How would you write a program to pull stock price data, store it, analyze it, and predict the next day's price?

A typical pipeline for pulling, storing, analyzing, and predicting stock prices in Python looks like this: 1. Data acquisition – Use a public API such as yfinance, Alpha Vantage, or IEX Cloud to fetch historical OHLCV data. 2. Storage – Load the data into a pandas DataFrame, then persist it to a relational database (PostgreSQL) or a time‑series store (InfluxDB) for durability. 3. Feature engineering – Compute technical indicators (moving averages, RSI, MACD), lagged returns, and volatility measures. 4. Model training – Split the data into train/validation sets. For a simple baseline, fit an ARIMA model (statsmodels.tsa.arima.model.ARIMA). For a more advanced approach, build a shallow LSTM using TensorFlow or PyTorch. 5. Evaluation – Use metrics such as MAE, RMSE, or directional accuracy on the validation set. 6. Prediction – Generate a one‑day ahead forecast and store the result in a predictions table. 7. Automation – Schedule the pipeline with Airflow or cron to run daily. 8. Monitoring – Track model drift and retrain when performance degrades. Complexity is linear in the number of data points (O(n)) for most steps, with model training being the most expensive part (O(n log n) for ARIMA, O(n × epochs) for neural nets).