ML_Final_Project.ipynb

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  "metadata": {
    "colab": {
      "name": "ML_Final_Project.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EstevanRamos/ML_cryptocurrency_prediction/blob/main/ML_Final_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading Data"
      ],
      "metadata": {
        "id": "nqHjNzhc9B_v"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8LGaqbldkPQ",
        "outputId": "ce88c1c5-2376-4420-94d0-f0f84aa31321"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import distutils\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Model\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.activations import *\n",
        "from keras.utils.vis_utils import plot_model\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from datetime import datetime\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TcVylt7hy9iu"
      },
      "source": [
        "from zipfile import ZipFile\n",
        "file_name = '/content/drive/MyDrive/Machine Learning/g-research-crypto-forecasting.zip'\n",
        "\n",
        "with ZipFile(file_name, 'r') as zip:\n",
        "  zip.extractall('g-research-crypto-forecasting')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWlyjws-z9Zm"
      },
      "source": [
        "data_folder = \"g-research-crypto-forecasting/\"\n",
        "crypto_df = pd.read_csv(data_folder + 'train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "--lK-rZM0yhP",
        "outputId": "fb568c82-6421-4078-d966-290a832db602"
      },
      "source": [
        "crypto_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>Asset_ID</th>\n",
              "      <th>Count</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>VWAP</th>\n",
              "      <th>Target</th>\n",
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              "  </thead>\n",
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              "      <td>8.530000</td>\n",
              "      <td>8.530000</td>\n",
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              "      <td>8.530000</td>\n",
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              "      <td>13827.062093</td>\n",
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              "    <tr>\n",
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              "      <td>32.0</td>\n",
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              "      <td>7.657713</td>\n",
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              "      <th>24236801</th>\n",
              "      <td>1632182400</td>\n",
              "      <td>9</td>\n",
              "      <td>775.0</td>\n",
              "      <td>157.181571</td>\n",
              "      <td>157.250000</td>\n",
              "      <td>156.700000</td>\n",
              "      <td>156.943857</td>\n",
              "      <td>4.663725e+03</td>\n",
              "      <td>156.994319</td>\n",
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              "      <td>34.0</td>\n",
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              "      <td>2438.000000</td>\n",
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              "      <td>380.0</td>\n",
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              "      <td>0.091527</td>\n",
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              "<p>24236806 rows × 10 columns</p>\n",
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            ],
            "text/plain": [
              "           timestamp  Asset_ID  Count  ...        Volume          VWAP    Target\n",
              "0         1514764860         2   40.0  ...  1.923301e+01   2373.116392 -0.004218\n",
              "1         1514764860         0    5.0  ...  7.838000e+01      8.530000 -0.014399\n",
              "2         1514764860         1  229.0  ...  3.155006e+01  13827.062093 -0.014643\n",
              "3         1514764860         5   32.0  ...  6.626713e+03      7.657713 -0.013922\n",
              "4         1514764860         7    5.0  ...  1.210873e+02     25.891363 -0.008264\n",
              "...              ...       ...    ...  ...           ...           ...       ...\n",
              "24236801  1632182400         9  775.0  ...  4.663725e+03    156.994319       NaN\n",
              "24236802  1632182400        10   34.0  ...  3.975460e+00   2434.818747       NaN\n",
              "24236803  1632182400        13  380.0  ...  2.193732e+06      0.091388       NaN\n",
              "24236804  1632182400        12  177.0  ...  1.828508e+05      0.282134       NaN\n",
              "24236805  1632182400        11   48.0  ...  1.035123e+02    232.569697       NaN\n",
              "\n",
              "[24236806 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 483
        },
        "id": "NTFaIA4V3yq0",
        "outputId": "41a9f242-4614-45ec-fae8-95a3401bb165"
      },
      "source": [
        "asset_details = pd.read_csv(data_folder + 'asset_details.csv')\n",
        "asset_details"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Asset_ID</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Asset_Name</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>Bitcoin Cash</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>4.304065</td>\n",
              "      <td>Binance Coin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>6.779922</td>\n",
