Recorded Future Day Trading

At Recorded Future, we often get asked how our news analytic technology can fit into an automated trading strategy. The answer: We offer a rich web service API that provides near real-time access to content as it is processed by our system. We've just posted some example R code to our Google Code site which illustrates how one might incorporate our API into this kind of strategy. At a high level, the code works as follows:

1) Load up a query which monitors for new content related to S&P 500 companies.
2) Every five minutes, poll the Recorded Future API for new content related to these companies (on the basis of the time the document was analyzed by our system).
3) If we see a new occurrence of one of these companies in source content, check to see whether that occurrence matches the following criteria:

• Is this content truly relevant to the company at hand (using our new "relevance" score)
• Does the content have sufficient positive (and insufficient negative) sentiment associated with it?
• Does the company in question have sufficiently high momentum?
• Is the company NOT already in our portfolio?

4) If the occurrence matches those criteria, we execute a paper "buy" order on the basis of current stock price. (Using near real-time quotes from Google Finance)
5) At the end of the day, get closing prices for every stock in our paper portfolio, and execute a paper sell at this price.
6) Calculate profits and losses on the basis of the trades made during the day.

Let's have a look at the results of this strategy, which was run on live data from Friday, October 1, 2010:

	ticker	trade_time	price	close	returns(%)
1	GE	09-55-00	16.48	16.36	-0.728155340
2	ORCL	09-55-00	27.37	27.24	-0.474972598
3	GOOG	09-55-00	528.17	525.62	-0.482799099
4	WFC	10-00-00	25.42	25.56	0.550747443
5	BAC	10-15-00	13.15	13.30	1.140684411
6	T	10-20-00	28.82	28.81	-0.034698126
7	MSFT	10-25-00	24.50	24.38	-0.489795918
8	AMZN	10-30-00	152.99	153.71	0.470618995
9	KFT	10-40-00	30.92	31.21	0.937904269
10	AAPL	10-45-00	283.13	282.52	-0.215448734
11	WMT	10-50-00	53.29	53.36	0.131356727
12	HPQ	11-00-00	40.86	40.77	-0.220264317
14	MOT	11-20-00	8.55	8.56	0.116959064
15	VZ	11-30-00	32.87	32.89	0.060845756
16	S	11-35-00	4.65	4.72	1.505376344
17	F	12-10-00	12.38	12.26	-0.969305331
18	YHOO	12-15-00	14.18	14.27	0.634696756
19	GS	13-05-40	147.44	147.70	0.176342919
20	INTC	13-25-49	19.29	19.32	0.155520995
21	C	15-01-14	4.09	4.09	0.000000000
22	IBM	15-51-26	135.65	135.64	-0.007371913
23	NYT	15-51-26	7.83	7.85	0.255427842

You can see that we executed 23 "buys" at various times throughout the day. Our average profits were +11bp/trade, with our best being +115bp, worst being -96bp.

This is obviously a naive trading strategy, and customers are using much more sophisticated approaches. This approach does not include trading costs, carries with it an extremely small sample size, and has no risk control parameters. The purpose of this example is to show how one could include the Recorded Future API into a live trading strategy and to make available sample code for performing these operations. Via the API, we also offer this data historically for the purposes of modeling and strategy building. Take a look at our Google Code Site for more information about our API and contact sales@recordedfuture.com for more information about getting access.