It is easy to write programs that look for streams to predict and submit scenarios.
0. Install microprediction
pip install microprediction
Or see
microprediction from PyPI.
1. Get a key
2. Run the default crawler
from microprediction import SimpleCrawler
crawler = MicroCrawler(write_key="YOUR_WRITE_KEY_HERE")
crawler.run()
3. Watch it run
You can use the
dashboard to monitor performance. Performance is also available via the API, or Python client:
mw.get_performance()
mw.get_overall()
mw.get_home()
mw.get_errors()
mw.get_transactions()
You are advised to read the MicroWriter
code, the MicroCrawler
code and package readme. There is also a series of
articles on LinkedIn covering topics such as crawler
navigation, predicting
bivariate streams and
overview of the mechanics of prediction and reward.
4. Improve it
Take a look at how this
example modifies the all important
sample method of the crawler. Crawlers provide samples, which can be interpreted as percentile estimates. Any time series model can be shoe-horned into a crawler. It is worth reviewing this
article also that explains the mechanics of this site and scoring.
You can also read this
article showing
how to change the nagivation of the crawler, thus determining which streams it visits and how far ahead it decides to try to predict.
5. Let it go further afield
If you derive from MicroCrawler rather than SimpleCrawler you can visit zscore, bivariate and trivariate streams. The z1~ streams are described in the
article mentioned above. You can also read this
article about bivariate prediction of badminton, or this
article about trivariate prediction of crytocurrencies to understand the z2~ and z3~ streams you see.
6. Let it run indefinitely
There are many ways to do this. One is suggested towards the end of this
article.