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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, incl...

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发现没有 中文 讨论的地方, 于是自己建了一个,

question

## 🌟 Feature Description Hello, please add an alternative financial data source: https://financialdata.net/documentation ## Motivation 1. An alternative source for those who are looking for a wider range of financial...

enhancement

## 🐛 Bug Description When using the backtestmodule, the application crashes abruptly due to two interconnected issues 1. ​​Internal gym dependency​​: The backtest module implicitly relies on the gymlibrary, which...

bug

## 🌟 Feature Description Integrate Bodo Dataframe's or compiler into qlib as an optional execution backend. Bodo Dataframe's are a drop in replacement for Pandas dataframes and offer expression tree...

enhancement

不使用回测模块,如何基于预测得到的pred score和strategy得到未来一个交易日的调仓信息呢

question

## 🐛 Bug Description ## To Reproduce Steps to reproduce the behavior: 1.python scripts/data_collector/yahoo/collector.py normalize_data --source_dir ~/.qlib/stock_data/source/cn_data --normalize_dir ~/.qlib/stock_data/normalize/cn_data --region CN --interval 1d 2.我已经下载了cn_data数据,normalize 过程中,在第341 sh600489.csv 发生错误 ## Expected Behavior...

bug

…ng the normalize process ## Description ## Motivation and Context ## How Has This Been Tested? - [ ] Pass the test by running: `pytest qlib/tests/test_all_pipeline.py` under upper directory of...

## 🌟 Feature Description 对于字符串特征直接存储字符串的原始值。不需要预编码成整数。 ## Motivation 1. Application scenario 对于深度模型来说,直接把字符串映射到一个emb,应该会比把字符串编码成整数再映射到emb,会省一些内存。对于一些新闻文本来说,直接由模型来学习文本向量会更加端到端。目前的qlib似乎不支持string类型的特征。这个是否能够添加支持。

enhancement

## 🐛 Bug Description When updating DiskExpressionCache, it raises KeyError because it tries to get data uri from the DEFAULT_FREQ while I have specified several other freqs. ## To Reproduce...

bug

File: qlib/contrib/model/gbdt.py Line 43: x, y = df["feature"], df["label"] if y.values.ndim == 2 and y.values.shape[1] == 1: y = np.squeeze(y.values) else: raise ValueError("LightGBM doesn't support multi-label training") y is a...

bug