ECE1895_JuniorDesign_TradingBot.ipynb

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        "<a href=\"https://colab.research.google.com/github/shreydawg23/ECE1895-BopIt/blob/main/ECE1895_JuniorDesign_TradingBot.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# ECE1895 Junior Design - Design Project 3\n",
        "Python Trading Bot created by Shreyas Vivekanandan\n",
        "\n",
        "#How to run code\n",
        "\n",
        "\n",
        "1.   Click Connect in top right corner to activate VM\n",
        "2.   Click Runtime\n",
        "3.   Click Run All\n",
        "4.   Scroll down to enter what ticker you would like to see \n",
        "5.   Thats it! Everything else will be done for you. \n",
        "\n",
        "\n",
        "\n"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "#Install packages and dependencies:\n",
        "\n",
        "*   alpaca-trade-api\n",
        "*   backtrader\n",
        "*alpaca backtrader api\n",
        "\n",
        "The alpaca trade api is an interface that allows you to trade automatically with the stock broker Alpaca. More specifically, the trading API allows you to send orders directly to Alpaca's servers to automate the process of trading, bypassing a traditional client. With this, I am able to get real time market information and am able to chart these tickers.\n",
        "\n",
        "https://alpaca.markets/docs/\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
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            "  Attempting uninstall: chardet\n",
            "    Found existing installation: chardet 4.0.0\n",
            "    Uninstalling chardet-4.0.0:\n",
            "      Successfully uninstalled chardet-4.0.0\n",
            "  Attempting uninstall: websockets\n",
            "    Found existing installation: websockets 10.4\n",
            "    Uninstalling websockets-10.4:\n",
            "      Successfully uninstalled websockets-10.4\n",
            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 6.0\n",
            "    Uninstalling PyYAML-6.0:\n",
            "      Successfully uninstalled PyYAML-6.0\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.21.6\n",
            "    Uninstalling numpy-1.21.6:\n",
            "      Successfully uninstalled numpy-1.21.6\n",
            "  Attempting uninstall: async-timeout\n",
            "    Found existing installation: async-timeout 4.0.2\n",
            "    Uninstalling async-timeout-4.0.2:\n",
            "      Successfully uninstalled async-timeout-4.0.2\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "  Attempting uninstall: matplotlib\n",
            "    Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Attempting uninstall: aiohttp\n",
            "    Found existing installation: aiohttp 3.8.1\n",
            "    Uninstalling aiohttp-3.8.1:\n",
            "      Successfully uninstalled aiohttp-3.8.1\n",
            "  Attempting uninstall: alpaca-trade-api\n",
            "    Found existing installation: alpaca-trade-api 2.3.0\n",
            "    Uninstalling alpaca-trade-api-2.3.0:\n",
            "      Successfully uninstalled alpaca-trade-api-2.3.0\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",
            "plotnine 0.8.0 requires matplotlib>=3.1.1, but you have matplotlib 2.2.5 which is incompatible.\n",
            "mizani 0.7.3 requires matplotlib>=3.1.1, but you have matplotlib 2.2.5 which is incompatible.\n",
            "datascience 0.17.5 requires matplotlib>=3.0.0, but you have matplotlib 2.2.5 which is incompatible.\n",
            "arviz 0.12.1 requires matplotlib>=3.0, but you have matplotlib 2.2.5 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed PyYAML-5.4.1 aiohttp-3.7.4 alpaca-trade-api-1.4.3 alpaca_backtrader_api-0.15.0 async-timeout-3.0.1 chardet-3.0.4 exchange-calendars-3.4 matplotlib-2.2.5 msgpack-1.0.2 numpy-1.21.2 pandas-1.3.2 pyluach-2.0.2 websockets-9.1\n"
          ]
        }
      ],
      "source": [
        "!pip install alpaca-trade-api\n",
        "!pip install backtrader\n",
        "!pip install alpaca_backtrader_api\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Set API websocket using account key, secret key, and URL websocket"
      ],
      "metadata": {
        "id": "knBp9gE8cmI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import alpaca_trade_api as alpaca\n",
        "\n",
        "\n",
        "api = alpaca.REST('PK80TAZMKNWDRL1YSDIC', 'h8EnTTRSjjkgzeWldWcPNe4bXaQONgk2aiyHd2sN', \"https://paper-api.alpaca.markets\")"
      ],
      "metadata": {
        "id": "8zUQClIZ5MQ5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Show account information\n"
      ],
      "metadata": {
        "id": "zRWV19aydFlf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "api.get_account()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38q3MLv2dAyE",
