Simulating Leveraged Crypto Trend Following with Autocorrelation and Jumps

Summary

This article uses simulated cryptocurrency price paths to explore how often a leveraged trend strategy might need rebalancing to manage drawdowns. The author builds a geometric Brownian motion simulator with autocorrelated returns and random jumps, using a C++ routine called from R for the path-dependent components. A simple strategy enters after a new all-time high and holds for five days. Leverage starts high and declines linearly as portfolio equity falls from its peak, reaching zero at the specified drawdown threshold.

In the illustrated simulation, the portfolio performs poorly, yet daily rebalancing keeps leverage within the intended drawdown rule. The example offers intuition about how rebalance frequency and risk controls interact under noisy, jumpy prices. It is not a robust estimate of strategy performance: only one simulated universe is analyzed, and the results depend on chosen model parameters. The author recommends testing multiple parameterizations and simulations; illiquidity and the possibility of total capital loss also limit practical conclusions.

Key ideas

• The simulator combines geometric Brownian motion with autoregressive returns and random jumps.
• The example strategy enters after an all-time high and holds a position for five days.
• Leverage declines linearly as portfolio drawdown grows, reaching zero at the chosen limit.
• Daily rebalancing kept leverage within the target rule in the illustrated simulation.
• A single simulated universe gives intuition, not reliable probability estimates of future outcomes.

Tags

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