Stock_HIGH_LOW_Prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Stock HIGH/LOW  Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "53FKxuLfDlhQ",
        "colab_type": "text"
      },
      "source": [
        "# Problem\n",
        "\n",
        "To predict stock’s day HIGH and LOW prices based on its OPEN and historical daily price.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6HP8E3ifY-b",
        "colab_type": "text"
      },
      "source": [
        "## Data Description\n",
        "\n",
        "The data was downloaded from the Yahoo Finance. The underlying stock is Tencent (700.hk). I renamed the column names as the following:\n",
        "\n",
        "**DATE**: Trading date\n",
        "\n",
        "**OPEN**:Current day open price\n",
        "\n",
        "**CLOSE**:Current day last traded price recorded on exchange\n",
        "\n",
        "**HIGH**:Current day highest trade price\n",
        "\n",
        "**LOW**:Current day lowest trade price\n",
        "\n",
        "**VOLUME**:Current day trade volume\n",
        "\n",
        "\n",
        "## Data File Path\n",
        "\n",
        "The data file has been uploaded as https://mryinglee.github.io/DailyPrices.csv\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vV0La3gE0Ck",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATA_PATH='https://mryinglee.github.io/DailyPrices.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qC1q1T0e9Hk4",
        "colab_type": "text"
      },
      "source": [
        "# Academic Review\n",
        "\n",
        "The time-varying volatility and volatility clustering are often observed in the financial market. High and low price can be treated as an indicator of volatility. There are some papers to research on prediction of high and low price. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rRjqfKJN37u2",
        "colab_type": "text"
      },
      "source": [
        "## High and Low Prices Research Review\n",
        "(Mainly digested from  **High and Low Intraday Commodity\n",
        "Prices: A Fractional Integration and\n",
        "Cointegration Approach**\n",
        "Yaya, OlaOluwa S and Gil-Alana, Luis A.\n",
        "University of Navarra, Spain, University of Ibadan, Nigeria\n",
        ", 5 December 2018)\n",
        "\n",
        "\n",
        "In financial economics, the difference between high and low intraday or daily prices is known\n",
        "as the range. Volatility can be expected to be higher if the range is wider. Parkinson (1980)\n",
        "showed that, in fact, the price range is a more efficient volatility estimator than alternatives\n",
        "such as the return-based estimator. It is also frequently used in technical analysis by traders in\n",
        "financial markets (see, e.g., Taylor and Allen, 1992). However, as pointed out by Cheung et al.\n",
        "(2009), focusing on the range itself might be useful if one’s only purpose is to obtain an\n",
        "efficient proxy for the underlying volatility, but it also means discarding useful information\n",
        "about price behaviour that can be found in its components. Therefore, in their study, Cheung\n",
        "et al. (2009) analyse simultaneously both the range and daily highs and lows using daily data\n",
        "for various stock market indices. Because of the observation that the latter two variables\n",
        "generally do not diverge significantly over time, having found that they both exhibit unit roots\n",
        "by carrying out Augmented Dickey-Fuller (ADF) tests (Dickey and Fuller, 1979), they model\n",
        "their behaviour using a cointegration framework as in Johansen (1991) and Johansen and\n",
        "Juselius (1990) to investigate whether they are linked through a long-run equilibrium\n",
        "relationship, and interpreting the range as a stationary error correction term. They then show\n",
        "that such a model has better in-sample properties than rival ARMA specifications but does not\n",
        "clearly outperform them in terms of its out-of-sample properties.\n",
        "\n",
        "Following on from Cheung et al. (2009), the present study makes a twofold contribution\n",
        "to the literature. First, it uses fractional integration and cointegration methods that are more\n",
        "general than the standard framework based on the I(0) versus I(1) dichotomy. According to the\n",
        "efficient market hypothesis (EMH), asset prices should be unpredictable and follow a random\n",
        "walk (see Fama, 1970), i.e. they should be integrated of order 1 or I(1). However, the choice\n",
        "between stationary I(0) and nonstationary I(1) processes is too restrictive for most financial\n",
        "series (Barunik and Dvorakova, 2015). Diebold and Rudebusch (1991) and Hasslers and\n",
        "3\n",
        "Wolters (1994) showed that in fact unit root tests have very low power in the context of\n",
        "fractional integration. Therefore our analysis below allows the differencing parameter for the\n",
        "individual series to take fractional values. Moreover, we adopt a fractional cointegration\n",
        "approach to test for the long-run relationships. Fiess and MacDonald (2002), Cheung (2007)\n",
        "and Cheung et al. (2009) all modelled high and low prices together with the range in a\n",
        "cointegration framework to analyse the foreign exchange and stock markets, respectively.\n",
        "However, their studies restrict the cointegrating parameter to unity (even though this is not\n",
        "imposed in Granger’s (1986) seminal paper). \n",
        "Fractional cointegration models (see also Robinson and Yajima, 2002; Nielsen and\n",
        "Shimotsu, 2007; etc.) are more general and have already been shown to be more suitable for\n",
        "many financial series (see, e.g., Caporale and Gil-Alana, 2014 and Erer et al., 2016). The\n",
        "FCVAR model in particular has a number of advantages over the fractional cointegration setup\n",
        "of Robinson and Marrinuci (2003): it allows for multiple time series and long-run\n",
        "equilibrium relationships to be determined using the statistical test of MacKinnon and Nielsen\n",
        "(2014), and it jointly estimates the adjustment coefficients and the cointegrating relations.1\n",
        "Nielsen and Popiel (2018) provide a Matlab package for the calculation of the estimators and\n",
        "test statistics. Dolatabadi et al. (2016) applied the FCVAR model to analyse the relationship\n",
        "between spot and futures prices in future commodity markets and found more support for\n",
        "cointegration compared to the case when the cointegration parameters are restricted to unity."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AhFLDod43OuN",
        "colab_type": "text"
      },
      "source": [
        "## On the predictability of stock prices: a case for high and low prices\n",
        "Caporin, M., Ranaldo, A. and Santucci de Magistris, P. (2013). On the predictability of stock\n",
        "prices: a case for high and low prices. Journal of Banking and Finance, 37(12), 5132–5146. argued:\n",
        "\n",
        "*Contrary to the common wisdom that asset prices are hardly possible to forecast, we\n",
        "show that high and low prices of equity shares are largely predictable. We propose to model\n",
        "them using a simple implementation of a fractional vector autoregressive model with error\n",
        "correction (FVECM). This model captures two fundamental patterns of high and low prices:\n",
        "their cointegrating relationship and the long memory of their difference (i.e. the range),\n",
        "which is a measure of realized volatility. Investment strategies based on FVECM predictions\n",
        "of high/low US equity prices as exit/entry signals deliver a superior performance even on a\n",
        "risk-adjusted basis.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aDADTCQU5xtk",
        "colab_type": "text"
      },
      "source": [
        "## Some FCVAR related implementation\n",
        "\n",
        "[LeeMorinUCF/FCVAR](https://github.com/LeeMorinUCF/FCVAR): Fractionally Cointegrated VAR Model based on Matlab.\n",
        "\n",
        "[Tinkat/FCVAR](https://github.com/Tinkat/FCVAR): Fractionally cointegrated vector autoregressive model based on R.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6laHbooEOF-S",
        "colab_type": "text"
      },
      "source": [
        "# Why Machine Learning?\n",
        "\n",
        "Machine learning is a morden tools to process stock data. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WKH1ydCzkAD6",
        "colab_type": "text"
      },
      "source": [
        "## Why LSTM model?\n",
        "LSTM is a good model to deal with process with memory effect. I have a summary on LSTM model on Github ([Stock-Price-Specific-LSTM](https://github.com/MRYingLEE/Stock-Price-Specific-LSTM))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0MTQvfBeT5RO",
        "colab_type": "text"
      },
      "source": [
        "# Setup Environment\n",
        "\n",
        "We use Keras + Tensorflow along with other Python ecosystem packages, such as numpy, pandas and matplotlib."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sHEdIovSDZ0l",
        "colab_type": "code",
        "outputId": "9797cbac-9396-42e3-c259-b65fccfc75fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        }
      },
      "source": [
        "!pip install tensorflow-gpu"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ybtJWhHLWwRW",
        "colab_type": "code",
        "outputId": "69df9374-11d9-4e9b-9b4f-ff61cf71d64c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        }
      },
      "source": [
        "!pip install mpl_finance"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBXq-Wb_K1XR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import rc\n",
        "from tensorflow.keras.layers import Bidirectional, Dropout, Activation, Dense, LSTM\n",
        "from tensorflow.python.keras.layers import CuDNNLSTM\n",
        "from tensorflow.keras.models import Sequential\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "rcParams['figure.figsize'] = 28, 16\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJqf7SORU8ED",
        "colab_type": "text"
      },
      "source": [
        "# Load Data and Overview Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KygGYtyThF3R",
        "colab_type": "code",
        "outputId": "30b2a7f7-10cf-4149-caa1-fd3137cf0cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        }
      },
      "source": [
        "df=pd.read_csv(DATA_PATH,parse_dates=['DATE'])\n",
        "df.set_index('DATE',inplace=True)\n",
        "df = df.sort_values('DATE')\n",
        "df.head"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of              OPEN   HIGH    LOW  CLOSE  ADJ CLOSE    VOLUME\n",
              "DATE                                                       \n",
              "2015-01-21  126.2  128.9  125.2  128.7      127.1  33788560\n",
              "2015-01-22  130.5  132.0  129.7  131.6      130.0  39063807\n",
              "2015-01-23  134.6  134.9  131.1  132.7      131.1  29965533\n",
              "2015-01-26  136.7  137.1  134.3  137.0      135.3  34952624\n",
              "2015-01-27  138.0  138.0  133.0  136.0      134.3  24455759\n",
              "...           ...    ...    ...    ...        ...       ...\n",
              "2020-01-14  410.0  413.0  396.6  400.4      400.4  26827634\n",
              "2020-01-15  397.2  403.0  396.2  398.8      398.8  15938138\n",
              "2020-01-16  399.0  403.0  396.4  400.0      400.0  13770626\n",
              "2020-01-17  400.0  400.6  396.0  399.0      399.0  13670846\n",
              "2020-01-20  405.0  405.0  396.0  396.0      396.0  13282412\n",
              "\n",
              "[1231 rows x 6 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t-xB1zK0C0mG",