              "      <td>Bitcoin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>5</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>EOS.IO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>Ethereum Classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>5.894403</td>\n",
              "      <td>Ethereum</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>2.397895</td>\n",
              "      <td>Litecoin</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>11</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>Monero</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>13</td>\n",
              "      <td>1.791759</td>\n",
              "      <td>TRON</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>12</td>\n",
              "      <td>2.079442</td>\n",
              "      <td>Stellar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>3</td>\n",
              "      <td>4.406719</td>\n",
              "      <td>Cardano</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>8</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>IOTA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>10</td>\n",
              "      <td>1.098612</td>\n",
              "      <td>Maker</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>4</td>\n",
              "      <td>3.555348</td>\n",
              "      <td>Dogecoin</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Asset_ID    Weight        Asset_Name\n",
              "0          2  2.397895      Bitcoin Cash\n",
              "1          0  4.304065      Binance Coin\n",
              "2          1  6.779922           Bitcoin\n",
              "3          5  1.386294            EOS.IO\n",
              "4          7  2.079442  Ethereum Classic\n",
              "5          6  5.894403          Ethereum\n",
              "6          9  2.397895          Litecoin\n",
              "7         11  1.609438            Monero\n",
              "8         13  1.791759              TRON\n",
              "9         12  2.079442           Stellar\n",
              "10         3  4.406719           Cardano\n",
              "11         8  1.098612              IOTA\n",
              "12        10  1.098612             Maker\n",
              "13         4  3.555348          Dogecoin"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Helper Methods"
      ],
      "metadata": {
        "id": "zUUJC2iz9qNf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_results(history):\n",
        "  fig, ax = plt.subplots()\n",
        "  ax.plot(history.history['RMSE'],label = 'train')\n",
        "  ax.plot(history.history['val_RMSE'],label = 'test')\n",
        "  ax.set_title('RMSE')\n",
        "  ax.legend(loc='lower right')\n",
        "  fig, ax = plt.subplots()\n",
        "  ax.plot(history.history['MAPE'],label = 'train')\n",
        "  ax.plot(history.history['val_MAPE'],label = 'test')\n",
        "  ax.set_title('MAPE')\n",
        "  ax.legend(loc='lower right')\n",
        "  fig, ax = plt.subplots()\n",
        "  ax.plot(history.history['loss'],label = 'train')\n",
        "  ax.plot(history.history['val_loss'],label = 'test')\n",
        "  ax.set_title('MSE')\n",
        "  ax.legend(loc='upper right')"
      ],
      "metadata": {
        "id": "4b9q1PhJhXwy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function to conver d/m/y to time stamp making it easier to split data\n",
        "def to_time_stamp(s):\n",
        "  import time\n",
        "  return np.int32(time.mktime(datetime.strptime(s, \"%d/%m/%Y\").timetuple()))"
      ],
      "metadata": {
        "id": "ydgYEhmTXJfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#function to get the fixed coin data frame given coin id\n",
        "def get_crypto_data(crypto_df, id):\n",
        "  #creates specific coin df\n",
        "  coin = crypto_df[crypto_df[\"Asset_ID\"]==id].set_index(\"timestamp\")\n",
        "\n",
        "  #Replaces Null values\n",
        "  coin.fillna(method ='ffill', inplace = True)\n",
        "\n",
        "  #removes gaps in time\n",
        "  coin = coin.reindex(range(coin.index[0],coin.index[-1]+60,60),method='pad')\n",
        "  \n",
        "  return coin"
      ],
      "metadata": {
        "id": "p6vUGNCSDJAo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#gets the coin data frame and converts it into numpy train test split (used for testing)\n",
        "def get_split_data_random(coin):\n",
        "  from sklearn.preprocessing import MinMaxScaler\n",
        "  # Scalaing data is important\n",
        "  scaler = MinMaxScaler()\n",
        "\n",
        "  #converts df to numpy\n",
        "  coin = coin.to_numpy()\n",
        "\n",
        "  #gets target price\n",
        "  y = coin[:, -1]\n",
        "\n",
        "  #removes id column and target column\n",
        "  X = np.delete(coin , (0,8), axis=1)\n",
        "\n",
        "  #creates split\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "\n",
        "  #scales data\n",
        "  X_train = scaler.fit_transform(X_train)\n",
        "  X_test = scaler.transform(X_test)\n",
        "\n",
        "  return  X_train, X_test, y_train, y_test"
      ],
      "metadata": {
        "id": "1lPZmzrMD3T9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_split_data(coin):\n",
        "  from sklearn.preprocessing import MinMaxScaler\n",
        "  # Scalaing data is important\n",
        "  scaler = MinMaxScaler()\n",
        "\n",
        "  # 80/20 split for time \n",
