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LSTM Stock Analysis
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lstm_stock_analysis.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "3.stock_prediction.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/ptenteromano/stock-analysis/blob/master/lstm_crypto_and_stock.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sHEdIovSDZ0l",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "198b3a5f-f006-4a53-e25f-8964de2095dd"
},
"source": [
"%tensorflow_version 2.x\n",
"!pip uninstall -y tensorflow\n",
"!pip install tensorflow-gpu==2.0.0-alpha0\n",
"!pip install fastquant"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
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"Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt>=1.30.59->fastquant) (2.20)\n",
"Building wheels for collected packages: yfinance\n",
" Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for yfinance: filename=yfinance-0.1.59-py2.py3-none-any.whl size=23455 sha256=a7d8b6a9634fb90761fa66c82b3f02a74ad73756de667a7b9ec34181c06948ce\n",
" Stored in directory: /root/.cache/pip/wheels/f8/2a/0f/4b5a86e1d52e451757eb6bc17fd899629f0925c777741b6d04\n",
"Successfully built yfinance\n",
"\u001b[31mERROR: kapre 0.3.5 requires tensorflow>=2.0.0, which is not installed.\u001b[0m\n",
"\u001b[31mERROR: requests 2.23.0 has requirement urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1, but you'll have urllib3 1.26.6 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: black 21.6b0 has requirement regex>=2020.1.8, but you'll have regex 2019.12.20 which is incompatible.\u001b[0m\n",
"Installing collected packages: multidict, yarl, async-timeout, aiohttp, pycares, aiodns, cryptography, ccxt, cfgv, distlib, virtualenv, identify, nodeenv, pyyaml, pre-commit, nltk, urllib3, pytz, soupsieve, beautifulsoup4, pathspec, mypy-extensions, typed-ast, black, lxml, backtrader, yfinance, croniter, fastquant\n",
" Found existing installation: PyYAML 3.13\n",
" Uninstalling PyYAML-3.13:\n",
" Successfully uninstalled PyYAML-3.13\n",
" Found existing installation: nltk 3.2.5\n",
" Uninstalling nltk-3.2.5:\n",
" Successfully uninstalled nltk-3.2.5\n",
" Found existing installation: urllib3 1.24.3\n",
" Uninstalling urllib3-1.24.3:\n",
" Successfully uninstalled urllib3-1.24.3\n",
" Found existing installation: pytz 2018.9\n",
" Uninstalling pytz-2018.9:\n",
" Successfully uninstalled pytz-2018.9\n",
" Found existing installation: beautifulsoup4 4.6.3\n",
" Uninstalling beautifulsoup4-4.6.3:\n",
" Successfully uninstalled beautifulsoup4-4.6.3\n",
" Found existing installation: lxml 4.2.6\n",
" Uninstalling lxml-4.2.6:\n",
" Successfully uninstalled lxml-4.2.6\n",
"Successfully installed aiodns-2.0.0 aiohttp-3.7.4.post0 async-timeout-3.0.1 backtrader-1.9.76.123 beautifulsoup4-4.9.3 black-21.6b0 ccxt-1.52.18 cfgv-3.3.0 croniter-1.0.15 cryptography-3.4.7 distlib-0.3.2 fastquant-0.1.5.0 identify-2.2.10 lxml-4.6.3 multidict-5.1.0 mypy-extensions-0.4.3 nltk-3.6.2 nodeenv-1.6.0 pathspec-0.8.1 pre-commit-2.13.0 pycares-4.0.0 pytz-2021.1 pyyaml-5.4.1 soupsieve-2.2.1 typed-ast-1.4.3 urllib3-1.26.6 virtualenv-20.4.7 yarl-1.6.3 yfinance-0.1.59\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pytz",
"urllib3",
"yaml"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "VBXq-Wb_K1XR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9847e3d9-3f94-4a81-b6a4-e96fade6ee73"
},
"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 sklearn.preprocessing import MinMaxScaler\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",
"sns.set(style='whitegrid', palette='muted', font_scale=1.5)\n",
"\n",
"rcParams['figure.figsize'] = 14, 8\n",
"\n",
"RANDOM_SEED = 42\n",
"\n",
"np.random.seed(RANDOM_SEED)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
"/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
" np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vuAToIVxfpgk"
},
"source": [
"import datetime\n",
"from fastquant import get_crypto_data, get_stock_data\n",
"import plotly.express as px\n",
"import plotly.graph_objects as go"
],
"execution_count": 222,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"id": "6X0fjgrirUZT",
"outputId": "5764a2a9-0555-4397-d124-df8919b1a913"
},
"source": [
