lstm_ceemdan.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# LSTM-CEEMDAN model\n",
    "\n",
    "## Python requirements\n",
    "\n",
    "```\n",
    "!pip install plotly\n",
    "!pip install cufflinks\n",
    "!pip install chart_studio\n",
    "!pip install ipywidgets\n",
    "!pip install yfinance\n",
    "!pip install EMD-signal\n",
    "!pip install sklearn\n",
    "!pip install keras\n",
    "!pip install tensorflow\n",
    "```\n",
    "\n",
    "## Model\n",
    "\n",
    "The **LSTM-CEEMDAN** works with daily data from equities: open, high, low, close, volume. Hereinafter we are calling these \"features\".\n",
    "\n",
    "### Data processing steps:\n",
    "\n",
    "1. Data gathering\n",
    "\n",
    "We use yfinance package to get the daily ticker history. We obtain a dataframe with one time series for each of the features, for each of the tickers we are working with.\n",
    "\n",
    "Here we are considering the top 10 most liquid tickers in Brazilian B3 exchange in period of **2019-01-01 until 2020-05-01**:\n",
    "\n",
    "**PETR4, VALE3, BOVA11, ITUB4, BBDC4, B3SA3, BBAS3, ABEV3, MGLU3, VVAR3**\n",
    "\n",
    "2. Decomposition\n",
    "\n",
    "We decompose the data with the complete ensemble empirical mode decomposition with adative noise (CEEMDAN) algorithm, using the [PyEMD public package from Dawid Laszuk](github.com/laszukdawid/PyEMD).\n",
    "\n",
    "As a result, we obtain a set of instrinsic mode functions (IMFs) time series for each of the features, for each of the tickers. The number of IMFs resulting from a decomposition may vary, and depends upon CEEMDAN hyperparameters such as scale of added noise, specific series characteristics, and mainly **series length**.\n",
    "\n",
    "3. Data transformation\n",
    "\n",
    "For each of the IMFs series obtained, we tranform the data according to the following equation in order to work with data values between 0 and 1.\n",
    "\n",
    "$$x' = \\frac{x-x_{min}}{x_{max}-x_{min}}$$\n",
    "\n",
    "Where x is any element in the series to be transformed, x' is the equivalent transformed element. The equation also takes into account the maximum and minimum values of the series.\n",
    "\n",
    "4. Windowing\n",
    "\n",
    "Besides the mathematical transformation, each series undergo a vectorial transformation in order to split the data into windows.\n",
    "\n",
    "The process is examplified:\n",
    "\n",
    "```\n",
    "original single dimensional series = [1,2,3,4,5,6,7,8]\n",
    "Splitting into windows of length 4.\n",
    "Windowed data = [[1,2,3,4],[2,3,4,5],[3,4,5,6],[4,5,6,7],[5,6,7,8]]\n",
    "```\n",
    "This way the resulting series will have $n-w+1$ elements, where $n$ is the length of the orginal series and $w$ is the window length.\n",
    "\n",
    "### Processed data availability\n",
    "\n",
    "After the processing method, the final data is available in a dataframe format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
     "metadata": {}
    },
    {
     "output_type": "display_data",
     "data": {
      "text/html": "        <script type=\"text/javascript\">\n        window.PlotlyConfig = {MathJaxConfig: 'local'};\n        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n        if (typeof require !== 'undefined') {\n        require.undef(\"plotly\");\n        requirejs.config({\n            paths: {\n                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n            }\n        });\n        require(['plotly'], function(Plotly) {\n            window._Plotly = Plotly;\n        });\n        }\n        </script>\n        "
     },
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    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "Using TensorFlow backend.\n"
    }
   ],
   "source": [
    "from datetime import timedelta, datetime\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import cufflinks as cf\n",
    "import chart_studio.plotly as plotly\n",
    "import plotly.offline\n",
    "cf.go_offline()\n",
    "cf.set_config_file(offline=True, world_readable=False)\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from keras.preprocessing.sequence import TimeseriesGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, LeakyReLU\n",
    "import yfinance as yf\n",
    "from PyEMD import CEEMDAN\n",
    "\n",
    "# iplot layout\n",
    "space =  {\n",
    "            'legend' : {'bgcolor':'#1A1A1C','font':{'color':'#D9D9D9',\"size\":12}},\n",
    "            'paper_bgcolor' : '#1A1A1C',\n",
    "            'plot_bgcolor' : '#1A1A1C',\n",
    "            \"title\" : {\"font\":{\"color\":\"#D9D9D9\"},\"x\":0.5},\n",
    "            'yaxis' : {\n",
    "                'tickfont' : {'color':'#C2C2C2', \"size\":12},\n",
    "                'gridcolor' : '#434343',\n",
    "                'titlefont' : {'color':'#D9D9D9'},\n",
    "                'zerolinecolor' : '#666570',\n",
    "                'showgrid' : True\n",
    "            },\n",
    "            'xaxis' : {\n",
    "                'tickfont' : {'color':'#C2C2C2', \"size\":12},\n",
    "                'gridcolor' : '#434343',\n",
    "                'titlefont' : {'color':'#D9D9D9'},\n",
