stock_price_pred_v2.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This ipynb file contains lstm model with preprocessing"
      ],
      "metadata": {
        "id": "Wq8HCIeplBEG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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          "height": 1000
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        "id": "_i3UyZaMF5Uv",
        "outputId": "5e41db4f-5a46-4896-b1e4-1e71f4aa7b29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "Successfully built bs4 bt finviz fred oandapyV20 pandas-ta screeninfo svglib onetimepass sgmllib3k user_agent\n",
            "Installing collected packages: y-py, textwrap3, squarify, sgmllib3k, screeninfo, python-i18n, python-coinmarketcap, pyprind, pure-eval, oandapyV20, monotonic, korean-lunar-calendar, json5, iso8601, FundamentalAnalysis, executing, commonmark, widgetsnbextension, websockets, watchdog, valinvest, validators, user_agent, urllib3, uri-template, ujson, tzdata, smmap, setuptools, setproctitle, ruamel.yaml.clib, rich, rfc3986-validator, rfc3339-validator, retrying, reportlab, python-json-logger, python-dotenv, pyrsistent, pyotp, pympler, pyluach, pyflakes, pydantic, pycryptodome, pycodestyle, protobuf, property-cached, onetimepass, numpy, mypy-extensions, mccabe, marshmallow, loguru, jupyter-ydoc, jsonpointer, jedi, isodate, interface-meta, inflection, hijri-converter, h11, fqdn, feedparser, ecdsa, dnspython, detecta, cssselect, comm, colorama, base58, backoff, asttokens, astor, ansiwrap, aiosqlite, aiofiles, zope.interface, ypy-websocket, typing-inspect, stack-data, setuptools-scm, ruamel.yaml, rdflib, pywry, pytz-deprecation-shim, python-jose, pydeck, pycares, openapi-schema-pydantic, jupyter-client, intrinio-sdk, httpcore, holidays, gitdb, flake8, cssselect2, bs4, ascii-magic, arrow, vaderSentiment, update-checker, tzlocal, tradingview-ta, tokenterminal, tiktoken, svglib, stocksera, statsmodels, sparqlwrapper, robin-stocks, requests-futures, Quandl, pytrends, pyhdfe, pycoingecko, prawcore, posthog, pandas-ta, openai, mstarpy, langsmith, isoduration, ipython, httpx, GitPython, fredapi, fred, formulaic, finviz, finnhub-python, financedatabase, exchange-calendars, degiro-connector, datetime, dataclasses-json, alpha-vantage, aiodns, yahooquery, seaborn, pythclient, praw, pandas-market-calendars, nixtlats, mplfinance, linearmodels, langchain, ipywidgets, finvizfinance, ffn, dateparser, ccxt, arch, thepassiveinvestor, streamlit, python-binance, pmaw, llama-index, jupyter-events, ipympl, bt, papermill, jupyter-server, jupyterlab-server, jupyter-server-fileid, jupyter-server-ydoc, jupyterlab, openbb\n",
            "  Attempting uninstall: widgetsnbextension\n",
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            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\n",
            "  Attempting uninstall: jupyter-server\n",
            "    Found existing installation: jupyter-server 1.24.0\n",
            "    Uninstalling jupyter-server-1.24.0:\n",
            "      Successfully uninstalled jupyter-server-1.24.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cvxpy 1.3.2 requires setuptools>65.5.1, but you have setuptools 65.4.1 which is incompatible.\n",
            "google-api-core 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery 3.10.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery-connection 1.12.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-bigquery-storage 2.22.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-datastore 2.15.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-firestore 2.11.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-functions 1.13.2 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-language 2.9.1 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-cloud-translate 3.11.3 requires protobuf!=3.20.0,!=3.20.1,!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.11.0 which is incompatible.\n",