        "  train_window = [to_time_stamp(\"01/01/2018\"), to_time_stamp(\"23/12/2020\")]\n",
        "  test_window = [to_time_stamp(\"24/12/2020\"), to_time_stamp(\"21/09/2021\")]\n",
        "\n",
        "  #Y is Target price\n",
        "  Y = coin.Target\n",
        "\n",
        "  #creates x_train and x_test with the time windows\n",
        "  X_train = coin.loc[train_window[0]:train_window[1]].to_numpy()\n",
        "  X_test = coin.loc[test_window[0]:test_window[1]].to_numpy()\n",
        "\n",
        "  #we remove the id and target col from X\n",
        "  X_train = np.delete(X_train, (0,8), axis = 1)\n",
        "  X_test = np.delete(X_test, (0,8), axis = 1)\n",
        "\n",
        "  #create Y with time windows\n",
        "  y_train = Y.loc[train_window[0]:train_window[1]].to_numpy()\n",
        "  y_test = Y.loc[test_window[0]:test_window[1]].to_numpy()\n",
        "\n",
        "  #scale the data\n",
        "  X_train = scaler.fit_transform(X_train)\n",
        "  X_test = scaler.transform(X_test)\n",
        "\n",
        "  return X_train, X_test, y_train, y_test\n"
      ],
      "metadata": {
        "id": "YG9aDzWUahx4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Given a coin dataframe, prints out random forest regression results\n",
        "def randomForestRegression(coin):\n",
        "  from sklearn.ensemble import RandomForestRegressor\n",
        "  #gets data\n",
        "  X_train, X_test, y_train, y_test = get_split_data(coin)\n",
        "\n",
        "  #creates random forest\n",
        "  RF = RandomForestRegressor(n_estimators= 50 , max_depth = 10)\n",
        "  RF.fit(X_train , y_train)\n",
        "  \n",
        "  #predicts targets\n",
        "  pred = RF.predict(X_test)\n",
        "\n",
        "  print('Mean absolute error: {:.4f}'.format(mean_absolute_error(y_test,pred)))\n",
        "  print('Mean squared error: {:.4f}'.format(mean_squared_error(y_test,pred)))\n"
      ],
      "metadata": {
        "id": "gsSoIHIaCLka"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def linearRegression(coin):\n",
        "  from sklearn.linear_model import LinearRegression\n",
        "  X_train, X_test, y_train, y_test = get_split_data(coin)\n",
        "\n",
        "  LR = LinearRegression()\n",
        "  LR.fit(X_train, y_train)\n",
        "  pred = LR.predict(X_test)\n",
        "  print('Mean Squared error: {:.10f}'.format(mean_squared_error(y_test,pred)))\n",
        "  print('Test score for LR baseline:', f\"{np.corrcoef(pred, y_test)[0,1]:.10f}\")"
      ],
      "metadata": {
        "id": "hNFGVlz4ClS3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Baselines for BTC , ETH , LTC"
      ],
      "metadata": {
        "id": "StkEAi1RB-_S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "btc = get_crypto_data(crypto_df , 1)\n",
        "print(\"Metrics for BTC:\")\n",
        "linearRegression(btc)\n",
        "print()\n",
        "print(\"Metrics for ETH:\")\n",
        "eth = get_crypto_data(crypto_df , 6)\n",
        "linearRegression(eth)\n",
        "print()\n",
        "print(\"Metrics for LTC:\")\n",
        "ltc = get_crypto_data(crypto_df , 9)\n",
        "linearRegression(ltc)\n",
        "print()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCli2oXiA4zr",
        "outputId": "7eddd00a-a6b6-47e8-c32e-cff4fc879f00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics for BTC:\n",
            "Mean Squared error: 0.0000064366\n",
            "Test score for LR baseline: 0.0077411506\n",
            "\n",
            "Metrics for ETH:\n",
            "Mean Squared error: 0.0000085400\n",
            "Test score for LR baseline: 0.0077458411\n",
            "\n",
            "Metrics for LTC:\n",
            "Mean Squared error: 0.0000160751\n",
            "Test score for LR baseline: -0.0038230615\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "btc = get_crypto_data(crypto_df , 1)\n",
        "X_train, X_test, y_train, y_test = get_split_data(btc)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "f = plt.figure(figsize=(30,5))\n",
        "\n",
        "ax = f.add_subplot(131)\n",
        "combined = np.concatenate([X_train[:,4],X_test[:,4]])\n",
        "plt.plot(combined,color = 'red' ,label='Test')\n",
        "ax.plot(X_train[:,4], label='Train')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Bitcoin')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "eb1eER2AgXzY",
        "outputId": "0fdf8beb-e385-4a37-f0d8-90bcaa23f9d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Code for drawing graphs"
      ],
      "metadata": {
        "id": "OU3ovcxGVzXb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "f = plt.figure(figsize=(30,5))\n",
        "\n",
        "ax = f.add_subplot(131)\n",
        "plt.plot(btc['Close'], label='BTC')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Bitcoin')\n",
        "\n",
        "ax2 = f.add_subplot(132)\n",
        "ax2.plot(eth['Close'], color='red', label='ETH')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Ethereum')\n",
        "\n",
        "ax3 = f.add_subplot(133)\n",
        "ax3.plot(ltc['Close'], color='green', label='LTC')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('Litecoin')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 233
        },
        "id": "CABIhXDQHswE",
        "outputId": "632ab1e6-76ab-442e-b51e-fbb56d1f8c31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x360 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = plt.figure(figsize=(30,5))\n",
