sezer12138 / finance-tool

SECTION 1 : IRS-PM-ISY5002-2022-10-26-GROUP19-Finance-Tool

Finance-Tool

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SECTION 2 : EXECUTIVE SUMMARY / PAPER ABSTRACT

Finance serves a purpose. ... Investors are lured to gamble their wealth on wide hunches originated by charlatans and encouraged by mass media. One day in the near future, ML will dominate finance, science will curtail guessing, and investing will not mean gambling. Our mission is to set up a system to efficiently automate trading.

• Help Users make decisions.
• Make the Profit maximum.
• Generate Users Financial Report.

We use multiple Deep Reinforcement Learning algorithm to train the policy and Q-value. The users just load our pre-trained models and the system will return the accumulative profit and everyday actions.We believe applying these intellectual and engineering system to finance will initiate a paradigm shift from the conventional trading routine to an automated machine learning approach, even RLOps in finance. \Deep Reinforcement Learning is a type of supervised machine learning. However, DRL doesn’t need large labeled training datasets. This is a significant advantage since the amount of data grows exponentially today, it becomes very time-and-labor-consuming to label a large dataset. DRL uses a reward function to optimize future rewards, in contrast to an ML regression/classification model that predicts the probability of future outcomes.

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SECTION 3 : CREDITS / PROJECT CONTRIBUTION

Official Full Name	Student ID (MTech Applicable)	Work Items (Who Did What)	Email (Optional)
Yang Wenkai	A0261636A	Backend Application Deveopment, Algorithm/Model Development and Training	[email protected]
Huang Runxiang	A0261627A	Backend Application Deveopment, UI/UX Development	[email protected]
Chen Haoyang	A0261905E	Backend Application Deveopment, UI/UX Development	[email protected]

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SECTION 4 : VIDEO OF SYSTEM MODELLING & USE CASE DEMO

Please refer to the video at Youtube

<Video Link> : https://www.youtube.com/watch?v=OVacIsCKnGA

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SECTION 5 : USER GUIDE

Installation

Run this command in your termainal pip install -r requirements.txt

Run Code

cd finance-tool

streamlit run home.py

Copy your Local URL in your web browser.

Run it on Internet

You can also visit our website by click https://sezer12138-finance-tool-home-rnjqsu.streamlitapp.com/

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SECTION 6 : PROJECT REPORT / PAPER

Please refer to Project report at Github Folder: Project_Report

<File Link> : https://github.com/sezer12138/finance-tool/blob/main/Project_Report.pdf

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SECTION 7 : MISCELLANEOUS

Please refer to the Jupyter Notebook 'Train.ipynb' for data analysis at Github Folder: Algorithms

<File Link> : https://github.com/sezer12138/finance-tool/blob/main/Algorithms/Train.ipynb

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finance-tool's Issues

The problem in pyfolio, there is no return in create_full_tear_sheet

Because the pakage pyfolio function create_full_tear_sheet has not return, it can only use in Jupyter. So if you want to solve this problem you can modify pyfolio/tears.py in create_full_tear_sheet and create_returns_tear_sheet, let them return a figure. The APP could run successfully.

attributeerror: 'numpy.int64' object has no attribute 'to_pydatetime'

fixed this by changing line 893 in file timeseries.py in pyfolio pakage.
valley = underwater.index[np.argmin(underwater)] # end of the period