            "googleapis-common-protos 1.60.0 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0.dev0,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "grpc-google-iam-v1 0.12.6 requires protobuf!=3.20.0,!=3.20.1,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.19.5, but you have protobuf 3.20.1 which is incompatible.\n",
            "plotnine 0.12.2 requires statsmodels>=0.14.0, but you have statsmodels 0.13.5 which is incompatible.\n",
            "prophet 1.1.4 requires holidays>=0.25, but you have holidays 0.14.2 which is incompatible.\n",
            "tensorflow 2.12.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 3.20.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed FundamentalAnalysis-0.2.14 GitPython-3.1.32 Quandl-3.7.0 aiodns-3.0.0 aiofiles-22.1.0 aiosqlite-0.19.0 alpha-vantage-2.3.1 ansiwrap-0.8.4 arch-5.6.0 arrow-1.2.3 ascii-magic-1.6 astor-0.8.1 asttokens-2.2.1 backoff-2.2.1 base58-2.1.1 bs4-0.0.1 bt-0.2.9 ccxt-4.0.79 colorama-0.4.6 comm-0.1.4 commonmark-0.9.1 cssselect-1.2.0 cssselect2-0.7.0 dataclasses-json-0.5.14 dateparser-1.1.8 datetime-5.2 degiro-connector-2.0.21 detecta-0.0.5 dnspython-2.4.2 ecdsa-0.18.0 exchange-calendars-4.2.8 executing-1.2.0 feedparser-6.0.10 ffn-0.3.6 financedatabase-2.1.0 finnhub-python-2.4.18 finviz-1.4.6 finvizfinance-0.14.6rc2 flake8-6.1.0 formulaic-0.6.4 fqdn-1.5.1 fred-3.1 fredapi-0.4.3 gitdb-4.0.10 h11-0.14.0 hijri-converter-2.3.1 holidays-0.14.2 httpcore-0.17.3 httpx-0.24.1 inflection-0.5.1 interface-meta-1.3.0 intrinio-sdk-6.25.0 ipympl-0.8.4 ipython-8.11.0 ipywidgets-8.1.0 iso8601-0.1.16 isodate-0.6.1 isoduration-20.11.0 jedi-0.19.0 json5-0.9.14 jsonpointer-2.4 jupyter-client-7.4.1 jupyter-events-0.7.0 jupyter-server-1.23.6 jupyter-server-fileid-0.9.0 jupyter-server-ydoc-0.8.0 jupyter-ydoc-0.2.5 jupyterlab-3.6.5 jupyterlab-server-2.24.0 korean-lunar-calendar-0.3.1 langchain-0.0.266 langsmith-0.0.28 linearmodels-4.31 llama-index-0.8.13 loguru-0.7.0 marshmallow-3.20.1 mccabe-0.7.0 monotonic-1.6 mplfinance-0.12.10b0 mstarpy-0.0.4 mypy-extensions-1.0.0 nixtlats-0.1.11 numpy-1.23.4 oandapyV20-0.6.3 onetimepass-1.0.1 openai-0.27.10 openapi-schema-pydantic-1.2.4 openbb-3.2.2 pandas-market-calendars-3.2 pandas-ta-0.3.14b0 papermill-2.4.0 pmaw-3.0.0 posthog-3.0.2 praw-7.7.1 prawcore-2.3.0 property-cached-1.6.4 protobuf-3.20.1 pure-eval-0.2.2 pycares-4.3.0 pycodestyle-2.11.0 pycoingecko-3.1.0 pycryptodome-3.18.0 pydantic-1.10.12 pydeck-0.8.0 pyflakes-3.1.0 pyhdfe-0.2.0 pyluach-2.2.0 pympler-1.0.1 pyotp-2.9.0 pyprind-2.11.3 pyrsistent-0.19.3 pythclient-0.1.12 python-binance-1.0.19 python-coinmarketcap-0.2 python-dotenv-1.0.0 python-i18n-0.3.9 python-jose-3.3.0 python-json-logger-2.0.7 pytrends-4.9.2 pytz-deprecation-shim-0.1.0.post0 pywry-0.5.14 rdflib-7.0.0 reportlab-3.6.13 requests-futures-1.0.1 retrying-1.3.4 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rich-12.6.0 robin-stocks-2.1.0 ruamel.yaml-0.17.32 ruamel.yaml.clib-0.2.7 screeninfo-0.6.7 seaborn-0.11.2 setproctitle-1.3.2 setuptools-65.4.1 setuptools-scm-7.1.0 sgmllib3k-1.0.0 smmap-5.0.0 sparqlwrapper-2.0.0 squarify-0.4.3 stack-data-0.6.2 statsmodels-0.13.5 stocksera-0.1.21 streamlit-1.26.0 svglib-1.5.1 textwrap3-0.9.2 thepassiveinvestor-1.2.1 tiktoken-0.4.0 tokenterminal-1.0.1 tradingview-ta-3.3.0 typing-inspect-0.9.0 tzdata-2023.3 tzlocal-4.3.1 ujson-5.8.0 update-checker-0.18.0 uri-template-1.3.0 urllib3-1.26.16 user_agent-0.1.10 vaderSentiment-3.3.2 validators-0.21.2 valinvest-0.0.2 watchdog-2.3.1 websockets-11.0.3 widgetsnbextension-4.0.8 y-py-0.6.0 yahooquery-2.3.2 ypy-websocket-0.8.4 zope.interface-6.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "_distutils_hack",
                  "google",
                  "numpy",
                  "pkg_resources",
                  "setuptools"
                ]
              }
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "!pip install openbb"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import numpy as np\n",
        "\n",
        "import keras\n",
        "from keras.layers import Dense, Dropout\n",
        "from keras.models import Sequential\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "import pylab as pl"
      ],
      "metadata": {
