After many years of focused research in financial-market automation, I have brought a comprehensive framework for building trading robots to roughly 80 % completion. Written entirely in MQL5, the platform is ready for a skilled team to help drive it across the finish line and release it commercially.summery:
1. Providing a unified and applicable structure for all strategies to accelerate coding and facilitate code reuse. This structure is easy to learn for any trader, allowing them to effortlessly implement their strategies within the algorithm. Consequently, coding will be much simpler for developers. (In later stages, the code will be generated automatically by AI.)
2. Simulating the thinking process of a manual trader by managing strategies based on various market phase factors, market direction, and automatically comparing signals against these factors.
3. Establishing a well-defined framework for risk management and capital allocation.
4. Executing multiple strategies simultaneously and managing their signals efficiently.
5. Providing essential tools for traders, such as position management based on news significance and the issuing country, as well as controlling the time gap between signals.
6. Automating signal evaluation and decision-making by utilizing MetaTrader’s Strategy Tester for each strategy separately. The performance of each strategy is tested individually in the market, and the results are stored in a database. During live trading, the robot assesses signals based on backtesting results, assigns scores, and determines trade volumes accordingly. 7. Implementing a novel and highly practical classification-based AI model to estimate the success rate of each signal.
8. Optimizing input parameters by running the robot with multiple strategies across various symbols. The primary goal of this platform is to establish a reliable core for trading robots capable of handling numerous strategies. With the structured approach provided for strategy development, the robot will automatically eliminate weak strategies and prioritize strong ones using its scoring system. Ultimately, the system will function in such a way that new strategies can be added, while the robot autonomously manages the rest. Consequently, as new strategies are introduced over time, the robot will continuously improve and become more effective.
Where we are headedThe core vision is a self-evolving trading platform that automatically weeds out weak strategies and scales up strong ones—continually improving as new ideas are added.Why I’m reaching outI have carried this project solo for three years and now seek:MQL5 engineers to polish, test, and extend the codebase.Investors to fund final development, infrastructure, and go-to-market efforts.The code repository and detailed technical documentation are ready for due-diligence review.If you are interested in partnering to complete and commercialize this platform, I would be happy to share a full walkthrough.
1. Providing a unified and applicable structure for all strategies to accelerate coding and facilitate code reuse. This structure is easy to learn for any trader, allowing them to effortlessly implement their strategies within the algorithm. Consequently, coding will be much simpler for developers. (In later stages, the code will be generated automatically by AI.)
2. Simulating the thinking process of a manual trader by managing strategies based on various market phase factors, market direction, and automatically comparing signals against these factors.
3. Establishing a well-defined framework for risk management and capital allocation.
4. Executing multiple strategies simultaneously and managing their signals efficiently.
5. Providing essential tools for traders, such as position management based on news significance and the issuing country, as well as controlling the time gap between signals.
6. Automating signal evaluation and decision-making by utilizing MetaTrader’s Strategy Tester for each strategy separately. The performance of each strategy is tested individually in the market, and the results are stored in a database. During live trading, the robot assesses signals based on backtesting results, assigns scores, and determines trade volumes accordingly. 7. Implementing a novel and highly practical classification-based AI model to estimate the success rate of each signal.
8. Optimizing input parameters by running the robot with multiple strategies across various symbols. The primary goal of this platform is to establish a reliable core for trading robots capable of handling numerous strategies. With the structured approach provided for strategy development, the robot will automatically eliminate weak strategies and prioritize strong ones using its scoring system. Ultimately, the system will function in such a way that new strategies can be added, while the robot autonomously manages the rest. Consequently, as new strategies are introduced over time, the robot will continuously improve and become more effective.
Where we are headedThe core vision is a self-evolving trading platform that automatically weeds out weak strategies and scales up strong ones—continually improving as new ideas are added.Why I’m reaching outI have carried this project solo for three years and now seek:MQL5 engineers to polish, test, and extend the codebase.Investors to fund final development, infrastructure, and go-to-market efforts.The code repository and detailed technical documentation are ready for due-diligence review.If you are interested in partnering to complete and commercialize this platform, I would be happy to share a full walkthrough.