"today = datetime.date.today()\n",
"yesterday = today - datetime.timedelta(days=1)\n",
"yesterday = yesterday.strftime(\"%Y-%m-%d\")\n",
"yesterday\n",
"\n",
"newCols = {\"dt\": \"Date\", \"open\": \"Open\", \"high\": \"High\", \"low\": \"Low\", \"close\": \"Close\", \"volume\": \"Volumne\"}"
],
"execution_count": 80,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2021-06-29'"
]
},
"metadata": {
"tags": []
},
"execution_count": 80
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "y5o6dHO2LLz-",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "5be5f36b-37f1-4ef5-bdf6-ed31d5226829"
},
"source": [
"# Data comes from:\n",
"# https://finance.yahoo.com/quote/BTC-USD/history?period1=1279314000&period2=1556053200&interval=1d&filter=history&frequency=1d\n",
"df = crypto = get_crypto_data(\"BTC/USDT\", \"2018-12-01\", yesterday)\n",
"df = df.reset_index()\n",
"df = df.sort_values('dt')\n",
"df = df.rename(columns=newCols)\n",
"df.tail()\n",
"# csv_path = \"https://raw.githubusercontent.com/curiousily/Deep-Learning-For-Hackers/master/data/3.stock-prediction/BTC-USD.csv\"\n",
"# csv_path = \"https://raw.githubusercontent.com/curiousily/Deep-Learning-For-Hackers/master/data/3.stock-prediction/AAPL.csv\"\n",
"# df = pd.read_csv(csv_path, parse_dates=['Date'])"
],
"execution_count": 190,
"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>Date</th>\n",
" <th>Open</th>\n",
" <th>High</th>\n",
" <th>Low</th>\n",
" <th>Close</th>\n",
" <th>Volumne</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>937</th>\n",
" <td>2021-06-25</td>\n",
" <td>34663.08</td>\n",
" <td>35500.00</td>\n",
" <td>31275.00</td>\n",
" <td>31584.45</td>\n",
" <td>116061.130356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>938</th>\n",
" <td>2021-06-26</td>\n",
" <td>31576.09</td>\n",
" <td>32730.00</td>\n",
" <td>30151.00</td>\n",
" <td>32283.65</td>\n",
" <td>107820.375287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>939</th>\n",
" <td>2021-06-27</td>\n",
" <td>32283.65</td>\n",
" <td>34749.00</td>\n",
" <td>31973.45</td>\n",
" <td>34700.34</td>\n",
" <td>96613.244211</td>\n",
" </tr>\n",
" <tr>\n",
" <th>940</th>\n",
" <td>2021-06-28</td>\n",
" <td>34702.49</td>\n",
" <td>35297.71</td>\n",
" <td>33862.72</td>\n",
" <td>34494.89</td>\n",
" <td>82222.267819</td>\n",
" </tr>\n",
" <tr>\n",
" <th>941</th>\n",
" <td>2021-06-29</td>\n",
" <td>34494.89</td>\n",
" <td>36600.00</td>\n",
" <td>34225.43</td>\n",
" <td>35911.73</td>\n",
" <td>90788.796220</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Open High Low Close Volumne\n",
"937 2021-06-25 34663.08 35500.00 31275.00 31584.45 116061.130356\n",
"938 2021-06-26 31576.09 32730.00 30151.00 32283.65 107820.375287\n",
"939 2021-06-27 32283.65 34749.00 31973.45 34700.34 96613.244211\n",
"940 2021-06-28 34702.49 35297.71 33862.72 34494.89 82222.267819\n",
"941 2021-06-29 34494.89 36600.00 34225.43 35911.73 90788.796220"
]
},
"metadata": {
"tags": []
},
"execution_count": 190
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nX1R880aw4Ja"
},
"source": [
"def plot_chart(df, xCol='Date', yCol='Close'):\n",
" # ax = df.plot(x=xCol, y=yCol);\n",
" # ax.set_xlabel(\"Date\")\n",
" # ax.set_ylabel(\"Close Price (USD)\")\n",
" px.line(df, x=xCol, y=yCol).show()"
],
"execution_count": 191,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4XyoR5lG3Jxv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"outputId": "7e577b72-d879-43da-a66b-f6580596bf0d"
},
"source": [
"plot_chart(df)"
],
"execution_count": 192,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
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]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7twAH-I-qO9Z"
},
"source": [
"# Normalization"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Ux2FtgMAqObq"
},
"source": [
"scaler = MinMaxScaler()\n",
"\n",
"close_price = df.Close.values.reshape(-1, 1)\n",
"\n",
"scaled_close = scaler.fit_transform(close_price)"
],
"execution_count": 107,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UprkaMWFvQOG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fe3b9773-d764-4afe-d7a3-169e494b7e01"
},
"source": [
"scaled_close.shape"
],
"execution_count": 108,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1307, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 108
