Financial_Data_Structures_(Bars).ipynb

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  "metadata": {
    "colab": {
      "name": "Financial Data Structures (Bars)",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
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      "metadata": {
        "id": "EvB1LpsOkny7"
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      "source": [
        "Original Article:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7TcmAAFaktFM"
      },
      "source": [
        "Installing and Importing Requisite Packages\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L8sdqn7IOpgj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b9fb2dd-e6f2-4268-cc6a-06b95a1b90f3"
      },
      "source": [
        "pip install shrimpy-python"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting shrimpy-python\n",
            "  Downloading shrimpy_python-0.0.15-py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: requests>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from shrimpy-python) (2.23.0)\n",
            "Collecting websockets\n",
            "  Downloading websockets-10.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (111 kB)\n",
            "\u001b[K     |████████████████████████████████| 111 kB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->shrimpy-python) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->shrimpy-python) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->shrimpy-python) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.13.0->shrimpy-python) (2021.10.8)\n",
            "Installing collected packages: websockets, shrimpy-python\n",
            "Successfully installed shrimpy-python-0.0.15 websockets-10.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6s5ZC0eGEbW"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from datetime import datetime, timedelta\n",
        "import math\n",
        "import plotly\n",
        "import shrimpy\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2mau5iaKkxAP"
      },
      "source": [
        "Collecting the Data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "C2ek2VKJNe7i",
        "outputId": "b0a3a338-6785-4949-fc27-e08df10a63ad"
      },
      "source": [
        "#collecting the historical candlestick data\n",
        "shrimpy_public_key ='...'\n",
        "shrimpy_secret_key = '...'\n",
        "client = shrimpy.ShrimpyApiClient(shrimpy_public_key, shrimpy_secret_key)\n",
        "candles = client.get_candles(\n",
        "    'coinbasepro', # exchange\n",
        "    'BTC',     # base_trading_symbol\n",
        "    'USD',     # quote_trading_symbol\n",
        "    '1m'       # interval\n",
        ")\n",
        "\n",
        "time = []\n",
        "open = []\n",
        "high = []\n",
        "low = []\n",
        "close = []\n",
        "vol =[]\n",
        "\n",
        "#formatting the data to match the plotting library\n",
        "for candle in candles:\n",
        "    time.append(candle['time'])\n",
        "    open.append(candle['open'])\n",
        "    high.append(candle['high'])\n",
        "    low.append(candle['low'])\n",
        "    close.append(candle['close'])\n",
        "    vol.append(candle['volume'])\n",
        "\n",
        "#plotting the candlesticks\n",
        "fig = go.Figure(data=[go.Candlestick(x=time,\n",
        "                       open=open, high=high,\n",
        "                       low=low, close=close)])\n",
        "fig.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IbXGTSwbk3MF"
      },
      "source": [
        "Formatting Data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMOa1U-KOcGh"
      },
      "source": [
        "#create dataframe from lists\n",
        "df = pd.DataFrame(\n",
        "    {'time': [datetime.strptime(x,\"%Y-%m-%dT%H:%M:%S.%fZ\") for x in time], #formatting our timestamp\n",
        "     'open': [float(x.strip('0')) for x in open], #removing trailing 0's and converting to float\n",
        "     'high': [float(x.strip('0')) for x in high],\n",
        "     'low': [float(x.strip('0')) for x in low],\n",
        "     'close': [float(x.strip('0')) for x in close],\n",
        "     'vol': [float(x.strip('0')) for x in vol]\n",
        "    })\n",
        "\n",
        "#create dictionary from dataframe\n",
        "df_dict = df.to_dict('records') "
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1mWOyjhtlDWA"
      },
      "source": [
        "Defining Dollar Bar Function\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7FdebBTUQu0L"
      },
      "source": [
        "def get_dollar_bars(time_bars, dollar_threshold):\n",
        "\n",
        "    # initialize an empty list of dollar bars\n",
        "    dollar_bars = []\n",
        "\n",
        "    # initialize the running dollar volume at zero\n",
        "    running_volume = 0\n",
        "\n",
        "    # initialize the running high and low with placeholder values\n",
        "    running_high, running_low = 0, math.inf\n",
        "\n",
        "    # for each time bar...\n",
        "    for i in range(len(time_bars)):\n",
        "\n",
        "        # get the timestamp, open, high, low, close, and volume of the next bar\n",
        "        next_close, next_high, next_low, next_open, next_timestamp, next_volume = [time_bars[i][k] for k in ['close', 'high', 'low', 'open', 'time', 'vol']]\n",
        "\n",
        "        # get the midpoint price of the next bar (the average of the open and the close)\n",
        "        midpoint_price = ((next_open) + (next_close))/2\n",
        "\n",
