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This project is about creating a RoboAdvisor diversification portfolio for users looking to understand their investment portfolio recommendation. It gives the users some idea on the mix of ETFs that they should invest in depending on their investment criteria as well level of risk. This project has been launched with the use of AWS Lex and React so users can have a smoother experience. A live demo of the frontend hooked up to the AWS Lex chatbot can be found here.
This project leverages python version 3.8.5 with the following packages and modules:
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pandas - This was used to be able to easily manipulate dataframes.
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AWS Lambda - To build the functionality for the tool.
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AWS Lex - A service for building conversational interfaces into any application using voice and text. This was used in combination with the functionality created on AWS Lambda.
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Numpy - This provides the ability to work with arrays and use different mathematical calculations on arrays.
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Yahoo API - This was used to be able to get closing prices for the ETFs that we will be using in the list of investments.
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Alpaca Trade API - This is used to get API trading data for this project. To have access to Alpaca's API key and API secret keys, the user needs to register for personal account and save their own keys.
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Scikit Learn - version 0.24.2 - This package has a lot of different tools and model that could be use to create a machine learning model.
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linear model - This allows the model to call the Logisitic Regression model to run our machine learning.
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Standard Scaler - Standardize features by removing the mean and scaling to unit variance.
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Jupyter Lab - This was used to be able to create and share documents that contain live code, equations, visualizations and narrative text.
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Numpy - This provides the ability to work with arrays and use different mathematical calculations on arrays.
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TensorFlow - version 2.6.0 - This is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.
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Keras - version 2.6.0 - This is an API designed for human beings, not machines. Keras follows best practices for reducing cognitive load: it offers consistent & simple APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages. It also has a free open source Python library for developing and evaluating deep learning models.
- models Sequential - This is used appropriately for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
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React - We used this to create a frontend user website that the user can interactive with the RoboAdvisor Diversification chatbot.
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TQDM - We used this library to create a progress bar for Python for easy viewing when loading models.
On the terminal, under the conda dev environment, type the code below:
pip install jupyterlab
If you have Jupyter Lab already installed, to open your Notebook and be able to view your hidden files, please type this while on your conda dev environment:
jupyter lab --ContentsManager.allow_hidden=True
Once you click ENTER, this will open on your default browser, which will allow you to read the .ipynb file.
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To install the Scikit-learn, check that your development environment is active, and then run the following command:
pip install -U scikit-learn -
To check if scikit-learn is already installed, you can run the following code on your dev environment:
conda list scikit-learn
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To install the TensorFlow, check that your development environment is active, and then run the following command:
pip install --upgrade tensorflow -
To verify if TensorFlow is already installed, you can run the following code on your dev environment:
python -c "import tensorflow as tf;print(tf.__version__)"
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To verify if Keras is already installed, you can run the following code on your dev environment:
python -c "import tensorflow as tf;print(tf.keras.__version__)"
5. Create AWS free account to access AWS Lambda and AWS Lex. Use VSCODE to view and edit lambda_function.py file.
6. To install ReactJS on your machine, please go to your dev environment and type below, but make sure the location is where you have a cloned of the this repo:
npm install
npm start
npm install -g serve
serve -s build
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Once built, a html website address will show, see pic below. You'll need to copy and paste this on your browser to view the user interface. It may have a different local host number than the photo.
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Additionally you might need an Alpaca API key to run the React website. It'd be best to create an .env file with this file name:
REACT_APP_API_KEY= REACT_APP_APCA_API_SECRET_KEY=
pip install yfinance --upgrade --no-cache-dir
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Go to AWS Lambda first. Create a new Lambda function from scratch, and name it
roboAdvisorDiversification. Choose Python 3.7 as the runtime programming language. -
In the online code editor, delete the AWS-generated default lines of code, and then paste the new
lambda_function.pythat is provided with this file.
- The user needs to input an utterance first i.e "I want to invest.", then it will prompt to input his/her first name.
- Then, it will ask for your age. Your age needs to be below the retirement age.
- Then, it will ask how much amount your annual income is, which needs to be at least $130,000.
- Then, it will ask how much your annual debt is, which needs to be 30% less than your annual income.
- Then, it will ask how much you would like to invest, which needs to be at least $25,000.
- Then, once you make a confirmation to proceed, the user can check their portfolio on the React website.
Here are some of the example of the project:
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AWS RoboAdvisor Chatbot
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Sample Random Portfolios
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Sample React Website
Click HERE to learn more from our presentation.
Copyright (c) [2021] [UW Fintech Bootcamp: Colin Benjamin | Justine Cho | Chris Henderson | Nathan Patterson]
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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