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qOg2HX9AuTk-",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "dc6fe24d-cede-435c-98f5-8e5195234b4e"
},
"source": [
"np.isnan(scaled_close).any()"
],
"execution_count": 109,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {
"tags": []
},
"execution_count": 109
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "evHUrll9ukht"
},
"source": [
"scaled_close = scaled_close[~np.isnan(scaled_close)]"
],
"execution_count": 110,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "gCyOvwnqvTSw"
},
"source": [
"scaled_close = scaled_close.reshape(-1, 1)"
],
"execution_count": 111,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eSJxuT3-u6K2",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8aa9baa9-4a41-4e2c-c230-3697bcf71ec8"
},
"source": [
"np.isnan(scaled_close).any()"
],
"execution_count": 112,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"False"
]
},
"metadata": {
"tags": []
},
"execution_count": 112
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1O4OM6XBkugh",
"outputId": "8b9c83fc-0b39-4d52-d778-d3d3b6e43db3"
},
"source": [
"scaled_close.shape"
],
"execution_count": 113,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1307, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 113
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CQgrA2va4Jzx"
},
"source": [
"# Preprocessing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "NS5O4-Vtq-N1"
},
"source": [
"SEQ_LEN = 100\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",
"\n",
" return np.array(d)\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, :]\n",
"\n",
" X_test = data[num_train:, :-1, :]\n",
" y_test = data[num_train:, -1, :]\n",
"\n",
" return X_train, y_train, X_test, y_test\n",
"\n",
"\n",
"X_train, y_train, X_test, y_test = preprocess(scaled_close, SEQ_LEN, train_split = 0.95)"
],
"execution_count": 114,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "XNBTUeaXreni",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2a16517f-9a6e-4a26-a4c1-a2b15fb40628"
},
"source": [
"X_train.shape"
],
"execution_count": 115,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1146, 99, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 115
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4IOoiN3pUUgO",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "bcf32305-b8e1-415e-cea5-5e34cd7e6c6f"
},
"source": [
"X_test.shape"
],
"execution_count": 116,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(61, 99, 1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 116
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Yhhv26c34YTW"
},
"source": [
"# Model"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a3dw5qTasJoy"
},
"source": [
"DROPOUT = 0.2\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=1))\n",
"\n",
"model.add(Activation('linear'))"
],
"execution_count": 117,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "pjBan-K27L8d"
},
"source": [
"# Training"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zvc-LMgOHkWJ"
},
"source": [
"model.compile(\n",
" loss='mean_squared_error', \n",
" optimizer='adam',\n",
")"
],
"execution_count": 118,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "m1aU2xDvsvrN",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "e448ec3a-f2d7-49f4-d881-ae2b81dc8dd7"
},
"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=0.2\n",
")"
],
"execution_count": 119,
"outputs": [
{
"output_type": "stream",
"text": [
"Train on 916 samples, validate on 230 samples\n",
"Epoch 1/100\n",
"916/916 [==============================] - 2s 2ms/sample - loss: 0.0028 - val_loss: 0.1952\n",
"Epoch 2/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 0.0015 - val_loss: 0.1295\n",
"Epoch 3/100\n",
"916/916 [==============================] - 1s 835us/sample - loss: 0.0027 - val_loss: 0.1318\n",
"Epoch 4/100\n",
"916/916 [==============================] - 1s 835us/sample - loss: 0.0023 - val_loss: 0.0756\n",
"Epoch 5/100\n",
"916/916 [==============================] - 1s 835us/sample - loss: 0.0019 - val_loss: 0.0668\n",
"Epoch 6/100\n",
"916/916 [==============================] - 1s 838us/sample - loss: 0.0015 - val_loss: 0.0534\n",
"Epoch 7/100\n",
"916/916 [==============================] - 1s 829us/sample - loss: 0.0012 - val_loss: 0.0802\n",
