In Market Analysis 3 we change its component to improve performance and speed, plus that we merge all the component in two main programes Analyzer and Applier, Also add a lot of Mobility for Analyzing the market.
Pull the Dataset for any symbol in any period of time in Forex market that contain the basic Features (open, high, low, close).
We do it by using fxcm python library (PYFXCM).
In Feature-Calculator we calculate the machine learning Feature based on this research (here).
- We build it in python .
- It based on the main Features Dataset and some functions in this research (here)..
Different Machine Learning models that we used to learn from the Feature.
- We build the the models in python using scikit-learn.
- It learn from our predefined Feature.
- And then save it after finsh traning in PKL file.
- Decision Tree.
- k-nearest neighbor.
- RandomForest.
- Support vector machine.
- Neural-network-MLPClassifier.
Use to backtest a strategy that based on the prediction of the Models from any period of time.
- We do it by build our algorithmic trading strategy in python.
- And load our Models in Tester component.
- Then calculate the profits by saving the enter price and the Action then subtract from it the close price and subtract the spreed.
The advantage of Market-Analysis-3 is the mobility and in Algorithmic Trading we can have more the one Algorithm till now i add two algorithm:
- Algo_1 Called every NTime see the prediction:
Enter : if all is equal then do the action.
Hold : if not equal.
Close : if all equal in the opesit side.
- Algo_2 Called every NTime and NTime+1 see the prediction:
Enter : if all is equal in NTime and NTime+1 then do the action.
Hold : if not equal.
Close : if all equal in the opesit side.
- Profit
- Total number of trades
- Sum of wining trades
- Sum of loss trades
- Max drawdown
- Best trade
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