> For the complete documentation index, see [llms.txt](https://docs.maicrotrader.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.maicrotrader.com/4.-open-beta/4.3-dual-model-architecture.md).
# 4.3 Dual-Model Architecture
Our system ”ensembles” two Random Forest models operating on distinct temporal resolutions, one derived using **intraday features calculated from hourly bars** and **intraday features across days**.
* The intraday model processes hourly data, developing metrics that quantify aggressive trading behavior, volume price divergences, and cross-venue arbitrage signals.
* The daily model operates on lower-frequency data, identifying persistent trends and structural shifts in market dynamics. Its feature set encompasses multi-period momentum indicators, volatility regime metrics, and cross-asset correlation structures.
At the time of writing, we have over 20 different variables that we combine in the modeling exercise across the **intraday** and **daily** frequencies. Going forward, we will continue to update the list of predictors that enter our model.
The prediction target is formulated as the return:
$$
r\_{i,t+1} = \frac{P\_{i,t+1}}{P\_{i,t}} - 1
$$
\
\
with the Random Forest out-putting continuous return predictions:
$$
\hat{r}\_{t+1} = f(\mathbf{X}\_t)
$$
where ***X****t* represents the feature vector at time ***t***.
Feature preprocessing employs rank transformation and winsorization[^1] to mitigate the impact of extreme observations common in cryptocurrency data. The feature engineering emphasizes normalized measures that remain stationary across varying market conditions. The models undergo periodic retraining on a frequent basis, ensuring adaptation to evolving market conditions while maintaining sufficient historical context for robust pattern recognition. We set our hyperparameters for the models to be conservative.
**After both models are trained separately, the models are combined through a risk parity framework that allocates weights such that both the intraday and daily models contribute equally to portfolio risk, preventing dominance by the more volatile predictions.**
[^1]:
---
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