JupyterDocker/notebooks/NeuralNetworkTrainingExample.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ttt_rBykurBo"
      },
      "source": [
        "# Neural Network Training Example\n",
        "\n",
        "---\n",
        "\n",
        "In this notebook, I want to show you how to convert historical crypto tick data into a datastream, suitable for training a neural network, which will predict the future direction of the price movement.\n",
        "\n",
        "The process is done step-by-step with explanations at every step. You can start and run the notebook in Google Colab, where it takes around 20-25 minute to run completely. Of course, you can also execute it step-by-step.\n",
        "\n",
        "The accoring Git Repo can be found under: [The link to the repo](https://github.com/girsigit/CryptoCrystalBall)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "YLUhtllmz_kL",
        "outputId": "1680b397-44a8-45c4-d06f-d7538c114864"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running in Colab\n",
            "Cloning into 'CryptoCrystalBall'...\n",
            "remote: Enumerating objects: 282, done.\u001b[K\n",
            "remote: Counting objects: 100% (282/282), done.\u001b[K\n",
            "remote: Compressing objects: 100% (179/179), done.\u001b[K\n",
            "remote: Total 282 (delta 113), reused 241 (delta 76), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (282/282), 15.72 MiB | 17.57 MiB/s, done.\n",
            "Resolving deltas: 100% (113/113), done.\n",
            "/content/CryptoCrystalBall/JupyterDocker/notebooks\n"
          ]
        }
      ],
      "source": [
        "# Check if the notebook is run in google colab, if so, import some stuff\n",
        "import sys\n",
        "import os\n",
        "\n",
        "colab = 'google.colab' in sys.modules\n",
        "\n",
        "if colab:\n",
        "    print(\"Running in Colab\")\n",
        "\n",
        "    # Clone the whole repo to get all data and code if not already done\n",
        "    if not os.path.exists(\"/content/CryptoCrystalBall\"):\n",
        "      !git clone https://github.com/girsigit/CryptoCrystalBall\n",
        "\n",
        "      # cd into the notebooks directory --> Necessary to match all paths for importing\n",
        "    %cd /content/CryptoCrystalBall/JupyterDocker/notebooks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VIeTggmwJlZk"
      },
      "outputs": [],
      "source": [
        "# Try importing the Ta-Lib library, if this fails, try to install it and\n",
        "# import it again afterwards\n",
        "try:\n",
        "    import talib\n",
        "except:\n",
        "    !wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz\n",
        "    !tar -xzvf ta-lib-0.4.0-src.tar.gz\n",
        "    %cd ta-lib\n",
        "    !./configure --prefix=/usr\n",
        "    !make\n",
        "    !make install\n",
        "    !pip install Ta-Lib\n",
        "    %cd ..\n",
        "\n",
        "    import talib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J5lm4kZqsHl6"
      },
      "outputs": [],
      "source": [
        "import logging\n",
        "logger = logging.getLogger()\n",
        "logger.setLevel(logging.INFO)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cOanV_FML2Zx"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import copy\n",
        "import matplotlib.pyplot as plt\n",
        "from tqdm import tqdm\n",
        "import datetime"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nbt7oQxzL2Zy"
      },
      "source": [
        "---\n",
        "# Add custom import path for DataStreamCreator and IndicatorCalculator\n",
        "\n",
        "These libs are not in the standard python directory, so their paths have to be added to the import paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ICDL0OwbL2Zz",
        "outputId": "2717aecf-14e2-4f2f-ab1e-61db78ce9c5f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['', 'content', 'CryptoCrystalBall', 'JupyterDocker', 'notebooks']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Get the current directory\n",
        "current_dir = os.getcwd()\n",
        "current_dir_splitted = current_dir.split(os.sep)\n",
        "current_dir_splitted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJ8l6O2gL2Z1",
        "outputId": "1c584295-b3e9-4acf-aee6-008f6fd60ee0"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dsc_dir: /content/CryptoCrystalBall/DataStreamCreator\n",
            "ind_dir: /content/CryptoCrystalBall/IndicatorCalculator\n"
          ]
        }
      ],
      "source": [
        "# Create the import directories for the DataStreamCreator and the IndicatorCalculator\n",
        "dsc_dir = os.path.join(os.sep, *current_dir_splitted[:-2], 'DataStreamCreator')\n",
        "print(f\"dsc_dir: {dsc_dir}\")\n",
        "\n",
        "ind_dir = os.path.join(os.sep, *current_dir_splitted[:-2], 'IndicatorCalculator')\n",
        "print(f\"ind_dir: {ind_dir}\")\n",
        "\n",
        "# Add them to the import paths\n",
        "sys.path.insert(0, dsc_dir)\n",
        "sys.path.insert(0, ind_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a-9z-QVIqwcZ"
      },
      "outputs": [],
      "source": [
        "# Import the actual classes\n",
        "from IndicatorCalculator import IndicatorCalculator\n",
        "import DataStreamCreator"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zPsG4dqRL2Z5"
      },
      "source": [
        "---\n",
        "# Define all the parameters and variables"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5J8ODl45L2Z6",
        "outputId": "c8f2d175-4940-41f4-ed67-12ebef8d6fd3"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "DATA_PATH: /content/CryptoCrystalBall/DemoData\n"
          ]
        }
      ],
      "source": [
        "# Define the tick data path\n",
        "DATA_PATH = os.path.join(os.sep, *current_dir_splitted[:-2], 'DemoData')\n",
        "print(f\"DATA_PATH: {DATA_PATH}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7TUeLGiOL2Z7",
        "outputId": "a93dca1d-cca5-4dee-d6cd-667643f734da"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "IMG_SAVE_PATH: /content/CryptoCrystalBall/Documentation/Images\n"
          ]
        }
      ],
      "source": [
        "# Define the chart image save path\n",
        "IMG_SAVE_PATH = os.path.join(os.sep, *current_dir_splitted[:-2], 'Documentation', 'Images')\n",
        "print(f\"IMG_SAVE_PATH: {IMG_SAVE_PATH}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rfq5Vbzq_sxh"
      },
      "outputs": [],
      "source": [
        "# Define a global random seed\n",
        "RANDOM_SEED = 11\n",
        "\n",
        "# Set the seed in np\n",
        "np.random.seed(RANDOM_SEED)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6fcaN9Sxq0gO",
        "outputId": "42a1178d-5f41-4879-d28b-69c7ecc1e598"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/CryptoCrystalBall/DemoData/BTC-USDT.csv',\n",
              " '/content/CryptoCrystalBall/DemoData/ETH-USDT.csv']"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Define the paths to two example tick (OHLCV) files. There are only two files\n",
        "# to keep the example tidy\n",
        "# This step would be done by scanning a directory in a real application\n",
        "EXAMPLE_FILE_PATHS = [os.path.join(DATA_PATH, \"BTC-USDT.csv\"), os.path.join(DATA_PATH, \"ETH-USDT.csv\")]\n",
        "EXAMPLE_FILE_PATHS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g9gBeRtnxKMD"
      },
      "outputs": [],
      "source": [
        "# X_BLOCK_LENGHT defines how far into the past a 'slice of a chart' shall be\n",
        "# See: https://github.com/girsigit/CryptoCrystalBall/tree/main/DataStreamCreator#xblockgenerator\n",
        "X_BLOCK_LENGHT = 512"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sh5dsBKr5Ko-"
      },
      "outputs": [],
      "source": [
        "# How many examples shall be processed at the same time, limited by GPU memory\n",
        "BATCH_SIZE = 256"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1JVT1Z2U0lW8"
      },
      "outputs": [],
      "source": [
        "# Finanical indicator timespans\n",
        "# See: https://github.com/girsigit/CryptoCrystalBall/tree/main/IndicatorCalculator\n",
        "SHORTSPAN = 6\n",
        "MIDSPAN = 48\n",
        "LONGSPAN = 120"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p3TWGOZ5L2al"
      },
      "source": [
        "---\n",
        "# Prepare data source\n",
        "\n",
