Multifactorial Design & Input Variables
The Risk Model is multifactorial, meaning it considers vast amount market factors and data points to compute the risk. Some key attributes include asset growth models for capturing asset-specific patterns, regression to model time and seasonality, and traditional TA metrics for price-based classification.

Self-Adjusting Mechanism
The model is self-adjusting, updating and refining itself daily as new data becomes available. This continuous improvement enhances its accuracy and confidence level over time. The algorithm is powered by a range of techniques including data analysis, statistical modeling, and automation.

Historical vs. Live Data
It’s crucial to understand that while the algorithm improves daily, the historical risk levels reflect the data shown on our live risk meter for each specific day. The model is not retrofitted or predictive; it classifies the current price on a daily basis.

Naturally, the exact mathematical components of our model remain proprietary. However, you can dive a lot deeper into its build, input variables, and other attributes in our whitepaper.

Proof of Profit (PoP) Framework

Before publishing any metrics or indicators, we subject them to our rigorous testing environment known as Proof of Profit (PoP). As a member, you gain access to detailed and interactive charts that show how our Risk Model has performed historically.

1. Dataset Optimization
   The first step in PoP involves meticulous dataset sourcing and comparison. We prioritize reliable and diverse datasets while mitigating challenges like multicollinearity, biased data, and potential data gaps.
   
   
2. Backtesting & Benchmarking
   Our model undergoes extensive backtesting, benchmarked against three widely recognized trading and investing strategies: HODL, DCA, and a trend-following strategy. You can view these backtesting results in our whitepaper.
   
   
3. Forward-and Stress Testing
   The model is subjected to various simulated scenarios and stressful environments to evaluate its resilience. This step allows us to assess how the model performs under a wide variety of future price trajectories. AlphaSquared is the only platform in the crypto space currently offering this level of forward-testing capabilities. You can inspect this approach in our Whitepaper.

No. The model does not predict what will happen in the future, as history has proven that predicting market movements is not productive. Instead, the uniqueness of it lies in its ability to tell you what is happening right now. 

The model computes the current market risk with high precision. This means that whenever the market is overheated, or oversold, the model will tell you. It cannot tell when in time that will occur. Being a macro model, this gives you more than enough time to either calmly DCA out of the market and take profits, or reduce your investments. This same principle also applies to major bottoms. 

In short, you invest more when the risk is low, and less when the risk is high, or take profits if you wish. This earns you more BTC per dollar invested and reduces your risk.

You can follow pre-constructed strategies available in the member dashboard, or create your own strategy which is suited to your personal risk tolerance and goals.