How Grid Count Affects Crypto Grid Strategy Returns

Summary

This article examines how the number of price levels in a crypto grid strategy affects trading frequency, per-grid allocation, fee burden, and returns. It describes logarithmically spaced grid levels across a chosen price range, divides starting capital among them, buys when a candle reaches a level on a decline, and sells when price reaches the next level on a rise. The example backtest uses daily Binance spot data, tracks inventory and fees, and compares several grid counts for one selected asset.

The reported experiment says net return rises and then falls as grid count increases, with the best result among the tested settings at 15 grids. That finding is specific to the sample and implementation; it does not establish a generally optimal count. The article notes the trade-off between missed moves with sparse grids and smaller trades or higher fees with dense grids. Its demonstration uses a fixed price range derived from historical closes and does not show out-of-sample validation, so results may depend heavily on the chosen market and period.

Key ideas

• Grid count determines level spacing, trade frequency, and the capital assigned to each level.
• The example spaces levels logarithmically between selected minimum and maximum prices.
• The backtest simulates buys on downward moves and sells on upward moves, while accumulating fees.
• For the tested asset and sample, net returns peaked at a grid count of 15 before declining at higher counts.
• The reported optimum is sample-specific and lacks demonstrated out-of-sample validation.

Tags

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