        "id": "ec98M9V1Kusp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from openbb_terminal.sdk import openbb"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "WhCz0L1hG4-6",
        "outputId": "7fdaf434-cc42-48a9-8a94-fbe024e897e3"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[91mlibwebkit2gtk-4.0.so.37: cannot open shared object file: No such file or directory\u001b[0m\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[33mForecasting Toolkit is disabled. To use the Forecasting features please install the toolkit following the instructions here: https://my.openbb.co/app/sdk/installation\u001b[0m\n",
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000\">Forecasting Toolkit is disabled. To use the Forecasting features please install the toolkit following the instructions here: https://my.openbb.co/app/sdk/installation</span>\n",
              "\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[33mPortfolio Optimization Toolkit is disabled. To use the Optimization features please install the toolkit following the instructions here: https://my.openbb.co/app/sdk/installation\u001b[0m\n",
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #808000; text-decoration-color: #808000\">Portfolio Optimization Toolkit is disabled. To use the Optimization features please install the toolkit following the instructions here: https://my.openbb.co/app/sdk/installation</span>\n",
              "\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\u001b[31mPyWry is not installed or missing required linux dependencies.\u001b[0m\n",
              "\n",
              "\u001b[33mInstall PyWry\u001b[0m\n",
              "\u001b[32mpip install pywry --upgrade\u001b[0m\n",
              "\n",
              "\u001b[33mPlatform-specific notes\u001b[0m\n",
              "Here is the underlying web engine each platform uses you might need to install.\n",
              "\n",
              "\u001b[32mLinux\u001b[0m\n",
              "Pywry uses gtk-rs and its related libraries for window creation and Wry also needs WebKitGTK for WebView.\n",
              "To activate interactive plots/tables in pywry window, please make sure the following packages are installed:\n",
              "\n",
              "\u001b[33mArch Linux \u001b[0m\u001b[33m/\u001b[0m\u001b[33m Manjaro:\u001b[0m\n",
              "\u001b[32msudo pacman -S webkit2gtk\u001b[0m\n",
              "\n",
              "\u001b[33mDebian \u001b[0m\u001b[33m/\u001b[0m\u001b[33m Ubuntu:\u001b[0m\n",
              "\u001b[32msudo apt install libwebkit2gtk-\u001b[0m\u001b[1;32m4.0\u001b[0m\u001b[32m-dev\u001b[0m\n",
              "\n",
              "\u001b[33mFedora \u001b[0m\u001b[33m/\u001b[0m\u001b[33m CentOS \u001b[0m\u001b[33m/\u001b[0m\u001b[33m AlmaLinux:\u001b[0m\n",
              "\u001b[32msudo dnf install gtk3-devel webkit2gtk3-devel\u001b[0m\n",
              "\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "<span style=\"color: #800000; text-decoration-color: #800000\">PyWry is not installed or missing required linux dependencies.</span>\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Install PyWry</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">pip install pywry --upgrade</span>\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Platform-specific notes</span>\n",
              "Here is the underlying web engine each platform uses you might need to install.\n",
              "\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">Linux</span>\n",
              "Pywry uses gtk-rs and its related libraries for window creation and Wry also needs WebKitGTK for WebView.\n",
              "To activate interactive plots/tables in pywry window, please make sure the following packages are installed:\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Arch Linux / Manjaro:</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">sudo pacman -S webkit2gtk</span>\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Debian / Ubuntu:</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">sudo apt install libwebkit2gtk-</span><span style=\"color: #008000; text-decoration-color: #008000; font-weight: bold\">4.0</span><span style=\"color: #008000; text-decoration-color: #008000\">-dev</span>\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Fedora / CentOS / AlmaLinux:</span>\n",
