This Stock Market Dataset is designed for predictive analysis and machine learning applications in financial markets. It includes 13647 records of simulated stock trading data with features commonly used in stock price forecasting.

🔹 Key Features Date – Trading day timestamps (business days only) Open, High, Low, Close – Simulated stock prices Volume – Trading volume per day RSI (Relative Strength Index) – Measures market momentum MACD (Moving Average Convergence Divergence) – Trend-following momentum indicator Sentiment Score – Simulated market sentiment from financial news & social media Target – Binary label (1: Price goes up, 0: Price goes down) for next-day prediction This dataset is useful for training hybrid deep learning models such as LSTM, CNN, and Attention-based networks for stock market forecasting. It enables financial analysts, traders, and AI researchers to experiment with market trends, technical analysis, and sentiment-based predictions.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The market for Technical Analysis Tools for Traders is on an upward trend, with a significant CAGR and a market size in the millions. The rising interest in technical analysis among traders, the proliferation of cloud-based trading platforms, and the increasing use of artificial intelligence (AI) and machine learning (ML) in trading are driving this growth. The market is also benefiting from the growing adoption of mobile trading platforms, which allow traders to access technical analysis tools on the go. The market for Technical Analysis Tools for Traders is segmented by type (cloud-based, on-premise), application (price indicators, support and resistance levels, momentum indicators, volume indicators, oscillators, and statistical price movement indicators), and region (North America, South America, Europe, Middle East & Africa, and Asia Pacific). Leading companies in the market include Trading Central, Ally Invest, Charles Schwab, E-Trade, Fidelity Investments, Interactive Brokers, Lightspeed, Thinkorswim, TradeStation, and Tradier. The market is highly competitive, with new players entering the market and established players expanding their capabilities through acquisitions and partnerships. The market is also characterized by the increasing use of open-source technical analysis tools, which are gaining popularity due to their cost-effectiveness and flexibility.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dollars and Sense: The Correlation Between US Total Reserves and the Dollar Index

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The global market for Technical Analysis Tools for Traders is experiencing robust growth, driven by the increasing adoption of online trading platforms and a surge in retail investor participation. While precise figures for market size and CAGR aren't provided, considering the rapid digitalization of financial markets and the growing popularity of technical analysis among both novice and experienced traders, a reasonable estimate places the 2025 market size at approximately $2.5 billion. This substantial market is projected to grow at a Compound Annual Growth Rate (CAGR) of around 12% from 2025 to 2033, reaching an estimated value exceeding $7 billion by 2033. This growth is fueled by several key drivers: the proliferation of sophisticated, user-friendly software incorporating advanced technical indicators (such as price indicators, momentum indicators, and oscillators), the rise of algorithmic trading, and the increasing demand for real-time data and charting capabilities. The market is segmented by deployment (cloud-based and on-premise) and application (various types of technical indicators). Cloud-based solutions are gaining traction due to their accessibility, scalability, and cost-effectiveness. The dominance of North America, especially the United States, in this market reflects the region's well-established financial markets and technologically advanced infrastructure. However, Asia-Pacific is poised for significant growth, fueled by the expansion of online trading in rapidly developing economies like India and China. Market restraints include the complexity of technical analysis, requiring considerable expertise and potentially leading to misinterpretations, along with regulatory concerns and cybersecurity risks associated with online trading platforms. The competitive landscape is characterized by a mix of established financial institutions (like Charles Schwab and Fidelity Investments) integrating technical analysis tools into their platforms and specialized providers (like Trading Central and TradeStation) offering dedicated solutions. The future of this market hinges on continuous innovation in technical indicators, the integration of artificial intelligence (AI) and machine learning (ML) for enhanced predictive capabilities, and the increasing accessibility of advanced tools for a wider range of traders. The continued expansion of online brokerage and the growing sophistication of trading strategies will further drive market expansion in the coming years.

Meta stock price for past 10 years. Following technical indicators added.

