Please help select an Algo based on Accuracy and Confusion Matrix I am very new to Data Science would appreciate your advice big time. 

Got a task: predict if a trade will be profitable or not, based on a set of data. 
I have prepared, cleaned and tested data. Selected a few two-class algorithms since the answer requires a boolean result (true or false (1/0)). After testing selected two best from my opinion: Random Forest and Logistic Regression CV (cross validation). Below are the results. Can't decide which one to use in production. Also, please note: it is better not to trade than trade and loose money, therefore false negatives are better than false positives. 

Random Forest results:



Logistic Regression CV (cross validation) results:



Added a sample of dataset for clarification:
There must be definitely some leakage in the data, which is why the Logistic Regression is predicting almost all as the positive class. You may want to check on that first before you proceed. The only reason Random Forest may seem better in that case is that it, by default, only takes a subset of variables.