              "<span style=\"color: #008000; text-decoration-color: #008000\">sudo dnf install gtk3-devel webkit2gtk3-devel</span>\n",
              "\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\n",
              "\n",
              "\u001b[33mContinuing with default plotly behaviour \u001b[0m\u001b[1;33m(\u001b[0m\u001b[33mopen in browser\u001b[0m\u001b[1;33m)\u001b[0m\u001b[33m.\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
              "\n",
              "<span style=\"color: #808000; text-decoration-color: #808000\">Continuing with default plotly behaviour </span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">(</span><span style=\"color: #808000; text-decoration-color: #808000\">open in browser</span><span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">)</span><span style=\"color: #808000; text-decoration-color: #808000\">.</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "openbb.stocks.candle('AAPL')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 558
        },
        "id": "t_rmXoNDG88p",
        "outputId": "36643605-73bf-42ae-86a8-d6261452ad16"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading Daily data for AAPL with starting period 2020-08-25.\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for AAPL with starting period 2020-08-25.\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
              "<head><meta charset=\"utf-8\" /></head>\n",
              "<body>\n",
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              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "train=openbb.stocks.load(\"AAPL\", \"2010-01-01\",1440,\"2017-01-01\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "id": "Vtpq8AsFHCZt",
        "outputId": "9ec99857-6c2f-4095-8cec-8b41d2a8e5ae"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading Daily data for AAPL with starting period 2010-01-04.\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for AAPL with starting period 2010-01-04.\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test=openbb.stocks.load(\"AAPL\", \"2017-01-01\",1440,\"2019-01-01\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "id": "piqe3dz5hUp6",
        "outputId": "4525f79a-9d88-461c-92ab-4509363a91e0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading Daily data for AAPL with starting period 2017-01-03.\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">Loading Daily data for AAPL with starting period 2017-01-03.\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "index=[]\n",
        "for i in range(len(train)):\n",
        "  index.append(i)\n",
        "\n",
        "train[\"index\"]=index"
      ],
      "metadata": {
        "id": "URvSjlKyHTI6"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "index=[]\n",
        "for i in range(len(test)):\n",
        "  index.append(i)\n",
        "\n",
        "test[\"index\"]=index"
      ],
      "metadata": {
        "id": "BOHdjwQ3HxP2"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=[]\n",
        "y=[]\n",
        "x_test=[]\n",
        "y_test=[]"
      ],
      "metadata": {
        "id": "TnfpHlB-H6Fg"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "close_list=[]\n",
        "close_list.append(train[\"Close\"][0])"
      ],
      "metadata": {
        "id": "7tsfGT-kIALQ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_close=[]\n",
        "test_close.append(test[\"Close\"][0])\n"
      ],
      "metadata": {
        "id": "sxEIsXmtILDT"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,len(train)):\n",
        "  date=int(str(train.index[i]).split()[0].split(\"-\")[2])\n",
        "  prev_date = int(str(train.index[i-1]).split()[0].split(\"-\")[2])\n",
        "\n",
        "\n",
        "  if date > (prev_date+1) and date < (prev_date+3) and date != 1:\n",