        "colab_type": "code",
        "outputId": "1539bf02-f936-4068-bc3d-a4474b234d8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1231, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "doxMbaaLGJHM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Po40-ZYGJggV",
        "colab_type": "code",
        "outputId": "671ce80b-6ecf-458e-e74a-77cc2485489d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1231, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y_zp_iEzEcuo",
        "colab_type": "code",
        "outputId": "7449637e-6acc-41f0-f660-59e2c7b82fb4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        }
      },
      "source": [
        "import matplotlib.dates as mdates\n",
        "from mpl_finance import candlestick_ohlc\n",
        "df_candle = df[['OPEN', 'HIGH', 'LOW', 'CLOSE']]\n",
        "\n",
        "df_candle.reset_index(inplace=True)\n",
        "df_candle['DATE'] = df_candle['DATE'].map(mdates.date2num)\n",
        "# df_candle.drop(columns='DATE',inplace=True)\n",
        "df_candle=df_candle.astype(float)\n",
        "df_candle=df_candle[['DATE','OPEN', 'HIGH', 'LOW', 'CLOSE']]\n",
        "df_candle.head"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of           DATE   OPEN   HIGH    LOW  CLOSE\n",
              "0     735619.0  126.2  128.9  125.2  128.7\n",
              "1     735620.0  130.5  132.0  129.7  131.6\n",
              "2     735621.0  134.6  134.9  131.1  132.7\n",
              "3     735624.0  136.7  137.1  134.3  137.0\n",
              "4     735625.0  138.0  138.0  133.0  136.0\n",
              "...        ...    ...    ...    ...    ...\n",
              "1226  737438.0  410.0  413.0  396.6  400.4\n",
              "1227  737439.0  397.2  403.0  396.2  398.8\n",
              "1228  737440.0  399.0  403.0  396.4  400.0\n",
              "1229  737441.0  400.0  400.6  396.0  399.0\n",
              "1230  737444.0  405.0  405.0  396.0  396.0\n",
              "\n",
              "[1231 rows x 5 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_nnk8uvHZ2A",
        "colab_type": "code",
        "outputId": "240a0ff7-f96d-40d2-a9da-76c01e27abc5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 938
        }
      },
      "source": [
        "\n",
        "# Define a new subplot\n",
        "ax = plt.subplot()\n",
        "\n",
        "# Plot the candlestick chart and put date on x-Axis\n",
        "candlestick_ohlc(ax, df_candle.values, width=5, colorup='g', colordown='r')\n",
        "ax.xaxis_date()\n",
        "\n",
        "# Turn on grid\n",
        "ax.grid(True)\n",
        "\n",
        "# Show plot\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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RrZeKjgYAABUhAAAAzDY98+KKGRhM2nlSdAQAAIyTCAEAAGC9pWlEkkQkSVRPiw4GAIBN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\n91GCBsA4yfr1M/WamNeQs+WBsoo2pEefS2SDrpHaVm3k/Tc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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRJc8Qw9K7wY",
        "colab_type": "text"
      },
      "source": [
        "Obviously, there are **gaps** in the candle chart. \n",
        "In other words, the current day's price range[LOW, HIGH] has no overlap to that of the previous day."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SznNAWtDYcMK",
        "colab_type": "text"
      },
      "source": [
        "### OPEN price instead of the previous prices is a better indicator for HIGH/LOW\n",
        "1. In theory, market price is to response to market news, which most likely is released between trading days. And news could trigger the price jump up or down, which makes the previous price range cannot be a good leading indicator for the current price range. So that the OPNE price has priced in more news.\n",
        "2. Even for the situation of dividends, the OPEN price as an indicator can deal with, which is better than previous prices.\n",
        "3. The OPEN is always in the range of [LOW, HIGH].\n",
        "4. It's pratical to predict HIGH/LOW after the market is open. Especially for a day trader.\n",
        "5. Technically if we use OPEN as a benchmark, OPEN/HIGH and LOW/OPEN are both belongs to (0,1], which is good for deep learning to optimize.\n",
        "\n",
        "### If we don't use OPEN as an indicator, in feature engineering, we should use other capping tricks to make sure the relative of HIGH and LOW are within (0,1]."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qOq58vfIMBs5",
        "colab_type": "text"
      },
      "source": [
        "## To Check the Tick Movement Frequency Distribution\n",
        "\n",
        "We need to check the tick distribution to standardize the tick data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SE92HSSNuPZF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "TICK_UNIT=0.2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xe9fsc-i0dVD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['HIGH_tick']=(np.round((df['HIGH']-df['OPEN'])/TICK_UNIT)).astype('int32')\n",
        "df['LOW_tick']=(np.round((df['OPEN']-df['LOW'])/TICK_UNIT)).astype('int32')\n",
        "\n",
        "df['RANGE_TICKS']=df['HIGH_tick']+df['LOW_tick'] ## ticks between day low and day high"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8PkFV9x1mdT",
        "colab_type": "code",
        "outputId": "c602b560-5aef-4ae1-9c1c-c5bb79a0f11a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "df.max()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OPEN                 474.0\n",
              "HIGH                 476.6\n",
              "LOW                  466.8\n",
              "CLOSE                474.6\n",
              "ADJ CLOSE            472.3\n",
              "VOLUME         308436765.0\n",
              "HIGH_tick            102.0\n",
              "LOW_tick              84.0\n",
              "RANGE_TICKS          108.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EUVnuGkn2BPP",
        "colab_type": "code",
        "outputId": "a90b855a-8e9e-48f3-d790-aedc25a58072",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        }
      },
      "source": [
        "df.mean()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OPEN           2.691491e+02\n",
              "HIGH           2.717566e+02\n",
              "LOW            2.661037e+02\n",
              "CLOSE          2.688868e+02\n",
              "ADJ CLOSE      2.676347e+02\n",
              "VOLUME         2.027254e+07\n",
              "HIGH_tick      1.303574e+01\n",
              "LOW_tick       1.522177e+01\n",
              "RANGE_TICKS    2.825751e+01\n",
              "dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pV6qQXqn2Obr",
        "colab_type": "code",
        "outputId": "409a55fe-6efd-4ee3-c860-32d04905a996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df[[\"HIGH_tick\",\"LOW_tick\"]].groupby(\"HIGH_tick\").count().head(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
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              "</div>"
            ],
            "text/plain": [
              "           LOW_tick\n",
              "HIGH_tick          \n",
              "0               183\n",
              "1                48\n",
              "2                64\n",
              "3                55\n",
              "4                63\n",
              "5                77\n",
              "6                28\n",
              "7                42\n",
              "8                48\n",
              "9                43\n",
              "10               40\n",
              "11               33\n",
              "12               32\n",
              "13               33\n",
              "14               40\n",
              "15               36\n",
              "16               17\n",
              "17               24\n",
              "18               22\n",
              "19               28\n",
              "20               19\n",
              "21               16\n",
              "22               12\n",
              "23               11\n",
              "24               17\n",
              "25               12\n",
              "26               12\n",
              "27               10\n",
              "28               11\n",
              "29               11\n",
              "30                6\n",
              "31                1\n",
              "32               15\n",
              "33                8\n",
              "34               10\n",
              "35                9\n",
              "36                4\n",
              "37                7\n",
              "38                6\n",
              "39                4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sZhSeJotIigE",
        "colab_type": "code",
        "outputId": "0e870586-7f59-44ae-f264-db21af39c9bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "df[[\"HIGH_tick\",\"LOW_tick\"]].groupby(\"LOW_tick\").count().head(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <td>43</td>\n",
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              "      <td>21</td>\n",
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              "      <td>29</td>\n",
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              "      <td>32</td>\n",
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              "      <td>20</td>\n",
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              "      <td>20</td>\n",
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              "      <td>19</td>\n",
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              "      <td>11</td>\n",
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              "      <th>25</th>\n",
              "      <td>20</td>\n",
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              "      <td>15</td>\n",
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              "      <th>27</th>\n",
              "      <td>13</td>\n",
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              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>10</td>\n",
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              "      <th>30</th>\n",
              "      <td>16</td>\n",
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              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>12</td>\n",
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              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          HIGH_tick\n",
              "LOW_tick           \n",
              "0               108\n",
              "1                37\n",
              "2                50\n",
              "3                42\n",
              "4                47\n",
              "5                58\n",
              "6                39\n",
              "7                60\n",
              "8                53\n",
              "9                47\n",
              "10               57\n",
              "11               37\n",
              "12               45\n",
              "13               43\n",
              "14               21\n",
              "15               42\n",
              "16               19\n",
              "17               29\n",
              "18               21\n",
              "19               25\n",
              "20               32\n",
              "21               20\n",
              "22               20\n",
              "23               19\n",
              "24               11\n",
              "25               20\n",
              "26               15\n",
              "27               13\n",
              "28                8\n",
              "29               10\n",
              "30               16\n",
              "31               11\n",
              "32                8\n",
              "33               11\n",
              "34               12\n",
              "35                3\n",
              "36                4\n",
