src/models/BTC.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 = \"BTC\"\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": [],
   "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",
      "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 7ms/step - loss: 0.1212 - val_loss: 0.0905\n",
      "Epoch 2/150\n",
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      "Epoch 3/150\n",
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      "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",
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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",
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      "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",
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      "Epoch 30/150\n",
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      "Epoch 31/150\n",
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      "Epoch 32/150\n",
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      "Epoch 34/150\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 81/150\n",
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      "Epoch 82/150\n",
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      "Epoch 83/150\n",
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      "Epoch 84/150\n",
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      "Epoch 85/150\n",
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      "Epoch 86/150\n",
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      "Epoch 87/150\n",
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      "Epoch 88/150\n",
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      "Epoch 89/150\n",
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      "Epoch 90/150\n",
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      "Epoch 91/150\n",
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      "Epoch 92/150\n",
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      "Epoch 93/150\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.3374e-04 - val_loss: 0.0075\n",
      "Epoch 94/150\n",
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      "Epoch 95/150\n",
      "33/33 [==============================] - 0s 3ms/step - loss: 2.2208e-04 - val_loss: 0.0084\n",
      "Epoch 96/150\n",
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      "Epoch 97/150\n",
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      "Epoch 100/150\n",
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      "Epoch 110/150\n",
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      "Epoch 111/150\n",
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      "Epoch 112/150\n",
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      "Epoch 113/150\n",
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      "Epoch 114/150\n",
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      "Epoch 115/150\n",
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      "Epoch 116/150\n",
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      "Epoch 117/150\n",
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      "Epoch 118/150\n",
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      "Epoch 119/150\n",
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      "Epoch 120/150\n",
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      "Epoch 121/150\n",
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      "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 916us/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 4ms/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",
      "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",
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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 21/150\n",
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      "21/21 [==============================] - 0s 4ms/step - loss: 3.9776e-04\n",
      "Epoch 131/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 4.6730e-04\n",
      "Epoch 132/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.8025e-04\n",
      "Epoch 133/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 4.5721e-04\n",
      "Epoch 134/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 4.9514e-04\n",
      "Epoch 135/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 5.2629e-04\n",
      "Epoch 136/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 4.0786e-04\n",
      "Epoch 137/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 3.7201e-04\n",
      "Epoch 138/150\n",
      "21/21 [==============================] - 0s 3ms/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 3ms/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 3ms/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 6ms/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 3ms/step - loss: 3.5463e-04\n",
      "Epoch 148/150\n",
      "21/21 [==============================] - 0s 3ms/step - loss: 3.5714e-04\n",
      "Epoch 149/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.6257e-04\n",
      "Epoch 150/150\n",
      "21/21 [==============================] - 0s 4ms/step - loss: 3.7201e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x237a6b773d0>"
      ]
     },
     "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 4ms/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>0.001395</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-09</th>\n",
       "      <td>0.100396</td>\n",
       "      <td>0.000614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-12</th>\n",
       "      <td>0.047346</td>\n",
       "      <td>0.061528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-13</th>\n",
       "      <td>-0.104497</td>\n",
       "      <td>0.052149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-14</th>\n",
       "      <td>0.002631</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>0.011681</td>\n",
       "      <td>0.063270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-21</th>\n",
       "      <td>0.013918</td>\n",
       "      <td>0.031912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-22</th>\n",
       "      <td>-0.030875</td>\n",
       "      <td>0.027677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-23</th>\n",
       "      <td>0.037610</td>\n",
       "      <td>-0.016522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-03-24</th>\n",
       "      <td>-0.030171</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               0.001395         0.000000\n",
       "2022-09-09               0.100396         0.000614\n",
       "2022-09-12               0.047346         0.061528\n",
       "2022-09-13              -0.104497         0.052149\n",
       "2022-09-14               0.002631        -0.046887\n",
       "...                           ...              ...\n",
       "2023-03-20               0.011681         0.063270\n",
       "2023-03-21               0.013918         0.031912\n",
