Neural-Finance/XGboost_Index-Enhancement-Strategy

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Python
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MIT License
Created over 5 years ago
Updated almost 2 years ago

Neural-Finance/XGboost_Index-Enhancement-Strategy

Neural-Finance

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【Framework】A Multi Factor Strategy based on XGboost, its my homework project in Tsinghua, the Introduction to Quantitative Finance, 2019 Spring.

Option_Pricing_Python

I use Python3 to try the experiments on the classic book <Options, Futures and other Derivatives>, the BS model and the sensitivity analysis on Greek Letters.

Financial_Feature_Engineering_cross_sample

【Framework】Let the neural network 'freely' learn the relationship between different stocks. An intuitive example in quantitative finance, tensorflow 1.3.0.

Captcha-Recognition-Digit-Number

【Framework】This is a captcha recognition algorithm tailored for 4 length digit recognition. Written in CNN, Tensorflow 1.3-1.7.

Tricks-in-Tensorflow

【Slide】Less attention questions about how to pruning a neural network.

Jupyter Notebook

Neural-Finance

【State-of-the-Art】Some truely worked idea, about how to use aritificial intelligence in quantitative finance.

Quantitative-Investment-Slide-THU

【Slide】It's a pre-class document for <Introduction to Quantitative Finance, Tsinghua, Master Program Course> written by me (Alex Fang). It includes statistics, prob, programming (python), visualization tools, etc.

Jupyter Notebook

Leading-Papers-Robustness-Oriented

Some leading papers in deep learning, which focused on robustness. I think it will be helpful to NeuralFinance.

Summary_LSTM_Stock_Price

【LSTM & Stock Price】Although all new birds proudly tell you that they can use lstm for stock prediction, all old bird will tell you the conclusion that LSTM can't do this job well. For me, I only want to tell you that, different settings really matter. You are sure about the best way to generalize the raw data, output length, network deepth, etc. These things worth your time and patience.