    "                'zerolinecolor' : '#666570',\n",
    "                'showgrid' : True\n",
    "            },\n",
    "            'titlefont' : {'color':'#D9D9D9'}\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n[*********************100%***********************]  1 of 1 completed\n"
    }
   ],
   "source": [
    "tickers = [\"PETR4\", \"VALE3\", \"BOVA11\", \"ITUB4\", \"BBDC4\", \"B3SA3\", \"BBAS3\", \"ABEV3\", \"MGLU3\", \"VVAR3\"]\n",
    "\n",
    "tickers = [f\"{ticker}.SA\" for ticker in tickers]\n",
    "\n",
    "start_datetime = datetime(year=2018, month=7, day=20)\n",
    "end_datetime = datetime(year=2019, month=12, day=2)\n",
    "\n",
    "history_data = {ticker.split('.')[0]:yf.download(ticker, start=start_datetime, end=end_datetime).drop(['Close','Open', 'High', 'Low', 'Volume'], axis=1).dropna() for ticker in tickers}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[PETR4] Decomposing...\n[VALE3] Decomposing...\n[BOVA11] Decomposing...\n[ITUB4] Decomposing...\n[BBDC4] Decomposing...\n[B3SA3] Decomposing...\n[BBAS3] Decomposing...\n[ABEV3] Decomposing...\n[MGLU3] Decomposing...\n[VVAR3] Decomposing...\nCPU times: user 1min 7s, sys: 1.04 s, total: 1min 8s\nWall time: 1min 54s\n"
    }
   ],
   "source": [
    "%%time\n",
    "ceemdan = CEEMDAN()\n",
    "decomposed_data = {}\n",
    "decomposed_ticker_features_series = {}\n",
    "scalers = {}\n",
    "for ticker in history_data:\n",
    "    # iterating every ticker\n",
    "    print(f'[{ticker}] Decomposing...')\n",
    "    ticker_dataframe = history_data[ticker]\n",
    "    decomposed_ticker_features_series[ticker] = {}\n",
    "    scalers[ticker] = {}\n",
    "    for column in ticker_dataframe.columns:\n",
    "        # iterating evey feature\n",
    "        scaler = MinMaxScaler()\n",
    "        decomposed_ticker_features_series[ticker][column] = {}\n",
    "        series = ticker_dataframe[column].values.reshape(-1,1)\n",
    "        scaler.fit(series)\n",
    "        scalers[ticker][column] = scaler\n",
    "        ticker_feature_time_series = np.frombuffer(scaler.transform(series))\n",
    "        ticker_feature_time_series_imfs = ceemdan(ticker_feature_time_series, max_imf=10)\n",
    "        for i, imf_series in enumerate(ticker_feature_time_series_imfs):\n",
    "            # iterating every IMF \n",
    "            if i < len(ticker_feature_time_series_imfs):\n",
    "                decomposed_ticker_features_series[ticker][column][f'IMF{i+1}'] = imf_series\n",
    "            else:\n",
    "                decomposed_ticker_features_series[ticker][column][f'Rsd'] = imf_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "outputPrepend",
     "outputPrepend",
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# Data organisation\n",
    "features_in_order = ['Adj Close']\n",
    "\n",
    "max_window_size = 10\n",
    "windows_sizes_for_imf_level = {\n",
    "    'IMF1': 2,\n",
    "    'IMF2': 2,\n",
    "    'IMF3': 3,\n",
    "    'IMF4': 3,\n",
    "    'IMF5': 4,\n",
    "    'IMF6': 4,\n",
    "    'IMF7': 5,\n",
    "    'IMF8': 5,\n",
    "    'Rsd': 6,\n",
    "    'DEFAULT': 4\n",
    "}\n",
    "\n",
    "# Coupling together the IMFs of the same level for different features to create exogenous input\n",
    "# The number of imfs for each feature decomposition may differ, thus some of the last imfs may not match in number of features\n",
    "series = {}\n",
    "for ticker in decomposed_ticker_features_series:\n",
    "    series[ticker] = {}\n",
    "    for feature in decomposed_ticker_features_series[ticker]:\n",
    "        imfs = pd.DataFrame.from_dict(decomposed_ticker_features_series[ticker][feature])\n",
    "        for imf in imfs:\n",
    "            if imf not in series[ticker]:\n",
    "                series[ticker][imf] = []\n",
    "            _series = imfs[imf].values\n",
    "            _series = _series.reshape((len(_series),1))\n",
    "            series[ticker][imf] += [_series] # reshaping to get into column format\n",
    "\n",
    "dataset = {}\n",
    "# # horizontal stack\n",
    "for ticker in series:\n",
    "    dataset[ticker] = {}\n",
    "    for imf_level in series[ticker]:\n",
    "        dataset[ticker][imf_level] = np.hstack(tuple(series[ticker][imf_level]))\n",
    "\n",
    "# # data set split rates\n",
    "train = 0.7\n",
    "validation = 0.2\n",
    "test = 0.1\n",
    "\n",
    "train_dataset = {}\n",
    "validation_dataset = {}\n",
    "test_dataset = {}\n",
    "\n",
    "train_generators = {}\n",
    "validation_generators = {}\n",
    "test_generators = {}\n",
    "\n",
    "for ticker in dataset:\n",
    "\n",
    "    train_dataset[ticker] = {}\n",
    "    validation_dataset[ticker] = {}\n",
    "    test_dataset[ticker] = {}\n",
    "\n",
    "    train_generators[ticker] = {}\n",
    "    validation_generators[ticker] = {}\n",
    "    test_generators[ticker] = {}\n",
    "\n",
    "    for imf_level in dataset[ticker]:\n",
    "        # splitting data sets according to rates\n",
    "        train_dataset[ticker][imf_level] = dataset[ticker][imf_level][:round(train*dataset[ticker][imf_level].shape[0]),:]\n",
