Trading performance

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



Deep Learning

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

Signals and alerts

Buy/Sell signals based on the predictions and current prices.
Get notifications when it is time to trade.

Stocks screener

Find best stocks with maximum PnL, minimum volatility or highest forecasting accuracy

Portfolio management

Construct your portfolio based to reduce risk and maximize profit

Corporate events, news and global trends

Take into account corporate events and market news.

Account for risks related to 2019 novel coronavirus COVID-19 and protect your assets from the market turmoil.

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:

  • Loading of the historical market data from the Quandl and Cryptocompare premium datasets
  • Data normalization
  • Selection of the optimal Kalman filter parameters using our innovative approach or using causal CNNs for automated feature extraction.
  • Smoothing of the source data with Kalman filter using optimal parameters or using causal CNNs for detecting features at different abstraction levels and generalization.
  • Optimization of the Recurrent Neural Network or CNN hyperparameters
  • Training and validation of the Recurrent Neural Network or CNN
  • Model backtesting
Models are being retrained on a regular basis.

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.

Track record

Manual trading

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.

Automated trading

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:

  • Annualized return: 26.8%
  • Annualized volatility: 6.8%
  • Sortino ratio: 8.43
  • Sharpe ratio: 3.49

We are constantly tracking the accuracy and performance of our models:

  • % of Profitable Deals: up to 80% and more
  • Annual percentage rate (APR): up to 33% and more
  • Sortino ratio : up to 9.0 and more

* 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.