src/models/ADA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\thome\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential, Model\n",
    "from tensorflow.keras.layers import LSTM, Dense, Input\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import random\n",
    "\n",
    "import os \n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "122751\n"
     ]
    }
   ],
   "source": [
    "CURRENCY = \"ADA\"\n",
    "CLOSE = f\"Close_{CURRENCY}\"\n",
    "\n",
    "seed = random.randint(0, 1000000)\n",
    "seed = 122751\n",
    "print(seed)\n",
    "tf.random.set_seed(seed)\n",
    "np.random.seed(seed)\n",
    "random.seed(seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_pickle = '../../data/federalfinancegestion/dataset.pkl'\n",
    "df = pd.read_pickle(dataset_pickle)\n",
    "\n",
    "# Remove all string (object type) columns from the dataset\n",
    "data_numeric = df.select_dtypes(exclude=['object'])\n",
    "\n",
    "# Finding the column that closely matches \"Close_BTC\" in the numeric dataset\n",
    "close_btc_column_numeric = [col for col in data_numeric.columns if \"Close_BTC\" in col]\n",
    "\n",
    "close_btc_column_numeric = close_btc_column_numeric[0]\n",
    "# Calculate the correlation of all columns with Close_BTC in the numeric dataset\n",
    "correlation_with_close_btc_numeric = data_numeric.corr()[close_btc_column_numeric].sort_values(ascending=False)\n",
    "\n",
    "# Extract the top 10 most correlated feature names\n",
    "top_feature = correlation_with_close_btc_numeric.head(10).index.tolist()\n",
    "include = top_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df.fillna(0, inplace=True)\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df.set_index('date', inplace=True)\n",
    "\n",
    "start_date_training = pd.to_datetime('2017-08-01')\n",
    "end_date_training = pd.to_datetime('2022-08-31')\n",
    "start_date_testing = pd.to_datetime('2022-09-01')\n",
    "end_date_testing = pd.to_datetime('2023-04-30')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df = df.loc[start_date_training:end_date_training]\n",
    "test_df = df.loc[start_date_testing:end_date_testing]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thome\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\pandas\\core\\arraylike.py:396: RuntimeWarning: divide by zero encountered in log\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Calculating the logarithmic difference of 'Close'\n",
    "df['Close_Log'] = np.log(df[CLOSE])  # First, compute the log of 'Close'\n",
    "df['Log_Diff_Close'] = df['Close_Log'].diff().fillna(0)  # Then compute the difference and handle NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Include features\n",
    "train = train_df[include].values\n",
    "test = test_df[include].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the data\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "train_scaled = scaler.fit_transform(train)\n",
    "test_scaled = scaler.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\thome\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\keras\\src\\backend.py:1398: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\thome\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Epoch 1/150\n",
      "WARNING:tensorflow:From C:\\Users\\thome\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python39\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "33/33 [==============================] - 1s 6ms/step - loss: 0.1212 - val_loss: 0.0905\n",
      "Epoch 2/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0340 - val_loss: 0.0543\n",
      "Epoch 3/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0104 - val_loss: 0.0210\n",
      "Epoch 4/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0070 - val_loss: 0.0159\n",
      "Epoch 5/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0052 - val_loss: 0.0147\n",
      "Epoch 6/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0041 - val_loss: 0.0142\n",
      "Epoch 7/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0036 - val_loss: 0.0136\n",
      "Epoch 8/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0032 - val_loss: 0.0141\n",
      "Epoch 9/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0137\n",
      "Epoch 10/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0024 - val_loss: 0.0132\n",
      "Epoch 11/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0021 - val_loss: 0.0130\n",
      "Epoch 12/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0019 - val_loss: 0.0130\n",
      "Epoch 13/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0016 - val_loss: 0.0126\n",
      "Epoch 14/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0014 - val_loss: 0.0119\n",
      "Epoch 15/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0013 - val_loss: 0.0119\n",
      "Epoch 16/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0012 - val_loss: 0.0123\n",
      "Epoch 17/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0012 - val_loss: 0.0110\n",
      "Epoch 18/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0011 - val_loss: 0.0111\n",
      "Epoch 19/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0010 - val_loss: 0.0108\n",
      "Epoch 20/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0010 - val_loss: 0.0120\n",
      "Epoch 21/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 9.6384e-04 - val_loss: 0.0108\n",
      "Epoch 22/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 9.1985e-04 - val_loss: 0.0111\n",
      "Epoch 23/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.9167e-04 - val_loss: 0.0112\n",
      "Epoch 24/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.7055e-04 - val_loss: 0.0108\n",
      "Epoch 25/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.2774e-04 - val_loss: 0.0106\n",
      "Epoch 26/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.0012e-04 - val_loss: 0.0107\n",
      "Epoch 27/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.1073e-04 - val_loss: 0.0098\n",
      "Epoch 28/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 7.5846e-04 - val_loss: 0.0106\n",
      "Epoch 29/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 7.0618e-04 - val_loss: 0.0102\n",
      "Epoch 30/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 6.8114e-04 - val_loss: 0.0100\n",
      "Epoch 31/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 6.4546e-04 - val_loss: 0.0102\n",