"Epoch 8/100\n",
"916/916 [==============================] - 1s 828us/sample - loss: 6.9919e-04 - val_loss: 0.0445\n",
"Epoch 9/100\n",
"916/916 [==============================] - 1s 828us/sample - loss: 5.5428e-04 - val_loss: 0.0781\n",
"Epoch 10/100\n",
"916/916 [==============================] - 1s 829us/sample - loss: 4.8968e-04 - val_loss: 0.0366\n",
"Epoch 11/100\n",
"916/916 [==============================] - 1s 842us/sample - loss: 1.5680e-04 - val_loss: 0.0683\n",
"Epoch 12/100\n",
"916/916 [==============================] - 1s 845us/sample - loss: 1.1991e-04 - val_loss: 0.0452\n",
"Epoch 13/100\n",
"916/916 [==============================] - 1s 838us/sample - loss: 1.0597e-04 - val_loss: 0.0650\n",
"Epoch 14/100\n",
"916/916 [==============================] - 1s 833us/sample - loss: 1.9041e-04 - val_loss: 0.0426\n",
"Epoch 15/100\n",
"916/916 [==============================] - 1s 846us/sample - loss: 1.2951e-04 - val_loss: 0.0482\n",
"Epoch 16/100\n",
"916/916 [==============================] - 1s 864us/sample - loss: 1.2799e-04 - val_loss: 0.0424\n",
"Epoch 17/100\n",
"916/916 [==============================] - 1s 824us/sample - loss: 9.2110e-05 - val_loss: 0.0488\n",
"Epoch 18/100\n",
"916/916 [==============================] - 1s 832us/sample - loss: 1.0460e-04 - val_loss: 0.0428\n",
"Epoch 19/100\n",
"916/916 [==============================] - 1s 839us/sample - loss: 9.9657e-05 - val_loss: 0.0479\n",
"Epoch 20/100\n",
"916/916 [==============================] - 1s 849us/sample - loss: 1.5428e-04 - val_loss: 0.0409\n",
"Epoch 21/100\n",
"916/916 [==============================] - 1s 858us/sample - loss: 1.3997e-04 - val_loss: 0.0478\n",
"Epoch 22/100\n",
"916/916 [==============================] - 1s 850us/sample - loss: 1.9579e-04 - val_loss: 0.0436\n",
"Epoch 23/100\n",
"916/916 [==============================] - 1s 826us/sample - loss: 1.6763e-04 - val_loss: 0.0511\n",
"Epoch 24/100\n",
"916/916 [==============================] - 1s 830us/sample - loss: 2.0572e-04 - val_loss: 0.0479\n",
"Epoch 25/100\n",
"916/916 [==============================] - 1s 830us/sample - loss: 1.6396e-04 - val_loss: 0.0546\n",
"Epoch 26/100\n",
"916/916 [==============================] - 1s 835us/sample - loss: 1.7396e-04 - val_loss: 0.0516\n",
"Epoch 27/100\n",
"916/916 [==============================] - 1s 836us/sample - loss: 1.3521e-04 - val_loss: 0.0583\n",
"Epoch 28/100\n",
"916/916 [==============================] - 1s 845us/sample - loss: 1.5523e-04 - val_loss: 0.0524\n",
"Epoch 29/100\n",
"916/916 [==============================] - 1s 831us/sample - loss: 1.2401e-04 - val_loss: 0.0610\n",
"Epoch 30/100\n",
"916/916 [==============================] - 1s 828us/sample - loss: 1.7473e-04 - val_loss: 0.0516\n",
"Epoch 31/100\n",
"916/916 [==============================] - 1s 834us/sample - loss: 1.2692e-04 - val_loss: 0.0629\n",
"Epoch 32/100\n",
"916/916 [==============================] - 1s 845us/sample - loss: 1.9563e-04 - val_loss: 0.0528\n",
"Epoch 33/100\n",
"916/916 [==============================] - 1s 840us/sample - loss: 1.4166e-04 - val_loss: 0.0648\n",
"Epoch 34/100\n",
"916/916 [==============================] - 1s 832us/sample - loss: 2.0121e-04 - val_loss: 0.0543\n",
"Epoch 35/100\n",
"916/916 [==============================] - 1s 827us/sample - loss: 1.3690e-04 - val_loss: 0.0665\n",
"Epoch 36/100\n",
"916/916 [==============================] - 1s 823us/sample - loss: 1.8258e-04 - val_loss: 0.0546\n",
"Epoch 37/100\n",
"916/916 [==============================] - 1s 821us/sample - loss: 1.1750e-04 - val_loss: 0.0671\n",
"Epoch 38/100\n",
"916/916 [==============================] - 1s 814us/sample - loss: 1.7196e-04 - val_loss: 0.0517\n",
"Epoch 39/100\n",
"916/916 [==============================] - 1s 822us/sample - loss: 1.0968e-04 - val_loss: 0.0664\n",
"Epoch 40/100\n",
"916/916 [==============================] - 1s 822us/sample - loss: 1.8706e-04 - val_loss: 0.0502\n",
"Epoch 41/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 1.2045e-04 - val_loss: 0.0666\n",
"Epoch 42/100\n",
"916/916 [==============================] - 1s 821us/sample - loss: 2.0619e-04 - val_loss: 0.0508\n",
"Epoch 43/100\n",
"916/916 [==============================] - 1s 829us/sample - loss: 1.2845e-04 - val_loss: 0.0682\n",
