Fractal Turning Points and SMMA Signals with ATR Trailing Stops

Summary

This strategy combines price-fractal turning points with a smoothed moving average to generate long and short entries. The accompanying logic approximates a fractal using local highs or lows, then checks the prior close against the SMMA: a low fractal with price below the average supports a long, while a high fractal with price above it supports a short. ATR-based trailing stop prices are calculated for open positions, and the document gives example settings for the average, fractal period, and trailing-stop coefficient.

The article also describes restricting entries to selected trading sessions and exiting on reverse signals, but those rules are not evident in the supplied code excerpt; the code also does not appear to use the stated stop-loss percentage. It lists whipsaws, moving-average lag, and poorly chosen stop distances as risks and suggests parameter tuning and additional filters. Published backtest metadata identifies BTC/USDT futures and a date range, but no performance evidence is provided, so the claimed advantages remain unverified.

Key ideas

• The strategy uses local high and low patterns as approximate fractal turning points.
• SMMA position helps determine whether a fractal supports a long or short entry.
• ATR is used to calculate trailing stop prices for open positions.
• The article discusses session filters and reverse-signal exits, while the supplied code excerpt does not clearly implement those rules.
• Whipsaws, indicator lag, and stop-distance choices are identified as risks, and no backtest results are reported.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.