Deep reinforcement learning (DRL) has shown huge potentials in quantitative finance recently. However, due to the high complexity of real-world markets, raw historical financial data often involve large noise and may not reflect the future of markets, degrading the performance of DRL agents in practice. By simulating the trading mechanism of real markets on processed datasets, market simulation environments play important roles in addressing this issue. However, the simulation accuracy heavily relies on the quality of processed datasets, while building and using datasets is often artisanal -- painstaking and expensive. Moreover, training DRL agents on large datasets imposes a challenge on simulation speed. In this paper, we present a NeoFinRL framework that includes tens of \underline{N}ear real-market \underline{e}nvironments f\underline{o}r data-driven \underline{Fin}ancial \underline{R}einforcement \underline{L}earning. First, NeoFinRL separates financial data processing from the design pipeline of DRL-based strategy and provides open-source data engineering tools. Second, NeoFinRL provides tens of standard market environments for various trading tasks. Third, NeoFinRL enables massively parallel simulations by exploiting thousands of GPU cores.