3.40215340e+01]\n",
            " [ 5.37377625e+01]\n",
            " [ 7.93655443e+00]\n",
            " [ 2.84518838e+00]\n",
            " [ 3.40864587e+00]\n",
            " [ 7.16834688e+00]\n",
            " [ 9.71092415e+00]\n",
            " [ 1.07485666e+01]\n",
            " [ 1.27349834e+01]\n",
            " [ 2.01121330e+01]\n",
            " [ 2.16919098e+01]\n",
            " [ 2.32530136e+01]\n",
            " [ 1.93203926e+01]\n",
            " [ 2.36702194e+01]\n",
            " [ 3.30804749e+01]\n",
            " [ 5.64639626e+01]\n",
            " [ 3.69588890e+01]\n",
            " [ 3.75611534e+01]\n",
            " [ 4.01875572e+01]\n",
            " [ 4.21473579e+01]\n",
            " [ 3.90895691e+01]\n",
            " [ 3.47403908e+01]\n",
            " [ 4.62082214e+01]\n",
            " [ 4.56230202e+01]\n",
            " [ 2.47652302e+01]\n",
            " [ 2.91099548e+01]\n",
            " [ 2.53773613e+01]\n",
            " [ 2.22593536e+01]\n",
            " [ 2.15746803e+01]\n",
            " [ 1.92223377e+01]\n",
            " [ 1.88435345e+01]\n",
            " [ 1.71224136e+01]\n",
            " [ 2.02467785e+01]\n",
            " [ 2.00028591e+01]\n",
            " [ 2.15877342e+01]\n",
            " [ 1.99692993e+01]\n",
            " [ 1.78338261e+01]\n",
            " [ 1.89050827e+01]\n",
            " [ 1.68500881e+01]\n",
            " [ 1.98613853e+01]\n",
            " [ 2.37146282e+01]\n",
            " [ 2.45623245e+01]\n",
            " [ 1.93431320e+01]\n",
            " [ 2.21355705e+01]\n",
            " [ 1.46236839e+01]\n",
            " [ 1.93965511e+01]\n",
            " [ 1.92098389e+01]\n",
            " [ 1.82903404e+01]\n",
            " [ 1.88118000e+01]\n",
            " [ 1.18801460e+01]\n",
            " [ 1.00383015e+01]\n",
            " [ 1.03913822e+01]\n",
            " [ 1.07913628e+01]\n",
            " [ 1.05027161e+01]\n",
            " [ 8.36676598e+00]\n",
            " [ 6.42370081e+00]\n",
            " [ 5.63569880e+00]\n",
            " [ 4.02606249e+00]\n",
            " [ 3.23705697e+00]\n",
            " [ 9.80793238e-01]\n",
            " [-7.11875677e-01]\n",
            " [-3.66214347e+00]\n",
            " [-4.95236540e+00]\n",
            " [-5.64674139e+00]\n",
            " [-4.94336271e+00]\n",
            " [-2.16260123e+00]\n",
            " [ 3.99945855e+00]\n",
            " [ 3.89574838e+00]\n",
            " [ 2.35104394e+00]\n",
            " [-3.70233297e-01]\n",
            " [-1.49126601e+00]\n",
            " [ 3.39862084e+00]\n",
            " [ 1.31622772e+01]\n",
            " [ 1.51895971e+01]\n",
            " [ 1.51915369e+01]\n",
            " [ 1.66711769e+01]\n",
            " [ 1.80643463e+01]\n",
            " [ 1.54909897e+01]\n",
            " [ 1.36935463e+01]\n",
            " [ 1.34412460e+01]\n",
            " [ 1.31609688e+01]\n",
            " [ 1.32909813e+01]\n",
            " [ 1.93686905e+01]\n",
            " [ 1.59213600e+01]\n",
            " [ 1.42994576e+01]\n",
            " [ 1.72295494e+01]\n",
            " [ 1.94959927e+01]\n",
            " [ 2.55409431e+01]\n",
            " [ 2.23325272e+01]\n",
            " [ 2.28635063e+01]\n",
            " [ 1.88411064e+01]\n",
            " [ 1.66380386e+01]\n",
            " [ 1.81477585e+01]\n",
            " [ 1.88106308e+01]\n",
            " [ 2.69932404e+01]\n",
            " [ 2.67291069e+01]\n",
            " [ 2.48168297e+01]\n",
            " [ 2.36984825e+01]\n",
            " [ 2.13826084e+01]\n",
            " [ 1.83001862e+01]\n",
            " [ 1.79765511e+01]\n",
            " [ 1.73645477e+01]\n",
            " [ 1.67273178e+01]\n",
            " [ 1.54597683e+01]\n",
            " [ 1.12084198e+01]\n",
            " [ 1.01124229e+01]\n",
            " [ 9.23855400e+00]\n",
            " [ 7.18760538e+00]\n",
            " [ 5.40736818e+00]\n",
            " [ 5.28913546e+00]\n",
            " [ 5.29034567e+00]\n",
            " [ 5.36804247e+00]\n",
            " [ 3.85429025e+00]\n",
            " [ 9.40143824e-01]\n",
            " [ 8.06469202e-01]\n",
            " [-1.33258414e+00]\n",
            " [-2.87492126e-02]\n",
            " [ 2.73407197e+00]\n",
            " [ 4.08042192e+00]\n",
            " [ 5.71479273e+00]\n",
            " [ 7.94873857e+00]\n",
            " [ 4.97180986e+00]\n",
            " [ 1.25185623e+01]\n",
            " [ 1.37631607e+01]\n",
            " [ 1.01786652e+01]\n",
            " [ 1.68054123e+01]\n",
            " [ 1.75257378e+01]\n",
            " [ 1.84429893e+01]\n",
            " [ 1.96235504e+01]\n",
            " [ 2.00934410e+01]\n",
            " [ 2.08363190e+01]\n",
            " [ 2.05331535e+01]\n",
            " [ 1.99858170e+01]\n",
            " [ 1.80590477e+01]\n",
            " [ 1.54163857e+01]\n",
            " [ 1.34959621e+01]\n",
            " [ 1.10655403e+01]\n",
            " [ 9.79060745e+00]\n",
            " [ 8.06352234e+00]\n",
            " [ 5.96503496e+00]\n",
            " [ 3.32112908e+00]\n",
            " [ 3.23917627e-01]\n",
            " [ 2.58153558e+00]\n",
            " [ 4.94162703e+00]\n",
            " [ 7.29266119e+00]\n",
            " [ 6.41920900e+00]\n",
            " [ 6.41867876e+00]\n",
            " [ 4.13154364e+00]\n",
            " [ 3.86964536e+00]\n",
            " [ 2.99449468e+00]\n",
            " [ 3.36609769e+00]\n",
            " [ 6.70288706e+00]\n",
            " [ 7.95597696e+00]\n",
            " [ 7.24500895e+00]\n",
            " [ 7.82266665e+00]\n",
            " [ 5.31761599e+00]\n",
            " [ 1.04781036e+01]\n",
            " [ 1.16976223e+01]\n",
            " [ 1.34836292e+01]\n",
            " [ 1.67278500e+01]\n",
            " [ 1.85922413e+01]\n",
            " [ 2.00804138e+01]\n",
            " [ 1.50362968e+01]\n",
            " [ 1.43365307e+01]\n",
            " [ 1.64753704e+01]\n",
            " [ 1.93612003e+01]\n",
            " [ 2.17695198e+01]\n",
            " [ 1.99807053e+01]\n",
            " [ 1.93046417e+01]\n",
            " [ 1.28872242e+01]\n",
            " [ 1.53316402e+01]\n",
            " [ 1.87378006e+01]\n",
            " [ 2.03546772e+01]\n",
            " [ 2.46850471e+01]\n",
            " [ 2.73476639e+01]\n",
            " [ 2.29230499e+01]\n",
            " [ 1.99130688e+01]\n",
            " [ 2.17600136e+01]\n",
            " [ 2.65084820e+01]\n",
            " [ 2.60911102e+01]\n",
            " [ 2.62055817e+01]\n",
            " [ 2.57670746e+01]\n",
            " [ 2.63728867e+01]\n",
            " [ 2.59033508e+01]\n",
            " [ 2.51465683e+01]\n",
            " [ 2.20141373e+01]\n",
            " [ 1.94970665e+01]\n",
            " [ 1.75919304e+01]\n",
            " [ 1.41783180e+01]\n",
            " [ 1.49167976e+01]\n",
            " [ 1.47929420e+01]\n",
            " [ 1.30782433e+01]\n",
            " [ 1.17919121e+01]\n",
