Discretionary trading is becoming increasingly difficult and even unprofitable, as the markets have become more efficient. This is why smart traders now make use of data-driven strategies. But what are they?
Data-driven strategies are essentially a method of trading that is based on data analysis and automated trading using computer algorithms. That is, trading algorithms are computed to analyze price and volume data, identify potential trade setups, and execute and manage those trades. Something is backed by data.
What are data-driven trading strategies?
Data-driven strategies are essentially a method of trading that is based on data analysis and automated trading using computer algorithms. They involve the use of computer algorithms and programs to identify and execute available trading opportunities. This trading approach is based on quantitative analysis, which uses research and measurement to break down complex behavior patterns into numerical values.
Since the markets have become digitalized and virtually every market data is available online, smart traders are now using technology to analyze loads of trading data and different markets to identify trading opportunities and automate their trading. With data-driven strategies, they set up computer algorithms that analyze price and volume data, identify potential trade setups, and execute and manage those trades.
How data-driven strategies work
Data-driven trading strategies work by constantly analyzing market data to determine the probability of a certain outcome happening based on an already-configured model. Unlike the discretionary way of trading, this form of trading relies solely on market data, not gut feelings, and it requires a lot of computational power to extensively research and make conclusive hypotheses out of numerous numerical data sets.
This is the method most top financial institutions and high-net-worth individuals have been using to trade for a long time. Retail traders like us are only getting to join the game now to enjoy the benefits that come with automated trading.
Advantages of data-driven strategies
The main advantage of data-driven trading methods is to use market data to calculate the probability of executing a profitable trade at all times. While a discretionary trader may effectively monitor, analyze and make trading decisions on a limited number of securities, they cannot do it for many securities at once because the amount of data would overwhelm their decision-making process. With data-driven strategies, the process of monitoring and analyzing the markets and executing trades is completely automated.
Another benefit is that it removes emotions from trade executions and management. Traders often allow emotions to get in the way when trading. Emotions, such as fear and greed, can stifle rational thinking, which usually leads to losses. With automated trading, this problem is eliminated.
Limitations of data-driven strategies
The market conditions are always changing, and as such, data-driven models may not work well all the time. The strategies need to be frequently updated to reflect the current market conditions.
Another limitation is that it is expensive to set up a data-driven trading method. Renting a cloud server that would ensure you have no downtime might cost some money and so is hiring a programmer to code your strategy.
How to make data driven trading strategies
The first and most obvious way to create data driven trading strategies is to use computer simulations, for example by using backtesting. You simply upload quotes and old data into a trading program and you make some variables and parameters to see if your idea has performed well in the past. If it hasn’t performed well in the past, then you can (safely) skip this idea and go to the next one.
The options and parameters are almost endless and only your imagination limits you. But computer tools are available for all and the most sophisticated players spend millions on IT and programs. The competition is fierce.
However, the strategy is only as good as the assumptions put in the model – garbage in garbage out. The risk of curve fitting is also pretty high. This is why you need to have a basic understanding of markets and trading. You need knowledge and experience.
For example, we know a hedge fund manager who employed Ph.d. graduates in math to do research. They were spectacular in math, but their lack of market understanding made the project worthless, because….:
Data driven trading is worthless without knowledge
Data without any background knowledge is worth zero.
Nassim Taleb is correct. To be a good mechanical trader you need to have a basic understanding of the markets. Our own experience is that many programmers and coders believe that they can make it big because of their programming skills. But we disagree:
Data driven trading strategies – ending remarks
We base all our trading on backtested data, and thus we are data driven traders. It works great. We believe this is the best way to approach the market for most (because it’s backed by data), if not all, traders.