        "For training a neural network, first the data source has to be prepared. For this purpose, the method `FileListToDataStream` from the `DataStreamCreator` class is used. This method creates a stream of `X-Block` and `y-data` arrays out of a list of .csv file names, pointing to tick tables (called `EXAMPLE_FILE_PATHS` in this example). For details about `X-Blocks` and `y-data`, please refer to the documentation of the `XBlockGenerator` and the `YDataGenerator` under https://github.com/girsigit/CryptoCrystalBall/tree/main/DataStreamCreator.\n",
        "\n",
        "<br>\n",
        "\n",
        "It is important to mention that the last timestamp of an X-Block (a frame from the past) matches the timestamp of the y-data entry (lookout into the future of the price).\n",
        "\n",
        "<br>\n",
        "\n",
        "As a target value (y data type), the future direction and its derviation of the price have been chosen, as they are easy to visualize and understand.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lufqj36NZXWB"
      },
      "outputs": [],
      "source": [
        "# Import the required TensorFlow modules\n",
        "# Todo: Remove unused ones\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.layers import Input, Dense, ReLU, Add, Flatten, Concatenate, LayerNormalization, UpSampling2D, Activation, LSTM, Multiply, Dropout, Reshape, Permute, BatchNormalization, MaxPooling1D, AveragePooling1D, MaxPooling3D, AveragePooling2D, LayerNormalization, MaxPooling2D, UnitNormalization\n",
        "from keras.layers import Conv1D, Conv2D\n",
        "from keras.models import Model\n",
        "from keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HJ4LsByIL2am",
        "outputId": "9a8aa802-d506-4804-aecf-7806c107bda3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'dataType': 0,\n",
              " 'direction_ma_timespan': 200,\n",
              " 'derivation_ma_timespan': 100,\n",
              " 'direction_derivation_shift_span': 0}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Set direction and derivation information as y target\n",
        "# Both y values (direction & derivation) are in the interval [-1.0,1.0]\n",
        "\n",
        "Y_TYPE_DICT = copy.deepcopy(DataStreamCreator.YDataGenerator.PARAM_DICT_TEMPLATE_Y_DATA_TYPE_DIRECTION_FLOAT)\n",
        "Y_TYPE_DICT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OWbH3rQYcEn",
        "outputId": "ab87511b-64cc-486d-c832-bd0106f51cde"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/CryptoCrystalBall/DemoData/BTC-USDT.csv']"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# For training, use the first example file 'BTC-USDT.csv'.\n",
        "# The FileListToDataStream generator requires a list, even if its only one file.\n",
        "\n",
        "TRAIN_FILES = [(EXAMPLE_FILE_PATHS[0])]\n",
        "TRAIN_FILES"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kcACn54jagr3"
      },
      "source": [
        "---\n",
        "# Get the feature dimension of the dataset for creating the NN\n",
        "\n",
        "This is required for setting up the neural network. Maybe this could be done at runtime, but is not necessary, as the indicator configuration, which leads to the feature dimension, has to be the same in the predication phase afterwards and therefore has to be known."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mi4qrGuLasSQ",
        "outputId": "7130ba78-e6bd-4b0e-81c9-5a61b565793a"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of X-Block array: (BATCH_SIZE, X_BLOCK_LENGHT, FEATURES) -> (256, 512, 226)\n",
            "Shape of y-data array: (BATCH_SIZE, DIRECTION & DERVIATION) -> (256, 2)\n",
            "FEATURES: 226\n"
          ]
        }
      ],
      "source": [
        "# Initialize the FileListToDataStream generator\n",
        "dataStreamTraining = DataStreamCreator.FileListToDataStream(fileList = TRAIN_FILES,\n",
        "                                                     batch_size = BATCH_SIZE,\n",
        "                                                     X_Block_lenght = X_BLOCK_LENGHT,\n",
        "                                                     y_type_dict=Y_TYPE_DICT\n",
        "                                                    )\n",
        "# Get the first batch\n",
        "batch = next(dataStreamTraining)\n",
        "XShape = batch['X'].shape\n",
        "yShape = batch['y'].shape\n",
        "FEATURES = XShape[-1]\n",
        "\n",
        "print(f\"Shape of X-Block array: (BATCH_SIZE, X_BLOCK_LENGHT, FEATURES) -> {XShape}\")\n",
        "print(f\"Shape of y-data array: (BATCH_SIZE, DIRECTION & DERVIATION) -> {yShape}\")\n",
        "print(f\"FEATURES: {FEATURES}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQH8n4LaardV"
      },
      "source": [
        "---\n",
        "# Create the actual data stream for training\n",
        "\n",
        "Re-Create the data stream used for finding the feature count to start from a fresh generator.\n",
        "\n",
        "Please note that the `shuffle` flag is set to `true` here. This provides shuffing of both the input file list (irrelevant in this example) and position where the `X-Blocks` and `y-data` informations are collected.\n",
        "\n",
        "<br>\n",
        "\n",
        "## Attention, weird behavior!\n",
        "A python generator function has to be applied on the dataStream, as the return provided by `FileListToDataStream` method is not a 'real' generator and therefore cannot be used by TF.\n",
        "```\n",
        "Todo: Bugfix --> Include this into the FileListToDataStream\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dfar_q67kQS3"
      },
      "outputs": [],
      "source": [
        "# A python generator function has to be applied on the dataStream\n",
        "\n",
        "def pythonGeneratorTraining():\n",
        "  # Initialize the FileListToDataStream generator\n",
        "  dataStreamTraining = DataStreamCreator.FileListToDataStream(fileList = TRAIN_FILES,\n",
        "                                                      batch_size = BATCH_SIZE,\n",
        "                                                      X_Block_lenght = X_BLOCK_LENGHT,\n",
        "                                                      y_type_dict=Y_TYPE_DICT,\n",
        "                                                      shuffle=True\n",
        "                                                      )\n",
        "  \n",
        "  # This while has to integrated into the FileListToDataStream method\n",
        "  while True:  \n",
        "    try:\n",
        "      ne = next(dataStreamTraining)\n",
        "      _X = ne['X']\n",
        "      _y = ne['y']\n",
        "      \n",
        "      yield (_X, _y)\n",
        "    except StopIteration as si:\n",
        "      logging.warning(\"StopIteration in pythonGenerator\")\n",
        "      logging.warning(si)\n",
        "      return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zr72SRsNjZRr",
        "outputId": "392c6391-5909-49ac-a81e-8ed6a2fbb26b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<FlatMapDataset element_spec=(TensorSpec(shape=(256, 512, 226), dtype=tf.float32, name=None), TensorSpec(shape=(256, 2), dtype=tf.float32, name=None))>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create a Tensorflow dataset out of the python generator, which can be fed to the network\n",
        "tfGenTraining = tf.data.Dataset.from_generator(pythonGeneratorTraining, \n",
        "                                               output_types = (tf.float32, tf.float32), # Todo: Automatically determine data type from dataStreamTraining (float, int for categories)\n",
        "                                               output_shapes=((XShape), (yShape))\n",
        "                                               )\n",
        "tfGenTraining.prefetch(buffer_size=2)\n",
        "tfGenTraining"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N2AuhEfdZFqz"
      },
      "source": [
        "---\n",
        "# Create the neural network\n",
        "\n",
        "In the example, two Conv1D pyramids are used to extract features at different levels out of the feature and the time dimension.\n",
        "\n",
        "It is also possible to use different architecures, like LSTM, Conv2D, Attention-Based ones or even NLP-based models. I have tried many different ones and will publish them into the model zoo.\n",
        "\n",
        "```\n",
        "Todo: Create Model Zoo\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8tuPwwdBZ80K",
        "outputId": "5acada1d-fbf3-42cb-f783-57b80ec8fdda"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"ModelExample\"\n",
