From a0f22571deb629069924a325e6a0fb58a91fabb3 Mon Sep 17 00:00:00 2001 From: you-n-g Date: Thu, 28 Jan 2021 09:44:03 +0800 Subject: [PATCH] Update README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 37058573e..27346aadc 100644 --- a/README.md +++ b/README.md @@ -31,7 +31,7 @@ For more details, please refer to our paper ["Qlib: An AI-oriented Quantitative - [Run a single model](#run-a-single-model) - [Run multiple models](#run-multiple-models) - [**Quant Dataset Zoo**](#quant-dataset-zoo) -- [High frequency execution](#high-frequency-execution) +- [High-frequency execution](#high-frequency-execution) - [More About Qlib](#more-about-qlib) - [Offline Mode and Online Mode](#offline-mode-and-online-mode) - [Performance of Qlib Data Server](#performance-of-qlib-data-server) @@ -271,12 +271,12 @@ Dataset plays a very important role in Quant. Here is a list of the datasets bui [Here](https://qlib.readthedocs.io/en/latest/advanced/alpha.html) is a tutorial to build dataset with `Qlib`. Your PR to build new Quant dataset is highly welcomed. -# High Frequency Execution +# High-Frequency Execution High-frequency order execution is a very important problem in the financial market. It aims to maximize the profit of order execution by intraday timing. AI has the potential to mine patterns from a huge mass of high-frequency trading data and helps users make better decisions during intraday trading. Here is a list of solutions built on `Qlib`. -- [Universal Trading for Order Execution with Oracle Policy Distillation](qlib/examples/trade/) +- [Universal Trading for Order Execution with Oracle Policy Distillation](examples/trade/) # More About Qlib