stock_price_pred_v3.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "This ipynb file contains only dense model with preprocessing"
      ],
      "metadata": {
        "id": "Wq8HCIeplBEG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_i3UyZaMF5Uv",
        "outputId": "5e41db4f-5a46-4896-b1e4-1e71f4aa7b29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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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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              "            Plotly.purge(gd);\n",
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              "\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": "28817c19-8878-4dc2-fed0-d3c2e619dfc4"
      },
      "execution_count": 40,
      "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": "4c964688-c94b-4a0e-e3e3-312eac17ac3f"
      },
      "execution_count": 41,
      "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": 42,
      "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": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=[]\n",
        "y=[]\n",
        "x_test=[]\n",
        "y_test=[]"
      ],
      "metadata": {
        "id": "TnfpHlB-H6Fg"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "close_list=[]\n",
        "close_list.append(train[\"Close\"][0])"
      ],
      "metadata": {
        "id": "7tsfGT-kIALQ"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_close=[]\n",
        "test_close.append(test[\"Close\"][0])\n"
      ],
      "metadata": {
        "id": "sxEIsXmtILDT"
      },
      "execution_count": 46,
      "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": "db1a19da-a38b-46be-fd40-b4c62e7211ed"
      },
      "execution_count": 47,
      "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": "f6c16957-9191-4173-be2a-7f57164d77f7"
      },
      "execution_count": 48,
      "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": 49,
      "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": 50,
      "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": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x))\n",
        "print(len(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DJxeycuwKJma",
        "outputId": "289f26b7-9f21-46f3-9557-76cfebf339cc"
      },
      "execution_count": 52,
      "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": 53,
      "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": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "original = y[:]"
      ],
      "metadata": {
        "id": "bkdD3ys_Kjoi"
      },
      "execution_count": 55,
      "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": 56,
      "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": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x=x[:354]"
      ],
      "metadata": {
        "id": "FWjnDkgaS_IA"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x))\n",
        "print(len(y))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qo1WrR1TLD5d",
        "outputId": "07dcb393-53e8-4aac-999c-658f90dddf62"
      },
      "execution_count": 59,
      "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": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(x_test))\n",
        "print(len(y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVH6ymNtTE1c",
        "outputId": "3b36bc42-1617-4a03-eb81-8100ad7cfcda"
      },
      "execution_count": 61,
      "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 Dense\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Dense(10, activation='relu', input_shape=(4,)))\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='adam')\n",
        "\n"
      ],
      "metadata": {
        "id": "N81t3WpmLGnx"
      },
      "execution_count": 62,
      "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": "94451c09-8ed3-4be0-cfe8-5e1b960045d2"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7bcfefeaddb0>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = model.evaluate(x_test, y_test, verbose=1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ezxxx0osLpX7",
        "outputId": "143eaf5a-4cca-4781-c490-5f8c7cc56d7a"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step - loss: 0.0015\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H5EzkmVOL1Jk",
        "outputId": "c5c4a196-d90b-4ed0-f2cb-66b5521e7d7e"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0015181356575340033\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": 661
        },
        "id": "vlomP6m3MF8K",