            "____________________________________________________________________________________________________________________________________________________________________________________________________________________________\n",
            " Layer (type)                                                           Output Shape                                     Param #                   Connected to                                                             \n",
            "============================================================================================================================================================================================================================\n",
            " inputTicksAndIndicators (InputLayer)                                   [(None, 512, 226)]                               0                         []                                                                       \n",
            "                                                                                                                                                                                                                            \n",
            " UnitNormalizationInput (UnitNormalization)                             (None, 512, 226)                                 0                         ['inputTicksAndIndicators[0][0]']                                        \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_Conv1D_0 (Conv1D)                                              (None, 512, 256)                                 636672                    ['UnitNormalizationInput[0][0]']                                         \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_MaxPooling1D_0 (MaxPooling1D)                                  (None, 512, 128)                                 0                         ['Feature_Conv1D_0[0][0]']                                               \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_Conv1D_1 (Conv1D)                                              (None, 512, 128)                                 180352                    ['Feature_MaxPooling1D_0[0][0]']                                         \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_MaxPooling1D_1 (MaxPooling1D)                                  (None, 512, 64)                                  0                         ['Feature_Conv1D_1[0][0]']                                               \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_Conv1D_2 (Conv1D)                                              (None, 512, 64)                                  45120                     ['Feature_MaxPooling1D_1[0][0]']                                         \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_MaxPooling1D_2 (MaxPooling1D)                                  (None, 512, 32)                                  0                         ['Feature_Conv1D_2[0][0]']                                               \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_Conv1D_3 (Conv1D)                                              (None, 512, 32)                                  11296                     ['Feature_MaxPooling1D_2[0][0]']                                         \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_MaxPooling1D_3 (MaxPooling1D)                                  (None, 512, 16)                                  0                         ['Feature_Conv1D_3[0][0]']                                               \n",
            "                                                                                                                                                                                                                            \n",
            " Feature_Filters_Concatenate (Concatenate)                              (None, 512, 240)                                 0                         ['Feature_MaxPooling1D_0[0][0]',                                         \n",
            "                                                                                                                                                    'Feature_MaxPooling1D_1[0][0]',                                         \n",
            "                                                                                                                                                    'Feature_MaxPooling1D_2[0][0]',                                         \n",
            "                                                                                                                                                    'Feature_MaxPooling1D_3[0][0]']                                         \n",
            "                                                                                                                                                                                                                            \n",
            " Permute_Time_Features (Permute)                                        (None, 240, 512)                                 0                         ['Feature_Filters_Concatenate[0][0]']                                    \n",
            "                                                                                                                                                                                                                            \n",
            " Time_Conv1D_0 (Conv1D)                                                 (None, 240, 64)                                  360512                    ['Permute_Time_Features[0][0]']                                          \n",
            "                                                                                                                                                                                                                            \n",
            " Time_MaxPooling1D_0 (MaxPooling1D)                                     (None, 240, 32)                                  0                         ['Time_Conv1D_0[0][0]']                                                  \n",
            "                                                                                                                                                                                                                            \n",
            " Time_Conv1D_1 (Conv1D)                                                 (None, 240, 32)                                  11296                     ['Time_MaxPooling1D_0[0][0]']                                            \n",
            "                                                                                                                                                                                                                            \n",
            " Time_MaxPooling1D_1 (MaxPooling1D)                                     (None, 240, 16)                                  0                         ['Time_Conv1D_1[0][0]']                                                  \n",
            "                                                                                                                                                                                                                            \n",
            " Time_Conv1D_2 (Conv1D)                                                 (None, 240, 16)                                  2832                      ['Time_MaxPooling1D_1[0][0]']                                            \n",
            "                                                                                                                                                                                                                            \n",
            " Time_MaxPooling1D_2 (MaxPooling1D)                                     (None, 240, 8)                                   0                         ['Time_Conv1D_2[0][0]']                                                  \n",
            "                                                                                                                                                                                                                            \n",
            " Time_Filters_Concatenate (Concatenate)                                 (None, 240, 56)                                  0                         ['Time_MaxPooling1D_0[0][0]',                                            \n",
