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Real-time market prediction tool tracking public opinion in specialized newsgroups and informative market data persuades investors of financial markets. Previous works mainly used lexicon-based sentiment analysis for financial markets prediction, while recently proposed transformer-based sentiment analysis promise good results for cross-domain sentiment analysis. This work considers temporal relationships between consecutive snapshots of informative market data and mood time series for market price prediction. We calculate the sentiment mood time series via the probability distribution of news embedding generated through a BERT-based transformer language model fine-tuned for financial domain sentiment analysis. We then use a deep recurrent neural network for feature extraction followed by a dense layer for price regression. We implemented our approach as an open-source API for real-time price …