      "Epoch 32/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 6.2936e-04 - val_loss: 0.0106\n",
      "Epoch 33/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 5.8980e-04 - val_loss: 0.0097\n",
      "Epoch 34/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 5.5331e-04 - val_loss: 0.0092\n",
      "Epoch 35/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 5.2970e-04 - val_loss: 0.0095\n",
      "Epoch 36/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 5.1025e-04 - val_loss: 0.0108\n",
      "Epoch 37/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.8364e-04 - val_loss: 0.0103\n",
      "Epoch 38/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.8407e-04 - val_loss: 0.0091\n",
      "Epoch 39/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.5632e-04 - val_loss: 0.0100\n",
      "Epoch 40/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.2619e-04 - val_loss: 0.0093\n",
      "Epoch 41/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.1461e-04 - val_loss: 0.0086\n",
      "Epoch 42/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.2562e-04 - val_loss: 0.0102\n",
      "Epoch 43/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.1660e-04 - val_loss: 0.0095\n",
      "Epoch 44/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.8619e-04 - val_loss: 0.0087\n",
      "Epoch 45/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.8474e-04 - val_loss: 0.0090\n",
      "Epoch 46/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.7827e-04 - val_loss: 0.0087\n",
      "Epoch 47/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.7673e-04 - val_loss: 0.0088\n",
      "Epoch 48/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.6345e-04 - val_loss: 0.0085\n",
      "Epoch 49/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.5277e-04 - val_loss: 0.0086\n",
      "Epoch 50/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.4255e-04 - val_loss: 0.0086\n",
      "Epoch 51/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.4050e-04 - val_loss: 0.0083\n",
      "Epoch 52/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.3805e-04 - val_loss: 0.0098\n",
      "Epoch 53/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.2869e-04 - val_loss: 0.0084\n",
      "Epoch 54/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.2230e-04 - val_loss: 0.0086\n",
      "Epoch 55/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.2286e-04 - val_loss: 0.0086\n",
      "Epoch 56/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.1315e-04 - val_loss: 0.0084\n",
      "Epoch 57/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.1112e-04 - val_loss: 0.0077\n",
      "Epoch 58/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.0427e-04 - val_loss: 0.0081\n",
      "Epoch 59/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.0827e-04 - val_loss: 0.0079\n",
      "Epoch 60/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.9911e-04 - val_loss: 0.0084\n",
      "Epoch 61/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.0220e-04 - val_loss: 0.0087\n",
      "Epoch 62/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.9110e-04 - val_loss: 0.0082\n",
      "Epoch 63/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.8072e-04 - val_loss: 0.0082\n",
      "Epoch 64/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.8888e-04 - val_loss: 0.0084\n",
      "Epoch 65/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.7579e-04 - val_loss: 0.0085\n",
      "Epoch 66/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.7551e-04 - val_loss: 0.0085\n",
      "Epoch 67/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.6786e-04 - val_loss: 0.0082\n",
      "Epoch 68/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.6386e-04 - val_loss: 0.0082\n",
      "Epoch 69/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5932e-04 - val_loss: 0.0079\n",
      "Epoch 70/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.6639e-04 - val_loss: 0.0084\n",
      "Epoch 71/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5879e-04 - val_loss: 0.0085\n",
      "Epoch 72/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.6112e-04 - val_loss: 0.0088\n",
      "Epoch 73/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5270e-04 - val_loss: 0.0080\n",
      "Epoch 74/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5175e-04 - val_loss: 0.0082\n",
      "Epoch 75/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5215e-04 - val_loss: 0.0090\n",
      "Epoch 76/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5939e-04 - val_loss: 0.0080\n",
      "Epoch 77/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.5012e-04 - val_loss: 0.0075\n",
      "Epoch 78/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.4430e-04 - val_loss: 0.0085\n",
      "Epoch 79/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3969e-04 - val_loss: 0.0081\n",
      "Epoch 80/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3803e-04 - val_loss: 0.0086\n",
      "Epoch 81/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.4452e-04 - val_loss: 0.0085\n",
      "Epoch 82/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3858e-04 - val_loss: 0.0080\n",
      "Epoch 83/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3465e-04 - val_loss: 0.0087\n",
      "Epoch 84/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3594e-04 - val_loss: 0.0082\n",
      "Epoch 85/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3345e-04 - val_loss: 0.0086\n",
      "Epoch 86/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.2919e-04 - val_loss: 0.0078\n",
      "Epoch 87/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.2609e-04 - val_loss: 0.0085\n",
      "Epoch 88/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3068e-04 - val_loss: 0.0080\n",
      "Epoch 89/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.4051e-04 - val_loss: 0.0083\n",
      "Epoch 90/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3802e-04 - val_loss: 0.0079\n",
      "Epoch 91/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1573e-04 - val_loss: 0.0071\n",
      "Epoch 92/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.2069e-04 - val_loss: 0.0081\n",
      "Epoch 93/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3374e-04 - val_loss: 0.0075\n",
      "Epoch 94/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3273e-04 - val_loss: 0.0083\n",
      "Epoch 95/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.2208e-04 - val_loss: 0.0084\n",