            "                                                                                                                                                    'Time_MaxPooling1D_1[0][0]',                                            \n",
            "                                                                                                                                                    'Time_MaxPooling1D_2[0][0]']                                            \n",
            "                                                                                                                                                                                                                            \n",
            " Regressor_Flatten (Flatten)                                            (None, 13440)                                    0                         ['Time_Filters_Concatenate[0][0]']                                       \n",
            "                                                                                                                                                                                                                            \n",
            " Regressor_Dense_1 (Dense)                                              (None, 32)                                       430112                    ['Regressor_Flatten[0][0]']                                              \n",
            "                                                                                                                                                                                                                            \n",
            " Regressor_Dense_2 (Dense)                                              (None, 32)                                       1056                      ['Regressor_Dense_1[0][0]']                                              \n",
            "                                                                                                                                                                                                                            \n",
            " Output (Dense)                                                         (None, 2)                                        66                        ['Regressor_Dense_2[0][0]']                                              \n",
            "                                                                                                                                                                                                                            \n",
            "============================================================================================================================================================================================================================\n",
            "Total params: 1,679,314\n",
            "Trainable params: 1,679,314\n",
            "Non-trainable params: 0\n",
            "____________________________________________________________________________________________________________________________________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Create the example model\n",
        "\n",
        "def CreateModelExample():\n",
        "  # Define model parameters\n",
        "  CNN_INITIAL_FILTERS_FEATURES = 256\n",
        "  CNN_NUMBER_FILTERS_FEATURES = 4\n",
        "\n",
        "  CNN_INITIAL_FILTERS_TIME = 64\n",
        "  CNN_NUMBER_FILTERS_TIME = 3\n",
        "  \n",
        "  CNN_KERNEL_SIZE = 11\n",
        "\n",
        "  DENSE_SIZE_REGRESSOR = 32\n",
        "  \n",
        "  # Create the model input  \n",
        "  inputTicksAndIndicators = Input(shape=(X_BLOCK_LENGHT, FEATURES), name='inputTicksAndIndicators', dtype='float32')\n",
        "\n",
        "  # Normalize the input data, as it has a wide value spread (range of 1e8)\n",
        "  normalized = UnitNormalization(name=\"UnitNormalizationInput\", axis=-2)(inputTicksAndIndicators)\n",
        "\n",
        "  # ----------------------------------------------------------------------------\n",
        "\n",
        "  # As an example a 1D-CNN pyramid is used to extract higher-level features out\n",
        "  # of the indicator+tick dimension\n",
        "  feature_conv_layers = []\n",
        "\n",
        "  for i in range(CNN_NUMBER_FILTERS_FEATURES):\n",
        "    if 0 == i:\n",
        "      source_layer = normalized\n",
        "    else:\n",
        "      source_layer = feature_conv\n",
        "    \n",
        "    feature_conv = Conv1D(filters=int(CNN_INITIAL_FILTERS_FEATURES/(2**i)), \n",
        "                          kernel_size=CNN_KERNEL_SIZE, \n",
        "                          strides=1, \n",
        "                          padding=\"same\",\n",
        "                          name=f\"Feature_Conv1D_{i}\")(source_layer)\n",
        "\n",
        "    feature_conv = MaxPooling1D(pool_size=2,\n",
        "                                padding=\"same\",\n",
        "                                data_format=\"channels_first\",\n",
        "                                name=f\"Feature_MaxPooling1D_{i}\")(feature_conv)\n",
        "\n",
        "    feature_conv_layers.append(feature_conv)\n",
        "\n",
        "  # Concaternate all extracted filters of the indicator+tick dimension together\n",
        "  indicator_filters = Concatenate(name=\"Feature_Filters_Concatenate\")(feature_conv_layers)\n",
        "\n",
        "  # ----------------------------------------------------------------------------\n",
        "\n",
        "  # A pyramid is also applied on the time dimension\n",
        "  time_conv_layers = []\n",
        "\n",
        "  for i in range(CNN_NUMBER_FILTERS_TIME):\n",
        "    if 0 == i:\n",
        "      # Flip the order of time and feature dimension to apply the Conv1D on time\n",
        "      source_layer = Permute((2,1), name=\"Permute_Time_Features\")(indicator_filters)\n",
        "    else:\n",
        "      source_layer = time_conv\n",
        "    \n",
        "    time_conv = Conv1D(filters=int(CNN_INITIAL_FILTERS_TIME/(2**i)), \n",
        "                       kernel_size=CNN_KERNEL_SIZE, \n",
        "                       strides=1, \n",
        "                       padding=\"same\",\n",
        "                       name=f\"Time_Conv1D_{i}\")(source_layer)\n",
        "\n",
        "    time_conv = MaxPooling1D(pool_size=2,\n",
        "                             padding=\"same\",\n",
        "                             data_format=\"channels_first\",\n",
        "                             name=f\"Time_MaxPooling1D_{i}\")(time_conv)\n",
        "\n",
        "    time_conv_layers.append(time_conv)\n",
        "\n",
        "  # Concaternate all extracted filters of the time dimension together\n",
        "  time_filters = Concatenate(name=\"Time_Filters_Concatenate\")(time_conv_layers)\n",
        "\n",
        "  # ----------------------------------------------------------------------------\n",
        "\n",
        "  # Regressor - Dense layers to calculate the direction and its derivation\n",
        "  regr = Flatten(name=\"Regressor_Flatten\")(time_filters)\n",
        "  regr = Dense(DENSE_SIZE_REGRESSOR, name=\"Regressor_Dense_1\", activation='tanh')(regr)\n",
        "  regr = Dense(DENSE_SIZE_REGRESSOR, name=\"Regressor_Dense_2\", activation='relu')(regr)\n",
        "\n",
        "  # ----------------------------------------------------------------------------\n",
        "\n",
        "  # The output are two values (direction and derivation) in the range [-1.0, 1.0]\n",
        "  output = Dense(2, activation='tanh', name=\"Output\")(regr)\n",
        "  outputs = [output]\n",
        "\n",
        "  # Combine it all into a model object\n",
        "  model = Model(inputs=inputTicksAndIndicators, outputs=outputs, name='ModelExample')\n",
        "\n",
        "  return model\n",
        "\n",
        "model = CreateModelExample()\n",
        "model.summary(line_length=220)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x2d_i2DHiGxF"
      },
      "outputs": [],
      "source": [
        "# Create an optimizer\n",
        "\n",
        "optimizer = Adam(\n",
        "    learning_rate=1e-04,\n",
        "    epsilon=1e-05,\n",
        "    decay=0.01)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "31ABdeTfiJ2k"
      },
      "outputs": [],
      "source": [
        "# Compile the model\n",
        "# To predict the float values of direction and derivation, the 'Mean Squared Error' loss is used\n",
        "# In addition, the 'Mean Average Error' is calculated\n",