        "outputId": "3a7d9c1a-3a98-43c0-8f19-3342b1f3b12b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Account({   'account_blocked': False,\n",
              "    'account_number': 'PA3FSNZKJ9IT',\n",
              "    'accrued_fees': '0',\n",
              "    'balance_asof': '2023-01-06',\n",
              "    'bod_dtbp': '0',\n",
              "    'buying_power': '101826.17',\n",
              "    'cash': '291.18',\n",
              "    'created_at': '2022-10-31T20:24:45.558013Z',\n",
              "    'crypto_status': 'ACTIVE',\n",
              "    'crypto_tier': 0,\n",
              "    'currency': 'USD',\n",
              "    'daytrade_count': 0,\n",
              "    'daytrading_buying_power': '0',\n",
              "    'effective_buying_power': '101826.17',\n",
              "    'equity': '101534.99',\n",
              "    'id': '9c3df13c-4502-4a60-ba57-644d7ee56e41',\n",
              "    'initial_margin': '50621.905',\n",
              "    'last_equity': '101570.03',\n",
              "    'last_maintenance_margin': '30383.66',\n",
              "    'long_market_value': '101243.81',\n",
              "    'maintenance_margin': '30373.143',\n",
              "    'multiplier': '2',\n",
              "    'non_marginable_buying_power': '48913.08',\n",
              "    'pattern_day_trader': False,\n",
              "    'pending_transfer_in': '0',\n",
              "    'portfolio_value': '101534.99',\n",
              "    'position_market_value': '101243.81',\n",
              "    'regt_buying_power': '101826.17',\n",
              "    'short_market_value': '0',\n",
              "    'shorting_enabled': True,\n",
              "    'sma': '99803.38',\n",
              "    'status': 'ACTIVE',\n",
              "    'trade_suspended_by_user': False,\n",
              "    'trading_blocked': False,\n",
              "    'transfers_blocked': False})"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "use get api method to retrieve data of ticker\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "C8VwuUZwhymp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datetime import date\n",
        "\n",
        "today = date.today()\n",
        "\n",
        "date = today.strftime(\"%Y-%m-%d\")\n",
        "\n",
        "\n",
        "ticker = input('Enter Ticker: ')\n",
        "\n"
      ],
      "metadata": {
        "id": "pkYSftkNdKOV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24165eac-7fe9-41ce-c975-ce59baaa3498"
      },
      "execution_count": 7,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Ticker: SPY\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "import plotly library to use graphing objects to plot the recently acquired data\n",
        "import numpy to apply mathmatical equations on data\n",
        "import pandas for more data extrapolation and manipulation\n"
      ],
      "metadata": {
        "id": "_a8h8ER1mmTB"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "eznFKxlgYZae"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.graph_objects as go\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import pandas as pd\n",
        "import datetime as dt\n",
        "import matplotlib.pyplot as plt\n",
        "import math\n",
        "plt.style.use('fivethirtyeight')\n",
        "\n",
        "from plotly.subplots import make_subplots\n",
        "from alpaca_trade_api import REST, TimeFrame\n",
        "\n",
        "#get historical data of the last year\n",
        "df = api.get_bars(symbol=ticker, start = \"2022-01-01\",timeframe = '1Day', limit = 355).df\n",
        "\n",
        "#set the date as the index in our table\n",
        "df['Date'] = df.index.date\n",
        "df['Time'] = df.index.time\n",
        "df.set_index(df['Date'])\n",
        "\n",
        "#graph data\n",
        "\n",
        "plt.figure(figsize = (12,4))\n",
        "plt.plot(df.close)\n",
        "plt.title(ticker)\n",
        "plt.xlabel('Date')\n",
        "plt.ylabel('Price ($USD)')\n",
        "\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "7rLKXh5Vimo9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "outputId": "0f50e449-9850-4848-d37f-31f4f99ff518"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Below we are calculating Fibonacci Retracement Levels based on the datas min and max.\n",
        "\n",
        "Fibonacci retracement is a method of technical analysis for determining support and resistance levels. It is named after the Fibonacci sequence of numbers, whose ratios provide price levels to which markets tend to retrace a portion of a move, before a trend continues in the original direction. There are many other ratio levels people use but I decided to stick to the main 5 with the .618 ratio being the most important as its know as the \"golden ratio.\""
      ],
      "metadata": {
        "id": "OpHOg3VO73BL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate Fibonacci Retracement Levels\n",
