src/models/LTC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\codya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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 = \"LTC\"\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\\codya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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\\codya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From c:\\Users\\codya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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\\codya\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\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 8ms/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",
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      "Epoch 5/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0052 - val_loss: 0.0147\n",
      "Epoch 6/150\n",
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      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 0.0028 - val_loss: 0.0137\n",
      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "Epoch 14/150\n",
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      "Epoch 15/150\n",
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      "Epoch 16/150\n",
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      "Epoch 17/150\n",
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      "Epoch 18/150\n",
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      "Epoch 19/150\n",
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      "Epoch 20/150\n",
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      "Epoch 21/150\n",
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      "Epoch 22/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 9.1985e-04 - val_loss: 0.0111\n",
      "Epoch 23/150\n",
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      "Epoch 24/150\n",
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      "Epoch 25/150\n",
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      "Epoch 26/150\n",
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      "Epoch 27/150\n",
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      "Epoch 28/150\n",
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      "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",
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      "Epoch 32/150\n",
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      "Epoch 33/150\n",
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      "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",
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      "Epoch 37/150\n",
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      "Epoch 38/150\n",
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      "Epoch 39/150\n",
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      "Epoch 40/150\n",
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      "Epoch 41/150\n",
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      "Epoch 42/150\n",
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      "Epoch 44/150\n",
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      "Epoch 56/150\n",
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      "Epoch 57/150\n",
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      "Epoch 58/150\n",
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      "Epoch 59/150\n",
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      "Epoch 60/150\n",
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      "Epoch 61/150\n",
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      "Epoch 72/150\n",
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      "Epoch 73/150\n",
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      "Epoch 75/150\n",
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      "Epoch 76/150\n",
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      "Epoch 77/150\n",
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      "Epoch 78/150\n",
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      "Epoch 79/150\n",
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      "Epoch 80/150\n",
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      "Epoch 81/150\n",
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      "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",
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      "Epoch 90/150\n",
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      "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",
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      "Epoch 100/150\n",
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      "Epoch 101/150\n",
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      "Epoch 102/150\n",
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      "Epoch 103/150\n",
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      "Epoch 104/150\n",
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      "Epoch 105/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0477e-04 - val_loss: 0.0080\n",
      "Epoch 106/150\n",
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      "Epoch 107/150\n",
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      "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 3ms/step - loss: 1.9307e-04 - val_loss: 0.0082\n",
      "Epoch 110/150\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 1.8547e-04 - val_loss: 0.0079\n",
      "Epoch 111/150\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 1.8383e-04 - val_loss: 0.0080\n",
      "Epoch 112/150\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 1.8572e-04 - val_loss: 0.0081\n",
      "Epoch 113/150\n",
      "33/33 [==============================] - 0s 3ms/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 3ms/step - loss: 1.8432e-04 - val_loss: 0.0087\n",
      "Epoch 118/150\n",
      "33/33 [==============================] - 0s 3ms/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 3ms/step - loss: 1.7766e-04 - val_loss: 0.0079\n",
      "Epoch 121/150\n",
      "33/33 [==============================] - 0s 3ms/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 3ms/step - loss: 1.7270e-04 - val_loss: 0.0077\n",
      "Epoch 129/150\n",
      "33/33 [==============================] - 0s 4ms/step - loss: 1.7527e-04 - val_loss: 0.0082\n",
      "Epoch 130/150\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 1.6962e-04 - val_loss: 0.0081\n",
      "Epoch 131/150\n",
      "33/33 [==============================] - 0s 3ms/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 3ms/step - loss: 1.5458e-04 - val_loss: 0.0085\n",
      "Epoch 144/150\n",
      "33/33 [==============================] - 0s 3ms/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 843us/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",
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      "Epoch 2/150\n",
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      "Epoch 3/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 0.0033\n",
      "Epoch 4/150\n",
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      "Epoch 5/150\n",
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      "Epoch 6/150\n",
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      "Epoch 7/150\n",
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      "Epoch 8/150\n",
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      "Epoch 9/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 9.7843e-04\n",
      "Epoch 10/150\n",
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      "Epoch 11/150\n",
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      "Epoch 12/150\n",
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      "Epoch 13/150\n",
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      "21/21 [==============================] - 0s 5ms/step - loss: 3.8933e-04\n",