              "37               11\n",
              "38               10\n",
              "39                7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z1jCS4Jn4_rr",
        "colab_type": "code",
        "outputId": "ea9c5c44-8db4-411c-a544-85bc0db3c0b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "df_low_freq=pd.DataFrame({'count' : df[[\"HIGH_tick\",\"LOW_tick\"]].groupby(\"LOW_tick\").size()}).reset_index()\n",
        "plt.bar(df_low_freq[\"LOW_tick\"], df_low_freq[\"count\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 72 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        },
        {
          "output_type": "display_data",
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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xw5u1rUC41OV",
        "colab_type": "code",
        "outputId": "cf718ff0-2157-4fb2-98a4-452aee108530",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "df_high_freq=pd.DataFrame({'count' : df[[\"HIGH_tick\",\"LOW_tick\"]].groupby(\"HIGH_tick\").size()}).reset_index()\n",
        "plt.bar(df_high_freq[\"HIGH_tick\"], df_high_freq[\"count\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 73 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4FT5Ya5JmwJ",
        "colab_type": "code",
        "outputId": "770f2cf3-ec72-456f-f022-08321249f72a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 955
        }
      },
      "source": [
        "df_range_freq=pd.DataFrame({'count' : df[[\"RANGE_TICKS\",\"LOW_tick\"]].groupby(\"RANGE_TICKS\").size()}).reset_index()\n",
        "plt.bar(df_range_freq[\"RANGE_TICKS\"], df_range_freq[\"count\"])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 89 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBNfzCuzzw4_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "MAX_RANGE_TICKS=100 # We use this to standardize the RANGE_TICKS"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uVHJRWrQOcM1",
        "colab_type": "text"
      },
      "source": [
        "### Findings on Tick Movement Frequency Distribution\n",
        "\n",
        "Based on OPEN data, the up/down tick movement can be 0-100.\n",
        "\n",
        "For the situations of outliers, we may miss some better opportunity to make money, but the model can be more stable if we ignore the outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XUDB0oR_XvbC",
        "colab_type": "text"
      },
      "source": [
        "## Overview Conclusion\n",
        "There are no date conflicts, such as duplicate dates.\n",
        "There are no N/A values.\n",
        "But there are many price gaps. So even both of the next HIGH/LOW could be out of the range of the current day.\n",
        "\n",
        "**OPEN price instead of the previous prices is a better indicator for HIGH/LOW.**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "DOYSQw-yQpDd"
      },
      "source": [
        "# Feature Engineering"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PIUtKiucLv-4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df['CLOSE-1']=df['CLOSE'].shift(1)       # The LAST price of the previous day\n",
        "df['OPEN/LAST-1']=df['OPEN']/df['CLOSE-1'] # The ratio of the current OPEN with the LAST price of the prevous day\n",
        "df['OPEN+1']=df['OPEN/LAST-1'].shift(-1) # The next OPEN price\n",
        "df['OPEN/HIGH']=df['OPEN']/df['HIGH'] # The ratio of OPEN with HIGH, which belongs to (0,1]\n",
        "df['LOW/OPEN']=df['LOW']/df['OPEN'] # The ratio of LOW with OPEN, which belongs to (0,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Im7lIZXLQpDO",
        "colab_type": "code",
        "outputId": "c2e079ab-5fad-475a-8ecf-153eb4b44599",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "df.dropna(inplace=True)\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OPEN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>LOW</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>ADJ CLOSE</th>\n",
              "      <th>VOLUME</th>\n",
              "      <th>HIGH_tick</th>\n",
              "      <th>LOW_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>CLOSE-1</th>\n",
              "      <th>OPEN/LAST-1</th>\n",
              "      <th>OPEN+1</th>\n",
              "      <th>OPEN/HIGH</th>\n",
              "      <th>LOW/OPEN</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2015-01-22</th>\n",
              "      <td>130.5</td>\n",
              "      <td>132.0</td>\n",
              "      <td>129.7</td>\n",
              "      <td>131.6</td>\n",
              "      <td>130.0</td>\n",
              "      <td>39063807</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>128.7</td>\n",
              "      <td>1.013986</td>\n",
              "      <td>1.022796</td>\n",
              "      <td>0.988636</td>\n",
              "      <td>0.993870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-23</th>\n",
              "      <td>134.6</td>\n",
              "      <td>134.9</td>\n",
              "      <td>131.1</td>\n",
              "      <td>132.7</td>\n",
              "      <td>131.1</td>\n",
              "      <td>29965533</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>20</td>\n",
              "      <td>131.6</td>\n",
              "      <td>1.022796</td>\n",
              "      <td>1.030143</td>\n",
              "      <td>0.997776</td>\n",
              "      <td>0.973997</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26</th>\n",
              "      <td>136.7</td>\n",
              "      <td>137.1</td>\n",
              "      <td>134.3</td>\n",
              "      <td>137.0</td>\n",
              "      <td>135.3</td>\n",
              "      <td>34952624</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>132.7</td>\n",
              "      <td>1.030143</td>\n",
              "      <td>1.007299</td>\n",
              "      <td>0.997082</td>\n",
              "      <td>0.982443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27</th>\n",
              "      <td>138.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>134.3</td>\n",
              "      <td>24455759</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1.007299</td>\n",
              "      <td>1.006618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.963768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-28</th>\n",
              "      <td>136.9</td>\n",
              "      <td>137.0</td>\n",
              "      <td>134.7</td>\n",
              "      <td>136.9</td>\n",
              "      <td>135.2</td>\n",
              "      <td>16216906</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1.006618</td>\n",
              "      <td>0.993426</td>\n",
              "      <td>0.999270</td>\n",
              "      <td>0.983930</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             OPEN   HIGH    LOW  ...    OPEN+1  OPEN/HIGH  LOW/OPEN\n",
              "DATE                             ...                               \n",
              "2015-01-22  130.5  132.0  129.7  ...  1.022796   0.988636  0.993870\n",
              "2015-01-23  134.6  134.9  131.1  ...  1.030143   0.997776  0.973997\n",
              "2015-01-26  136.7  137.1  134.3  ...  1.007299   0.997082  0.982443\n",
              "2015-01-27  138.0  138.0  133.0  ...  1.006618   1.000000  0.963768\n",
              "2015-01-28  136.9  137.0  134.7  ...  0.993426   0.999270  0.983930\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BCcAycM4h68j",
        "colab_type": "text"
      },
      "source": [
        "## Outliers Processing by a customized scaler\n",
        "To standardize the tick distribution\n",
        "\n",
        "We will use our customized scale function to turn the tick data to the value of (0,1]. All outliers will be assign 1. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cDBF9QOjp_fP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def capped_with_1(tick_value):\n",
        "        return min(1, tick_value)\n",
        "        \n",
        "df[\"HIGH_tick_std\"] = df.apply(lambda x: capped_with_1(x[\"HIGH_tick\"]/MAX_RANGE_TICKS),axis=1)\n",
        "df[\"LOW_tick_std\"] = df.apply(lambda x: capped_with_1(x[\"LOW_tick\"]/MAX_RANGE_TICKS),axis=1)\n",
        "df[\"RANGE_TICKS_std\"]=df.apply(lambda x: capped_with_1(x[\"RANGE_TICKS\"]/MAX_RANGE_TICKS),axis=1)\n",
        "#df[\"LOW_tick_std\"].max()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b-URvratkm9L",
        "colab_type": "code",
        "outputId": "9f72b54f-af10-4c40-c32c-0349cbeec511",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        }
      },
      "source": [
        "df.dropna(inplace=True) # Due to shift(1), the first row has N/A data\n",
        "df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OPEN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>LOW</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>ADJ CLOSE</th>\n",
              "      <th>VOLUME</th>\n",
              "      <th>HIGH_tick</th>\n",
              "      <th>LOW_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>CLOSE-1</th>\n",
              "      <th>OPEN/LAST-1</th>\n",
              "      <th>OPEN+1</th>\n",
              "      <th>OPEN/HIGH</th>\n",
              "      <th>LOW/OPEN</th>\n",
              "      <th>HIGH_tick_std</th>\n",
              "      <th>LOW_tick_std</th>\n",
              "      <th>RANGE_TICKS_std</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DATE</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>2015-01-22</th>\n",
              "      <td>130.5</td>\n",
              "      <td>132.0</td>\n",
              "      <td>129.7</td>\n",
              "      <td>131.6</td>\n",
              "      <td>130.0</td>\n",
              "      <td>39063807</td>\n",
              "      <td>8</td>\n",
              "      <td>4</td>\n",
              "      <td>12</td>\n",
              "      <td>128.7</td>\n",
              "      <td>1.013986</td>\n",
              "      <td>1.022796</td>\n",
              "      <td>0.988636</td>\n",
              "      <td>0.993870</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-23</th>\n",
              "      <td>134.6</td>\n",
              "      <td>134.9</td>\n",
              "      <td>131.1</td>\n",
              "      <td>132.7</td>\n",
              "      <td>131.1</td>\n",
              "      <td>29965533</td>\n",
              "      <td>2</td>\n",
              "      <td>18</td>\n",
              "      <td>20</td>\n",
              "      <td>131.6</td>\n",
              "      <td>1.022796</td>\n",
              "      <td>1.030143</td>\n",
              "      <td>0.997776</td>\n",
              "      <td>0.973997</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.18</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-26</th>\n",
              "      <td>136.7</td>\n",
              "      <td>137.1</td>\n",
              "      <td>134.3</td>\n",
              "      <td>137.0</td>\n",
              "      <td>135.3</td>\n",
              "      <td>34952624</td>\n",
              "      <td>2</td>\n",
              "      <td>12</td>\n",
              "      <td>14</td>\n",
              "      <td>132.7</td>\n",
              "      <td>1.030143</td>\n",
              "      <td>1.007299</td>\n",
              "      <td>0.997082</td>\n",
              "      <td>0.982443</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-27</th>\n",
              "      <td>138.0</td>\n",
              "      <td>138.0</td>\n",
              "      <td>133.0</td>\n",
              "      <td>136.0</td>\n",
              "      <td>134.3</td>\n",
              "      <td>24455759</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>25</td>\n",
              "      <td>137.0</td>\n",
              "      <td>1.007299</td>\n",
              "      <td>1.006618</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.963768</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-28</th>\n",