      "Epoch 96/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1641e-04 - val_loss: 0.0082\n",
      "Epoch 97/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0491e-04 - val_loss: 0.0086\n",
      "Epoch 98/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1172e-04 - val_loss: 0.0073\n",
      "Epoch 99/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3521e-04 - val_loss: 0.0082\n",
      "Epoch 100/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1615e-04 - val_loss: 0.0083\n",
      "Epoch 101/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9903e-04 - val_loss: 0.0081\n",
      "Epoch 102/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9563e-04 - val_loss: 0.0086\n",
      "Epoch 103/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9869e-04 - val_loss: 0.0083\n",
      "Epoch 104/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9985e-04 - val_loss: 0.0087\n",
      "Epoch 105/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0477e-04 - val_loss: 0.0080\n",
      "Epoch 106/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8950e-04 - val_loss: 0.0085\n",
      "Epoch 107/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0299e-04 - val_loss: 0.0081\n",
      "Epoch 108/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9421e-04 - val_loss: 0.0083\n",
      "Epoch 109/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9307e-04 - val_loss: 0.0082\n",
      "Epoch 110/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8547e-04 - val_loss: 0.0079\n",
      "Epoch 111/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8383e-04 - val_loss: 0.0080\n",
      "Epoch 112/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8572e-04 - val_loss: 0.0081\n",
      "Epoch 113/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8544e-04 - val_loss: 0.0083\n",
      "Epoch 114/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9539e-04 - val_loss: 0.0087\n",
      "Epoch 115/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9066e-04 - val_loss: 0.0083\n",
      "Epoch 116/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8638e-04 - val_loss: 0.0083\n",
      "Epoch 117/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8432e-04 - val_loss: 0.0087\n",
      "Epoch 118/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8386e-04 - val_loss: 0.0081\n",
      "Epoch 119/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7805e-04 - val_loss: 0.0084\n",
      "Epoch 120/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7766e-04 - val_loss: 0.0079\n",
      "Epoch 121/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8361e-04 - val_loss: 0.0080\n",
      "Epoch 122/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8779e-04 - val_loss: 0.0084\n",
      "Epoch 123/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1702e-04 - val_loss: 0.0078\n",
      "Epoch 124/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7587e-04 - val_loss: 0.0083\n",
      "Epoch 125/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7296e-04 - val_loss: 0.0082\n",
      "Epoch 126/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8058e-04 - val_loss: 0.0088\n",
      "Epoch 127/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8986e-04 - val_loss: 0.0081\n",
      "Epoch 128/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7270e-04 - val_loss: 0.0077\n",
      "Epoch 129/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7527e-04 - val_loss: 0.0082\n",
      "Epoch 130/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6962e-04 - val_loss: 0.0081\n",
      "Epoch 131/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6518e-04 - val_loss: 0.0087\n",
      "Epoch 132/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6368e-04 - val_loss: 0.0084\n",
      "Epoch 133/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6554e-04 - val_loss: 0.0078\n",
      "Epoch 134/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8424e-04 - val_loss: 0.0083\n",
      "Epoch 135/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.7982e-04 - val_loss: 0.0076\n",
      "Epoch 136/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6047e-04 - val_loss: 0.0083\n",
      "Epoch 137/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6087e-04 - val_loss: 0.0080\n",
      "Epoch 138/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5765e-04 - val_loss: 0.0078\n",
      "Epoch 139/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5807e-04 - val_loss: 0.0084\n",
      "Epoch 140/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.6452e-04 - val_loss: 0.0081\n",
      "Epoch 141/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5774e-04 - val_loss: 0.0085\n",
      "Epoch 142/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5817e-04 - val_loss: 0.0078\n",
      "Epoch 143/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5458e-04 - val_loss: 0.0085\n",
      "Epoch 144/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5827e-04 - val_loss: 0.0079\n",
      "Epoch 145/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.4894e-04 - val_loss: 0.0083\n",
      "Epoch 146/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5159e-04 - val_loss: 0.0083\n",
      "Epoch 147/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.4477e-04 - val_loss: 0.0078\n",
      "Epoch 148/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.5186e-04 - val_loss: 0.0080\n",
      "Epoch 149/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.4384e-04 - val_loss: 0.0079\n",
      "Epoch 150/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.4936e-04 - val_loss: 0.0086\n",
      "42/42 [==============================] - 0s 732us/step\n"
     ]
    }
   ],
   "source": [
    "# # # SDAE\n",
    "input_layer = Input(shape=(len(include),))\n",
    "encoded = Dense(64, activation='relu')(input_layer)\n",
    "encoded = Dense(32, activation='relu')(encoded)\n",
    "encoded = Dense(16, activation='relu')(encoded)\n",
    "\n",
    "decoded = Dense(32, activation='relu')(encoded)\n",
    "decoded = Dense(64, activation='relu')(decoded)\n",
    "decoded = Dense(len(include), activation='sigmoid')(decoded)\n",
    "\n",
    "autoencoder = Model(input_layer, decoded)\n",
    "autoencoder.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "# Train the autoencoder\n",
    "autoencoder.fit(train_scaled, train_scaled, epochs=150, batch_size=32, shuffle=True, validation_split=0.2)\n",
    "\n",
