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View Code? Open in Web Editor NEWQ-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange
License: MIT License
Q-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange
License: MIT License
"feed free to join...". I think it should be "feel free to join ..."
import ray.dataframe as pd
https://news.ycombinator.com/item?id=16510610
@bshaw19 I'm not sure what date format you intend to use here. When I wrote this code for the LSTM stock model the input date worked without specifying a formatting argument to the arrow.get() function.
I was running this today and ran into problems with the "YYYY-MM-DD" format as without a formatting argument, it assumes another format and thus returns tweets for the wrong dates. When we know our date format we can add the argument to avoid this problem.
Arrow documentation: http://arrow.readthedocs.io/en/latest/
Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis
http://cs229.stanford.edu/proj2015/029_report.pdf
share some papers about algo trading of crypto here
Hey! Another variable you could add to your agent would maybe be "Google Trends". ;-)
I feel as if it would be a better idea to have a regression for predicted price for the next time window (whatever increment we end up choosing).
This is because, in the scenario the price goes up, it is only profitable to trade when the expected profit exceeds trade costs (gas). If we had context for the price increase we could avoid losing money on negligible increases.
Thoughts?
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