volarixs - applied AI & ML to finance

Explore our latest posts on machine learning, market dynamics, strategy architecture and design

Feature Engineering

Shrinking the Feature Space: PCA & Autoencoders

Many features are redundant or noisy. High dimensionality = harder to generalize.

How Asset Managers Can Implement AI & Machine Learning

Part 2: Infrastructure, Governance & Roadmap. What it takes to implement AI in asset management.

AI Implementation

Neural Networks for Market Data: MLPs, CNNs & LSTMs

We are selective with deep learning. Expensive to train, easy to overfit, harder to debug.

Neural Networks

Signal Half-Life and Decay: How Long Do ML Edges Really Last?

If you discover a signal today, how long will it work?

Edge Persistence

How Asset Managers Can Use AI & Machine Learning in Investment Decisions

Part 1: Use Cases & Value. Real-world use cases: idea generation, regime analysis, risk management.

Asset Management

Modeling Market Turbulence: GARCH, EGARCH & HAR

Volatility ≠ returns: heavy tails, clustering, mean reversion. Dedicated volatility models are essential.

ARIMA, SARIMAX & VAR: When Classical Time-Series Still Win

Explicitly model temporal dependence with transparent structure.

Volatility Forecasting Benchmarks: GARCH, HAR, and ML

Compare GARCH, HAR, and ML models for volatility forecasting.

Machine Learning

How Market Regimes Break ML Models

Financial machine learning rarely fails because the model is 'bad'. It fails because the market regime changed.

Boosted Trees for Alpha: XGBoost & LightGBM

Gradient boosting dominates tabular ML. Learn how XGBoost and LightGBM deliver strong performance.

The 19 Most Important Features for Equity Return Forecasting

Most ML performance in finance doesn't come from the model — it comes from the features.

Rolling Windows for Financial ML: A Complete Guide

If you use financial data and your model does not use a rolling window, the backtest is wrong.

Rolling Windows

Beyond Sharpe: A Research Framework for Evaluating ML Trading Strategies

Sharpe ratio is dangerously incomplete for ML strategies.

Random Forests in Finance: Nonlinear Signals Without the Drama

Tree-based ensembles capture nonlinearities and interactions in market data.

From Linear Regression to Lasso: Fast, Interpretable Baselines

Linear and regularized regressions still do serious work in finance.

Linear Regression

Market Regimes, Clusters & HMMs: Teaching Models to Respect the Environment

Episodes where statistical properties are stable enough: high vol vs low vol, risk-on vs risk-off.

Building a Universe-Wide Prediction Grid

An alpha factory needs predictions for every asset at multiple horizons from multiple models.

Prediction Grid

Regime-Conditioned Performance: Measuring ML Robustness

Most backtests report a single Sharpe. But ML models fail by regime.

volarixs - applied AI & ML to finance

Explore our latest posts on machine learning, market dynamics, strategy architecture and design

Feature Engineering

Shrinking the Feature Space: PCA & Autoencoders

Many features are redundant or noisy. High dimensionality = harder to generalize.

How Asset Managers Can Implement AI & Machine Learning

Part 2: Infrastructure, Governance & Roadmap. What it takes to implement AI in asset management.

AI Implementation

Neural Networks for Market Data: MLPs, CNNs & LSTMs

We are selective with deep learning. Expensive to train, easy to overfit, harder to debug.

Neural Networks

Signal Half-Life and Decay: How Long Do ML Edges Really Last?

If you discover a signal today, how long will it work?

Edge Persistence

How Asset Managers Can Use AI & Machine Learning in Investment Decisions

Part 1: Use Cases & Value. Real-world use cases: idea generation, regime analysis, risk management.

Asset Management

Modeling Market Turbulence: GARCH, EGARCH & HAR

Volatility ≠ returns: heavy tails, clustering, mean reversion. Dedicated volatility models are essential.

ARIMA, SARIMAX & VAR: When Classical Time-Series Still Win

Explicitly model temporal dependence with transparent structure.

Volatility Forecasting Benchmarks: GARCH, HAR, and ML

Compare GARCH, HAR, and ML models for volatility forecasting.

Machine Learning

How Market Regimes Break ML Models

Financial machine learning rarely fails because the model is 'bad'. It fails because the market regime changed.

Boosted Trees for Alpha: XGBoost & LightGBM

Gradient boosting dominates tabular ML. Learn how XGBoost and LightGBM deliver strong performance.

