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
Some additional details about Fetcher: https://www.quantopian.com/help (You will need to scroll down to the Fetcher section -- couldn't find anchor links)
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.
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.
Unfortunately I wasn't able to attend February's Big Data Meetup, so I missed your talk. EDIT: (I did just view it online, my comment takes what you said into account.)
My point about intraday data with corporate actions wasn't really about the difficulty with corporate actions, but rather the leaky abstraction of trading present in the slides. To give you credit, I don't know what issues Quantopian addresses. On the other hand, has any one tried running Zipline-backtested strategies in real life? Does any one know what issues aren't addressed by Quantopian? Corp actions was one part, T-cost model was hinted at in the slides, but was any though given to borrow costs and availability? There leaks everywhere. It is not that you don't seem like smart guys who made this cool thing freely generously available to everyone, but that you seem like you spent too little time downtown NYC.
PS. Fully adjusted bars are nice, but they have an epoch to be adjusted to. Unfortunately having an epoch that is not today() means you can't add today's data to it. If you can't add today's data to it, you can use this system to generate real trades to trade. Now you need two sets of data and two sets of code to work with. Good luck.
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/
Commissions are the same as your E*TRADE account; list price is $9.99 for equities although if you are doing higher volumes you can negotiate discounts ($7.99 if you do 150 trades / quarter, and call them up if you are doing a lot of trades for lower pricing).
If you're looking to do API trading as a retail customer, Interactive Brokers is the best I've found. 1/2 cent per share per trade with a $1 minimum commission per trade.
A downside is that they are between you and the exchanges at least when you use their SMART routing, so I get the impression your fills will be optimized for their benefit (via their Timber Hill subsidiary), not yours. Years ago, you could get filled substantially better than your limit order especially at the open for example, but no more.
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.
The unrealistic aspect of that backtest is the total leverage - the algorithm ends up borrowing something like $700k on an initial balance of $10k.
Why not prevent this borrowing in the backtest? Quantopian's philosophy is to report the results, rather than block you from trying outrageous scenarios.
Backtests are not predictive; they are a tool to investigate the behavior of your algorithm.
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