    "# Get the encoded representation of the data\n",
    "encoded_features = autoencoder.predict(train_scaled)\n",
    "\n",
    "def create_sequences(data, sequence_length):\n",
    "    xs, ys = [], []\n",
    "    for i in range(len(data) - sequence_length):\n",
    "        x = data[i:(i + sequence_length), :]\n",
    "        y = data[i + sequence_length, 0]\n",
    "        xs.append(x)\n",
    "        ys.append(y)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "sequence_length = 5  # Number of days used to predict the next day\n",
    "X_train, y_train = create_sequences(train_scaled, sequence_length)\n",
    "X_test, y_test = create_sequences(test_scaled, sequence_length)\n",
    "\n",
    "# Reshape inputs for LSTM [samples, time steps, features]\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], len(include)))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], len(include)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 5, 50)             12200     \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 50)                20200     \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 25)                1275      \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 26        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 33701 (131.64 KB)\n",
      "Trainable params: 33701 (131.64 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Build the LSTM model\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(sequence_length, len(include))))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "21/21 [==============================] - 3s 5ms/step - loss: 0.0241\n",
      "Epoch 2/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 0.0052\n",
      "Epoch 3/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0033\n",
      "Epoch 4/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0021\n",
      "Epoch 5/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 0.0015\n",
      "Epoch 6/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 7/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 8/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0010\n",
      "Epoch 9/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 9.7843e-04\n",
      "Epoch 10/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0010\n",
      "Epoch 11/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0010\n",
      "Epoch 12/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 13/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0012\n",
      "Epoch 14/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 9.1198e-04\n",
      "Epoch 15/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 8.4839e-04\n",
      "Epoch 16/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 9.2613e-04\n",
      "Epoch 17/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 9.1904e-04\n",
      "Epoch 18/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.6802e-04\n",
      "Epoch 19/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.7747e-04\n",
      "Epoch 20/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.9561e-04\n",
      "Epoch 21/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.9815e-04\n",
      "Epoch 22/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.4893e-04\n",
      "Epoch 23/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.7126e-04\n",
      "Epoch 24/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.0795e-04\n",
      "Epoch 25/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.1364e-04\n",
      "Epoch 26/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.5758e-04\n",
      "Epoch 27/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.6973e-04\n",
      "Epoch 28/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.3505e-04\n",
      "Epoch 29/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 9.1592e-04\n",
      "Epoch 30/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.7170e-04\n",
      "Epoch 31/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.4929e-04\n",
      "Epoch 32/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.4938e-04\n",
      "Epoch 33/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0011\n",
      "Epoch 34/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.5463e-04\n",
      "Epoch 35/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.0288e-04\n",
      "Epoch 36/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.0372e-04\n",
      "Epoch 37/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.9809e-04\n",
      "Epoch 38/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.7464e-04\n",
      "Epoch 39/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.8837e-04\n",
      "Epoch 40/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 8.3061e-04\n",
      "Epoch 41/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.7300e-04\n",
      "Epoch 42/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.5791e-04\n",
      "Epoch 43/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.9651e-04\n",
      "Epoch 44/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.1366e-04\n",
      "Epoch 45/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.2372e-04\n",
      "Epoch 46/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.6276e-04\n",
      "Epoch 47/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.0428e-04\n",
      "Epoch 48/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.6010e-04\n",
      "Epoch 49/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 6.2767e-04\n",
      "Epoch 50/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 7.1235e-04\n",
      "Epoch 51/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 7.4930e-04\n",
      "Epoch 52/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.8182e-04\n",
      "Epoch 53/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.2473e-04\n",
      "Epoch 54/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.5020e-04\n",
      "Epoch 55/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.5492e-04\n",
      "Epoch 56/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.1511e-04\n",
      "Epoch 57/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.6874e-04\n",
      "Epoch 58/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.9887e-04\n",
      "Epoch 59/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.5956e-04\n",
      "Epoch 60/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.0439e-04\n",
      "Epoch 61/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.1668e-04\n",
