What's New

From Raw Market Data to Machine Learning — All in One Interface

Feature engineering and machine learning should not require jumping between notebooks, pipelines, and disconnected tools.

We just added a new workflow inside the KlickAnalytics web interface that lets users transform market data and prepare machine learning models directly from the browser on any global symbols. Equities, ETFs, Funds, Crypto and more…

Now you can work with data for any global symbol and turn raw market data into research-ready features:

• Structure changes.
• Math between columns.
• Scaling and normalization.
• Rolling statistics.
• Rolling Z-scores.
• Percentile ranks.
• Correlations.
• OHLC microstructure features.
• Technical indicators like SMA, EMA, RSI, MACD, Bollinger Bands, ATR, OBV, MFI, ADX, and more.

And now, once the features are ready, you can move directly into the Machine Learning Workspace.

• Choose your target.
• Select features.
• Configure model settings.
• Use chronological data splits.
• Optimize for metrics like RMSE.
• Run walk-forward validation.
• Train models on live datasets.

The goal is simple: Go from raw global market data → engineered features → machine learning workflow in one interface.

• No complex setup.
• No manual spreadsheet formulas.
• No moving files between tools.
• No waiting on engineering teams.

Just select a symbol, transform the data, define the prediction outcome, and start building.

This is another step toward making financial research, quant workflows, and market analytics more accessible to anyone working with global markets. To access: Click on Tools > Quant Tools > feature enginnering