Algorithmic Trading has been making waves lately on Wall Street, but what's the secret behind its success? Is there a secret formula to generate profits by the click of a mouse? Or is it all about chance? Well, the answer is neither. What you need to do is learn how to pick winning trends and exploit them. Then, you can use algorithms to trade accordingly and earn money by the pound.
There are two main types of trading strategies: quantitative trading and fundamental trading. Quantitative trading strategies deal with trends in the markets-past and present. Fundamental strategies analyze market behavior around the clock looking for underlying security/trends that will breakout. Both strategies essentially use the same mathematical equations to identify profitable opportunities; however, they do employ very different approaches.
Many traders use quantitative trading algorithms to identify trends in the markets. These algorithms allow them to "see" where the action is likely to take place next, enabling them to trade early and ahead of the curve. This is especially useful in volatile markets where movement can be rapid from one minute to the next. However, some quantitative traders stick to the rules-and in doing so, they become trapped in a vicious cycle of chasing bad trends (in the quantitative world) and chasing good trends (out of the quant world). If this seems like you, then you might want to reconsider your strategies quantitative trading is not for everyone.
The second approach used by quantitative traders is backtesting. Backtesting, as it is known, is the process of running the same backtest, same trade, over again with the same parameters. Each time you run the backtest, you are taking the chance that the stock or other asset will break out or not. While this may seem like an ineffective way to go about learning algorithms for trading, it does have one major advantage: it lets you observe how a machine learning algorithm performs in the real markets, not just backtesting alone.
Learning how to pick stocks using an algorithmic trading strategy using backtesting is not as difficult as you might think. There are many great books available that teach you the basics and key concepts required to implement this method in your own campaigns. One such book, day trading with Python by Mike Griffiths and Matthew Guerrier, provides the exact scripts and policies required to backtest your algorithms in the real markets using historical data. Not only does this provide you with a solid foundation for learning, but it will also help you build more sophisticated backtesting policies and scripts to better gauge your results.
Learning how to use these types of strategies is not just about figuring out how to predict market behavior, but it's about using your past performances to effectively gauge where you should place your money. Traders who know how to learn algorithmic trading strategies can capitalize on their past mistakes to guide their future picks. Just as in the world of options trading, it's possible to lose money when putting a lot of money in a risky position, so too can traders who don't take the time to develop a solid system. With a little bit of effort and practice, you can create a system that will consistently return high profits for years to come - even while you're sleeping.