        "\n",
        "model.compile(\n",
        "    optimizer = optimizer,\n",
        "    loss = ['mse'], \n",
        "    metrics=['mae'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uQCV9PE1iZHk"
      },
      "outputs": [],
      "source": [
        "# Load the TensorBoard notebook extension for visualization\n",
        "\n",
        "%load_ext tensorboard\n",
        "\n",
        "log_dir = \"/content/logs/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "\n",
        "tensorboard_callback = tf.keras.callbacks.TensorBoard(\n",
        "    log_dir=log_dir,\n",
        "    histogram_freq=1,\n",
        "    update_freq=1\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gh2U89XbjOqf"
      },
      "source": [
        "---\n",
        "# Train the network\n",
        "\n",
        "**Hint:** There is a warning that there were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators. This is ok, as for this example only one training file is provided. In a real application, there would be more files.\n",
        "\n",
        "The training for 5 epochs takes about 12 minutes on a colab GPU. The epoch count can be increased to provide 'deeper' learning, while in this example this would not lead to better results, as the network would overfit on the 'BTC-USDT' price and would not be able to predict other currencies.\n",
        "\n",
        "In a real application, the tuning of these hyperparameters would be crucial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NJauZ7z-jOcu",
        "outputId": "ff060167-a279-4d32-8270-9e4fd3ba6da6"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    190/Unknown - 136s 653ms/step - loss: 0.0674 - mae: 0.1644"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:StopIteration in pythonGenerator\n",
            "WARNING:root:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r190/190 [==============================] - 137s 658ms/step - loss: 0.0674 - mae: 0.1644\n",
            "Epoch 2/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "190/190 [==============================] - 124s 642ms/step - loss: 0.0538 - mae: 0.1444\n",
            "Epoch 3/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "190/190 [==============================] - 126s 650ms/step - loss: 0.0505 - mae: 0.1398\n",
            "Epoch 4/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "190/190 [==============================] - 126s 655ms/step - loss: 0.0485 - mae: 0.1372\n",
            "Epoch 5/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "190/190 [==============================] - 127s 655ms/step - loss: 0.0470 - mae: 0.1352\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9c189c0400>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "# Train the model\n",
        "\n",
        "model.fit(tfGenTraining, \n",
        "          epochs=5, \n",
        "          verbose=1, \n",
        "          callbacks=[tensorboard_callback]\n",
        "          )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvTc1W46mavi",
        "outputId": "19eed2cf-7b42-411d-877a-6bd9c491e09c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    190/Unknown - 107s 549ms/step - loss: 0.0447 - mae: 0.1324"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:StopIteration in pythonGenerator\n",
            "WARNING:root:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r190/190 [==============================] - 107s 551ms/step - loss: 0.0447 - mae: 0.1324\n",
            "\n",
            "\n",
            "MSE Training: 0.04468248412013054\n",
            "MAE Training: 0.13235564529895782\n"
          ]
        }
      ],
      "source": [
        "# Calculate the metrics after training on the train dataset\n",
        "\n",
        "metrics_train = model.evaluate(tfGenTraining)\n",
        "mse_train, mae_train = metrics_train\n",
        "print(\"\\n\")\n",
        "print(f\"MSE Training: {mse_train}\")\n",
        "print(f\"MAE Training: {mae_train}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "52yiY10-DChA"
      },
      "outputs": [],
      "source": [
        "# Show the metrics in TensorBoard\n",
        "# Hint: If it says 'no data' just switch the categories in the top bar, then it refreshes and works\n",
        "%tensorboard --logdir /content/logs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ka64VHO6oDyJ"
      },
      "source": [
        "---\n",
        "# Try to Re-Predict the values\n",
        "\n",
        "Use the trained network to re-predict the direction and derivation values of the train dataset. For the visualization, the `shuffle` flag has to be set to `False`.\n",
        "\n",
        "**Important: A batch size of 1 is used to avoid loss of data.**\n",
        "The FileListToDataStream generator would stop if there is less data than the batch size, so the last few values would not been predicted."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KfQclhaf6kW1"
      },
      "outputs": [],
      "source": [
        "# A python generator function has to be applied on the dataStream\n",
        "# Todo: Include this into the FileListToDataStream\n",
        "def pythonGeneratorEval():\n",
        "  # Initialize the FileListToDataStream generator\n",
        "  dataStreamEval = DataStreamCreator.FileListToDataStream(fileList = TRAIN_FILES,\n",
        "                                                      batch_size = 1,\n",
        "                                                      X_Block_lenght = X_BLOCK_LENGHT,\n",
        "                                                      y_type_dict=Y_TYPE_DICT,\n",
        "                                                      shuffle=False # For visualization\n",
        "                                                      )\n",
        "  while True:  \n",
        "    try:\n",
        "      ne = next(dataStreamEval)\n",
        "      _X = ne['X']\n",
        "      _y = ne['y']\n",
        "      \n",
        "      yield (_X, _y)\n",
        "    except StopIteration as si:\n",
        "      logging.warning(\"StopIteration in pythonGenerator\")\n",
        "      logging.warning(si)\n",
        "      return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gav35hd27Z7S",
        "outputId": "a068b761-453f-4429-a5df-9700c19f2b72"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<FlatMapDataset element_spec=(TensorSpec(shape=(1, 512, 226), dtype=tf.float32, name=None), TensorSpec(shape=(1, 2), dtype=tf.float32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# Create a Tensorflow dataset, which can be fed to the network\n",
        "# Batch size is set to 1\n",
        "\n",
        "tfGenEval = tf.data.Dataset.from_generator(pythonGeneratorEval, \n",
        "                                               output_types = (tf.float32, tf.float32), # Todo: Automatically from dataStreamTraining\n",
        "                                               output_shapes=((1, XShape[1], XShape[2]), (1, yShape[1]))\n",
        "                                               )\n",
        "tfGenEval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YajNrxUcoO-S",
        "outputId": "e7227389-fbf9-4069-d3bc-f73484b02033"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  48885/Unknown - 252s 5ms/step"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:StopIteration in pythonGenerator\n",
            "WARNING:root:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r48891/48891 [==============================] - 252s 5ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48891, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "# Predict the values\n",
        "p = model.predict(tfGenEval)\n",
        "p.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdt2_aUhFQX9",
        "outputId": "856a63e4-0028-4da2-9aaf-4c88e1b49d36"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'BTC-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1000\n",
            "2000\n",
            "3000\n",
            "4000\n",
            "5000\n",
            "6000\n",
            "7000\n",
            "8000\n",
            "9000\n",
            "10000\n",
            "11000\n",
            "12000\n",
            "13000\n",
            "14000\n",
            "15000\n",
            "16000\n",
            "17000\n",
            "18000\n",
            "19000\n",
            "20000\n",
            "21000\n",
            "22000\n",
            "23000\n",
            "24000\n",
            "25000\n",
            "26000\n",
            "27000\n",
            "28000\n",
            "29000\n",
