{{ status: wip - work in progress }}
NeuralFund
AI for Investment
Overview
This project is derived from Siraj Raval's Making Money with Tensorflow 2.0 video wherein he demonstrates an app called NeuralFund that uses deep learning to make investment decisions.
Dependencies
- Tensorflow 2.0
- flask
- Tensorflow serving
Instructions
NeuralFund is a combination of simple tensorflow serving web app skeleton as the project base, and flask boilerplate code from my last video for the user authentication + MySQL database integration it had implemented.
In that app, the author integrates TF Servng with Flask to create a structure that allows for a continous training pipeline. Download that code and run it locally.
TODO:
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Step 1 - Merge the two repositories by starting with the simple TF serving demo. Copy and paste the user auth + SQL code from the boilerplate demo into the simple TF serving demo.
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Step 2 - In the 'train.py' file in the simple TF serving demo, under main(): add this code snippet to pull real-time stock data from the web. It will do that dynamically as per the continous training pipeline.
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Step 3 - Add this trading view widget anywhere on the front end for a nice stock visualization.
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Step 4 - The model will be able to make time series predictions, but what if it could also predict which stock to buy? Have 3 seperate models train on 3 different stock prices simulatenously. When done training, have them perform inference to predict the next price. Use the prediction that offers the highest increase from the previous price.
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Step 5 - Have 3 more models train on 3 news datasets via the google news API for each of the stocks. perform sentiment analysis using a pretrained model like BERT to do this. Pick the stock that has the highest sentiment and price prediction.
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Step 6 - Figure out a way to implement Deep Reinforcement Learning in tensorflow serving, i haven't yet seen an example of this done on GitHub. I might just do this in my next video. Treat the market as a markov decision process, the agents actions are buy sell or hold.
Credits
toebit3hub, tensorflow team, cedrickchee, my parents, my Wizards, all humans who came before me, thank you i am but a temporary vessel of knowledge