The Science of Tradespoon

We built our platform to embody our philosophy of value investing: essentially, an investor should only buy a stock when it is priced below its fundamental value. Our tools are designed to help all investors—not just institutional investors with deep pockets. We believe that any self-directed investor can use technology typically reserved for hedge funds to pinpoint these opportunities with precision.

Most retail investment technology services use traditional technical analysis, composed of simple mathematical formulas, standard deviations, and general statistical formulas. These formulas are applied to elaborate real-time data structures but fail to offer the user a true edge. The stock market- as a collection of always-changing data sets- is extremely chaotic, full of unstructured noise and hidden relationships every day. Making sense of this data requires living, breathing mechanisms that can self-learn in real time.

This is where Tradespoon is different: we use an ongoing learning and feedback loop- reliant on hyper-fast data processing and advanced artificial intelligence called Neural Networks. Artificial neural networks behave like humans--making assumptions and rigorously testing them to reach the most logical conclusions. In the case of Tradespoon, a neural network performs back-testing against its own algorithms. Other advanced components of this system include Adaptive Harmonics, Statistical Spectral Analysis, and Digital Filtration.

Honed over 15 years for unmatched predictive precision, our system monitors a universe of ~3,000 U.S. stocks based on recent news and price actions for each stock. Our team of trading industry veterans then conducts technical and fundamental analysis of the stocks ranked by our system. The result is a robust set of tools and recommendation services which allow the average self-directed investor to:

  • Find stocks that may be undervalued, and rank them based on probability of a rise in value.

  • Generate short and long term predictions for open and close prices, as well as support and resistance levels.

  • Determine the statistical probability of a stock closing between or outside of target low and high prices.

  • Analyze seasonal trends and pinpoint optimal entry and exit points to consistently capture profits and mitigate risk.