At Xeconds Strategy, we believe there is no single strategy that can work in all market conditions. Our goal is to create alpha multi-strategies which have the capabilities to perform and adapt to harsh trading environments.
Nothing like that existed for normal traders like us. This is for traders
The ideas behind our roots are that of our founders:
“Whilst working as a proprietary trader and fund manager, we came to realize that most humans are not flexible in adapting to changes. Often, we are married to our trades and ideas. Thing is, the markets are always on the side of exuberance and fear. All along, greed has the better of it. Market trends are caused by human emotions more than fundamental reasons! Yet, we fail to correct our mistakes because of shame and ego.”
Good trading is boring in almost all instances. We are genetically framed to focus on finding the best price with the best entry but there is never the best. We tend to bring trades into a high-impact event where the market is in a state of uncertainty and flux, discounting original trading systems/ideas.
Combining the power of professional traders, programmers and strategy testers with open data access, Xeconds Strategy was born to create alpha multi-strategies and give traders the access to it.
We assembled a community of professional traders with verified trade records combining the power of professional traders, programmers and strategy testers with open data access.
Strategies are coded in multiple programming languages and harness our cluster of hundreds of servers to run tick to tick backtest to analyze and deploy trading strategies in Equities, FX, CFD, Options or Futures Markets.
Out-of-sample testing and forward performance testing provide further confirmation regarding a system’s effectiveness and can show a system’s true colors before real cash is on the line and is vital for determining the viability of a trading system.
To optimize the strategy to the market in real time, we need to periodically run the detector on current market data and find the best parameters as we would do in a static case. The idea is to use the test outcome immediately for adjusting the strategy parameters on-the-fly. This way we will get a feedback loop which works almost in real time.