This blog is aimed to share the topics learned about building successful systematic and quantitative strategies using algorithmic trading. Remove the sentiment and the psychology while building a successful trading career.
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At following, you can find the tags of the topics that you will find in this blog.
Algorithmic Trading Algorithms Arbitrage Backtesting Basics Data Science Data Sources Derivative Markets EPAT Financial Engineering Forecasting Fundamental Analysis Hedging Strategies Histograms Historical Stock Data Interactive Brokers Learning Machine Learning Market Corrections NASDAQ Oil Market Oil Trading Options Trading Option Trading Python Quantamental QuantInsti Quantitative Analysis Quantitative Trading Quantra Quant Trading R Returns Risk Analysis Risk Management Statistical Arbitrage Stocks Systematic Trading Testing Trading Trading Strategies Training Vertical Spreads WorldQuant University WTI Prices
Also check our partner Data Enigma for information about Data Science and Artificial Intelligence.
Why algorithmic trading?
According to the Algorithmic Trading Market report, the market size is expected to grow from USD 11.1 billion in 2019 to USD 18.8 billion by 2024, at a CAGR of 11.1% during the forecast period. The key driven factors for this grow are:
Raise demand for fast, reliable, and effective order execution
Reduction of transactional costs
Increasing government regulations, and growing demand for market surveillance.
The Exchange-Traded Fund (ETF) is expected to grow at the highest CAGR during the forecast period. This segment provides low average costs to traders so they could gain maximum profits out of them.
North America is expected to hold the largest market size, while Asia Pacific (APAC) is expected to grow at the highest CAGR.
The emergence of AI in the financial service sector is expected to be a major factor aiding in the growth of the algorithmic trading market.