The 19 Most Important Features for Equity Return Forecasting

Most ML performance in finance doesn't come from the model — it comes from the features.

Rolling Windows for Financial ML: A Complete Guide

If you use financial data and your model does not use a rolling window, the backtest is wrong.

Rolling Windows

Beyond Sharpe: A Research Framework for Evaluating ML Trading Strategies

Sharpe ratio is dangerously incomplete for ML strategies.

Random Forests in Finance: Nonlinear Signals Without the Drama

Tree-based ensembles capture nonlinearities and interactions in market data.

From Linear Regression to Lasso: Fast, Interpretable Baselines

Linear and regularized regressions still do serious work in finance.

Linear Regression

Market Regimes, Clusters & HMMs: Teaching Models to Respect the Environment

Episodes where statistical properties are stable enough: high vol vs low vol, risk-on vs risk-off.

Building a Universe-Wide Prediction Grid

An alpha factory needs predictions for every asset at multiple horizons from multiple models.

Prediction Grid

Regime-Conditioned Performance: Measuring ML Robustness

Most backtests report a single Sharpe. But ML models fail by regime.

Regimes

December 12, 2025

11 min read

Market Regimes, Clusters & HMMs: Teaching Models to Respect the Environment

Episodes where statistical properties are stable enough: high vol vs low vol, risk-on vs risk-off. Regime awareness is essential for robust models.

1. What Is a Regime?

Episodes where statistical properties are "stable enough": high vol vs low vol, risk-on vs risk-off, etc.

Why regime awareness matters:

Same signal can mean opposite things in different regimes.
Model performance is not stationary.

2. Clustering Methods

K-Means: partitions points into K clusters via distance.

GMM: probabilistic clustering, soft assignments (cluster probabilities).

3. Hidden Markov Models (HMMs)

Discrete latent states (regimes) with transition probabilities. Markov chain gives us expected regime path.

4. How volarixs Uses Regimes

Regime labeling is real in volarixs today: the platform tracks macro state — inflation, policy, and liquidity regimes — and surfaces historical analogues for the current environment. Every signal also carries a regime-clarity component in its confidence score, and the regime context is stored with each run.

That gives you the raw material to:

Label regimes from returns, volatility, and macro state.
Read each model's prediction history alongside the regime it was made in.
Frame the expected regime path as a lens on how a signal might hold up as conditions shift.

A full per-regime performance breakdown — Sharpe, hit ratio, and drawdown sliced by regime — is the direction this data is built to support, not a finished dashboard.

K-Means

GMM

HMM

Regimes

Get new research in your inbox

Applied AI & ML for the buy-side — new research on signals, regimes, and strategy design, straight to your inbox. No noise.

Ready to detect market regimes?

Build regime-aware models in volarixs.

Explore More Research

Regimes

December 12, 2025

11 min read

Market Regimes, Clusters & HMMs: Teaching Models to Respect the Environment

Episodes where statistical properties are stable enough: high vol vs low vol, risk-on vs risk-off. Regime awareness is essential for robust models.

1. What Is a Regime?

Episodes where statistical properties are "stable enough": high vol vs low vol, risk-on vs risk-off, etc.

Why regime awareness matters:

Same signal can mean opposite things in different regimes.
Model performance is not stationary.

2. Clustering Methods

K-Means: partitions points into K clusters via distance.

GMM: probabilistic clustering, soft assignments (cluster probabilities).

3. Hidden Markov Models (HMMs)

Discrete latent states (regimes) with transition probabilities. Markov chain gives us expected regime path.

4. How volarixs Uses Regimes

Regime labeling is real in volarixs today: the platform tracks macro state — inflation, policy, and liquidity regimes — and surfaces historical analogues for the current environment. Every signal also carries a regime-clarity component in its confidence score, and the regime context is stored with each run.

That gives you the raw material to:

Label regimes from returns, volatility, and macro state.
Read each model's prediction history alongside the regime it was made in.
Frame the expected regime path as a lens on how a signal might hold up as conditions shift.

A full per-regime performance breakdown — Sharpe, hit ratio, and drawdown sliced by regime — is the direction this data is built to support, not a finished dashboard.

K-Means

GMM

HMM

Regimes

Get new research in your inbox

Applied AI & ML for the buy-side — new research on signals, regimes, and strategy design, straight to your inbox. No noise.

Ready to detect market regimes?

Build regime-aware models in volarixs.

Explore More Research