Signal trading pioneers across multiple asset classes, gitFlex has become the target benchmark among the signal service community. gitFlex dedicate themselves to producing the best in class education and signal service for new and seasoned traders alike.
It’s simple! You receive a text message or an email where we tell you WHAT, HOW, WHY and WHEN to trade a particular instrument.
Signals are sent via encrypted messaging services such as WhatsApp or Telegram but also via email to ensure you don’t miss a single opportunity.
We cover all trading hours across the globe. In short, it doesn’t matter if you’re in Paris, New-York or even Tokyo – rest easy knowing you’ll be notified of high probability trades across 6 asset classes.
The gitFlex approach rests in our ability to decrypt traditional technical analysis by taking a statically based recommendation for each trade signal.
Every client receives dedicated support as long as the markets are open. Since the beginning of the cryptocurrency era, we’ve been available 24 hours a day, 7 days a week
Every month, we deliver a trading summary covering a breakdown of every signal, the win loss ratio, asset class and performance review. We’ll send you a copy, or see the latest summary on our website.
Our global presence allows us to cover all trading hours and bring you appropriate signals for your region – regardless of where in the world you are located.
Learn to spot trading opportunities efficiently and make your way toward financial independence with gitFlex help.
Here at gitFlex we invite feedback from clients to allow us to continually improve our offering and service. You’re a the heart of what we do – hear from some of our traders and their experience with our signals service.
gitFlex is an advanced, fully automated Robot developed to trade with EURUSD.
As well as the Robot uses unique artificial intelligence technology for market analysis to find the best entry points. EA contains self-adaptive market algorithms with reinforcement learning elements. Reinforcement machine learning differs from supervised learning in a way that it does not need labeled input/output pairs to be present, and it does not need sub-optimal actions to be explicitly corrected. Instead, it focuses on finding a balance between exploration (of uncharted territory) and exploitation… More