Synalytica LLC's repositories
fastapi-svelte-template
SvelteJS + fastapi + docker-compose (with mongodb and material ui support)
fastapi-template
FastAPI based template application
svelte-template
Minimal svelte3 template with router support
timescale-superset-template
TimescaleDB with Apache superset for visualization
airflow-template
Airflow template for various ETL scheduling and orchestration
freqtrade-runner
Runner to drive/backtest freqtrade strategies
nginx-multi-host
Docker service to connect multiple sub-compose apps together under one
racket-tutorial
Incrementally building a complex language parser and interpreter
stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
synalytica.github.io
Synalytica LLC homepage
tutorial-data-science
Data science techniques and approaches relevant to stock information analysis
visualization-engine
Visualize trade history for a variety of assets given a standard static json file input
data-science-test
Blanket data-science task to test proficiency
twitter-algorithm
Source code for Twitter's Recommendation Algorithm