I started with the open NLP packages, but I rewrote pretty much everything, including my own ML algos. Typically based on the open source one. I kept running into scaling and use case performance issues. On some occasions things I did turned out to be solutions I'd find buried in the language of some google paper, which is mildly frustrating.
Forex is a tricky space, volatility and fees can really funk you up. I do a little there because the returns tend to justify it. Be wary of smaller and or unknown names. Manually check out anything you allow your algos to trade... at least till you come up with a comprehensive set of rules you can trust.
I tend to stick to US equities. Just because my risk tolerance is low for what amounts to a side project using my personal funds. Which I'm often not looking at until all the trading is done for the day.
Have you considered using your bot in the cryptocurrency market? There are no high-frequency traders to worry about because the technology has not reached that level yet. Volatility is high so your algos won't be sitting on the sidelines as much.
Would you mind putting an email in your profile? I'd like to contact you privately.
I am curious where do you get your data feed for trading. Do you pay for the data or are you using a free api? The main i reason i choose forex was due to ease of API use for data and trading.
Also follow up question, what sort of inputs are you using for training. Are you using mainly sentiment and price history or mostly technical indicators?
Level I and Level II data from my broker's API (Interactive Brokers). It's a free service as long as you have enough trading fee's every month. TDAmeritrade has an OK API as well. Both services have some limitations on data feeds (number of symbols you can actively review). There's some lag in data (sub what a human could notice) but again these are essentially free, and unless you're trying to be an HFT or scraping stocks this shouldn't matter. Neither of those activities are recommended, the people you'd be going up against have a lot more money and much better resources.
News data is aggregated from a variety of sources and paid rss news feeds.
Yes to sentiment, yes to history (though I keep my own history for the sake of granularity and not paying twice for data), and sort of for technical indicators... technical indicators are helpful for humans, but if your ML algo is using a data set that spans a multi-day window already a technical indicator of the direction of movement in the last few days is just telling your algo something it already knows.
I am surprised to hear that your placing less importance on technical indicators. I have been going under the assumption that those should be my main focus for input as they can be easily calculated and translated into simple number inputs.
My thought process is that the more indicators i can feed in with some history the higher the possibility that unique patterns are discovered. I originally thought that sentiment and news would be to complex to break down into a simple input for the network. I want to include them but i was going to place allot less emphasis on them.
Are you simply breaking down sentiment as good vs bad by analyzing text or are you doing something more complex?
I am sorry to be asking so many questions, but i have literally just started working on this for less then a week and I find your success very interesting. At this stage i am treating this as a long experiment.
Forex is a tricky space, volatility and fees can really funk you up. I do a little there because the returns tend to justify it. Be wary of smaller and or unknown names. Manually check out anything you allow your algos to trade... at least till you come up with a comprehensive set of rules you can trust.
I tend to stick to US equities. Just because my risk tolerance is low for what amounts to a side project using my personal funds. Which I'm often not looking at until all the trading is done for the day.