Quantopian's company blog on algorithmic trading, its open-source Zipline backtester, and strategies built by its user community.
Commenters praise its open-source approach to backtesting and cite its posts on trust and on how poorly backtests predict live trading results.
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 your algorithms, I hope you'll trust us with a bit of your time. Come share some of what you've learned over the years with our community.
Certainly that's always a risk. Zipline does however support simulating transaction-costs and slippage so those will be accounted for (as best as possible).
As for market-paradigm shifts, I totally agree. One way to deal with that is to constantly re-optimize the parameters based on recent data. This is also known as walk-forward optimization on which there is a slide at the very end (see http://blog.quantopian.com/parameter-optimization/ for a more information).
Finally, as to running multiple strategies with different parameter settings. The OLMAR paper that describes the algorithm (http://arxiv.org/abs/1206.4626) has a variation of this where they use a range of different-length moving averages, rather than just one.
Nah, bro. Unless you want help on how to lose money on the market and also give more commission to your broker. If you're in the right area, they have Algo Trading meetup's or regular trading meetup's where some peeps maybe do algo-trading. e.g.,
You may find this helpful for what you're trying to do for equity trading,
http://blog.quantopian.com/gary-chan-on-pairs-trading-presentation-from-nyc-algorithmic-trading-meetup/
http://www.godotfinance.com/workingpapers/
One of Quantopian's studies shows that backtesting poorly predicts live trading results: https://blog.quantopian.com/using-machine-learning-to-predict-out-of-sample-performance-of-trading-algorithms/. From the forums, it looks like 90% of the effort is devoted to getting a good backtest, a yardstick which might not have any bearing on reality. How do real quant traders deal with this discrepancy?
(Days later)
"7 Best Community-Built Value Investing Algorithms Using Fundamentals" https://blog.quantopian.com/fundamentals-contest-winners/
(The Zipline backtesting library also builds upon Pandas)
How can we factor ESG/sustainability reporting into these fundamentals-driven algorithms in order to save the world?
Quantopian presented at the New York Software Engineers meetup last week. As somebody who has built proprietary backtesting software before, I applaud their open-source approach to this problem. They have some recorded presentations on their blog for anybody who is interested http://blog.quantopian.com/