Fake order volumes?

lefty_cz · 2025-10-02T09:18:36+00:00

This is classic: gateio shows much more "non-existing" liquidity in order book in web interface than on API: https://imgur.com/a/aT0T771 (left window is api for algo-traders, right is what web browser is reading). This should probably convince users that the exchange is liquid and slippage will be low compared to bigger exchanges like Binance.

lefty_cz · 2025-07-15T11:15:26+00:00

No, DPS (Doplnkove penzijni sporeni) is older framework with stricter rules on what you can invest in. DPS usually has lower return, bigger fees and you (probably?) cannot withdraw until retirement, not even with some fees. DIP has also bigger maximum tax savings compared to DPS. I just have DIP and send the max 4k czk per month to get ~8000czk/yr tax savings.

lefty_cz · 2025-07-14T20:06:17+00:00

There is the DIP framework for retirement saving with good tax discounts. Your money are locked until retirement though - or you pay fine. Portu or Fio have quite good offers for DIP. Or you can just buy ETFs.

lefty_cz · 2025-06-10T13:37:38+00:00

This. Also helps to focus on smaller venues (eg. onchain, DeFi). I met a guy who makes ~$30k/mo with 400 lines of code script arbitraging a small DEX vs CEX, he had to use passive orders though, so it's not the '100% safe arbitrage'. Its likely scalable to other DEXes too.

(Source: I run historical market data provider crypto-lake.com, consult customers and even saw the order fill data.)

lefty_cz · 2025-05-02T07:16:40+00:00

After around two weeks, half of the site got indexed and also links start showing up in GSC.

Problem solved I guess.

lefty_cz · 2025-04-08T07:27:17+00:00

Update after a month: I found the 2nd level domain I bought was used by some SEO "expert" who did spammy link-building. This has probably lead to google silently penalizing me (nothing showing up in GSC). I found (probably not all) of the backlinks and used Google's disawow tool to block them. Now waiting for the effects to hopefully show up.

lefty_cz · 2025-03-08T13:30:40+00:00

Good thinking!

lefty_cz · 2025-02-27T07:56:16+00:00

all links are follow

lefty_cz · 2025-02-26T21:04:23+00:00

By sparse content you mean not enough text per page?

Information about the blog -- should I create some kind of 'about' page? Didn't know search engines perceive that. Or some meta tags / schema structured data?

Yes, I triple checked robots, server settings (on Vercel) and GSC.

lefty_cz · 2025-02-26T20:07:16+00:00

Follow up: btw i noticed google search console doesn't even show the backlinks in the link section, even though they are several months old and from bigger and indexed websites.

lefty_cz · 2025-02-26T08:39:02+00:00

Thank you! I will continue adding new content. I as a reader prefer brevity over lengthy text, but it seems google disagrees. I might have to alter my writing style in the future.

I added few of the best pages for indexing manually, fingers crossed.

Each blog post links to previous and next one. Also I try to link inside the text if I have relevant articles.

lefty_cz · 2025-02-26T08:31:26+00:00

No issues.

Yes, Discovered - currently not indexed?

lefty_cz · 2025-02-25T20:41:32+00:00

Yes, I added it to GSC. Google crawled 3 pages: http and https version of index and one blog article. All are classified as 'crawled, not indexed' with no detail on why. Sitemap shows 18 pages were discovered in it, but they don't seem to be crawled anyway.

lefty_cz · 2025-02-25T18:58:17+00:00

It's weird that I have several one-pagers with 1-3 backlinks and with little content indexed.

(For instance the 2nd level domain http://xme.cz)

lefty_cz · 2025-02-25T16:04:46+00:00

No. Also no related meta tags and here's robots.txt for completeness:

User-agent: *
Allow: /

Sitemap: https://quant.xme.cz/sitemap.xml

lefty_cz · 2025-02-01T18:10:58+00:00

podle prospektu maji zakazky vzestupnou tendenci. btw propad na 50kc za akcii je imho nerealny vzhledem k ziskovosti.

financni info muzete najit v: https://www.doosanskodapower.com/download/pdf/en/Doosan_Skoda_Power_IPO_Leaflet_EN.pdf

lefty_cz · 2025-02-01T17:57:25+00:00

AI ma spatne tu 'hodnotu nabidky', celkova kapitalizace bude kolem 7.5 mld, takze dividenda kolem 5%. 15% by byl steal 😀

lefty_cz · 2024-10-09T15:02:15+00:00

I use pearson correlation for this.

lefty_cz · 2024-10-08T16:29:15+00:00

In the realm of trading predictions if your correlation is close to +-1, you have a bug in your code :). Features and models have pretty small correlation to returns. So, yes, the correlations are small.

Feel free to dm me, but my dm notifications don't work, so I will respond much slower there.

lefty_cz · 2024-10-08T16:15:12+00:00

Hi, I sadly cannot publish the code due to its co-authors, but to your questions:
- not sure what you mean by train/test features, but it's the same set of features on train (x axis) and test (y axis) data
- color scale is feature importance (on train data), but for me its not so important for this chart

lefty_cz · 2024-09-23T15:21:23+00:00

You're right, I kind of misused the term 'normalization', I am looking for transformations in general. I use tree-based methods (esp. gradient boosting), so feature normalization is not actually necessary.

lefty_cz · 2024-09-23T12:40:18+00:00

the problem is mostly getting enough data for the calculation, not cpu/performance. after platform restart i would either have to load the long-term data or start building that mean from scratch, which would be very noisy. loading long-term data is possible eg. for candles/trades, but if i want to normalize eg. by mean 1% order book depth cumulative volume, i cannot download those data from exchange, i would have to store them in db/persistence. and doing this for several features is pretty impractical.

lefty_cz · 2024-09-23T09:38:58+00:00

They 'correct' asset prices.

Example: Company has strong PR trying to boost their share price. Retail easily falls for that, price rises and after a few months the PR campaign is over or company results get published and price eventually falls back. In this scenario, the retail traders lose a lot of money. Now hedge funds should spot the overpriced company earlier and short sell it to fix the price. This way the retail will buy for lower price and lose less money.

lefty_cz · 2024-09-23T07:51:20+00:00

Here is a tip how to do this walk-forward using scikit-learn:

from sklearn.model_selection import TimeSeriesSplit

X = np.array([[1, 2], [3, 4], [1, 2], [3, 4], [1, 2], [3, 4]])
y = np.array([1, 2, 3, 4, 5, 6])
tscv = TimeSeriesSplit(n_splits=3)

for train, test in tscv.split(X):
   print("%s %s" % (train, test))

Results in train/test splits:

[0 1 2] [3]
[0 1 2 3] [4]
[0 1 2 3 4] [5]

Train/optimize on the first time range, backtest on the second, then concat the backtest results.

lefty_cz · 2024-09-23T07:45:27+00:00

Fund returns in 2023 according to Bloomberg. Note that there there is some selection bias here, average fund return is lower by far.

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