Algorithmic Trading uses computer algorithms to track, buy and sell stocks. As financial markets are extremely volatile, rule-based and quantitate automated trading should be devised on some guidelines if you want to increase the odds of success in stock market with algorithmic trading.
Flexibility for Rapid Changes Must be There
While you will need to have some sort of rules or constants in your algorithm, you ought to leave space and flexibility to make your algorithm subjective and changeable based on the market conditions. Even if you can’t change stop losses, price targets and entries daily, you should program and implement your algorithm so that you can tweak and fine tune the central strategy of your algorithmic trading algorithm whenever you can. For this you will need to have a mastery over computer programming.
Backtesting is the Key
No matter which algorithmic trading technique you use, backtesting should be an indispensable part of it. Backtesting uses historical data to analyze profitability and risks of automated stock trading. If the results of backtesting supports your algorithm’s rationale, you can go ahead and launch that algorithm with confidence. Always backtest and use a demo account before putting real capital in action. If you don’t want to program your own backtests, you should use a fully developed back-testing engine like Trading Blox.
Learn Programming, Tools and Finance
The odds of success in computer-driven automated stock trading can never be increased if you don’t have a full grasp on designing your own algorithms. For that, you will need a full mastery of programming and stock markets. Learn your way through the world of algorithmic trading by reading online resources and books. For a start, I’d recommend reading Quantitative Trading by Ernest Chan. This book gives a step-by-step guide on how to setup a quantitative trading system using MatLab or Excel. Another excellent book is “Algorithmic Trading” by the same author. This book gives extensive guide on how to design your own algorithms for stock market trading and then implement them using Matlab, Python, C, C++. There’s also an excellent course on Algorithmic Trading by Quantra. This course has helped thousands of people to increase their chances of success in automated stock trading.
Use Robo Advisors
The role of Robo Advisors is quintessential in algorithmic trading. If you want to increase chances of success in automated trading, always incorporate a good robo advisor while designing your strategy.
Use All Market Dynamics in your Algorithm
Your algorithmic trading strategy should be holistic enough to inculcate market metrics like fundamental analysis, macroeconomic news, statistical analysis and technical analysis. These metrics are prone to changes so always leave flexibility in your program.