Algorithm DCA+
Let CALC’s sentiment analysis machine learning algorithm DCA for you.
Last updated
Let CALC’s sentiment analysis machine learning algorithm DCA for you.
Last updated
DCA+ is a series of strategies that take advantage of cutting-edge statistical and Machine Learning techniques with the aim of improving returns without sacrificing the favorable risk profile of a conventional DCA approach. With a DCA+ strategy, live data is used to update capital allocation dynamically, through modification of both buy or swap-amount and time-of-swap. CALC's algorithms continuously monitor trends and data so that you don’t have to! A true set-and-forget tool. Read the whitepaper , or for a simplified version you can read this .
In short:
The non-custodial, dynamic DCA algorithm that outperforms traditional DCA by 20% on average, while maintaining the same favourable risk profile. More assets, no added risk.
We monitor data - both on and off-chain - on a wide range of variables associated with crypto and stock market performance
We process the data with a range of tools, from simple statistical methods to advanced machine-learning techniques and genetic algorithms
This processed data is used to inform the next swap amount in the strategic allocation of a user’s deposited sum
Each strategy has been tested comprehensively, using a combination of back-testing and historical sampling methods to ensure that each stands the best chance of outperforming a conventional DCA approach.
Note that because swap-amount and time-of-swap are not linear in DCA+ strategies, the time to allocate a user’s total capital will vary depending on market conditions. On average, most of our DCA+ strategies will deploy a user’s total capital over a period of 4-6 months.