        "\n",
        "ax = f.add_subplot(131)\n",
        "plt.plot(btc['Target'], label='BTC')\n",
        "ax.plot(eth['Target'], color='red', label='ETH')\n",
        "ax.plot(ltc['Target'], color='green', label='LTC')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('target')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "Y97eDeDqZyyS",
        "outputId": "e2bf34fc-9746-4056-a5bb-60335a579af3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "btc = get_crypto_data(crypto_df , 1)\n",
        "X_train, X_test, y_train, y_test = get_split_data(btc)"
      ],
      "metadata": {
        "id": "8wgEqIJyiB5d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eth = get_crypto_data(crypto_df , 6)\n",
        "X_train, X_test, y_train, y_test = get_split_data(eth)"
      ],
      "metadata": {
        "id": "JFuigiFxxVE1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ltc = get_crypto_data(crypto_df , 9)\n",
        "X_train, X_test, y_train, y_test = get_split_data(ltc)"
      ],
      "metadata": {
        "id": "ahQtrg3uxVjM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dense_model(inputs = 7,hidden_1=512, hidden_2=256):\n",
        "  model = tf.keras.models.Sequential()\n",
        "  model.add(Dense(hidden_1, input_shape=(inputs,)))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(hidden_2))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(128))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(64))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(1))\n",
        "  return model\n",
        "\n",
        "dense = dense_model()\n",
        "dense.summary() \n",
        "dense.compile(optimizer=tf.keras.optimizers.Adam(), loss=\"mse\", metrics=[tf.keras.metrics.RootMeanSquaredError(\"RMSE\") , \"MAPE\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1BwrbEEjgyvU",
        "outputId": "a65adb68-faf4-4832-f69b-3ca35dd34370"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 512)               4096      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 512)               0         \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 512)              2048      \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 256)              1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 128)              512       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 64)               256       \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 180,481\n",
            "Trainable params: 178,561\n",
            "Non-trainable params: 1,920\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = dense.fit(\n",
        "    X_train, y_train,\n",
        "    epochs =  10, \n",
        "    batch_size= 2048, \n",
        "    verbose = 1,\n",
        "    validation_data=(X_test, y_test),\n",
        ")\n",
        "\n",
        "\n",
        "print('Final accuracy on training set: {:.10f}'.format(history.history['loss'][-1]))\n",
        "print('Final accuracy on test set: {:.10f}'.format(history.history['val_loss'][-1]))\n",
        "print('Mean Squared Error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_loss'][-5:])))\n",
        "print('Mean Root Mean Squared error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_RMSE'][-5:])))\n",
        "print('Mean Apolute Percentage error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_MAPE'][-5:])))"
      ],
      "metadata": {
        "id": "hVovhh_8wCZc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_results(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "0VqXgPwIrasv",
        "outputId": "0818a87b-70b2-4eca-b574-a6ffef399ecc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = dense(X_test).numpy()\n",
        "pred= pred.reshape(-1)\n",
        "\n",
        "f = plt.figure(figsize=(30,5))\n",
        "\n",
        "ax = f.add_subplot(131)\n",
        "ax.plot(y_test, label = 'actual')\n",
        "ax.plot(pred , label = 'predicted' , color = 'red')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('target')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "riVUcCfH1Qzc",
        "outputId": "cdee3906-93a9-48af-ea27-fad017492156"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Augmentation"
      ],
      "metadata": {
        "id": "5rxzoX5ioU5l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#adds upper shadow, lowershadow, high/low, and open - close to data \n",
        "def get_features(df):\n",
        "    df_feat = df[['Asset_ID','Count', 'Open', 'High', 'Low', 'Close', 'Volume', 'VWAP', 'Target']].copy()\n",
        "    df_feat['upper_Shadow'] = df['High'] - np.maximum(df['Close'], df['Open'])\n",
        "    df_feat['lower_Shadow'] = np.minimum(df['Close'], df['Open']) - df['Low']\n",
        "    df_feat[\"high_div_low\"] = df_feat[\"High\"] / df_feat[\"Low\"]\n",
        "    df_feat[\"open_sub_close\"] = df_feat[\"Open\"] - df_feat[\"Close\"]\n",
        "    return df_feat"
      ],
      "metadata": {
        "id": "Svc6snEqzb_Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rolling_mean(series, window): return series.rolling(window).mean()\n",
        "def rolling_std(series, window): return series.rolling(window).std()\n",
        "def rolling_sum(series, window): return series.rolling(window).sum()\n",
        "def ewma(series, span, min_periods): return series.ewm(span = span, min_periods = min_periods).mean()"
      ],
      "metadata": {
        "id": "sfViBl1v2zWE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Moving Average\n",
