final_HMM.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SamruddhiMetkar/stock-price-forecasting/blob/main/final_HMM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uSfnSw-jFbwx",
        "outputId": "7177e884-63ba-4365-91b0-3b652b0b4661"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting hmmlearn\n",
            "  Downloading hmmlearn-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (161 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/161.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━\u001b[0m \u001b[32m153.6/161.1 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m161.1/161.1 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.10 in /usr/local/lib/python3.10/dist-packages (from hmmlearn) (1.25.2)\n",
            "Requirement already satisfied: scikit-learn!=0.22.0,>=0.16 in /usr/local/lib/python3.10/dist-packages (from hmmlearn) (1.2.2)\n",
            "Requirement already satisfied: scipy>=0.19 in /usr/local/lib/python3.10/dist-packages (from hmmlearn) (1.11.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.22.0,>=0.16->hmmlearn) (1.4.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn!=0.22.0,>=0.16->hmmlearn) (3.4.0)\n",
            "Installing collected packages: hmmlearn\n",
            "Successfully installed hmmlearn-0.3.2\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 252/252 [06:05<00:00,  1.45s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "worst baseline HMM 2.026084083090855\n",
            "current baseline HMM 1.9931715748057708\n",
            "Running time of HMM: 365.65849256515503\n",
            "worst baseline KF 2.026084083090855\n",
            "current baseline KF 1.9661323496348688\n",
            "Running time of KF: 0.0016841888427734375\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 252/252 [06:08<00:00,  1.46s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "worst baseline HMM 2.026084083090855\n",
            "current baseline HMM 1.9976187708405726\n",
            "Running time of HMM: 368.56691813468933\n",
            "Mean Absolute Percentage Error (KF) in log scale: 0.33872678965847763\n",
            "Mean Absolute Percentage Error (HMM) in log scale: 0.3443339381687143\n",
            "Mean SE (HMM) in log scale: 0.0008541115791004633\n",
            "Mean SE (KF) in log scale: 0.0008074767135454761\n",
            "RMSE (KF) in log scale: 0.028416134739712155\n",
            "RMSE (HMM) in log scale: 0.029225187409158956\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x672 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1280x672 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"Hidden Markov Model\n",
        "\n",
        "Automatically generated by Colaboratory.\n",
        "\n",
        "Original file is located at\n",
        "    https://colab.research.google.com/drive/1iMQgMTNYtyw5_rppw4pUG3-qTqkk4z3C\n",
        "\"\"\"\n",
        "\n",
        "!pip install hmmlearn\n",
        "from hmmlearn.hmm import GaussianHMM\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from hmmlearn.hmm import GaussianHMM\n",
        "import itertools\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "plt.rcParams.update({'font.size': 14})\n",
        "\n",
        "## load the data and split them to train and test\n",
        "data = pd.read_csv(\"/content/ADANIPOWER.NS.csv\")\n",
        "\n",
        "# We omit the dates, adjusted close and the volume columns. And we take only the data for 2018 and 2019\n",
        "data = data.values[-555:,:-2]\n",
        "\n",
        "# We train on 2018 data and we consider only open and high price\n",
        "train = data[:-252, 1:5]\n",
        "\n",
        "# We test on 2019 data\n",
        "test = data[-252:, 1:5]\n",
        "\n",
        "# We train on 2018 data and we consider only open and high price\n",
