DLQuant
Unverified ML strategy on Multi by yuriak. BotFinder score 18 out of 100.
Applying Deep Learning and NLP in Quantitative Trading
Source: github
BotFinder analysis pending.
DLQuant
Applying Deep Learning and NLP in Quantitative Trading Overview This repository is the preliminary work of RLQuant including 5 data crawlers and 2 models. Crawlers - China stock crawler - Based on tushare - daily stock and index data - Sina news crawler - Based on 新浪财经24小时新闻(Sina financial news) - Securities and companies short news - Open-Europe crawler - Based on Open Europe Daily News - Reuters news crawler - Based on Reuters Financial News Models - Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks - A relatively standard NLP workflow - Bag of keywords, Polarity score, Category tag were used - Predict the sign of next day's return rate - Deep learning for event-driven stock prediction - OpenIE (Information Extraction) for extracting Events(subject, predicate, object) - Neural Tensor Network for learning Event Embedding - DNN, CNN can be used to predict movements - RNN Auto-encoder for encoding news titles - A sequence 2 sequence AE - GRU was used - Elmo - Using Elmo for news title encoding - please find tf version in PaperRevie
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Verification ledger
How the score has moved
Recalculated at each data collection. Transparency means showing the bad weeks too.
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Alerts on changes: coming soon
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Open-source maintainer on GitHub.
Data-completeness & trust index (not a profitability rating)