FinancialDeepLearning
所属分类：加密解密
开发工具：Jupyter Notebook
文件大小：60406KB
下载次数：0
上传日期：2021-06-06 19:43:18
上 传 者：
sh-1993
说明： 应用于加密新闻和价格数据的深度学习方法
(Deep learning methods applied to crypto news and price data)
文件列表:
ALE_plots.R (4529, 2021-06-07)
Crypto_EDA.ipynb (3405773, 2021-06-07)
Crypto_EDA_presentation.pdf (1457310, 2021-06-07)
Crypto_RNN.ipynb (553612, 2021-06-07)
Crypto_RNN_ALE_modeling.ipynb (2755493, 2021-06-07)
Crypto_RNN_presentation.pdf (1162314, 2021-06-07)
Data (0, 2021-06-07)
Data\BinanceCoin_runs1layer.csv (21721, 2021-06-07)
Data\IOTA_runs1layer.csv (21747, 2021-06-07)
Data\XRP_runs1layer.csv (21745, 2021-06-07)
Data\analyzed_sentiment.csv (43691904, 2021-06-07)
Data\btc_runs1layer.csv (21749, 2021-06-07)
Data\coin_BinanceCoin.csv (169790, 2021-06-07)
Data\coin_Bitcoin.csv (365784, 2021-06-07)
Data\coin_Ethereum.csv (263613, 2021-06-07)
Data\coin_Iota.csv (170701, 2021-06-07)
Data\coin_XRP.csv (364494, 2021-06-07)
Data\crypto-markets.csv (88582391, 2021-06-07)
Data\cryptonews.csv (22634803, 2021-06-07)
Data\eth_runs1layer.csv (21741, 2021-06-07)
Paper.pdf (5043416, 2021-06-07)
Paper_draft_presentation.pdf (1954085, 2021-06-07)
Results (0, 2021-06-07)
Results\binance_gru.RDS (9020, 2021-06-07)
Results\binance_gru.png (98662, 2021-06-07)
Results\binance_lstm.RDS (8995, 2021-06-07)
Results\binance_lstm.png (107474, 2021-06-07)
Results\bitcoin_gru.RDS (9049, 2021-06-07)
Results\bitcoin_gru.png (114607, 2021-06-07)
Results\bitcoin_lstm.RDS (9962, 2021-06-07)
Results\bitcoin_lstm.png (98844, 2021-06-07)
Results\eth_gru.RDS (9704, 2021-06-07)
Results\eth_gru.png (113419, 2021-06-07)
Results\eth_lstm.RDS (9700, 2021-06-07)
Results\eth_lstm.png (105024, 2021-06-07)
Results\iota_gru.RDS (8588, 2021-06-07)
Results\iota_gru.png (101208, 2021-06-07)
... ...
# FinancialDeepLearning
LSTM and GRU deep learning models applied to analyze the effect of scraped past 30-day Coindesk news sentiment on future 10-day volatility for five different cryptocurrencies: Bitcoin, Binance Coin, Ether, and XRP. These models are used together with accumulated local effects to visualize the effects of the sentiment.
The research paper is available [here](https://github.com/KaroRonty/FinancialDeepLearning/blob/main/Paper.pdf). The code in this repo is mostly based on Jupyter Notebooks and might be incomplete.
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