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Welcome to DiNapoli AI

DiNapoli AI introduction

Our Mission

Project DiNapoli AI was launched in late 2019 with its mission, to employ artificial intelligence to support trading strategies based on DiNapoli support and resistance Levels. This Project is entirely developed in Poland by technology specialists and Polish authorized DiNapoli experts. The tools and solutions are designed to aid the global DiNapoli trading community.

At the core of the project are two disruptive technologies: cloud computing and artificial intelligence. Since 2020, we have managed to achieve a strong collaboration between actual traders and the technicians that understand AI. We work together as a team. This project received no external funding. The tools and solutions were made by traders for traders.

Role of technology

Everything is developed in the cloud environment where our artificial intelligence code scans worldwide financial markets. A dedicated team of data center engineers maintain and monitor the cloud environment on a daily basis. All data generated in the cloud is protected against accidental loss, leakage, and unauthorized access.

The Railroad Track (RRT) weekly report (paid subscription)

US stocks, commodities and forex

The first commercial spin off from the DiNapoli AI project is the location of RRT patterns, a pattern detailed in Chapter 6 of "Trading with DiNapoli Levels". All RRT patterns are potential trading ideas. A subscription is available for those who are interested in trading DiNapoli's directional indicators. The RRT is among the most powerful of all DiNapoli patterns. Patterns are scanned on US stocks, commodity markets and Forex, as well as Polish issue stocks on the Warsaw Stock Exchange.

Our reports contain RRT patterns in terms of their shape and fundamental background. Subscribers can choose from a variety of proposed RRT patterns taking into consideration their own trading preferences, current market view, and individual experience. For beginners, it is a good opportunity to gain new skills in recognizing RRT patterns in the market. Again, these patterns are among the most powerful of DiNapoli Directional indicators.

How it works - connecting the dots

1. OHLC / EOD market data are downloaded into cloud
2. OHLC ticker data are transformed into a bar chart
3. The chart is transformed into a matrix (an array of numbers)
4. The Matrix is entered into the neural network for the analysis process
5. The neural network evaluates the data to determine if the RRT pattern is present.
6. All instruments with a high RRT probability go into a preliminary report
7. The team of Polish DiNapoli experts conduct an analysis based on the AI findings
8. All instruments selected by DiNapoli experts (RRT patterns) go into the final report.

Watch a real time example trade on Alibaba stock trade ( BABA )

What are our future plans for this project?

During the first year of our project we have managed to build a sophisticated AI RRT scanner in the cloud. Currently we are continuing the training process of the neural network to optimize RRT recognition. Simultaneously, we are using machine learning techniques to find unique traits attempting to ascertain improvements in the directional indicators first developed by Joe DiNapoli throughout his long trading career.

          Disclaimer. The information contained herein is subject to change without notice and was obtained from sources believed to be reliable, but is not guaranteed as to accuracy or completeness. Those using the materials for trading purposes are responsible for their own actions. No guarantee is made that trading signals or methods of analysis will be profitable or will not result in losses. It should not be assumed that performance will equal or exceed past results.
          Trademarks and Copyrights: FibNodes, DiNapoli Levels, Oscillator Predictor, MACD Predictor and D-Levels are trademarks of Coast Investment Software Inc. All contents of this website are Copyrighted © 1998-2019, Coast Investment Software Inc. All Rights reserved worldwide.
          NOTICE: Testimonials are believed to be true based on the representations of the persons providing the testimonials, but facts stated in testimonials have not been independently audited or verified. Nor has there been any attempt to determine whether any testimonials are representative of the experiences of all persons using the methods described herein or to compare the experiences of the persons giving the testimonials after the testimonials were given. The average reader should not necessarily expect the same or similar results. Past performance is not necessarily indicative of future results. No person was compensated for providing a testimonial.