        "\n",
        "max_price = df['close'].max()\n",
        "min_price = df['close'].min()\n",
        "\n",
        "#diffenece between the max and min to create ratio levels calculated in the next step\n",
        "difference = max_price - min_price\n",
        "\n",
        "l1 = max_price- difference *0.236\n",
        "l2 = max_price - difference * 0.382\n",
        "l3 = max_price - difference * 0.5\n",
        "l4 = max_price - difference * 0.618 \n",
        "l5 = max_price - difference * 0.786\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "BMqhBZimxeNV"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this step we are calculating the datas Exponential Moving Averages(EMA) in the short and long term to be used in the Moving Averages Convergence/Divergence. I first calculated the short EMA using a 12 day average and then calculated the longEMA using the 26 day average as this is a common standard. From there we subtract the short from the long to get the EMA which is now used to calculate the signal line. The signal line is the moving average of the MACD and we use the 9 day average as this is standard. "
      ],
      "metadata": {
        "id": "sm_yxGkc9qpl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate MACD lines, Signal Lines, EMA\n",
        "\n",
        "\n",
        "#Short Term Exponential Moving Average(EMA)\n",
        "shortEMA = df.close.ewm(span = 12, adjust = False).mean()\n",
        "\n",
        "#Long Term Exponential Moving Average\n",
        "longEMA = df.close.ewm(span = 26, adjust = False).mean()\n",
        "\n",
        "#Calc EMA Convergence and Divergance\n",
        "MACD = shortEMA - longEMA\n",
        "\n",
        "#Signal line calculation\n",
        "sig = MACD.ewm(span = 9, adjust = False).mean()\n"
      ],
      "metadata": {
        "id": "PcGImjvnz7UZ"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this step we plot all the data calculated in the previous step using matplotlib.pyplot\n"
      ],
      "metadata": {
        "id": "IPnqcTQODfNL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot Fibs,MACD, signal\n",
        "\n",
        "new_df = df\n",
        "\n",
        "#Fibs plot\n",
        "plt.figure(figsize=(12.33,9.5))\n",
        "plt.subplot(2,1,1)\n",
        "\n",
        "plt.plot(new_df.index, new_df['close'])\n",
        "\n",
        "plt.axhline(max_price, linestyle = '--', alpha=0.5, color ='black')\n",
        "plt.axhline(l1, linestyle = '--', alpha=0.5, color ='red')\n",
        "plt.axhline(l2, linestyle = '--', alpha=0.5, color ='orange')\n",
        "plt.axhline(l3, linestyle = '--', alpha=0.5, color ='blue')\n",
        "plt.axhline(l4, linestyle = '--', alpha=0.5, color ='gold')\n",
        "plt.axhline(l5, linestyle = '--', alpha=0.5, color ='green')\n",
        "plt.axhline(min_price, linestyle = '--', alpha=0.5, color ='black')\n",
        "plt.ylabel('Fibonacci')\n",
        "\n",
        "#Plot the MACD and Signal\n",
        "plt.subplot(2,1,2)\n",
        "plt.plot(new_df.index, MACD)\n",
        "plt.plot(new_df.index, sig)\n",
        "plt.ylabel('MACD')\n",
        "plt.xticks(rotation = 45)\n"
      ],
      "metadata": {
        "id": "OU6mSoKC06Js",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "b36505a3-3849-4935-ef63-a9f726e1096d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([738156., 738215., 738276., 738337., 738399., 738460., 738521.]),\n",
              " <a list of 7 Text major ticklabel objects>)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 887.76x684 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create columns for data frame for signal in our dataset\n",
        "\n",
        "df['MACD'] = MACD\n",
        "df['Signal Line'] = sig\n",
        "\n",
        "df\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "orjbSer3P6zs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "e01dc27b-f1f1-4d1e-bc7f-21a6fbc1368a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             open    high       low   close     volume  \\\n",
              "timestamp                                                                \n",
              "2022-01-03 05:00:00+00:00  476.30  477.85  473.8500  477.71   72604064   \n",
              "2022-01-04 05:00:00+00:00  479.22  479.98  475.5800  477.55   71129958   \n",
              "2022-01-05 05:00:00+00:00  477.16  477.98  468.2900  468.38  102085598   \n",
              "2022-01-06 05:00:00+00:00  467.89  470.82  465.4300  467.94   86499197   \n",
              "2022-01-07 05:00:00+00:00  467.95  469.20  464.6500  466.09   84895774   \n",
              "...                           ...     ...       ...     ...        ...   \n",
              "2022-12-30 05:00:00+00:00  380.64  382.58  378.4300  382.43   83474793   \n",
              "2023-01-03 05:00:00+00:00  384.37  386.43  377.8310  380.82   74330774   \n",