        "\t\t\tprint(\"date: {}, prev: {}\".format(date,prev_date))\n",
        "\t\t\tclose_list.append(train[\"Close\"][i-1])\n",
        "\n",
        "  close_list.append(train[\"Close\"][i])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMpMn4osISR8",
        "outputId": "660d3065-9657-45f0-c10d-f69caef4ac58"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "date: 26, prev: 24\n",
            "date: 25, prev: 23\n",
            "date: 5, prev: 3\n",
            "date: 23, prev: 21\n",
            "date: 26, prev: 24\n",
            "date: 5, prev: 3\n",
            "date: 29, prev: 27\n",
            "date: 26, prev: 24\n",
            "date: 28, prev: 26\n",
            "date: 26, prev: 24\n",
            "date: 27, prev: 25\n",
            "date: 25, prev: 23\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(1,len(test)):\n",
        "  date = int(str(test.index[i]).split()[0].split(\"-\")[2])\n",
        "  prev_date = int(str(test.index[i-1]).split()[0].split(\"-\")[2])\n",
        "\n",
        "  if date > (prev_date+1) and date < (prev_date+3) and date != 1:\n",
        "    print(\"date: {}, prev: {}\".format(date,prev_date))\n",
        "    test_close.append(test[\"Close\"][i-1])\n",
        "\n",
        "  test_close.append(test[\"Close\"][i])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-BXg1a2JF4X",
        "outputId": "265e2344-599f-4a9c-9233-82e44449f9ce"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "date: 5, prev: 3\n",
            "date: 24, prev: 22\n",
            "date: 5, prev: 3\n",
            "date: 23, prev: 21\n",
            "date: 6, prev: 4\n",
            "date: 26, prev: 24\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_close.insert(60, test_close[59])"
      ],
      "metadata": {
        "id": "wwJJjmzeJbo-"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(close_list)):\n",
        "  ap=i+1\n",
        "  if ap%5!=0:\n",
        "    x.append(close_list[i])\n",
        "  else:\n",
        "    y.append(close_list[i])\n",
        "    count=0\n"
      ],
      "metadata": {
        "id": "Cxsr7DqOJeq9"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(test_close)):\n",
        "  bp=i+1\n",
        "  if bp%5!=0:\n",
        "    x_test.append(test_close[i])\n",
        "  else:\n",
        "    y_test.append(test_close[i])\n",
        "    count=0\n"
      ],
      "metadata": {
        "id": "s_hD7JtSJ72Z"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x))\n",
        "print(len(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJxeycuwKJma",
        "outputId": "131fe1b0-871c-4a86-b4a5-a0eb9c529323"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1420\n",
            "354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.array(x).reshape(-1,4)\n",
        "y=np.array(y).reshape(-1,1)\n"
      ],
      "metadata": {
        "id": "ZHO0g-0KKO-6"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=np.array(x_test).reshape(-1,4)\n",
        "y_test=np.array(y_test).reshape(-1,1)"
      ],
      "metadata": {
        "id": "EOg6GhM1KcJY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "original = y[:]"
      ],
      "metadata": {
        "id": "bkdD3ys_Kjoi"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_scaler=MinMaxScaler()\n",
        "y_scaler=MinMaxScaler()\n",
        "y=y_scaler.fit_transform(y)\n",
        "x=x_scaler.fit_transform(x)\n"
      ],
      "metadata": {
        "id": "tEFAnP_wKmwB"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=x_scaler.fit_transform(x_test)\n",
        "y_test=y_scaler.fit_transform(y_test)\n",
        "x_test=np.array(x_test).reshape(-1,4,1)\n",
        "x=np.array(x).reshape(-1,4,1)"
      ],
      "metadata": {
        "id": "yJT6UMtfK7rQ"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=x[:354]"
      ],
      "metadata": {
        "id": "FWjnDkgaS_IA"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x))\n",
        "print(len(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qo1WrR1TLD5d",
        "outputId": "e370458e-6578-4194-be7f-bfeedaeec7bb"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "354\n",