              "      <td>136.9</td>\n",
              "      <td>137.0</td>\n",
              "      <td>134.7</td>\n",
              "      <td>136.9</td>\n",
              "      <td>135.2</td>\n",
              "      <td>16216906</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>136.0</td>\n",
              "      <td>1.006618</td>\n",
              "      <td>0.993426</td>\n",
              "      <td>0.999270</td>\n",
              "      <td>0.983930</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             OPEN   HIGH    LOW  ...  HIGH_tick_std  LOW_tick_std  RANGE_TICKS_std\n",
              "DATE                             ...                                              \n",
              "2015-01-22  130.5  132.0  129.7  ...           0.08          0.04             0.12\n",
              "2015-01-23  134.6  134.9  131.1  ...           0.02          0.18             0.20\n",
              "2015-01-26  136.7  137.1  134.3  ...           0.02          0.12             0.14\n",
              "2015-01-27  138.0  138.0  133.0  ...           0.00          0.25             0.25\n",
              "2015-01-28  136.9  137.0  134.7  ...           0.00          0.11             0.11\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQgrA2va4Jzx",
        "colab_type": "text"
      },
      "source": [
        "# Model 1: Return based LSTM model\n",
        "\n",
        "If we can predict relative HIGH, LOW based on OPEN price, we can get the correponding real HIGH and LOW."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zf9nRdwNZA7o",
        "colab_type": "text"
      },
      "source": [
        "**Range Prediction**\n",
        "\n",
        "The most difficluty for this model is that the task is too predict one HIGH and one LOW value, which define a range. If there is no constraint on the prediction, the predicted LOW could be higher the predicted HIGH. So we have to predict a reasonable range.\n",
        "\n",
        "The trick we do is that we use OPEN/HIGH and OPEN/LOW as the indicators and the data to be predicted. These 2 generated columns have the range of (0,1]. And all combines of OPEN/HIGH and OPEN/LOW means a reasonable range. The hinted HIGH is always higher or equal to the LOW.\n",
        "\n",
        "**The critical activation function for the output layer**\n",
        "\n",
        "For the predicted values lies on (0,1], and they have no direct relation between them. So that sigmoid aactivation function suites to the output layer.\n",
        "\n",
        "![Sigmoid](https://upload.wikimedia.org/wikipedia/commons/8/88/Logistic-curve.svg)\n",
        "\n",
        "Of course, there are a few possible activation cadidates also. Such as: TanH, SQNL，Gaussian and SQ-RBF. But Sigmoid has good feature that the predicted values concentrate to 1, which fits our situation.\n",
        "\n",
        "The detail on activation function can be found at:\n",
        "\n",
        "[Activation function](https://en.wikipedia.org/wiki/Activation_function)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bWoZh1EWn218",
        "colab_type": "text"
      },
      "source": [
        "## Preprocess"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vju9ECu0NsWy",
        "colab_type": "code",
        "outputId": "554c7714-f3b5-4cfb-b6e3-eb59c0b57dfd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "history_values = df[[\"RANGE_TICKS_std\",\"OPEN/HIGH\",\t\"LOW/OPEN\"]].values\n",
        "history_values.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1229, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NS5O4-Vtq-N1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "SEQ_LEN = 20 # about 1 month\n",
        "\n",
        "\n",
        "def to_sequences(data, seq_len):\n",
        "    d = []\n",
        "\n",
        "    for index in range(len(data) - seq_len):\n",
        "        d.append((data[index: index + seq_len]))\n",
        "        #d.append(data[index: index + seq_len])\n",
        "\n",
        "    na=np.array(d)\n",
        "    #print(na.shape)\n",
        "    #print(na[:2])\n",
        "    return na\n",
        "\n",
        "def preprocess(data_raw, seq_len, train_split):\n",
        "\n",
        "    data = to_sequences(data_raw, seq_len)\n",
        "\n",
        "    num_train = int(train_split * data.shape[0])\n",
        "\n",
        "    X_train = data[:num_train, :-1,: ]\n",
        "    y_train = data[:num_train, -1, -2:]\n",
        "\n",
        "    X_test = data[num_train:, :-1, :]\n",
        "    y_test = data[num_train:,-1, -2:]\n",
        "\n",
        "    return X_train, y_train, X_test, y_test\n",
        "\n",
        "\n",
        "X_train, y_train, X_test, y_test = preprocess(history_values, SEQ_LEN, train_split = 0.95)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XNBTUeaXreni",
        "colab_type": "code",
        "outputId": "e3f9fd22-7873-41c3-ba0b-efc5dfb20f1b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1148, 19, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "58uT2htLjK_X",
        "colab_type": "text"
      },
      "source": [
        "## Model Define\n",
        "This model is copied from a model to predict close price of a crypto currency.\n",
        "This model is selectd for the author argued the model has some good technical features.\n",
        "\n",
        "A common LSTM should be fine too.\n",
        "\n",
        "The difference is that here are 2 outputs with sigmoid as activation function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a3dw5qTasJoy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DROPOUT = 0.2 # In case overfitting\n",
        "WINDOW_SIZE = (SEQ_LEN - 1)\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM(WINDOW_SIZE, return_sequences=True),\n",
        "                        input_shape=(WINDOW_SIZE, X_train.shape[-1])))\n",
        "model.add(Dropout(rate=DROPOUT))\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM((WINDOW_SIZE * 2), return_sequences=True)))\n",
        "model.add(Dropout(rate=DROPOUT))\n",
        "\n",
        "model.add(Bidirectional(CuDNNLSTM(WINDOW_SIZE, return_sequences=False)))\n",
        "\n",
        "model.add(Dense(units=2))\n",
        "\n",
        "model.add(Activation('sigmoid')) # So the value output is in the (0,1] "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pjBan-K27L8d",
        "colab_type": "text"
      },
      "source": [
        "## Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvc-LMgOHkWJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "    loss='mean_squared_error', \n",
        "    optimizer=keras.optimizers.Adam(\n",
        "    learning_rate=0.001,\n",
        "    beta_1=0.9,\n",
        "    beta_2=0.999,\n",
        "    epsilon=1e-09,# We choose a much smaller value for the predicted value is always near 1, so the loss can be always very small. In order to distinguish 1 tick difference, we need a very small epsilon.\n",
        "    amsgrad=False,\n",
        "    name='Adam')\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gZjbH91Lc69d",
        "outputId": "c52c10a0-ef42-48aa-a1c4-12ead1448beb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "BATCH_SIZE = 64\n",
        "\n",
        "history = model.fit(\n",
        "    X_train, \n",
        "    y_train, \n",
        "    epochs=100, \n",
        "    batch_size=BATCH_SIZE, \n",
        "    shuffle=False,\n",
        "    validation_split=0.1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 1033 samples, validate on 115 samples\n",
            "Epoch 1/100\n",
            "1033/1033 [==============================] - 3s 3ms/sample - loss: 0.0961 - val_loss: 0.0075\n",
            "Epoch 2/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 0.0026 - val_loss: 4.7194e-04\n",
            "Epoch 3/100\n",
            "1033/1033 [==============================] - 0s 244us/sample - loss: 3.0440e-04 - val_loss: 1.7221e-04\n",
            "Epoch 4/100\n",
            "1033/1033 [==============================] - 0s 213us/sample - loss: 1.6948e-04 - val_loss: 1.1866e-04\n",
            "Epoch 5/100\n",
            "1033/1033 [==============================] - 0s 195us/sample - loss: 1.3693e-04 - val_loss: 9.9681e-05\n",
            "Epoch 6/100\n",
            "1033/1033 [==============================] - 0s 204us/sample - loss: 1.2298e-04 - val_loss: 8.9734e-05\n",
            "Epoch 7/100\n",
            "1033/1033 [==============================] - 0s 232us/sample - loss: 1.1647e-04 - val_loss: 8.3405e-05\n",
            "Epoch 8/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 1.1164e-04 - val_loss: 7.9033e-05\n",
            "Epoch 9/100\n",
            "1033/1033 [==============================] - 0s 208us/sample - loss: 1.0853e-04 - val_loss: 7.5922e-05\n",
            "Epoch 10/100\n",
            "1033/1033 [==============================] - 0s 200us/sample - loss: 1.0615e-04 - val_loss: 7.3603e-05\n",
            "Epoch 11/100\n",
            "1033/1033 [==============================] - 0s 209us/sample - loss: 1.0452e-04 - val_loss: 7.1876e-05\n",
            "Epoch 12/100\n",
            "1033/1033 [==============================] - 0s 214us/sample - loss: 1.0337e-04 - val_loss: 7.0569e-05\n",
            "Epoch 13/100\n",
            "1033/1033 [==============================] - 0s 201us/sample - loss: 1.0279e-04 - val_loss: 6.9561e-05\n",
            "Epoch 14/100\n",
            "1033/1033 [==============================] - 0s 195us/sample - loss: 1.0178e-04 - val_loss: 6.8787e-05\n",
            "Epoch 15/100\n",
            "1033/1033 [==============================] - 0s 232us/sample - loss: 1.0155e-04 - val_loss: 6.8164e-05\n",
            "Epoch 16/100\n",
            "1033/1033 [==============================] - 0s 209us/sample - loss: 1.0162e-04 - val_loss: 6.7673e-05\n",
            "Epoch 17/100\n",
            "1033/1033 [==============================] - 0s 213us/sample - loss: 1.0108e-04 - val_loss: 6.7302e-05\n",
            "Epoch 18/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0108e-04 - val_loss: 6.6995e-05\n",
            "Epoch 19/100\n",
            "1033/1033 [==============================] - 0s 202us/sample - loss: 1.0079e-04 - val_loss: 6.6748e-05\n",
            "Epoch 20/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 1.0082e-04 - val_loss: 6.6542e-05\n",
            "Epoch 21/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 1.0052e-04 - val_loss: 6.6374e-05\n",
            "Epoch 22/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0039e-04 - val_loss: 6.6251e-05\n",
            "Epoch 23/100\n",
            "1033/1033 [==============================] - 0s 202us/sample - loss: 1.0069e-04 - val_loss: 6.6142e-05\n",
            "Epoch 24/100\n",
            "1033/1033 [==============================] - 0s 201us/sample - loss: 1.0019e-04 - val_loss: 6.6041e-05\n",
            "Epoch 25/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0061e-04 - val_loss: 6.5965e-05\n",
            "Epoch 26/100\n",
            "1033/1033 [==============================] - 0s 216us/sample - loss: 1.0044e-04 - val_loss: 6.5926e-05\n",
            "Epoch 27/100\n",
            "1033/1033 [==============================] - 0s 220us/sample - loss: 1.0040e-04 - val_loss: 6.5886e-05\n",
            "Epoch 28/100\n",
            "1033/1033 [==============================] - 0s 202us/sample - loss: 1.0050e-04 - val_loss: 6.5855e-05\n",
            "Epoch 29/100\n",
            "1033/1033 [==============================] - 0s 196us/sample - loss: 1.0057e-04 - val_loss: 6.5817e-05\n",
            "Epoch 30/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0065e-04 - val_loss: 6.5786e-05\n",
            "Epoch 31/100\n",
            "1033/1033 [==============================] - 0s 210us/sample - loss: 1.0036e-04 - val_loss: 6.5753e-05\n",
            "Epoch 32/100\n",
            "1033/1033 [==============================] - 0s 214us/sample - loss: 1.0031e-04 - val_loss: 6.5729e-05\n",
            "Epoch 33/100\n",