        "outputId": "bcfaf51e-85d5-4250-e02a-7de90aeaa59b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict=model.predict(x_test,batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0DjfcNVMMhV",
        "outputId": "60523484-8ccc-4af6-f82f-84b8d7704b44"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/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": "c386120e-379a-426a-dc3b-5b69260b1c1f"
      },
      "execution_count": 68,
      "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": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diff=pred-actual"
      ],
      "metadata": {
        "id": "qYSwTgkiM1vE"
      },
      "execution_count": 70,
      "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": "7dcb7e80-6521-4b55-ccab-0b375eefd47f"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "predicted | original | difference | accuracy\n",
            "[29.429861] | [27.72093773] | [1.70892334] | [0.93835261]\n",
            "[29.435505] | [27.95623016] | [1.47927475] | [0.94708604]\n",
            "[29.45132] | [27.94924927] | [1.50207138] | [0.94625718]\n",
            "[29.619438] | [28.27074432] | [1.34869385] | [0.95229366]\n",
            "[30.497044] | [30.64236832] | [-0.14532471] | [0.99525739]\n",
            "[31.28974] | [31.5918026] | [-0.30206299] | [0.99043856]\n",
            "[32.130093] | [32.08081818] | [0.04927444] | [0.99846405]\n",
            "[32.33484] | [32.70787811] | [-0.37303925] | [0.98859482]\n",
            "[33.019005] | [32.5230484] | [0.49595642] | [0.98475062]\n",
            "[32.877415] | [32.86465073] | [0.01276398] | [0.99961162]\n",
            "[33.11811] | [33.08926392] | [0.02884674] | [0.99912821]\n",
            "[33.60995] | [33.7210083] | [-0.11105728] | [0.99670658]\n",
            "[34.137077] | [33.87309647] | [0.26398087] | [0.99220677]\n",
            "[34.06947] | [33.13840485] | [0.93106461] | [0.97190376]\n",
            "[33.459072] | [32.91612625] | [0.54294586] | [0.98350517]\n",
            "[33.836216] | [33.61807632] | [0.21813965] | [0.99351124]\n",
            "[34.20055] | [34.40890884] | [-0.20835876] | [0.99394463]\n",
            "[35.229473] | [35.85957718] | [-0.63010406] | [0.98242857]\n",
            "[36.8989] | [35.30040741] | [1.59849167] | [0.95471747]\n",
            "[36.39504] | [36.02638626] | [0.36865234] | [0.98976716]\n",
            "[36.50833] | [35.98879242] | [0.51953888] | [0.98556387]\n",
            "[36.90555] | [36.41404724] | [0.49150085] | [0.98650244]\n",
            "[34.897526] | [33.90013504] | [0.99739075] | [0.97057856]\n",
            "[34.444645] | [34.21495438] | [0.22969055] | [0.99328684]\n",
            "[34.922596] | [33.75682831] | [1.16576767] | [0.96546572]\n",
            "[34.27092] | [33.53362274] | [0.73729706] | [0.9780132]\n",
            "[34.470654] | [34.71774673] | [-0.2470932] | [0.9928828]\n",
            "[35.675095] | [35.32155228] | [0.35354233] | [0.98999075]\n",
            "[36.1165] | [35.37323761] | [0.74326324] | [0.97898798]\n",
            "[36.189564] | [36.55031204] | [-0.36074829] | [0.99013009]\n",
            "[37.662704] | [36.63488007] | [1.0278244] | [0.9719441]\n",
            "[37.90935] | [37.23397064] | [0.67538071] | [0.98186117]\n",
            "[37.669514] | [37.56655121] | [0.10296249] | [0.9972592]\n",
            "[38.467884] | [38.68221283] | [-0.21432877] | [0.99445924]\n",
            "[38.82573] | [37.41559982] | [1.41012955] | [0.96231172]\n",
            "[38.304966] | [37.71043777] | [0.5945282] | [0.98423439]\n",
            "[37.528816] | [35.82584] | [1.70297623] | [0.95246514]\n",
            "[36.268036] | [36.35183334] | [-0.08379745] | [0.99769482]\n",
            "[37.011314] | [36.63016129] | [0.38115311] | [0.98959456]\n",
            "[37.203312] | [37.02877426] | [0.17453766] | [0.99528643]\n",
            "[37.860035] | [36.85424042] | [1.00579453] | [0.97270885]\n",
            "[37.514996] | [38.45812988] | [-0.94313431] | [0.97547633]\n",
            "[40.150726] | [40.68707657] | [-0.53635025] | [0.98681768]\n",
            "[41.612408] | [41.34700394] | [0.26540375] | [0.99358106]\n",
            "[41.28042] | [40.27705002] | [1.00336838] | [0.97508833]\n",
            "[41.150932] | [41.41801834] | [-0.26708603] | [0.99355145]\n",
            "[41.499077] | [40.49010086] | [1.00897598] | [0.97508092]\n",
            "[40.60288] | [40.09241867] | [0.5104599] | [0.98726792]\n",
            "[41.15974] | [41.18130875] | [-0.0215683] | [0.99947626]\n",
            "[41.904057] | [41.42749023] | [0.47656631] | [0.98849637]\n",
            "[40.61151] | [40.77652359] | [-0.16501236] | [0.99595325]\n",