            "354\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test=x_test[:101]"
      ],
      "metadata": {
        "id": "NYk2AfF7TIjf"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x_test))\n",
        "print(len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVH6ymNtTE1c",
        "outputId": "f1dd0a9d-aa32-4dce-a665-24344c5c9121"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101\n",
            "101\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(LSTM(10, return_sequences=True, input_shape=(4, 1)))\n",
        "model.add(LSTM(10))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(10, activation='relu'))\n",
        "model.add(Dense(1, activation='linear'))\n",
        "\n",
        "model.compile(loss='mse', optimizer=keras.optimizers.Adam(lr=0.01))\n"
      ],
      "metadata": {
        "id": "N81t3WpmLGnx"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x,y,epochs=20,batch_size=32,verbose=0,validation_data=(x_test,y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KBInwOU-LMN0",
        "outputId": "43cac981-c46a-463c-9d65-867a1cf01fb4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7bcff1977160>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezxxx0osLpX7",
        "outputId": "875cc273-b506-4e8a-b5e9-136bce875ae8"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 7ms/step - loss: 7.8563e-04\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5EzkmVOL1Jk",
        "outputId": "6a7cd515-e312-4efc-cb8f-250658574140"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0007856272859498858\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "plot_model(model, to_file='model.png')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        },
        "id": "vlomP6m3MF8K",
        "outputId": "a3a72590-b884-4735-ca47-8d54be19737b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=model.predict(x_test,batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0DjfcNVMMhV",
        "outputId": "797da99d-2faf-4f91-c760-17acbafab5d4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 1s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_fit=model.predict(x,batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH_vn7VWMbzM",
        "outputId": "fbcc049d-b0eb-4771-9d83-573196f8621e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "12/12 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred=y_scaler.inverse_transform(y_predict)\n",
        "actual=y_scaler.inverse_transform(y_test)\n",
        "fit=y_scaler.inverse_transform(y_fit)\n",
        "trainy=y_scaler.inverse_transform(y)"
      ],
      "metadata": {
        "id": "qNUFR2mLMgKw"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diff=pred-actual"
      ],
      "metadata": {
        "id": "qYSwTgkiM1vE"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"predicted | original | difference | accuracy\")\n",
        "for i in range(len(y_test)):\n",
        "  print(\"{} | {} | {} | {}\".format(pred[i], actual[i], diff[i] , (1-(abs(diff[i]/actual[i])))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJJ4zXsJM5Ss",
        "outputId": "daeac43e-4258-4b4f-a97d-7bfff9ef429e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted | original | difference | accuracy\n",
            "[27.92249] | [27.72093773] | [0.20155144] | [0.99272927]\n",
            "[27.92249] | [27.95623589] | [-0.03374672] | [0.99879287]\n",
            "[28.066832] | [27.94924736] | [0.11758423] | [0.99579294]\n",
            "[28.418657] | [28.27074242] | [0.14791489] | [0.99476792]\n",
            "[30.348764] | [30.64236832] | [-0.2936039] | [0.99041837]\n",
            "[31.218708] | [31.59180641] | [-0.37309837] | [0.98819003]\n",
            "[32.043015] | [32.08081055] | [-0.03779602] | [0.99882185]\n",
            "[32.11431] | [32.70787811] | [-0.59356689] | [0.98185248]\n",
            "[32.64613] | [32.52303696] | [0.12309265] | [0.99621522]\n",
            "[32.539864] | [32.86464691] | [-0.32478333] | [0.99011755]\n",
            "[32.751957] | [33.08926773] | [-0.33731079] | [0.98980604]\n",