        "        # get the approximate dollar volume of the bar using the volume and the midpoint price\n",
        "        dollar_volume = next_volume * midpoint_price\n",
        "\n",
        "        # update the running high and low\n",
        "        running_high, running_low = max(running_high, next_high), min(running_low, next_low)\n",
        "\n",
        "        # if the next bar's dollar volume would take us over the threshold...\n",
        "        if dollar_volume + running_volume >= dollar_threshold:\n",
        "\n",
        "            # set the timestamp for the dollar bar as the timestamp at which the bar closed (i.e. one minute after the timestamp of the last minutely bar included in the dollar bar)\n",
        "            bar_timestamp = next_timestamp + timedelta(minutes=1)\n",
        "            \n",
        "            # add a new dollar bar to the list of dollar bars with the timestamp, running high/low, and next close\n",
        "            dollar_bars += [{'timestamp': bar_timestamp, 'open': next_open, 'high': running_high, 'low': running_low, 'close': next_close}]\n",
        "\n",
        "            # reset the running volume to zero\n",
        "            running_volume = 0\n",
        "\n",
        "            # reset the running high and low to placeholder values\n",
        "            running_high, running_low = 0, math.inf\n",
        "\n",
        "        # otherwise, increment the running volume\n",
        "        else:\n",
        "            running_volume += dollar_volume\n",
        "\n",
        "    # return the list of dollar bars\n",
        "    return dollar_bars"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M65Qb8Y3lGKR"
      },
      "source": [
        "Creating Dollar Bars"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqWBKCZaR5Np",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "af0e6b1d-3177-4136-8134-e21fc11f9616"
      },
      "source": [
        "dollar_bars = get_dollar_bars(df_dict, 5000000) #5,000,000 is an arbitrarily selected threshold\n",
        "df = pd.DataFrame(dollar_bars)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>timestamp</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-12-17 04:36:00</td>\n",
              "      <td>21856.52</td>\n",
              "      <td>21913.44</td>\n",
              "      <td>21817.07</td>\n",
              "      <td>21828.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-12-17 04:54:00</td>\n",
              "      <td>21820.45</td>\n",
              "      <td>21876.06</td>\n",
              "      <td>21784.55</td>\n",
              "      <td>21824.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-12-17 05:07:00</td>\n",
              "      <td>21842.77</td>\n",
              "      <td>21891.68</td>\n",
              "      <td>21800.00</td>\n",
              "      <td>21865.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-12-17 05:19:00</td>\n",
              "      <td>21987.50</td>\n",
              "      <td>22004.64</td>\n",
              "      <td>21857.38</td>\n",
              "      <td>21992.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-12-17 05:31:00</td>\n",
              "      <td>21994.19</td>\n",
              "      <td>22013.31</td>\n",
              "      <td>21961.70</td>\n",
              "      <td>21981.49</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            timestamp      open      high       low     close\n",
              "0 2020-12-17 04:36:00  21856.52  21913.44  21817.07  21828.86\n",
              "1 2020-12-17 04:54:00  21820.45  21876.06  21784.55  21824.54\n",
              "2 2020-12-17 05:07:00  21842.77  21891.68  21800.00  21865.59\n",
              "3 2020-12-17 05:19:00  21987.50  22004.64  21857.38  21992.26\n",
              "4 2020-12-17 05:31:00  21994.19  22013.31  21961.70  21981.49"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
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    {
      "cell_type": "code",
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        "id": "4Yzr5pYSHkgq",
        "outputId": "d1db26ff-f1f9-421b-81ca-2ce084217edf"
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      "source": [
        "fig = go.Figure(data=[go.Candlestick(x=df.timestamp,\n",
        "                     open=df.open, high=df.high,\n",
        "                     low=df.low, close=df.close)])\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d7LKJwJbwk3h"
      },
      "source": [
        "Plotting both bars together\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rtya87-lKBxJ",
        "outputId": "c7938cc0-6998-4485-ce22-938719728589"
      },
      "source": [
        "trace1 = go.Candlestick(x=time,\n",
        "                       open=open, high=high,\n",
        "                       low=low, close=close,\n",
        "                       name = 'time_bars'\n",
        ")\n",
        "            \n",
        "trace2 = go.Candlestick(x=df.timestamp,\n",
        "                       open=df.open, high=df.high,\n",
        "                       low=df.low, close=df.close,\n",
        "                       name = 'dollar_bars',\n",
        "                       increasing_line_color= 'cyan', decreasing_line_color= 'black'\n",
        "\n",
        ")\n",
        "\n",
        "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
        "fig.add_trace(trace1)\n",
        "fig.add_trace(trace2,secondary_y=True)\n",
        "\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
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