      "Epoch 62/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.7773e-04\n",
      "Epoch 63/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.4751e-04\n",
      "Epoch 64/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.5360e-04\n",
      "Epoch 65/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.2889e-04\n",
      "Epoch 66/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.0007e-04\n",
      "Epoch 67/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.3804e-04\n",
      "Epoch 68/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.3421e-04\n",
      "Epoch 69/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.1371e-04\n",
      "Epoch 70/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.7432e-04\n",
      "Epoch 71/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.4371e-04\n",
      "Epoch 72/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.5146e-04\n",
      "Epoch 73/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.9319e-04\n",
      "Epoch 74/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.8278e-04\n",
      "Epoch 75/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 6.4715e-04\n",
      "Epoch 76/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.0730e-04\n",
      "Epoch 77/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.2227e-04\n",
      "Epoch 78/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.8937e-04\n",
      "Epoch 79/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.7733e-04\n",
      "Epoch 80/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.9739e-04\n",
      "Epoch 81/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6656e-04\n",
      "Epoch 82/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.0858e-04\n",
      "Epoch 83/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.6799e-04\n",
      "Epoch 84/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6761e-04\n",
      "Epoch 85/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.8408e-04\n",
      "Epoch 86/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6713e-04\n",
      "Epoch 87/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.8302e-04\n",
      "Epoch 88/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.3011e-04\n",
      "Epoch 89/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 7.3241e-04\n",
      "Epoch 90/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 5.7290e-04\n",
      "Epoch 91/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6788e-04\n",
      "Epoch 92/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6951e-04\n",
      "Epoch 93/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5231e-04\n",
      "Epoch 94/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.4272e-04\n",
      "Epoch 95/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.7132e-04\n",
      "Epoch 96/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2907e-04\n",
      "Epoch 97/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.1806e-04\n",
      "Epoch 98/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5062e-04\n",
      "Epoch 99/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.3681e-04\n",
      "Epoch 100/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.4267e-04\n",
      "Epoch 101/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.4014e-04\n",
      "Epoch 102/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6211e-04\n",
      "Epoch 103/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.1546e-04\n",
      "Epoch 104/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2437e-04\n",
      "Epoch 105/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.4803e-04\n",
      "Epoch 106/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.4814e-04\n",
      "Epoch 107/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2992e-04\n",
      "Epoch 108/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.9948e-04\n",
      "Epoch 109/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.9843e-04\n",
      "Epoch 110/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5193e-04\n",
      "Epoch 111/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2568e-04\n",
      "Epoch 112/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.1013e-04\n",
      "Epoch 113/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.0638e-04\n",
      "Epoch 114/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2885e-04\n",
      "Epoch 115/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.2992e-04\n",
      "Epoch 116/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.1375e-04\n",
      "Epoch 117/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5671e-04\n",
      "Epoch 118/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5500e-04\n",
      "Epoch 119/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7892e-04\n",
      "Epoch 120/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8875e-04\n",
      "Epoch 121/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.0655e-04\n",
      "Epoch 122/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.0494e-04\n",
      "Epoch 123/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.8700e-04\n",
      "Epoch 124/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.0928e-04\n",
      "Epoch 125/150\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 4.1522e-04\n",
      "Epoch 126/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5581e-04\n",
      "Epoch 127/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8933e-04\n",
      "Epoch 128/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.1587e-04\n",
      "Epoch 129/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.0976e-04\n",
      "Epoch 130/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.9776e-04\n",
      "Epoch 131/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.6730e-04\n",
      "Epoch 132/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.8025e-04\n",
      "Epoch 133/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5722e-04\n",
      "Epoch 134/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.9514e-04\n",
      "Epoch 135/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 5.2629e-04\n",
      "Epoch 136/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.0786e-04\n",
      "Epoch 137/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7201e-04\n",
      "Epoch 138/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8920e-04\n",
      "Epoch 139/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.4502e-04\n",
