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Analyzing Cryptocurrency Markets Using Python

blog.patricktriest.com

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Re: Analyzing Cryptocurrency Markets Using Python

#51

Post by mrchicity »

ghgr wrote: post #39
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…

Panther was a one-man shop that did nothing but market manipulation. Yes, they used automation but for every Panther, there are 1000 manual traders being fined by CME for spoofing every year. Automation doesn't imply state of the art HFT. The Flash Crash spoofer was using a Trading Technologies system that any small trader can rent with a few scripts loaded in.

Look at real players with hundreds of employees. None of them make their money from market manipulation. It's all from low-latency arbitrage. If you're fast, you can make way, way more money doing legitimate trading than market manipulation.

Re: Analyzing Cryptocurrency Markets Using Python

#52

Post by Kranar »

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.

As a fellow quant I wanted to clarify that we use changes of log prices. Your sentence suggests calculating the change in price and then taking the log rather than taking the log of the prices and then calculating the change.

Re: Analyzing Cryptocurrency Markets Using Python

#53

Post by sbenitoj »

Finnucane wrote: post #46

Is it possible to get data on economic activity in cryptocoins? Which is to say, not just the coin trading, but goods and services being paid for with coin tokens? Every now and then you see a post to the effect of 'so-and-so is taking payment in Bitcoin.' Does anyone actually do that? Or are the coins just hoarded for their investment value?

At the moment, there aren't a ton of real-world use cases aside, but the future will be very different!

Check out Open Bazaar for an entire market place that's facilitated via cryptocurrencies.

Re: Analyzing Cryptocurrency Markets Using Python

#54

Post by gnaritas »

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.

> What's the point of correlation?

Knowledge.

> If I put 2 buys either I will have double profit or double loss. If I put 1 buy 1 sell I get zero.

Yea, so it pays to know if something is correlated to avoid this. It also pays to know if something is correlated and their spread gets out of whack short term allowing you to profit from the expectation they'll return to being correlated.

Re: Analyzing Cryptocurrency Markets Using Python

#55

Post by RealAnalysis »

zebrafish wrote: post #24
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.

I think it helps to "step out" with correlation/regression to understand why something may be spurious or why you get the high correlation values that you do. In some cases, there is no logical connection between variables which leads to spurious regression. However, in this case, if you "step out", the reason for the correlation is pretty obvious. There has been a lot of money pouring into crypto recently because mo…

what does "step out" mean?

Re: Analyzing Cryptocurrency Markets Using Python

#56

Post by hodder »

Kranar wrote: post #52
hodder wrote: post #38

Earlier quoted context omitted.

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

As a fellow quant I wanted to clarify that we use changes of log prices. Your sentence suggests calculating the change in price and then taking the log rather than taking the log of the prices and then calculating the change.

Yes. Thanks. My comment was poorly worded.
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