chanlun

To identify Chanlun price patterns and generate trading signals, you process OHLCV data through a pattern recognition engine that detects FX, BI, and ZS formations across multiple timeframes to output deterministic buy and sell decisions.

Chanlun multi-timeframe analysis signals are generated by recognizing structural formations like fractals, strokes, and pivot zones across different time periods, classifying 3, 5, 7, 9, and 11-stroke patterns to produce actionable buy or sell decisions.

Yes, Chanlun pattern recognition supports multi-market applications including stocks, futures, and cryptocurrency, requiring OHLCV input data to automatically detect formations and output corresponding trading signals across these markets.

To generate deterministic buy/sell decisions using czsc, prepare your OHLCV data and run the signal engine, which will automatically detect fractals, strokes, and pivot zones to output actionable trading signals for your analysis.

Yes, you need the czsc library to detect fractals, strokes, and pivot zones, as it serves as the underlying engine for automatically identifying these Chanlun-based price patterns and generating the corresponding trading signals.

Automated Chanlun multi-timeframe signal generation is limited by its dependency on accurate OHLCV input data and the czsc library, meaning signal accuracy relies entirely on the quality of the historical price data provided.