In order to accurately represent your data in pipeline and avoid lookahead bias, your data will be collected, stored, and surfaced in a point-in-time nature similar to Quantopian Partner Data. The list includes commodity, interest rate, equity, and currency futures.24 hour data is available for these futures, 5 days a week (Sunday at 6pm - Friday at 6pm ET).For most people, the hardest part of writing a good algorithm is finding the idea or pricing "edge." The research environment is the place to do that kind of research and analysis.In the research environment you have access to all of Quantopian's data - price, volume, corporate fundamentals, and partner datasets including sentiment data, earnings calendars, and more.For futures, as-traded prices are derived from electronic trade data.When your algorithm calls for historical equity price or volume data, it is adjusted for splits, mergers, and dividends as of the current simulation date.
It is common to use the fundamentals metrics within the trading logic of the algorithm.
What kind of data sources would you like us to have? The research environment is a customized IPython server.
With this open-ended platform, you are free to explore new investing ideas.
All of our datasets are point-in-time, which is important for backtest accuracy.
Since our event-based system sends trading events to you serially, your algorithm receives accurate historical data without any bias towards the present.