This dataset is about books. It has 1 row and is filtered where the book is Trend following mindset : the genius of legendary trader Tom Basso. It features 7 columns including author, publication date, language, and book publisher.

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Algorithmic Trading Market Size 2025-2029

The algorithmic trading market size is forecast to increase by USD 18.74 billion, at a CAGR of 15.3% between 2024 and 2029.

The market is experiencing significant growth, driven primarily by the increasing demand for market surveillance and regulatory compliance. Advanced technologies, such as machine learning and artificial intelligence, are revolutionizing trading strategies, enabling faster and more accurate decision-making. However, this market's landscape is not without challenges. In the Asia Pacific region, for instance, the widening bid-ask spread poses a significant obstacle for algorithmic trading firms, necessitating innovative solutions to mitigate this issue. As market complexity increases, players must navigate these challenges to capitalize on the opportunities presented by this dynamic market.
Companies seeking to succeed in this space must invest in advanced technologies, maintain regulatory compliance, and develop strategies to address regional challenges, ensuring their competitive edge in the ever-evolving algorithmic trading landscape.

What will be the Size of the Algorithmic Trading Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the dynamic and ever-evolving world of algorithmic trading, market activities continue to unfold with intricacy and complexity. Order management systems, real-time data processing, and sharpe ratio are integral components, enabling traders to optimize returns and manage risk tolerance. Regulatory frameworks and compliance regulations shape the market landscape, with cloud computing and order routing facilitating seamless integration of data analytics and algorithmic strategies. Natural language processing and market data feeds inform trading decisions, while trading psychology and sentiment analysis provide valuable insights into market sentiment. Position sizing, technical analysis, and profitability metrics are essential for effective portfolio optimization and asset allocation.

Market making, automated trading platforms, and foreign exchange are sectors that significantly benefit from these advancements. Return on investment, risk management, and execution algorithms are crucial for maximizing profits and minimizing losses. Machine learning models and deep learning algorithms are increasingly being adopted for trend following and mean reversion strategies. Trading signals, latency optimization, and trading indicators are essential tools for high-frequency traders, ensuring efficient trade execution and profitability. Network infrastructure and api integration are vital for ensuring low latency and reliable connectivity, enabling traders to capitalize on market opportunities in real-time. The ongoing integration of these technologies and techniques continues to reshape the market, offering new opportunities and challenges for traders and investors alike.

How is this Algorithmic Trading Industry segmented?

The algorithmic trading industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Solutions
  Services


End-user

  Institutional investors
  Retail investors
  Long-term investors
  Short-term investors


Deployment

  Cloud
  On-premise
  Cloud
  On-premise


Type

  Foreign Exchange (FOREX)
  Stock Markets
  Exchange-Traded Fund (ETF)
  Bonds
  Cryptocurrencies
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The solutions segment is estimated to witness significant growth during the forecast period.

The market encompasses a range of solutions, primarily software, employed by traders for automated trading. Algorithmic trading, characterized by the execution of large orders using pre-programmed software, is a common practice among proprietary trading firms, hedge funds, and investment banks. High-frequency trading (HFT) relies heavily on these software solutions for speed and efficiency. The integration of advanced software in trading systems allows traders to optimize price, timing, and quantity, ultimately increasing profitability. companies offer a diverse array of software solutions, catering to various investment objectives and risk tolerances. Market making, mean reversion, trend following, and machine learning models are among the algorithmic strategies employed.

Real-time data processing, sentiment analysis, and position sizing are integral components of these solutions. Network infrastructure,

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The data includes the iterviews from 24 activists (PA) and 24 non-activists (PN). The interviews were semi-structured. The study was conducted using Zaltman Metaphor Elicitation technique and thus each interview is accompanied by a corresponding set of images.

The data is accompanied by the experimental protocol (in word) and participant preparation template (in powerpoint).

NOTE: UPR stands for Unilaterally Price Rising situation and UPF stands for Unilaterally Price Falling situation.

