AIHPC: next-generation AWS cloud product to help uncover the hidden values of your big datasets with Big Data container platforms, HPC cloud clusters, AI platforms, AI cyber defense and cloud container computing.
DSS: a group of enterprise Data Science servers in the cloud to enable predictive analytics, anomaly detection, factor and conjoint analytics for data-driven decisions.
DevOps: a DevOps continuous integration (CI) server optimized in the cloud to enable and visualize software build and deploy in minutes.
CES: Continuous Enterprise Security.
PNN Cloud (forward tests) - an AI cloud platform for financial institutions (e.g., ETF, mutual, and hedge funds) to automatically train and validate deep learning models to predict trends of security prices - Anchoring: Google DeepMind (sold for $400 million)
PNN Mobile (back tests) - Android and iPhone AI apps for retail investors to automatically predict trends of security prices
Shiller and Fama were awarded Nobel Prize in 2013 for demonstrating accurate predictions of long-term asset prices manually. PNN is the first artificial intelligence (deep learning) machine to automatically predict and visualize security price movements with very high back-test accuracy.
PNN AI machine makes sophisticated security trendspotting available to investors as PNN charts. It optimizes and compares over ten modeling methods that include neural-network sentiment algorithms and advanced financial models (e.g., GARCH, BS, SAPE and BATS algorithms). PNN and MCPS are being patented by USPTO.
PNN AI machine runs on a hybrid cloud of MCPS that has built in Big Data platform as Hadoop for massively parallel processing (MPP) and linear scaling. PNN may process asset trendspotting requests within minutes for 10,000+ stocks, 1500 ETFs, and 1900 mutual funds, 24 by 7.
Extending Yale Professor Shiller's Nobel work, PNN is the first AI (deep learning) machine to trendspot security prices for investors of little advanced training in quantitative modeling. PNN enables automated security trendspotting with instant and visual insights for investment, trading and financial markets.
Many investors face challenges to trendspot securities manually. It takes investors four weeks and over $150,000 to manually trendspot a financial security (e.g., AAPL). PNN automatically creates and sends on-demand predictive charts (of the highest back-test accuracy and cross validations) to investors within minutes. The back-test accuracy of PNN charts is the highest of ten plus algorithmic modeling approaches, among them are 4 Nobel algorithms: 1. Black and Scholes on option pricing; 2. Kahneman on loss aversion; 3. Engle on GARCH volatility; 4. Shiller and Fama on asset trendspotting
PNN AI machine trendspots multiple relevant securities automatically to cross validate the mega trends. For example, along with high back-test validities, cross-validating SCO and UCO daily charts builds up confidence for investors in forecasting the moving directions of the oil industry.
PNN AI machine is designed by Yeswici LLC, a private U.S. company in Maryland specializing in computerized quantitative modeling, Data Science, and cloud solutions.
PNN AI machine makes complicated trendspotting available to you as PNN charts. To purchase PNN charts, text Lucy at 7706880801 or email email@example.com, Atlanta, GA, USA