This project presents an in-depth SQL analysis of historical data of PT Bank Central Asia Tbk (BBCA) stocks. The dataset includes daily trading records, which are analyzed using structured SQL to explore annual growth, price volatility, extreme returns, and long-term investment strategy simulations.
🗂️ Dataset: Historical daily stock data for BBCA, sourced from kaggle
🛠️ Tools Used: SQL (MySQL) | Window Functions | Aggregate Functions | Subqueries | Microsoft Power BI (Data Visualization)
🎯 Goal: Showcase business acumen and data analytics skill by translating raw stock data into meaningful financial insights
BCA-STOCK-ANALYSIS/ ├── datasets/ │ └── bbca_stock.sql ├── sql/ │ ├── annual_growth_percentage.sql │ ├── average_annual_price_and_volume.sql │ ├── buy_and_hold_simulation.sql │ ├── correlation_between_price_and_volume.sql │ ├── data_timestamp.sql │ ├── stable_vs_volatile_years.sql │ ├── top_5_daily_gainer_and_loser.sql ├── visualizations/ | ├── Bank Central Asia Stock Analysis Dashboard.jpg | ├── Bank Central Asia Stock Analysis Dashboard.pbix ├── .gitattributes ├── README.md
| File | Insight |
|---|---|
data_timestamp.sql |
Displays the first and last year in the dataset, along with the total number of unique years |
annual_growth_percentage.sql |
Calculates the percentage of stock price growth from the beginning to the end of each year |
top_5_daily_gainer_and_loser.sql |
Identifies the top 5 days with the highest gains and biggest drops (daily return) |
average_annual_price_and_volume.sql |
Shows the average closing price and trading volume per year |
stable_vs_volatile_years.sql |
Highlights the 3 most stable and 3 most volatile years based on standard deviation |
correlation_between_price_and_volume.sql |
Measures the Pearson correlation between stock price and trading volume |
buy_and_hold_simulation.sql |
Simulates a buy-and-hold investment strategy: total return over the entire period |
Power BI dashboard for visual exploration of BBCA shares
With a structured SQL approach and powerful visualizations, this project bridges data and business insights. Whether for novice investors, data analysts, or financial professionals — the insights from this project can be used for better decision-making based on historical data.