{'fraction_non_alphanumeric': 0.015698587127158554, 'fraction_numerical': 0.0, 'mean_word_length': 5.7105263157894735, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Multi-objective Deep Reinforcement Learning Approach for Stock Index Future’s Intraday Trading

Modern artificial intelligence has been widely discussed to practice in automated financial asserts trading. Automated intraday trading means that the agent can react to the market conditions automatically, while simultaneously make the right decisions. Besides, the profits will be made within a day considering transaction cost charged by the brokerage company. In this paper, we introduce a multiobjective deep reinforcement learning approach for intraday financial signal representation and trading. We design the deep neural networks to automatically discover the dynamic market features, then a reinforcement learning method implemented by a special kind of recurrent neural network (LSTM) is applied to make continuous trading decisions. In terms of balancing the profit and risk, we implement a multi-objective structure which includes two objectives with different weights. We conduct experiments on stock index futures data, and our analysis and experiments not only offer insights into financial market features mining, but also provide a straightforward and reliable method to make profits, which sheds light on its wide application on automated financial trading.