Data mining applications in financial sectors are very common since investors can apply the resultant rules to make profits. Profit mining algorithms in particular, such as PRMiner, can generate profit rules that meet the expectations of investors regarding profit, risk, and win rate. However, most of such algorithms are not efficient due to the long processing time involving going through the whole search space in complex dynamical systems of financial markets. Hence, we propose a new approach in this paper to solve the problem by using closed itemsets to obtain profit rules without processing the entire trading rules. Based on the inter-day modeling, we analyze inter-transactions and conduct trading simulations to predict trading results for efficient profit rule generation. We develop two algorithms of JCMiner and ATMiner to process closed itemsets, which have better performance than the approach of PRMiner, especially for the large number of itemsets and large datasets. According to the experimental results, our algorithms outperform PRMiner in various experimental scenarios, i.e., mining parameters, the number of items in a transaction, and the number of transactions in a dataset.