1. Date: This column represents the date for which the data is recorded.
2. Open: The opening price of a stock on a particular trading day.
3. High: The highest price at which a stock traded during the trading day.
4. Low: The lowest price at which a stock traded during the trading day.
5. Close: The closing price of a stock on a particular trading day. This is the final price at which the stock is valued for the day.
6. Volume: The number of shares or contracts traded in a security or an entire market during a given period, usually one trading day.
7. RSI_7: 7-day Relative Strength Index. It's a momentum indicator measuring the magnitude of recent price changes to evaluate overbought or oversold conditions in the price of a stock.
8. RSI_14: 14-day Relative Strength Index. Similar to RSI_7 but calculated over 14 days.
9. CCI_7: 7-day Commodity Channel Index. It’s a technical indicator that measures the difference between the current price and the historical average price. When calculated over 7 days, it gives short-term trends.
10. CCI_14: 14-day Commodity Channel Index. Like CCI_7, but over 14 days for more medium-term trends.
11. SMA_50: 50-day Simple Moving Average. It averages the closing prices of a stock over the past 50 days.
12. EMA_50: 50-day Exponential Moving Average. Similar to SMA_50, but gives more weight to recent prices, making it more responsive to new information.
13. SMA_100: 100-day Simple Moving Average. It averages the closing prices over the past 100 days.
14. EMA_100: 100-day Exponential Moving Average. Like SMA_100, but more responsive to recent price changes.
15. MACD: Moving Average Convergence Divergence. This indicator shows the relationship between two moving averages of a stock’s price.
16. Bollinger: Bollinger Bands. A type of price envelope developed by John Bollinger.
17. TrueRange: Typically used in calculating the Average True Range (ATR), it is a measure of volatility that considers the range between the high, low, and previous close of a stock.
18. ATR_7: 7-day Average True Range. It measures market volatility by decomposing the entire range of a stock for that period.
19. ATR_14: 14-day Average True Range. Similar to ATR_7, but calculated over 14 days.

Target

Next_Day_Close: Represents the closing price of the stock for the next day. It is useful for predictive models trying to forecast future prices.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Market Analysis for Technical Analysis Tools for Traders The global market for technical analysis tools for traders is estimated to reach approximately USD 5.2 billion by 2033, exhibiting a CAGR of 4.2% during the forecast period (2025-2033). The surge in trading activities and the growing adoption of algorithmic trading strategies among financial institutions and individual investors are key drivers of this growth. Cloud-based solutions and advancements in artificial intelligence (AI) and machine learning (ML) are further fueling the demand for advanced technical analysis tools. Market segmentation reveals that price indicators, support and resistance levels, momentum indicators, volume indicators, oscillators, and statistical price movement indicators are the most widely used technical analysis tools. Key market players include Trading Central, Ally Invest, Charles Schwab, E-Trade, Fidelity Investments, Interactive Brokers, Lightspeed, Thinkorswim, TradeStation, and Tradier. North America holds the dominant market share due to the high concentration of financial institutions and a large number of active traders. However, Asia Pacific is expected to witness substantial growth in the coming years, driven by the increasing number of retail investors and the adoption of mobile trading platforms.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tech Titans' Next Frontier?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This package contains the datasets and source codes used in the PhD thesis entitled Predicting the Brazilian stock market using sentiment analysis, technical indicators and stock prices. The following files are included: File Labeled.zip - financial news labeled in two classes (Positive and Negative), organized to train Sentiment Analysis models. Part of these news were initially presented in [1]. Besides the news in this file, in the related PhD thesis the training dataset was complemented with the labeled news presented in [2]. File Unlabeled.zip - general unlabeled financial news collected during the period 2010-2020 from the following online sources: G1, Folha de São Paulo and Estadão. This file contains news from the Bovespa index and from the following companies: Banco do Brasil, Itau, Gerdau and Ambev. File Stocks.zip - stock prices from the companies Banco do Brasil, Itau, Gerdau, Ambev, and the Bovespa index. The considered period ranges from 2010 to 2020. File Models.zip - contains the source codes of the models used in the PhD thesis (i.e., Multilayer Perceptron, Long Short-Term Memory, Bidirectional Long Short-Term Memory, Convolutional Neural Network, and Support Vector Machines). File Utils.zip - contains the source codes of the preprocessing step designed for the methodology of this work (i.e., load data and generate the word embeddings), alongside with stocks manipulation, and investment evaluation. [1] Carosia, A. E. D. O., Januário, B. A., da Silva, A. E. A., & Coelho, G. P. (2021). Sentiment Analysis Applied to News from the Brazilian Stock Market. IEEE Latin America Transactions, 100. DOI: 10.1109/TLA.2022.9667151 [2] MARTINS, R. F.; PEREIRA, A.; BENEVENUTO, F. An approach to sentiment analysis of web applications in portuguese. Proceedings of the 21st Brazilian Symposium on Multimedia and the Web, ACM, p. 105–112, 2015. DOI: 10.1145/2820426.2820446

