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Analyzing Cryptocurrency Markets Using Python

blog.patricktriest.com

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Re: Analyzing Cryptocurrency Markets Using Python

#31

Post by jvannistelrooy »

bkolobara wrote: post #9
jvannistelrooy wrote: post #6

Earlier quoted context omitted.

Could you help me to put your opinion in the right context by answering a few questions? - Do you own Stellar Lumens? - Are you involved in any Stellar related projects?

I won Lumens in the previous build challenges, run a community forum and are building a company that uses the Stellar network. I'm not trying to give investment advice in my post. You should definitely not be buying Lumens if you don't need them. Most of them are going to be given away: https://www.stellar.org/about/mandate/

Thanks, I appreciate your openness.

Re: Analyzing Cryptocurrency Markets Using Python

#32

Post by jetti »

grenkatost wrote: post #17

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

Interesting site. It would be an interesting exercise to try and arbitrage the price differences of BTC on different exchanges.

Re: Analyzing Cryptocurrency Markets Using Python

#33

Post by mrchicity »

Layering, Spoofing and Momentum Ignition are not "HFT Strategies." They're illegal market manipulation techniques, most often used by manual traders. You don't need to be ultra fast to bully prices around or enter large non bonafide orders. I've never heard of a legitimate proprietary trading firm (i.e. one that pays a salary and hires highly qualified people, not a boiler room operation) intentionally using these techniques. Most are in the arbitrage, market making or statarb business.

It would be much more interesting to see the author dive into which events lead to fast price changes. How quickly are price shocks on other markets are reflected on GDAX? How are changes in outright contracts like BTC/USD and ETH/USD reflected in ETH/BTC? Do USD denominated pairs drive price discovery more or less than EUR denominated ones, and does this change when US based traders are asleep? Lots of stuff to look at here.

Re: Analyzing Cryptocurrency Markets Using Python

#34

Post by macroexchange »

ghgr wrote: post #26

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…

very interesting. do you have a contact email somwhere?

Re: Analyzing Cryptocurrency Markets Using Python

#35

Post by csomar »

johnt113 wrote: post #13

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.

If you are looking into diversification. You probably don't want to buy 2 1/1 correlated stocks or assets, since basically buying one of them will do the thing.

Re: Analyzing Cryptocurrency Markets Using Python

#36

Post by ghgr »

macroexchange wrote: post #34
ghgr wrote: post #26

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…

very interesting. do you have a contact email somwhere?

sure! I just updated my profile

Re: Analyzing Cryptocurrency Markets Using Python

#37

Post by ghgr »

jetti wrote: post #30
ghgr wrote: post #26

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…

What are you using to listen to GDAX sockets? I've been able to successfully connect using their node library but when trying to connect directly without using their library I'm unable to connect.

I use the Python library WebSocketClient. Here is the code: https://github.com/ghgr/HFT_Bitcoin/blob/master/download_boo...

Re: Analyzing Cryptocurrency Markets Using Python

#38

Post by hodder »

quotable_cow wrote: post #4
Galanwe wrote: post #2

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.

(As a quant) when calculating correlations of returns, we use log changes.

Re: Analyzing Cryptocurrency Markets Using Python

#39

Post by ghgr »

mrchicity wrote: post #33

Layering, Spoofing and Momentum Ignition are not "HFT Strategies." They're illegal market manipulation techniques, most often used by manual traders. You don't need to be ultra fast to bully prices around or enter large non bonafide orders. I've never heard of a legitimate proprietary trading firm (i.e. one that pays a salary and hires highly qualified people, not a boiler room operation) intentionally using these te…

Directly from Wikipedia [1]:

> In July 2013, it was reported that Panther Energy Trading LLC was ordered to pay $4.5 million to U.S. and U.K. regulators on charges that the firm's high-frequency trading activities manipulated commodity markets. Panther's computer algorithms placed and quickly canceled bids and offers in futures contracts including oil, metals, interest rates and foreign currencies, the U.S. Commodity Futures Trading Commission said.[109] In October 2014, Panther's sole owner Michael Coscia was charged with six counts of commodities fraud and six counts of "spoofing". The indictment stated that Coscia devised a high-frequency trading strategy to create a false impression of the available liquidity in the market, "and to fraudulently induce other market participants to react to the deceptive market information he created".[110]

I guess it's HFT if it's executed fast enough.

[1] https://en.wikipedia.org/wiki/High-frequency_trading#Strateg...

Re: Analyzing Cryptocurrency Markets Using Python

#40

Post by zebrafish »

hodder wrote: post #38
quotable_cow wrote: post #4

Earlier quoted context omitted.

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.

(As a quant) when calculating correlations of returns, we use log changes.

Rather than smoothing, have you tried looking at the correlations of the residuals?
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