      "Epoch 140/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7793e-04\n",
      "Epoch 141/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.6709e-04\n",
      "Epoch 142/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.0145e-04\n",
      "Epoch 143/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.7213e-04\n",
      "Epoch 144/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8674e-04\n",
      "Epoch 145/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8142e-04\n",
      "Epoch 146/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7391e-04\n",
      "Epoch 147/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.5463e-04\n",
      "Epoch 148/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.5714e-04\n",
      "Epoch 149/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.6257e-04\n",
      "Epoch 150/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7201e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x2108a684ca0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train, batch_size=64, epochs=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 2ms/step\n"
     ]
    }
   ],
   "source": [
    "# Predict values\n",
    "predictions = model.predict(X_test)\n",
    "predictions = scaler.inverse_transform(np.concatenate((predictions, np.zeros((predictions.shape[0], len(include)-1))), axis=1))[:, 0]\n",
    "\n",
    "# Slice the actual prices to align with the predictions\n",
    "actual_prices_aligned = df.loc[start_date_testing:end_date_testing, CLOSE][-len(predictions):]\n",
    "predictions = pd.DataFrame(predictions, index=actual_prices_aligned.index, columns=['Predicted_Close'])\n",
    "\n",
    "# Convert actual_prices_aligned to a DataFrame\n",
    "actual_prices_aligned_df = pd.DataFrame(actual_prices_aligned, columns=[CLOSE])\n",
    "\n",
    "# Calculating the logarithm of predictions and then the logarithmic difference\n",
    "actual_prices_aligned_df['Predicted_Close'] = predictions\n",
    "actual_prices_aligned_df['Predicted_Close'] = np.log(actual_prices_aligned_df['Predicted_Close'])\n",
    "log_diff_predicted = actual_prices_aligned_df['Predicted_Close'].diff().fillna(0)\n",
    "log_diff_actual = pd.DataFrame(df[\"Log_Diff_Close\"], index=actual_prices_aligned.index, columns=['Log_Diff_Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Actual Log Diff Close</th>\n",
       "      <th>Predicted_Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022-09-08</th>\n",
       "      <td>0.000627</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-09</th>\n",
       "      <td>0.047298</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>-0.001993</td>\n",
       "      <td>0.061529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>-0.078199</td>\n",
       "      <td>0.052149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>0.036853</td>\n",
       "      <td>-0.046887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-20</th>\n",
       "      <td>-0.050781</td>\n",
       "      <td>0.063270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-21</th>\n",
       "      <td>0.106985</td>\n",
       "      <td>0.031912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-22</th>\n",
       "      <td>-0.027429</td>\n",
       "      <td>0.027677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-23</th>\n",
       "      <td>0.032557</td>\n",
       "      <td>-0.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24</th>\n",
       "      <td>-0.032557</td>\n",
       "      <td>0.014109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual Log Diff Close  Predicted_Close\n",
       "date                                              \n",
       "2022-09-08               0.000627         0.000000\n",
       "2022-09-09               0.047298         0.000614\n",
       "2022-09-12              -0.001993         0.061529\n",
       "2022-09-13              -0.078199         0.052149\n",
       "2022-09-14               0.036853        -0.046887\n",
       "...                           ...              ...\n",
       "2023-03-20              -0.050781         0.063270\n",
       "2023-03-21               0.106985         0.031912\n",
       "2023-03-22              -0.027429         0.027677\n",
       "2023-03-23               0.032557        -0.016522\n",
       "2023-03-24              -0.032557         0.014109\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction_df = pd.concat([log_diff_actual, log_diff_predicted], axis=1)\n",
    "prediction_df.rename(columns={'Log_Diff_Close': 'Actual Log Diff Close', 'Predicted Close': 'Predicted Log Diff Close'}, inplace=True)\n",
    "output_path = os.path.join(os.getcwd(), '../../data/output', \"prediction_\"+CLOSE+\".xlsx\")\n",
    "prediction_df.to_excel(output_path, index=False)\n",
    "\n",
    "prediction_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:7: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  money_buy_and_hold = money_buy_and_hold * (1 + log_diff_actual.iloc[i][0])\n",
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:9: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  if log_diff_predicted[i] >= 0:\n",
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:10: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  money = money * (1 + log_diff_actual.iloc[i][0])\n",
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:17: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  cur_prediction = \"bull\" if log_diff_predicted[i] > 0 else \"bear\"\n",
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:19: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  cur_rendement_observe = log_diff_actual.iloc[i][0]\n",
      "C:\\Users\\thome\\AppData\\Local\\Temp\\ipykernel_4088\\731473146.py:12: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`\n",
      "  money = money * (1 - log_diff_actual.iloc[i][0])\n"
     ]
    },
    {
     "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>date</th>\n",
       "      <th>rendement_predit</th>\n",
       "      <th>vol_empirique</th>\n",
       "      <th>prediction</th>\n",
       "      <th>alpha(expo_crypto)</th>\n",
       "      <th>rendement_observe</th>\n",
       "      <th>valeur_portefeuille</th>\n",
       "      <th>valeur_crypto</th>\n",
       "      <th>valeur_portefeuille_buy_and_hold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-09-08</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>?</td>\n",