    "        validation_dataset[ticker][imf_level] = dataset[ticker][imf_level][round(train*dataset[ticker][imf_level].shape[0]):round((train+validation)*dataset[ticker][imf_level].shape[0]),:]\n",
    "        test_dataset[ticker][imf_level] = dataset[ticker][imf_level][round((train+validation)*dataset[ticker][imf_level].shape[0]):,:]\n",
    "\n",
    "        if imf_level in windows_sizes_for_imf_level:\n",
    "            window_size = windows_sizes_for_imf_level[imf_level]\n",
    "        else: \n",
    "            window_size = windows_sizes_for_imf_level['DEFAULT']\n",
    "        # windowing\n",
    "        train_generators[ticker][imf_level] = TimeseriesGenerator(train_dataset[ticker][imf_level], train_dataset[ticker][imf_level], length=window_size, batch_size=1)\n",
    "        validation_generators[ticker][imf_level] = TimeseriesGenerator(validation_dataset[ticker][imf_level], validation_dataset[ticker][imf_level], length=window_size, batch_size=1)\n",
    "        test_generators[ticker][imf_level] = TimeseriesGenerator(test_dataset[ticker][imf_level], test_dataset[ticker][imf_level], length=window_size, batch_size=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Training model [PETR4][IMF1]\nTraining model [PETR4][IMF2]\nTraining model [PETR4][IMF3]\nTraining model [PETR4][IMF4]\nTraining model [PETR4][IMF5]\nTraining model [VALE3][IMF1]\nTraining model [VALE3][IMF2]\nTraining model [VALE3][IMF3]\nTraining model [VALE3][IMF4]\nTraining model [VALE3][IMF5]\nTraining model [VALE3][IMF6]\nTraining model [BOVA11][IMF1]\nTraining model [BOVA11][IMF2]\nTraining model [BOVA11][IMF3]\nTraining model [BOVA11][IMF4]\nTraining model [BOVA11][IMF5]\nTraining model [ITUB4][IMF1]\nTraining model [ITUB4][IMF2]\nTraining model [ITUB4][IMF3]\nTraining model [ITUB4][IMF4]\nTraining model [ITUB4][IMF5]\nTraining model [ITUB4][IMF6]\nTraining model [BBDC4][IMF1]\nTraining model [BBDC4][IMF2]\nTraining model [BBDC4][IMF3]\nTraining model [BBDC4][IMF4]\nTraining model [BBDC4][IMF5]\nTraining model [BBDC4][IMF6]\nTraining model [B3SA3][IMF1]\nTraining model [B3SA3][IMF2]\nTraining model [B3SA3][IMF3]\nTraining model [B3SA3][IMF4]\nTraining model [B3SA3][IMF5]\nTraining model [B3SA3][IMF6]\nTraining model [BBAS3][IMF1]\nTraining model [BBAS3][IMF2]\nTraining model [BBAS3][IMF3]\nTraining model [BBAS3][IMF4]\nTraining model [BBAS3][IMF5]\nTraining model [BBAS3][IMF6]\nTraining model [ABEV3][IMF1]\nTraining model [ABEV3][IMF2]\nTraining model [ABEV3][IMF3]\nTraining model [ABEV3][IMF4]\nTraining model [ABEV3][IMF5]\nTraining model [ABEV3][IMF6]\nTraining model [MGLU3][IMF1]\nTraining model [MGLU3][IMF2]\nTraining model [MGLU3][IMF3]\nTraining model [MGLU3][IMF4]\nTraining model [MGLU3][IMF5]\nTraining model [MGLU3][IMF6]\nTraining model [VVAR3][IMF1]\nTraining model [VVAR3][IMF2]\nTraining model [VVAR3][IMF3]\nTraining model [VVAR3][IMF4]\nTraining model [VVAR3][IMF5]\nTraining model [VVAR3][IMF6]\nTraining model [VVAR3][IMF7]\nCPU times: user 12min 4s, sys: 52.5 s, total: 12min 57s\nWall time: 12min 16s\n"
    }
   ],
   "source": [
    "%%time\n",
    "# Model Training\n",
    "\n",
    "models = {}\n",
    "\n",
    "model_epochs = {\n",
    "    'IMF1': 2000,\n",
    "    'IMF2': 1500,\n",
    "    'IMF3': 1000,\n",
    "    'IMF4': 1000,\n",
    "    'IMF5': 1000,\n",
    "    'IMF6': 800,\n",
    "    'IMF7': 800,\n",
    "    'IMF8': 600,\n",
    "    'Rsd': 500,\n",
    "    'DEFAULT': 1000\n",
    "}\n",
    "\n",
    "for ticker in train_generators:\n",
    "    models[ticker] = {}\n",
    "    for imf_level in train_generators[ticker]:\n",
    "        print(f'Training model [{ticker}][{imf_level}]')\n",
    "        # Prediction model\n",
    "        model = Sequential()\n",
    "        current_dataset = train_dataset[ticker][imf_level]\n",
    "        n_features = current_dataset.shape[1]\n",
    "        cur_tmp_gen = train_generators[ticker][imf_level]\n",
    "\n",
    "        if imf_level in windows_sizes_for_imf_level:\n",
    "            window_size = windows_sizes_for_imf_level[imf_level]\n",
    "        else: \n",
    "            window_size = windows_sizes_for_imf_level['DEFAULT']\n",
    "\n",
    "        model.add(LSTM(128, activation='tanh', return_sequences=True, input_shape=(window_size, n_features)))\n",
    "        model.add(LSTM(64, activation='tanh', input_shape=(window_size, 128)))\n",
    "        model.add(Dense(16))\n",
    "        model.add(Dense(4))\n",
    "        model.add(Dense(n_features))\n",
    "        model.compile(optimizer='adam', loss='mse')\n",
    "\n",
    "        number_of_epochs = model_epochs[imf_level]\n",
    "        # fit model\n",
    "        model.fit_generator(cur_tmp_gen, steps_per_epoch=1, epochs=number_of_epochs, verbose=0)\n",
    "\n",
