Input: your historical system trade data with various system parameters(like, rsi, supertrend , atr, etc) along with status or trade(winning or losing)
1. Connect to your db or csv, loads the trade data e.g input features (columns-rsi,supertrend,atr or any other parameter you feel is making difference in decision) (5/n)
3. Use train data for training to Machine learning models
- SVM(with linear, polynomial,rbf)
- Logistic regression
- Decision tree/Random forest
- K-nearest Neighbor
- Naive Bayes
....baggin and boosting algos....and many more
6. Now use test data to see the performance of your models on unseen data.
1. get the input data in same format before putting trade/order
2. give this input data to above selected model
3. if output is winning then place order else don't
- Trade data has a pattern with respect to winning/losing trade
- Broadly, it is possible to separate winning trade and losing trade.
- Missing classification errors (type-i or type -2 ) are not significant