        "def MA(df, n = 15):\n",
        "    MA = pd.Series(rolling_mean(df['Close'], n), name = 'MA_' + str(n))\n",
        "    df['MA'] = MA\n",
        "    return df\n",
        "\n",
        "#Exponential Moving Average\n",
        "def EMA(df, n = 15):\n",
        "    EMA = pd.Series(ewma(df['Close'], span = n, min_periods = n - 1), name = 'EMA_' + str(n))\n",
        "    df['EMA'] = EMA\n",
        "    return df\n",
        "\n",
        "#Momentum\n",
        "def MOM(df, n = 15):\n",
        "    M = pd.Series(df['Close'].diff(n), name = 'Momentum_' + str(n))\n",
        "    df['MOM'] = M\n",
        "    return df\n",
        "\n",
        "#Rate of Change\n",
        "def ROC(df, n = 15):\n",
        "    M = df['Close'].diff(n - 1)\n",
        "    N = df['Close'].shift(n - 1)\n",
        "    ROC = pd.Series(M / N, name = 'ROC_' + str(n))\n",
        "    df['ROC'] = ROC\n",
        "    return df\n",
        "\n",
        "\n",
        "#after you call these you have to use .fillna(method = 'bfill')"
      ],
      "metadata": {
        "id": "_se0O4cF21AL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing Data Augmentation"
      ],
      "metadata": {
        "id": "QbGIkyApki00"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "btc = get_crypto_data(crypto_df , 1)\n",
        "btc = get_features(btc)\n",
        "btc = MA(btc)\n",
        "btc = EMA(btc)\n",
        "btc = MOM(btc)\n",
        "btc = ROC(btc)\n",
        "\n",
        "btc.fillna(method = 'bfill' , inplace = True)\n",
        "btc_X_train, btc_X_test, btc_y_train, btc_y_test = get_split_data(btc)"
      ],
      "metadata": {
        "id": "pjyEAOoc4rHR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "eth = get_crypto_data(crypto_df , 6)\n",
        "eth = get_features(btc)\n",
        "eth = MA(eth)\n",
        "eth = EMA(eth)\n",
        "eth = MOM(eth)\n",
        "eth = ROC(eth)\n",
        "\n",
        "eth.fillna(method = 'bfill' , inplace = True)\n",
        "eth_X_train, eth_X_test, eth_y_train, eth_y_test = get_split_data(eth)"
      ],
      "metadata": {
        "id": "ZcvQyPrJTzos"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ltc = get_crypto_data(crypto_df , 9)\n",
        "ltc = get_features(ltc)\n",
        "ltc = MA(ltc)\n",
        "ltc = EMA(ltc)\n",
        "ltc = MOM(ltc)\n",
        "ltc = ROC(ltc)\n",
        "\n",
        "ltc.fillna(method = 'bfill' , inplace = True)\n",
        "ltc_X_train, ltc_X_test, ltc_y_train, ltc_y_test = get_split_data(ltc)"
      ],
      "metadata": {
        "id": "RqBUBThGT0VV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dense_model(inputs = 7,hidden_1=512, hidden_2=256):\n",
        "  model = tf.keras.models.Sequential()\n",
        "  model.add(Dense(hidden_1, input_shape=(inputs,)))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(hidden_2))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(128))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(64))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(1))\n",
        "  return model\n",
        "\n",
        "dense = dense_model(inputs=14)\n",
        "dense.summary() \n",
        "dense.compile(optimizer=tf.keras.optimizers.Adam(), loss=\"mse\", metrics=[tf.keras.metrics.RootMeanSquaredError(\"RMSE\") , \"MAPE\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnXqfPE9Pb6H",
        "outputId": "ee172539-5e27-40b5-a35b-0723aeb926de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_40 (Dense)            (None, 512)               7680      \n",
            "                                                                 \n",
            " dropout_32 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " batch_normalization_32 (Bat  (None, 512)              2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_41 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_33 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " batch_normalization_33 (Bat  (None, 256)              1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_42 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_34 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " batch_normalization_34 (Bat  (None, 128)              512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_43 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_35 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " batch_normalization_35 (Bat  (None, 64)               256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_44 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 184,065\n",
            "Trainable params: 182,145\n",
            "Non-trainable params: 1,920\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = dense.fit(\n",
        "    btc_X_train, btc_y_train,\n",
        "    epochs =  10, \n",
        "    batch_size= 2048, \n",
        "    verbose = 1,\n",
        "    validation_data=(btc_X_test, btc_y_test),\n",
        ")\n",
        "\n",
        "\n",
        "print('Final accuracy on training set: {:.10f}'.format(history.history['loss'][-1]))\n",
        "print('Final accuracy on test set: {:.10f}'.format(history.history['val_loss'][-1]))\n",
        "print('Mean Squared Error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_loss'][-5:])))\n",
        "print('Mean Root Mean Squared error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_RMSE'][-5:])))\n",
        "print('Mean Apolute Percentage error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_MAPE'][-5:])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IF-MWfj0yATu",