       "      <td>bear</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.000627</td>\n",
       "      <td>10006.265010</td>\n",
       "      <td>0.4790</td>\n",
       "      <td>10006.265010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-09-09</td>\n",
       "      <td>0.000614</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.047298</td>\n",
       "      <td>10479.539825</td>\n",
       "      <td>0.5022</td>\n",
       "      <td>10479.539825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>0.061529</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>-0.001993</td>\n",
       "      <td>10458.651758</td>\n",
       "      <td>0.5012</td>\n",
       "      <td>10458.651758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-09-13</td>\n",
       "      <td>0.052149</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>-0.078199</td>\n",
       "      <td>9640.797343</td>\n",
       "      <td>0.4635</td>\n",
       "      <td>9640.797343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-09-14</td>\n",
       "      <td>-0.046887</td>\n",
       "      <td>?</td>\n",
       "      <td>bear</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.036853</td>\n",
       "      <td>9285.505392</td>\n",
       "      <td>0.4809</td>\n",
       "      <td>9996.089293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2023-03-20</td>\n",
       "      <td>0.063270</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>-0.050781</td>\n",
       "      <td>9435.296547</td>\n",
       "      <td>0.3321</td>\n",
       "      <td>6103.570542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2023-03-21</td>\n",
       "      <td>0.031912</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.106985</td>\n",
       "      <td>10444.733743</td>\n",
       "      <td>0.3696</td>\n",
       "      <td>6756.562327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2023-03-22</td>\n",
       "      <td>0.027677</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>-0.027429</td>\n",
       "      <td>10158.244752</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>6571.236328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2023-03-23</td>\n",
       "      <td>-0.016522</td>\n",
       "      <td>?</td>\n",
       "      <td>bear</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.032557</td>\n",
       "      <td>9827.527232</td>\n",
       "      <td>0.3715</td>\n",
       "      <td>6785.173188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2023-03-24</td>\n",
       "      <td>0.014109</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>-0.032557</td>\n",
       "      <td>9507.576737</td>\n",
       "      <td>0.3596</td>\n",
       "      <td>6564.271279</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  rendement_predit vol_empirique prediction alpha(expo_crypto)  \\\n",
       "0    2022-09-08          0.000000             ?       bear               100%   \n",
       "1    2022-09-09          0.000614             ?       bull               100%   \n",
       "2    2022-09-12          0.061529             ?       bull               100%   \n",
       "3    2022-09-13          0.052149             ?       bull               100%   \n",
       "4    2022-09-14         -0.046887             ?       bear               100%   \n",
       "..          ...               ...           ...        ...                ...   \n",
       "137  2023-03-20          0.063270             ?       bull               100%   \n",
       "138  2023-03-21          0.031912             ?       bull               100%   \n",
       "139  2023-03-22          0.027677             ?       bull               100%   \n",
       "140  2023-03-23         -0.016522             ?       bear               100%   \n",
       "141  2023-03-24          0.014109             ?       bull               100%   \n",
       "\n",
       "     rendement_observe  valeur_portefeuille  valeur_crypto  \\\n",
       "0             0.000627         10006.265010         0.4790   \n",
       "1             0.047298         10479.539825         0.5022   \n",
       "2            -0.001993         10458.651758         0.5012   \n",
       "3            -0.078199          9640.797343         0.4635   \n",
       "4             0.036853          9285.505392         0.4809   \n",
       "..                 ...                  ...            ...   \n",
       "137          -0.050781          9435.296547         0.3321   \n",
       "138           0.106985         10444.733743         0.3696   \n",
       "139          -0.027429         10158.244752         0.3596   \n",
       "140           0.032557          9827.527232         0.3715   \n",
       "141          -0.032557          9507.576737         0.3596   \n",
       "\n",
       "     valeur_portefeuille_buy_and_hold  \n",
       "0                        10006.265010  \n",
       "1                        10479.539825  \n",
       "2                        10458.651758  \n",
       "3                         9640.797343  \n",
       "4                         9996.089293  \n",
       "..                                ...  \n",
       "137                       6103.570542  \n",
       "138                       6756.562327  \n",
       "139                       6571.236328  \n",
       "140                       6785.173188  \n",
       "141                       6564.271279  \n",
       "\n",
       "[142 rows x 9 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "money = 10000\n",
    "money_buy_and_hold = 10000\n",
    "money_evolution = []\n",
    "money_evolution_buy_and_hold = []\n",
    "backtest_values = []\n",
    "for i in range(len(log_diff_predicted)):\n",
    "    money_buy_and_hold = money_buy_and_hold * (1 + log_diff_actual.iloc[i][0])\n",
    "    money_evolution_buy_and_hold.append(money_buy_and_hold)\n",
    "    if log_diff_predicted[i] >= 0:\n",
    "        money = money * (1 + log_diff_actual.iloc[i][0])\n",
    "    else:\n",
    "        money = money * (1 - log_diff_actual.iloc[i][0])\n",
    "    money_evolution.append(money)\n",
    "    cur_date = log_diff_actual.index[i].strftime(\"%Y-%m-%d\")\n",
    "    cur_rendement_predit = log_diff_predicted.iloc[i]\n",
    "    cur_vol_empirique = \"?\"\n",
    "    cur_prediction = \"bull\" if log_diff_predicted[i] > 0 else \"bear\"\n",
    "    cur_alpha = \"100%\"\n",
    "    cur_rendement_observe = log_diff_actual.iloc[i][0]\n",
    "    cur_valeur_portefeuille = money\n",