    "        models[ticker][imf_level] = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Predicting: [PETR4][IMF1]\nPredicting: [PETR4][IMF2]\nPredicting: [PETR4][IMF3]\nPredicting: [PETR4][IMF4]\nPredicting: [PETR4][IMF5]\nPredicting: [VALE3][IMF1]\nPredicting: [VALE3][IMF2]\nPredicting: [VALE3][IMF3]\nPredicting: [VALE3][IMF4]\nPredicting: [VALE3][IMF5]\nPredicting: [VALE3][IMF6]\nPredicting: [BOVA11][IMF1]\nPredicting: [BOVA11][IMF2]\nPredicting: [BOVA11][IMF3]\nPredicting: [BOVA11][IMF4]\nPredicting: [BOVA11][IMF5]\nPredicting: [ITUB4][IMF1]\nPredicting: [ITUB4][IMF2]\nPredicting: [ITUB4][IMF3]\nPredicting: [ITUB4][IMF4]\nPredicting: [ITUB4][IMF5]\nPredicting: [ITUB4][IMF6]\nPredicting: [BBDC4][IMF1]\nPredicting: [BBDC4][IMF2]\nPredicting: [BBDC4][IMF3]\nPredicting: [BBDC4][IMF4]\nPredicting: [BBDC4][IMF5]\nPredicting: [BBDC4][IMF6]\nPredicting: [B3SA3][IMF1]\nPredicting: [B3SA3][IMF2]\nPredicting: [B3SA3][IMF3]\nPredicting: [B3SA3][IMF4]\nPredicting: [B3SA3][IMF5]\nPredicting: [B3SA3][IMF6]\nPredicting: [BBAS3][IMF1]\nPredicting: [BBAS3][IMF2]\nPredicting: [BBAS3][IMF3]\nPredicting: [BBAS3][IMF4]\nPredicting: [BBAS3][IMF5]\nPredicting: [BBAS3][IMF6]\nPredicting: [ABEV3][IMF1]\nPredicting: [ABEV3][IMF2]\nPredicting: [ABEV3][IMF3]\nPredicting: [ABEV3][IMF4]\nPredicting: [ABEV3][IMF5]\nPredicting: [ABEV3][IMF6]\nPredicting: [MGLU3][IMF1]\nPredicting: [MGLU3][IMF2]\nPredicting: [MGLU3][IMF3]\nPredicting: [MGLU3][IMF4]\nPredicting: [MGLU3][IMF5]\nPredicting: [MGLU3][IMF6]\nPredicting: [VVAR3][IMF1]\nPredicting: [VVAR3][IMF2]\nPredicting: [VVAR3][IMF3]\nPredicting: [VVAR3][IMF4]\nPredicting: [VVAR3][IMF5]\nPredicting: [VVAR3][IMF6]\nPredicting: [VVAR3][IMF7]\n"
    }
   ],
   "source": [
    "results = {}\n",
    "\n",
    "for ticker in models:\n",
    "    results[ticker] = {}\n",
    "\n",
    "    # initializing results dicitionary\n",
    "    for feature in features_in_order:\n",
    "        results[ticker][feature] = {}\n",
    "        for imf_level in models[ticker]:\n",
    "            results[ticker][feature][imf_level] = {\n",
    "                'real_train': [],\n",
    "                'predicted_train': [],\n",
    "                'real_validation': [],\n",
    "                'predicted_validation': [],\n",
    "                'real_test': [],\n",
    "                'predicted_test': [],\n",
    "                'x_axis_train': [],\n",
    "                'x_axis_validation': [],\n",
    "                'x_axis_test': []\n",
    "            }\n",
    "\n",
    "    for imf_level in models[ticker]:\n",
    "        model = models[ticker][imf_level]\n",
    "        \n",
    "        print(f'Predicting: [{ticker}][{imf_level}]')\n",
    "\n",
    "        cur_train_gen = train_generators[ticker][imf_level]\n",
    "        cur_validation_gen = validation_generators[ticker][imf_level]\n",
    "        cur_test_gen = test_generators[ticker][imf_level]\n",
    "\n",
    "        # predicting train\n",
    "        day_counter = 0\n",
    "        for i in range(len(cur_train_gen)):\n",
    "            x, y = cur_train_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][features_in_order[j]][imf_level]['real_train'] += [y[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['predicted_train'] += [yhat[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['x_axis_train'] += [day_counter]\n",
    "            day_counter += 1\n",
    "\n",
    "        # predicting validation\n",
    "        for i in range(len(cur_validation_gen)):\n",
    "            x, y = cur_validation_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][features_in_order[j]][imf_level]['real_validation'] += [y[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['predicted_validation'] += [yhat[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['x_axis_validation'] += [day_counter]\n",
    "            day_counter += 1\n",
    "\n",
    "        # predicting test\n",
    "        for i in range(len(cur_test_gen)):\n",
    "            x, y = cur_test_gen[i]\n",
    "            yhat = model.predict(x, verbose=0)\n",
    "\n",
    "            for j in range(yhat.shape[1]):\n",
    "                results[ticker][features_in_order[j]][imf_level]['real_test'] += [y[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['predicted_test'] += [yhat[:,j][0]]\n",
    "                results[ticker][features_in_order[j]][imf_level]['x_axis_test'] += [day_counter]\n",
    "            day_counter += 1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# organizing imf prediction results, concatenating train, validation and test\n",
    "concatenated_results = {}\n",
    "\n",
    "for ticker in results:\n",
    "    concatenated_results[ticker] = {}\n",
    "    for feature in results[ticker]:\n",
    "        concatenated_results[ticker][feature] = {}\n",
    "        for imf_level in results[ticker][feature]:\n",
    "            \n",
    "            df_result = pd.DataFrame.from_dict(results[ticker][feature][imf_level], orient='index').T\n",
    "            df_train = df_result[['real_train','predicted_train','x_axis_train']].set_index('x_axis_train').dropna(axis=0)\n",
    "            df_train.index.name = 'x'\n",
    "            df_validation = df_result[['real_validation','predicted_validation','x_axis_validation']].set_index('x_axis_validation').dropna(axis=0)\n",
    "            df_validation.index.name = 'x'\n",
    "            df_test = df_result[['real_test','predicted_test','x_axis_test']].set_index('x_axis_test').dropna(axis=0)\n",
    "            df_test.index.name = 'x'\n",
    "\n",
    "            df_concatenated = pd.concat([df_train,df_validation,df_test], axis=1)\n",