            " [ 9.07634735e+00]\n",
            " [ 5.12357759e+00]\n",
            " [ 3.94267678e+00]\n",
            " [ 9.14383173e-01]\n",
            " [-1.13227057e+00]\n",
            " [-6.52507544e-01]\n",
            " [-1.28827262e+00]\n",
            " [-2.00244308e+00]\n",
            " [ 1.75945535e-01]\n",
            " [ 3.57988596e-01]\n",
            " [-2.25789770e-01]\n",
            " [ 3.62703949e-02]\n",
            " [ 1.18477273e+00]\n",
            " [ 2.85854578e-01]\n",
            " [ 8.69304895e-01]\n",
            " [ 2.09691644e+00]\n",
            " [ 5.91226244e+00]\n",
            " [ 6.63578463e+00]\n",
            " [ 7.70921755e+00]\n",
            " [ 8.35614395e+00]\n",
            " [ 8.93899536e+00]\n",
            " [ 8.30607224e+00]\n",
            " [ 4.92342806e+00]\n",
            " [ 3.36497903e+00]\n",
            " [ 8.29269648e-01]\n",
            " [ 6.74698114e-01]\n",
            " [ 1.52106881e+00]\n",
            " [ 2.31250596e+00]\n",
            " [ 3.50540948e+00]\n",
            " [ 4.90759897e+00]\n",
            " [ 6.51210642e+00]\n",
            " [ 5.96347857e+00]\n",
            " [ 5.22732878e+00]\n",
            " [ 4.75674295e+00]\n",
            " [ 7.91565561e+00]\n",
            " [ 5.41536188e+00]\n",
            " [ 4.01594019e+00]\n",
            " [ 8.99946976e+00]\n",
            " [ 1.21821060e+01]\n",
            " [ 1.42826996e+01]\n",
            " [ 1.57805099e+01]\n",
            " [ 1.60151405e+01]\n",
            " [ 1.56261520e+01]\n",
            " [ 1.76417160e+01]\n",
            " [ 1.82307014e+01]\n",
            " [ 1.90286064e+01]\n",
            " [ 2.00870895e+01]\n",
            " [ 2.05337830e+01]\n",
            " [ 2.79499512e+01]\n",
            " [ 5.96086655e+01]\n",
            " [ 6.04506645e+01]\n",
            " [ 3.37199211e+01]\n",
            " [ 5.74852676e+01]\n",
            " [ 6.05241241e+01]\n",
            " [ 3.20991135e+01]\n",
            " [ 3.07792587e+01]\n",
            " [ 1.29678078e+01]\n",
            " [ 5.37780809e+00]\n",
            " [ 4.22727251e+00]\n",
            " [ 6.13021326e+00]]\n",
            "     Predictions\n",
            "0        11.4639\n",
            "1        13.8426\n",
            "2        14.4405\n",
            "3        15.9302\n",
            "4        17.4010\n",
            "..           ...\n",
            "481      30.7793\n",
            "482      12.9678\n",
            "483       5.3778\n",
            "484       4.2273\n",
            "485       6.1302\n",
            "\n",
            "[486 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LSTM 모델의 예측 값을 6개 지표 값과 함께 생성 모델 입력값으로 넣기 위해\n",
        "\n",
        "# T 타임 스텝 만큼의 길이를 고려하여 LSTM 예측값 넣기\n",
        "final_df[\"LSTM_Predictions\"] = np.nan\n",
        "final_df[\"LSTM_Predictions\"].iloc[-len(predictions_df):] = predictions_df[\"Predictions\"].values\n",
        "\n",
        "# NaN 값 제거\n",
        "final_df.dropna(inplace=True)\n",
        "\n",
        "# 다시 시퀀스로 변환\n",
        "final_df_values = final_df.values\n",
        "\n",
        "final_df_sequences = []\n",
        "for i in range(len(data_values) - T):\n",
        "    final_df_sequences.append(final_df_values[i:i+T])\n",
        "\n",
        "X = np.array(final_df_sequences)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0D30KciFv41v",
        "outputId": "b88a4e4e-1e74-48db-b636-716ab021dc9c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-8-f45a5bfa5946>:17: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.array(final_df_sequences)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 생성 및 구분 모델\n",
        "\n",
        "from tensorflow.keras.layers import Reshape, Flatten, LeakyReLU, BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# 생성 모델 G (주식 가격의 시퀀스를 생성)\n",
        "def build_generator(input_shape=(T, 7)):  # 입력 값 : LSTM 모델 예측 + 6개 지표 값\n",
        "    model = Sequential()\n",
        "\n",
        "    # LSTM 레이어와 BatchNormalization, LeakyReLU 활성화 함수를 이용하여 시퀀스를 학습\n",
        "    model.add(LSTM(128, return_sequences=True, input_shape=input_shape))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "\n",
        "    # Flatten layer를 제거하고, T 길이의 시퀀스를 생성하도록 수정\n",
        "    model.add(LSTM(128))\n",
        "    model.add(Dense(T, activation='linear'))\n",
        "\n",
        "    noise = tf.keras.layers.Input(shape=input_shape)\n",
        "    generated_sequence = model(noise)\n",
        "\n",
        "    return Model(noise, generated_sequence)\n",
        "\n",
        "# 구분 모델 D (주가의 실제 시퀀스와 생성된 시퀀스를 구분)\n",
        "def build_discriminator(input_shape=(T, 1)):\n",
        "    model = Sequential()\n",
        "\n",
        "    # LSTM 레이어와 LeakyReLU 활성화 함수를 사용하여 시퀀스를 처리\n",
        "    model.add(LSTM(128, input_shape=input_shape))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "    sequence = tf.keras.layers.Input(shape=input_shape)\n",
        "    validity = model(sequence)\n",
        "\n",
        "    # 주어진 시퀀스가 실제인지 생성된 것인지에 대한 확률\n",
        "    return Model(sequence, validity)"
      ],
      "metadata": {
        "id": "ohs5vg8CwAk4"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습 함수 (생성된 주식 가격 시퀀스와 실제 주식 가격 시퀀스를 사용하여 판별자를 학습)\n",
        "\n",
        "def train_gan(generator, discriminator, combined, epochs, batch_size=32):\n",
        "    valid = np.ones((batch_size, 1))\n",
        "    fake = np.zeros((batch_size, 1))\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
        "        sequences = X_train[idx]\n",
        "\n",
        "        # 주식 예측값 생성\n",
        "        predicted_stock = model.predict(sequences)\n",
        "\n",
        "        # LSTM 예측값을 sequences에 추가\n",
        "        predicted_stock_reshaped = predicted_stock.reshape(batch_size, 1, 1)  # (batch_size, 1, 1) 형태로 변환\n",
        "        predicted_stock_expanded = np.repeat(predicted_stock_reshaped, T, axis=1)  # predicted_stock_reshaped를 T 타임 스텝만큼 확장\n",
        "        X_train_combined = np.concatenate([sequences, predicted_stock_expanded], axis=2)\n",
        "\n",
        "        generated_stock = generator.predict(X_train_combined)\n",
        "        generated_stock_reshaped = generated_stock.reshape(batch_size, T, 1)\n",
        "\n",
        "        # 판별자 학습\n",
        "        d_loss_real = discriminator.train_on_batch(predicted_stock_expanded, valid)\n",
        "        d_loss_fake = discriminator.train_on_batch(generated_stock_reshaped, fake)\n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "        # 생성자 학습\n",
        "        g_loss = combined.train_on_batch(X_train_combined, valid)\n",
        "\n",
        "        print(f\"{epoch}/{epochs} [D loss: {d_loss[0]} | D accuracy: {100 * d_loss[1]}] [G loss: {g_loss}]\")"
      ],
      "metadata": {
        "id": "c95qvP6YwHqn"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 판별자 및 생성자 모델 초기화, 생성자와 판별자를 결합하여\n",