"Epoch 44/100\n",
"916/916 [==============================] - 1s 821us/sample - loss: 2.0514e-04 - val_loss: 0.0530\n",
"Epoch 45/100\n",
"916/916 [==============================] - 1s 819us/sample - loss: 1.2372e-04 - val_loss: 0.0687\n",
"Epoch 46/100\n",
"916/916 [==============================] - 1s 811us/sample - loss: 1.8043e-04 - val_loss: 0.0531\n",
"Epoch 47/100\n",
"916/916 [==============================] - 1s 838us/sample - loss: 1.0306e-04 - val_loss: 0.0670\n",
"Epoch 48/100\n",
"916/916 [==============================] - 1s 810us/sample - loss: 1.5344e-04 - val_loss: 0.0486\n",
"Epoch 49/100\n",
"916/916 [==============================] - 1s 810us/sample - loss: 8.5510e-05 - val_loss: 0.0649\n",
"Epoch 50/100\n",
"916/916 [==============================] - 1s 823us/sample - loss: 1.5965e-04 - val_loss: 0.0434\n",
"Epoch 51/100\n",
"916/916 [==============================] - 1s 817us/sample - loss: 8.4951e-05 - val_loss: 0.0651\n",
"Epoch 52/100\n",
"916/916 [==============================] - 1s 816us/sample - loss: 2.0979e-04 - val_loss: 0.0432\n",
"Epoch 53/100\n",
"916/916 [==============================] - 1s 812us/sample - loss: 1.0925e-04 - val_loss: 0.0675\n",
"Epoch 54/100\n",
"916/916 [==============================] - 1s 819us/sample - loss: 2.4019e-04 - val_loss: 0.0478\n",
"Epoch 55/100\n",
"916/916 [==============================] - 1s 830us/sample - loss: 1.2391e-04 - val_loss: 0.0695\n",
"Epoch 56/100\n",
"916/916 [==============================] - 1s 826us/sample - loss: 2.1161e-04 - val_loss: 0.0501\n",
"Epoch 57/100\n",
"916/916 [==============================] - 1s 818us/sample - loss: 9.8424e-05 - val_loss: 0.0709\n",
"Epoch 58/100\n",
"916/916 [==============================] - 1s 812us/sample - loss: 1.8733e-04 - val_loss: 0.0460\n",
"Epoch 59/100\n",
"916/916 [==============================] - 1s 809us/sample - loss: 8.8062e-05 - val_loss: 0.0746\n",
"Epoch 60/100\n",
"916/916 [==============================] - 1s 819us/sample - loss: 2.4529e-04 - val_loss: 0.0409\n",
"Epoch 61/100\n",
"916/916 [==============================] - 1s 800us/sample - loss: 9.1910e-05 - val_loss: 0.0793\n",
"Epoch 62/100\n",
"916/916 [==============================] - 1s 813us/sample - loss: 3.2412e-04 - val_loss: 0.0434\n",
"Epoch 63/100\n",
"916/916 [==============================] - 1s 812us/sample - loss: 1.0721e-04 - val_loss: 0.0755\n",
"Epoch 64/100\n",
"916/916 [==============================] - 1s 812us/sample - loss: 2.3752e-04 - val_loss: 0.0435\n",
"Epoch 65/100\n",
"916/916 [==============================] - 1s 818us/sample - loss: 8.9643e-05 - val_loss: 0.0755\n",
"Epoch 66/100\n",
"916/916 [==============================] - 1s 799us/sample - loss: 2.0101e-04 - val_loss: 0.0389\n",
"Epoch 67/100\n",
"916/916 [==============================] - 1s 815us/sample - loss: 8.4231e-05 - val_loss: 0.0739\n",
"Epoch 68/100\n",
"916/916 [==============================] - 1s 815us/sample - loss: 2.1395e-04 - val_loss: 0.0363\n",
"Epoch 69/100\n",
"916/916 [==============================] - 1s 829us/sample - loss: 8.2069e-05 - val_loss: 0.0688\n",
"Epoch 70/100\n",
"916/916 [==============================] - 1s 815us/sample - loss: 1.9432e-04 - val_loss: 0.0358\n",
"Epoch 71/100\n",
"916/916 [==============================] - 1s 807us/sample - loss: 7.5714e-05 - val_loss: 0.0634\n",
"Epoch 72/100\n",
"916/916 [==============================] - 1s 805us/sample - loss: 1.6453e-04 - val_loss: 0.0356\n",
"Epoch 73/100\n",
"916/916 [==============================] - 1s 818us/sample - loss: 6.7439e-05 - val_loss: 0.0599\n",
"Epoch 74/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 1.4331e-04 - val_loss: 0.0361\n",
"Epoch 75/100\n",
"916/916 [==============================] - 1s 822us/sample - loss: 6.4266e-05 - val_loss: 0.0580\n",
"Epoch 76/100\n",
"916/916 [==============================] - 1s 811us/sample - loss: 1.3887e-04 - val_loss: 0.0353\n",
"Epoch 77/100\n",
"916/916 [==============================] - 1s 819us/sample - loss: 6.4453e-05 - val_loss: 0.0564\n",
"Epoch 78/100\n",
"916/916 [==============================] - 1s 814us/sample - loss: 1.4364e-04 - val_loss: 0.0343\n",
"Epoch 79/100\n",
