zipline

Have you considered python for the research/algo side of things? I work in a hedge fund in London at the moment and there's a massive shift away from R towards python in the algorithmic shops that I know about here. There's also the great work that quantopian are doing on their backtesting framework zipline [1] [1] https://github.com/quantopian/zipline

(disclaimer: I work for Quantopian) I can't speak for the author of the algo, but from the algo, it looks like the relevant lines for your question are 34 (order(stock,indicator * context.bet_amount)) and 35 (log.info("%f shares of %s sold." %(context.bet_amount * indicator,stock)). Our backtester (Zipline) will only order whole number of shares, obviously. If you pass it a fractional number, we take the floor: https://github.com/quantopian/zipline/blob/master/zipline/ge... The log line you're seeing should probably flip the sign of the number of shares before logging. order(-63, sid(6109)) means sell 63 shares of security 6109. The log line is simply logging the negative value instead of the positive one. Users can log anything they want in their backtest. As for the slow performance, apologies - being on HN has resulted in a lot of

That's currently not implemented, but definitely something we would like include. What I'd really like to see is a client for a bitcoin exchange. See our contribution request page for more details: https://github.com/quantopian/zipline/wiki/Contribution-Requ... Finally, live trading is something which we are working on to enable on Quantopian (which uses zipline for backtesting your algorithms). The syntax is very similar so you could just convert your zipline strategy to Quantopian (which is free to use).

That's a very interesting system you've built. The the thing about backtesting a strategy is that it is very easy to make a mistake in your backtester. Look ahead bias is the most common mistake. Another challenge is the data. Are you testing against a history of stocks that includes bankruptcies? If not you have survivorship bias. I suggest you take a look at my website, www.quantopian.com. Look at our open-sourced backtester, https://github.com/quantopian/zipline. Between the two we can help you get past those two sources of error.

Trust is essential (http://blog.quantopian.com/on-trust/). We see trust as something we earn over time with transparency and openness. We even open sourced our core intellectual property, the backtester itself: https://github.com/quantopian/zipline We also believe setting our own incentives to match our members' needs is key to sustainable trust. That's why Quantopian doesn't invest its own capital - we want to be purely focused on and motivated by serving quants. Being a successful quant takes many things - talent, mentorship, access to data, great systems, discipline. We started with backtesting because many aspiring quants end up skipping rigorous backtesting because of the time necessary to develop the test harness. We focused on community from the start to help connect new talent with mentors. If you're not ready to trust us with you

Hi undrcvr, I work at Quantopian. We'd love to hear your thoughts on our open-sourced Zipline backtester: https://github.com/quantopian/zipline and learn more about what you're building - there are lots of interesting challenges in this space.

You might be interested in zipline, an open source backtester written in Python. Zipline, Quantopian's open source backtester (yes, I work at Quantopian). The backtester is designed to also be a trading engine. You feed zipline data in "events" where each event is a point in time. Zipline doesn't care if the events are live or replayed from a database - it processes the data and executes any trades in the order book. The tricky part here is that zipline is more aimed at implementing trading algorithms. You'd have to write a simple algorithm to implement the buy/sell orders coming in. https://github.com/quantopian/zipline