Hello Luca!
Blueshift is a platform to research and trade systematic investment strategies in Python. It is fast, flexible and reliable - anything you can code, it can run. It is asset-class and instruments agnostic - i.e. it supports multiple asset classes and instruments like FX, Equities, Futures.
In addition to that, whether you use factor strategies, technical indicators, or advanced machine-learning, Blueshift can do it all.
The stability of the algorithm / Max DD or any other metric is dependent on what parameters and strategies you choose.
If you are confident with the backtest results, you can live trade using the Blueshift platform itself by configuring a supported broker account.
Hope this helps!
Ok, I tried to include 0.0001 pip slippage and the result are totally different and obviously in loss.
I think, we should always take in consideration a possible slippage of 1 pip (trading eur/usd for example).
How can I do it?
Slippage simulation can be a complex aspect when we take into account all the factors associated with the same. More details on that can be found here.
For naive slippage calculations, you can simply offset the price of entry/exit by 1 pip against your favour to get an indicative result. It is not exactly accurate, but in most low volume trade scenarios gives a result closer to what happens in actual trading.