    "\n",
    "            concatenated_results[ticker][feature][imf_level] = df_concatenated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# plotting partial result\n",
    "plot_ticker = 'PETR4'\n",
    "plot_feature = 'Adj Close'\n",
    "plot_imf = 'IMF1'\n",
    "\n",
    "concatenated_results[plot_ticker][plot_feature][plot_imf].iplot(title=f'{plot_ticker} {plot_feature} {plot_imf}', asFigure=True, layout=space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                     PETR4       VALE3      BOVA11        ITUB4         BBDC4  \\\nmse_test_IMF1     0.000520    0.001428    0.000394     0.001116  3.807898e-04   \nmape_test_IMF1  116.647175  218.315512  126.297964   281.829390  1.247580e+02   \nmse_test_IMF2     0.000056    0.000431    0.000093     0.000113  1.675280e-04   \nmape_test_IMF2  354.248916  156.253707  230.914252   147.552508  2.048975e+02   \nmse_test_IMF3     0.000013    0.000050    0.000014     0.000012  3.837209e-06   \nmape_test_IMF3  542.215471   37.249069  102.445544    18.887421  4.671012e+01   \nmse_test_IMF4     0.000505    0.000213    0.000165     0.000770  2.344182e-04   \nmape_test_IMF4   97.773401   93.212107   89.500047  4674.600112  6.707502e+01   \nmse_test_IMF5     0.000104    0.000384    0.000088     0.000603  2.177930e-04   \nmape_test_IMF5    2.268386   51.039605    1.829608    32.900192  8.580111e+00   \nmse_test_IMF6          NaN    0.000018         NaN     0.000017  8.583141e-07   \nmape_test_IMF6         NaN    0.696210         NaN     0.629859  1.336114e-01   \nmse_test_IMF7          NaN         NaN         NaN          NaN           NaN   \nmape_test_IMF7         NaN         NaN         NaN          NaN           NaN   \n\n                     B3SA3       BBAS3       ABEV3        MGLU3         VVAR3  \nmse_test_IMF1     0.000143    0.000359    0.000763     0.000066  5.390423e-04  \nmape_test_IMF1  104.238041  546.930024  129.645248   119.776442  1.384503e+02  \nmse_test_IMF2     0.000031    0.000086    0.000120     0.000016  8.467471e-05  \nmape_test_IMF2   97.578355  194.101283  135.289368  1074.033999  7.543673e+01  \nmse_test_IMF3     0.000011    0.000007    0.000020     0.000005  1.596280e-05  \nmape_test_IMF3  118.779281   29.018442   45.167111    39.139082  2.922832e+01  \nmse_test_IMF4     0.000082    0.000137    0.000666     0.000008  5.255099e-04  \nmape_test_IMF4  278.198179  369.985390  103.211202    62.595827  7.019924e+02  \nmse_test_IMF5     0.000013    0.000148    0.000051     0.000101  2.989821e-04  \nmape_test_IMF5   27.207704   10.504605  987.385383    64.225295  8.995575e+01  \nmse_test_IMF6     0.000036    0.000001    0.000016     0.000002  9.224837e-05  \nmape_test_IMF6    2.086399    0.146651    0.836498     0.781330  7.948641e+01  \nmse_test_IMF7          NaN         NaN         NaN          NaN  2.905681e-07  \nmape_test_IMF7         NaN         NaN         NaN          NaN  1.310693e-01  ",
      "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>PETR4</th>\n      <th>VALE3</th>\n      <th>BOVA11</th>\n      <th>ITUB4</th>\n      <th>BBDC4</th>\n      <th>B3SA3</th>\n      <th>BBAS3</th>\n      <th>ABEV3</th>\n      <th>MGLU3</th>\n      <th>VVAR3</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>mse_test_IMF1</th>\n      <td>0.000520</td>\n      <td>0.001428</td>\n      <td>0.000394</td>\n      <td>0.001116</td>\n      <td>3.807898e-04</td>\n      <td>0.000143</td>\n      <td>0.000359</td>\n      <td>0.000763</td>\n      <td>0.000066</td>\n      <td>5.390423e-04</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF1</th>\n      <td>116.647175</td>\n      <td>218.315512</td>\n      <td>126.297964</td>\n      <td>281.829390</td>\n      <td>1.247580e+02</td>\n      <td>104.238041</td>\n      <td>546.930024</td>\n      <td>129.645248</td>\n      <td>119.776442</td>\n      <td>1.384503e+02</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF2</th>\n      <td>0.000056</td>\n      <td>0.000431</td>\n      <td>0.000093</td>\n      <td>0.000113</td>\n      <td>1.675280e-04</td>\n      <td>0.000031</td>\n      <td>0.000086</td>\n      <td>0.000120</td>\n      <td>0.000016</td>\n      <td>8.467471e-05</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF2</th>\n      <td>354.248916</td>\n      <td>156.253707</td>\n      <td>230.914252</td>\n      <td>147.552508</td>\n      <td>2.048975e+02</td>\n      <td>97.578355</td>\n      <td>194.101283</td>\n      <td>135.289368</td>\n      <td>1074.033999</td>\n      <td>7.543673e+01</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF3</th>\n      <td>0.000013</td>\n      <td>0.000050</td>\n      <td>0.000014</td>\n      <td>0.000012</td>\n      <td>3.837209e-06</td>\n      <td>0.000011</td>\n      <td>0.000007</td>\n      <td>0.000020</td>\n      <td>0.000005</td>\n      <td>1.596280e-05</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF3</th>\n      <td>542.215471</td>\n      <td>37.249069</td>\n      <td>102.445544</td>\n      <td>18.887421</td>\n      <td>4.671012e+01</td>\n      <td>118.779281</td>\n      <td>29.018442</td>\n      <td>45.167111</td>\n      <td>39.139082</td>\n      <td>2.922832e+01</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF4</th>\n      <td>0.000505</td>\n      <td>0.000213</td>\n      <td>0.000165</td>\n      <td>0.000770</td>\n      <td>2.344182e-04</td>\n      <td>0.000082</td>\n      <td>0.000137</td>\n      <td>0.000666</td>\n      <td>0.000008</td>\n      <td>5.255099e-04</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF4</th>\n      <td>97.773401</td>\n      <td>93.212107</td>\n      <td>89.500047</td>\n      <td>4674.600112</td>\n      <td>6.707502e+01</td>\n      <td>278.198179</td>\n      <td>369.985390</td>\n      <td>103.211202</td>\n      <td>62.595827</td>\n      <td>7.019924e+02</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF5</th>\n      <td>0.000104</td>\n      <td>0.000384</td>\n      <td>0.000088</td>\n      <td>0.000603</td>\n      <td>2.177930e-04</td>\n      <td>0.000013</td>\n      <td>0.000148</td>\n      <td>0.000051</td>\n      <td>0.000101</td>\n      <td>2.989821e-04</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF5</th>\n      <td>2.268386</td>\n      <td>51.039605</td>\n      <td>1.829608</td>\n      <td>32.900192</td>\n      <td>8.580111e+00</td>\n      <td>27.207704</td>\n      <td>10.504605</td>\n      <td>987.385383</td>\n      <td>64.225295</td>\n      <td>8.995575e+01</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF6</th>\n      <td>NaN</td>\n      <td>0.000018</td>\n      <td>NaN</td>\n      <td>0.000017</td>\n      <td>8.583141e-07</td>\n      <td>0.000036</td>\n      <td>0.000001</td>\n      <td>0.000016</td>\n      <td>0.000002</td>\n      <td>9.224837e-05</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF6</th>\n      <td>NaN</td>\n      <td>0.696210</td>\n      <td>NaN</td>\n      <td>0.629859</td>\n      <td>1.336114e-01</td>\n      <td>2.086399</td>\n      <td>0.146651</td>\n      <td>0.836498</td>\n      <td>0.781330</td>\n      <td>7.948641e+01</td>\n    </tr>\n    <tr>\n      <th>mse_test_IMF7</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>2.905681e-07</td>\n    </tr>\n    <tr>\n      <th>mape_test_IMF7</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>1.310693e-01</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "concatenated_results_copy = concatenated_results.copy()\n",
    "accuracies_per_imf_detailed = {}\n",
    "\n",
    "for ticker in concatenated_results_copy:\n",
    "    accuracies_per_imf_detailed[ticker] = {}\n",
    "    for feature in concatenated_results_copy[ticker]:\n",
    "        # we are predicting only Adj Close, so we don't need to specify which feature we are predicting here\n",
    "        # accuracies_per_imf_detailed[ticker][feature] = {}\n",
    "        for imf_level in concatenated_results_copy[ticker][feature]:\n",
    "            real_test = concatenated_results_copy[ticker][feature][imf_level]['real_test'].values\n",
    "            predicted_test = concatenated_results_copy[ticker][feature][imf_level]['predicted_test'].values\n",
    "            real_validation = concatenated_results_copy[ticker][feature][imf_level]['real_validation'].values\n",
    "            predicted_validation = concatenated_results_copy[ticker][feature][imf_level]['predicted_validation'].values\n",
    "            real_train = concatenated_results_copy[ticker][feature][imf_level]['real_train'].values\n",
    "            predicted_train = concatenated_results_copy[ticker][feature][imf_level]['predicted_train'].values\n",
    "\n",
    "            # removing offset nan\n",
    "            real_test = real_test[~np.isnan(real_test)]\n",
    "            predicted_test = predicted_test[~np.isnan(predicted_test)]\n",
    "            real_validation = real_validation[~np.isnan(real_validation)]\n",
    "            predicted_validation = predicted_validation[~np.isnan(predicted_validation)]\n",
    "            real_train = real_train[~np.isnan(real_train)]\n",
    "            predicted_train = predicted_train[~np.isnan(predicted_train)]\n",
    "\n",
    "            accuracies_per_imf_detailed[ticker][f'mse_test_{imf_level}'] = mean_squared_error(real_train,predicted_train)\n",
    "            accuracies_per_imf_detailed[ticker][f'mape_test_{imf_level}'] = np.mean(np.abs((real_train - predicted_train) / real_train)) * 100\n",
    "\n",
    "            # we sre only interested in the test accuracy here, so no need for specifying train and validation accuracies\n",
    "            # accuracies_per_imf_detailed[ticker] = {\n",
    "            #     'mse_train':mean_squared_error(real_test,predicted_test),\n",
    "            #     'mse_validation':mean_squared_error(real_validation,predicted_validation),\n",
    "            #     'mse_test':mean_squared_error(real_train,predicted_train),\n",
    "            #     'mape_train':np.mean(np.abs((real_test - predicted_test) / real_test)) * 100,\n",
    "            #     'mape_validation':np.mean(np.abs((real_validation - predicted_validation) / real_validation)) * 100,\n",
    "            #     'mape_test':np.mean(np.abs((real_train - predicted_train) / real_train)) * 100,\n",
    "            # }\n",
    "\n",
    "df_accuracies_by_imfs = pd.DataFrame.from_dict(accuracies_per_imf_detailed)\n",
    "df_accuracies_by_imfs.to_csv(f'exp_records_accuracy_per_imf/lstm_ceemdan_per_imf_{datetime.now().strftime(\"%H_%M_%S_%m_%d_%Y\")}.csv', sep=',', encoding='utf-8')\n",
