Has anyone played with Tensorflow to train it to just make positive returns from the market? I'm sure someone has done something with this -- but I think the one real way to make this effective would be to actually have a general AI that is capable of scanning news stories and then hacking into the phone system to listen to calls from the CEO, etc. Just kidding about the last part -- but has anyone played around with machine learning and hooked it into an API?
I have not worked with Tensorflow, but I am currently building my own custom solution in GoLang. The concept is simple. Below is some code from the package I am working on comp := make(map[int][]string) targSym := "SPY" tf := "daily" allSyms := []string{"SPY", "QQQ"} bbc := BollingerBandsConf{14, 2, 2} mac := MaConf{200} cf642 := CompareFloat64{tf, "monthly", "monthly", LowerBollingerBand, bbc, Close, nil, Dynamic, 0, 2} cf644 := CompareFloat64{tf, "daily", "daily", SimpleMa, mac, Close, nil, GreaterThan, 0, 1} ccc := CloseChangeConf{14, 1, 1.5} cicd1 := CompareString{tf, "daily", deCloseChange, 1, ccc} cicd2 := CompareString{tf, "daily", deCloseChange, 2, ccc} cicd3 := CompareString{tf, "daily", deCloseChange, 3, ccc} cicw1 := CompareString{tf, "weekly", deCloseChange, 2, ccc} cicw2 := CompareString{tf, "weekly", deCloseChange, 2, ccc} cicw3 := CompareString{tf, "weekly", deCloseChange, 2, ccc} imc := IsMinConf{"low", 12} cb := CompareBool{tf, "monthly", deIsMin, 3, imc} // req defines which map elements are required for a backtest run req := []int{0, 1} comp[0] = []string{cf644.GetComponentDef()} comp[1] = []string{cf642.GetComponentDef()} comp[2] = []string{cb.GetComponentDef()} comp[3] = []string{cicd1.GetComponentDef(), cicd2.GetComponentDef(), cicd3.GetComponentDef()} comp[4] = []string{cicw1.GetComponentDef(), cicw2.GetComponentDef(), cicw3.GetComponentDef()} Based on the code above, 32 combination permutations were generated with over 11,000 strategies created on the fly, tested and results logged. Also note that 3 different time frames are being used. On my laptop it took about 10 seconds to run. I love Go fan27
It analyzed all available daily, weekly and monthly data for SPY and QQQ. The idea is to identify strategy candidates on a very small subset of tickers and then run those candidates on a larger universe of tickers.
I am learning a ML program called Weka via a 3 courses. (I'm part way through course 2) Department of Computer Science University of Waikato, New Zealand http://cs.waikato.ac.nz/ Class1.2
Over the past few months I’ve built an automated system in python using Keras/Tensorflow and the Python API for Interactive Brokers. I’ve been messing with Forex but it is flexible. Keras is a very nice front end for Tensorflow that makes it much easier to use. Honestly, getting a truly useful system out of Keras/Tensorflow is the hard part. Even the much hyped machine learning is no magic path to a edge on the market. But hey, if it were easy....
I am releasing a beta version of a C# .NET Core custom built machine learning SDK sometime in Q1 of next year. It is designed specifically for quantitative finance. From what I have seen thus far out there, it is a very novel and intuitive approach. I will be looking for beta testers so stay tuned!!
Hello, I am Emanuel Baltensperger from Switzerland. I am searching for AI based on the technology of Tensorflow to use for trading on the stock market. Is your product working and is it possible to use it as a customer? Actually, i think the best way to use is if it is working on Metatrader4
I am trying to find Programmer that combinate Tensorflow AlphGo Zero with Metatrader 4. Are you please willing to help me?
I should have a beta product available by the end of January that I will let users try out. That version will not be able to convert found strategies to MetaTrader code but later versions will. Will let you know when it is ready.