              "2023-01-04 05:00:00+00:00  383.18  385.88  380.0000  383.76   85425119   \n",
              "2023-01-05 05:00:00+00:00  381.72  381.76  378.7600  379.38   76079710   \n",
              "2023-01-06 05:00:00+00:00  382.61  389.25  379.4127  388.08  103741286   \n",
              "\n",
              "                           trade_count        vwap        Date      Time  \\\n",
              "timestamp                                                                  \n",
              "2022-01-03 05:00:00+00:00       534803  476.526964  2022-01-03  05:00:00   \n",
              "2022-01-04 05:00:00+00:00       565259  477.868706  2022-01-04  05:00:00   \n",
              "2022-01-05 05:00:00+00:00       784945  473.338984  2022-01-05  05:00:00   \n",
              "2022-01-06 05:00:00+00:00       803296  468.480666  2022-01-06  05:00:00   \n",
              "2022-01-07 05:00:00+00:00       623749  466.940793  2022-01-07  05:00:00   \n",
              "...                                ...         ...         ...       ...   \n",
              "2022-12-30 05:00:00+00:00       544591  381.220108  2022-12-30  05:00:00   \n",
              "2023-01-03 05:00:00+00:00       585048  380.961150  2023-01-03  05:00:00   \n",
              "2023-01-04 05:00:00+00:00       628498  383.151245  2023-01-04  05:00:00   \n",
              "2023-01-05 05:00:00+00:00       523811  380.264217  2023-01-05  05:00:00   \n",
              "2023-01-06 05:00:00+00:00       683816  385.248218  2023-01-06  05:00:00   \n",
              "\n",
              "                               MACD  Signal Line  \n",
              "timestamp                                         \n",
              "2022-01-03 05:00:00+00:00  0.000000     0.000000  \n",
              "2022-01-04 05:00:00+00:00 -0.012764    -0.002553  \n",
              "2022-01-05 05:00:00+00:00 -0.754128    -0.152868  \n",
              "2022-01-06 05:00:00+00:00 -1.361475    -0.394589  \n",
              "2022-01-07 05:00:00+00:00 -1.969379    -0.709547  \n",
              "...                             ...          ...  \n",
              "2022-12-30 05:00:00+00:00 -3.235339    -1.975497  \n",
              "2023-01-03 05:00:00+00:00 -3.292801    -2.238958  \n",
              "2023-01-04 05:00:00+00:00 -3.065766    -2.404319  \n",
              "2023-01-05 05:00:00+00:00 -3.202354    -2.563926  \n",
              "2023-01-06 05:00:00+00:00 -2.578856    -2.566912  \n",
              "\n",
              "[255 rows x 11 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ea5f206b-a66c-4249-b62e-39e57483df68\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "    }\n",
              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>trade_count</th>\n",
              "      <th>vwap</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Signal Line</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-03 05:00:00+00:00</th>\n",
              "      <td>476.30</td>\n",
              "      <td>477.85</td>\n",
              "      <td>473.8500</td>\n",
              "      <td>477.71</td>\n",
              "      <td>72604064</td>\n",
              "      <td>534803</td>\n",
              "      <td>476.526964</td>\n",
              "      <td>2022-01-03</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-04 05:00:00+00:00</th>\n",
              "      <td>479.22</td>\n",
              "      <td>479.98</td>\n",
              "      <td>475.5800</td>\n",
              "      <td>477.55</td>\n",
              "      <td>71129958</td>\n",
              "      <td>565259</td>\n",
              "      <td>477.868706</td>\n",
              "      <td>2022-01-04</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-0.012764</td>\n",
              "      <td>-0.002553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05 05:00:00+00:00</th>\n",
              "      <td>477.16</td>\n",
              "      <td>477.98</td>\n",
              "      <td>468.2900</td>\n",
              "      <td>468.38</td>\n",
              "      <td>102085598</td>\n",
              "      <td>784945</td>\n",
              "      <td>473.338984</td>\n",
              "      <td>2022-01-05</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-0.754128</td>\n",
              "      <td>-0.152868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06 05:00:00+00:00</th>\n",
              "      <td>467.89</td>\n",
              "      <td>470.82</td>\n",
              "      <td>465.4300</td>\n",
              "      <td>467.94</td>\n",
              "      <td>86499197</td>\n",
              "      <td>803296</td>\n",
              "      <td>468.480666</td>\n",
              "      <td>2022-01-06</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-1.361475</td>\n",
              "      <td>-0.394589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07 05:00:00+00:00</th>\n",