"916/916 [==============================] - 1s 811us/sample - loss: 6.4595e-05 - val_loss: 0.0550\n",
"Epoch 80/100\n",
"916/916 [==============================] - 1s 819us/sample - loss: 1.3831e-04 - val_loss: 0.0348\n",
"Epoch 81/100\n",
"916/916 [==============================] - 1s 842us/sample - loss: 6.2970e-05 - val_loss: 0.0539\n",
"Epoch 82/100\n",
"916/916 [==============================] - 1s 811us/sample - loss: 1.3758e-04 - val_loss: 0.0347\n",
"Epoch 83/100\n",
"916/916 [==============================] - 1s 831us/sample - loss: 6.0175e-05 - val_loss: 0.0552\n",
"Epoch 84/100\n",
"916/916 [==============================] - 1s 832us/sample - loss: 1.4161e-04 - val_loss: 0.0347\n",
"Epoch 85/100\n",
"916/916 [==============================] - 1s 814us/sample - loss: 6.4711e-05 - val_loss: 0.0540\n",
"Epoch 86/100\n",
"916/916 [==============================] - 1s 810us/sample - loss: 1.4141e-04 - val_loss: 0.0345\n",
"Epoch 87/100\n",
"916/916 [==============================] - 1s 807us/sample - loss: 6.3762e-05 - val_loss: 0.0545\n",
"Epoch 88/100\n",
"916/916 [==============================] - 1s 834us/sample - loss: 1.3590e-04 - val_loss: 0.0343\n",
"Epoch 89/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 6.0285e-05 - val_loss: 0.0552\n",
"Epoch 90/100\n",
"916/916 [==============================] - 1s 824us/sample - loss: 1.3781e-04 - val_loss: 0.0347\n",
"Epoch 91/100\n",
"916/916 [==============================] - 1s 817us/sample - loss: 6.2376e-05 - val_loss: 0.0541\n",
"Epoch 92/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 1.3976e-04 - val_loss: 0.0353\n",
"Epoch 93/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 5.9785e-05 - val_loss: 0.0544\n",
"Epoch 94/100\n",
"916/916 [==============================] - 1s 813us/sample - loss: 1.3143e-04 - val_loss: 0.0366\n",
"Epoch 95/100\n",
"916/916 [==============================] - 1s 820us/sample - loss: 6.1002e-05 - val_loss: 0.0558\n",
"Epoch 96/100\n",
"916/916 [==============================] - 1s 831us/sample - loss: 1.4166e-04 - val_loss: 0.0362\n",
"Epoch 97/100\n",
"916/916 [==============================] - 1s 805us/sample - loss: 6.1394e-05 - val_loss: 0.0559\n",
"Epoch 98/100\n",
"916/916 [==============================] - 1s 812us/sample - loss: 1.3857e-04 - val_loss: 0.0363\n",
"Epoch 99/100\n",
"916/916 [==============================] - 1s 808us/sample - loss: 6.1488e-05 - val_loss: 0.0561\n",
"Epoch 100/100\n",
"916/916 [==============================] - 1s 816us/sample - loss: 1.3762e-04 - val_loss: 0.0359\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "L9JumIeP40xv",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1623fa4a-615c-44c1-88f1-23780157571a"
},
"source": [
"model.evaluate(X_test, y_test)"
],
"execution_count": 120,
"outputs": [
{
"output_type": "stream",
"text": [
"61/61 [==============================] - 0s 580us/sample - loss: 0.0399\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.03989965544983012"
]
},
"metadata": {
"tags": []
},
"execution_count": 120
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2_9ibR1e5DnQ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 535
},
"outputId": "da8fa743-f65a-41d0-bbb9-9eb93f578881"
},
"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": 121,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Xf9STH2D4_hP"
},
"source": [
"# Prediction"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Lui1XTi0xVsF",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 535
},
"outputId": "91210742-6f65-4770-ed9c-288de93b54ed"
},
"source": [
"y_hat = model.predict(X_test)\n",
"\n",
"y_test_inverse = scaler.inverse_transform(y_test)\n",
"y_hat_inverse = scaler.inverse_transform(y_hat)\n",
" \n",
"plt.plot(y_test_inverse, label=\"Actual Price\", color='green')\n",
"plt.plot(y_hat_inverse, label=\"Predicted Price\", color='red')\n",
" \n",
"plt.title('Bitcoin price prediction')\n",
"plt.xlabel('Time [days]')\n",
"plt.ylabel('Price')\n",
"plt.legend(loc='best')\n",
" \n",
"plt.show();"
],
"execution_count": 122,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1008x576 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_rRRlXFktQWY"
},
"source": [
"# Stocks"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UgfsShgrqPvh"
},
"source": [