The market for Technical Analysis Tools for Traders is experiencing robust growth, driven by increasing retail investor participation, the proliferation of online trading platforms, and a growing preference for data-driven investment strategies. This market, estimated at $2.5 billion in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several key trends, including the rising adoption of cloud-based solutions offering accessibility and scalability, the increasing sophistication of indicators and algorithms incorporated into these tools, and the expansion of the market into emerging economies. The segmentation of the market into different application types (price indicators, support/resistance, momentum, volume, oscillators, statistical indicators) and deployment types (cloud-based, on-premise) reflects the diverse needs of traders across skill levels and trading styles. While data security concerns and the need for continuous updates and maintenance represent some challenges, the overall market outlook remains positive, driven by ongoing technological innovation and the persistent demand for tools that can enhance trading performance. The competitive landscape is characterized by a mix of established financial institutions offering integrated trading platforms and specialized technology providers focusing on advanced analytical tools. Key players like Trading Central, Ally Invest, Charles Schwab, and others are constantly innovating to improve their offerings, leading to increased market competition and driving further improvements in the quality and affordability of technical analysis tools. The geographic distribution of the market is broad, with North America currently holding a significant share, followed by Europe and Asia-Pacific. However, emerging markets in Asia and Latin America present significant growth opportunities as investor sophistication and online trading penetration increase in those regions. The forecast period anticipates continued expansion across all segments, driven by technological advancements and increasing adoption among both professional and retail traders globally.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Context

Using TA-Lib : Technical Analysis Library. Backtest on the SPY Index data, using support and resistance indicators or any other indicator.

Content

Data contains daily SPY Index: Date Open High Low Close Adj Close Volume

Acknowledgements

Support for Resistance: Technical Analysis WIKI link: https://en.wikipedia.org/wiki/Support_and_resistance

Inspiration

Do your best for the backtest and technical indicator implementation

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

US Dollar Index: Bullish Break or Bearish Trap?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The global technical analysis tools for traders market size was valued at USD 1.6 billion in 2025 and is projected to reach USD 3.1 billion by 2033, exhibiting a CAGR of 8.6% during the forecast period. The increasing popularity of online trading platforms and the growing adoption of mobile trading apps are major factors driving market growth. Additionally, the rising demand for real-time data and analytics to make informed trading decisions is further fueling market expansion. The market is segmented into application, type, and region. By application, the price indicators segment held the largest market share in 2025 and is anticipated to maintain its dominance throughout the forecast period. Support and resistance levels, momentum indicators, and volume indicators are other key segments contributing to the market growth. By type, the cloud-based segment is expected to witness significant growth during the forecast period due to its flexibility, scalability, and cost-effectiveness. The on-premise segment, however, is likely to retain a substantial share owing to data security concerns among traders. Regionally, North America and Europe are expected to remain the dominant markets throughout the forecast period, while Asia-Pacific is anticipated to witness the fastest growth rate due to the rising number of retail traders in the region.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

SAP Stock Forecast & Analysis

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones U.S. Select Insurance Index: Poised for a Rebound?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones Tech Index Forecast: Mixed Signals Ahead

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data