            "1033/1033 [==============================] - 0s 195us/sample - loss: 1.0033e-04 - val_loss: 6.5703e-05\n",
            "Epoch 34/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0028e-04 - val_loss: 6.5698e-05\n",
            "Epoch 35/100\n",
            "1033/1033 [==============================] - 0s 214us/sample - loss: 1.0046e-04 - val_loss: 6.5679e-05\n",
            "Epoch 36/100\n",
            "1033/1033 [==============================] - 0s 201us/sample - loss: 1.0024e-04 - val_loss: 6.5660e-05\n",
            "Epoch 37/100\n",
            "1033/1033 [==============================] - 0s 219us/sample - loss: 1.0041e-04 - val_loss: 6.5664e-05\n",
            "Epoch 38/100\n",
            "1033/1033 [==============================] - 0s 251us/sample - loss: 1.0028e-04 - val_loss: 6.5672e-05\n",
            "Epoch 39/100\n",
            "1033/1033 [==============================] - 0s 272us/sample - loss: 1.0076e-04 - val_loss: 6.5673e-05\n",
            "Epoch 40/100\n",
            "1033/1033 [==============================] - 0s 263us/sample - loss: 1.0044e-04 - val_loss: 6.5665e-05\n",
            "Epoch 41/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0038e-04 - val_loss: 6.5641e-05\n",
            "Epoch 42/100\n",
            "1033/1033 [==============================] - 0s 198us/sample - loss: 1.0013e-04 - val_loss: 6.5626e-05\n",
            "Epoch 43/100\n",
            "1033/1033 [==============================] - 0s 204us/sample - loss: 1.0086e-04 - val_loss: 6.5625e-05\n",
            "Epoch 44/100\n",
            "1033/1033 [==============================] - 0s 210us/sample - loss: 1.0020e-04 - val_loss: 6.5636e-05\n",
            "Epoch 45/100\n",
            "1033/1033 [==============================] - 0s 219us/sample - loss: 1.0055e-04 - val_loss: 6.5639e-05\n",
            "Epoch 46/100\n",
            "1033/1033 [==============================] - 0s 202us/sample - loss: 1.0035e-04 - val_loss: 6.5633e-05\n",
            "Epoch 47/100\n",
            "1033/1033 [==============================] - 0s 218us/sample - loss: 1.0024e-04 - val_loss: 6.5619e-05\n",
            "Epoch 48/100\n",
            "1033/1033 [==============================] - 0s 201us/sample - loss: 1.0057e-04 - val_loss: 6.5620e-05\n",
            "Epoch 49/100\n",
            "1033/1033 [==============================] - 0s 208us/sample - loss: 1.0051e-04 - val_loss: 6.5636e-05\n",
            "Epoch 50/100\n",
            "1033/1033 [==============================] - 0s 198us/sample - loss: 1.0027e-04 - val_loss: 6.5627e-05\n",
            "Epoch 51/100\n",
            "1033/1033 [==============================] - 0s 228us/sample - loss: 1.0042e-04 - val_loss: 6.5623e-05\n",
            "Epoch 52/100\n",
            "1033/1033 [==============================] - 0s 230us/sample - loss: 1.0031e-04 - val_loss: 6.5624e-05\n",
            "Epoch 53/100\n",
            "1033/1033 [==============================] - 0s 210us/sample - loss: 1.0083e-04 - val_loss: 6.5628e-05\n",
            "Epoch 54/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 1.0062e-04 - val_loss: 6.5620e-05\n",
            "Epoch 55/100\n",
            "1033/1033 [==============================] - 0s 192us/sample - loss: 9.9961e-05 - val_loss: 6.5611e-05\n",
            "Epoch 56/100\n",
            "1033/1033 [==============================] - 0s 219us/sample - loss: 1.0025e-04 - val_loss: 6.5611e-05\n",
            "Epoch 57/100\n",
            "1033/1033 [==============================] - 0s 212us/sample - loss: 1.0031e-04 - val_loss: 6.5617e-05\n",
            "Epoch 58/100\n",
            "1033/1033 [==============================] - 0s 208us/sample - loss: 1.0006e-04 - val_loss: 6.5610e-05\n",
            "Epoch 59/100\n",
            "1033/1033 [==============================] - 0s 199us/sample - loss: 9.9959e-05 - val_loss: 6.5617e-05\n",
            "Epoch 60/100\n",
            "1033/1033 [==============================] - 0s 236us/sample - loss: 1.0061e-04 - val_loss: 6.5602e-05\n",
            "Epoch 61/100\n",
            "1033/1033 [==============================] - 0s 200us/sample - loss: 1.0094e-04 - val_loss: 6.5575e-05\n",
            "Epoch 62/100\n",
            "1033/1033 [==============================] - 0s 198us/sample - loss: 1.0054e-04 - val_loss: 6.5582e-05\n",
            "Epoch 63/100\n",
            "1033/1033 [==============================] - 0s 215us/sample - loss: 1.0034e-04 - val_loss: 6.5594e-05\n",
            "Epoch 64/100\n",
            "1033/1033 [==============================] - 0s 196us/sample - loss: 1.0010e-04 - val_loss: 6.5617e-05\n",
            "Epoch 65/100\n",
            "1033/1033 [==============================] - 0s 215us/sample - loss: 1.0074e-04 - val_loss: 6.5623e-05\n",
            "Epoch 66/100\n",
            "1033/1033 [==============================] - 0s 207us/sample - loss: 1.0019e-04 - val_loss: 6.5614e-05\n",
            "Epoch 67/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0051e-04 - val_loss: 6.5615e-05\n",
            "Epoch 68/100\n",
            "1033/1033 [==============================] - 0s 210us/sample - loss: 1.0033e-04 - val_loss: 6.5619e-05\n",
            "Epoch 69/100\n",
            "1033/1033 [==============================] - 0s 200us/sample - loss: 9.9957e-05 - val_loss: 6.5611e-05\n",
            "Epoch 70/100\n",
            "1033/1033 [==============================] - 0s 199us/sample - loss: 1.0029e-04 - val_loss: 6.5619e-05\n",
            "Epoch 71/100\n",
            "1033/1033 [==============================] - 0s 205us/sample - loss: 1.0055e-04 - val_loss: 6.5605e-05\n",
            "Epoch 72/100\n",
            "1033/1033 [==============================] - 0s 245us/sample - loss: 1.0030e-04 - val_loss: 6.5615e-05\n",
            "Epoch 73/100\n",
            "1033/1033 [==============================] - 0s 268us/sample - loss: 1.0030e-04 - val_loss: 6.5584e-05\n",
            "Epoch 74/100\n",
            "1033/1033 [==============================] - 0s 261us/sample - loss: 1.0030e-04 - val_loss: 6.5587e-05\n",
            "Epoch 75/100\n",
            "1033/1033 [==============================] - 0s 233us/sample - loss: 1.0057e-04 - val_loss: 6.5606e-05\n",
            "Epoch 76/100\n",
            "1033/1033 [==============================] - 0s 205us/sample - loss: 1.0042e-04 - val_loss: 6.5615e-05\n",
            "Epoch 77/100\n",
            "1033/1033 [==============================] - 0s 197us/sample - loss: 1.0070e-04 - val_loss: 6.5608e-05\n",
            "Epoch 78/100\n",
            "1033/1033 [==============================] - 0s 200us/sample - loss: 1.0035e-04 - val_loss: 6.5590e-05\n",
            "Epoch 79/100\n",
            "1033/1033 [==============================] - 0s 208us/sample - loss: 1.0048e-04 - val_loss: 6.5571e-05\n",
            "Epoch 80/100\n",
            "1033/1033 [==============================] - 0s 217us/sample - loss: 1.0038e-04 - val_loss: 6.5579e-05\n",
            "Epoch 81/100\n",
            "1033/1033 [==============================] - 0s 204us/sample - loss: 1.0042e-04 - val_loss: 6.5575e-05\n",
            "Epoch 82/100\n",
            "1033/1033 [==============================] - 0s 198us/sample - loss: 1.0046e-04 - val_loss: 6.5576e-05\n",
            "Epoch 83/100\n",
            "1033/1033 [==============================] - 0s 197us/sample - loss: 1.0043e-04 - val_loss: 6.5589e-05\n",
            "Epoch 84/100\n",
            "1033/1033 [==============================] - 0s 204us/sample - loss: 1.0046e-04 - val_loss: 6.5606e-05\n",
            "Epoch 85/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0077e-04 - val_loss: 6.5593e-05\n",
            "Epoch 86/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0013e-04 - val_loss: 6.5593e-05\n",
            "Epoch 87/100\n",
            "1033/1033 [==============================] - 0s 195us/sample - loss: 1.0090e-04 - val_loss: 6.5612e-05\n",
            "Epoch 88/100\n",
            "1033/1033 [==============================] - 0s 199us/sample - loss: 1.0062e-04 - val_loss: 6.5616e-05\n",
            "Epoch 89/100\n",
            "1033/1033 [==============================] - 0s 205us/sample - loss: 1.0020e-04 - val_loss: 6.5622e-05\n",
            "Epoch 90/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0005e-04 - val_loss: 6.5604e-05\n",
            "Epoch 91/100\n",
            "1033/1033 [==============================] - 0s 204us/sample - loss: 1.0025e-04 - val_loss: 6.5594e-05\n",
            "Epoch 92/100\n",
            "1033/1033 [==============================] - 0s 216us/sample - loss: 1.0036e-04 - val_loss: 6.5606e-05\n",
            "Epoch 93/100\n",
            "1033/1033 [==============================] - 0s 199us/sample - loss: 9.9865e-05 - val_loss: 6.5617e-05\n",
            "Epoch 94/100\n",
            "1033/1033 [==============================] - 0s 209us/sample - loss: 1.0043e-04 - val_loss: 6.5621e-05\n",
            "Epoch 95/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0086e-04 - val_loss: 6.5626e-05\n",
            "Epoch 96/100\n",
            "1033/1033 [==============================] - 0s 203us/sample - loss: 1.0056e-04 - val_loss: 6.5624e-05\n",
            "Epoch 97/100\n",
            "1033/1033 [==============================] - 0s 206us/sample - loss: 1.0028e-04 - val_loss: 6.5647e-05\n",
            "Epoch 98/100\n",
            "1033/1033 [==============================] - 0s 200us/sample - loss: 1.0040e-04 - val_loss: 6.5624e-05\n",
            "Epoch 99/100\n",
            "1033/1033 [==============================] - 0s 221us/sample - loss: 1.0010e-04 - val_loss: 6.5639e-05\n",
            "Epoch 100/100\n",
            "1033/1033 [==============================] - 0s 213us/sample - loss: 1.0039e-04 - val_loss: 6.5660e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrhK9YPHtMXw",
        "colab_type": "text"
      },
      "source": [
        "## Training Log"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2_9ibR1e5DnQ",
        "colab_type": "code",
        "outputId": "2bdfad80-3f43-48ed-cb04-1a6cb5c710f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 968
        }
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuPd-Ukkrld7",
        "colab_type": "code",
        "outputId": "f05c0389-6f28-4d6c-8f1d-12bf1e4a5241",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 968
        }
      },
      "source": [
        "plt.plot(history.history['loss'][-50:])\n",
        "plt.plot(history.history['val_loss'][-50:])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2016x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L9JumIeP40xv",
        "colab_type": "code",
        "outputId": "1e8f0bea-7c38-4d0f-a9b7-b1597e366c6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "61/61 [==============================] - 0s 293us/sample - loss: 4.5586e-05\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4.558617681633208e-05"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RxROVKdbsUjD",
        "colab_type": "text"
      },
      "source": [
        "## Model Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ORsuXxm_ALZC",
        "colab_type": "code",
        "outputId": "736fef7a-fb2a-40b0-b567-b72d544b364b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_test.shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ziwDEBos01z_",
        "colab_type": "code",
        "outputId": "ccf429c0-2f8d-4bcc-c774-da96c2354409",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "y_hat = model.predict(X_test)\n",
        "df_test= df[-X_test.shape[0]:].reset_index()\n",
        "df_test"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>OPEN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>LOW</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>ADJ CLOSE</th>\n",
              "      <th>VOLUME</th>\n",
              "      <th>HIGH_tick</th>\n",
              "      <th>LOW_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>CLOSE-1</th>\n",