        "\n",
        "discriminator = build_discriminator()\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])\n",
        "\n",
        "generator = build_generator()\n",
        "z = tf.keras.layers.Input(shape=(T, 7))\n",
        "generated_sequence = generator(z)\n",
        "\n",
        "discriminator.trainable = False\n",
        "validity = discriminator(generated_sequence)\n",
        "\n",
        "combined = Model(z, validity)\n",
        "combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))\n",
        "\n",
        "#학습 시작\n",
        "train_gan(generator, discriminator, combined, epochs=10, batch_size=32)\n",
        "\n",
        "# 학습 후 최종 예측을 수행하는 함수\n",
        "def final_predictions(generator, test_data, batch_size=32):\n",
        "    predicted_stock_lstm = model.predict(test_data)\n",
        "\n",
        "    # LSTM 예측값을 test_data에 추가\n",
        "    predicted_stock_expanded = np.repeat(predicted_stock_lstm[:, np.newaxis], T, axis=1)  # (batch_size, T)로 형태 변경\n",
        "    test_data_combined = np.concatenate([test_data, predicted_stock_expanded], axis=2)\n",
        "\n",
        "    # G 모델을 사용하여 합성 주가를 생성\n",
        "    generated_stock = generator.predict(test_data_combined)\n",
        "\n",
        "    # LSTM 예측과 G 모델의 예측을 평균\n",
        "    final_predicted_stock = (predicted_stock_lstm + generated_stock.mean(axis=1)) / 2.0\n",
        "\n",
        "    return final_predicted_stock\n",
        "\n",
        "# 학습 후 예측\n",
        "after_gan_predictions = final_predictions(generator, X_test, batch_size=32)\n",
        "\n",
        "# 전체 예측 출력\n",
        "print(after_gan_predictions)\n",
        "\n",
        "# 최종 예측 출력 (평균)\n",
        "average_prediction = np.mean(after_gan_predictions)\n",
        "print(average_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMa2AB2NwKs9",
        "outputId": "cdf5ebc6-8aa5-4d81-9303-04254d473839"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 1s 789ms/step\n",
            "0/10 [D loss: 0.6467320322990417 | D accuracy: 48.4375] [G loss: 0.6923903822898865]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/10 [D loss: 0.6353811025619507 | D accuracy: 48.4375] [G loss: 0.6917588710784912]\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "2/10 [D loss: 0.621012419462204 | D accuracy: 57.8125] [G loss: 0.6911375522613525]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "3/10 [D loss: 0.609836995601654 | D accuracy: 65.625] [G loss: 0.6899081468582153]\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "4/10 [D loss: 0.5848531275987625 | D accuracy: 82.8125] [G loss: 0.6919918656349182]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "5/10 [D loss: 0.572172611951828 | D accuracy: 75.0] [G loss: 0.690685510635376]\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "6/10 [D loss: 0.5602634102106094 | D accuracy: 73.4375] [G loss: 0.6893919706344604]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "7/10 [D loss: 0.554436445236206 | D accuracy: 79.6875] [G loss: 0.6892921924591064]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "8/10 [D loss: 0.5505445152521133 | D accuracy: 68.75] [G loss: 0.6864761710166931]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "9/10 [D loss: 0.556775376200676 | D accuracy: 59.375] [G loss: 0.6840354204177856]\n",
            "16/16 [==============================] - 0s 5ms/step\n",
            "16/16 [==============================] - 1s 9ms/step\n",
            "[[5.6647887 5.6450806 5.625985  ... 5.920446  5.9143167 5.90173  ]\n",
            " [6.8541455 6.8344374 6.815342  ... 7.1098027 7.1036735 7.091087 ]\n",
            " [7.1531034 7.133395  7.1143    ... 7.4087605 7.4026313 7.3900447]\n",
            " ...\n",
            " [2.6217601 2.602052  2.5829563 ... 2.8774176 2.871288  2.8587012]\n",
            " [2.0464923 2.0267842 2.0076885 ... 2.3021498 2.2960203 2.2834334]\n",
            " [2.9979627 2.9782546 2.959159  ... 3.2536201 3.2474906 3.2349038]]\n",
            "9.016599\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 코드\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import LSTM, Dense\n",
        "import yfinance as yf\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "# 야후 파이낸스 주식 정보 가져오기\n",
        "# 주식 종목 지정 필수\n",
        "\n",
        "ticker = 'NVDA'\n",
        "end_date = datetime.now()\n",
        "start_date = end_date - timedelta(days=3650)\n",
        "data = yf.download(ticker, start=start_date, end=end_date)\n",
        "\n",
        "closing_prices = data['Close']\n",
        "volume = data['Volume']\n",
        "moving_average = closing_prices.rolling(window=120).mean()\n",
        "\n",
        "# 6개 알고리즘 (Bollinger, MACD, MOK, RSI, STCK, WR) 전체 코드\n",
        "\n",
        "\n",
        "# Bollinger\n",
        "\n",
        "def calculate_bollinger_bands(closing_prices, window=20, num_std=2):\n",
        "    rolling_mean = closing_prices.rolling(window=window).mean()\n",
        "    rolling_std = closing_prices.rolling(window=window).std()\n",
        "\n",
        "    upper_band = rolling_mean + num_std * rolling_std\n",
        "    lower_band = rolling_mean - num_std * rolling_std\n",
        "    return upper_band, lower_band\n",
        "\n",
        "upper_band, lower_band = calculate_bollinger_bands(closing_prices)\n",
        "bollinger_bands_data = pd.DataFrame({'Upper Band': upper_band, 'Lower Band': lower_band})\n",
        "\n",
        "bollinger_bands_data.dropna(inplace=True)\n",
        "result = np.where(closing_prices > upper_band, (closing_prices - upper_band) / (upper_band - lower_band), np.where(closing_prices < lower_band, (closing_prices - lower_band) / (lower_band - upper_band), np.nan))\n",
        "bollinger_bands_data['Result'] = pd.Series(result, index=closing_prices.index)\n",
        "\n",
        "bollinger_bands_data['Result'].fillna(method='ffill', inplace=True)\n",
        "\n",
        "bollinger_bands_data['Prediction'] = bollinger_bands_data['Result'].shift(-1)\n",
        "bollinger_bands_data.dropna(inplace=True)\n",
        "bollinger_bands_data['Trend'] = bollinger_bands_data['Prediction'].diff()\n",
        "bollinger_bands_data['Trend'] = (bollinger_bands_data['Trend'] + 1) / 2\n",
        "\n",
        "min_pred = bollinger_bands_data['Prediction'].min()\n",
        "max_pred = bollinger_bands_data['Prediction'].max()\n",
        "bollinger_bands_data['Prediction'] = (bollinger_bands_data['Prediction'] - min_pred) / (max_pred - min_pred)\n",
        "\n",
        "min_trend = bollinger_bands_data['Trend'].min()\n",
        "max_trend = bollinger_bands_data['Trend'].max()\n",
        "bollinger_bands_data['Trend'] = (bollinger_bands_data['Trend'] - min_trend) / (max_trend - min_trend)*100\n",
        "\n",
        "pd.set_option('display.float_format', '{:.4f}'.format)\n",
        "\n",
        "\n",
        "# MACD\n",
        "\n",