        "train_X = train[:,0]\n",
        "train_Y = train[:,3]\n",
        "# We test on 2019 data\n",
        "test_X = test[:,0]\n",
        "test_Y = test[:,3]\n",
        "\n",
        "\n",
        "def transform_train_data(data):\n",
        "        # frac_change = (close - open) / open\n",
        "        #These lines compute the fractional changes, highs, and lows based on the input data. Specifically,\n",
        "        #frac_change computes the fractional change in closing price, frac_high computes the fractional change in high price, and frac_low computes the fractional change in low price.\n",
        "        frac_change = (data[:,3] - data[:,0]) / data[:,0]\n",
        "\n",
        "        # frac_high = (high - open) / open\n",
        "        frac_high = (data[:,1] - data[:,0]) / data[:,0]\n",
        "\n",
        "        # frac_low = (open - low) / open\n",
        "        frac_low = (data[:,0] - data[:,2]) / data[:,0]\n",
        "\n",
        "        return np.vstack((frac_change, frac_high, frac_low)).T\n",
        "\n",
        "## train the model\n",
        "class HMM_model():\n",
        "    def __init__(self, n_components):\n",
        "      #This line initializes the GaussianHMM model with the specified number of components (n_components).\n",
        "        self.model = GaussianHMM(n_components=n_components, algorithm='map', covariance_type='diag')\n",
        "\n",
        "    def transform_data(self, data):\n",
        "        # frac_change = (close - open) / open\n",
        "        frac_change = (data[:,3] - data[:,0]) / data[:,0]\n",
        "\n",
        "        # frac_high = (high - open) / open\n",
        "        frac_high = (data[:,1] - data[:,0]) / data[:,0]\n",
        "\n",
        "        # frac_low = (open - low) / open\n",
        "        frac_low = (data[:,0] - data[:,2]) / data[:,0]\n",
        "\n",
        "        return np.vstack((frac_change, frac_high, frac_low)).T\n",
        "\n",
        "    def fit(self, train_data, sequence_len=-1):\n",
        "        train_preprocessed = self.transform_data(train_data)\n",
        "        #This line preprocesses the training data using the transform_data method defined earlier.\n",
        "        if sequence_len != -1:\n",
        "            lengths = divide_to_seq(train_preprocessed, sequence_len)\n",
        "            self.model.fit(train_preprocessed, lengths)\n",
        "        else:\n",
        "            self.model.fit(train_preprocessed)\n",
        "\n",
        "    def compute_predecessors(self, train, test, test_case_indice, latency):\n",
        "        if test_case_indice == 0:\n",
        "            predecessors = train[-latency:]\n",
        "            return predecessors\n",
        "        elif test_case_indice < latency:\n",
        "            predecessors = np.concatenate((train[- latency + test_case_indice:], test[:test_case_indice]), axis=0)\n",
        "            return predecessors\n",
        "        elif test_case_indice >= latency:\n",
        "            predecessors = test[test_case_indice - latency : test_case_indice]\n",
        "            return predecessors\n",
        "\n",
        "    def predict_for_one(self, predecessors, combinations):\n",
        "        scores = []\n",
        "        # compute the scores using the MAP\n",
        "        for elt in combinations:\n",
        "            elt_to_score = np.concatenate((predecessors, elt.reshape(-1,3)), axis=0)\n",
        "            score = self.model.score(elt_to_score)\n",
        "            scores.append(score)\n",
        "        # returning the argmax\n",
        "        scores = np.array(scores)\n",
        "        return combinations[np.argmax(scores)]\n",
        "\n",
