daily update
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David Brazda
287
to_explore/pyquantnews/15_StockPriceDatabase.ipynb
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287
to_explore/pyquantnews/15_StockPriceDatabase.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b99b374d",
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"metadata": {},
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"source": [
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"<div style=\"background-color:#000;\"><img src=\"pqn.png\"></img></div>"
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]
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},
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{
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"cell_type": "markdown",
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"id": "db4c147e",
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"metadata": {},
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"source": [
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"This notebook fetches financial data for specified stock symbols from Yahoo Finance and stores it in a SQLite database. It provides functions to download stock data over a given date range and save the data into the database. Additionally, it can save data for the latest trading session. This is useful for maintaining a local database of historical stock prices for analysis and backtesting. The code demonstrates usage with example stock symbols and queries the stored data."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d3000251",
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"metadata": {},
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"outputs": [],
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"source": [
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"from sys import argv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "280a44ca",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import yfinance as yf\n",
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"import sqlite3"
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]
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},
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{
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"cell_type": "markdown",
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"id": "6223c51a",
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"metadata": {},
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"source": [
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"Establish a connection to the SQLite database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b6b88ae",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"con = sqlite3.connect(\"market_data.sqlite\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "a7a5eed8",
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"metadata": {},
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"source": [
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"Fetch stock data for a given symbol and date range from Yahoo Finance"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "03765086",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def get_stock_data(symbol, start, end):\n",
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" \"\"\"Fetch stock data from Yahoo Finance.\n",
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" \n",
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" Downloads stock data for a specified symbol and date range and processes it.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" symbol : str\n",
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" The stock ticker symbol.\n",
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" start : str\n",
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" The start date (YYYY-MM-DD).\n",
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" end : str\n",
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" The end date (YYYY-MM-DD).\n",
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" \n",
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" Returns\n",
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" -------\n",
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" data : pd.DataFrame\n",
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" A DataFrame containing the stock data.\n",
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" \"\"\"\n",
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" \n",
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" # Download the stock data from Yahoo Finance\n",
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" data = yf.download(symbol, start=start, end=end)\n",
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" \n",
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" # Reset the DataFrame index to use integer indexing\n",
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" data.reset_index(inplace=True)\n",
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"\n",
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" # Rename the columns to match database schema\n",
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" data.rename(columns={\n",
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" \"Date\": \"date\",\n",
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" \"Open\": \"open\",\n",
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" \"Low\": \"low\",\n",
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" \"Close\": \"close\",\n",
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" \"Adj Close\": \"adj_close\",\n",
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" \"Volume\": \"volume\"\n",
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" }, inplace=True)\n",
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" \n",
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" # Add a column for the stock symbol\n",
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" data['symbol'] = symbol\n",
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" \n",
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" return data"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1d907550",
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"metadata": {},
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"source": [
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"Save stock data for a given symbol and date range to the SQLite database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7117438c",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def save_data_range(symbol, start, end):\n",
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" \"\"\"Save stock data to database.\n",
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" \n",
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" Fetches and saves stock data for a specified symbol and date range.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" symbol : str\n",
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" The stock ticker symbol.\n",
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" start : str\n",
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" The start date (YYYY-MM-DD).\n",
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" end : str\n",
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" The end date (YYYY-MM-DD).\n",
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" \n",
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" Returns\n",
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" -------\n",
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" None\n",
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" \"\"\"\n",
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" \n",
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" # Get stock data for the specified symbol and date range\n",
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" data = get_stock_data(symbol, start, end)\n",
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" \n",
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" # Save the data to the SQLite database\n",
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" data.to_sql(\n",
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" \"stock_data\", \n",
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" con, \n",
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" if_exists=\"append\", \n",
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" index=False\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1cd30bfc",
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"metadata": {},
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"source": [
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"Save stock data for the latest trading session to the SQLite database"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3cb23ec7",
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"metadata": {
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"lines_to_next_cell": 1
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},
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"outputs": [],
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"source": [
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"def save_last_trading_session(symbol):\n",
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" \"\"\"Save the latest trading session data.\n",
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" \n",
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" Fetches and saves stock data for the latest trading session.\n",
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" \n",
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" Parameters\n",
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" ----------\n",
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" symbol : str\n",
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" The stock ticker symbol.\n",
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" \n",
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" Returns\n",
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" -------\n",
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" None\n",
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" \"\"\"\n",
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" \n",
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" # Get today's date\n",
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" today = pd.Timestamp.today()\n",
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" \n",
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" # Get stock data for the latest trading session\n",
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" data = get_stock_data(symbol, today, today)\n",
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" \n",
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" # Save the data to the SQLite database\n",
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" data.to_sql(\n",
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" \"stock_data\", \n",
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" con, \n",
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" if_exists=\"append\", \n",
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" index=False\n",
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" )"
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]
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},
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{
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"cell_type": "markdown",
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"id": "42704aa5",
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"metadata": {},
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"source": [
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"Fetch and save data for the symbol \"TLT\" from 2022-01-01 to 2022-10-21"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d84f5d39",
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"metadata": {},
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"outputs": [],
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"source": [
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"save_data_range(\"TLT\", \"2022-01-01\", \"2022-10-21\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9bb24b46",
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"metadata": {},
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"source": [
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"Query the SQLite database to fetch data for the symbol \"SPY\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a34f05ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_sql_query(\"SELECT * from stock_data where symbol='SPY'\", con)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "97dd546f",
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"metadata": {},
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"source": [
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"Display the fetched data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "481992be",
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"metadata": {},
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"outputs": [],
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"source": [
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"df"
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]
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},
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{
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"cell_type": "markdown",
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"id": "fcf58b0f",
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"metadata": {},
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"source": [
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"<a href=\"https://pyquantnews.com/\">PyQuant News</a> is where finance practitioners level up with Python for quant finance, algorithmic trading, and market data analysis. Looking to get started? Check out the fastest growing, top-selling course to <a href=\"https://gettingstartedwithpythonforquantfinance.com/\">get started with Python for quant finance</a>. For educational purposes. Not investment advise. Use at your own risk."
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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