        "def calculate_macd(closing_prices):\n",
        "\n",
        "    short_ema = closing_prices.ewm(span=26, adjust=False).mean()\n",
        "    long_ema = closing_prices.ewm(span=12, adjust=False).mean()\n",
        "\n",
        "    dif = short_ema - long_ema\n",
        "    signal_line = dif.ewm(span=9, adjust=False).mean()\n",
        "    histogram = dif - signal_line\n",
        "\n",
        "    return dif, signal_line, histogram\n",
        "\n",
        "dif, signal_line, histogram = calculate_macd(closing_prices)\n",
        "macd_data = pd.DataFrame({'DIF': dif, 'Signal Line': signal_line, 'Histogram': histogram})\n",
        "macd_data['Result'] = np.where(macd_data['DIF'] > macd_data['Signal Line'], 1, 0)\n",
        "\n",
        "macd_data['Result'] = (macd_data['Result'].rolling(window=20, min_periods=1).mean())*100\n",
        "macd_data['Prediction'] = macd_data['Result'].shift(-1)\n",
        "macd_data.dropna(inplace=True)\n",
        "\n",
        "# MOK\n",
        "\n",
        "def calculate_mok(closing_prices, period=14, ma_period=20):\n",
        "    returns = closing_prices.pct_change()\n",
        "\n",
        "    moving_average = closing_prices.rolling(window=ma_period).mean()\n",
        "    momentum = closing_prices.diff(period)\n",
        "    normalized_momentum = 100 * (momentum - np.min(momentum)) / (np.max(momentum) - np.min(momentum))\n",
        "\n",
        "    return normalized_momentum\n",
        "\n",
        "mok_values = calculate_mok(closing_prices)\n",
        "\n",
        "# RSI\n",
        "\n",
        "def calculate_rsi(closing_prices, window=20):\n",
        "    price_changes = closing_prices.diff()\n",
        "    up_changes = price_changes.where(price_changes > 0, 0)\n",
        "    down_changes = -price_changes.where(price_changes < 0, 0)\n",
        "\n",
        "    avg_up_changes = up_changes.rolling(window=window, min_periods=1).mean()\n",
        "    avg_down_changes = down_changes.rolling(window=window, min_periods=1).mean()\n",
        "\n",
        "    rs = avg_up_changes / avg_down_changes\n",
        "    rsi = 100 - (100 / (1 + rs))\n",
        "\n",
        "    return rsi\n",
        "\n",
        "rsi_result = calculate_rsi(closing_prices, window=len(closing_prices)//10)  # 기간을 데이터의 1/10로 동적으로 설정\n",
        "rsi_result.dropna(inplace=True)\n",
        "\n",
        "# STCK\n",
        "\n",
        "def calculate_stck(closing_prices, window=20):\n",
        "    lowest_low = closing_prices.rolling(window=window).min()\n",
        "    highest_high = closing_prices.rolling(window=window).max()\n",
        "\n",
        "    stck = 100 * (closing_prices - lowest_low) / (highest_high - lowest_low)\n",
        "\n",
        "    stck_ma = stck.rolling(window=20).mean()\n",
        "\n",
        "    return stck_ma\n",
        "\n",
        "stck_result = calculate_stck(closing_prices)\n",
        "stck_result.dropna(inplace=True)\n",
        "\n",
        "# WR\n",
        "\n",
        "def calculate_williams_r(closing_prices, period=14):\n",
        "    high_prices = data['High']\n",
        "    low_prices = data['Low']\n",
        "    moving_average = closing_prices.rolling(window=20).mean()\n",
        "\n",
        "    highest_high = high_prices.rolling(window=period).max()\n",
        "    lowest_low = low_prices.rolling(window=period).min()\n",
        "\n",
        "    williams_r = (highest_high - moving_average) / (highest_high - lowest_low) * -100\n",
        "\n",
        "    normalized_williams_r = 100 * (williams_r + 100) / 100\n",
        "\n",
        "    return normalized_williams_r\n",
        "\n",
        "wr_result = calculate_williams_r(closing_prices)\n",
        "\n",
        "# 6개 지표들을 하나의 데이터 프레임에 합치기\n",
        "\n",
        "final_df = pd.DataFrame({\n",
        "    'Bollinger': bollinger_bands_data['Trend'],\n",
        "    'MACD': macd_data['Prediction'],\n",
        "    'MOK': mok_values,\n",
        "    'RSI': rsi_result,\n",
        "    'STCK': stck_result,\n",
        "    'WR': wr_result\n",
        "})\n",
        "\n",
        "# NaN 값 제거\n",
        "final_df.dropna(inplace=True)\n",
        "\n",
        "# 최종 데이터 프레임 출력\n",
        "print(final_df)\n",
        "\n",
        "# LSTM 모델\n",
        "\n",
        "\n",
        "final_df_values = final_df.values\n",
        "data_values = data.values\n",
        "\n",
        "# 입력 시퀀스에 대한 타임 스텝(T)을 정의합니다.\n",
        "\n",
        "T = 10  # 원하는대로 조정할 수 있습니다.\n",
        "\n",
        "# 입력 및 타겟을 위한 데이터 시퀀스 생성\n",
        "final_df_sequences = []\n",
        "data_sequences = []\n",
        "\n",
        "for i in range(len(data_values) - T):\n",
        "    final_df_sequences.append(final_df_values[i:i+T])\n",
        "    data_sequences.append(closing_prices.iloc[i+T])\n",
        "\n",
        "filtered_final_df_sequences = []\n",
        "filtered_data_sequences = []\n",
        "\n",
        "# 시퀀스 길이 맞추기\n",
        "for i, seq in enumerate(final_df_sequences):\n",
        "    if len(seq) == 10:\n",
        "        filtered_final_df_sequences.append(seq)\n",
        "        filtered_data_sequences.append(data_sequences[i])\n",
        "\n",
        "final_df_sequences = filtered_final_df_sequences\n",
        "data_sequences = filtered_data_sequences\n",
        "\n",
        "# 시퀀스를 넘파이 배열로 변환\n",
        "X = np.array(final_df_sequences)\n",
        "y = np.array(data_sequences)\n",
        "\n",
        "\n",
        "# 데이터를 훈련 및 테스트 세트로 분할\n",
        "split_ratio = 0.8  # 분할 비율을 조정할 수 있습니다.\n",
        "split_index = int(split_ratio * len(X))\n",
        "\n",
        "X_train, X_test = X[:split_index], X[split_index:]\n",
        "y_train, y_test = y[:split_index], y[split_index:]\n",
        "\n",
        "# LSTM 모델 구축\n",
        "model = Sequential()\n",
        "\n",
        "# 첫 번째 LSTM 레이어 (시퀀스 출력을 반환하여 다음 레이어로 전달)\n",
        "model.add(LSTM(50, activation='relu', return_sequences=True, input_shape=(T, 6)))\n",
        "\n",
        "# 두 번째 LSTM 레이어\n",
        "model.add(LSTM(50, activation='relu', return_sequences=True))\n",
        "\n",
        "# 세 번째 LSTM 레이어 (시퀀스 출력을 반환하지 않음)\n",
        "model.add(LSTM(50, activation='relu'))\n",
        "\n",
        "# 출력 레이어\n",
        "model.add(Dense(1))\n",
        "\n",
        "# 모델 컴파일\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "\n",
        "# 모델 훈련\n",
        "model.fit(X_train, y_train, epochs=10, batch_size=32, validation_split=0.2)\n",
        "\n",
        "# 테스트 데이터에서 모델 평가\n",
        "loss = model.evaluate(X_test, y_test)\n",
        "print(f'Test Loss: {loss}')\n",
        "\n",
        "# 예측 수행\n",
        "predictions = model.predict(X_test)\n",
        "print(predictions)\n",
        "\n",
        "# 예측 결과를 데이터 프레임으로 변환\n",
        "predictions_df = pd.DataFrame(predictions, columns=[\"Predictions\"])\n",
        "print(predictions_df)\n",
