Follow this step-by-step guide to discover how to include sophisticated and powerful machine-learning tools in your investment process.
Limex Ai’s user-friendly interface allows traders to access a wide range of investable universes, risk-reward optimization goals, data series and statistical analysis, filters and conditions, etc.
The Limex platform allows you to use your individual Ai-powered strategies to trade on your personal accounts, provide investment ideas to other investors, mentor newer traders, or eventually manage a Limex-funded trading account.
Three Steps
Develop, test, and execute
Strategy development
Define your priorities – which stocks to include, risk levels, and other variables like dividend yields, diversification rules, etc. Pick an optimization goal, a back-testing period, and run multiple simulations with historical data.
Execute
Select the strategies that best achieve your investment goals and start trading.
Real-time testing
Once you settle on one or more strategies based on the back-testing results, turn it “live” and run it with actual data.
Try different strategies until you find the one that best achieves your goals
What sets us apart
Access user-friendly Ai
Limex interface connects traders with cutting edge machine-learning infrastructure to build individualized investment strategies.
Improve your trading
Complete the course with the skills to manage assets and mentor within the Limex community
Fund your trading
Trade real capital on a Limex-funded account, developing your track record while sharing profits.
Program
Identify the preliminary steps required prior to start testing investment strategies.
Input your parameters for the infrastructure to design your Ai-powered models.
Simply run the models. The machine will run millions of simulations and learn patterns over 10+ years of data including dozens of variables, and will provide full detailed historical data.
Check the performance results – absolute and relative performance, volatility, trading volume, etc. If you are dissatisfied change the model’s inputs and run new simulations.
Once you obtain a performance that you like, simply turn the model “Live” and it will automatically update in the frequency you chose.
The model will continue improving through its machine-learning capacity.