This will be a long article but see the random walk at the end
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Here is a Quantopian Python example using Gibbs
Have you ever heard of standard deviation reversion like Bollinger band?
Simplest mean reversion is moving averages but how effective is it and which timeframes work best for crypto? You can pick 5 best long and 5 worst outlook for pair trading in crypto but Correlated relationships don’t last too long due to crazy volatile moves.
Qunatopian has some interesting discussion on other mean reverting trading ideas
RSI2 and Long stocks at minus two times Average True Range(ATR) looks interesting? As for moving average, you need to find the fight parameters for moving average against which timeframe?
This could be extra complicated with overlaying linear regression over mean reversion?
Is this useful with typical mean reversion?
Hurst Exponent is most reliable to find when a time series is random walk (Geometric Brownian Motion), mean revert, and trend follow/momentum.
decent entry/exit description with basic mean reverting strategies
Another easy Quantopian Python logic example
Z-Score can be highly reliable to measure cointegration
Some say it is possible to beat random walk but I doubt that
I am no expert here from a math point of view but I believe in this headline which means avoid random walk
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