            "[41.306114] | [41.26652527] | [0.03958893] | [0.99904065]\n",
            "[41.782593] | [42.39565277] | [-0.61306] | [0.98553956]\n",
            "[42.540394] | [41.24048615] | [1.29990768] | [0.96847982]\n",
            "[40.634277] | [39.63319778] | [1.00107956] | [0.97474139]\n",
            "[39.656853] | [37.76551437] | [1.89133835] | [0.9499189]\n",
            "[37.74779] | [39.78052139] | [-2.0327301] | [0.94890137]\n",
            "[41.28629] | [40.99982071] | [0.28646851] | [0.99301293]\n",
            "[42.49986] | [41.59402466] | [0.9058342] | [0.97822201]\n",
            "[42.102894] | [42.05511856] | [0.04777527] | [0.99886398]\n",
            "[43.02575] | [42.46155548] | [0.56419373] | [0.98671283]\n",
            "[41.745953] | [40.13229752] | [1.61365509] | [0.95979161]\n",
            "[40.325306] | [39.87797546] | [0.44733047] | [0.98878252]\n",
            "[40.49637] | [40.02057648] | [0.47579193] | [0.98811132]\n",
            "[41.439266] | [41.52984238] | [-0.09057617] | [0.99781901]\n",
            "[41.84977] | [39.38834381] | [2.46142578] | [0.93750878]\n",
            "[39.501244] | [38.58024216] | [0.92100143] | [0.97612764]\n",
            "[40.4388] | [43.69273376] | [-3.25393295] | [0.92552691]\n",
            "[44.772377] | [44.99694061] | [-0.2245636] | [0.99500936]\n",
            "[44.783123] | [44.45294189] | [0.33018112] | [0.99257234]\n",
            "[44.932564] | [44.99455261] | [-0.06198883] | [0.9986223]\n",
            "[45.323715] | [45.76999664] | [-0.44628143] | [0.99024948]\n",
            "[46.080635] | [45.62682724] | [0.45380783] | [0.99005393]\n",
            "[45.534225] | [45.0327301] | [0.50149536] | [0.98886376]\n",
            "[44.498917] | [43.46515274] | [1.03376389] | [0.97621626]\n",
            "[44.227398] | [44.66051865] | [-0.43312073] | [0.99030193]\n",
            "[44.522385] | [45.47174835] | [-0.94936371] | [0.9791219]\n",
            "[45.44005] | [45.55047989] | [-0.11043167] | [0.99757562]\n",
            "[45.69869] | [45.71750259] | [-0.01881409] | [0.99958847]\n",
            "[45.99386] | [45.31188965] | [0.68196869] | [0.98494945]\n",
            "[47.495674] | [49.88339615] | [-2.38772202] | [0.95213393]\n",
            "[49.3908] | [50.01045609] | [-0.61965561] | [0.98760948]\n",
            "[50.889603] | [51.5883255] | [-0.69872284] | [0.9864558]\n",
            "[51.41159] | [52.18212891] | [-0.77053833] | [0.98523367]\n",
            "[53.27683] | [54.6770134] | [-1.40018463] | [0.97439171]\n",
            "[52.936222] | [53.59716797] | [-0.66094589] | [0.98766827]\n",
            "[52.553146] | [52.25394821] | [0.29919815] | [0.99427415]\n",
            "[52.528366] | [53.19970703] | [-0.67134094] | [0.98738074]\n",
            "[53.41166] | [54.89730072] | [-1.48564148] | [0.9729378]\n",
            "[54.12415] | [54.32025528] | [-0.19610596] | [0.99638982]\n",
            "[51.220943] | [53.19012833] | [-1.96918488] | [0.96297838]\n",
            "[52.26462] | [53.32901001] | [-1.06438828] | [0.9800411]\n",
            "[51.280766] | [51.07114792] | [0.20961761] | [0.99589558]\n",
            "[50.988968] | [48.7893486] | [2.19961929] | [0.95491599]\n",
            "[48.51476] | [46.18687439] | [2.32788467] | [0.94959857]\n",
            "[44.931454] | [42.52277756] | [2.40867615] | [0.94335563]\n",
            "[42.780495] | [41.86444473] | [0.91604996] | [0.97811866]\n",
            "[44.227726] | [42.45310974] | [1.77461624] | [0.9581982]\n",
            "[42.207375] | [40.51654053] | [1.69083405] | [0.95826805]\n",
            "[40.770622] | [39.90145493] | [0.86916733] | [0.97821715]\n",
            "[37.704433] | [35.27867126] | [2.42576218] | [0.93123998]\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": "d60e03cc-bcae-4fae-91b4-8dbb9a589ff9"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall accuracy:  [0.97976699]\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": "03a7e90f-30aa-4428-dd03-682536750ab9"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bcfefb1b580>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "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": "d4b79e10-c8ff-44e5-b27e-34996a47b0a4"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bcfefcec040>]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "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": "108433ff-aa3e-445d-8421-360883e4e544"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7bcfefbe5330>]"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oymKvZ1dnOR9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}