SMI Crossovers with Pivot-Point Entry Filtering

Summary

The document describes a momentum strategy that pairs Stochastic Momentum Index (SMI) crossovers with standard pivot levels. It outlines an SMI calculation based on a price source’s position within its recent high–low range, followed by two moving-average smoothing steps. A bullish crossover is treated as a buy signal and a bearish crossover as a sell signal, with the accompanying description proposing trades only when price is near a pivot level.

There is a notable gap between that description and the supplied strategy source: the source generates entries from SMI crossovers but does not calculate or check pivot points. The document gives no performance results, and its published backtest covers only a short period on BTC/USDT futures. It flags moving-average lag, choppy-market crossovers, and parameter sensitivity; it also suggests testing additional filters and exit rules. The method is therefore best read as a strategy outline, not evidence that pivot filtering improves results.

Key ideas

• The described entry filter combines SMI crossovers with price proximity to standard pivot levels.
• The source calculates smoothed SMI values and enters long or short when the SMI crosses its signal line.
• The supplied source does not implement the described pivot-point condition.
• Moving-average smoothing can delay signals, while range-bound markets may produce repeated crossovers.
• The document provides no performance results and describes only a short BTC/USDT futures backtest period.

Tags

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