       "2023-03-22              -0.030875         0.027677\n",
       "2023-03-23               0.037610        -0.016522\n",
       "2023-03-24              -0.030171         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",
       "      <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>0.001395</td>\n",
       "      <td>10013.948813</td>\n",
       "      <td>19319.77</td>\n",
       "      <td>10013.948813</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>0.100396</td>\n",
       "      <td>11019.311621</td>\n",
       "      <td>21360.11</td>\n",
       "      <td>11019.311621</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>0.047346</td>\n",
       "      <td>11541.027523</td>\n",
       "      <td>22395.74</td>\n",
       "      <td>11541.027523</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>-0.104497</td>\n",
       "      <td>10335.019803</td>\n",
       "      <td>20173.57</td>\n",
       "      <td>10335.019803</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>0.002631</td>\n",
       "      <td>10307.831711</td>\n",
       "      <td>20226.71</td>\n",
       "      <td>10362.207895</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>0.011681</td>\n",
       "      <td>14715.489126</td>\n",
       "      <td>27717.01</td>\n",
       "      <td>13111.505011</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>0.013918</td>\n",
       "      <td>14920.298055</td>\n",
       "      <td>28105.47</td>\n",
       "      <td>13293.989826</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>-0.030875</td>\n",
       "      <td>14459.632323</td>\n",
       "      <td>27250.97</td>\n",
       "      <td>12883.536527</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>0.037610</td>\n",
       "      <td>13915.798555</td>\n",
       "      <td>28295.41</td>\n",
       "      <td>13368.092570</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>-0.030171</td>\n",
       "      <td>13495.950501</td>\n",
       "      <td>27454.47</td>\n",
       "      <td>12964.769136</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          0.061528             ?       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             0.001395         10013.948813       19319.77   \n",
       "1             0.100396         11019.311621       21360.11   \n",
       "2             0.047346         11541.027523       22395.74   \n",
       "3            -0.104497         10335.019803       20173.57   \n",
       "4             0.002631         10307.831711       20226.71   \n",
       "..                 ...                  ...            ...   \n",
       "137           0.011681         14715.489126       27717.01   \n",
       "138           0.013918         14920.298055       28105.47   \n",
       "139          -0.030875         14459.632323       27250.97   \n",
       "140           0.037610         13915.798555       28295.41   \n",
       "141          -0.030171         13495.950501       27454.47   \n",
       "\n",
       "     valeur_portefeuille_buy_and_hold  \n",
       "0                        10013.948813  \n",
       "1                        11019.311621  \n",
       "2                        11541.027523  \n",
       "3                        10335.019803  \n",
       "4                        10362.207895  \n",
       "..                                ...  \n",
       "137                      13111.505011  \n",
       "138                      13293.989826  \n",
       "139                      12883.536527  \n",
       "140                      13368.092570  \n",
       "141                      12964.769136  \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",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>2243454.062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Root Mean Square Error</td>\n",
       "      <td>1497.816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mean Absolute Error</td>\n",
       "      <td>1272.794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Root Mean Square Error (log)</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Mean Absolute Error (log)</td>\n",
       "      <td>0.033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mean Absolute Percentage Error</td>\n",
       "      <td>6.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>U-Theil1 Statistic</td>\n",
       "      <td>67.668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>U-Theil2 Statistic</td>\n",
       "      <td>0.210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Diebold-Mariano Test</td>\n",
       "      <td>35.591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Rendement Absolue</td>\n",
       "      <td>0.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Accuracy of correct prediction side</td>\n",
       "      <td>0.451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Indicator Name        Value\n",
       "0                     Mean Square Error  2243454.062\n",
       "1                Root Mean Square Error     1497.816\n",
       "2                   Mean Absolute Error     1272.794\n",
       "3          Root Mean Square Error (log)        0.047\n",
       "4             Mean Absolute Error (log)        0.033\n",
       "5        Mean Absolute Percentage Error        6.272\n",
       "6                    U-Theil1 Statistic       67.668\n",
       "7                    U-Theil2 Statistic        0.210\n",
       "8                  Diebold-Mariano Test       35.591\n",
       "9                     Rendement Absolue        0.350\n",
       "10  Accuracy of correct prediction side        0.451"
      ]
     },
     "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",
    "# using log accuracy \n",
    "accuracy = np.sum(np.sign(log_diff_predicted) == np.sign(log_diff_actual.values.flatten())) / len(log_diff_predicted)\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', 'Accuracy of correct prediction side'],\n",
    "    'Value': [mse, rmse, mae, rmse_log, mae_log, mape, u_theil1, u_theil2, dm_stat, absolute_rendement, accuracy]\n",
    "}\n",
    "\n",
    "table = pd.DataFrame(data)\n",
    "table['Value'] = table['Value'].apply(lambda x: round(x, 3))\n",
    "\n",
    "\n",
    "output_path = os.path.join(os.getcwd(), '../../data/output', \"performance_\"+CLOSE+\".xlsx\")\n",
    "table.to_excel(output_path, index=False)\n",
    "table\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(money_evolution_buy_and_hold, label='Buy and hold', color='blue')\n",
    "plt.plot(money_evolution, label='Applied strategy', color='red')\n",
    "plt.title(f'Current Holdings ({CURRENCY})')\n",
    "plt.ylabel('Money')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(log_diff_actual, label='Actual', color='blue')\n",
    "plt.plot(log_diff_predicted, label='Predicted', color='red')\n",
    "plt.title(f'Logarithmic Difference')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Log Difference')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}