    "    cur_valeur_crypto = df.loc[cur_date, CLOSE]\n",
    "    backtest_values.append([cur_date, cur_rendement_predit, cur_vol_empirique, cur_prediction, cur_alpha, cur_rendement_observe, cur_valeur_portefeuille, cur_valeur_crypto, money_buy_and_hold])\n",
    "backtest = pd.DataFrame(backtest_values, columns=['date', 'rendement_predit', 'vol_empirique', 'prediction', 'alpha(expo_crypto)', 'rendement_observe', 'valeur_portefeuille', 'valeur_crypto', 'valeur_portefeuille_buy_and_hold'])\n",
    "output_path = os.path.join(os.getcwd(), '../../data/output', \"backtest_\"+CLOSE+\".xlsx\")\n",
    "backtest.to_excel(output_path, index=False)\n",
    "backtest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mean Square Error</td>\n",
       "      <td>3.653294e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Root Mean Square Error</td>\n",
       "      <td>1.911359e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mean Absolute Error</td>\n",
       "      <td>1.890859e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Root Mean Square Error (log)</td>\n",
       "      <td>5.600000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mean Absolute Error (log)</td>\n",
       "      <td>4.100000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean Absolute Percentage Error</td>\n",
       "      <td>5.344501e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>U-Theil1 Statistic</td>\n",
       "      <td>1.464124e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U-Theil2 Statistic</td>\n",
       "      <td>5.254407e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Diebold-Mariano Test</td>\n",
       "      <td>-8.070600e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rendement Absolue</td>\n",
       "      <td>-4.900000e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Indicator Name         Value\n",
       "0               Mean Square Error  3.653294e+08\n",
       "1          Root Mean Square Error  1.911359e+04\n",
       "2             Mean Absolute Error  1.890859e+04\n",
       "3    Root Mean Square Error (log)  5.600000e-02\n",
       "4       Mean Absolute Error (log)  4.100000e-02\n",
       "5  Mean Absolute Percentage Error  5.344501e+06\n",
       "6              U-Theil1 Statistic  1.464124e+07\n",
       "7              U-Theil2 Statistic  5.254407e+04\n",
       "8            Diebold-Mariano Test -8.070600e+01\n",
       "9               Rendement Absolue -4.900000e-02"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = mean_squared_error(actual_prices_aligned, predictions)\n",
    "\n",
    "# Calculate RMSE\n",
    "rmse = math.sqrt(mean_squared_error(actual_prices_aligned, predictions))\n",
    "\n",
    "# Calculate MAE\n",
    "mae = mean_absolute_error(actual_prices_aligned, predictions)\n",
    "\n",
    "# Calculate RMSE Log diff\n",
    "rmse_log = math.sqrt(mean_squared_error(log_diff_actual, log_diff_predicted))\n",
    "\n",
    "# Calculate MAE Log diff\n",
    "mae_log = mean_absolute_error(log_diff_actual, log_diff_predicted)\n",
    "\n",
    "# Convert predictions DataFrame column to a NumPy array and flatten\n",
    "predictions_array = predictions.iloc[:, 0].to_numpy().flatten()\n",
    "\n",
    "# Now calculate MAPE\n",
    "# Ensure actual_prices_aligned is also a NumPy array for consistency\n",
    "actual_prices_aligned_array = actual_prices_aligned.to_numpy()\n",
    "\n",
    "mape = np.mean(np.abs((actual_prices_aligned_array - predictions_array) / actual_prices_aligned_array)) * 100\n",
    "\n",
    "# Handle division by zero or NaN values\n",
    "mape = np.nan_to_num(mape)\n",
    "\n",
    "\n",
    "# Ensure actual_prices_aligned and predictions are correctly aligned and are numpy arrays\n",
    "actual_values = actual_prices_aligned.values\n",
    "predicted_values = predictions.values\n",
    "\n",
    "# U-Theil1\n",
    "u_theil1 = np.sqrt(np.sum(np.square(predicted_values - actual_values)) / np.sum(np.square(np.diff(actual_values))))\n",
    "\n",
    "# U-Theil2\n",
    "u_theil2 = (np.sqrt(np.mean(np.square(predicted_values - actual_values))) / np.sqrt(np.mean(np.square(actual_values))))\n",
    "\n",
    "\n",
    "# Calculate the Dielbod-Mariano\n",
    "# Error from your model\n",
    "model_errors = actual_prices_aligned_array - predictions_array\n",
    "\n",
    "# Error from a naive model (using previous value as prediction)\n",
    "naive_errors = actual_prices_aligned_array - np.roll(actual_prices_aligned_array, 1)\n",
    "naive_errors[0] = 0  # handle the first element which doesn't have a previous value\n",
    "# Difference in errors\n",
    "error_diff = model_errors - naive_errors\n",
    "\n",
    "# Mean difference in errors\n",
    "mean_diff = np.mean(error_diff)\n",
    "\n",
    "# Variance of the differences\n",
    "var_diff = np.var(error_diff)\n",
    "\n",
    "# Number of observations\n",
    "n = len(error_diff)\n",
    "\n",
    "# Calculate DM test statistic\n",
    "dm_stat = mean_diff / np.sqrt(var_diff / n)\n",
    "\n",
    "absolute_rendement = money_evolution[-1] / 10000 - 1\n",
    "\n",
    "\n",
    "data = {\n",
    "    'Indicator Name': ['Mean Square Error','Root Mean Square Error', 'Mean Absolute Error','Root Mean Square Error (log)', 'Mean Absolute Error (log)', 'Mean Absolute Percentage Error',\n",
    "                       'U-Theil1 Statistic', 'U-Theil2 Statistic', 'Diebold-Mariano Test', 'Rendement Absolue'],\n",
    "    'Value': [mse, rmse, mae, rmse_log, mae_log, mape, u_theil1, u_theil2, dm_stat, absolute_rendement]\n",
    "}\n",
    "\n",
    "table = pd.DataFrame(data)\n",
    "table['Value'] = table['Value'].apply(lambda x: round(x, 3))\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), '../../data/output', \"performance_\"+CLOSE+\".xlsx\")\n",
    "table.to_excel(output_path, index=False)\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(money_evolution_buy_and_hold, label='Buy and hold', color='blue')\n",
    "plt.plot(money_evolution, label='Applied strategy', color='red')\n",
    "plt.title(f'Current Holdings ({CURRENCY})')\n",
    "plt.ylabel('Money')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(log_diff_actual, label='Actual', color='blue')\n",
    "plt.plot(log_diff_predicted, label='Predicted', color='red')\n",
    "plt.title(f'Logarithmic Difference')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Log Difference')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}