        "    def predict(self, train_data, test_data, latency):\n",
        "        # span the possible values\n",
        "        frac_change_space = np.linspace(-0.1, 0.1, 50)\n",
        "        frac_high_space = np.linspace(0, 0.1, 10)\n",
        "        frac_low_space = np.linspace(0, 0.1, 10)\n",
        "\n",
        "        # all possible combinations\n",
        "        combinations = np.array(list(itertools.product(*[frac_change_space, frac_high_space, frac_low_space])))\n",
        "\n",
        "        # transform the old train and test to get the new features\n",
        "        train_data_preprocessed = self.transform_data(train_data)\n",
        "        test_data_preprocessed = self.transform_data(test_data)\n",
        "\n",
        "        predictions = []\n",
        "        l = len(test_data_preprocessed)\n",
        "        for i in tqdm(range(l)):\n",
        "            predecessors = self.compute_predecessors(train_data_preprocessed, test_data_preprocessed, i, latency=latency)\n",
        "            pred = self.predict_for_one(predecessors, combinations)\n",
        "            predictions.append(pred)\n",
        "\n",
        "        predictions = np.vstack(predictions)\n",
        "\n",
        "        return predictions\n",
        "\n",
        "def divide_to_seq(data, len_):\n",
        "    n = int(len(data)/len_)\n",
        "    lengths = []\n",
        "\n",
        "    for i in range(n):\n",
        "        elt = data[i*len_: i*len_+len_]\n",
        "        lengths.append(len(elt))\n",
        "\n",
        "    if n*len_ != len(data):\n",
        "        elt = data[n*len_:]\n",
        "        lengths.append(len(elt))\n",
        "\n",
        "    return lengths\n",
        "\n",
        "## test the model\n",
        "def test_model_make_plots(train, test, n_components=5, latency=10, sequence_len=-1):\n",
        "    since = time.time()\n",
        "\n",
        "    model = HMM_model(n_components)\n",
        "    model.fit(train, sequence_len)\n",
        "    predictions = model.predict(train, test, latency=latency)\n",
        "\n",
        "    time_elapsed = time.time() - since\n",
        "\n",
        "    predicted_close = predictions[:,0] * test[:, 0] + test[:, 0]\n",
        "\n",
        "    plt.figure(figsize=(6.4*2, 4.8*1.4))\n",
        "    plt.plot(np.arange(len(test[:50])), test[:50, 3])\n",
        "    plt.plot(np.arange(len(predicted_close[:50])), predicted_close[:50])\n",
        "    '''plt.legend(['True values', 'Predicted values'])\n",
        "    plt.xlabel('Timesteps')\n",
        "    plt.ylabel('Close price')\n",
        "    plt.grid()'''\n",
        "\n",
        "    # we define the worst baseline as the opening price, if you can't do better you are better off predicting this price\n",
        "    print('worst baseline HMM', np.mean(np.abs((test[:, 3] - test[:, 0]) / test[:, 3])) * 100)\n",
        "    print('current baseline HMM', np.mean(np.abs((test[:, 3] - predicted_close) / test[:, 3])) * 100)\n",
        "    print('Running time of HMM:', time_elapsed)\n",
        "    return predictions\n",
        "    print('Predictions:', predicted_close)\n",
        "\n",
        "\n",
        "def pre_filtering(x, a, b, c, f):\n",
        "\n",
        "  T = len(x)\n",
        "\n",
        "  mu = np.zeros(shape=(T))\n",
        "  p = np.zeros(shape=(T))\n",
        "  k = np.zeros(shape=(T))\n",
        "  p[0] = 10000\n",
        "  mu[0] = x[0]\n",
        "\n",
        "  # example we start by \\mu_{1|0} and \\p_{1|0} assuming we have x = (x_1, x_2,..., x_N) a vector of N elements\n",
        "  for t in range(1,T):\n",
        "    mu[t] = a + b*mu[t-1]\n",
        "    p[t] = (b**2) * p[t-1] + c**2\n",
        "    k[t] = p[t] / (p[t] + f**2)\n",
        "\n",