        "\n",
        "# LSTM 모델의 예측 값을 6개 지표 값과 함께 생성 모델 입력값으로 넣기 위해\n",
        "\n",
        "# T 타임 스텝 만큼의 길이를 고려하여 LSTM 예측값 넣기\n",
        "final_df[\"LSTM_Predictions\"] = np.nan\n",
        "final_df[\"LSTM_Predictions\"].iloc[-len(predictions_df):] = predictions_df[\"Predictions\"].values\n",
        "\n",
        "# NaN 값 제거\n",
        "final_df.dropna(inplace=True)\n",
        "\n",
        "# 다시 시퀀스로 변환\n",
        "final_df_values = final_df.values\n",
        "\n",
        "final_df_sequences = []\n",
        "for i in range(len(data_values) - T):\n",
        "    final_df_sequences.append(final_df_values[i:i+T])\n",
        "\n",
        "X = np.array(final_df_sequences)\n",
        "\n",
        "# 생성 및 구분 모델\n",
        "\n",
        "from tensorflow.keras.layers import Reshape, Flatten, LeakyReLU, BatchNormalization\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "# 생성 모델 G (주식 가격의 시퀀스를 생성)\n",
        "def build_generator(input_shape=(T, 7)):  # 입력 값 : LSTM 모델 예측 + 6개 지표 값\n",
        "    model = Sequential()\n",
        "\n",
        "    # LSTM 레이어와 BatchNormalization, LeakyReLU 활성화 함수를 이용하여 시퀀스를 학습\n",
        "    model.add(LSTM(128, return_sequences=True, input_shape=input_shape))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(BatchNormalization(momentum=0.8))\n",
        "\n",
        "    # Flatten layer를 제거하고, T 길이의 시퀀스를 생성하도록 수정\n",
        "    model.add(LSTM(128))\n",
        "    model.add(Dense(T, activation='linear'))\n",
        "\n",
        "    noise = tf.keras.layers.Input(shape=input_shape)\n",
        "    generated_sequence = model(noise)\n",
        "\n",
        "    return Model(noise, generated_sequence)\n",
        "\n",
        "# 구분 모델 D (주가의 실제 시퀀스와 생성된 시퀀스를 구분)\n",
        "def build_discriminator(input_shape=(T, 1)):\n",
        "    model = Sequential()\n",
        "\n",
        "    # LSTM 레이어와 LeakyReLU 활성화 함수를 사용하여 시퀀스를 처리\n",
        "    model.add(LSTM(128, input_shape=input_shape))\n",
        "    model.add(LeakyReLU(alpha=0.2))\n",
        "    model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "    sequence = tf.keras.layers.Input(shape=input_shape)\n",
        "    validity = model(sequence)\n",
        "\n",
        "    # 주어진 시퀀스가 실제인지 생성된 것인지에 대한 확률\n",
        "    return Model(sequence, validity)\n",
        "\n",
        "    # 학습 함수 (생성된 주식 가격 시퀀스와 실제 주식 가격 시퀀스를 사용하여 판별자를 학습)\n",
        "\n",
        "def train_gan(generator, discriminator, combined, epochs, batch_size=32):\n",
        "    valid = np.ones((batch_size, 1))\n",
        "    fake = np.zeros((batch_size, 1))\n",
        "\n",
        "    for epoch in range(epochs):\n",
        "        idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
        "        sequences = X_train[idx]\n",
        "\n",
        "        # 주식 예측값 생성\n",
        "        predicted_stock = model.predict(sequences)\n",
        "\n",
        "        # LSTM 예측값을 sequences에 추가\n",
        "        predicted_stock_reshaped = predicted_stock.reshape(batch_size, 1, 1)  # (batch_size, 1, 1) 형태로 변환\n",
        "        predicted_stock_expanded = np.repeat(predicted_stock_reshaped, T, axis=1)  # predicted_stock_reshaped를 T 타임 스텝만큼 확장\n",
        "        X_train_combined = np.concatenate([sequences, predicted_stock_expanded], axis=2)\n",
        "\n",
        "        generated_stock = generator.predict(X_train_combined)\n",
        "        generated_stock_reshaped = generated_stock.reshape(batch_size, T, 1)\n",
        "\n",
        "        # 판별자 학습\n",
        "        d_loss_real = discriminator.train_on_batch(predicted_stock_expanded, valid)\n",
        "        d_loss_fake = discriminator.train_on_batch(generated_stock_reshaped, fake)\n",
        "        d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
        "\n",
        "        # 생성자 학습\n",
        "        g_loss = combined.train_on_batch(X_train_combined, valid)\n",
        "\n",
        "        print(f\"{epoch}/{epochs} [D loss: {d_loss[0]} | D accuracy: {100 * d_loss[1]}] [G loss: {g_loss}]\")\n",
        "\n",
        "        # 판별자 및 생성자 모델 초기화, 생성자와 판별자를 결합하여\n",
        "\n",
        "discriminator = build_discriminator()\n",
        "discriminator.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5), metrics=['accuracy'])\n",
        "\n",
        "generator = build_generator()\n",
        "z = tf.keras.layers.Input(shape=(T, 7))\n",
        "generated_sequence = generator(z)\n",
        "\n",
        "discriminator.trainable = False\n",
        "validity = discriminator(generated_sequence)\n",
        "\n",
        "combined = Model(z, validity)\n",
        "combined.compile(loss='binary_crossentropy', optimizer=Adam(0.0002, 0.5))\n",
        "\n",
        "#학습 시작\n",
        "train_gan(generator, discriminator, combined, epochs=10, batch_size=32)\n",
        "\n",
        "# 학습 후 최종 예측을 수행하는 함수\n",
        "def final_predictions(generator, test_data, batch_size=32):\n",
        "    predicted_stock_lstm = model.predict(test_data)\n",
        "\n",
        "    # LSTM 예측값을 test_data에 추가\n",
        "    predicted_stock_expanded = np.repeat(predicted_stock_lstm[:, np.newaxis], T, axis=1)  # (batch_size, T)로 형태 변경\n",
        "    test_data_combined = np.concatenate([test_data, predicted_stock_expanded], axis=2)\n",
        "\n",
        "    # G 모델을 사용하여 합성 주가를 생성\n",
        "    generated_stock = generator.predict(test_data_combined)\n",
        "\n",
        "    # LSTM 예측과 G 모델의 예측을 평균\n",
        "    final_predicted_stock = (predicted_stock_lstm + generated_stock.mean(axis=1)) / 2.0\n",
        "\n",
        "    return final_predicted_stock\n",
        "\n",
        "# 학습 후 예측\n",
        "after_gan_predictions = final_predictions(generator, X_test, batch_size=32)\n",
        "\n",
        "# 전체 예측 출력\n",
        "print(after_gan_predictions)\n",
        "\n",
        "# 최종 예측 출력 (평균)\n",
        "average_prediction = np.mean(after_gan_predictions)\n",
        "print(average_prediction)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDSo_MwLwk-J",
        "outputId": "fc1f7575-447b-4c8c-9270-5dc549171a62"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r[*********************100%%**********************]  1 of 1 completed\n",
            "            Bollinger    MACD     MOK     RSI    STCK      WR\n",
            "Date                                                         \n",
            "2014-02-13    43.8505 55.0000 40.3087 55.0434 50.1006 27.0795\n",
            "2014-02-14    28.7469 55.0000 40.3868 56.6366 51.5429 25.5513\n",
            "2014-02-18    42.4711 55.0000 40.3676 56.6001 53.3726 28.8534\n",
            "2014-02-19    51.9447 55.0000 40.4145 57.2606 54.9226 29.2618\n",
            "2014-02-20    47.6999 55.0000 40.4602 58.9275 56.5726 28.8429\n",
            "...               ...     ...     ...     ...     ...     ...\n",
            "2023-10-10    47.6999 60.0000 57.2054 60.5174 35.5890 51.8735\n",