      "Epoch 128/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 5.1587e-04\n",
      "Epoch 129/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.0976e-04\n",
      "Epoch 130/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.9776e-04\n",
      "Epoch 131/150\n",
      "21/21 [==============================] - 0s 5ms/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 5ms/step - loss: 4.5721e-04\n",
      "Epoch 134/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.9514e-04\n",
      "Epoch 135/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 5.2629e-04\n",
      "Epoch 136/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.0786e-04\n",
      "Epoch 137/150\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 3.7201e-04\n",
      "Epoch 138/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.8920e-04\n",
      "Epoch 139/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.4502e-04\n",
      "Epoch 140/150\n",
      "21/21 [==============================] - 0s 5ms/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 5ms/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 5ms/step - loss: 3.7391e-04\n",
      "Epoch 147/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.5463e-04\n",
      "Epoch 148/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 3.5714e-04\n",
      "Epoch 149/150\n",
      "21/21 [==============================] - 0s 5ms/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 0x18a33d31410>"
      ]
     },
     "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>0.011278</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-09</th>\n",
       "      <td>0.053250</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>0.004081</td>\n",
       "      <td>0.061528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>-0.037853</td>\n",
       "      <td>0.052149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>0.018109</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>-0.091284</td>\n",
       "      <td>0.063270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-21</th>\n",
       "      <td>0.055206</td>\n",
       "      <td>0.031912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-22</th>\n",
       "      <td>0.056451</td>\n",
       "      <td>0.027677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-23</th>\n",
       "      <td>0.070130</td>\n",
       "      <td>-0.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24</th>\n",
       "      <td>-0.000321</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               0.011278         0.000000\n",
       "2022-09-09               0.053250         0.000614\n",
       "2022-09-12               0.004081         0.061528\n",
       "2022-09-13              -0.037853         0.052149\n",
       "2022-09-14               0.018109        -0.046887\n",
       "...                           ...              ...\n",
       "2023-03-20              -0.091284         0.063270\n",
       "2023-03-21               0.055206         0.031912\n",
       "2023-03-22               0.056451         0.027677\n",
       "2023-03-23               0.070130        -0.016522\n",
       "2023-03-24              -0.000321         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": [
    {
     "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",
       "    </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>0.011278</td>\n",
       "      <td>10112.779889</td>\n",
       "      <td>57.96</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>0.053250</td>\n",
       "      <td>10651.281642</td>\n",
       "      <td>61.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-09-12</td>\n",
       "      <td>0.061528</td>\n",
       "      <td>?</td>\n",
       "      <td>bull</td>\n",
       "      <td>100%</td>\n",
       "      <td>0.004081</td>\n",
       "      <td>10694.752772</td>\n",
       "      <td>61.38</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>-0.037853</td>\n",
       "      <td>10289.922961</td>\n",
       "      <td>59.10</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>0.018109</td>\n",
       "      <td>10103.581236</td>\n",
       "      <td>60.18</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",
       "    </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>-0.091284</td>\n",
       "      <td>5583.755424</td>\n",
       "      <td>78.05</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>0.055206</td>\n",
       "      <td>5892.013303</td>\n",
       "      <td>82.48</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>0.056451</td>\n",
       "      <td>6224.622883</td>\n",
       "      <td>87.27</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>0.070130</td>\n",
       "      <td>5788.087253</td>\n",
       "      <td>93.61</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>-0.000321</td>\n",
       "      <td>5786.231998</td>\n",
       "      <td>93.58</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 8 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          0.061528             ?       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             0.011278         10112.779889          57.96  \n",
       "1             0.053250         10651.281642          61.13  \n",
       "2             0.004081         10694.752772          61.38  \n",
       "3            -0.037853         10289.922961          59.10  \n",
       "4             0.018109         10103.581236          60.18  \n",
       "..                 ...                  ...            ...  \n",
       "137          -0.091284          5583.755424          78.05  \n",
       "138           0.055206          5892.013303          82.48  \n",
       "139           0.056451          6224.622883          87.27  \n",
       "140           0.070130          5788.087253          93.61  \n",
       "141          -0.000321          5786.231998          93.58  \n",
       "\n",
       "[142 rows x 8 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>3.625410e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Root Mean Square Error</td>\n",
       "      <td>1.904051e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mean Absolute Error</td>\n",
       "      <td>1.883581e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Root Mean Square Error (log)</td>\n",
       "      <td>6.100000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mean Absolute Error (log)</td>\n",
       "      <td>4.300000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean Absolute Percentage Error</td>\n",
       "      <td>2.668563e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>U-Theil1 Statistic</td>\n",
       "      <td>6.532712e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U-Theil2 Statistic</td>\n",
       "      <td>2.544870e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Diebold-Mariano Test</td>\n",
       "      <td>-8.061900e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rendement Absolue</td>\n",
       "      <td>-4.210000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Indicator Name         Value\n",
       "0               Mean Square Error  3.625410e+08\n",
       "1          Root Mean Square Error  1.904051e+04\n",
       "2             Mean Absolute Error  1.883581e+04\n",
       "3    Root Mean Square Error (log)  6.100000e-02\n",
       "4       Mean Absolute Error (log)  4.300000e-02\n",
       "5  Mean Absolute Percentage Error  2.668563e+04\n",
       "6              U-Theil1 Statistic  6.532712e+04\n",
       "7              U-Theil2 Statistic  2.544870e+02\n",
       "8            Diebold-Mariano Test -8.061900e+01\n",
       "9               Rendement Absolue -4.210000e-01"
      ]
     },
     "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",
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