Inspiration

Inspired by stock-markets, pretty graphs, and making money.

What it does

Attempts to predict trends in bitcoin cryptocurrency to suggest when to buy and sell, in hopes of making a potential profit over the baseline.

How we (attempted to) build it

We attempted build it with using a variety of tools and data from different sources.

  • We tried predictions with between two correlated currencies (ethereum and bitcoin)
  • Looked at twitter data and combined with sentiment analysis via IBM Watson Tone Analysis
  • " " for Reddit posts via /r/Bitcoin
  • Examined Google Trends for keywords such as "Bitcoin" and "Ethereum"
  • Built framework for processing real-time data for utilizing predictive models

Challenges we ran into

It's difficult to predict... Cryptocurrencies are much more volatile than standard stock markets, so related models and intuitive correlations didn't quite apply. Many of the indicators didn't provide strong enough correlation in the data to confidently make decisions.

Accomplishments that we're proud of

We learned a lot. We experimented with AWS for analyzing and aggregating our transactional data for BTC and ETH cryptocurrencies; as well as loaded data into IBM Watson's Tone Analysis for Twitter data, which was pretty interesting to see how it analyzed the tones of various tweets.

What we learned

A lot. Much of what we're proud of are things we learned that were relatively new to us.

Future Challenges

  • Get access to more raw Bitcoin transaction data (our source is ~7% of all transactions, and USD is a small fraction of that)
  • Choose a time window in which the relationship between Bitcoin and the regressors is unchanging
  • Could be altered based on sentiment analysis of Bitcoin and Ethereum tweets / news
  • Choose a time periodicity and training window that allows for the strongest leading relationship of the regressors and lowest error rate of forecasts
  • Get enough historical twitter data at regular intervals to cross-correlate with Bitcoin movements Find useful, free, and complete (no or few nulls) historical datasets for initial model training

What's next for derbyhacks2018

  1. Analyze data
  2. Make good predictors
  3. ???
  4. Profit
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