            "2023-10-11    47.6999 55.0000 67.9281 60.7924 38.7841 45.8897\n",
            "2023-10-12    47.6999 50.0000 65.7451 60.8578 41.9000 41.5991\n",
            "2023-10-13    47.6999 45.0000 55.6667 60.1984 45.1522 38.4900\n",
            "2023-10-16    47.6999 40.0000 60.2106 60.6209 49.3747 40.2701\n",
            "\n",
            "[2435 rows x 6 columns]\n",
            "Epoch 1/10\n",
            "49/49 [==============================] - 5s 26ms/step - loss: 618.4954 - val_loss: 10554.1094\n",
            "Epoch 2/10\n",
            "49/49 [==============================] - 1s 17ms/step - loss: 428.5431 - val_loss: 10460.1533\n",
            "Epoch 3/10\n",
            "49/49 [==============================] - 1s 18ms/step - loss: 392.8344 - val_loss: 10198.5518\n",
            "Epoch 4/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 370.1914 - val_loss: 10655.4551\n",
            "Epoch 5/10\n",
            "49/49 [==============================] - 1s 19ms/step - loss: 364.7289 - val_loss: 10306.7109\n",
            "Epoch 6/10\n",
            "49/49 [==============================] - 1s 28ms/step - loss: 397.6345 - val_loss: 10552.8896\n",
            "Epoch 7/10\n",
            "49/49 [==============================] - 1s 28ms/step - loss: 445.0551 - val_loss: 10308.0479\n",
            "Epoch 8/10\n",
            "49/49 [==============================] - 1s 17ms/step - loss: 388.7135 - val_loss: 9680.7246\n",
            "Epoch 9/10\n",
            "49/49 [==============================] - 1s 16ms/step - loss: 377.8710 - val_loss: 9853.7695\n",
            "Epoch 10/10\n",
            "49/49 [==============================] - 1s 19ms/step - loss: 393.6985 - val_loss: 10444.5195\n",
            "16/16 [==============================] - 0s 5ms/step - loss: 41754.7070\n",
            "Test Loss: 41754.70703125\n",
            "16/16 [==============================] - 0s 4ms/step\n",
            "[[ 37.937496  ]\n",
            " [ 34.11591   ]\n",
            " [ 45.942245  ]\n",
            " [ 34.511982  ]\n",
            " [ 19.204563  ]\n",
            " [ 66.97609   ]\n",
            " [ 75.569496  ]\n",
            " [ 62.11281   ]\n",
            " [ 53.297596  ]\n",
            " [ 42.03127   ]\n",
            " [ 34.1794    ]\n",
            " [ 36.75917   ]\n",
            " [ 36.46301   ]\n",
            " [ 40.04215   ]\n",
            " [ 35.757454  ]\n",
            " [ 28.640553  ]\n",
            " [ 39.541607  ]\n",
            " [ 35.055473  ]\n",
            " [ 40.056667  ]\n",
            " [ 39.851433  ]\n",
            " [ 36.99797   ]\n",
            " [-11.742714  ]\n",
            " [  3.7435117 ]\n",
            " [ 49.152576  ]\n",
            " [ 48.287266  ]\n",
            " [ 24.361391  ]\n",
            " [ 31.644413  ]\n",
            " [ 27.550245  ]\n",
            " [ 46.08325   ]\n",
            " [ 45.199734  ]\n",
            " [ 22.392715  ]\n",
            " [ 27.139189  ]\n",
            " [ 12.995855  ]\n",
            " [ 18.915031  ]\n",
            " [ 20.664022  ]\n",
            " [ 21.090769  ]\n",
            " [ 18.707859  ]\n",
            " [ 18.858742  ]\n",
            " [ 19.480064  ]\n",
            " [ 18.359354  ]\n",
            " [ 19.435274  ]\n",
            " [ 20.374983  ]\n",
            " [ 18.377491  ]\n",
            " [ 18.05667   ]\n",
            " [ 17.41704   ]\n",
            " [ 18.019901  ]\n",
            " [ 19.235743  ]\n",
            " [ 15.499111  ]\n",
            " [ 16.600372  ]\n",
            " [ 18.507332  ]\n",
            " [ 20.038013  ]\n",
            " [ 19.475464  ]\n",
            " [ 20.373808  ]\n",
            " [ 21.009853  ]\n",
            " [ 22.962317  ]\n",
            " [ 25.162882  ]\n",
            " [ 25.398365  ]\n",
            " [ 25.414312  ]\n",
            " [ 25.936203  ]\n",
            " [ 24.882223  ]\n",
            " [ 23.033482  ]\n",
            " [ 22.483532  ]\n",
            " [ 22.205368  ]\n",
            " [ 23.01647   ]\n",
            " [ 22.389503  ]\n",
            " [ 24.216316  ]\n",
            " [ 25.89466   ]\n",
            " [ 26.849222  ]\n",
            " [ 27.565815  ]\n",
            " [ 26.140366  ]\n",
            " [ 29.594385  ]\n",
            " [ 34.358234  ]\n",
            " [ 39.778458  ]\n",
            " [ 34.309917  ]\n",
            " [ 31.082275  ]\n",
            " [ 27.358011  ]\n",
            " [ 19.614037  ]\n",
            " [ 13.298377  ]\n",
            " [  9.393975  ]\n",
            " [ 10.064836  ]\n",
            " [ 17.492353  ]\n",
            " [ 23.626211  ]\n",
            " [ 24.81298   ]\n",
            " [ 25.685352  ]\n",
            " [ 21.204847  ]\n",
            " [ 26.751846  ]\n",
            " [ 29.710295  ]\n",
            " [ 29.533524  ]\n",
            " [ 30.37536   ]\n",
            " [ 28.46222   ]\n",
            " [ 27.67465   ]\n",
            " [ 26.90263   ]\n",
            " [ 26.011662  ]\n",
            " [ 25.521982  ]\n",
            " [ 23.980837  ]\n",
            " [ 26.564362  ]\n",
            " [ 23.389751  ]\n",
            " [  7.209719  ]\n",
            " [  4.5165973 ]\n",
            " [ 11.869259  ]\n",
            " [ -3.5673487 ]\n",
            " [ 37.48868   ]\n",
            " [ 21.67619   ]\n",
            " [ -3.5584693 ]\n",
            " [ 24.907337  ]\n",
            " [ 20.932556  ]\n",
            " [ 21.064495  ]\n",
            " [ 22.939215  ]\n",
            " [ 29.703445  ]\n",
            " [ 26.605473  ]\n",
            " [ 19.330347  ]\n",
            " [ 21.768114  ]\n",
            " [ 30.741602  ]\n",
            " [ 40.594425  ]\n",
            " [ 31.956318  ]\n",
            " [ 34.26242   ]\n",
            " [ 28.374819  ]\n",
            " [ 11.713279  ]\n",
            " [ 18.127754  ]\n",
            " [ 26.69025   ]\n",
            " [ 29.050282  ]\n",
            " [ 28.87295   ]\n",
            " [ 25.872105  ]\n",
            " [ 23.851217  ]\n",
            " [ 24.484934  ]\n",
            " [ 22.476122  ]\n",
            " [ 23.366     ]\n",
            " [ 17.24873   ]\n",
            " [ 23.064045  ]\n",
            " [ 23.742624  ]\n",
            " [ 23.591803  ]\n",
            " [ 23.30347   ]\n",
            " [ 23.56455   ]\n",
            " [ 24.252174  ]\n",
            " [ 27.092451  ]\n",
            " [ 29.491522  ]\n",
            " [ 30.416815  ]\n",
            " [ 31.0926    ]\n",
            " [ 30.554419  ]\n",
            " [ 30.394794  ]\n",
            " [ 29.453167  ]\n",
            " [ 28.727129  ]\n",
            " [ 28.80932   ]\n",
            " [ 30.25103   ]\n",
            " [ 35.46399   ]\n",
            " [ 39.754223  ]\n",
            " [ 45.046173  ]\n",
            " [ 42.734436  ]\n",
            " [ 41.910107  ]\n",
            " [ 41.54137   ]\n",
            " [ 40.91837   ]\n",
