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Q-Learning Based Cryptocurrency Trader and Portfolio Optimizer for the Poloniex Exchange

License: MIT License

Python 100.00%
poloniex machine-learning portfolio-optimization cryptocurrency-trader reinforcement-learning sentiment-analysis technical-analysis

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andrewvaliente avatar bshaw2019 avatar enummela avatar

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andrewvaliente xtaraim manly armoreal 4skynet yhyuan kinwo uploadskill hedgefair parrondo chegeek liam-f snowsky liquidgenius mdiby feynman0825 darkstar4401 arslan-ahmad sahanduiuc shonsy jacketme afcarl davidhuang123 eos21 anoopky 21jumpstart wy6688 tradv nivilo crypto-currency-projects 7leekee7 w1r2p1 balker0322 wesley-yang velles messorem7 seseadrian jifengthu webclinic017

crypto_trader's Issues

Readme typo

"feed free to join...". I think it should be "feel free to join ..."

We need to specify date format for arrow.get()

https://github.com/bshaw19/Crypto_Trader/blob/2f0f416c78b917d00f99b7c2ec0babcfbf33bc7e/Variable_Functions/Twitter_Sentiment.py#L16

@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/

Binary Classification vs Regression

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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