            "[33.557568] | [33.72101974] | [-0.16345215] | [0.99515281]\n",
            "[33.566273] | [33.8731041] | [-0.30683136] | [0.99094174]\n",
            "[33.456245] | [33.1383934] | [0.31785202] | [0.99040835]\n",
            "[33.041637] | [32.91612244] | [0.12551498] | [0.99618682]\n",
            "[33.73145] | [33.61806107] | [0.11338806] | [0.99662717]\n",
            "[34.388077] | [34.40891266] | [-0.02083588] | [0.99939446]\n",
            "[35.85568] | [35.85957336] | [-0.00389481] | [0.99989139]\n",
            "[36.406055] | [35.30040741] | [1.10564804] | [0.96867889]\n",
            "[36.014977] | [36.02637863] | [-0.01140213] | [0.99968351]\n",
            "[35.77862] | [35.98879623] | [-0.21017456] | [0.99416]\n",
            "[36.454224] | [36.41404343] | [0.04018021] | [0.99889657]\n",
            "[34.074165] | [33.90014267] | [0.17402267] | [0.99486661]\n",
            "[34.107613] | [34.21496964] | [-0.10735703] | [0.99686228]\n",
            "[34.17543] | [33.75682068] | [0.41860962] | [0.98759926]\n",
            "[33.78195] | [33.53362656] | [0.24832535] | [0.99259474]\n",
            "[34.125263] | [34.71774292] | [-0.59247971] | [0.98293438]\n",
            "[35.3547] | [35.32154846] | [0.03314972] | [0.99906149]\n",
            "[35.917892] | [35.37322998] | [0.54466248] | [0.98460241]\n",
            "[36.911896] | [36.55031586] | [0.3615799] | [0.99010734]\n",
            "[37.750145] | [36.63487244] | [1.11527252] | [0.96955708]\n",
            "[37.85424] | [37.23397827] | [0.62026215] | [0.9833415]\n",
            "[37.684467] | [37.56655121] | [0.11791611] | [0.99686114]\n",
            "[38.366787] | [38.68220139] | [-0.31541443] | [0.99184601]\n",
            "[38.017174] | [37.41559982] | [0.60157394] | [0.98392184]\n",
            "[37.304348] | [37.71043396] | [-0.40608597] | [0.98923147]\n",
            "[36.070484] | [35.82585526] | [0.24462891] | [0.99317172]\n",
            "[35.987434] | [36.35182571] | [-0.36439133] | [0.98997598]\n",
            "[36.595562] | [36.63014984] | [-0.03458786] | [0.99905575]\n",
            "[36.71366] | [37.02877426] | [-0.31511307] | [0.99149005]\n",
            "[36.648655] | [36.85422897] | [-0.20557404] | [0.99442197]\n",
            "[37.091747] | [38.45812988] | [-1.3663826] | [0.9644709]\n",
            "[39.44057] | [40.68707275] | [-1.24650192] | [0.96936369]\n",
            "[41.097157] | [41.34701157] | [-0.24985504] | [0.99395712]\n",
            "[40.324417] | [40.27706146] | [0.04735565] | [0.99882425]\n",
            "[40.99227] | [41.41802216] | [-0.42575073] | [0.98972064]\n",
            "[40.49959] | [40.49010468] | [0.00948715] | [0.99976569]\n",
            "[39.84106] | [40.09241486] | [-0.25135422] | [0.99373063]\n",
            "[40.46604] | [41.18130112] | [-0.71525955] | [0.98263145]\n",
            "[41.13207] | [41.42749405] | [-0.29542542] | [0.99286886]\n",
            "[39.7698] | [40.7765274] | [-1.00672913] | [0.97531106]\n",
            "[40.87538] | [41.26652145] | [-0.39113998] | [0.99052161]\n",
            "[41.281765] | [42.39564514] | [-1.11388016] | [0.97372654]\n",
            "[41.588894] | [41.24048233] | [0.34841156] | [0.99155171]\n",
            "[39.363106] | [39.63319397] | [-0.2700882] | [0.9931853]\n",
            "[38.637936] | [37.76551056] | [0.87242508] | [0.97689889]\n",
            "[38.758408] | [39.78051758] | [-1.02210999] | [0.97430627]\n",
            "[40.369827] | [40.99981308] | [-0.62998581] | [0.98463442]\n",
            "[41.91228] | [41.59401703] | [0.31826401] | [0.99234832]\n",
            "[41.2084] | [42.05511856] | [-0.84671783] | [0.97986647]\n",
            "[41.999477] | [42.4615593] | [-0.46208191] | [0.98911764]\n",
            "[40.367958] | [40.1322937] | [0.23566437] | [0.99412781]\n",
            "[39.295578] | [39.87796783] | [-0.58238983] | [0.9853957]\n",
            "[40.680492] | [40.02058029] | [0.65991211] | [0.98351068]\n",
            "[41.050358] | [41.52984238] | [-0.47948456] | [0.98845446]\n",