              "      <td>467.95</td>\n",
              "      <td>469.20</td>\n",
              "      <td>464.6500</td>\n",
              "      <td>466.09</td>\n",
              "      <td>84895774</td>\n",
              "      <td>623749</td>\n",
              "      <td>466.940793</td>\n",
              "      <td>2022-01-07</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-1.969379</td>\n",
              "      <td>-0.709547</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-30 05:00:00+00:00</th>\n",
              "      <td>380.64</td>\n",
              "      <td>382.58</td>\n",
              "      <td>378.4300</td>\n",
              "      <td>382.43</td>\n",
              "      <td>83474793</td>\n",
              "      <td>544591</td>\n",
              "      <td>381.220108</td>\n",
              "      <td>2022-12-30</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.235339</td>\n",
              "      <td>-1.975497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-03 05:00:00+00:00</th>\n",
              "      <td>384.37</td>\n",
              "      <td>386.43</td>\n",
              "      <td>377.8310</td>\n",
              "      <td>380.82</td>\n",
              "      <td>74330774</td>\n",
              "      <td>585048</td>\n",
              "      <td>380.961150</td>\n",
              "      <td>2023-01-03</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.292801</td>\n",
              "      <td>-2.238958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-04 05:00:00+00:00</th>\n",
              "      <td>383.18</td>\n",
              "      <td>385.88</td>\n",
              "      <td>380.0000</td>\n",
              "      <td>383.76</td>\n",
              "      <td>85425119</td>\n",
              "      <td>628498</td>\n",
              "      <td>383.151245</td>\n",
              "      <td>2023-01-04</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.065766</td>\n",
              "      <td>-2.404319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-05 05:00:00+00:00</th>\n",
              "      <td>381.72</td>\n",
              "      <td>381.76</td>\n",
              "      <td>378.7600</td>\n",
              "      <td>379.38</td>\n",
              "      <td>76079710</td>\n",
              "      <td>523811</td>\n",
              "      <td>380.264217</td>\n",
              "      <td>2023-01-05</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.202354</td>\n",
              "      <td>-2.563926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-06 05:00:00+00:00</th>\n",
              "      <td>382.61</td>\n",
              "      <td>389.25</td>\n",
              "      <td>379.4127</td>\n",
              "      <td>388.08</td>\n",
              "      <td>103741286</td>\n",
              "      <td>683816</td>\n",
              "      <td>385.248218</td>\n",
              "      <td>2023-01-06</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-2.578856</td>\n",
              "      <td>-2.566912</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255 rows × 11 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#function to be used in strat to get upper/lower fibs based on where current price is trading\n",
        "\n",
        "def getFibLevels(price):\n",
        "  if price >= l1:\n",
        "    return(max_price, l1)\n",
        "  elif price >= l2:\n",
        "    return(l1,l2)\n",
        "  elif price >=l3:\n",
        "    return(l2,l3)\n",
        "  elif price >=l4:\n",
        "    return(l3,l4)\n",
        "  elif price >= l5:\n",
        "    return(l4,l5)\n",
        "  else:\n",
        "    return(l5,min_price)\n"
      ],
      "metadata": {
        "id": "FiWwsojU-yTL"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function for my trading strategy\n",
        "\n",
        "1) When signal line crosses ABOVE the MACD Line and the current price corssed above or below the last fibs level then BUY\n",
        "\n",
        "2) When signal line crosses BELOW the MACD Line and then current price crossed above or below the last fibs level then SELL\n",
        "\n",
        "3) Never sell at a price thats lower than what I bought it at\n",
        "\n",
        "\n",
        "function returns buy and sell arrays for each trade"
      ],
      "metadata": {
        "id": "KoBNo0CwGfC9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def strategy_algo(df):\n",
        "\n",
        "  buy_list = []\n",
        "  sell_list = []\n",
        "\n",
        "  flag = 0 #flag for buy or sell 0 - SELL 1 - BUY\n",
        "\n",
        "  last_buy_price = 0;\n",
        "\n",
        "  #loop through the data\n",
        "  for i in range(0, df.shape[0]):\n",
        "    price = df['close'][i]\n",
        "\n",
        "    #if this is the first data point within the data set, then get the level above or below it\n",