              "      <th>OPEN/LAST-1</th>\n",
              "      <th>OPEN+1</th>\n",
              "      <th>OPEN/HIGH</th>\n",
              "      <th>LOW/OPEN</th>\n",
              "      <th>HIGH_tick_std</th>\n",
              "      <th>LOW_tick_std</th>\n",
              "      <th>RANGE_TICKS_std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>329.6</td>\n",
              "      <td>330.2</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>13947162</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>331.0</td>\n",
              "      <td>0.995770</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.998183</td>\n",
              "      <td>0.985437</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-10-22</td>\n",
              "      <td>325.0</td>\n",
              "      <td>327.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>327.6</td>\n",
              "      <td>327.6</td>\n",
              "      <td>10448427</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>324.8</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.991458</td>\n",
              "      <td>0.999385</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-10-23</td>\n",
              "      <td>324.8</td>\n",
              "      <td>325.8</td>\n",
              "      <td>319.6</td>\n",
              "      <td>320.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>19855257</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>31</td>\n",
              "      <td>327.6</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>0.996931</td>\n",
              "      <td>0.983990</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-10-24</td>\n",
              "      <td>319.0</td>\n",
              "      <td>320.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>319.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>18472498</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.995009</td>\n",
              "      <td>0.992476</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>320.4</td>\n",
              "      <td>320.4</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>15789881</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>319.0</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.999368</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988140</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>400.0</td>\n",
              "      <td>406.4</td>\n",
              "      <td>397.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>27570261</td>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>45</td>\n",
              "      <td>398.6</td>\n",
              "      <td>1.003512</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.984252</td>\n",
              "      <td>0.993500</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>410.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>396.6</td>\n",
              "      <td>400.4</td>\n",
              "      <td>400.4</td>\n",
              "      <td>26827634</td>\n",
              "      <td>15</td>\n",
              "      <td>67</td>\n",
              "      <td>82</td>\n",
              "      <td>406.4</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>0.992736</td>\n",
              "      <td>0.967317</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>397.2</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.2</td>\n",
              "      <td>398.8</td>\n",
              "      <td>398.8</td>\n",
              "      <td>15938138</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>34</td>\n",
              "      <td>400.4</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>0.985608</td>\n",
              "      <td>0.997482</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>399.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.4</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>13770626</td>\n",
              "      <td>20</td>\n",
              "      <td>13</td>\n",
              "      <td>33</td>\n",
              "      <td>398.8</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990074</td>\n",
              "      <td>0.993484</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.6</td>\n",
              "      <td>396.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>13670846</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>23</td>\n",
              "      <td>400.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.015038</td>\n",
              "      <td>0.998502</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 18 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE   OPEN   HIGH  ...  HIGH_tick_std  LOW_tick_std  RANGE_TICKS_std\n",
              "0  2019-10-21  329.6  330.2  ...           0.03          0.24             0.27\n",
              "1  2019-10-22  325.0  327.8  ...           0.14          0.01             0.15\n",
              "2  2019-10-23  324.8  325.8  ...           0.05          0.26             0.31\n",
              "3  2019-10-24  319.0  320.6  ...           0.08          0.12             0.20\n",
              "4  2019-10-25  320.4  320.4  ...           0.00          0.19             0.19\n",
              "..        ...    ...    ...  ...            ...           ...              ...\n",
              "56 2020-01-13  400.0  406.4  ...           0.32          0.13             0.45\n",
              "57 2020-01-14  410.0  413.0  ...           0.15          0.67             0.82\n",
              "58 2020-01-15  397.2  403.0  ...           0.29          0.05             0.34\n",
              "59 2020-01-16  399.0  403.0  ...           0.20          0.13             0.33\n",
              "60 2020-01-17  400.0  400.6  ...           0.03          0.20             0.23\n",
              "\n",
              "[61 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pWeHU0Nc1DQW",
        "colab_type": "code",
        "outputId": "2ab81c4f-c8c7-4911-fccc-9f51b178f807",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "\n",
        "df_hat = pd.DataFrame(data =y_hat,columns=['OPEN/HIGH<hat>','LOW/OPEN<hat>'])\n",
        "df_hat"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OPEN/HIGH&lt;hat&gt;</th>\n",
              "      <th>LOW/OPEN&lt;hat&gt;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.990460</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.990460</td>\n",
              "      <td>0.988910</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.990456</td>\n",
              "      <td>0.988902</td>\n",
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              "      <th>3</th>\n",
              "      <td>0.990457</td>\n",
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              "      <th>4</th>\n",
              "      <td>0.990456</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <th>56</th>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988909</td>\n",
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              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988913</td>\n",
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              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>0.990467</td>\n",
              "      <td>0.988908</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988905</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>0.990483</td>\n",
              "      <td>0.988921</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    OPEN/HIGH<hat>  LOW/OPEN<hat>\n",
              "0         0.990460       0.988908\n",
              "1         0.990460       0.988910\n",
              "2         0.990456       0.988902\n",
              "3         0.990457       0.988905\n",
              "4         0.990456       0.988903\n",
              "..             ...            ...\n",
              "56        0.990469       0.988909\n",
              "57        0.990469       0.988913\n",
              "58        0.990467       0.988908\n",
              "59        0.990469       0.988905\n",
              "60        0.990483       0.988921\n",
              "\n",
              "[61 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lui1XTi0xVsF",
        "colab_type": "code",
        "outputId": "12bff4a2-c262-4d3c-8e7e-1533a5a5f903",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "df_compare=pd.concat([df_test, df_hat], axis=1)\n",
        "\n",
        "df_compare"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>OPEN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>LOW</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>ADJ CLOSE</th>\n",
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              "      <th>HIGH_tick</th>\n",
              "      <th>LOW_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>CLOSE-1</th>\n",
              "      <th>OPEN/LAST-1</th>\n",
              "      <th>OPEN+1</th>\n",
              "      <th>OPEN/HIGH</th>\n",
              "      <th>LOW/OPEN</th>\n",
              "      <th>HIGH_tick_std</th>\n",
              "      <th>LOW_tick_std</th>\n",
              "      <th>RANGE_TICKS_std</th>\n",
              "      <th>OPEN/HIGH&lt;hat&gt;</th>\n",
              "      <th>LOW/OPEN&lt;hat&gt;</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>329.6</td>\n",
              "      <td>330.2</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>13947162</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>331.0</td>\n",
              "      <td>0.995770</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.998183</td>\n",
              "      <td>0.985437</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.24</td>\n",
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              "      <td>0.990460</td>\n",
              "      <td>0.988908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-10-22</td>\n",
              "      <td>325.0</td>\n",
              "      <td>327.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>327.6</td>\n",
              "      <td>327.6</td>\n",
              "      <td>10448427</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>324.8</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.991458</td>\n",
              "      <td>0.999385</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.990460</td>\n",
              "      <td>0.988910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-10-23</td>\n",
              "      <td>324.8</td>\n",
              "      <td>325.8</td>\n",
              "      <td>319.6</td>\n",
              "      <td>320.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>19855257</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>31</td>\n",
              "      <td>327.6</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>0.996931</td>\n",
              "      <td>0.983990</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.990456</td>\n",
              "      <td>0.988902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-10-24</td>\n",
              "      <td>319.0</td>\n",
              "      <td>320.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>319.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>18472498</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.995009</td>\n",
              "      <td>0.992476</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.990457</td>\n",
              "      <td>0.988905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>320.4</td>\n",
              "      <td>320.4</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>15789881</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>319.0</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.999368</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988140</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.990456</td>\n",
              "      <td>0.988903</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>400.0</td>\n",
              "      <td>406.4</td>\n",
              "      <td>397.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>27570261</td>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>45</td>\n",