    "df_accuracies_by_imfs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "# recomposing prediction by arithmetically adding the IMF curves\n",
    "\n",
    "final_prediction_results = {}\n",
    "max_window_size = 10\n",
    "\n",
    "for ticker in concatenated_results:\n",
    "    final_prediction_results[ticker] = {}\n",
    "    for feature in concatenated_results[ticker]:\n",
    "        addition_train = None\n",
    "        addition_validation = None\n",
    "        addition_test = None\n",
    "\n",
    "        addition_real_train = None\n",
    "        addition_real_validation = None\n",
    "        addition_real_test = None\n",
    "\n",
    "        # recomposing predictions\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding test\n",
    "            can_sum = True\n",
    "            if addition_test is None:\n",
    "                addition_test = concatenated_results[ticker][feature][imf_level]['predicted_test'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_test'].values\n",
    "                cur_length = addition_test.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_test = addition_test[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_test = np.add(addition_test,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding train\n",
    "            can_sum = True\n",
    "            if addition_train is None:\n",
    "                addition_train = concatenated_results[ticker][feature][imf_level]['predicted_train'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_train'].values\n",
    "                cur_length = addition_train.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_train = addition_train[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_train = np.add(addition_train,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding validation\n",
    "            can_sum = True\n",
    "            if addition_validation is None:\n",
    "                addition_validation = concatenated_results[ticker][feature][imf_level]['predicted_validation'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['predicted_validation'].values\n",
    "                cur_length = addition_validation.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_validation = addition_validation[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_validation = np.add(addition_validation,np_array_to_be_added)\n",
    "\n",
    "        # recomposing real\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding test\n",
    "            can_sum = True\n",
    "            if addition_real_test is None:\n",
    "                addition_real_test = concatenated_results[ticker][feature][imf_level]['real_test'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_test'].values\n",
    "                cur_length = addition_real_test.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_test = addition_real_test[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_test = np.add(addition_real_test,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding train\n",
    "            can_sum = True\n",
    "            if addition_real_train is None:\n",
    "                addition_real_train = concatenated_results[ticker][feature][imf_level]['real_train'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_train'].values\n",
    "                cur_length = addition_real_train.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_train = addition_real_train[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_train = np.add(addition_real_train,np_array_to_be_added)\n",
    "\n",
    "        for imf_level in concatenated_results[ticker][feature]:\n",
    "            # adding validation\n",
    "            can_sum = True\n",
    "            if addition_real_validation is None:\n",
    "                addition_real_validation = concatenated_results[ticker][feature][imf_level]['real_validation'].values\n",
    "            else:\n",
    "                np_array_to_be_added = concatenated_results[ticker][feature][imf_level]['real_validation'].values\n",
    "                cur_length = addition_real_validation.shape[0]\n",
    "                next_np_array_length = np_array_to_be_added.shape[0]\n",
    "                if cur_length < next_np_array_length:\n",
    "                    if next_np_array_length-cur_length < max_window_size:\n",
    "                        np_array_to_be_added = np_array_to_be_added[next_np_array_length-cur_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                else: \n",
    "                    if cur_length-next_np_array_length < max_window_size:\n",
    "                        addition_real_validation = addition_real_validation[cur_length-next_np_array_length:]\n",
    "                    else:\n",
    "                        can_sum = False\n",
    "                \n",
    "                if can_sum:\n",
    "                    addition_real_validation = np.add(addition_real_validation,np_array_to_be_added)\n",
    "        \n",
    "        scaler = scalers[ticker][feature]\n",
    "\n",
    "        final_prediction_results[ticker][feature] = {\n",