        "    if i == 0:\n",
        "      upper_lvl, lower_lvl = getFibLevels(price)\n",
        "      buy_list.append(np.nan)\n",
        "      sell_list.append(np.nan)\n",
        "    \n",
        "    #else if the current price is greater than or equal to the upper_lvl, or less than or equal to the lower_lvl then we know price has hit/crossed a new fib level\n",
        "    elif price >= upper_lvl or price <= lower_lvl:\n",
        "\n",
        "      #Check if MACD line crossed above or below the signal line\n",
        "      if df['Signal Line'][i] > df['MACD'][i] and flag == 0:\n",
        "        last_buy_price = price\n",
        "        buy_list.append(price)\n",
        "        sell_list.append(np.nan)\n",
        "\n",
        "        flag = 1\n",
        "\n",
        "      elif df['Signal Line'][i] < df['MACD'][i] and flag == 1 and price >= last_buy_price:\n",
        "        buy_list.append(np.nan)\n",
        "        sell_list.append(price)\n",
        "\n",
        "        flag = 0\n",
        "      \n",
        "      else:\n",
        "        buy_list.append(np.nan)\n",
        "        sell_list.append(np.nan)\n",
        "    \n",
        "    else:\n",
        "      buy_list.append(np.nan)\n",
        "      sell_list.append(np.nan)\n",
        "      \n",
        "      #update new levels\n",
        "      upper_lvl, lower_lvl = getFibLevels(price)\n",
        "  \n",
        "  return buy_list, sell_list\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "KnvOOzIqL716"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create buy and sell columns\n",
        "\n",
        "\n",
        "buy, sell = strategy_algo(df)\n",
        "\n",
        "df['Buy_Signal_Price'] = buy\n",
        "df['Sell_Signal_Price'] = sell\n",
        "\n",
        "df"
      ],
      "metadata": {
        "id": "yegTSKa9UkJO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "outputId": "365b6b61-8193-4ced-d8c7-9845e93af3f6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             open    high       low   close     volume  \\\n",
              "timestamp                                                                \n",
              "2022-01-03 05:00:00+00:00  476.30  477.85  473.8500  477.71   72604064   \n",
              "2022-01-04 05:00:00+00:00  479.22  479.98  475.5800  477.55   71129958   \n",
              "2022-01-05 05:00:00+00:00  477.16  477.98  468.2900  468.38  102085598   \n",
              "2022-01-06 05:00:00+00:00  467.89  470.82  465.4300  467.94   86499197   \n",
              "2022-01-07 05:00:00+00:00  467.95  469.20  464.6500  466.09   84895774   \n",
              "...                           ...     ...       ...     ...        ...   \n",
              "2022-12-30 05:00:00+00:00  380.64  382.58  378.4300  382.43   83474793   \n",
              "2023-01-03 05:00:00+00:00  384.37  386.43  377.8310  380.82   74330774   \n",
              "2023-01-04 05:00:00+00:00  383.18  385.88  380.0000  383.76   85425119   \n",
              "2023-01-05 05:00:00+00:00  381.72  381.76  378.7600  379.38   76079710   \n",
              "2023-01-06 05:00:00+00:00  382.61  389.25  379.4127  388.08  103741286   \n",
              "\n",
              "                           trade_count        vwap        Date      Time  \\\n",
              "timestamp                                                                  \n",
              "2022-01-03 05:00:00+00:00       534803  476.526964  2022-01-03  05:00:00   \n",
              "2022-01-04 05:00:00+00:00       565259  477.868706  2022-01-04  05:00:00   \n",
              "2022-01-05 05:00:00+00:00       784945  473.338984  2022-01-05  05:00:00   \n",
              "2022-01-06 05:00:00+00:00       803296  468.480666  2022-01-06  05:00:00   \n",
              "2022-01-07 05:00:00+00:00       623749  466.940793  2022-01-07  05:00:00   \n",
              "...                                ...         ...         ...       ...   \n",
              "2022-12-30 05:00:00+00:00       544591  381.220108  2022-12-30  05:00:00   \n",
              "2023-01-03 05:00:00+00:00       585048  380.961150  2023-01-03  05:00:00   \n",
              "2023-01-04 05:00:00+00:00       628498  383.151245  2023-01-04  05:00:00   \n",
              "2023-01-05 05:00:00+00:00       523811  380.264217  2023-01-05  05:00:00   \n",
              "2023-01-06 05:00:00+00:00       683816  385.248218  2023-01-06  05:00:00   \n",
              "\n",
              "                               MACD  Signal Line  Buy_Signal_Price  \\\n",
              "timestamp                                                            \n",
              "2022-01-03 05:00:00+00:00  0.000000     0.000000               NaN   \n",
              "2022-01-04 05:00:00+00:00 -0.012764    -0.002553               NaN   \n",