        "outputId": "4a41729f-698d-443c-d6a6-6a054777d7fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 3.9088e-06 - RMSE: 0.0020 - MAPE: 367.6672 - val_loss: 6.4630e-06 - val_RMSE: 0.0025 - val_MAPE: 308.4181\n",
            "Epoch 2/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 4.2640e-06 - RMSE: 0.0021 - MAPE: 467.7773 - val_loss: 4.2099e-05 - val_RMSE: 0.0065 - val_MAPE: 3481.7952\n",
            "Epoch 3/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 4.1865e-06 - RMSE: 0.0020 - MAPE: 456.4111 - val_loss: 1.6451e-04 - val_RMSE: 0.0128 - val_MAPE: 7315.0146\n",
            "Epoch 4/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 4.2367e-06 - RMSE: 0.0021 - MAPE: 463.8040 - val_loss: 1.2669e-04 - val_RMSE: 0.0113 - val_MAPE: 6385.3745\n",
            "Epoch 5/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 4.2008e-06 - RMSE: 0.0020 - MAPE: 483.3719 - val_loss: 7.4638e-06 - val_RMSE: 0.0027 - val_MAPE: 676.2129\n",
            "Epoch 6/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 4.0551e-06 - RMSE: 0.0020 - MAPE: 473.7209 - val_loss: 7.6079e-06 - val_RMSE: 0.0028 - val_MAPE: 785.2060\n",
            "Epoch 7/10\n",
            "765/765 [==============================] - 11s 14ms/step - loss: 4.0459e-06 - RMSE: 0.0020 - MAPE: 545.1631 - val_loss: 3.8290e-04 - val_RMSE: 0.0196 - val_MAPE: 11345.8789\n",
            "Epoch 8/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 3.3859e-06 - RMSE: 0.0018 - MAPE: 641.0724 - val_loss: 1.1468e-05 - val_RMSE: 0.0034 - val_MAPE: 1708.6301\n",
            "Epoch 9/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 2.0173e-06 - RMSE: 0.0014 - MAPE: 612.9561 - val_loss: 1.5500e-05 - val_RMSE: 0.0039 - val_MAPE: 2122.8040\n",
            "Epoch 10/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 1.3291e-06 - RMSE: 0.0012 - MAPE: 494.1524 - val_loss: 2.3306e-06 - val_RMSE: 0.0015 - val_MAPE: 567.6539\n",
            "Final accuracy on training set: 0.0000013291\n",
            "Final accuracy on test set: 0.0000023306\n",
            "Mean Squared Error in last 5 epochs: 0.0000839622\n",
            "Mean Root Mean Squared error in last 5 epochs: 0.0062352623\n",
            "Mean Apolute Percentage error in last 5 epochs: 3306.0345825195\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = dense.fit(\n",
        "    eth_X_train, eth_y_train,\n",
        "    epochs =  10, \n",
        "    batch_size= 2048, \n",
        "    verbose = 1,\n",
        "    validation_data=(eth_X_test, eth_y_test),\n",
        ")\n",
        "\n",
        "\n",
        "print('Final accuracy on training set: {:.10f}'.format(history.history['loss'][-1]))\n",
        "print('Final accuracy on test set: {:.10f}'.format(history.history['val_loss'][-1]))\n",
        "print('Mean Squared Error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_loss'][-5:])))\n",
        "print('Mean Root Mean Squared error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_RMSE'][-5:])))\n",
        "print('Mean Apolute Percentage error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_MAPE'][-5:])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLiWDe_7Phq1",
        "outputId": "d820f3dc-af43-4035-d05e-c5cd8cd54c5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 3.9493e-06 - RMSE: 0.0020 - MAPE: 380.1080 - val_loss: 1.3408e-04 - val_RMSE: 0.0116 - val_MAPE: 6575.6729\n",
            "Epoch 2/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 4.2537e-06 - RMSE: 0.0021 - MAPE: 466.6099 - val_loss: 1.2279e-04 - val_RMSE: 0.0111 - val_MAPE: 6310.1104\n",
            "Epoch 3/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 4.2376e-06 - RMSE: 0.0021 - MAPE: 470.9196 - val_loss: 1.4463e-04 - val_RMSE: 0.0120 - val_MAPE: 6833.8335\n",
            "Epoch 4/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 4.2897e-06 - RMSE: 0.0021 - MAPE: 494.2488 - val_loss: 2.3380e-05 - val_RMSE: 0.0048 - val_MAPE: 2436.7532\n",
            "Epoch 5/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 4.1802e-06 - RMSE: 0.0020 - MAPE: 483.5397 - val_loss: 8.9887e-05 - val_RMSE: 0.0095 - val_MAPE: 5309.1870\n",
            "Epoch 6/10\n",
            "765/765 [==============================] - 11s 14ms/step - loss: 4.1880e-06 - RMSE: 0.0020 - MAPE: 503.4239 - val_loss: 9.1188e-05 - val_RMSE: 0.0095 - val_MAPE: 5404.5840\n",
            "Epoch 7/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 3.9616e-06 - RMSE: 0.0020 - MAPE: 555.7694 - val_loss: 1.5579e-04 - val_RMSE: 0.0125 - val_MAPE: 7127.8521\n",
            "Epoch 8/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 2.9079e-06 - RMSE: 0.0017 - MAPE: 656.6475 - val_loss: 1.2800e-05 - val_RMSE: 0.0036 - val_MAPE: 1757.9257\n",
            "Epoch 9/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 1.4882e-06 - RMSE: 0.0012 - MAPE: 520.3658 - val_loss: 9.2210e-06 - val_RMSE: 0.0030 - val_MAPE: 1577.1979\n",
            "Epoch 10/10\n",
            "765/765 [==============================] - 11s 14ms/step - loss: 1.0849e-06 - RMSE: 0.0010 - MAPE: 441.1752 - val_loss: 1.1310e-04 - val_RMSE: 0.0106 - val_MAPE: 6177.0557\n",
            "Final accuracy on training set: 0.0000010849\n",
            "Final accuracy on test set: 0.0001130999\n",
            "Mean Squared Error in last 5 epochs: 0.0000764206\n",
            "Mean Root Mean Squared error in last 5 epochs: 0.0078560358\n",
