Our primary objective is predicting the future trends for stock market data with machine learning. We present a new innovative prediction model, based on centuries old Japanese candlesticks and modern NLP algorithm Word2Vec. Suggested model constructs a simple language of Japanese candlesticks with a very limited vocabulary. In the following steps the prediction model uses Word2Vec to discover semantic context of each word within the language vocabulary. To test the model we develop a simulation tool that takes into account the most important aspect of stock market trading -- trade fees. To compare the success of the suggested prediction model we also develop simple TA models such as: Buy and Hold, Simple Moving Averages and MACD. Analysis of the results show the superiority of suggested prediction model over previously mentioned models in the test data-set. Additional testing is done with validation data-set in order to verify the results.