            "30000\n",
            "31000\n",
            "32000\n",
            "33000\n",
            "34000\n",
            "35000\n",
            "36000\n",
            "37000\n",
            "38000\n",
            "39000\n",
            "40000\n",
            "41000\n",
            "42000\n",
            "43000\n",
            "44000\n",
            "45000\n",
            "46000\n",
            "47000\n",
            "48000\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:root:StopIteration in pythonGenerator\n",
            "WARNING:root:\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(48891, 2)\n"
          ]
        }
      ],
      "source": [
        "# Get the real y values (-> Ground Truth) out of the tfGenEval generator for plotting and comparison\n",
        "\n",
        "# Create numpy generator\n",
        "it = tfGenEval.as_numpy_iterator()\n",
        "\n",
        "# Init the values\n",
        "yReal = None\n",
        "i = 0\n",
        "\n",
        "# Get the data\n",
        "while True:\n",
        "  try:\n",
        "    X,y = next(it)\n",
        "\n",
        "    if yReal is None:\n",
        "      yReal = y\n",
        "    else:\n",
        "      yReal = np.concatenate([yReal, y])\n",
        "\n",
        "    i += 1\n",
        "    if i % 1000 == 0:\n",
        "      print(i)\n",
        "\n",
        "  except StopIteration:\n",
        "    break\n",
        "\n",
        "print(yReal.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iQbED4Sj7_4g"
      },
      "outputs": [],
      "source": [
        "# Split the predicted data into two values for easier processing\n",
        "p_dir = p[:,0]\n",
        "p_dir_derivation = p[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8r8FGk1Ubveu"
      },
      "outputs": [],
      "source": [
        "# Also split the real y values\n",
        "real_y_dir = yReal[:,0]\n",
        "real_y_dir_derivation = yReal[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 641
        },
        "id": "6ZtGxY0V79jm",
        "outputId": "a70d8805-c74f-4a6f-87dd-ede84b74d7e2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Direction')"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the prediction and the ground truth\n",
        "chart_name=\"Predicted direction of price movement\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "_ = ax1.plot(real_y_dir, color=\"gray\", label=\"Real y direction (Ground truth)\")\n",
        "_ = ax1.plot(p_dir, color=\"blue\", label=\"Predicted y direction\")\n",
        "\n",
        "_= plt.legend(loc=\"upper left\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Direction\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnwYgiNwFLsZ"
      },
      "source": [
        "---\n",
        "# Plot the direction info over the price"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "EOa2odZuFP3x",
        "outputId": "9e0c4454-b993-4690-becd-afb4a7e9aa86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    open          high           low         close     volume  \\\n",
              "startsAt                                                                        \n",
              "1449928800    420.000000    420.000000    420.000000    420.000000   0.000304   \n",
              "1449932400    420.000000    420.000000    420.000000    420.000000   0.000000   \n",
              "1449936000    420.000000    420.000000    420.000000    420.000000   0.000000   \n",
              "1449939600    420.000000    420.000000    420.000000    420.000000   0.000000   \n",
              "1449943200    420.000000    420.000000    420.000000    420.000000   0.000000   \n",
              "...                  ...           ...           ...           ...        ...   \n",
              "1627761600  41312.243514  41819.689357  41174.024002  41504.090831   6.954679   \n",
              "1627765200  41532.010689  41974.904927  41532.010689  41960.070000   7.271290   \n",
              "1627768800  41961.294435  41977.030664  41645.385796  41870.548474   6.137356   \n",
              "1627772400  41838.657138  41942.798100  41324.944231  41448.430802  10.411857   \n",
              "1627776000  41460.515600  41779.055816  41148.417800  41697.318684  18.597236   \n",
              "\n",
              "              quoteVolume  \n",
              "startsAt                   \n",
              "1449928800       0.127596  \n",
              "1449932400       0.000000  \n",
              "1449936000       0.000000  \n",
              "1449939600       0.000000  \n",
              "1449943200       0.000000  \n",
              "...                   ...  \n",
              "1627761600  288855.157694  \n",
              "1627765200  303437.038256  \n",
              "1627768800  256691.373423  \n",
              "1627772400  434411.360678  \n",
              "1627776000  772063.318313  \n",
              "\n",
              "[49403 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8821ef18-674b-424c-87ca-3027e35f5814\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>quoteVolume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>startsAt</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1449928800</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>0.000304</td>\n",
              "      <td>0.127596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449932400</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449936000</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449939600</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1449943200</th>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>420.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627761600</th>\n",
              "      <td>41312.243514</td>\n",
              "      <td>41819.689357</td>\n",
              "      <td>41174.024002</td>\n",
              "      <td>41504.090831</td>\n",
              "      <td>6.954679</td>\n",
              "      <td>288855.157694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627765200</th>\n",
              "      <td>41532.010689</td>\n",
              "      <td>41974.904927</td>\n",
              "      <td>41532.010689</td>\n",
              "      <td>41960.070000</td>\n",
              "      <td>7.271290</td>\n",
              "      <td>303437.038256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627768800</th>\n",
              "      <td>41961.294435</td>\n",
              "      <td>41977.030664</td>\n",
              "      <td>41645.385796</td>\n",
              "      <td>41870.548474</td>\n",
              "      <td>6.137356</td>\n",
              "      <td>256691.373423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627772400</th>\n",
              "      <td>41838.657138</td>\n",
              "      <td>41942.798100</td>\n",
              "      <td>41324.944231</td>\n",
              "      <td>41448.430802</td>\n",
              "      <td>10.411857</td>\n",
              "      <td>434411.360678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1627776000</th>\n",
              "      <td>41460.515600</td>\n",
              "      <td>41779.055816</td>\n",
              "      <td>41148.417800</td>\n",
              "      <td>41697.318684</td>\n",
              "      <td>18.597236</td>\n",
              "      <td>772063.318313</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>49403 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8821ef18-674b-424c-87ca-3027e35f5814')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8821ef18-674b-424c-87ca-3027e35f5814 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8821ef18-674b-424c-87ca-3027e35f5814');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "# Load the according tick data\n",
        "tickdata = pd.read_csv(EXAMPLE_FILE_PATHS[0])\n",
        "tickdata.set_index(\"startsAt\", inplace = True) # Todo: Take care of the index name\n",
        "tickdata.sort_index()\n",
        "\n",
        "tickdata"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dD9thju8KiTF"
      },
      "source": [
        "## **Hint:** Prediction size mismatch\n",
        "\n",
        "The predicted array is shorter than the tick table in the time dimension, namely by the value of `X_BLOCK_LENGHT`. This is because the first complete X-Block can be generated at table index 127 (-> 128 elements), therefore the generation process starts there.\n",
        "\n",
        "To match the ticks in the table for plotting or mapping the predictions, the first `X_BLOCK_LENGHT` rows of `tickData` have to be dropped."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs0i5hDYFkJ0",
