We implement the extension_factor default as 16 and the sampling inspection default time as 16, so the security level will be 80 bits. Polynomial commitment: we calculate the hash value of the computation trace and corresponding user balance, user ID, and the value of the corresponding constraint polynomial for each row, and generate a Merkle tree. The Merkle root is the commitment value of the polynomial. Generating sampling proof: using the Merkle root as a random source, we sample the data.

This example shows the revision of a new size. result = tradeAPI.amend_order( instId = "BTC-USDT-SWAP", ordId = "505073046126960640", newSz = "80" ) print(result)10. How can I get the list of open orders for derivatives trading with the Jupyter Notebook using Get order List? result = tradeAPI.get_order_list() print(result)11. How can I get order history for derivatives trading with the Jupyter Notebook via Get order history (last 7 days) and Get order history (last 3 months)?