              "      <td>398.6</td>\n",
              "      <td>1.003512</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.984252</td>\n",
              "      <td>0.993500</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>410.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>396.6</td>\n",
              "      <td>400.4</td>\n",
              "      <td>400.4</td>\n",
              "      <td>26827634</td>\n",
              "      <td>15</td>\n",
              "      <td>67</td>\n",
              "      <td>82</td>\n",
              "      <td>406.4</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>0.992736</td>\n",
              "      <td>0.967317</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>397.2</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.2</td>\n",
              "      <td>398.8</td>\n",
              "      <td>398.8</td>\n",
              "      <td>15938138</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>34</td>\n",
              "      <td>400.4</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>0.985608</td>\n",
              "      <td>0.997482</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.990467</td>\n",
              "      <td>0.988908</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>399.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.4</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>13770626</td>\n",
              "      <td>20</td>\n",
              "      <td>13</td>\n",
              "      <td>33</td>\n",
              "      <td>398.8</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990074</td>\n",
              "      <td>0.993484</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.6</td>\n",
              "      <td>396.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>13670846</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>23</td>\n",
              "      <td>400.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.015038</td>\n",
              "      <td>0.998502</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.990483</td>\n",
              "      <td>0.988921</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 20 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE   OPEN   HIGH  ...  RANGE_TICKS_std  OPEN/HIGH<hat>  LOW/OPEN<hat>\n",
              "0  2019-10-21  329.6  330.2  ...             0.27        0.990460       0.988908\n",
              "1  2019-10-22  325.0  327.8  ...             0.15        0.990460       0.988910\n",
              "2  2019-10-23  324.8  325.8  ...             0.31        0.990456       0.988902\n",
              "3  2019-10-24  319.0  320.6  ...             0.20        0.990457       0.988905\n",
              "4  2019-10-25  320.4  320.4  ...             0.19        0.990456       0.988903\n",
              "..        ...    ...    ...  ...              ...             ...            ...\n",
              "56 2020-01-13  400.0  406.4  ...             0.45        0.990469       0.988909\n",
              "57 2020-01-14  410.0  413.0  ...             0.82        0.990469       0.988913\n",
              "58 2020-01-15  397.2  403.0  ...             0.34        0.990467       0.988908\n",
              "59 2020-01-16  399.0  403.0  ...             0.33        0.990469       0.988905\n",
              "60 2020-01-17  400.0  400.6  ...             0.23        0.990483       0.988921\n",
              "\n",
              "[61 rows x 20 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C3ppeF-0EN0q",
        "colab_type": "text"
      },
      "source": [
        "## From Prediction to Ticks (Practical Price)\n",
        "\n",
        "The prediction is actually OPEN based HIGH and LOW return. We need to convert it back to real price in the units of TICK.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQlxg3KWIScY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_compare['HIGH_hat']=np.floor((df_compare['OPEN']/df_compare['OPEN/HIGH<hat>'])/TICK_UNIT)*TICK_UNIT\n",
        "df_compare['LOW_hat']=np.ceil((df_compare['OPEN']*df_compare['LOW/OPEN<hat>'])/TICK_UNIT)*TICK_UNIT\n",
        "df_compare['HIGH_diff']=df_compare['HIGH_hat']-df_compare['HIGH']\n",
        "df_compare['LOW_diff']=df_compare['LOW_hat']-df_compare['LOW']\n",
        "df_compare['HIGH_diff_tick']=(np.round(df_compare['HIGH_diff']/TICK_UNIT)).astype('int32')\n",
        "df_compare['LOW_diff_tick']=(np.round(df_compare['LOW_diff']/TICK_UNIT)).astype('int32')\n",
        "df_compare['RANGE_TICKS_hat']=(np.round((df_compare['HIGH_hat']-df_compare['LOW_hat'])/TICK_UNIT)).astype('int32')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_q4Xc-l76nAT",
        "colab_type": "code",
        "outputId": "65de210e-0fd7-44b5-eeb4-95e25a34acab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "source": [
        "df_compare[['DATE', 'OPEN', 'CLOSE', 'HIGH', 'HIGH_hat','HIGH_diff_tick', 'LOW','LOW_hat','LOW_diff_tick','RANGE_TICKS','RANGE_TICKS_hat']]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>OPEN</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>HIGH_hat</th>\n",
              "      <th>HIGH_diff_tick</th>\n",
              "      <th>LOW</th>\n",
              "      <th>LOW_hat</th>\n",
              "      <th>LOW_diff_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>RANGE_TICKS_hat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>329.6</td>\n",
              "      <td>324.8</td>\n",
              "      <td>330.2</td>\n",
              "      <td>332.6</td>\n",
              "      <td>12</td>\n",
              "      <td>324.8</td>\n",
              "      <td>326.0</td>\n",
              "      <td>6</td>\n",
              "      <td>27</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-10-22</td>\n",
              "      <td>325.0</td>\n",
              "      <td>327.6</td>\n",
              "      <td>327.8</td>\n",
              "      <td>328.0</td>\n",
              "      <td>1</td>\n",
              "      <td>324.8</td>\n",
              "      <td>321.4</td>\n",
              "      <td>-17</td>\n",
              "      <td>15</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-10-23</td>\n",
              "      <td>324.8</td>\n",
              "      <td>320.0</td>\n",
              "      <td>325.8</td>\n",
              "      <td>327.8</td>\n",
              "      <td>10</td>\n",
              "      <td>319.6</td>\n",
              "      <td>321.2</td>\n",
              "      <td>8</td>\n",
              "      <td>31</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-10-24</td>\n",
              "      <td>319.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>320.6</td>\n",
              "      <td>322.0</td>\n",
              "      <td>7</td>\n",
              "      <td>316.6</td>\n",
              "      <td>315.6</td>\n",
              "      <td>-5</td>\n",
              "      <td>20</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>320.4</td>\n",
              "      <td>316.6</td>\n",
              "      <td>320.4</td>\n",
              "      <td>323.4</td>\n",
              "      <td>15</td>\n",
              "      <td>316.6</td>\n",
              "      <td>317.0</td>\n",
              "      <td>2</td>\n",
              "      <td>19</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>400.0</td>\n",
              "      <td>406.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>403.8</td>\n",
              "      <td>-13</td>\n",
              "      <td>397.4</td>\n",
              "      <td>395.6</td>\n",
              "      <td>-9</td>\n",
              "      <td>45</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>410.0</td>\n",
              "      <td>400.4</td>\n",
              "      <td>413.0</td>\n",
              "      <td>413.8</td>\n",
              "      <td>4</td>\n",
              "      <td>396.6</td>\n",
              "      <td>405.6</td>\n",
              "      <td>45</td>\n",
              "      <td>82</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>397.2</td>\n",
              "      <td>398.8</td>\n",
              "      <td>403.0</td>\n",
              "      <td>401.0</td>\n",
              "      <td>-10</td>\n",
              "      <td>396.2</td>\n",
              "      <td>392.8</td>\n",
              "      <td>-17</td>\n",
              "      <td>34</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>399.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>402.8</td>\n",
              "      <td>-1</td>\n",
              "      <td>396.4</td>\n",
              "      <td>394.6</td>\n",
              "      <td>-9</td>\n",
              "      <td>33</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>400.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>400.6</td>\n",
              "      <td>403.8</td>\n",
              "      <td>16</td>\n",
              "      <td>396.0</td>\n",
              "      <td>395.6</td>\n",
              "      <td>-2</td>\n",
              "      <td>23</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE   OPEN  CLOSE  ...  LOW_diff_tick  RANGE_TICKS  RANGE_TICKS_hat\n",
              "0  2019-10-21  329.6  324.8  ...              6           27               33\n",
              "1  2019-10-22  325.0  327.6  ...            -17           15               33\n",
              "2  2019-10-23  324.8  320.0  ...              8           31               33\n",
              "3  2019-10-24  319.0  319.0  ...             -5           20               32\n",
              "4  2019-10-25  320.4  316.6  ...              2           19               32\n",
              "..        ...    ...    ...  ...            ...          ...              ...\n",
              "56 2020-01-13  400.0  406.4  ...             -9           45               41\n",
              "57 2020-01-14  410.0  400.4  ...             45           82               41\n",
              "58 2020-01-15  397.2  398.8  ...            -17           34               41\n",
              "59 2020-01-16  399.0  400.0  ...             -9           33               41\n",
              "60 2020-01-17  400.0  399.0  ...             -2           23               41\n",
              "\n",
              "[61 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBh4_QDz8ARz",
        "colab_type": "code",
        "outputId": "d5fc458e-2be2-4711-925c-b5f35bf939ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        }
      },
      "source": [
        "df_compare"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>DATE</th>\n",
              "      <th>OPEN</th>\n",
              "      <th>HIGH</th>\n",
              "      <th>LOW</th>\n",
              "      <th>CLOSE</th>\n",
              "      <th>ADJ CLOSE</th>\n",
              "      <th>VOLUME</th>\n",
              "      <th>HIGH_tick</th>\n",
              "      <th>LOW_tick</th>\n",
              "      <th>RANGE_TICKS</th>\n",
              "      <th>CLOSE-1</th>\n",
              "      <th>OPEN/LAST-1</th>\n",
              "      <th>OPEN+1</th>\n",
              "      <th>OPEN/HIGH</th>\n",
              "      <th>LOW/OPEN</th>\n",
              "      <th>HIGH_tick_std</th>\n",
              "      <th>LOW_tick_std</th>\n",
              "      <th>RANGE_TICKS_std</th>\n",
              "      <th>OPEN/HIGH&lt;hat&gt;</th>\n",
              "      <th>LOW/OPEN&lt;hat&gt;</th>\n",
              "      <th>HIGH_hat</th>\n",
              "      <th>LOW_hat</th>\n",
              "      <th>HIGH_diff</th>\n",
              "      <th>LOW_diff</th>\n",
              "      <th>HIGH_diff_tick</th>\n",
              "      <th>LOW_diff_tick</th>\n",
              "      <th>RANGE_TICKS_hat</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-10-21</td>\n",
              "      <td>329.6</td>\n",
              "      <td>330.2</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>13947162</td>\n",
