Futures
Access hundreds of perpetual contracts
TradFi
Gold
One platform for global traditional assets
Options
Hot
Trade European-style vanilla options
Unified Account
Maximize your capital efficiency
Demo Trading
Futures Kickoff
Get prepared for your futures trading
Futures Events
Join events to earn rewards
Demo Trading
Use virtual funds to experience risk-free trading
Launch
CandyDrop
Collect candies to earn airdrops
Launchpool
Quick staking, earn potential new tokens
HODLer Airdrop
Hold GT and get massive airdrops for free
Launchpad
Be early to the next big token project
Alpha Points
Trade on-chain assets and earn airdrops
Futures Points
Earn futures points and claim airdrop rewards
#分享我的交易
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.