              "2022-01-05 05:00:00+00:00 -0.754128    -0.152868               NaN   \n",
              "2022-01-06 05:00:00+00:00 -1.361475    -0.394589               NaN   \n",
              "2022-01-07 05:00:00+00:00 -1.969379    -0.709547               NaN   \n",
              "...                             ...          ...               ...   \n",
              "2022-12-30 05:00:00+00:00 -3.235339    -1.975497               NaN   \n",
              "2023-01-03 05:00:00+00:00 -3.292801    -2.238958               NaN   \n",
              "2023-01-04 05:00:00+00:00 -3.065766    -2.404319               NaN   \n",
              "2023-01-05 05:00:00+00:00 -3.202354    -2.563926               NaN   \n",
              "2023-01-06 05:00:00+00:00 -2.578856    -2.566912               NaN   \n",
              "\n",
              "                           Sell_Signal_Price  \n",
              "timestamp                                     \n",
              "2022-01-03 05:00:00+00:00                NaN  \n",
              "2022-01-04 05:00:00+00:00                NaN  \n",
              "2022-01-05 05:00:00+00:00                NaN  \n",
              "2022-01-06 05:00:00+00:00                NaN  \n",
              "2022-01-07 05:00:00+00:00                NaN  \n",
              "...                                      ...  \n",
              "2022-12-30 05:00:00+00:00                NaN  \n",
              "2023-01-03 05:00:00+00:00                NaN  \n",
              "2023-01-04 05:00:00+00:00                NaN  \n",
              "2023-01-05 05:00:00+00:00                NaN  \n",
              "2023-01-06 05:00:00+00:00                NaN  \n",
              "\n",
              "[255 rows x 13 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>trade_count</th>\n",
              "      <th>vwap</th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>MACD</th>\n",
              "      <th>Signal Line</th>\n",
              "      <th>Buy_Signal_Price</th>\n",
              "      <th>Sell_Signal_Price</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-03 05:00:00+00:00</th>\n",
              "      <td>476.30</td>\n",
              "      <td>477.85</td>\n",
              "      <td>473.8500</td>\n",
              "      <td>477.71</td>\n",
              "      <td>72604064</td>\n",
              "      <td>534803</td>\n",
              "      <td>476.526964</td>\n",
              "      <td>2022-01-03</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-04 05:00:00+00:00</th>\n",
              "      <td>479.22</td>\n",
              "      <td>479.98</td>\n",
              "      <td>475.5800</td>\n",
              "      <td>477.55</td>\n",
              "      <td>71129958</td>\n",
              "      <td>565259</td>\n",
              "      <td>477.868706</td>\n",
              "      <td>2022-01-04</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-0.012764</td>\n",
              "      <td>-0.002553</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-05 05:00:00+00:00</th>\n",
              "      <td>477.16</td>\n",
              "      <td>477.98</td>\n",
              "      <td>468.2900</td>\n",
              "      <td>468.38</td>\n",
              "      <td>102085598</td>\n",
              "      <td>784945</td>\n",
              "      <td>473.338984</td>\n",
              "      <td>2022-01-05</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-0.754128</td>\n",
              "      <td>-0.152868</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-06 05:00:00+00:00</th>\n",
              "      <td>467.89</td>\n",
              "      <td>470.82</td>\n",
              "      <td>465.4300</td>\n",
              "      <td>467.94</td>\n",
              "      <td>86499197</td>\n",
              "      <td>803296</td>\n",
              "      <td>468.480666</td>\n",
              "      <td>2022-01-06</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-1.361475</td>\n",
              "      <td>-0.394589</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-07 05:00:00+00:00</th>\n",
              "      <td>467.95</td>\n",
              "      <td>469.20</td>\n",
              "      <td>464.6500</td>\n",
              "      <td>466.09</td>\n",
              "      <td>84895774</td>\n",
              "      <td>623749</td>\n",
              "      <td>466.940793</td>\n",
              "      <td>2022-01-07</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-1.969379</td>\n",
              "      <td>-0.709547</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-12-30 05:00:00+00:00</th>\n",
              "      <td>380.64</td>\n",
              "      <td>382.58</td>\n",
              "      <td>378.4300</td>\n",
              "      <td>382.43</td>\n",
              "      <td>83474793</td>\n",
              "      <td>544591</td>\n",
              "      <td>381.220108</td>\n",
