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investment

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Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

  • Updated Jan 1, 2022
  • Python
backtesting.py
zillionare
zillionare commented Apr 30, 2021

this is how Buy & Hold Return is calculated:

        c = data.Close.values
        s.loc['Buy & Hold Return [%]'] = (c[-1] - c[0]) / c[0] * 100  # long-only return

so it's calced use day one and the day last.

Expected Behavior

Buy & Hold Return is used for compare with strategy gain. Therefore, I guess they should started at same time, since the strategy get enough data to w

Riskfolio-Lib
FinancePy
ahabre
ahabre commented Aug 8, 2021

Is there a way to calibrate a discount curve from traded fx forwards?

Taking USDJPY as an example. As an input I have the fx spot, 1M, 3M and 6M forwards , I have also built a USD OIS discount curve. I want to create a JPY discount curve such that I can reprice correctly all of the fx forwards I observe in the market. Is that possible with the current library?

As an extension to the above,

Qlib-Server is the data server system for Qlib. It enable Qlib to run in online mode. Under online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too.

  • Updated Oct 29, 2021
  • Python

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