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Carbon Credit Trading Platform Market Size 2024-2028

The carbon credit trading platform market size is forecast to increase by USD 313.8 billion at a CAGR of 27.77% between 2023 and 2028. The carbon credit trading market is experiencing significant growth due to increasing international sustainability initiatives and stricter environmental rules. As enterprises strive to reduce their carbon footprints and comply with emission regulations, the demand for emission reduction projects and carbon credits is on the rise. Market stability is a key trend, as more businesses recognize the long-term benefits of carbon credit trading. However, a lack of awareness and understanding of the process hinders widespread adoption. Greenhouse gas emissions continue to be a major concern for governments and organizations alike, making the carbon credit trading platform an essential tool for achieving emission reduction targets.

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The global focus on climate change and the adoption of renewable energy sources have led enterprises to prioritize emission reduction targets and environmental responsibility. Carbon credits have emerged as a financial tool to facilitate these efforts, enabling businesses to offset their carbon footprints by investing in emission reduction projects. Carbon capture technologies are gaining traction as essential components of the global transition towards a low-carbon economy. The increasing awareness of the environmental impact of greenhouse gas emissions has driven enterprises to seek sustainable practices and adhere to international sustainability initiatives.

Moreover, net zero goals have become a corporate mindset, with many organizations committing to reducing their carbon emissions in line with environmental regulations. Carbon credits provide a means for businesses to achieve these targets by investing in projects that reduce or remove greenhouse gas emissions from the atmosphere. The market is witnessing significant growth as more enterprises recognize the importance of carbon footprint reduction in their business strategies. Carbon credits offer a flexible and cost-effective solution for organizations to meet their emission reduction targets while supporting sustainable projects. The economic transition towards a low-carbon economy necessitates the adoption of carbon credits as a financial instrument.

Further, renewable energy sources, such as wind and solar power, are increasingly becoming the preferred choice for power generation, reducing the demand for fossil fuels and, consequently, carbon emissions. Carbon credits serve as a crucial financial mechanism in the context of environmental regulations. As governments worldwide implement stricter emission norms, businesses are turning to carbon credits to offset their carbon footprints and ensure compliance with these rules. Sustainability is a key concern for businesses, and carbon credits offer a tangible way to demonstrate environmental responsibility. By investing in emission reduction projects, organizations can reduce their carbon footprints and contribute to global efforts to mitigate climate change.

In conclusion, the market is expected to continue its growth trajectory, driven by the increasing demand for carbon credits from enterprises. The market's expansion is further fueled by the growing awareness of the importance of cybersecurity in the context of carbon credit trading platforms. In conclusion, the market plays a vital role in facilitating the transition towards a low-carbon economy by enabling enterprises to offset their carbon footprints and invest in emission reduction projects. As the global focus on climate change and sustainability intensifies, the demand for carbon credits and carbon credit trading platforms is expected to continue growing.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Voluntary carbon market
  Regulated carbon market


Service Type

  Cap and trade
  Baseline and credit


Geography

  Europe

    Germany
    UK
    Italy


  APAC

    China


  North America

    US


  South America



  Middle East and Africa

By Type Insights

The voluntary carbon market segment is estimated to witness significant growth during the forecast period. In The market, the voluntary segment held the largest share in 2022. This segment's popularity is on the rise as businesses increasingly commit to net zero goals and renewable energy adoption in response to climate change concerns. Voluntary carbon credits enable companies to offset their carbon emissions by investing in projects that reduce or remove greenhouse gas (GHG) emissions. These initiatives not only contribute to the fight against climate

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.

Mind Over Margin: (MIND) Stock's Future in Focus?

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

Credit report of Iron Mind General Trading L L C contains unique and detailed export import market intelligence with it's phone, email, Linkedin and details of each import and export shipment like product, quantity, price, buyer, supplier names, country and date of shipment.