            "Mean Apolute Percentage error in last 5 epochs: 4408.9230468750\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = dense.fit(\n",
        "    ltc_X_train, ltc_y_train,\n",
        "    epochs =  10, \n",
        "    batch_size= 2048, \n",
        "    verbose = 1,\n",
        "    validation_data=(ltc_X_test, ltc_y_test),\n",
        ")\n",
        "\n",
        "\n",
        "print('Final accuracy on training set: {:.10f}'.format(history.history['loss'][-1]))\n",
        "print('Final accuracy on test set: {:.10f}'.format(history.history['val_loss'][-1]))\n",
        "print('Mean Squared Error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_loss'][-5:])))\n",
        "print('Mean Root Mean Squared error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_RMSE'][-5:])))\n",
        "print('Mean Apolute Percentage error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_MAPE'][-5:])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoz6bGE8zL5s",
        "outputId": "f2d15c43-b065-4ac3-bc64-73be09fb02d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 6.8281e-07 - RMSE: 8.2633e-04 - MAPE: 149.0102 - val_loss: 2.1214e-08 - val_RMSE: 1.4565e-04 - val_MAPE: 40.4957\n",
            "Epoch 2/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 5.6355e-07 - RMSE: 7.5070e-04 - MAPE: 145.6690 - val_loss: 9.2331e-08 - val_RMSE: 3.0386e-04 - val_MAPE: 40.3049\n",
            "Epoch 3/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 5.0920e-07 - RMSE: 7.1358e-04 - MAPE: 123.8669 - val_loss: 4.0722e-07 - val_RMSE: 6.3814e-04 - val_MAPE: 241.3872\n",
            "Epoch 4/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 4.2358e-07 - RMSE: 6.5083e-04 - MAPE: 113.9955 - val_loss: 6.2138e-07 - val_RMSE: 7.8828e-04 - val_MAPE: 223.6984\n",
            "Epoch 5/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 3.7454e-07 - RMSE: 6.1199e-04 - MAPE: 99.9254 - val_loss: 4.6726e-07 - val_RMSE: 6.8357e-04 - val_MAPE: 245.7703\n",
            "Epoch 6/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 3.3111e-07 - RMSE: 5.7542e-04 - MAPE: 97.6923 - val_loss: 4.3736e-08 - val_RMSE: 2.0913e-04 - val_MAPE: 76.7644\n",
            "Epoch 7/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 3.1040e-07 - RMSE: 5.5713e-04 - MAPE: 93.0176 - val_loss: 1.9307e-07 - val_RMSE: 4.3940e-04 - val_MAPE: 56.6016\n",
            "Epoch 8/10\n",
            "765/765 [==============================] - 11s 15ms/step - loss: 2.8861e-07 - RMSE: 5.3723e-04 - MAPE: 87.9487 - val_loss: 8.2710e-08 - val_RMSE: 2.8759e-04 - val_MAPE: 93.5757\n",
            "Epoch 9/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 2.8893e-07 - RMSE: 5.3752e-04 - MAPE: 95.2090 - val_loss: 2.3573e-07 - val_RMSE: 4.8552e-04 - val_MAPE: 119.0775\n",
            "Epoch 10/10\n",
            "765/765 [==============================] - 12s 15ms/step - loss: 2.7740e-07 - RMSE: 5.2668e-04 - MAPE: 89.2493 - val_loss: 1.3971e-07 - val_RMSE: 3.7378e-04 - val_MAPE: 18.1289\n",
            "Final accuracy on training set: 0.0000002774\n",
            "Final accuracy on test set: 0.0000001397\n",
            "Mean Squared Error in last 5 epochs: 0.0000001390\n",
            "Mean Root Mean Squared error in last 5 epochs: 0.0003590843\n",
            "Mean Apolute Percentage error in last 5 epochs: 72.8296058655\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_results(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "HmCTeXvMQJ2h",
        "outputId": "090c9372-be80-42c1-c369-52f15a490da7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Combining Multiple coins"
      ],
      "metadata": {
        "id": "xsRmu1rsTgYb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_X_train = np.concatenate((btc_X_train, eth_X_train , ltc_X_train), axis=0)\n",
        "all_X_test = np.concatenate((btc_X_test, eth_X_test , ltc_X_test), axis=0)\n",
        "\n",
        "all_y_train = np.concatenate((btc_y_train, eth_y_train , ltc_y_train), axis=0)\n",
        "all_y_test = np.concatenate((btc_y_test, eth_y_test , ltc_y_test), axis=0)"
      ],
      "metadata": {
        "id": "HyTi-xeaTf0S"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def dense_model(inputs = 7,hidden_1=512, hidden_2=256):\n",
        "  model = tf.keras.models.Sequential()\n",
        "  model.add(Dense(hidden_1, input_shape=(inputs,)))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(hidden_2))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(128))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(64))\n",
        "  model.add(Dropout(.25))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(1))\n",
        "  return model\n",
        "\n",
        "dense = dense_model(inputs=14)\n",
        "dense.summary() \n",
        "dense.compile(optimizer=tf.keras.optimizers.Adam(), loss=\"mse\", metrics=[tf.keras.metrics.RootMeanSquaredError(\"RMSE\") , \"MAPE\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ez0kXaj2a1kV",
        "outputId": "4f508498-4942-4944-e6cf-15351bd3ae7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_35 (Dense)            (None, 512)               7680      \n",
            "                                                                 \n",
            " dropout_28 (Dropout)        (None, 512)               0         \n",
            "                                                                 \n",
            " batch_normalization_28 (Bat  (None, 512)              2048      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_36 (Dense)            (None, 256)               131328    \n",
