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You have general and specific questions, so I'll my best here. I have a forex robot that does 30% p.a. 8 years running. It's technical indicators. It's also using one set of rules that is aware of peoples-patterns. (Target prices that traders would commonly sell at). It must be people-aware because even an HFT (and some have failed in big ways) has human ...


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Another example might be, if companies that exhibit certain revenue growth metrics, or margin improvement, would that signal a potential buy opportunity? Or perhaps if certain words in their annual report, quarterly filings, press releases indicate this company is likely to do well? The keys to quantitative investment are research and data analysis. ...


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This may not directly answer your questions. There's a class offered by Georgia Tech called Machine Learning for Trading, you might find it useful. https://www.udacity.com/course/machine-learning-for-trading--ud501


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auto.arima has many unresolved issues. see: http://www.stat.pitt.edu/stoffer/tsa3/Rissues.htm


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Read Max Dama on Automated trading (PDF) - This is the best introduction to algorithmic trading out there: http://www.decal.org/download/2582


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HFT firms are liquidity providers. They post bids and offers at prices around what they believe the fair price of the stock is at the current moment. The distance between those bids and offers can be thought of as a confidence interval. So, to put it quite simply, they can use machine learning to better estimate the fair price of the asset or better estimate ...


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You can do it manually. Let x be the data series. The code below considers all moving-average lag orders between 0 and max.q and prints out the BIC-minimizing lag order and the corresponding estimated model: m=list() # I will save estimated ARIMA(1,0,q) models here BIC=c() # I will save the corresponding BIC values here max.q=10 # the maximum MA order you ...


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Here is library for time series modelling. There are exponential smoothing models (simple, double, triple) with maximum likelihood estimation and another time series utility classes: https://github.com/hawkular/hawkular-datamining http://www.hawkular.org/docs/components/datamining/index.html


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This should walk you through what you are looking for: https://www.quantstart.com/articles/Generalised-Autoregressive-Conditional-Heteroskedasticity-GARCH-p-q-Models-for-Time-Series-Analysis https://www.quantstart.com/articles/ARIMA-GARCH-Trading-Strategy-on-the-SP500-Stock-Market-Index-Using-R


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Look at the function "lag" and if you want a lag function that does not depend on some time series structure of the object then you can use this one: shift<-function(x,shift_by){ stopifnot(is.numeric(shift_by)) stopifnot(is.numeric(x)) if (length(shift_by)>1) return(sapply(shift_by,shift, x=x)) out<-NULL abs_shift_by=abs(shift_by) ...


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Typically due to news overnight, and sometimes, as you suggest, after hours trading. The stock prices you see in the chart are the prices at which trades occurred. Trades are discrete events, at discrete prices. The discontinuities you see in the chart are simply due to the market agreeing a different price on the following day. Those discontinuities occur ...


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Normally distributed and that's why the two first moments are sufficient to infer their statistical significance. Proof are rather technical (and sometimes are not specific to time-series models) and mainly depends of: The estimation method employed ( QMLE, Least Squares, Moment, Whittle...) The parameter space Moment restrictions ... These proofs ...


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Just ran into and solved this problem. Convert the timeSeries object into an xts object then change the indexClass to "Date" # returns is a timeSeries object r <- as.xts(returns) indexClass(r) <- "Date" a <- Return.portfolio(r, rebalance_on="quarters", verbose=TRUE) I'm not an R expert or anything so I don't know if changing the indexClass has ...



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