Stock_Price_perdiction_using_LSTM.ipynb

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      "name": "Stock Price perdiction using LSTM.ipynb",
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      "cell_type": "markdown",
      "source": [
        "LSTM refers to Long Short Term Memory Networks. It is a type of recurrent neural network that is commonly used for regression and time series forecasting in ML. It can memorize data for long periods, which differentiates LSTM neural networks from other neural networks. \n",
        "In this notebook, we will learn how to predict stock prices with LSTM!"
      ],
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        "!pip install yfinance"
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            "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (0.1.70)\n",
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            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.3.5)\n",
            "Requirement already satisfied: requests>=2.26 in /usr/local/lib/python3.7/dist-packages (from yfinance) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from yfinance) (1.21.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->yfinance) (2018.9)\n",
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      "cell_type": "markdown",
      "source": [
        "Stock Price Prediction with LSTM\n",
        "Using LSTM is one of the best machine learning approaches for time series forecasting. LSTMs are recurrent neural networks designed to remember data for a longer period. So, whenever you are working on a problem where your neural network fails to memorize data, you can use LSTM neural network. You can read more about LSTMs here (https://web.stanford.edu/class/cs379c/archive/2018/class_messages_listing/content/Artificial_Neural_Network_Technology_Tutorials/OlahLSTM-NEURAL-NETWORK-TUTORIAL-15.pdf)\n",
        "\n",
        "Now in this section, I will take you through the task of Stock price prediction with LSTM using the Python programming language. I will start this task by importing all the necessary Python libraries and collecting the latest stock price data of Apple:"
      ],
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      "source": [
        "import pandas as pd\n",
        "import yfinance as yf\n",
        "import datetime\n",
        "from datetime import date, timedelta\n",
        "today = date.today()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d1 = today.strftime(\"%Y-%m-%d\")\n",
        "end_date = d1\n",
        "d2 = date.today() - timedelta(days=5000)\n",
        "d2 = d2.strftime(\"%Y-%m-%d\")\n",
        "start_date = d2"
      ],
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        "id": "ol2Bxdy_pr1t"
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      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "data = yf.download('AAPL', \n",
        "                      start=start_date, \n",
        "                      end=end_date, \n",
        "                      progress=False)\n",
        "data[\"Date\"] = data.index\n",
        "data = data[[\"Date\", \"Open\", \"High\", \"Low\", \"Close\", \n",
        "             \"Adj Close\", \"Volume\"]]\n",
        "data.reset_index(drop=True, inplace=True)\n",
        "data.tail()"
      ],
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      "execution_count": 4,
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              "           Date        Open        High         Low       Close   Adj Close  \\\n",
              "3443 2022-03-23  167.990005  172.639999  167.649994  170.210007  170.210007   \n",
              "3444 2022-03-24  171.059998  174.139999  170.210007  174.070007  174.070007   \n",
              "3445 2022-03-25  173.880005  175.279999  172.750000  174.720001  174.720001   \n",
              "3446 2022-03-28  172.169998  175.729996  172.000000  175.600006  175.600006   \n",
              "3447 2022-03-29  176.690002  179.009995  176.339996  178.960007  178.960007   \n",
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      "cell_type": "markdown",
      "source": [
        "A candlestick chart gives a clear picture of the increase and decrease in stock prices, so let’s visualize a candlestick chart of the data before moving further:"
      ],
      "metadata": {
        "id": "S2JF5T46q-eK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "figure = go.Figure(data=[go.Candlestick(x=data[\"Date\"],\n",
        "                                        open=data[\"Open\"], \n",
        "                                        high=data[\"High\"],\n",
        "                                        low=data[\"Low\"], \n",
        "                                        close=data[\"Close\"])])\n",
        "figure.update_layout(title = \"Apple Stock Price Analysis\", \n",
        "                     xaxis_rangeslider_visible=False)\n",
        "figure.show()"
      ],
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          "metadata": {}
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    {
      "cell_type": "code",
      "source": [
        "correlation = data.corr()\n",
        "print(correlation[\"Close\"].sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uNUyx7rpvlQ",
        "outputId": "48805e7b-48cf-4ca9-f93f-ba8b5b7b7968"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Close        1.000000\n",
            "High         0.999887\n",
            "Low          0.999885\n",
            "Adj Close    0.999873\n",
            "Open         0.999774\n",
            "Volume      -0.485205\n",
            "Name: Close, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training LSTM for Stock Price Prediction - \n",
        "Now I will start with training an LSTM model for predicting stock prices. I will first split the data into training and test sets:"