            "                                                                 \n",
            " dropout_29 (Dropout)        (None, 256)               0         \n",
            "                                                                 \n",
            " batch_normalization_29 (Bat  (None, 256)              1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_37 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " dropout_30 (Dropout)        (None, 128)               0         \n",
            "                                                                 \n",
            " batch_normalization_30 (Bat  (None, 128)              512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_38 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_31 (Dropout)        (None, 64)                0         \n",
            "                                                                 \n",
            " batch_normalization_31 (Bat  (None, 64)               256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_39 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 184,065\n",
            "Trainable params: 182,145\n",
            "Non-trainable params: 1,920\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = dense.fit(\n",
        "    all_X_train, all_y_train,\n",
        "    epochs =  10, \n",
        "    batch_size= 2048, \n",
        "    verbose = 1,\n",
        "    validation_data=(all_X_test, all_y_test),\n",
        ")\n",
        "\n",
        "\n",
        "print('Final accuracy on training set: {:.10f}'.format(history.history['loss'][-1]))\n",
        "print('Final accuracy on test set: {:.10f}'.format(history.history['val_loss'][-1]))\n",
        "print('Mean Squared Error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_loss'][-5:])))\n",
        "print('Mean Root Mean Squared error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_RMSE'][-5:])))\n",
        "print('Mean Apolute Percentage error in last 5 epochs: {:.10f}'.format(np.mean(history.history['val_MAPE'][-5:])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ls3ueKWXa8Bl",
        "outputId": "8462ef7c-cad4-4cb5-cc22-6582e6aa7973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "2293/2293 [==============================] - 33s 14ms/step - loss: 7.9707e-07 - RMSE: 8.9279e-04 - MAPE: 255.5921 - val_loss: 1.6850e-05 - val_RMSE: 0.0041 - val_MAPE: 1330.4821\n",
            "Epoch 2/10\n",
            "2293/2293 [==============================] - 33s 14ms/step - loss: 5.7121e-07 - RMSE: 7.5578e-04 - MAPE: 195.6709 - val_loss: 1.6100e-05 - val_RMSE: 0.0040 - val_MAPE: 1187.3997\n",
            "Epoch 3/10\n",
            "2293/2293 [==============================] - 33s 14ms/step - loss: 4.9953e-07 - RMSE: 7.0678e-04 - MAPE: 179.6551 - val_loss: 1.6313e-05 - val_RMSE: 0.0040 - val_MAPE: 1118.0942\n",
            "Epoch 4/10\n",
            "2293/2293 [==============================] - 33s 15ms/step - loss: 4.8573e-07 - RMSE: 6.9694e-04 - MAPE: 176.8596 - val_loss: 1.0137e-05 - val_RMSE: 0.0032 - val_MAPE: 861.6619\n",
            "Epoch 5/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 4.6605e-07 - RMSE: 6.8268e-04 - MAPE: 175.7270 - val_loss: 1.0440e-05 - val_RMSE: 0.0032 - val_MAPE: 876.7526\n",
            "Epoch 6/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 4.6587e-07 - RMSE: 6.8254e-04 - MAPE: 177.8240 - val_loss: 1.0922e-05 - val_RMSE: 0.0033 - val_MAPE: 910.8614\n",
            "Epoch 7/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 4.2958e-07 - RMSE: 6.5542e-04 - MAPE: 172.9842 - val_loss: 7.8622e-06 - val_RMSE: 0.0028 - val_MAPE: 791.6816\n",
            "Epoch 8/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 4.1531e-07 - RMSE: 6.4445e-04 - MAPE: 167.0285 - val_loss: 7.9609e-06 - val_RMSE: 0.0028 - val_MAPE: 852.5338\n",
            "Epoch 9/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 4.1229e-07 - RMSE: 6.4209e-04 - MAPE: 172.3812 - val_loss: 6.5070e-06 - val_RMSE: 0.0026 - val_MAPE: 840.1049\n",
            "Epoch 10/10\n",
            "2293/2293 [==============================] - 34s 15ms/step - loss: 3.9327e-07 - RMSE: 6.2711e-04 - MAPE: 167.7045 - val_loss: 6.7986e-06 - val_RMSE: 0.0026 - val_MAPE: 699.5853\n",
            "Final accuracy on training set: 0.0000003933\n",
            "Final accuracy on test set: 0.0000067986\n",
            "Mean Squared Error in last 5 epochs: 0.0000080101\n",
            "Mean Root Mean Squared error in last 5 epochs: 0.0028177144\n",
            "Mean Apolute Percentage error in last 5 epochs: 818.9533935547\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_results(history)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "76SK-ZWOa5Bg",
        "outputId": "837d164d-756e-40cb-ff2c-a91c474b2a15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = dense(all_X_test).numpy()\n",
        "pred= pred.reshape(-1)\n",
        "\n",
        "f = plt.figure(figsize=(30,5))\n",
        "\n",
        "ax = f.add_subplot(131)\n",
        "ax.plot(all_y_test, label = 'actual')\n",
        "ax.plot(pred , label = 'predicted' , color = 'red')\n",
        "plt.legend()\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('target')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "8GqmtDQLbEzt",
        "outputId": "341e1a59-4905-4ae7-c8ff-a3adafa738f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2160x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}