System Design

Interactive Labs

Runnable distributed-systems implementations.


                        
                        
                        
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Data Science

Interactive Labs

Runnable experiments with preserved methodology, code, and results.

Classifies low, medium, and high forward-volatility regimes from trailing OHLCV features. The label is future realized volatility, but every feature is known at the prediction date, so the model is evaluated as a regime forecast rather than a hindsight classifier.

Time-series-safe classifier selection

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Estimates 21-day forward return from the same trailing feature matrix. This is intentionally tested against simple baselines because noisy return prediction can look sophisticated while adding no usable signal.

Fold-local return model pipeline

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Tunes the high-volatility mean-reversion branch. The branch estimates expected return from price reverting toward a trailing 21-day moving-average fair-value anchor, then applies a fixed-size signal only when the stretch clears the searched entry threshold.

Chronological constrained grid objective

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Combines the classifier, Ridge return model, and mean-reversion fair-value branch into one expected-price evaluation. The gate uses predicted regimes only: low predicted volatility selects Ridge expected return, high predicted volatility selects mean-reversion fair value, and medium predicted volatility stays neutral.

Predicted-regime strategy gate

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C++ Low Latency

About

Algofolio is a DSA recall system built around:

  • Pattern-first categorization by reasoning technique.
  • Consistent variable naming, notation, and C++23 style.
  • Fast review through folds, filters, and multiple solution methods.