    "            'train_predicted': scaler.inverse_transform(addition_train.reshape(-1,1)).reshape(-1),\n",
    "            'validation_predicted': scaler.inverse_transform(addition_validation.reshape(-1,1)).reshape(-1),\n",
    "            'test_predicted': scaler.inverse_transform(addition_test.reshape(-1,1)).reshape(-1),\n",
    "            'train_real': scaler.inverse_transform(addition_real_train.reshape(-1,1)).reshape(-1),\n",
    "            'validation_real': scaler.inverse_transform(addition_real_validation.reshape(-1,1)).reshape(-1),\n",
    "            'test_real': scaler.inverse_transform(addition_real_test.reshape(-1,1)).reshape(-1)\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
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     },
     "metadata": {}
    }
   ],
   "source": [
    "# plotting final result\n",
    "plot_ticker = 'PETR4'\n",
    "plot_feature = 'Adj Close'\n",
    "\n",
    "pd.DataFrame.from_dict(final_prediction_results[plot_ticker][plot_feature]).iplot(title=f'{plot_ticker} {plot_feature} {plot_imf}', layout=space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             mse      mape\nPETR4   0.850763  1.462580\nVALE3   1.490739  1.560848\nBOVA11  0.631973  0.625056\nITUB4   0.092269  1.348598\nBBDC4   0.461326  1.754286\nB3SA3   0.698235  1.943622\nBBAS3   0.273462  1.193850\nABEV3   0.152687  1.242173\nMGLU3   1.733704  2.590740\nVVAR3   0.016182  2.256926",
      "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>mse</th>\n      <th>mape</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>PETR4</th>\n      <td>0.850763</td>\n      <td>1.462580</td>\n    </tr>\n    <tr>\n      <th>VALE3</th>\n      <td>1.490739</td>\n      <td>1.560848</td>\n    </tr>\n    <tr>\n      <th>BOVA11</th>\n      <td>0.631973</td>\n      <td>0.625056</td>\n    </tr>\n    <tr>\n      <th>ITUB4</th>\n      <td>0.092269</td>\n      <td>1.348598</td>\n    </tr>\n    <tr>\n      <th>BBDC4</th>\n      <td>0.461326</td>\n      <td>1.754286</td>\n    </tr>\n    <tr>\n      <th>B3SA3</th>\n      <td>0.698235</td>\n      <td>1.943622</td>\n    </tr>\n    <tr>\n      <th>BBAS3</th>\n      <td>0.273462</td>\n      <td>1.193850</td>\n    </tr>\n    <tr>\n      <th>ABEV3</th>\n      <td>0.152687</td>\n      <td>1.242173</td>\n    </tr>\n    <tr>\n      <th>MGLU3</th>\n      <td>1.733704</td>\n      <td>2.590740</td>\n    </tr>\n    <tr>\n      <th>VVAR3</th>\n      <td>0.016182</td>\n      <td>2.256926</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "# calculating accuracy metrics\n",
    "\n",
    "adj_close_accuracies = {}\n",
    "accuracies_detailed = {}\n",
    "\n",
    "for ticker in final_prediction_results:\n",
    "    adj_close_accuracies[ticker] = {}\n",
    "    accuracies_detailed[ticker] = {}\n",
    "    for feature in final_prediction_results[ticker]:\n",
    "\n",
    "        y_train = final_prediction_results[ticker][feature]['train_predicted'][~np.isnan(final_prediction_results[ticker][feature]['train_predicted'])]\n",
    "        yhat_train = final_prediction_results[ticker][feature]['train_real'][~np.isnan(final_prediction_results[ticker][feature]['train_real'])]\n",
    "\n",
    "        y_validation = final_prediction_results[ticker][feature]['validation_predicted'][~np.isnan(final_prediction_results[ticker][feature]['validation_predicted'])]\n",
    "        yhat_validation = final_prediction_results[ticker][feature]['validation_real'][~np.isnan(final_prediction_results[ticker][feature]['validation_real'])]\n",
    "\n",
    "        y_test = final_prediction_results[ticker][feature]['test_predicted'][~np.isnan(final_prediction_results[ticker][feature]['test_predicted'])]\n",
    "        yhat_test = final_prediction_results[ticker][feature]['test_real'][~np.isnan(final_prediction_results[ticker][feature]['test_real'])]\n",
    "        accuracies_detailed[ticker][feature] = {\n",
    "            'mse':{\n",
    "                'train':mean_squared_error(y_train,yhat_train),\n",
    "                'validation':mean_squared_error(y_validation,yhat_validation),\n",
    "                'test':mean_squared_error(y_test,yhat_test),\n",
    "            },\n",
    "            'mape':{\n",
    "                'train':np.mean(np.abs((y_train - yhat_train) / y_train)) * 100,\n",
    "                'validation':np.mean(np.abs((y_validation - yhat_validation) / y_validation)) * 100,\n",
    "                'test':np.mean(np.abs((y_test - yhat_test) / y_test)) * 100,\n",
    "            }\n",
    "        }\n",
    "\n",
    "        if feature == 'Adj Close':\n",
    "            adj_close_accuracies[ticker] = {\n",
    "                'mse': mean_squared_error(y_test,yhat_test),\n",
    "                'mape': np.mean(np.abs((y_validation - yhat_validation) / y_validation)) * 100\n",
    "            }\n",
    "\n",
    "# pd.DataFrame.from_dict(accuracies_detailed[plot_ticker][plot_feature])\n",
    "df_close_accuracies = pd.DataFrame.from_dict(adj_close_accuracies).T\n",
    "df_close_accuracies.to_csv(f'exp_records/lstm_ceemdan_{datetime.now().strftime(\"%H_%M_%S_%m_%d_%Y\")}.csv', sep=',', encoding='utf-8')\n",
    "df_close_accuracies"
   ]
  }
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