      ],
      "metadata": {
        "id": "akJOuO6HrFfz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = data[[\"Open\", \"High\", \"Low\", \"Volume\"]]\n",
        "y = data[\"Close\"]\n",
        "x = x.to_numpy()\n",
        "y = y.to_numpy()\n",
        "y = y.reshape(-1, 1)\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "NrJ4hFLIpvik"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM\n",
        "\n",
        "# Preparing LSTM model \n",
        "model = Sequential()\n",
        "model.add(LSTM(128, return_sequences=True, input_shape= (xtrain.shape[1], 1)))\n",
        "model.add(LSTM(64, return_sequences=False))\n",
        "model.add(Dense(25))\n",
        "model.add(Dense(1))\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BI7wKO-OpvfN",
        "outputId": "07e67e9b-7d78-4c04-d770-4cc9e67867b8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 4, 128)            66560     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 64)                49408     \n",
            "                                                                 \n",
            " dense (Dense)               (None, 25)                1625      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 26        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 117,619\n",
            "Trainable params: 117,619\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train our model for stock price prediction\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "history = model.fit(xtrain, ytrain, batch_size=1, epochs=30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkDlNvvppvXV",
        "outputId": "bf584204-09b2-44dd-de10-38387e934ab3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "2758/2758 [==============================] - 31s 10ms/step - loss: 159.3900\n",
            "Epoch 2/30\n",
            "2758/2758 [==============================] - 18s 6ms/step - loss: 13.7035\n",
            "Epoch 3/30\n",
            "2758/2758 [==============================] - 15s 5ms/step - loss: 12.8779\n",
            "Epoch 4/30\n",
            "2758/2758 [==============================] - 15s 5ms/step - loss: 12.5821\n",
            "Epoch 5/30\n",
            "2758/2758 [==============================] - 15s 5ms/step - loss: 9.4496\n",
            "Epoch 6/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 7.4917\n",
            "Epoch 7/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 9.9144\n",
            "Epoch 8/30\n",
            "2758/2758 [==============================] - 18s 6ms/step - loss: 7.8939\n",
            "Epoch 9/30\n",
            "2758/2758 [==============================] - 17s 6ms/step - loss: 5.6634\n",
            "Epoch 10/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.6043\n",
            "Epoch 11/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 8.5266\n",
            "Epoch 12/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 6.6700\n",
            "Epoch 13/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.0062\n",
            "Epoch 14/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 7.1449\n",
            "Epoch 15/30\n",
            "2758/2758 [==============================] - 17s 6ms/step - loss: 3.9408\n",
            "Epoch 16/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.5667\n",
            "Epoch 17/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.6848\n",
            "Epoch 18/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 4.3060\n",
            "Epoch 19/30\n",
            "2758/2758 [==============================] - 17s 6ms/step - loss: 6.2376\n",
            "Epoch 20/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 4.1795\n",
            "Epoch 21/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.1284\n",
            "Epoch 22/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.1869\n",
            "Epoch 23/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 3.8296\n",
            "Epoch 24/30\n",
            "2758/2758 [==============================] - 17s 6ms/step - loss: 4.2077\n",
            "Epoch 25/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 5.4618\n",
            "Epoch 26/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 2.8481\n",
            "Epoch 27/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 4.4339\n",
            "Epoch 28/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 3.5210\n",
            "Epoch 29/30\n",
            "2758/2758 [==============================] - 16s 6ms/step - loss: 3.5734\n",
            "Epoch 30/30\n",
            "2758/2758 [==============================] - 18s 7ms/step - loss: 4.2106\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# list all data in model\n",
        "print(history.history.keys())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umFuRuvTtEc_",
        "outputId": "d3b5616a-bd47-4dca-d141-d5da4d34bbe0"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_keys(['loss'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summarize history for loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "2af4iNGewWir",
        "outputId": "f58c2beb-165c-4b5d-e3f4-894f8c365b9f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now let’s test this model by giving input values according to the features that we have used to train this model and predicting the final result:"
      ],
      "metadata": {
        "id": "HFsEagUNrXcF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# features = [Open, High, Low, Adj Close, Volume]\n",
        "features = np.array([[177.089996, 180.419998, 177.070007, 74919600]])\n",
        "model.predict(features)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RuHUeg1QpvL7",
        "outputId": "a054dd45-99ef-4d82-d6e3-5b399c65e58d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[174.67531]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}