20 – Real-World Python Projects – Stock Price Tracker

🎯 Project Objective

To build a Python-based Stock Price Tracker that fetches real-time stock market data, visualizes price trends, and sends alerts when prices cross certain thresholds.

🧠 Skills You’ll Learn

Using APIs (like Yahoo Finance or Alpha Vantage)
Working with JSON data
Data analysis using pandas
Plotting graphs with matplotlib
Automating alerts via email/SMS
Scheduling periodic updates

⚙️ Technology Stack

Library	Purpose
`requests`	Fetch stock data from APIs
`pandas`	Store and analyze stock data
`matplotlib`	Plot price trends
`time`, `datetime`	Handle time-based tracking
`smtplib` (optional)	Send email alerts
`schedule` (optional)	Automate periodic tracking

🧩 Step 1 — Auto-Install Required Libraries

import subprocess, sys

def install(package):
    try:
        __import__(package)
    except ImportError:
        subprocess.check_call([sys.executable, "-m", "pip", "install", package])

for pkg in ["requests", "pandas", "matplotlib", "schedule"]:
    install(pkg)

🧾 Step 2 — Import Required Modules

import requests
import pandas as pd
import matplotlib.pyplot as plt
import time
from datetime import datetime

📊 Step 3 — Fetch Real-Time Stock Data

Using the Yahoo Finance API (unofficial) via RapidAPI or public endpoint:

def get_stock_price(symbol):
    url = f"https://query1.finance.yahoo.com/v7/finance/quote?symbols={symbol}"
    response = requests.get(url)
    data = response.json()
    price = data['quoteResponse']['result'][0]['regularMarketPrice']
    return price

🧮 Step 4 — Track and Store Prices Over Time

symbol = "AAPL"  # Example: Apple Inc.
prices = []
timestamps = []

for i in range(5):  # Fetch 5 times for demo
    price = get_stock_price(symbol)
    prices.append(price)
    timestamps.append(datetime.now().strftime("%H:%M:%S"))
    print(f"{symbol} Price: ${price}")
    time.sleep(2)  # Wait before next check

df = pd.DataFrame({"Time": timestamps, "Price": prices})
print(df)

📈 Step 5 — Visualize Stock Price Trend

plt.plot(df["Time"], df["Price"], marker="o")
plt.title(f"{symbol} Stock Price Over Time")
plt.xlabel("Time")
plt.ylabel("Price (USD)")
plt.grid(True)
plt.show()

✅ This shows a live trend of stock price changes.

⚠️ Step 6 — Add Price Alerts

THRESHOLD = 210.0  # Example alert limit

if price >= THRESHOLD:
    print(f"🚨 Alert: {symbol} has crossed ${THRESHOLD}!")

📬 Step 7 — (Optional) Send Email Alert

import smtplib

def send_email_alert(symbol, price):
    sender = "youremail@gmail.com"
    receiver = "targetemail@gmail.com"
    password = "yourpassword"

    message = f"Subject: Stock Alert\n\n{symbol} price reached ${price}"

    with smtplib.SMTP("smtp.gmail.com", 587) as server:
        server.starttls()
        server.login(sender, password)
        server.sendmail(sender, receiver, message)

# Example usage:
# send_email_alert(symbol, price)

✅ You can replace the credentials with environment variables for safety.

🔄 Step 8 — Automate Daily Tracking

import schedule

def job():
    price = get_stock_price("AAPL")
    print(f"{datetime.now()} — AAPL: ${price}")

schedule.every(5).minutes.do(job)

while True:
    schedule.run_pending()
    time.sleep(1)

✅ Automatically checks every 5 minutes and logs updates.

📘 Full Combined Version

import subprocess, sys, time, requests, pandas as pd, matplotlib.pyplot as plt
from datetime import datetime

def install(package):
    try:
        __import__(package)
    except ImportError:
        subprocess.check_call([sys.executable, "-m", "pip", "install", package])

for pkg in ["requests", "pandas", "matplotlib"]:
    install(pkg)

def get_stock_price(symbol):
    url = f"https://query1.finance.yahoo.com/v7/finance/quote?symbols={symbol}"
    data = requests.get(url).json()
    return data['quoteResponse']['result'][0]['regularMarketPrice']

symbol = input("Enter Stock Symbol (e.g., AAPL, TSLA): ").upper()
prices, times = [], []

print(f"Tracking {symbol} stock price...")
for i in range(10):
    price = get_stock_price(symbol)
    prices.append(price)
    times.append(datetime.now().strftime("%H:%M:%S"))
    print(f"{symbol} Price: ${price}")
    time.sleep(5)

df = pd.DataFrame({"Time": times, "Price": prices})
df.to_csv(f"{symbol}_prices.csv", index=False)

plt.plot(df["Time"], df["Price"], marker="o", color="blue")
plt.title(f"{symbol} Stock Tracker")
plt.xlabel("Time")
plt.ylabel("Price (USD)")
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

🌍 Real-Life Applications

✅ Monitor personal investment portfolios
✅ Integrate into financial dashboards
✅ Automate buy/sell alerts
✅ Collect data for ML-based price prediction
✅ Power up Excel dashboards via Python

💡 Next-Level Enhancements

Feature	Description
📈 Multiple Stock Comparison	Track multiple tickers in one plot
🧾 Historical Data	Fetch past data using `yfinance`
📊 Candlestick Charts	Use `mplfinance` for advanced visualizations
🔔 Telegram/Discord Alerts	Send price updates to chat apps
☁️ Web Dashboard	Display live prices using Flask or Streamlit