fawce

Interview with co-founder of The Crowd’s Line

still here :)

plug, but our system provides very fast access to price, fundamentals, estimates, etc: https://factset.quantopian.com

Hi, I'm the founder of Q. You're right about the turnover, though it is a multi-faceted trade-off. While increasing the trading frequency accelerates the accumulation of data, it also increases drag from transaction costs and that tends to lower a strategy's capacity.

Here is a post and video that talks about many of the criteria we use to evaluate algorithms: https://www.quantopian.com/posts/how-to-get-an-allocation-wr...

I agree, this is the most important issue for innovation in MA.

I wonder if we can integrate so that an algo running at Q can trade on a Stockfighter exchange

To be fair, we invested our own money in the winner.

The composition of the algo portfolio for the fund != the winners only. Our challenge is to choose the optimal set of algorithms from a huge pool. Optimal means maximizing returns while also minimizing correlation between the strategies.

The holy grail of investing is 10 or so uncorrelated return streams. We're building a community and platform to consistently turn out algorithms with uncorrelated return streams.

Here's how I think about our contest: If you only had backtesting, you could win by fitting. If you only had 1 month of paper trading, you could win by luck (taking excessive risks).

But our contest is 2 years of backtesting + live trading for a month. I don't think it is likely you can both overfit and be lucky with one strategy.

Investing is planning for the future. It is a critical tool for society. Everyone from individuals to important institutions like universities and pensions need to invest. It is a need of the many, not a diversion for the few.

Because of HFT, it has become popular to blindly condemn investing.

I went to college because an endowment was able to provide me with a scholarship. Investing has a huge effect on society and on people.

Sector weighting equally is based on diversification. Market cap weighting will tend to bet more on winners (the market cap is going up), concentrating your investment there.

Another way to think about it: market cap weighting is a momentum strategy. Equal across sectors is mean reversion.

No, it places orders market orders in any of the ETFs we need to rebalance mid-morning. Orders go out around 10:15am NY time on rebalance days.

Also, the chart in the page is from actual trading on a roughly $25k account, and updates each day.

The strategy is not attempting to approximate the S&P 500, it is seeking to manage your exposure to these industries.

The algo author wrote a detailed explanation of the outperformance of the S&P 500 here (source code included): https://www.quantopian.com/posts/equal-weight-all-sector-str...

Also see Domino Data Lab (http://www.dominodatalab.com) which is in public beta. Similar, with more emphasis on reproducibility of past results.

thanks, we fixed the problem causing the slow loads.

zygomega, I'm one of the zipline maintainers and we'd love to collaborate with you. There are a few people doing HFT research with zipline, and there's a lot of work to do. At quantopian (my day job), we focus on longer hold periods, so there is room in the zipline ecosystem for you to do HFT.

The main benefit we've found with python as the algo language is that it allows for stat programming with pandas, but also OO or functional programming for the algo logic. This smoothes the transition from research to production, just as you're describing with R -> haskell, but you can stay in one language.

I think one of the biggest potential wins with parallelization is if you can assume all positions are closed overnight, most often true for HFT. That way, you can simulate all the trading days in a test range in parallel. This is quite similar to the parallel processing we do to handle the large number of concurrent backtests running at quantopian. We did all of that with python, but I'd be fascinated to see it done with haskell.

It would be cool to do that with this signal, if the algo was buying/selling on another signal. Maybe use the short interest signal as a gate on momentum investing for example.

You're speaking truth. We (quantopian) deal with all of those headaches, and test the algos with fully adjusted data. Splits, symbol changes, mergers, divestitures, dead companies, dividends - they're all covered.

what do you mean by trader input?

If you click on the code and search for commissions, you'll see how those costs are taken into account. The big missing thing is the market for borrowing the stock to do the short side of the trade.

No money has traded on my version no. But, I understand that asset management firms have licensed the more sophisticated one Jess wrote at TR, so I would think they use it with real money. From what I understand, firms look at numerous signals like this, and then make investments based on a combination of the signals.

what's an example of an external factor? maybe I can find it on quandl and backtest it.

I don't know enough about Galaxy to say that we want to be the Galaxy of finance, but I can tell you where we want to go.

There are several key steps in the process of creating quant investment strategies: research, development/testing, backtesting, paper-trading, and finally, live trading. We want to cover all of it.

I think Galaxy is most like the 'research' phase - you want to get your data assembled and in something like a pandas datapanel, which you can then adhoc plot. You're looking for patterns, very quickly testing hypotheses about the data.

We made the decision to build live trading next, and we're full tilt on that, but we will keep expanding the product and it will include the research phase.

drop me a line and we can talk :) -- fawce at quantopian dot com

here you are: https://www.quantopian.com/help#overview-fetcher

Here's an example of fetcher in action: https://www.quantopian.com/posts/new-feature-fetcher

You can clone and run the algo yourself over any time period since 2002.

Investing is one way people plan for the future. Helping people plan for the future is a good thing. Algorithmic trading has mostly focused on (ultra) short timescales, without much regard for future planning. I think the world would benefit tremendously if more of that effort went toward investing on longer timescales. Investing is still almost fully manual today, and packed with inefficiencies and costs that could be automated away.

Thanks for taking a look, and for the feedback. Zipline does model transaction costs, both commissions and price impact of your own trading (slippage). The commissions and slippage models are pluggable, so you can use what is there or roll your own.

Quantopian does not have data for stock borrowing costs or availability, and Zipline's slippage/cost model does not account for them either. We'll find a way to get that data and plug the hole. The challenge has been finding a clean way to get it from the brokers, or finding an aggregator with a reasonable price (any advice?). In the meantime, we've been open about this limitation, and the zipline code is opensource, so I think/hope anyone who cares to know does probably know.

Quantopian is building our live trading environment now, so we don't yet have comparisons between the backtest results and real trading.

Regarding your point about the epoch, I'm not sure I entirely follow you. Part of the point of zipline's design is to allow easy swapping of datasources, mainly to allow the transition from backtesting to paper trading and then to real trading to be seamless. One algo code can run either historically or live. Adjustments from splits and mergers are back-projected, so that current day prices need no adjustment. Dividends are dealt with as announce, ex, and pay events, meaning we do not smooth out the over-night drops, instead we increment/decrement cash.

I'm in NYC regularly to host the NYC Algorithmic Trading meetup - it would be awesome to talk to you about these issues in person, please consider coming: http://www.meetup.com/NYC-Algorithmic-Trading/

Don't forget mergers. I spoke about this problem recently at Matt Turck's Big Data Meetup (http://vimeo.com/60598560). Quantopian provides zipline powered backtesting over fully adjusted intra-day data (minute bars) for free.

How do commissions work? per trade? Does the api provide any data, or just execution?

We are building just that at https://www.quantopian.com. It runs on zipline, and we're hooking it up to brokers.

If you are up for porting Bateman to python, over at https://www.quantopian.com we let you backtest with high quality intraday data for free. You can also reference our opensource backtesting engine, http://zipline.io, to see how we handled modeling slippage and order simulation.