Day traders identify patterns in the market that tell them when to enter and exit a trade.  They never hold market position over night meaning profit targets for trades are expressed in minutes not days.  To further back their technical analysis they view the data in multiple time domains trying to locate so called "support" and "resistance" levels.  Many of the technical indicators are based on price alone.

This data set should allow a machine algorithm to form better technical indicators then a human, thus allowing it to predict probabilities for entry conditions better then a human day trader.

For this data set we analysed 7 years of NASDAQ100 data (from 2010 to mid 2017).  Every morning we wait until 90 minutes of trading history has occurred before scanning for a simple pattern.  This pattern is when the 15 minute EMA crosses over the 65 minute EMA.

Symbols included in the search:

• FB - Facebook

• BABA - Alibaba

• GOOG - Google class C

• AAPL - Apple

• TSLA - Tesla

• MSFT - Microsoft

• NVDA - NVidia

• AMZN - Amazon

• CRM - Salesforce

• GOOGL - Google class A

• ADBE - Adobe

• NFLX - Netflix

• INTC - Intel

• BIDU - Baidu

Content

once the pattern has been detected I give you 2400 minute (40 hours) of previous history.  As well as 20 minutes of future history.  
The data will be formatted as follows.

File name: dataNSYM.csv

• N is an incremental integer

• SYM is the stock symbol ticker (FB, BABA, ect..)

Inside each of the csv files you will find 2420 lines of comma separated values, with format:

ISO formatted date, closing price, volume.

Eg:

2017-10-17T14:18:00.000Z,201.87,55800.0
2017-10-17T14:19:00.000Z,201.21,137786.0
2017-10-17T14:20:00.000Z,201.852,103695.0
2017-10-17T14:21:00.000Z,201.6,81362.0
2017-10-17T14:22:00.000Z,201.54,30183.0
2017-10-17T14:23:00.000Z,201.43,72405.0
2017-10-17T14:24:00.000Z,201.15,79411.0
2017-10-17T14:25:00.000Z,201.48,125713.0

The task should report a probability that this will be a successful trade or not.

Further note: One should keep in mind that there are trading fees involved for the entry and exit of the trade.  So in order to profile you will need to beat this spread.

Acknowledgements

Further ideas and questions can be directed to http://daytrader.ai
Thanks and I hope you have some fun with this set :)
blog: https://medium.com/@coreyauger/daytrader-ai-machine-learning-applied-to-intraday-trading-a6b4e44b0274