文件列表:

code (0, 2020-02-19)
code\1_load_News_data.py (1646, 2020-02-19)
code\2_feature_engineer.py (4867, 2020-02-19)
code\3_data_process.py (9546, 2020-02-19)
code\4_cut_data.py (15716, 2020-02-19)
code\5_LSTM_wihtout_news_NIPS_sp500.py (5584, 2020-02-19)
code\6_LSTM_with_news_NIPS_5_dimention.py (6233, 2020-02-19)
code\7_DP_LSTM.py (12325, 2020-02-19)
code\8_plot_mean_prediction_accuracies.py (3299, 2020-02-19)
code\9_plot_prediction_result_of_LSTM_based_on_price.py (1990, 2020-02-19)
code\place_holder (1, 2020-02-19)
data (0, 2020-02-19)
data\SP500.csv (9964, 2020-02-19)
data\features_tfidf_names.npy (660128, 2020-02-19)
data\large_data (359, 2020-02-19)
data\sentiment_cnbc_groupday.csv (14197, 2020-02-19)
data\sentiment_cnbc_groupday1.csv (9102, 2020-02-19)
data\sentiment_fortune.csv (20276949, 2020-02-19)
data\sentiment_fortune_groupday.csv (13333, 2020-02-19)
data\sentiment_fortune_groupday1.csv (8519, 2020-02-19)
data\sentiment_reuters_groupday.csv (14187, 2020-02-19)
data\sentiment_reuters_groupday1.csv (9110, 2020-02-19)
data\sentiment_wsj.csv (22689913, 2020-02-19)
data\sentiment_wsj_groupday.csv (14323, 2020-02-19)
data\sentiment_wsj_groupday1.csv (9184, 2020-02-19)
data\source_price.csv (8047, 2020-02-19)
data\stockdata (0, 2020-02-19)
data\stockdata\Data (0, 2020-02-19)
data\stockdata\Data\part_1.zip (11321640, 2020-02-19)
data\stockdata\Data\part_2.zip (11420502, 2020-02-19)
data\stockdata\Data\part_3.zip (11338065, 2020-02-19)
data\stockdata\Data\part_4.zip (17216280, 2020-02-19)
data\stockdata\Data\place_holder (1, 2020-02-19)
data\stockdata\cutdata (0, 2020-02-19)
data\stockdata\cutdata\cutdata.zip (1725581, 2020-02-19)
data\stockdata\cutdata\place_holder (1, 2020-02-19)
data\stockdata\place_holder (1, 2020-02-19)
data\stockdata\result (0, 2020-02-19)
... ...

# DP-LSTM-Differential-Privacy-inspired-LSTM-for-Stock-Prediction-Using-Financial-News If you feel that our code is helpful to you, please cite our paper: Xinyi Li, Yinchuan Li, Hongyang Yang, Liuqing Yang, Xiao-Yang Liu, "DP-LSTM-Differential-Privacy-inspired-LSTM-for-Stock-Prediction-Using-Financial-News," 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) Workshop on Robust AI in Financial Services: Data, Fairness, Explainability, Trustworthiness, and Privacy, December 2019. (https://arxiv.org/pdf/1912.10806.pdf) ### Prerequisites Python 3.6 environment Anaconda3 Tensorflow ### Code and Data description Under code file 7_DP_LSTM is the main file for the DP-LSTM deep neural network. The S&P 500 stocks are in the data folder.

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