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Next step: prediction! As an active research subject, crypto-currencies may be the ideal candidate for using deep learning to forecast non-stationary time series data.
Theory and Algorithms for Forecasting Non-Stationary Time Series
You can watch a couple dozen the Bitcoin markets trade in real-time all on one chart with my site: https://bitcoin.clarkmoody.com/tickers/
It gets very interesting when the price really starts to move, since all the markets tend to move in lockstep. The response time reveals how active the trading bots are, making sure to reduce arbitrage opporunities.
I wanted to do something like that, eventually I did it on the elasticsearch. Then I wanted to share with the society and added grafana. The final result is real-time analytics of trading on main exchanges and for major pairs: https://cointradeanalysis.com
Wow, that is very cool. Some feedback/ideas: 1) things like xrp_usd need more decimal places for price (Max price 0.16, Min price 0.15, you really need at least 4 decimal places.) 2) What you call "Amount" should really be called "Volume".. "Count orders" should be "Count Trades" or perhaps just "Trades". 3) The buy/sell graph: i would place the buy/sell bars beside each other (instead of having sell underneath point…
2) Corrected, thank you very much.
3) The first option was like you describe, but something I did not like about it - I'll think about it :)
My 2 cents: - it is not really pertinent to compute the correlation between prices. This takes the currencies trend into account since prices, contrary to e.g. "returns", are non stationary. This will lead to a biased higher correlation. Just do a ".pct_change()" before the correlation. - also averaging the price between exchanges is a bit naive. It hides arbitrage opportunities and does not reflect the underlying tr…
Thanks for the tips! I'll update the tutorial code with that info in the next day or so. Until then, I've just added a note warning about the bias, and recommending the use of the ".pct_change()" method.
There has been a lot of money pouring into crypto recently because most people are speculating on the space as a whole. Bitcoin's price is too high for smaller investors to make a significant amount of money on, but when a big Bitcoin move makes the news, those investors want a piece. They then pour money into the smaller coins, hoping to get a larger return on their investment.
All of this is to say that I think this is the opposite of spurious correlation. However, that doesn't make the correlation meaningful in any way. When ETH or BTC jumps and makes the news, the other coins tend to follow because the whole space is speculative right now.
[1] https://nbviewer.jupyter.org/github/ghgr/HFT_Bitcoin/blob/ma...
What's the point of correlation ? If I put 2 buys either I will have double profit or double loss. If I put 1 buy 1 sell I get zero.
I'm surprised they didn't mentioned Machine Learning to further analyze the cryptocurrencies. Especially, that a few years ago an experiment shown, that if we would trade algorithmically on S&P500 via Machine Learning, traders could earn 8.5% returns comparing to the 5.6% by a random tactic. Here is a nice explanation: https://sigmoidal.io/machine-learning-for-trading/
ML can definitely help trading by using things like Ensemble Learning[2] but I would argue that for the novice it isn't going to add much to a trading strategy than doing other analysis on their own would do.
[1] https://cran.r-project.org/web/packages/caret/index.html
Nice and everything, but you can't make money with this, or can you?
It's great how readily available financial data is with cryptocurrencies! As a complement to this post, I've been recently working with Jupyter notebooks to analyze high-frequency trading activity in Bitcoin markets [1]. I've been listening to the GDAX socket since end July, so I have almost a month's worth of tick data (~ 7GB+ gzip compressed JSON data, it surely explodes to ~100 GB after extracting). If somebody is…