        "  return mu, p, k\n",
        "\n",
        "def filtering(x, y, a, b, c, f):\n",
        "  T = len(y)\n",
        "  mu, p, k = pre_filtering(x, a, b, c, f)\n",
        "\n",
        "  mu_filt = np.zeros(shape=(T))\n",
        "  p_filt = np.zeros(shape=(T))\n",
        "\n",
        "  for t in range(T):\n",
        "    mu_filt[t] = mu[t] + k[t]*(y[t] - mu[t])\n",
        "    p_filt[t] = (f**2) * k[t]\n",
        "\n",
        "  return mu_filt, p_filt\n",
        "\n",
        "def initialize_params(x, y):\n",
        "    T = len(y)\n",
        "\n",
        "    x_t = x[1:]\n",
        "    x_t_1 = x[:-1]\n",
        "\n",
        "    f_2 = ((y - x)**2).sum() / len(y)\n",
        "    b = ((T-1)*(x_t * x_t_1).sum() - x_t.sum() * x_t_1.sum())/((T-1)*((x_t_1**2).sum()) - x_t_1.sum()**2)\n",
        "    a = (x_t - b*x_t_1).sum() / (T-1)\n",
        "    c_2 = ((x_t - a - b*x_t_1)**2).sum() / (T-1)\n",
        "\n",
        "    return a, b, c_2, f_2\n",
        "\n",
        "test_model_make_plots(train, test, latency=10, sequence_len = -1)\n",
        "\n",
        "x_train = train_Y\n",
        "y_train = train_X\n",
        "x_test = test_Y\n",
        "y_test = test_X\n",
        "\n",
        "since_kf = time.time()\n",
        "\n",
        "a, b, c_2, f_2 = initialize_params(x_train, y_train)\n",
        "\n",
        "mu_fore, p_fore = filtering(x_test, y_test, a, b, np.sqrt(c_2), np.sqrt(f_2))\n",
        "\n",
        "time_elapsed_kf = time.time() - since_kf\n",
        "\n",
        "plt.plot(np.arange(50), mu_fore[:50])\n",
        "plt.fill_between(np.arange(50), mu_fore[:50] - p_fore[:50] / 2, mu_fore[:50] + p_fore[:50] / 2, color='gray', alpha=0.2)\n",
        "plt.legend(['True values', 'Predicted values HMM', 'Predicted values KF'])\n",
        "plt.xlabel('Timesteps')\n",
        "plt.ylabel('Close price')\n",
        "plt.grid()\n",
        "\n",
        "print('worst baseline KF', np.mean(np.abs((x_test - test[:,0]) / x_test))* 100)\n",
        "print('current baseline KF', np.mean(np.abs((x_test - mu_fore) / x_test))* 100)\n",
        "print('Running time of KF:', time_elapsed_kf)\n",
        "# # Convert x_test to a NumPy array with float64 data type\n",
        "# x_test_np = np.array(x_test, dtype=np.float64)\n",
        "\n",
        "# # Calculate the accuracy metrics in log scale\n",
        "# log_true_values = np.log(x_test_np)\n",
        "# log_predicted_values = np.log(mu_fore)\n",
        "\n",
        "# log_mae = np.mean(np.abs(log_true_values - log_predicted_values))\n",
        "# log_mse = np.mean((log_true_values - log_predicted_values)**2)\n",
        "# log_rmse = np.sqrt(log_mse)\n",
        "# log_mape = np.mean(np.abs((log_true_values - log_predicted_values) / log_true_values)) * 100\n",
        "\n",
        "# print(log_mae,log_mse,log_rmse,log_mape)\n",
        "\n",
        "\n",
        "# def test_model_make_plots(train, test, n_components=5, latency=10, sequence_len=-1):\n",
        "#     since = time.time()\n",
        "\n",
        "#     model = HMM_model(n_components)\n",
        "#     model.fit(train, sequence_len)\n",
        "#     predictions = model.predict(train, test, latency=latency)\n",
        "\n",
        "#     time_elapsed = time.time() - since\n",
        "\n",
        "#     predicted_close = predictions[:,0] * test[:, 0] + test[:, 0]\n",
        "\n",
        "#     plt.figure(figsize=(6.4*2, 4.8*1.4))\n",
        "#     plt.plot(np.arange(len(test[:50])), test[:50, 3])\n",
        "#     plt.plot(np.arange(len(predicted_close[:50])), predicted_close[:50])\n",
        "\n",
        "#     # we define the worst baseline as the opening price, if you can't do better you are better off predicting this price\n",
        "#     print('worst baseline HMM', np.mean(np.abs((test[:, 3] - test[:, 0]) / test[:, 3])) * 100)\n",