"https://towardsdatascience.com/time-series-forecasting-with-recurrent-neural-networks-74674e289816\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"id": "5tw36rM0qWVX",
"outputId": "70d5c0e7-7a9e-4dd1-f4f3-7a80f790b454"
},
"source": [
"from tensorflow.keras.preprocessing.sequence import TimeseriesGenerator\n",
"from tensorflow import keras\n",
"\n",
"stock = \"MGM\"\n",
"\n",
"stock_df = get_stock_data(stock, \"2015-01-01\", datetime.date.today())\n",
"stock_df = stock_df.reset_index()\n",
"stock_df = stock_df.sort_values('dt')\n",
"stock_df = stock_df.rename(columns=newCols)\n",
"stock_df.tail()"
],
"execution_count": 233,
"outputs": [
{
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"text": [
"\r[*********************100%***********************] 1 of 1 completed\n"
],
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" <th>1629</th>\n",
" <td>2021-06-23</td>\n",
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" <th>1631</th>\n",
" <td>2021-06-25</td>\n",
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" <th>1632</th>\n",
" <td>2021-06-28</td>\n",
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" <th>1633</th>\n",
" <td>2021-06-29</td>\n",
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"text/plain": [
" Date Open High Low Close Volumne\n",
"1629 2021-06-23 42.369999 43.439999 42.320000 43.119999 6326800\n",
"1630 2021-06-24 44.150002 45.340000 44.049999 44.060001 9579500\n",
"1631 2021-06-25 44.070000 44.200001 43.730000 43.889999 7529800\n",
"1632 2021-06-28 43.650002 43.750000 41.740002 42.389999 6589100\n",
"1633 2021-06-29 42.500000 42.830002 42.049999 42.189999 4218100"
]
},
"metadata": {
"tags": []
},
"execution_count": 233
}
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"source": [
"plot_chart(stock_df)"
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]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "oTk9mA_JskcN",
"outputId": "2f2c94a8-fe1b-4ec7-b48c-d3ddb030c4f4"
},
"source": [
"close_data = stock_df['Close'].values\n",
"close_data = close_data.reshape((-1,1))\n",
"\n",
"split_percent = 0.9\n",
"split = int(split_percent*len(close_data))\n",
"\n",
"close_train = close_data[:split]\n",
"close_test = close_data[split:]\n",
"\n",
"date_train = stock_df['Date'][:split]\n",
"date_test = stock_df['Date'][split:]\n",
"print(len(close_train))\n",
"print(len(close_test))"
],
"execution_count": 229,
"outputs": [
{
"output_type": "stream",
"text": [
"1470\n",
"164\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2YBjNXqI1NRn"
},
"source": [
"look_back = 15\n",
"\n",
"train_generator = TimeseriesGenerator(close_train, close_train, length=look_back, batch_size=20) \n",
"test_generator = TimeseriesGenerator(close_test, close_test, length=look_back, batch_size=1)"
],
"execution_count": 230,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dwMqQzh82Xjh",
"outputId": "200e75ad-3a8e-4d2a-bfe5-26229e9ca1dc"
},
"source": [
"\n",
"model = Sequential()\n",
"model.add(\n",
" LSTM(10,\n",
" activation='relu',\n",
" input_shape=(look_back,1))\n",
")\n",
"model.add(Dense(1))\n",
"model.compile(optimizer='adam', loss='mse')\n",
"\n",
"num_epochs = 25\n",
"model.fit_generator(train_generator, epochs=num_epochs, verbose=1)"
],
"execution_count": 231,
"outputs": [
{
"output_type": "stream",
"text": [
"WARNING:tensorflow:<tensorflow.python.keras.layers.recurrent.UnifiedLSTM object at 0x7f13a32c1410>: Note that this layer is not optimized for performance. Please use tf.keras.layers.CuDNNLSTM for better performance on GPU.\n",
"Epoch 1/25\n",
"73/73 [==============================] - 7s 92ms/step - loss: 5932.0816\n",
"Epoch 2/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 823.3322\n",
"Epoch 3/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 644.9152\n",
"Epoch 4/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 493.2084\n",
"Epoch 5/25\n",
"73/73 [==============================] - 5s 69ms/step - loss: 177.3352\n",
"Epoch 6/25\n",
"73/73 [==============================] - 5s 67ms/step - loss: 18.3552\n",
"Epoch 7/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 1.9578\n",
"Epoch 8/25\n",
"73/73 [==============================] - 5s 67ms/step - loss: 1.6157\n",
"Epoch 9/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 1.3778\n",
"Epoch 10/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 1.2927\n",
