Development resources at your finger tips
Build with the coolest Web3 projects
Recurring funding for Open Source
Learn about Web3 & earn rewards
Show appreciation for each other
Meet fellow developers, designers, futurists and more. Collaborate and BUIDL awesome projects together.
Discover great web3 organizations, work on meaningful projects and build relationships with like minded people. Browse Tribes
Meet the top hunters and contributors from our community.
Hello, Gitcoiners! At Gitcoin, we love bringing good news — new projects built, relationships formed, skills learned. Even better when we find …
Hello, Gitcoiners & Gitcoinerettes! It’s happening again – happy blockchain times are coming to San Francisco 🎉, as the San Francisco…
Gitcoin is GDPR complaint. Learn more in
Gitcoin's Terms & Conditions.
Check out the Issue Explorer
Looking to fund some work? You can submit a new Funded Issue here.
Ideas for evaluating / backtesting trading models.
From https://github.com/owocki/pytrader/issues/5#issuecomment-204250257 :
> A quick way would be create a theoretical control group: (if you bought 1 BTC/ETH and held it for the same two months) and compare the profits.
> A second much more rigorous method (what I would do before even investing 1 real BTC) would be to get a collection of data from different markets and simulate PyTrader at random segments on them. This would be a true experiment and give a good idea of the actual profitability of the algorithm in the general market. But, I imagine this would take quite a bit of computation power. Do you have a general estimate of how long it would take a $900 PC running 24/7 to simulate 2 months of trading? (assuming all the code is modified somehow for this to happen)
Thanks @jeff-hykin for these ideas. Lets discuss these and other ideas below.