Jan Skoda
@leftys
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Jan Skoda
@leftys
Tips and tricks to avoid over-fitting in algorithmic-trading. Over-fitting can cost you a lot of time and money. Here are a few trading-related tricks to avoid over-fitting that are not known outside the trading community. https://quant.xme.cz/overfitting-tricks/?utm_source=warpcast
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Jan Skoda
@leftys
I put together are few resources I found useful for algo-trading: strategy ZOO, analysis mashup, historical hft data provider and podcasts included! https://quant.xme.cz/algo-trading-resources-online/
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Jan Skoda
@leftys
Binance devs mocking FTX like: 😅
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Jan Skoda
@leftys
Starting with algo-trading is hard. Anonymous twitter users often just pretend to have experience to get attention. Industry veterans just shitpost all the time :) Where to turn? Books! Link below. https://quant.xme.cz/algo-trading-resources-books/
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Jan Skoda
@leftys
Simple code to plot deep order book (thousands of levels) from Crypto Lake data using Plotly and Pandas. Code is available in the link below including a full collab notebook. Very useful for strategy, features or lossy trade debugging. https://quant.xme.cz/plot-orderbook-python/?utm_source=warpcast&utm_campaign=orderbook
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Jan Skoda
@leftys
Is there any way to repost from warpcast to twitter or vice versa? 🤔 Would be really practical...
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Jan Skoda
@leftys
Sometimes we are approached by quants with a great backtest. They either want to sell their strategy or ask us to implement it and give them a share of the profits. (How) does this model work? https://quant.xme.cz/selling-backtests/
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Jan Skoda
@leftys
I started a quant blog from small crypto market maker perspective. Can you overfit strategies on one parameter? Can you overfit manually? How to detect or avoid overfitting? Answered at https://quant.xme.cz/strategy-overfitting/
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