Using KAMA for Adaptive Trend Entries and Exits in Cryptocurrency
Summary
The document explains Kaufman’s Adaptive Moving Average (KAMA), which adjusts its responsiveness using an efficiency ratio based on net price movement relative to total movement over a lookback period. Low efficiency makes the average smoother in choppy conditions, while high efficiency makes it react faster in directional markets. It outlines the calculation components and gives conventional trend rules: enter long when price is above a rising KAMA, enter short when price is below a falling KAMA, and exit when either condition reverses.
A sample cryptocurrency strategy uses a library implementation of KAMA and a signal filter to avoid overlapping entry and exit signals. The article reports a daily BitMEX XBTUSD backtest from July 2017 to July 2019 with two ticks of slippage on entries and exits, and describes favorable behavior in the 2018 bear market and 2019 bull market. Results are presented visually rather than with detailed numerical statistics. The strategy is illustrative; the article notes that additional filters and explicit profit-taking or stop-loss rules could be explored, and fixed parameters may not adapt to future regimes.
Key ideas
- KAMA adjusts smoothing according to the ratio of net price change to accumulated movement over a lookback window.
- The average responds more slowly in noisy conditions and more quickly when price movement is directional.
- Long and short entries combine price location relative to KAMA with the slope of the average.
- The example backtest uses daily XBTUSD data and includes slippage on entries and exits.
- The strategy remains a starting point and may need additional filters and explicit exit controls.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.