More than 80% of our top recommendations led to the successful trades
We track annual return, downside risk, Sortino ratio and other metrics of our models
Predictions are performed daily by the state-of-art neural networks models
We have trained models for the most of the S&P 500 Index constituents
Buy/Sell signals based on the predictions and current prices.
Get notifications when it is time to trade.
Find best stocks with maximum PnL, minimum volatility or highest forecasting accuracy
Construct your portfolio based to reduce risk and maximize profit
Take into account corporate events and market news
In the world where risk-free assets like banking deposits have close to zero or even negative returns, investors are seeking for ways to save and grow their assets.
StocksNeural.net analyzes and predicts stock prices using Deep Learning and provides useful trade recommendations (Buy/Sell signals) for the individual traders and asset management companies.
Predictive models based on Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are at the heart of our service. We constantly improve them, try new models and new scientific approaches. We believe our model are more accurate than competitors have and our service is much easier to use by either novice or experienced traders. We are communicating with some of the professional quant traders, and working together to make our system better.
Following steps are present in models training:
Daily pipeline for models includes steps required to load and preprocess new market data, calculate model's accuracy and performance metrics and generate trading recommendations according to forecast made and strategy parameters.
We apply this stock prices prediction method to our investment decision on the real stock market with success since 2014. Using calculated predictions as a base for the trading strategy, we were able to consistently outperform S&P 500 index.
We have also launched fully automated trading bot using solely recommendation provided by our Deep Learning models. It has demonstrated the following performance since December, 2016:
We are constantly tracking the accuracy and performance of our models:
* Those figures should be addressed with caution and solid risk management policy should be established and fulfilled. Past performance is not necessarily indicative of future results.