              "      <td>2022-12-30</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.235339</td>\n",
              "      <td>-1.975497</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-03 05:00:00+00:00</th>\n",
              "      <td>384.37</td>\n",
              "      <td>386.43</td>\n",
              "      <td>377.8310</td>\n",
              "      <td>380.82</td>\n",
              "      <td>74330774</td>\n",
              "      <td>585048</td>\n",
              "      <td>380.961150</td>\n",
              "      <td>2023-01-03</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.292801</td>\n",
              "      <td>-2.238958</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-04 05:00:00+00:00</th>\n",
              "      <td>383.18</td>\n",
              "      <td>385.88</td>\n",
              "      <td>380.0000</td>\n",
              "      <td>383.76</td>\n",
              "      <td>85425119</td>\n",
              "      <td>628498</td>\n",
              "      <td>383.151245</td>\n",
              "      <td>2023-01-04</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.065766</td>\n",
              "      <td>-2.404319</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-05 05:00:00+00:00</th>\n",
              "      <td>381.72</td>\n",
              "      <td>381.76</td>\n",
              "      <td>378.7600</td>\n",
              "      <td>379.38</td>\n",
              "      <td>76079710</td>\n",
              "      <td>523811</td>\n",
              "      <td>380.264217</td>\n",
              "      <td>2023-01-05</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-3.202354</td>\n",
              "      <td>-2.563926</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2023-01-06 05:00:00+00:00</th>\n",
              "      <td>382.61</td>\n",
              "      <td>389.25</td>\n",
              "      <td>379.4127</td>\n",
              "      <td>388.08</td>\n",
              "      <td>103741286</td>\n",
              "      <td>683816</td>\n",
              "      <td>385.248218</td>\n",
              "      <td>2023-01-06</td>\n",
              "      <td>05:00:00</td>\n",
              "      <td>-2.578856</td>\n",
              "      <td>-2.566912</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c70f616b-09cf-4c0e-b926-2c4c8c2516af')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c70f616b-09cf-4c0e-b926-2c4c8c2516af button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c70f616b-09cf-4c0e-b926-2c4c8c2516af');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot fibs levels along with close price and with the buy and sell signals\n",
        "\n",
        "new_df = df\n",
        "\n",
        "#Fibs plot\n",
        "plt.figure(figsize=(12.33,4.5))\n",
        "\n",
        "plt.plot(new_df.index, new_df['close'], alpha = 0.5)\n",
        "\n",
        "plt.scatter(new_df.index, new_df['Buy_Signal_Price'], color = 'green', marker = '^', alpha = 1)\n",
        "plt.scatter(new_df.index, new_df['Sell_Signal_Price'], color = 'red', marker = 'v', alpha = 1)\n",
        "\n",
        "plt.axhline(max_price, linestyle = '--', alpha=0.5, color ='black')\n",
        "plt.axhline(l1, linestyle = '--', alpha=0.5, color ='red')\n",
        "plt.axhline(l2, linestyle = '--', alpha=0.5, color ='orange')\n",
        "plt.axhline(l3, linestyle = '--', alpha=0.5, color ='blue')\n",
        "plt.axhline(l4, linestyle = '--', alpha=0.5, color ='gold')\n",
        "plt.axhline(l5, linestyle = '--', alpha=0.5, color ='green')\n",
        "plt.axhline(min_price, linestyle = '--', alpha=0.5, color ='black')\n",
        "plt.ylabel('Price USD($)')\n",
        "plt.xlabel('Date')\n",
        "plt.xticks(rotation = 45)\n",
        "\n",
        "plt.title(ticker)\n",
        "\n"
      ],
      "metadata": {
        "id": "mMagILE0VMX7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "d59255d7-a702-4f17-8f48-efcca259dd4b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'SPY')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 887.76x324 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate return rate for each buy and sell cycle as well as the average of each in the year\n",
        "\n",
        "buy_nums = [item for item in buy if not(math.isnan(item)) == True]\n",
        "\n",
        "sell_nums = [item for item in sell if not(math.isnan(item)) == True]\n",
        "\n",
        "print(buy_nums)\n",
        "print(sell_nums)\n",
        "\n"
      ],
      "metadata": {
        "id": "9mjOpUFIfJwL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3195e44c-8d72-42e5-c3b7-1e26c24cfe37"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[446.75, 434.23, 419.43, 448.77]\n",
            "[448.7, 437.89, 435.62]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0Zj5u0xCiBN0"
      },
      "execution_count": 17,
      "outputs": []
    }
  ]
}