Invest with
a Risk-Adjusted Approach
Dollar Cost Averaging (DCA) is a fixed, regular investment method that reduces portfolio volatility risks but can limit profits and capital efficiency.
In contrast, Dynamic DCA is a more responsive strategy which involves adjusting the amount you buy or sell according to the risk of the asset. Put simply, the lower the risk, the more you buy, the higher the risk, the more you sell.
Benefits of Dynamic DCA
Note! We do not retrofit any data. Risk levels calculated daily since 2021 remain unaltered historically.
Why use the model
With our risk model, you’re not predicting the future – you’re understanding the present.
The model condenses complex data into a single, actionable metric, helping you take the guesswork out of investing. This allows you to focus on and react to the present state of the market, rather than getting caught up in the noise of predictions and hype.
Data-driven innovation:
Construction of the risk model
Our model takes a variety of factors as input and applies quantitative tools to calculate a risk level between 0-100. The underlying algorithm is self-learning and improves itself daily as new data becomes available. The algorithm is built using data-analysis, statistics, and automation.
The factors taken into consideration are handpicked and based upon a strong domain knowledge in finance and bitcoin expertise.
Asset growth models for capturing asset-specific patterns
Traditional technical analysis for price-based classification
Regression for modeling time and seasonality
Machine learning for dynamically determining variable weight
Proof of Profit (PoP)
Discover how our custom testing framework (PoP) goes beyond traditional methods, ensuring the reliability of the model.
01 Dataset & Optimization
The 1st step involves meticulous dataset sourcing and comparison. We understand the importance of utilizing reliable and diverse datasets, but also the challenges in avoiding biased data, mitigating the effects of multicollinearity, and addressing potential data gaps or inconsistencies.
02 Backtesting & Benchmarking
The backtesting process runs in parallel with finding the optimal dataset. This crucial step involves benchmarking our model against three widely recognized trading and investing strategies: HODL (Hold), DCA (Dollar-Cost Averaging), and a trend-following strategy. View the backtesting results in the Whitepaper.
03 Forward-and Stresstesting
The most crucial step involves subjecting our model to various simulated scenarios and stressful environments to evaluate its resilience. By simulating a wide variety of future price trajectories, we can assess how our model performs under adverse circumstances. View the simulation results in the Whitepaper.
Get started for free
Try for free
"The BTC Risk Metric alone has easily paid for the lifetime membership 100 times over."
"Honestly $120 a year to have a stress-free, backtested, super simple strategy to maximize your crypto returns is a no brainer."
"I’m happy this is a selfcontained product without too many bells and whistles. I need a tool for me to be exposed to the market without having to think about it."
Free trial
Free trial includes
Risk Models
Access to BTC, ETH, SOL and many more.
Risk Notifications
Get notified of risk level changes
Strategy Backtesting
Test with historic data
API Access
Access live risk levels
No strings attached
No credit card or personal info required
Yearly plan
33% total savingsPlan includes
Risk Models
Access to BTC, ETH, SOL and many more.
Risk Notifications
Get notified of risk level changes
Strategy Backtesting
Test with historic data
API Access
Access live risk levels
20% Launch Discount
Limited time offer - Lock in this special rate now!
Lifetime Plan
Lifetime plan includes
Risk Models
Access to BTC, ETH, SOL and many more.
Risk Notifications
Get notified of risk level changes
Strategy Backtesting
Test with historic data
API Access
Access live risk levels
Lifetime Access
Single payment, lifetime benefits.
Inner workings of the
bitcoin risk model
The technical and fundamental underpinnings of the model
Detailed backtesting results, benchmarked against other popular strategies
In-depth process of constructing realistic price simulations
Forward-testing and stress-testing results using simulations
Our journey so far and our vision for the future
