pytrader
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owocki
add more data as inputs
- volume
- bid/ask spread
- RSI/MACD / other derivative metrics
- social sentiment analysis
ref: #13 #8 OBV / On Balance Volume Margin & Short positions etc... ( www.BFXdata.com ) Ichimoku Clouds mentioned here as being most profitable: ( http://www.babypips.com/school/elementary/common-chart-indicators/what-is-the-most-profitable-indicator.html ) [most aren't when rigidly followed]
Have you looked at all into using Social Mention or Google Alerts for social sentiment analysis?
Sifting and reacting to viral bursts of social approval could provide large gains with minimal trading.
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Update: (3/30/2016)
I wrote a simple HTML scraper in python that grabs the top four scores from Social Mention
Here’s a link to the code, at minimum it’s a proof of concept
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Update:(4/13/2016) for many accuracy reasons it looks like SocialMention is not a good bet.
Also my raspberry Pi 3 came today so I will be experimenting with data scraping with it in the next few weeks
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Goals: Collect Data with Raspberry Pi Organize & Save Data Analyze Data Get Market Data Machine learning: Attempt to Predict Market Spikes with Social Media Data Test Accuracy Analyze possibility of Overfitting Discover or Fail to Discover Profitability If Profitable, integrate with PyTrader
Done: Scrape Data Get Raspberry Pi
Nice @jeff-hykin , I didnt check the other issues. I will start to follow all of them in here. @owocki I already had filled the form, will you create a a group or we will be here by now?
(Just saving this for future use) Heres a potential data source @darcy mentioned on slack https://www.quandl.com/data/BCHAIN?keyword=bitcoin
And another mentioned by @nrpalao on slack https://www.quantopian.com/data
found this today, could be very useful https://blog.pusher.com/realtime-tweet-statistics-pusher/
Quandl looks super easy to integrate, there's also a Python library: https://www.quandl.com/help/python
