src/models/MATIC.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 = \"MATIC\"\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 695us/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 4ms/step - loss: 0.0052\n",
      "Epoch 3/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 0.0033\n",
      "Epoch 4/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 0.0021\n",
      "Epoch 5/150\n",
      "21/21 [==============================] - 0s 4ms/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 4ms/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",
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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 21/150\n",
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      "Epoch 22/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 8.4893e-04\n",
      "Epoch 23/150\n",
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      "Epoch 31/150\n",
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      "Epoch 33/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 0.0011\n",
      "Epoch 34/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 7.5463e-04\n",
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      "21/21 [==============================] - 0s 5ms/step - loss: 4.1522e-04\n",
      "Epoch 126/150\n",
      "21/21 [==============================] - 0s 5ms/step - loss: 4.5581e-04\n",
      "Epoch 127/150\n",
      "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 4ms/step - loss: 4.5722e-04\n",
      "Epoch 134/150\n",
      "21/21 [==============================] - 0s 5ms/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 5ms/step - loss: 3.7793e-04\n",
      "Epoch 141/150\n",
      "21/21 [==============================] - 0s 6ms/step - loss: 3.6709e-04\n",
      "Epoch 142/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.0145e-04\n",
      "Epoch 143/150\n",
      "21/21 [==============================] - 0s 4ms/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 5ms/step - loss: 3.7201e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1a560241ca0>"
      ]
     },
     "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 3ms/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>0.005222</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-09</th>\n",
       "      <td>0.052683</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>0.043509</td>\n",
       "      <td>0.061529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>-0.094655</td>\n",
       "      <td>0.052149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>0.021514</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>-0.103136</td>\n",
       "      <td>0.063270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-21</th>\n",
       "      <td>0.044259</td>\n",
       "      <td>0.031912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-22</th>\n",
       "      <td>-0.039734</td>\n",
       "      <td>0.027677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-23</th>\n",
       "      <td>0.025228</td>\n",
       "      <td>-0.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24</th>\n",
       "      <td>-0.035116</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               0.005222         0.000000\n",
       "2022-09-09               0.052683         0.000614\n",
       "2022-09-12               0.043509         0.061529\n",
       "2022-09-13              -0.094655         0.052149\n",
       "2022-09-14               0.021514        -0.046887\n",
       "...                           ...              ...\n",
       "2023-03-20              -0.103136         0.063270\n",
       "2023-03-21               0.044259         0.031912\n",
       "2023-03-22              -0.039734         0.027677\n",
       "2023-03-23               0.025228        -0.016522\n",
       "2023-03-24              -0.035116         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_13444\\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_13444\\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_13444\\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_13444\\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_13444\\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_13444\\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>0.005222</td>\n",
       "      <td>10052.219440</td>\n",
       "      <td>0.8448</td>\n",
       "      <td>10052.219440</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>0.052683</td>\n",
       "      <td>10581.802424</td>\n",
       "      <td>0.8905</td>\n",
       "      <td>10581.802424</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>0.043509</td>\n",
       "      <td>11042.206114</td>\n",
       "      <td>0.9301</td>\n",
       "      <td>11042.206114</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>-0.094655</td>\n",
       "      <td>9997.011048</td>\n",
       "      <td>0.8461</td>\n",
       "      <td>9997.011048</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>0.021514</td>\n",
       "      <td>9781.937860</td>\n",
       "      <td>0.8645</td>\n",
       "      <td>10212.084237</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>-0.103136</td>\n",
       "      <td>8316.180239</td>\n",
       "      <td>1.1027</td>\n",
       "      <td>10029.573442</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>0.044259</td>\n",
       "      <td>8684.242254</td>\n",
       "      <td>1.1526</td>\n",
       "      <td>10473.467742</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>-0.039734</td>\n",
       "      <td>8339.178521</td>\n",
       "      <td>1.1077</td>\n",
       "      <td>10057.310089</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>0.025228</td>\n",
       "      <td>8128.801672</td>\n",
       "      <td>1.1360</td>\n",
       "      <td>10311.031148</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>-0.035116</td>\n",
       "      <td>7843.346843</td>\n",
       "      <td>1.0968</td>\n",
       "      <td>9948.944122</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             0.005222         10052.219440         0.8448   \n",
       "1             0.052683         10581.802424         0.8905   \n",
       "2             0.043509         11042.206114         0.9301   \n",
       "3            -0.094655          9997.011048         0.8461   \n",
       "4             0.021514          9781.937860         0.8645   \n",
       "..                 ...                  ...            ...   \n",
       "137          -0.103136          8316.180239         1.1027   \n",
       "138           0.044259          8684.242254         1.1526   \n",
       "139          -0.039734          8339.178521         1.1077   \n",
       "140           0.025228          8128.801672         1.1360   \n",
       "141          -0.035116          7843.346843         1.0968   \n",
       "\n",
       "     valeur_portefeuille_buy_and_hold  \n",
       "0                        10052.219440  \n",
       "1                        10581.802424  \n",
       "2                        11042.206114  \n",
       "3                         9997.011048  \n",
       "4                        10212.084237  \n",
       "..                                ...  \n",
       "137                      10029.573442  \n",
       "138                      10473.467742  \n",
       "139                      10057.310089  \n",
       "140                      10311.031148  \n",
       "141                       9948.944122  \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>3.653057e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Root Mean Square Error</td>\n",
       "      <td>1.911297e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mean Absolute Error</td>\n",
       "      <td>1.890798e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Root Mean Square Error (log)</td>\n",
       "      <td>7.300000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mean Absolute Error (log)</td>\n",
       "      <td>5.000000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean Absolute Percentage Error</td>\n",
       "      <td>1.980431e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>U-Theil1 Statistic</td>\n",
       "      <td>3.590500e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U-Theil2 Statistic</td>\n",
       "      <td>1.937208e+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>-2.160000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Indicator Name         Value\n",
       "0               Mean Square Error  3.653057e+08\n",
       "1          Root Mean Square Error  1.911297e+04\n",
       "2             Mean Absolute Error  1.890798e+04\n",
       "3    Root Mean Square Error (log)  7.300000e-02\n",
       "4       Mean Absolute Error (log)  5.000000e-02\n",
       "5  Mean Absolute Percentage Error  1.980431e+06\n",
       "6              U-Theil1 Statistic  3.590500e+06\n",
       "7              U-Theil2 Statistic  1.937208e+04\n",
       "8            Diebold-Mariano Test -8.070600e+01\n",
       "9               Rendement Absolue -2.160000e-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",
    "plt.show()"
   ]
  }
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