        "outputId": "c0e62af2-5324-4f3a-88fe-41b796ce4e72"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Shape of the predicted values: (48891, 2)\n",
            "Shape of the loaded tick table: (49403, 6)\n",
            "\n",
            "Difference in the time dimension: 512\n",
            "X_BLOCK_LENGHT: 512\n"
          ]
        }
      ],
      "source": [
        "print(f\"Shape of the predicted values: {p.shape}\")\n",
        "print(f\"Shape of the loaded tick table: {tickdata.shape}\")\n",
        "\n",
        "print(f\"\\nDifference in the time dimension: {tickdata.shape[0] - p.shape[0]}\")\n",
        "print(f\"X_BLOCK_LENGHT: {X_BLOCK_LENGHT}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aFkyw8cBL0ei",
        "outputId": "43e658ff-0e77-4788-f72b-28e86716fe05"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(48891, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "# Crop the beginning of the tick table\n",
        "tickDataResized = tickdata.iloc[X_BLOCK_LENGHT:,]\n",
        "tickDataResized.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "vRGU6h9ucPgq",
        "outputId": "68d6ef84-09bd-48c7-d3fd-b2f6962e9036"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9b4c03e820>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the prediction again, now over the open price\n",
        "chart_name=\"Predicted direction of price movement with open price\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "_ = ax1.plot(p_dir, color=\"blue\", label=\"Predicted y direction\")\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "ax2.plot(tickDataResized.loc[:,'open'].values, color=\"black\", label=\"Price\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Direction\")\n",
        "ax2.set_ylabel(\"Price\")\n",
        "\n",
        "# Show a legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax2.legend(lines + lines2, labels + labels2, loc=\"upper left\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "5aoMlJTWL_sm",
        "outputId": "8f32dc4f-f007-42bb-a038-2e25d645128f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot the prediction once again over the open price, now only a slice\n",
        "SLICE_START_INDEX = 12000\n",
        "SLICE_END_INDEX = 15000\n",
        "\n",
        "chart_name=\"Predicted direction of price movement with open price - zoomed in slice\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "_ = ax1.plot(real_y_dir[SLICE_START_INDEX:SLICE_END_INDEX], color=\"gray\", label=\"Real y direction (Ground truth)\")\n",
        "_ = ax1.plot(p_dir[SLICE_START_INDEX:SLICE_END_INDEX], color=\"blue\", label=\"Predicted y direction\")\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "ax2.plot(tickDataResized.loc[:,'open'].values[SLICE_START_INDEX:SLICE_END_INDEX], color=\"black\", label=\"Price\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Direction\")\n",
        "ax2.set_ylabel(\"Price\")\n",
        "\n",
        "# Show a legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "_ = ax2.legend(lines + lines2, labels + labels2, loc=\"upper left\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "iREN4fH3unhe",
        "outputId": "49d1402f-a26d-45e7-9926-1022f7c4f9f5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9b364ca5b0>"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a colored price line depending on the predicted direction\n",
        "SLICE_START_INDEX = 12000\n",
        "SLICE_END_INDEX = 15000\n",
        "\n",
        "chart_name=\"Predicted direction of price movement with open price\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "# _ = ax1.plot(p_test_dir)\n",
        "\n",
        "priceValues = tickDataResized.loc[:,'open'].values[SLICE_START_INDEX:SLICE_END_INDEX]\n",
        "\n",
        "# Select the rising price values\n",
        "priceValuesRising = copy.deepcopy(priceValues)\n",
        "priceValuesRising[p_dir[SLICE_START_INDEX:SLICE_END_INDEX] < 0.2] = np.nan\n",
        "\n",
        "# Select the falling price values\n",
        "priceValuesFalling = copy.deepcopy(priceValues)\n",
        "priceValuesFalling[p_dir[SLICE_START_INDEX:SLICE_END_INDEX] > 0.1] = np.nan\n",
        "\n",
        "ax1.plot(priceValues, color=\"lightgray\", label=\"Price\")\n",
        "ax1.plot(priceValuesRising, color=\"green\", label=\"Price rising\")\n",
        "ax1.plot(priceValuesFalling, color=\"red\", label=\"Price falling\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Price\")\n",
        "\n",
        "# Show a legend\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKHIj4k6mHpC"
      },
      "source": [
        "---\n",
        "# Predict an unknown currency\n",
        "\n",
        "The next step is to use the network to predict a currency it has never seen before, in the case of this example, ETH-USDT."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKr0HQcOoLs4",
        "outputId": "a56925c4-83d1-45e3-b660-a0afed7d4e1e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['/content/CryptoCrystalBall/DemoData/ETH-USDT.csv']"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# For testing, use the second example file 'ETH-USDT.csv'. The FileListToDataStream generator requires a list, even if its only one file\n",
        "TEST_FILES = [(EXAMPLE_FILE_PATHS[1])]\n",
        "TEST_FILES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hsJPmreWn9i1"
      },
      "outputs": [],
      "source": [
        "# A python generator function has to be applied on the dataStream\n",
        "def pythonGeneratorTest():\n",
        "  # Initialize the FileListToDataStream generator\n",
        "  dataStreamTest = DataStreamCreator.FileListToDataStream(fileList = TEST_FILES,\n",
        "                                                      batch_size = BATCH_SIZE,\n",
        "                                                      X_Block_lenght = X_BLOCK_LENGHT,\n",
        "                                                      y_type_dict=Y_TYPE_DICT,\n",
        "                                                      shuffle=False # For visualization\n",
        "                                                      )\n",
        "  while True:  \n",
        "    try:\n",
        "      ne = next(dataStreamTest)\n",
        "      _X = ne['X']\n",
        "      _y = ne['y']\n",
        "      \n",
        "      yield (_X, _y)\n",
        "    except StopIteration as si:\n",
        "      logging.warning(\"StopIteration in pythonGenerator\")\n",
        "      logging.warning(si)\n",
        "      return"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWCsB4wDoHh2",
        "outputId": "33de1d69-7d68-49c2-8c4e-49e6ded18e78"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<FlatMapDataset element_spec=(TensorSpec(shape=(256, 512, 226), dtype=tf.float32, name=None), TensorSpec(shape=(256, 2), dtype=tf.float32, name=None))>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "# Create a Tensorflow dataset, which can be fed to the network\n",
        "tfGenTest = tf.data.Dataset.from_generator(pythonGeneratorTest, \n",
        "                                               output_types = (tf.float32, tf.float32), # Todo: Automatically from dataStreamTraining\n",
        "                                               output_shapes=((XShape), (yShape))\n",
        "                                               )\n",
        "tfGenTest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jZLv-AXtoz8p",
        "outputId": "7a9346c5-1100-47e1-9f29-e9ad44904462"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:WARNING: There were not enough files (only 1) to provide 4 parallel generators, falling back to 1 generators.\n",
            "INFO:root:File 'ETH-USDT.csv' loaded, 0 left\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    144/Unknown - 73s 498ms/step - loss: 0.0723 - mae: 0.1719"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:StopIteration in pythonGenerator\n",
            "WARNING:root:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r144/144 [==============================] - 74s 499ms/step - loss: 0.0723 - mae: 0.1719\n",
            "MSE Test: 0.07233753800392151 - for comparison, MSE Training: 0.04468248412013054\n",
            "MAE Test: 0.1719352751970291 - for comparison, MAE Training: 0.13235564529895782\n"
          ]
        }
      ],
      "source": [
        "# Calculate the networks metrics on the test dataset\n",
        "# For a comparsion, also the metric results from the evaluation on the train\n",
        "# set are printed.\n",
        "\n",
