Financial markets are well known for their regular behavioral change in terms of volatility, trend changes, or stagnation, due to a large number of qualitative and quantitative inputs that can’t be easily identified. However we can find patterns in price movement for some assets, and with some probability to predict future price moves and use this information to support investment decision-making (opening or closing a trading position).

The analysis of the price patterns of financial instruments can be divided into three basic categories:

These three categories are interconnected and complementary. Historical price trends include a footprint of the crowd’s psychology and fundamental information.

Our team is currently working on the PatternLab tool, which can automate search and statistically evaluate patterns (price, fundamental, crowd). Using a simple form, the user selects the required pattern and asset list for analysis. Patterns are divided into several categories – an indication of volatility, a continuation of the current trend, or trend reversal.

PatternLab user

PatternLab user is the one who needs to use hard statistical data on a regular basis in investment decision making and not only relies on its intuitive decision-making. A typical example is a portfolio manager who manages the trading positions of hundreds of clients, and his decision can have a big impact on managed accounts and his credibility related to new capital acquisitions.

Use case

The portfolio manager is in a situation where he decides after 10 months to close a trading position on the selected asset. He will use PatternLab for analysis and get a statistical evaluation that if he doesn’t close the trading position immediately, but will wait for next 5 trading days, he will make extra two percent.

PatternLab may, of course, be used by retail traders, brokers or marketing department for easier communication with clients while acquiring or communicating the current status of trading positions.

Uniqueness

The uniqueness of PatternLab lies in the expert know-how of behavioral patterns that are the result of a ten-year application in the investment decision-making process. This is not about the over-optimized output of Artificial Intelligence tools but verified expert knowledge combined with regular statistical analysis.

Jan Budík