Background
Intense competition in the stock market results in a very efficient marketplace, where finding such repeatable predictive patterns is increasingly difficult. The field of technical analysis seeks to find repeatable patterns in price movements for a given asset to gain a predictive advantage in assessing its future price in the short term.
As a result, it is essential to understand the algorithms and statistics required to efficiently discover new indicators in the ever-changing landscape of technical analysis.
Invention Description
Researchers at Arizona State University have developed a software platform that uses ideas from symbolic logic and technical analysis to identify high probability indicators of successful stock trades across multiple stock symbols for historical data.
The system supports various time resolutions, and it has been tested most extensively based on indicators on the hour resolution. This software connects to the Alpaca trading platform (and others as needed) to obtain information about prices. Indicators can be learned on a daily schedule and the user is alerted as indicators are met during the trading day. This software is designed to automatically execute trades (for indicators meeting certain criteria) on a given trading day.
This platform also contains methods to help select the most important/effective indicators in operational trading. The underlying approach is also explainable, so the reasoning for making a given trade or why an indicator is important is understood.
Potential Applications:
- Companies or individual traders invested in the stock market
Benefits and Advantages:
- Ability to sift through hundreds of indicators for thousands of stock symbols
- Ability to provide explanations/ meta-analysis of performance
- Time-saving
- Accurate analysis