        "metrics_test = model.evaluate(tfGenTest)\n",
        "mse_test, mae_test = metrics_test\n",
        "print(f\"MSE Test: {mse_test} - for comparison, MSE Training: {mse_train}\")\n",
        "print(f\"MAE Test: {mae_test} - for comparison, MAE Training: {mae_train}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A2Mag1d3qCUh"
      },
      "source": [
        "---\n",
        "# Visualize the predictions\n",
        "\n",
        "Other to the previous visualization, instead of a batch size of 1 the normal size is used to speed up the process. Doing this, some data in the end of the table is ignored, but that makes no difference here, as it is just for visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "QsisQyM_qWPk"
      },
      "outputs": [],
      "source": [
        "# Predict the direction and derivation - on colab, this takes around 3 1/2 minutes\n",
        "pTest = model.predict(tfGenTest)\n",
        "pTest.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "DwxoItwXmUFP"
      },
      "outputs": [],
      "source": [
        "# Get the real y values (-> Ground Truth) out of the tfGenTest generator for plotting and comparison\n",
        "\n",
        "# Create numpy generator\n",
        "it = tfGenTest.as_numpy_iterator()\n",
        "\n",
        "# Init the values\n",
        "yRealTest = None\n",
        "i = 0\n",
        "\n",
        "# Get the data\n",
        "while True:\n",
        "  try:\n",
        "    X,y = next(it)\n",
        "\n",
        "    if yRealTest is None:\n",
        "      yRealTest = y\n",
        "    else:\n",
        "      yRealTest = np.concatenate([yRealTest, y])\n",
        "\n",
        "    i += 1\n",
        "    if i % 1000 == 0:\n",
        "      print(i)\n",
        "\n",
        "  except StopIteration:\n",
        "    break\n",
        "\n",
        "print(yRealTest.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "WbnIzQ2bqrMZ"
      },
      "outputs": [],
      "source": [
        "# Split the predicted data into two values for easier processing\n",
        "p_test_dir = pTest[:,0]\n",
        "p_test_dir_derivation = pTest[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "ALPK60ADopl9"
      },
      "outputs": [],
      "source": [
        "# Also split the real y values\n",
        "test_real_y_dir = yRealTest[:,0]\n",
        "test_real_y_dir_derivation = yRealTest[:,1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "fgsKE7I1o5Jf"
      },
      "outputs": [],
      "source": [
        "# Plot the prediction and the ground truth\n",
        "chart_name=\"Predicted direction of price movement - unknown currency\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "_ = ax1.plot(test_real_y_dir, color=\"gray\", label=\"Real y direction (Ground truth)\")\n",
        "_ = ax1.plot(p_test_dir, color=\"blue\", label=\"Predicted y direction\")\n",
        "\n",
        "_= plt.legend(loc=\"upper left\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Direction\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "CQhjOkuTrtLa"
      },
      "outputs": [],
      "source": [
        "# Load and crop the according tick data\n",
        "tickdataTest = pd.read_csv(TEST_FILES[0])\n",
        "tickdataTest.set_index(\"startsAt\", inplace = True) # Todo: Take care of the index name\n",
        "tickdataTest.sort_index()\n",
        "\n",
        "tickDataTestResized = tickdataTest.iloc[X_BLOCK_LENGHT:,]\n",
        "tickDataTestResized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "M9Iys83LplSz"
      },
      "outputs": [],
      "source": [
        "# Plot the prediction again, now over the open price\n",
        "chart_name=\"Predicted direction of price movement with open price - unknown currency\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "_ = ax1.plot(p_test_dir, color=\"blue\", label=\"Predicted y direction\")\n",
        "\n",
        "ax2 = ax1.twinx()\n",
        "\n",
        "ax2.plot(tickDataTestResized.loc[:,'open'].values, color=\"black\", label=\"Price\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Direction\")\n",
        "ax2.set_ylabel(\"Price\")\n",
        "\n",
        "# Show a legend\n",
        "lines, labels = ax1.get_legend_handles_labels()\n",
        "lines2, labels2 = ax2.get_legend_handles_labels()\n",
        "ax2.legend(lines + lines2, labels + labels2, loc=\"upper left\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 640
        },
        "id": "yJpjb2k8pwHz",
        "outputId": "1d9f48c7-4877-4640-ee6a-9d8a632a53ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f9b362aaac0>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot a colored price line slice depending on the predicted direction\n",
        "SLICE_START_INDEX = 5000\n",
        "SLICE_END_INDEX = 7000\n",
        "\n",
        "chart_name=\"Predicted direction of price movement with open price - unknown currency\"\n",
        "fig, ax1 = plt.subplots(figsize=(20,10))\n",
        "ax1.set_title(chart_name, fontsize=14)\n",
        "\n",
        "priceValues = tickDataTestResized.loc[:,'open'].values[SLICE_START_INDEX:SLICE_END_INDEX]\n",
        "\n",
        "# Select the rising price values\n",
        "priceValuesRising = copy.deepcopy(priceValues)\n",
        "priceValuesRising[p_test_dir[SLICE_START_INDEX:SLICE_END_INDEX] < 0.2] = np.nan\n",
        "\n",
        "# Select the falling price values\n",
        "priceValuesFalling = copy.deepcopy(priceValues)\n",
        "priceValuesFalling[p_test_dir[SLICE_START_INDEX:SLICE_END_INDEX] > 0.1] = np.nan\n",
        "\n",
        "ax1.plot(priceValues, color=\"lightgray\", label=\"Price\")\n",
        "ax1.plot(priceValuesRising, color=\"green\", label=\"Price rising\")\n",
        "ax1.plot(priceValuesFalling, color=\"red\", label=\"Price falling\")\n",
        "\n",
        "ax1.set_xlabel(\"Timesteps\")\n",
        "ax1.set_ylabel(\"Price\")\n",
        "\n",
        "# Show a legend\n",
        "plt.legend(loc=\"upper left\")\n",
        "\n",
        "# Save the figure\n",
        "# fig_name = \"FileListToDataStreamExample_\" + chart_name.replace(\" \", \"_\").replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\") + \".svg\"\n",
        "# plt.savefig(os.path.join(IMG_SAVE_PATH, fig_name), bbox_inches=\"tight\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cjGZPvKUL2av"
      },
      "source": [
        "---\n",
        "# Conclusion\n",
        "\n",
        "Although this is a very simplified example, as a shallow network is trained on a single tick file, the predicted results on a currency the network never seen before are pretty satisfying.\n",
        "\n",
        "The next steps to take are:\n",
        "- Provide more data for training, for example my crypto train dataset providing hourly data from my Google Bucket: [Link to Train-Set](https://console.cloud.google.com/storage/browser/cryptocrystalball_public/CryptoDataset/Hourly/Train)\n",
        "- Evaluate the training progress on a independent data set, not the train set itself. For example, use the crypto eval dataset from my Google Bucket: [Link to Eval-Set](https://console.cloud.google.com/storage/browser/cryptocrystalball_public/CryptoDataset/Hourly/Eval)\n",
        "- Use an independent test set for checking prediction results, to calculate trade signals and test the trading strategy. Once again, take a look at the test set on my Google Bucket: [Link to Test-Set](https://console.cloud.google.com/storage/browser/cryptocrystalball_public/CryptoDataset/Hourly/Test) <bR> *Todo for me: Add a link to the trade stategy tester*\n",
        "- Create a more sophisticated neural network -> For example using different Frequency/Time scales (see [Link](https://towardsdatascience.com/how-to-use-convolutional-neural-networks-for-time-series-classification-56b1b0a07a57) and [Link](https://arxiv.org/pdf/1603.06995.pdf)), use LSTM layers, add Attention routes to the model or even treat the X-Blocks as a sentence and apply NLP networks. For NLP, there will come some code on the Git Repo.\n",
        "- Create trade signals out of the direction/derivation information.\n",
        "- Create and test a trading strategy\n",
        "- Convert it into a live system and earn (or loose, your risk!!) money! :D\n",
        "\n",
        "\n",
        "\n"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "cjGZPvKUL2av"
      ],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.7.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "224c8b2e1f718756ae9bc5c4f62786756774c1d0fb85b85781d494c44704ff9b"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}