chanlun

To automate Chan Theory technical analysis, you process OHLCV market data to automatically detect price structures like fractals, segments (BI), and centers (ZS) using the czsc library. This enables deterministic pattern recognition across multiple timeframes for stocks, crypto, and futures.

Chan Theory pattern recognition generates first, second, and third-class buy and sell points based on price structures. These signals help identify potential trend reversals or continuation patterns directly from historical OHLCV data without manual chart plotting.

Yes, you can use the czsc library for quantitative finance analysis by feeding historical OHLCV data into the Chan Theory methodology. It calculates deterministic price structures and market signals suitable for analyzing financial markets across multiple timeframes.

You need OHLCV market data to detect Chan Theory segments and centers. The analysis requires Open, High, Low, Close, and Volume data points to accurately identify fractals, calculate segments (BI), and establish centers (ZS) for signal generation.

Automated Chan Theory analysis works for multiple timeframes in trading by applying consistent pattern detection rules to OHLCV data across different intervals. This allows traders to scan historical market data for buy and sell points regardless of the specific timeframe.

Automated Chan Theory eliminates the complexity of manual technical analysis by programmatically identifying fractals, segments, and centers. This deterministic approach ensures consistent pattern detection and signal generation, removing human error from chart analysis.