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Synthetic Financial Time-Series Data
Synthetic Data Generation
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About
Provides realistic synthetic stock market data suitable for time-series forecasting, stock analysis, and financial modeling. The data is generated using advanced simulation techniques, specifically Geometric Brownian Motion for price movements and Markov Chains for predicting market trends. It is an excellent resource for developing and simulating algorithmic trading strategies. The dataset includes detailed financial metrics alongside standard pricing information.
Columns
- Date: The trading date in YYYY-MM-DD format.
- Company: The name of the stock, with examples like Apple, Tesla, or JPMorgan.
- Sector: The industry classification of the company, covering diverse sectors such as Technology, Finance, and Healthcare.
- Open: The opening price of the stock on a given trading day.
- High: The highest price of the stock reached during the trading day.
- Low: The lowest price of the stock recorded during the trading day.
- Close: The closing price of the stock at the end of the trading session.
- Volume: The total number of shares traded on that day.
- Market_Cap: The total market capitalization of the company, measured in USD.
- PE_Ratio: The Price-to-Earnings ratio, a key valuation metric for stocks.
- Dividend_Yield: The percentage of dividends relative to the stock price.
- Volatility: A measure indicating the stock price fluctuation.
- Sentiment_Score: Market sentiment quantified on a scale from -1 (extremely negative) to 1 (extremely positive).
- Trend: The stock market trend label, designated as Bullish, Bearish, or Stable.
Distribution
The dataset is typically provided in CSV format under the file name
synthetic_stock_data.csv and is approximately 214.35 kB in size. It contains 14 columns and features 1000 total records or days of trading simulation. The data is clean, with no reported missing or mismatched values.Usage
This dataset is designed for several key applications:
- Time-Series Forecasting: Training models like LSTMs, Transformers, or ARIMA for accurate stock price prediction.
- Algorithmic Trading: Developing and backtesting trading strategies based on trends and sentiment scores.
- Feature Engineering: Exploring correlations between various financial metrics (such as P/E Ratio or Volatility) and stock price movements.
- Quantitative Finance Research: Analyzing simulated market trends and testing financial models in a realistic environment.
Coverage
The data simulates 1000 days of trading activity, spanning a time range from January 1, 2022, to September 26, 2024. As the data is synthetic, there is no specific geographical scope. The coverage includes multiple companies across diverse industries, such as Technology, Finance, Healthcare, Energy, and Aerospace. There are 63 unique companies represented across 7 distinct sectors.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For training and validating machine learning models focused on financial time-series prediction.
- Quantitative Analysts: For rigorous testing of financial theories and analysis of simulated market mechanics.
- Algorithmic Traders: For developing and refining automated trading systems using trend and sentiment data.
- Academics and Students: For educational purposes related to financial modeling and stochastic processes.
Dataset Name Suggestions
- Synthetic Financial Time-Series Data
- Algorithmic Trading Simulation Data
- Realistic Stock Price Generator
- Financial Market Modeling Dataset
- Simulated Quantitative Finance Data
Attributes
Original Data Source: Synthetic Financial Time-Series Data
QUALITY