              "      <td>3</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>331.0</td>\n",
              "      <td>0.995770</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.998183</td>\n",
              "      <td>0.985437</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.990460</td>\n",
              "      <td>0.988908</td>\n",
              "      <td>332.6</td>\n",
              "      <td>326.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1.2</td>\n",
              "      <td>12</td>\n",
              "      <td>6</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-10-22</td>\n",
              "      <td>325.0</td>\n",
              "      <td>327.8</td>\n",
              "      <td>324.8</td>\n",
              "      <td>327.6</td>\n",
              "      <td>327.6</td>\n",
              "      <td>10448427</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>15</td>\n",
              "      <td>324.8</td>\n",
              "      <td>1.000616</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.991458</td>\n",
              "      <td>0.999385</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.990460</td>\n",
              "      <td>0.988910</td>\n",
              "      <td>328.0</td>\n",
              "      <td>321.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>1</td>\n",
              "      <td>-17</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-10-23</td>\n",
              "      <td>324.8</td>\n",
              "      <td>325.8</td>\n",
              "      <td>319.6</td>\n",
              "      <td>320.0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>19855257</td>\n",
              "      <td>5</td>\n",
              "      <td>26</td>\n",
              "      <td>31</td>\n",
              "      <td>327.6</td>\n",
              "      <td>0.991453</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>0.996931</td>\n",
              "      <td>0.983990</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.990456</td>\n",
              "      <td>0.988902</td>\n",
              "      <td>327.8</td>\n",
              "      <td>321.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>10</td>\n",
              "      <td>8</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-10-24</td>\n",
              "      <td>319.0</td>\n",
              "      <td>320.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>319.0</td>\n",
              "      <td>319.0</td>\n",
              "      <td>18472498</td>\n",
              "      <td>8</td>\n",
              "      <td>12</td>\n",
              "      <td>20</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0.996875</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.995009</td>\n",
              "      <td>0.992476</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.12</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.990457</td>\n",
              "      <td>0.988905</td>\n",
              "      <td>322.0</td>\n",
              "      <td>315.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>-5</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-10-25</td>\n",
              "      <td>320.4</td>\n",
              "      <td>320.4</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>316.6</td>\n",
              "      <td>15789881</td>\n",
              "      <td>0</td>\n",
              "      <td>19</td>\n",
              "      <td>19</td>\n",
              "      <td>319.0</td>\n",
              "      <td>1.004389</td>\n",
              "      <td>0.999368</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.988140</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.990456</td>\n",
              "      <td>0.988903</td>\n",
              "      <td>323.4</td>\n",
              "      <td>317.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>400.0</td>\n",
              "      <td>406.4</td>\n",
              "      <td>397.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>406.4</td>\n",
              "      <td>27570261</td>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>45</td>\n",
              "      <td>398.6</td>\n",
              "      <td>1.003512</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.984252</td>\n",
              "      <td>0.993500</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988909</td>\n",
              "      <td>403.8</td>\n",
              "      <td>395.6</td>\n",
              "      <td>-2.6</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-13</td>\n",
              "      <td>-9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>410.0</td>\n",
              "      <td>413.0</td>\n",
              "      <td>396.6</td>\n",
              "      <td>400.4</td>\n",
              "      <td>400.4</td>\n",
              "      <td>26827634</td>\n",
              "      <td>15</td>\n",
              "      <td>67</td>\n",
              "      <td>82</td>\n",
              "      <td>406.4</td>\n",
              "      <td>1.008858</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>0.992736</td>\n",
              "      <td>0.967317</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988913</td>\n",
              "      <td>413.8</td>\n",
              "      <td>405.6</td>\n",
              "      <td>0.8</td>\n",
              "      <td>9.0</td>\n",
              "      <td>4</td>\n",
              "      <td>45</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>2020-01-15</td>\n",
              "      <td>397.2</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.2</td>\n",
              "      <td>398.8</td>\n",
              "      <td>398.8</td>\n",
              "      <td>15938138</td>\n",
              "      <td>29</td>\n",
              "      <td>5</td>\n",
              "      <td>34</td>\n",
              "      <td>400.4</td>\n",
              "      <td>0.992008</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>0.985608</td>\n",
              "      <td>0.997482</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.990467</td>\n",
              "      <td>0.988908</td>\n",
              "      <td>401.0</td>\n",
              "      <td>392.8</td>\n",
              "      <td>-2.0</td>\n",
              "      <td>-3.4</td>\n",
              "      <td>-10</td>\n",
              "      <td>-17</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>2020-01-16</td>\n",
              "      <td>399.0</td>\n",
              "      <td>403.0</td>\n",
              "      <td>396.4</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.0</td>\n",
              "      <td>13770626</td>\n",
              "      <td>20</td>\n",
              "      <td>13</td>\n",
              "      <td>33</td>\n",
              "      <td>398.8</td>\n",
              "      <td>1.000502</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.990074</td>\n",
              "      <td>0.993484</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.990469</td>\n",
              "      <td>0.988905</td>\n",
              "      <td>402.8</td>\n",
              "      <td>394.6</td>\n",
              "      <td>-0.2</td>\n",
              "      <td>-1.8</td>\n",
              "      <td>-1</td>\n",
              "      <td>-9</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>2020-01-17</td>\n",
              "      <td>400.0</td>\n",
              "      <td>400.6</td>\n",
              "      <td>396.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>399.0</td>\n",
              "      <td>13670846</td>\n",
              "      <td>3</td>\n",
              "      <td>20</td>\n",
              "      <td>23</td>\n",
              "      <td>400.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.015038</td>\n",
              "      <td>0.998502</td>\n",
              "      <td>0.990000</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.23</td>\n",
              "      <td>0.990483</td>\n",
              "      <td>0.988921</td>\n",
              "      <td>403.8</td>\n",
              "      <td>395.6</td>\n",
              "      <td>3.2</td>\n",
              "      <td>-0.4</td>\n",
              "      <td>16</td>\n",
              "      <td>-2</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61 rows × 27 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         DATE   OPEN   HIGH  ...  HIGH_diff_tick  LOW_diff_tick  RANGE_TICKS_hat\n",
              "0  2019-10-21  329.6  330.2  ...              12              6               33\n",
              "1  2019-10-22  325.0  327.8  ...               1            -17               33\n",
              "2  2019-10-23  324.8  325.8  ...              10              8               33\n",
              "3  2019-10-24  319.0  320.6  ...               7             -5               32\n",
              "4  2019-10-25  320.4  320.4  ...              15              2               32\n",
              "..        ...    ...    ...  ...             ...            ...              ...\n",
              "56 2020-01-13  400.0  406.4  ...             -13             -9               41\n",
              "57 2020-01-14  410.0  413.0  ...               4             45               41\n",
              "58 2020-01-15  397.2  403.0  ...             -10            -17               41\n",
              "59 2020-01-16  399.0  403.0  ...              -1             -9               41\n",
              "60 2020-01-17  400.0  400.6  ...              16             -2               41\n",
              "\n",
              "[61 rows x 27 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_6kij5hUdOv4",
        "colab_type": "text"
      },
      "source": [
        "## The Results Are Not Good Although the Training Seems Good"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pgfsSwLp_mM4",
        "colab_type": "text"
      },
      "source": [
        "# How to improve\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X7eSmaUrlTRO",
        "colab_type": "text"
      },
      "source": [
        "## Data\n",
        "\n",
        "To improve the effectiveness of prediction, we need to add more data:\n",
        "1. Instead of a single name, we need a big pool of names to train the model. The single name provides very limited data, which is very limited to train a model. Also, we will use the model to predict not only a single name but also for other names. And it’s better to have the data of the market index.\n",
        "\n",
        "2. Instead of real HIGH and LOW values, we may need a more practical ones. The HIGH and LOW are extreme data, which is not stable and could be impacted by 1 price spike. Even we have predicted the extreme data correctly, which may be not feasible to fill an order. So instead of to predicate the HIGH and LOW, we may predicate the 95% confidence range based on the VWAP. Which could be more realistic.\n",
        "\n",
        "3. Instead of a daily volume, we use an array of volume at price for the day. People believe that more volume means more evidence. So the extreme data (high/ low) can be predicted with more confidence.  \n",
        "\n",
        "4. More reliable data, such as VWAP of AM, PM."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PiUpZQLblof_",
        "colab_type": "text"
      },
      "source": [
        "## Model\n",
        "\n",
        "1. We may try other non-machine learning models, such as Fractionally cointegrated vector autoregressive model.\n",
        "\n",
        "2. We may try other machine learning models, even auto-AI package.\n",
        "\n",
        "3. We may tune hyperparameters.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Hn5MIcaSVpz",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "This repository was digested from a big project I did. In that project more feature engineering and extra model have been done and the results are good.\n",
        "\n",
        "In this repsitory I didn't tune the hyperparameters yet. This is just for demo, but aim for a practical usage."
      ]
    }
  ]
}