        "#     print('current baseline HMM', np.mean(np.abs((test[:, 3] - predicted_close) / test[:, 3])) * 100)\n",
        "#     print('Running time of HMM:', time_elapsed)\n",
        "\n",
        "#     return predictions  # return the predictions\n",
        "\n",
        "\n",
        "\n",
        "#Call the function to get predictions\n",
        "predictions = test_model_make_plots(train, test, latency=10, sequence_len=-1)\n",
        "\n",
        "predicted_close = predictions[:,0] * test[:, 0] + test[:, 0]\n",
        "\n",
        "# Convert x_test to a NumPy array with float64 data type\n",
        "x_test_np = np.array(x_test, dtype=np.float64)\n",
        "predicted_close_np = np.array(predicted_close, dtype=np.float64)\n",
        "# Calculate the natural logarithm of the test data and predicted values\n",
        "log_true_values = np.log(x_test_np)\n",
        "log_predicted_values_kf = np.log(mu_fore)\n",
        "#log_predicted_values_hmm = np.log(predicted_close)\n",
        "log_predicted_values_hmm = np.log(predicted_close_np)\n",
        "\n",
        "# Calculate the absolute percentage error for KF predictions in log scale\n",
        "abs_percentage_error_kf = np.abs((log_true_values - log_predicted_values_kf) / log_true_values) * 100\n",
        "\n",
        "# Calculate the absolute percentage error for HMM predictions in log scale\n",
        "abs_percentage_error_hmm = np.abs((log_true_values - log_predicted_values_hmm) / log_true_values) * 100\n",
        "\n",
        "# Calculate the mean absolute percentage error for KF predictions in log scale\n",
        "mean_abs_percentage_error_kf = np.mean(abs_percentage_error_kf)\n",
        "\n",
        "# Calculate the mean absolute percentage error for HMM predictions in log scale\n",
        "mean_abs_percentage_error_hmm = np.mean(abs_percentage_error_hmm)\n",
        "\n",
        "\n",
        "log_mse_kf = np.mean((log_true_values - log_predicted_values_kf)**2)\n",
        "log_mse_hmm = np.mean((log_true_values - log_predicted_values_hmm)**2)\n",
        "\n",
        "log_rmse_hmm = np.sqrt(log_mse_hmm)\n",
        "log_rmse_kf = np.sqrt(log_mse_kf)\n",
        "\n",
        "print('Mean Absolute Percentage Error (KF) in log scale:', mean_abs_percentage_error_kf)\n",
        "print('Mean Absolute Percentage Error (HMM) in log scale:', mean_abs_percentage_error_hmm)\n",
        "print('Mean SE (HMM) in log scale:',log_mse_hmm )\n",
        "\n",
        "print('Mean SE (KF) in log scale:', log_mse_kf)\n",
        "print('RMSE (KF) in log scale:', log_rmse_kf)\n",
        "print('RMSE (HMM) in log scale:',log_rmse_hmm )\n",
        "\n",
        "# # Calculate the absolute percentage error for KF predictions in log scale\n",
        "# abs_percentage_error_kf = np.abs((x_test - mu_fore) / x_test) * 100\n",
        "\n",
        "# # Calculate the absolute percentage error for HMM predictions in log scale\n",
        "# abs_percentage_error_hmm = np.abs((test[:, 3] - predicted_close) / test[:, 3]) * 100\n",
        "\n",
        "# # Calculate the mean absolute percentage error for KF predictions in log scale\n",
        "# mean_abs_percentage_error_kf = np.mean(abs_percentage_error_kf)\n",
        "\n",
        "# # Calculate the mean absolute percentage error for HMM predictions in log scale\n",
        "# mean_abs_percentage_error_hmm = np.mean(abs_percentage_error_hmm)\n",
        "\n",
        "# print('Mean Absolute Percentage Error (KF) in log scale:', mean_abs_percentage_error_kf)\n",
        "# print('Mean Absolute Percentage Error (HMM) in log scale:', mean_abs_percentage_error_hmm)\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RauRhIMMGHsm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}