            " [ 31.350014  ]\n",
            " [ 38.04756   ]\n",
            " [ 41.441624  ]\n",
            " [ 42.57778   ]\n",
            " [ 31.678402  ]\n",
            " [ 30.36557   ]\n",
            " [ 35.66666   ]\n",
            " [ 29.790459  ]\n",
            " [ 29.488558  ]\n",
            " [ 29.06742   ]\n",
            " [ 27.295408  ]\n",
            " [ 28.06391   ]\n",
            " [ 21.187862  ]\n",
            " [ 24.533884  ]\n",
            " [ 28.071678  ]\n",
            " [ 27.020521  ]\n",
            " [ 27.725632  ]\n",
            " [ 28.236881  ]\n",
            " [ 28.877695  ]\n",
            " [ 28.846903  ]\n",
            " [ 28.961395  ]\n",
            " [ 28.325916  ]\n",
            " [ 27.598824  ]\n",
            " [ 27.028667  ]\n",
            " [ 26.385386  ]\n",
            " [ 26.025932  ]\n",
            " [ 26.18484   ]\n",
            " [ 27.163345  ]\n",
            " [ 30.801323  ]\n",
            " [ 30.355928  ]\n",
            " [ 29.340008  ]\n",
            " [ 17.843987  ]\n",
            " [ 21.679714  ]\n",
            " [ 19.798893  ]\n",
            " [ 16.28616   ]\n",
            " [ 19.207617  ]\n",
            " [ 23.078323  ]\n",
            " [ 26.54864   ]\n",
            " [ 26.783632  ]\n",
            " [ 25.667526  ]\n",
            " [ 25.5045    ]\n",
            " [ 21.406813  ]\n",
            " [ 20.824926  ]\n",
            " [ 12.745193  ]\n",
            " [ 20.165918  ]\n",
            " [ 28.969444  ]\n",
            " [ 21.947895  ]\n",
            " [ 36.35387   ]\n",
            " [ 22.120995  ]\n",
            " [ 21.76081   ]\n",
            " [ 17.789011  ]\n",
            " [ 19.537962  ]\n",
            " [ 26.281055  ]\n",
            " [ 13.841473  ]\n",
            " [ 18.9911    ]\n",
            " [ 29.69343   ]\n",
            " [ 17.141829  ]\n",
            " [ 25.043798  ]\n",
            " [ 28.551918  ]\n",
            " [ 28.127207  ]\n",
            " [ 28.648607  ]\n",
            " [ 29.277222  ]\n",
            " [ 24.912767  ]\n",
            " [ 23.829351  ]\n",
            " [ 22.955074  ]\n",
            " [ 22.045948  ]\n",
            " [ 12.940004  ]\n",
            " [ 11.767544  ]\n",
            " [ 30.029097  ]\n",
            " [ 22.788404  ]\n",
            " [ 21.592388  ]\n",
            " [ 20.954346  ]\n",
            " [ 22.642048  ]\n",
            " [ 22.179873  ]\n",
            " [ 26.119549  ]\n",
            " [ 25.706373  ]\n",
            " [ 13.170317  ]\n",
            " [ 21.051975  ]\n",
            " [ 21.650393  ]\n",
            " [ 23.51189   ]\n",
            " [ 24.801603  ]\n",
            " [ 25.964209  ]\n",
            " [ 28.384935  ]\n",
            " [ 30.703558  ]\n",
            " [ 35.468426  ]\n",
            " [ 39.724987  ]\n",
            " [ 42.193977  ]\n",
            " [ 45.375034  ]\n",
            " [ 46.485992  ]\n",
            " [ 48.278862  ]\n",
            " [ 49.33172   ]\n",
            " [ 52.036617  ]\n",
            " [ 55.020416  ]\n",
            " [ 58.776035  ]\n",
            " [ 59.746567  ]\n",
            " [ 57.64271   ]\n",
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            " [ 38.334896  ]\n",
            " [ 35.910618  ]\n",
            " [ 35.97935   ]\n",
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            " [ 35.933327  ]\n",
            " [ 34.104824  ]\n",
            " [ 29.619297  ]\n",
            " [ 23.934723  ]\n",
            " [ 20.691349  ]\n",
            " [ 21.401445  ]\n",
            " [ 19.75096   ]\n",
            " [ 18.538897  ]\n",
            " [ 18.870794  ]\n",
            " [ 18.330456  ]\n",
            " [ 25.631824  ]\n",
            " [ 26.841751  ]\n",
            " [ 15.542883  ]\n",
            " [ -9.373403  ]\n",
            " [ 19.687061  ]\n",
            " [ 22.412895  ]\n",
            " [ 22.349968  ]\n",
            " [ 23.656473  ]\n",
            " [ 24.269012  ]\n",
            " [ 20.673748  ]\n",
            " [ 21.702038  ]\n",
            " [ 21.618965  ]\n",
            " [ 16.096199  ]\n",
            " [ 40.913277  ]\n",
            " [ 32.460045  ]\n",
            " [ 36.79658   ]\n",
            " [ 30.553074  ]\n",
            " [ 33.06068   ]\n",
            " [ 28.863886  ]\n",
            " [ 29.003563  ]\n",
            " [ 31.936968  ]\n",
            " [ -8.608638  ]\n",
            " [ 42.960934  ]\n",
            " [ 32.334522  ]\n",
            " [ 34.17725   ]\n",
            " [ 53.783012  ]\n",
            " [ 41.097466  ]\n",
            " [ 30.850868  ]\n",
            " [ 32.238647  ]\n",
            " [ 29.669966  ]\n",
            " [ 20.997541  ]\n",
            " [  8.08787   ]\n",
            " [ 19.896126  ]\n",
            " [ 17.753075  ]\n",
            " [ 19.03549   ]\n",
            " [ 20.282341  ]\n",
            " [ 20.826134  ]\n",
            " [ 20.596136  ]\n",
            " [ 20.281755  ]\n",
            " [ 20.613615  ]\n",
            " [ 19.724539  ]\n",
            " [ 14.773161  ]\n",
            " [ 13.423766  ]\n",
            " [  9.895319  ]\n",
            " [ 13.84714   ]\n",
            " [ 21.281637  ]\n",
            " [ 11.371994  ]\n",
            " [ 14.511411  ]\n",
            " [ 25.650566  ]\n",
            " [-11.040136  ]\n",
            " [ 28.202751  ]\n",
            " [-13.705737  ]\n",
            " [ 39.855267  ]\n",
            " [ 58.55276   ]\n",
            " [ 37.02618   ]\n",
            " [ 36.784973  ]\n",
            " [ 45.555225  ]\n",
            " [ 31.248695  ]\n",
            " [ 20.866442  ]\n",
            " [ 19.63896   ]\n",
            " [ 28.089905  ]\n",
            " [ 41.506535  ]\n",
            " [ -0.5981001 ]\n",
            " [ 44.515163  ]\n",
            " [  4.650993  ]\n",
            " [ 31.333797  ]\n",
            " [ 10.490503  ]\n",
            " [ 42.42016   ]\n",
            " [ 12.203165  ]\n",
            " [ 35.43234   ]\n",
            " [ 39.377644  ]\n",
            " [ 25.725388  ]\n",
            " [ 44.61439   ]\n",
            " [ 35.723534  ]\n",
            " [ 31.642126  ]\n",
            " [ 40.95215   ]\n",
            " [ 54.25174   ]\n",
            " [ 12.613626  ]\n",
            " [  9.73942   ]\n",
            " [ 15.68398   ]\n",
            " [ 31.447138  ]\n",
            " [ 40.33948   ]\n",
            " [ 37.377815  ]\n",
            " [ 32.68563   ]\n",
            " [ 34.05866   ]\n",
            " [ 44.270443  ]\n",
            " [ 36.368984  ]\n",
            " [ 38.019115  ]\n",
            " [ 47.52495   ]\n",
            " [ 48.210846  ]\n",
            " [ 32.645256  ]\n",
            " [ 44.474834  ]\n",
            " [ 43.391087  ]\n",
            " [ 26.827835  ]\n",
            " [ 18.007471  ]\n",
            " [ 25.526329  ]\n",
            " [ 42.74217   ]\n",
            " [ 35.91424   ]\n",