"Epoch 11/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 1.1203\n",
"Epoch 12/25\n",
"73/73 [==============================] - 5s 68ms/step - loss: 1.0894\n",
"Epoch 13/25\n",
"73/73 [==============================] - 5s 67ms/step - loss: 1.0603\n",
"Epoch 14/25\n",
"73/73 [==============================] - 5s 68ms/step - loss: 0.9852\n",
"Epoch 15/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.9615\n",
"Epoch 16/25\n",
"73/73 [==============================] - 5s 65ms/step - loss: 0.9479\n",
"Epoch 17/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.9900\n",
"Epoch 18/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.9530\n",
"Epoch 19/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.9046\n",
"Epoch 20/25\n",
"73/73 [==============================] - 5s 67ms/step - loss: 0.9286\n",
"Epoch 21/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.9791\n",
"Epoch 22/25\n",
"73/73 [==============================] - 5s 66ms/step - loss: 0.8806\n",
"Epoch 23/25\n",
"73/73 [==============================] - 5s 65ms/step - loss: 0.8825\n",
"Epoch 24/25\n",
"73/73 [==============================] - 5s 67ms/step - loss: 0.8722\n",
"Epoch 25/25\n",
"73/73 [==============================] - 5s 68ms/step - loss: 0.8802\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tensorflow.python.keras.callbacks.History at 0x7f139e30f190>"
]
},
"metadata": {
"tags": []
},
"execution_count": 231
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 542
},
"id": "U5G8-nfH2YTj",
"outputId": "3da50ed5-76d9-4513-caa8-de299a9abcd9"
},
"source": [
"prediction = model.predict_generator(test_generator)\n",
"\n",
"close_train = close_train.reshape((-1))\n",
"close_test = close_test.reshape((-1))\n",
"prediction = prediction.reshape((-1))\n",
"\n",
"trace1 = go.Scatter(\n",
" x = date_train,\n",
" y = close_train,\n",
" mode = 'lines',\n",
" name = 'Data'\n",
")\n",
"trace2 = go.Scatter(\n",
" x = date_test,\n",
" y = prediction,\n",
" mode = 'lines',\n",
" name = 'Prediction'\n",
")\n",
"trace3 = go.Scatter(\n",
" x = date_test,\n",
" y = close_test,\n",
" mode='lines',\n",
" name = 'Ground Truth'\n",
")\n",
"layout = go.Layout(\n",
" title = stock,\n",
" xaxis = {'title' : \"Date\"},\n",
" yaxis = {'title' : \"Close\"}\n",
")\n",
"fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)\n",
"fig.show()"
],
"execution_count": 234,
"outputs": [
{
"output_type": "display_data",
"data": {
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},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"# Prediction"
]
},
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"cell_type": "code",
"metadata": {
"id": "ZO6yBh6I2xyG"
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"source": [
"\n",
"close_data = close_data.reshape((-1))\n",
"\n",
"def predict(num_prediction, model):\n",
" prediction_list = close_data[-look_back:]\n",
" \n",
" for _ in range(num_prediction):\n",
" x = prediction_list[-look_back:]\n",
" x = x.reshape((1, look_back, 1))\n",
" out = model.predict(x)[0][0]\n",
" prediction_list = np.append(prediction_list, out)\n",
" prediction_list = prediction_list[look_back-1:]\n",
" \n",
" return prediction_list\n",
" \n",
"def predict_dates(num_prediction):\n",
" last_date = stock_df['Date'].values[-1]\n",
" prediction_dates = pd.date_range(last_date, periods=num_prediction+1).tolist()\n",
" return prediction_dates\n",
"\n",
"num_prediction = 10\n",
"forecast = predict(num_prediction, model)\n",
"forecast_dates = predict_dates(num_prediction)"
],
"execution_count": 238,
"outputs": []
},
{
"cell_type": "code",
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"source": [
"len(forecast_dates)\n",
"forecast_dates"
],
"execution_count": 250,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
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" Timestamp('2021-06-30 00:00:00', freq='D'),\n",
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"source": [
"px.line(x=forecast_dates, y=forecast)"
],
"execution_count": 253,
"outputs": [
{
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]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UBXHWIer46Wh"
},
"source": [
""
],
"execution_count": 242,
"outputs": []
}
]
}
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