Recently, I executed a BTC/USDT grid trading during a period of BTC volatility. I set up a 50-grid grid within the $86,500-$89,200 range, with a profit rate of 0.8% per grid. The core logic of the strategy is: during consolidation phases lacking clear trends, automatically buy low and sell high to capture oscillation profits, while reducing the interference of subjective judgment.
Execution results: After running for 72 hours, the strategy triggered 47 trades in total. After deducting fees, the net profit was approximately 2.3%. Key review and insights:
1. Range setting needs to be dynamically adjusted: The initial range was based on recent ATR (Average True Range) and support/resistance levels, but if the price breaks the boundary, manual pause or adjustment is necessary to avoid continuous buying risks in a one-sided market. 2. Liquidity management: Reserved 30% USDT as backup, manually adding to positions when the price drops to the lower boundary of the range to strengthen grid density. 3. Tool value: Grid trading is an efficient “cash flow tool” in oscillating markets, but it should be combined with trend judgment— I also set an automatic take-profit grid that triggers when the price breaks above $89,500, switching to trend-following.
Summary: Grid strategy is not a “sit back and win” approach; it requires continuous monitoring of market volatility and dynamic management of positions and ranges. In inefficient markets, mechanical discipline often proves more reliable than subjective emotions.
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Recently, I executed a BTC/USDT grid trading during a period of BTC volatility.
I set up a 50-grid grid within the $86,500-$89,200 range, with a profit rate of 0.8% per grid. The core logic of the strategy is: during consolidation phases lacking clear trends, automatically buy low and sell high to capture oscillation profits, while reducing the interference of subjective judgment.
Execution results: After running for 72 hours, the strategy triggered 47 trades in total. After deducting fees, the net profit was approximately 2.3%.
Key review and insights:
1. Range setting needs to be dynamically adjusted: The initial range was based on recent ATR (Average True Range) and support/resistance levels, but if the price breaks the boundary, manual pause or adjustment is necessary to avoid continuous buying risks in a one-sided market.
2. Liquidity management: Reserved 30% USDT as backup, manually adding to positions when the price drops to the lower boundary of the range to strengthen grid density.
3. Tool value: Grid trading is an efficient “cash flow tool” in oscillating markets, but it should be combined with trend judgment— I also set an automatic take-profit grid that triggers when the price breaks above $89,500, switching to trend-following.
Summary: Grid strategy is not a “sit back and win” approach; it requires continuous monitoring of market volatility and dynamic management of positions and ranges. In inefficient markets, mechanical discipline often proves more reliable than subjective emotions.