Updating our cointegration model in real-time is crucial for dynamic portfolio management. Real-life trading requires adjusting stock weights with every new data point. Our approach caters to retail traders with modest resources, applying statistical arbitrage techniques to identify correlated assets and cointegrated stocks.
We've restructured our Python scripts for efficiency. The database now acts as the main interface, updating our trading model and storing relevant parameters. The pipeline includes statistical tests like Pearson correlation, Engle-Granger, ADF, and KPSS for comprehensive analysis.
On the MQL5 side, we've made strategy parameters flexible by integrating JSON parsing and global variables, facilitating seamless database interaction for real-time updates. Maintaining a clean architecture with most functions in an MQL5 header f...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Cointegration
We've restructured our Python scripts for efficiency. The database now acts as the main interface, updating our trading model and storing relevant parameters. The pipeline includes statistical tests like Pearson correlation, Engle-Granger, ADF, and KPSS for comprehensive analysis.
On the MQL5 side, we've made strategy parameters flexible by integrating JSON parsing and global variables, facilitating seamless database interaction for real-time updates. Maintaining a clean architecture with most functions in an MQL5 header f...
👉 Read | AlgoBook | @mql5dev
#MQL5 #MT5 #Cointegration
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