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Abstract Purpose: The purpose of the study is to examine the prices of some of the most widely traded stocks from Taiwan, Brazil and South Africa for indications of psychological barriers at round numbers. Design/methodology/approach: The sample under study includes a group of 24 stocks (8 for each one the emerging markets) during the period 2000-2014. We test for uniformity in the trailing digits of the stock prices and use regression and GARCH analysis to assess the differential impact of being above or below a possible barrier. Findings: We found no consistent psychological barriers in individual stock prices near round numbers. Moreover, we document that the relationship between risk and return tends to be weaker in the proximity of round numbers for about half of the stocks under study. Originality/value: This is the first study to examine the prices of single stocks from emerging markets for indications of psychological barriers at round numbers. Our results advocate special reflection regarding trading strategies linked to support and resistance levels in stock prices.

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Security Brokerage And Stock Exchange Services Market Size 2025-2029

The security brokerage and stock exchange services market size is forecast to increase by USD 917.8 billion at a CAGR of 9.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing demand for exchange-traded funds (ETFs) and the popularity of online trading platforms. These trends reflect the evolving preferences of investors, who seek convenience, cost-effectiveness, and diversification in their investment portfolios. Simultaneously, regulatory compliance with trading activities is on the rise, necessitating brokerage firms and stock exchanges to invest in advanced technologies and processes to ensure adherence. Data analytics and big data are also crucial tools for e-brokerage firms to gain insights and make informed decisions. These trends and challenges are shaping the future of the market. These factors present both opportunities and challenges for market participants. Companies that can effectively leverage technology to streamline operations, enhance customer experience, and comply with regulations will gain a competitive edge. Additionally, users of online trading platforms can easily monitor the performance of their assets thanks to real-time stock data. 
Conversely, those that fail to adapt may face operational inefficiencies and regulatory penalties, potentially impacting their market position and reputation. To capitalize on these opportunities and navigate challenges, market players must remain agile, innovative, and committed to delivering value to their customers.

What will be the Size of the Security Brokerage And Stock Exchange Services Market during the forecast period?

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The market encompasses a dynamic and intricate ecosystem of financial intermediaries facilitating the buying and selling of various securities, including equities, fixed income instruments, alternative investments, and digital assets. Market participants seek services such as commission rates and trading fees, account minimums, customer service, investment strategies, market insights, and personalized recommendations to optimize their portfolios. The market is witnessing significant growth due to the widespread use of smartphones and led technology, enabling investors to access real-time market data and trade securities such as ETFs and mutual funds from anywhere. Key trends include tax-efficient investing, estate planning, and the integration of advanced technologies like securities lending, prime brokerage, clearing and settlement, market making, order routing, and execution algorithms. Furthermore, the market is witnessing the emergence of innovative financial services, such as decentralized finance (DeFi), non-fungible tokens (NFTs), and digital assets, which are transforming traditional investment paradigms.
Risk appetite, trading psychology, and behavioral finance play crucial roles in market sentiment, as investors navigate economic indicators, geopolitical risks, global markets, and emerging markets. Additionally, investment banking services, including debt financing, equity financing, corporate finance, financial reporting, corporate governance, and Environmental, Social, and Governance (ESG) investing, continue to be essential components of the market.

How is this Security Brokerage And Stock Exchange Services Industry segmented?

The security brokerage and stock exchange services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Channel

  Offline
  Online


Type

  Derivatives and commodities brokerage
  Equities brokerage
  Bonds brokerage
  Stock exchanges
  Others


Source

  Banks
  Investment firms
  Exclusive brokers


Geography

  North America

    US
    Canada


  APAC

    China
    India
    Japan
    Singapore


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa



  South America

By Channel Insights

The offline segment is estimated to witness significant growth during the forecast period. Offline security brokerage and stock exchange services enable investors to collaborate with seasoned professionals, receiving customized advice based on their investment strategies and objectives. In this mode, investors can trade various securities, such as stocks, bonds, mutual funds, and more. One significant advantage of offline trading is the negotiation of security prices, which is not always feasible in online trading. This price negotiation can result in improved returns for investors, particularly those who benefit from the expertise of skilled brokers.

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The Offline segment was valu

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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.

(MIND) Mind Gym: Poised for Growth?

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.

Mind Gym (MIND) - Unlocking Potential: A Growth Story in the Making

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.

Mind Over Matter? ((MIND))

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

Cognitive model components.

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