[dunster]

If you find Quantblocks interesting, you should also look at Quantopian. (www.quantopian.com)

We're geared a bit more towards programmers. Rather than use blocks, our members develop their algorithms in Python. We have an in-browser IDE with a lot of smart auto-completion.

A few of our nifty features: * free access to 10 years of by-minute historical data for all US stocks * the writer of the algorithm owns the algorithm * batteries included - all of your favorite Python math and science packages including Pandas and NumPy * a robust backtester that models slippage, commissions, risk metrics, and more

We also have a community of quants and programmers who like talking about this kind of stuff. People share code, give advice, ask questions, etc.

Full disclosure: I work for Quantopian!

Happy hacking,

Dan Dunn

[jnewell]

Great work Dan, it is totally what I would be looking for when backtesting and strategy development. Do you mind sharing what js framework you used on the client side?

[dunster]

We wrote a bunch, and used a bunch, so there is no straight answer. A short list: highcharts for charting, jquery and underscore for the glue, crossfilter for data filtering, bootstrap for components, codemirror for the IDE, handlebars for templates, markdown for markdown, prettifier for code highlighting, and the list goes on.

[YuriNiyazov]

Do you guys have historical data for equity options?

[fawce]

Hi Yuri, not yet. We are looking at different possibilities for expanding the dataset, and the most popular right now is futures. Equity options do come up often, and are a close second for market data.

However, we are actually more excited about adding non-market data, because we want to bring more talent to 'algorithmic investment'. We hope our community can create algorithms that make buy/sell decisions based on more than just liquidity - fundamentals, reported data, qualitative news and research content. In other words, automating more of fundamental analysis and investment.