banta

Understand the BanTA Series struct Time field. Learn how bar synchronization and cache guarding ensure accurate data tracking and efficient updates for your financial time series.

Learn how to access the underlying data array in a BanTA Series. Use Get Range or RangeValid exported methods for safe data retrieval in your Go applications.

Discover how BanTA handles sub-series and column series. Learn about lazy caching with Subs and multi-value storage in Cols for efficient data management.

Discover the BarEnv structure in BanTA, a core context object in types.go that centralizes k-line data, price/volume series, and extensible storage for technical analysis.

Find the Bollinger Bands implementation in BanTA within the Go and Python files. This developer guide provides the exact locations for core logic and public APIs.

Learn how BanTA implements NaN compatibility in its Series struct. Discover how NaN values are handled for accurate financial data analysis.

Learn how to detect indicator crossovers with BanTA using vector and stateful methods. Understand `tav.Cross` for historical data and `Series.Cross` for live trading.

Explore 12 moving average types like SMA EMA HMA and KAMA supported by BanTA for Go and Python. Learn to implement them efficiently with slice functions and series methods.

Learn to calculate KDJ momentum indicators with BanTA in Go and Python. Explore state-caching for live bots and parallel computation for backtesting.

Implement Bollinger Bands using BanTA in Go and Python. Calculate BBANDS with ease using the BBANDS function and price series for powerful trading insights. Explore the banbox banta repository today.

Discover BanTA's state-caching mode for O(1) updates and eliminate O(N) recalculations. Achieve superior real-time trading performance with BanTA.

Learn to backtest trading strategies with BanTA. Create BarEnv, feed historical data, and use state-caching indicators for accurate live trading simulation.