data_check.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import asyncpg\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import pytz\n",
    "\n",
    "# Establishes a connection to the PostgreSQL database using provided credentials.\n",
    "async def create_db_connection():\n",
    "    conn = await asyncpg.connect(\n",
    "        host=\"localhost\",\n",
    "        port=\"your_port\",\n",
    "        user=\"your_username\",\n",
    "        password=\"your_password\",\n",
    "        database=\"your_database\",\n",
    "    )\n",
    "    return conn\n",
    "\n",
    "# Defines the base date for Sierra Chart timestamps.\n",
    "sc_epoch = datetime(1899, 12, 30, tzinfo=pytz.UTC)\n",
    "\n",
    "# Converts Sierra Chart timestamp (microseconds since sc_epoch) to a readable datetime object.\n",
    "def convert_sc_datetime(sc_datetime_ms):\n",
    "    delta = timedelta(microseconds=sc_datetime_ms)\n",
    "    actual_datetime = sc_epoch + delta\n",
    "    return actual_datetime\n",
    "\n",
    "# Retrieves all rows from the database and returns them as a pandas DataFrame.\n",
    "async def fetch_all_rows(conn):\n",
    "    query = 'SELECT * FROM public.\"esm24\" ORDER BY \"scdatetime\" DESC;'\n",
    "    rows = await conn.fetch(query)\n",
    "    df = pd.DataFrame(rows)\n",
    "    df.columns = ['scdatetime', 'price', 'quantity', 'side'] \n",
    "    df['actual_datetime'] = df['scdatetime'].apply(convert_sc_datetime)\n",
    "    df['actual_datetime'] = df['actual_datetime'].dt.tz_convert('America/Chicago')\n",
    "    df.set_index('actual_datetime', inplace=True)\n",
    "    df.drop(columns=['scdatetime'], inplace=True)\n",
    "    df = df.sort_index()\n",
    "    return df\n",
    "\n",
    "# Main function to establish database connection and fetch data.\n",
    "async def main():\n",
    "    conn = await create_db_connection()\n",
    "    df = await fetch_all_rows(conn)\n",
    "    return df\n",
    "\n",
    "# Initialize the asynchronous task and create the DataFrame.\n",
    "df = await main()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min value:  4963.5\n",
      "max value:  5333.5\n",
      "min value of quantity:  1\n",
      "max value of quantity:  2962\n",
      "min value of side:  0\n",
      "max value of side:  1\n",
      "min value of actual_datetime:  2024-02-22 18:07:25.394000-06:00\n",
      "max value of actual_datetime:  2024-05-10 15:59:59.478001-05:00\n",
      "total rows count:  43818522\n",
      "Number of null entries:\n",
      " price       0\n",
      "quantity    0\n",
      "side        0\n",
      "dtype: int64\n",
      "Unique values in 'side':  [1 0]\n",
      "Distribution of 'price' values:  price\n",
      "5211.75    95325\n",
      "5212.50    95262\n",
      "5211.50    93263\n",
      "5212.00    90905\n",
      "5212.75    90141\n",
      "           ...  \n",
      "4963.75      173\n",
      "5330.75      126\n",
      "5333.25      113\n",
      "4963.50       34\n",
      "5333.50        4\n",
      "Name: count, Length: 1481, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Display minimum and maximum values for the 'price' column.\n",
    "print(\"min value: \", df['price'].min())\n",
    "print(\"max value: \", df['price'].max())\n",
    "\n",
    "# Display minimum and maximum values for the 'quantity' column to ensure data consistency.\n",
    "print(\"min value of quantity: \", df['quantity'].min())\n",
    "print(\"max value of quantity: \", df['quantity'].max())\n",
    "\n",
    "# Verify the 'side' column only contains expected values (0 for bid, 1 for ask)\n",
    "print(\"min value of side: \", df['side'].min())\n",
    "print(\"max value of side: \", df['side'].max())\n",
    "\n",
    "# Display the earliest and latest datetime entries to check the data range.\n",
    "print(\"min value of actual_datetime: \", df.index.min())\n",
    "print(\"max value of actual_datetime: \", df.index.max())\n",
    "\n",
    "# Report the total number of rows to track data volume and detect any unusual changes.\n",
    "print(\"total rows count: \", len(df))\n",
    "\n",
    "# Check for null entries.\n",
    "print(\"Number of null entries:\\n\", df.isnull().sum())\n",
    "\n",
    "# Check the unique values in 'side'. 0 for bid, 1 for ask.\n",
    "print(\"Unique values in 'side': \", df['side'].unique())\n",
    "\n",
    "# Check the distribution of 'price' values.\n",
    "print(\"Distribution of 'price' values: \", df['price'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Calculate the total quantity for each price level\n",
    "df_total = df.groupby('price')['quantity'].sum().reset_index()\n",
    "\n",
    "# Calculate the number of bins\n",
    "bins = int((df_total['price'].max() - df_total['price'].min()) / 0.25)\n",
    "\n",
    "# Create the histogram\n",
    "plt.figure(figsize=(12,8))\n",
    "sns.histplot(df_total, y='price', weights='quantity', bins=bins, fill=True, color='b')\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Price Distribution')\n",
    "plt.ylabel('Price')\n",
    "plt.xlabel('Total Quantity')\n",
    "\n",
    "# Invert x axis\n",
    "plt.gca().invert_xaxis()\n",
    "\n",
    "# Move y axis to the right\n",
    "plt.gca().yaxis.tick_right()\n",
    "plt.gca().yaxis.set_label_position(\"right\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vbt-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}