Dark Mode
Historical NFLX Daily Stock Data
Stock & Market Data
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Netflix's stock price movements, charting the company's financial journey from its IPO in May 2002 to its current standing as a global streaming leader. It captures the evolution of Netflix (Ticker: NFLX) from a DVD rental service to a digital content powerhouse, reflecting market trends, strategic shifts like the move to original content, and periods of significant growth and volatility. This data offers a window into the company's market reactions to earnings reports, acquisitions, subscriber milestones, and major global events.
Columns
- Date: The trading day, formatted as YYYY-MM-DD.
- Open: The price at which the stock began trading when the market opened.
- High: The highest trading price of the day.
- Low: The lowest trading price of the day.
- Close: The final price of the stock when the market closed.
- Adj Close: The closing price adjusted for splits and dividends.
- Volume: The total number of shares traded throughout the day.
Distribution
The data is structured in a clean, ready-to-use CSV format. It contains 5,860 rows, with each row representing a single trading day's activity.
Usage
This dataset is ideal for a variety of financial analysis and data science applications, including:
- Time-series forecasting using models like ARIMA, Prophet, or LSTMs.
- Technical and trend analysis, such as calculating moving averages, RSI, or Bollinger Bands.
- Developing and testing predictive machine learning models.
- Simulating investment strategies.
- Creating stock market visualisations, storytelling projects, and financial dashboards with tools like Tableau, Power BI, or Streamlit.
Coverage
The dataset covers the daily trading activity of Netflix (NFLX) stock from 23 May 2002 to 05 September 2025. It is updated on a monthly basis. The data is specific to the financial market performance of Netflix and does not contain demographic information.
License
CC0: Public Domain
Who Can Use It
- Aspiring data scientists: For practicing exploratory data analysis (EDA) and modelling.
- Financial analysts and traders: To explore historical trends and market behaviour.
- AI researchers: For working on time-series prediction models.
- Students: To build machine learning projects with real-world data.
- Developers: For creating stock visualisation tools and applications.
Dataset Name Suggestions
- Netflix Stock Price History (2002-Present)
- Historical NFLX Daily Stock Data
- Netflix Financial Market Performance Data
- Daily Netflix Stock Prices and Volume
Attributes
Original Data Source: Historical NFLX Daily Stock Data
QUALITY