            " [ 33.24969   ]\n",
            " [-10.12674   ]\n",
            " [ 29.192732  ]\n",
            " [ 37.067482  ]\n",
            " [ 35.957317  ]\n",
            " [ 32.659367  ]\n",
            " [ 27.6067    ]\n",
            " [ 37.1615    ]\n",
            " [ 48.632935  ]\n",
            " [ 32.337994  ]\n",
            " [ 16.708511  ]\n",
            " [ 26.132687  ]\n",
            " [ 17.756279  ]\n",
            " [ 38.20164   ]\n",
            " [ 32.30915   ]\n",
            " [ 37.147434  ]\n",
            " [ 29.135052  ]\n",
            " [ 27.063053  ]\n",
            " [ 33.46548   ]\n",
            " [ 35.213127  ]\n",
            " [ 28.678078  ]\n",
            " [ 25.45829   ]\n",
            " [ 23.93806   ]\n",
            " [ 20.125797  ]\n",
            " [ 21.257387  ]\n",
            " [ 24.290047  ]\n",
            " [ 21.991688  ]\n",
            " [ 13.000979  ]\n",
            " [ 28.80169   ]\n",
            " [ 46.96004   ]\n",
            " [ 42.93757   ]\n",
            " [ 26.44618   ]\n",
            " [ 27.924393  ]\n",
            " [ 43.942764  ]\n",
            " [ 58.330536  ]\n",
            " [ 60.281487  ]\n",
            " [-26.73781   ]\n",
            " [ 37.753506  ]\n",
            " [ 20.501616  ]\n",
            " [ 53.183434  ]\n",
            " [ 44.582268  ]\n",
            " [ 31.174143  ]\n",
            " [ 33.62824   ]\n",
            " [ 59.951763  ]\n",
            " [ 50.119175  ]\n",
            " [ 33.57525   ]\n",
            " [ 23.035112  ]\n",
            " [ 35.4515    ]\n",
            " [ 38.25099   ]\n",
            " [ 48.446712  ]\n",
            " [ 41.96172   ]\n",
            " [ -7.9140267 ]\n",
            " [ -9.779675  ]\n",
            " [ 35.743755  ]\n",
            " [ 38.670742  ]\n",
            " [ 34.830826  ]\n",
            " [ 35.7486    ]\n",
            " [ 31.302736  ]\n",
            " [ 44.091618  ]\n",
            " [ 41.342663  ]\n",
            " [ 38.7541    ]\n",
            " [ 39.626797  ]\n",
            " [ 25.60918   ]\n",
            " [ 13.260598  ]\n",
            " [ 13.79868   ]\n",
            " [ 23.415813  ]\n",
            " [ 32.938137  ]\n",
            " [ 28.957697  ]\n",
            " [ 36.376835  ]\n",
            " [ 28.07489   ]\n",
            " [ 30.721611  ]\n",
            " [ 29.431156  ]\n",
            " [ 30.895868  ]\n",
            " [ 27.540674  ]\n",
            " [ 36.30811   ]\n",
            " [ 30.233147  ]\n",
            " [ 27.755882  ]\n",
            " [ 33.18522   ]\n",
            " [ 28.036545  ]\n",
            " [ 33.655766  ]\n",
            " [ 53.456524  ]\n",
            " [ 56.209347  ]\n",
            " [ 41.383713  ]\n",
            " [ 37.528645  ]\n",
            " [ 20.956524  ]\n",
            " [ 16.99579   ]\n",
            " [ 13.1187525 ]\n",
            " [ 19.496326  ]\n",
            " [ 20.242952  ]\n",
            " [ 19.049799  ]\n",
            " [ 19.16065   ]\n",
            " [ 17.944805  ]\n",
            " [ 16.037512  ]\n",
            " [ -0.24192058]\n",
            " [ 13.024741  ]\n",
            " [ 18.01659   ]\n",
            " [ 65.36339   ]\n",
            " [ 22.48246   ]\n",
            " [  9.399372  ]\n",
            " [ 13.697263  ]\n",
            " [ 27.12196   ]\n",
            " [ 30.32471   ]\n",
            " [ 22.99545   ]\n",
            " [ 23.921186  ]\n",
            " [ 21.45248   ]\n",
            " [ 22.228378  ]\n",
            " [ 28.761549  ]\n",
            " [ 32.640415  ]\n",
            " [ 52.55254   ]\n",
            " [ 34.832493  ]\n",
            " [ 41.90435   ]\n",
            " [ 38.956623  ]\n",
            " [ 30.202837  ]\n",
            " [ 33.40891   ]\n",
            " [ 38.193645  ]\n",
            " [ 43.726856  ]\n",
            " [ 22.279919  ]\n",
            " [ 24.275974  ]\n",
            " [ 23.092628  ]\n",
            " [ 17.400097  ]\n",
            " [ 20.762651  ]\n",
            " [ 21.27239   ]\n",
            " [ 20.394442  ]\n",
            " [ 14.85655   ]\n",
            " [ 13.470934  ]\n",
            " [ 25.813513  ]]\n",
            "     Predictions\n",
            "0        37.9375\n",
            "1        34.1159\n",
            "2        45.9422\n",
            "3        34.5120\n",
            "4        19.2046\n",
            "..           ...\n",
            "481      21.2724\n",
            "482      20.3944\n",
            "483      14.8566\n",
            "484      13.4709\n",
            "485      25.8135\n",
            "\n",
            "[486 rows x 1 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-254c8a9809bd>:257: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.array(final_df_sequences)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 1s 746ms/step\n",
            "0/10 [D loss: 0.730454295873642 | D accuracy: 15.625] [G loss: 0.6917109489440918]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/10 [D loss: 0.7123456001281738 | D accuracy: 15.625] [G loss: 0.692314624786377]\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "2/10 [D loss: 0.6933015584945679 | D accuracy: 48.4375] [G loss: 0.6927284002304077]\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "3/10 [D loss: 0.6804961264133453 | D accuracy: 75.0] [G loss: 0.693375825881958]\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "4/10 [D loss: 0.6684160828590393 | D accuracy: 93.75] [G loss: 0.6934597492218018]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "5/10 [D loss: 0.6569936573505402 | D accuracy: 100.0] [G loss: 0.694181501865387]\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "6/10 [D loss: 0.6436269581317902 | D accuracy: 100.0] [G loss: 0.6949526071548462]\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "7/10 [D loss: 0.6321579813957214 | D accuracy: 96.875] [G loss: 0.6948208212852478]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "8/10 [D loss: 0.6099732518196106 | D accuracy: 89.0625] [G loss: 0.6959587931632996]\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "9/10 [D loss: 0.603618860244751 | D accuracy: 93.75] [G loss: 0.6964036226272583]\n",
            "16/16 [==============================] - 0s 5ms/step\n",
            "16/16 [==============================] - 1s 10ms/step\n",
            "[[18.858595  18.858597  18.85802   ... 18.997744  18.970577  18.923082 ]\n",
            " [16.947802  16.947803  16.947227  ... 17.08695   17.059784  17.01229  ]\n",
            " [22.86097   22.860971  22.860395  ... 23.000118  22.972952  22.925457 ]\n",
            " ...\n",
            " [ 7.3181214  7.318124   7.3175483 ...  7.4572697  7.4301047  7.382609 ]\n",
            " [ 6.6253133  6.6253157  6.62474   ...  6.7644615  6.7372966  6.6898007]\n",
            " [12.796602  12.796605  12.796029  ... 12.935751  12.908586  12.86109  ]]\n",
            "13.95667\n"
          ]
        }
      ]
    }
  ]
}