deeptendies / legacy-deeptendies-library

Open source stock price forecast bringing quantitative trading to the masses!

What is deeptendies

deeptendies is an open source package to bring ML and quant analysis tools and

power to the players!

Repo Structure

Please see ./src/deeptendies/ for core package components.

See /notebooks/ for notebooks used to generate data in report.

Onboarding

https://github.com/deeptendies/deeptendies/wiki/Onboarding

Installation

mac & linux

# install dependencies
pip install -r https://raw.githubusercontent.com/deeptendies/deeptendies/master/requirements.txt
# install deeptendies
pip install git+https://github.com/deeptendies/deeptendies

one line

pip install -r https://raw.githubusercontent.com/deeptendies/deeptendies/master/requirements.txt && pip install git+https://github.com/deeptendies/deeptendies

reinstall

pip uninstall deeptendies && pip install git+https://github.com/deeptendies/deeptendies

windows

git clone https://github.com/deeptendies/deeptendies.git
cd deeptendies
python setup.py install

Official Channels

• r/deeptendies
• github.com/deeptendies

Disclaimer of Warranty.

There is no warranty for the program, to the extent permitted by Applicable law. Except when otherwise stated in writing the copyright Holders and/or other parties provide the program "As is" without warranty Of any kind, either expressed or implied, including, but not limited to, The implied warranties of merchantability and fitness for a particular Purpose. The entire risk as to the quality and performance of the program Is with you. Should the program prove defective, you assume the cost of All necessary servicing, repair or correction. (https://github.com/deeptendies/deeptendies/blob/master/LICENSE)

legacy-deeptendies-library's Issues

Setup long term data storage (data lake)

Evalute KubeFlow - Machine Learning Toolkits for Kubernetes

Provide Pros, Cons, and final recommendation(s)

https://www.kubeflow.org/

Evaluate low-latency data serving options (Redis, GCP MemStore, etc.)

Consider pros, cons, and cost. Make recommendaiton

Explore darts https://unit8co.github.io/darts/index.html

Implement technical indicators

rescope needed
We came across this library https://twopirllc.github.io/pandas-ta/, it seems like it has most of the technical indicators we need. let's add the library to our stack for now and only write our own ta logics if needed.

Let's think about how can we "grid search" a couple of indicators for each dataset (stocks). I think we can try something like:

• correlation analysis
• strategy evaluation

We can pick a couple of clusters, then based on each cluster, we pick the most correlated metric and use them as feature columns to train our timeseries.

Evaluate GlueonTS

Evaluate https://ts.gluon.ai/
figure out if it is suitable for statistical arbitrage use case, write some blurb about it for sharing the knowledge

• read the documentation
• follow the quick start
• run the quick start and share code
• write a blurb about the evaluation, write on medium
• pros/cons list, what does it looks like it's best for

add gridsearch with autokeras

def of done

• make a makeshift poc
• make it more robust

grid search on

1. dataset range
2. epoch
3. batch size

each autokeras timeseries trial should log the performance

Encapsulate ARIMA for model evaluation/comparison

Evaluate AutoArima , pmdarima

wrote a minimalist/lite fork of deeptendies. just single column autoarima (R's autoarima's python rewrite)
https://pypi.org/project/pmdarima/

        stock='gme',
        start='2021-05-10',
        end='2021-05-27',
        forecast_days=5,
        filter=['High']

>> 
/usr/bin/python3.8 /home/stan/github/deeptendies-lite/main.py
[272.1088269  279.6325006  287.15617429 294.67984799 302.20352168]

Process finished with exit code 0

Implement naive backtest algorithm for existing LSTM models

EG., make a weekly buy/sell pair based on next 5-day high/low price. Do not work about modelling market conditions yet, we just want to see a theoretical maximum ROI

Evaluate tslearn

Provide Pros, Cons, and final recommendation(s)

https://github.com/tslearn-team/tslearn

Evalute MLRun - Orchestration Framework

Provide Pros, Cons, and final recommendation(s)

Setup JIRA board and link deeptendies repo

Refs:
https://github.com/integrations/jira
https://support.atlassian.com/jira-cloud-administration/docs/integrate-with-github/

Setup feature store (processed data)

Evaluate ZenML - MLOps Framework to develop reproducible pipelines

Provide Pros, Cons, and final recommendation(s)

https://zenml.io/

Add get predicted / actual helper to utils class -> shoudl be robust enough to take an arbritary batch size

Evalute MLOps Workflow Tools

• Argo - Container-native workflow engine
• MLRun - Orechestration Framework
• KubeFlow - Machine Learning Toolkits for Kubernetes
• Prefect - Workflow managment system
• ZenML - MLOps framework to develop reproducible pipelines

TODO: Provide pros/cons list for each and a recommendaiton for the best solution for our usecase

Evalute Argo - Container-native workflow engine

Provide Pros, Cons, and final recommendation(s)

https://argoproj.github.io/

Spin up a celery scheduled tasks server

Spin up a scheduled task server

• pulls data daily
• save data to csv / jsonline buckitized
• build docker image for it

similar to update data in a public google sheet like this

boilerplate celery app: https://github.com/stancsz/celery-scheduler-app

Evalute Prefect - Workflow management system

Provide Pros, Cons, and final recommendation(s)

https://docs.prefect.io/

Evaluate BentoML, TensorFlow Serving, and TorchServe for Model serving

Provide Pros, Cons, and final recommendation(s)

https://docs.bentoml.org/en/latest/

Start a scraping/data mining project for Marko, pull financial reports from SEC

data scraping / data mining project started at:
https://github.com/deeptendies/mining-sec-edgar