You can't predict the market.
Neither can we.
But we can make sense of the present. That’s how we’re taking DCA to the next level.
Stress-free investing
Stay objective and make confident decisions when it matters the most by sticking to your personal Risk Strategy.
What is risk?
Risk is a score between 0 and 100 that represents the market’s pulse.
Computed by machine learning, our Risk Model distills vast market data into an easy-to-understand score.
Note! We do not retrofit any data. Risk levels calculated daily since 2021 remain unaltered historically.
Key Benefits
Filter out the noise and take confident decisions
Worry less. Accumulate more.
Test how Risk-based DCA drastically improves your returns and BTC accumulation.
Strategy | % Profit | Final Wealth in Dollars | Final Wealth in BTC | Amount Invested |
---|---|---|---|---|
Risk-based DCA | - | - | - | - |
DCA/HODL | - | - | - | - |
Visit us on a desktop device for enhanced chart visuals and comprehensive results.
The minds behind
Initially, the Risk Model was in our private toolbox. What began as an experiment in exploiting risk identification , rather than predicting prices, yielded impressive results.
Seeing the potential for a much wider application in finance than we initially anticipated, we felt compelled to share it with the world.
The Risk Model
- Real-time calculation & notifications
- Asset growth models, technical- and multivariate regression analysis
- Back- & Forwardtesting Custom Strategies
- Proof of Profit verified
- Proven track record since 2021
Risk Notifications
Strategy Backtesting
API Access
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Risk Notifications
Strategy Backtesting
API Access
17% off monthly
Risk Notifications
Strategy Backtesting
API Access
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Our FAQ has you covered.
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.
Our Bitcoin model has been live since mid-2021 and has consistently shown very convincing results since. The historical data you see is not backfitted; it is the actual risk calculated on that day. You can use our strategy builder to backtest based on this real data to see performance.
In addition to real world performance all our models go through a 3 step framework. It involves dataset optimization, backtesting and benchmarking against other strategies, and finally forward-and stress-testing.
AlphaSquared is the only platform in the crypto space currently offering forward-testing capabilities using simulations. You can try these in the strategy builder.
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The Risk Model is very intuitive to use. Put simply, the lower the risk level, the more you invest. The higher the risk, the more you sell (or simply invest less). This allows you to gradually enter and exit the market in sync with risk.Â
Our new knowledge base contains videos and tutorials covering topics like how buying, selling, and reinvesting works – making it very easy to follow and learn.
Yes. We have models for Ethereum, Solana, Cardano and the S&P500. We are currently building more.Â
You can find detailed videos on how we built these models here.
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