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 Arbitrage Backtesting Basics Complex Systems Data Sources Derivative Markets Emergence EPAT Factor Investing Financial Engineering Forecasting Fundamental Analysis Hedging Strategies Histograms Historical Stock Data Interactive Brokers Learning Machine Learning Market Corrections Market Dynamics NASDAQ Nonlinearities in Financial Markets Oil Market Oil Trading Options Trading Option Trading Python Quantamental Quant Industry QuantInsti Quantitative Analysis Quantitative Trading Quantra R Returns Statistical Arbitrage Stock Market Stocks Systematic Trading Trading Trading Strategies 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.
Trading Enigma 