            "[40.606148] | [39.38835144] | [1.21779633] | [0.96908232]\n",
            "[38.892204] | [38.58023834] | [0.31196594] | [0.99191384]\n",
            "[41.479126] | [43.69273376] | [-2.21360779] | [0.94933694]\n",
            "[44.795753] | [44.99694061] | [-0.20118713] | [0.99552887]\n",
            "[44.105186] | [44.45294189] | [-0.34775543] | [0.992177]\n",
            "[44.41728] | [44.99455643] | [-0.57727814] | [0.98717004]\n",
            "[45.107014] | [45.76998901] | [-0.66297531] | [0.98551507]\n",
            "[45.194332] | [45.62682724] | [-0.43249512] | [0.99052103]\n",
            "[44.531567] | [45.0327301] | [-0.50116348] | [0.98887113]\n",
            "[43.685474] | [43.46515656] | [0.22031784] | [0.99493116]\n",
            "[43.69525] | [44.66051865] | [-0.96526718] | [0.97838657]\n",
            "[44.479736] | [45.47175217] | [-0.99201584] | [0.97818391]\n",
            "[45.163105] | [45.55047607] | [-0.38737106] | [0.99149578]\n",
            "[45.17406] | [45.71749496] | [-0.54343414] | [0.98811321]\n",
            "[44.93824] | [45.31188965] | [-0.3736496] | [0.99175383]\n",
            "[48.859985] | [49.88340759] | [-1.02342224] | [0.97948371]\n",
            "[49.031834] | [50.01045227] | [-0.97861862] | [0.98043172]\n",
            "[51.615574] | [51.58832169] | [0.0272522] | [0.99947174]\n",
            "[51.099205] | [52.18211746] | [-1.08291245] | [0.97924744]\n",
            "[53.688534] | [54.6770134] | [-0.98847961] | [0.98192148]\n",
            "[51.357292] | [53.59717941] | [-2.23988724] | [0.95820886]\n",
            "[50.980053] | [52.25394821] | [-1.27389526] | [0.97562107]\n",
            "[52.363754] | [53.19970322] | [-0.83594894] | [0.98428659]\n",
            "[53.54676] | [54.89729691] | [-1.35053635] | [0.97539886]\n",
            "[52.77769] | [54.32025528] | [-1.54256439] | [0.97160241]\n",
            "[50.780235] | [53.19012451] | [-2.40988922] | [0.95469292]\n",
            "[52.09761] | [53.32900238] | [-1.23139191] | [0.97690953]\n",
            "[49.924877] | [51.07114792] | [-1.14627075] | [0.97755541]\n",
            "[47.70423] | [48.78935242] | [-1.08512115] | [0.97775906]\n",
            "[45.611557] | [46.18688202] | [-0.57532501] | [0.98754354]\n",
            "[43.72751] | [42.52278519] | [1.20472336] | [0.97166876]\n",
            "[41.741074] | [41.86444092] | [-0.12336731] | [0.99705317]\n",
            "[44.230167] | [42.4530983] | [1.77706909] | [0.95814041]\n",
            "[40.621857] | [40.51653671] | [0.10531998] | [0.99740057]\n",
            "[39.251133] | [39.90144348] | [-0.65031052] | [0.98370208]\n",
            "[35.25507] | [35.27867126] | [-0.02360153] | [0.999331]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy = 0\n",
        "for i in range(len(y_test)):\n",
        "  accuracy += (1-(abs(diff[i]/actual[i])))\n",
        "\n",
        "accuracy /= len(y_test)\n",
        "\n",
        "print(\"Overall accuracy: \" ,accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ap4s0XC-SB7b",
        "outputId": "616a261f-22b1-46db-a772-3f9ba63a9a5d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall accuracy:  [0.9867102]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(pred,label=\"predicted\")\n",
        "plt.plot(actual,label=\"original\")\n",
        "pl.xlabel('index')\n",
        "pl.ylabel('values')\n",
        "pl.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "tP51IAgyNHIn",
        "outputId": "b7f6d1bd-10c8-44fd-b077-7831f46f0c68"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcff180ff10>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(test[\"Close\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "fHO64sIkWz9X",
        "outputId": "c1c3bce3-df14-48c5-8552-ea70c966d07a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bcfeff71bd0>]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "cB-jJvfTXRkJ",
        "outputId": "f232c292-0396-467d-d7d0-504f7b6fe05c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f2a48991120>]"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    }
  ]
}