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Quantitative-Notebooks

Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy

ApplicationsSpecialistsJupyter Notebook
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创建于 2018-10-01 · 更新于 2026-10-13 · 今日第 10465 名
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LongOnly

LongOnly/Quantitative-Notebooks 排名第 10,465，有 1,395 个星标。

同类别比较

LongOnly/Quantitative-Notebooks 排名第 10,465，此处有 8 个同类条目；nilbuild/claude-statusline 排名第 10,461。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Applications
子类别Specialists
关键词Finance, Trading
星标数1,395
分叉数217
贡献者1
创建日期2018-10-01
更新日期2026-10-13
1 天星标数0
7 天星标数0
排名10,465
上次排名10,459
排名变化-6
7 天星标排名12,633
上次 7 天星标排名9,945
7 天星标排名变化-2,688
总星标排名8,584
上次总星标排名8,572
总星标排名变化-12
README 摘录

The main objective of this repo is idea generation! Some of these 'strategies' might not be appropriate for consumption ~~due to overfitting~~ (it's meant to be educational) Dependencies: Numpy; Pandas; Matplotlib and Requests (for fetching Yahoo Finance data) Difficulty Moderate: ML Based Pairs Trading - A simple Machine Learning example, Decision Tree Regressors applied to the previous pair (also requires Scikit-Learn) Basic: Long Only Pairs Trading - A simple pairs trading strategy focused on buying the loser! Signal is given by

rolling correlation Introductory: Dynamic Asset Allocation & Diversification - Exploring geographical diversification and optimizing capital allocation (also requires Scipy) Market data last updated at 2 July 2020 License This code has been released under the Apache 2.

常见问题

记录了多少星标和分叉？

LongOnly/Quantitative-Notebooks 有 1,395 个星标和 217 个分叉。

仓库排名是多少？

LongOnly/Quantitative-Notebooks 排名第 10,465。

记录的语言和类别是什么？

LongOnly/Quantitative-Notebooks 有 1,395 个星标，语言为 Jupyter Notebook，类别为 Applications。

哪个仓库排名最接近？

LongOnly/Quantitative-Notebooks 与 nilbuild/claude-statusline 比较，后者排名第 10,461，有 1,398 个星标。

README

The main objective of this repo is idea generation! Some of these 'strategies' might not be appropriate for consumption due to overfitting (it's meant to be educational)

Dependencies: Numpy; Pandas; Matplotlib and Requests (for fetching Yahoo Finance data)

Difficulty

Moderate:

ML Based Pairs Trading - A simple Machine Learning example, Decision Tree Regressors applied to the previous pair (also requires Scikit-Learn)

Basic:

Long Only Pairs Trading - A simple pairs trading strategy focused on buying the loser! Signal is given by rolling correlation

Introductory:

Dynamic Asset Allocation & Diversification - Exploring geographical diversification and optimizing capital allocation (also requires Scipy)

Market data last updated at 2 July 2020

License

This code has been released under the Apache 2.0 License

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