How Trade Ideas Use Statistical Analysis to Predict Price Movements

Traders across the globe are beginning to realize that they can increase their profits by utilizing artificial intelligence and advanced machine learning. In fact, most traders use some sort of automation as part of their normal trading routine. By automating some processes and automatically executing some traders, traders have been able to rely more on quantitative and data analysis to propel their trades.

In doing so, they often employ statistical analysis (sometimes they don’t even realize they are doing so); as a result, they can better predict how certain securities will move in the future.

This blog post will discuss how various trade ideas can be derived based on statistical and quantitative analysis. This involves computer programming and other technologies that you may not be familiar with. That is okay. You do not need to have a computer science background to harness the power of statistical analysis.

You simply need a fundamental understanding of what it is, where it comes from, and how it can impact your trading strategies. To do that, we will explore what statistical analysis is, how you can use it, and the benefits and drawbacks that are associated with it.

What is statistical analysis?

In essence, statistical analysis is a process that filters out unnecessary data and provides its user with essential information about common patterns and movements that will help them execute more accurate trades in the future. However, it is common knowledge that nothing can accurately predict and beat the market 100% of the time.

Regardless of how ground-breaking a software is, the market will often fluctuate randomly and continuously be influenced by external factors. The goal of the statistical analysis is not to eliminate this risk but to reduce the possibility of loss while executing a trade position.

An example of using statistical analysis without getting into the details of the code and algorithms created by the programmer could be that an algorithm scans through thousands of pieces of data on various stocks or sectors. The program could gauge the relationship between price, volatility, and risk/reward between multiple data points. It can then output this to the user to suggest that (for example), historically speaking, the price of Google rose three points after an announcement from the Fed said that inflation increased by .2% from the previous quarter.

The statistical analysis draws connections and patterns between data points and uses them to return probability-based trade ideas to the user. This is how those who use statistical analysis and quantitative modeling gain a competitive advantage over other traders. They have data to back their trade executions, while others only “go with their gut.”

What are some drawbacks or shortcomings of statistical analysis?

The most significant drawback is that no amount of data can predict future news. Data can be purchased and analyzed, but the market’s movement fluctuates based on global affairs and the general sentiment of traders. So even if there is a high probability that you profit from trading oil futures, for example, if news channels release that the current administration is placing an oil embargo on middle eastern countries, that trade will likely be unsuccessful.

This is why you must incorporate quantitative and statistical analysis into your trading strategy; you don’t want to rely on it blindly and completely.

Furthermore, greedy or fearful traders can greatly influence the price projection of companies, sectors, and industries. This is why you always must be involved in the trading community, understand sentiment and emotions, and watch and read the news. This will allow you to stay one step ahead of the competition, allowing your statistical analysis to better guide your actions.

Conclusion

Overall, statistical analysis is a numeric-based method of analyzing data and drawing connections to better predict probabilities of future movements. It is essential to understand and utilize this (there is much software that provides the technology behind the analysis) if you want to adopt the strategy that has resulted in millions of dollars of additional profit by thousands of traders.

But, you must always be wary and alert. Understand what drives sentiment in the market, and incorporate this into your data analysis. By combining both, you open your doors to maximize profits while also minimizing risks brought by external factors.