A Buy-on-dip algo for Alpaca API
This is a simple algo that trades every day refreshing portfolio based on the EMA ranking. Among the universe (e.g. SP500 stocks), it ranks by daily (price - EMA) percentage as of trading time and keep positions in sync with lowest ranked stocks.
The rationale behind this: low (price - EMA) vs price ratio indicates there is a big dip in a short time. Since the universe is SP500 which means there is some fundamental strengths, the belief is that the price should be recovered to some extent.
How to run
Set up your API key in environment variables first.
$ export APCA_API_KEY_ID=xxx $ export APCA_API_SECRET_KEY=yyy
The only dependency is alpaca-trade-api module. You can set up the environment by pipenv. If python 3 and the dependency is ready,
$ python main.py
Also, this repository is set up for Heroku. If you have a Heroku account, create a new app and run this as an application. It is only one worker app so make sure you set up worker type app.
universe.Universe is hard-coded. Easy customization is to change this to more dynamic set of stocks with some filters such as per-share price to be less than $50 or so. Some of the numbers are also hard-coded and it is meant to run in an account with about $500 deposit, with asuumption that one position to be up to $100, resulting in 5 positions at most. If your account size and position size preference are different, you can change these valuess.
EMA-5 is also very arbitrary choice. You could try something like 10, too.
There is btest.py that runs a simple simulation. This module needs more easy visualization and more integrated setup, possibly using jupyter and matplotlib.