chanlun

You can detect chanlun buy and sell signals by feeding raw OHLCV data into the engine, which automatically identifies FX, BI, and ZS patterns. It requires the czsc library and Python tooling to process multi-timeframe market data and output practical signals.

In czsc pattern recognition, first, second, and third buy points are core trading signals derived from FX, BI, and ZS structures. The engine identifies these points alongside corresponding sell signals and validates them using divergence checks to ensure signal accuracy.

Yes, you can use chanlun technical analysis for crypto and futures markets. The engine applies to any market with OHLCV history, supporting stocks, futures, and digital assets for multi-timeframe backtesting and real-time signal generation.

To identify ZS中枢 structures across multiple timeframes, the engine processes OHLCV data to detect overlapping price segments. It applies multi-timeframe analysis to map these consolidation zones and generates corresponding buy and sell trading signals.

Yes, you need the czsc library to run the chanlun signal engine. The engine explicitly depends on czsc for pattern detection and requires Python tooling to execute the scripts, analyze OHLCV data, and output the trading signals.

Yes, the engine validates trading signals with divergence and pan-bei checks. It identifies core buy and sell points and applies divergence validation to confirm signal reliability across multi-timeframe OHLCV data before outputting results.