Kim jest Pragmatyczna Sowa?
Konsultujemy, wdrażamy, analizujemy dane, eksperymentujemy z AI oraz pomagamy zwalczać CyberZagrożenia.
Szukamy ambitnych projektów / problemów w infrastrukturze IT, ciekawych case-studies w obszarze cyberbezpieczeństwa, oraz Data Science.
Nasze projekty
FinancialMarkets.cloud
a cloud-based financial information system powered by Artificial Intelligence
***Alpha release coming soon***
Opis elementów składowych prototypu w języku polskim
Prototype at a glance:
- it reads the market not the financial news!
- gathers financial data from independent sources (global stocks exchanges, CFTC, FED, volatility indices, FRED, Eurostat)
- performs analytics and data visualization
- shows the current state of global markets and economy (overview of indices and macroeconomic indicators, industry sectors and stock portfolios)
- uses AI to find strong predictive price patterns (DiNapoli's directional indicators) in stocks, indices and commodities, that might be early warnings of possible trend reversals
- it spots anomalies (volume spikes, COT report, stock market sessions) and similarities
- powered by Artificial intelligence
- cloud based
- accessible from everywhere via any web browser
- analyses markets globally (Asia, Europe, US)
- fully developed and maintained in Poland (GDPR friendly, Cloud Act free)
- no software installation or data subscription needed
- Artificial Intelligence trained by real traders
'the Railroad Man' (Kolejarz)
DiNapoli's directional indicator scanner powered by Artificial Intelligence
(Proof of concept / a working prototype, Spring 2020)
What is the Railroad Track directional pattern (indicator)?
According to Dinapoli's "Trading with DiNapoli Levels" book, the RRT directional pattern is one of the best directional signals that can be found on charts in any time interval. Typically, is accompanied by a high market volatility and depicts a rapid change in the price of a particular financial instrument followed by a relatively quick return to the point from where the rapid slope has started. For experienced DiNapoli traders, prompt identification of that pattern could be turned easily into profit.
Prototype at a glance:
- it uses disruptive technology such as convolutional neural network instead of traditional programming or algorithmic trading
- it is cloud based, hence it doesn't require your machine's computational power
- the whole process is fully automated: data download, deep learning analysis and report preparation (PDF easily accessible via web browser). Process is triggered multiple times during a day (Mon -Fri) according to the time zones of the stock exchanges in Asia, Europe and the US
- accessible from everywhere via any web browser
- it analyses markets globally (Asia, Europe, US)
- fully developed and maintained in Poland (GDPR friendly, Cloud Act free)
- no software installation or data subscription needed
In this project we are using deep learning, which allows us to teach the machine how to recognize the DiNapoli's directional patterns on charts. We are showing Artificial intelligence our RRT patterns (charts) that we have found manually beforehand, so AI can learn from those and find similar ones on the market.... As simple as that.
How the scanner works:
- OHLC data are downloaded to the cloud
- OHLC ticker's data are transformed into a bar chart
- Chart is transformed into a matrix (an array of numbers)
- Matrix enters to the pre-trained neural network for the analysis process
- Neural network tries to evaluate if the RRT pattern is present on the chart
- All charts with the high RRT probability go into a PDF report.
At this stage, the scanner has access to the following exchanges & markets:
- AMEX,NYSE,NASDAQ,BATS
- Cryptocurrencies, Global Indices, Commodities, FOREX
- London Exchange, XETRA Exchange, Warsaw Stock Exchange, Borsa Italiana, SIX Swiss Exchange
- National Stock Exchange of India, Shenzhen Exchange, Shanghai Exchange, Hong Kong Exchange, Thailand Exchange, Tokyo Stock Exchange
BacktestBefore.trade | backtest.pl | Financial Data Analysis Project
cot-report.online | Commitments of Traders Report
Nasze artykuły
FinancialMarkets.cloud | a cloud-based financial information system powered by Artificial IntelligenceHow to teach Artificial intelligence to recognize highly predictive price patterns on the stock market.
Hunting hidden patterns in soft commodities with machine learning algorithms
Breaking down the NSL-KDD dataset and its predecessor KDD 1999
Employing Deep Learning for Cyber Security :: Artificial neural network (ANN) case-study
Employing basic CNN for image recognition (Deep Learning case study)
Nasze "Case Studies"
Powrót na górę
Infrastruktura informatyczna: wdrożenia, projekty, modernizacje
- Bezpieczny serwer WWW dla Webaplikacji w chmurze
- Bezpieczny serwer plików dla mikrofirmy
- Bezpieczny serwer poczty z obsługą kilku domen
- Migracja "lokalnych usług serwerowych" do "bezpiecznej" chmury w modelu IaaS
- Bezpieczna sieć komputerowa przyjazna RODO
- Budowa i migracja do nowej bezpiecznej platformy e-learningowej
- Network deception strategy / Honeypot, odwarcamy uwagę od tego co najważniejsze
Zwalczanie cyberzagrożeń :: Reakcja na incydenty bezpieczeństwa :: "Cyfrowe" dochodzenia
- Wroga inwigilacja (rekonesans) na web-aplikację / serwer publiczny - czyli blue-chip vs mała innowacyjna firma
- Atak na cyfrową reputację firmy za pośrednictwem znanego serwisu opiniotwórczego
- Skradziona tożsamość CEO użyta do zniesławiania osoby trzeciej w social mediach
- Ochorna przed spear-phihsingiem
Pozostałe
- Podnoszenie poziomu "Secuirty awareness" wśród pracowików, rozwój "Security Culture" oraz "Knowledge Management" w małej organizacji
- Zdalny monitoring infrastruktury krytycznej i danych krytycznych przedsiębiorstwa
- Implementacja protokołu IPv6 w publicznej chmurze
- Opracowanie projektu bezpiecznej wymiany informacji dla wielo-poziomowego procesu rekrutacyjnego (Zgodność z RODO oraz z rozporządzeniem MSWiAz dn. 29 kwietnia 2014 r.)
- Konsultacje oraz pomoc w przygotowaniu klienta do audytów (aspekty związne z bezpieczeństwem systemów komputerowych i oceną ryzyka)
