Reentrancy bugs, one of the most severe vulnerabilities in smart contracts, has caused huge financial loss in recent years. Researchers have proposed general-purpose and rule-based approaches to detecting them. However, empirical studies have shown that they usually suffer from undesirable false positives and false negatives, especially when the code under detection involves the interaction between multiple smart contracts. In this paper, we propose an accurate and efficient cross-contract reentracy detection approach in practice. Rather than design rule-of-thumb heuristics, we conduct a large empirical study of 11714 real-world contracts from Etherscan against three well-known general-purpose security tools for reentrancy detection. We manually summarized the reentrancy scenarios where state-of-the-art approaches cannot address. Based on the empirical evidence, we present Clairvoyance, a cross-function and cross-contract static analysis to detect reentrancy vulnerabilities in real world with significantly higher accuracy. To reduce false negatives, we enable, for the first time, a cross-contract call chain analysis by tracking possibly tainted paths. To reduce false positives, we systematically summarized five major path protective techniques (PPTs) to support fast yet precise path feasibility checking. We implemented our approach and compared Clairvoyance with five state-of-the-art tools on 17770 real-worlds contracts. The results show that Clairvoyance yields the best detection accuracy among all the tools and also finds 101 unknown reentrancy vulnerabilities.