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6

Here's my favorite example of an intraday strategy on S&P500 futures that at least used to work: Intraday Share Price Volatility and Leveraged ETF Rebalancing I pull it out whenever people start talking about market efficiency. The strategy is very simple: if S&P500 futures are up or down more than 2% on the day with two hours left until close, ...


5

I was just like you when I started out: I had learned a lot about machine learning (mainly neural networks and genetic algorithms/programming) and used it heavily. I also had learned about classic statistics but not nearly as much as about ML. The problem with ML is - as I see it today - that you are often taking a sledgehammer to crack a nut, meaning: ...


4

One potential use I could imagine would be identifying paradigm shifts / regime change. Just as a quick toy example, maybe you're interested in how gold is often considered a hedge against downturns in the stock market. Say you are building a trading strategy based on that intuition, but want your model to be more flexible by identifying different regimes ...


3

The main reason to use traditional methods is interpretability. Specially when you are dealing with portfolios. Portfolios are nothing more than a linear combination of assets. Many Machine Learning methods are highly non-linear and therefore are hard to replicate with a real portfolio. For example if you want to minimize volatility of your emerging markets ...


3

Recently I attended a presentation by the first author of the following paper who gave us quite a creative and illuminating (kind of meta-)use of random forests in Quant Finance: All that Glitters Is Not Gold: Comparing Backtest and Out-of-Sample Performance on a Large Cohort of Trading Algorithms (March 2016) by Thomas Wiecki, Andrew Campbell, Justin Lent, ...


2

Such a complex question... Geometric Brownian Motion (GBM) will not typically work to aid one finding strategies based on technicals, as the pursuit of the technical trader is to find market deviations from a random walk. However, some strategies, for example a "take profit/stop loss" strategy can work, (or at a minimum one can change the risk/reward ...


2

R. The others in-play are Python and, increasingly, Scala. But if you're trying to create or test a machine learning algorithm for a new problem, it's R.


1

You need to assign each of the target variables to their own column and then train a model for each of your forecast horizons library(quantmod) symbol= getSymbols("AAPL",from="2010-03-01", auto.assign=F) close<-Cl(symbol) open<-Op(symbol) lc1<-lag(close) lc2<-lag(close,2) lc3<-lag(close,3) lo1<-lag(open) lo2<-lag(open,2) lo3<-lag(...


1

You should use Adj close price Using Adj close price gives you the adjusted values of close price, hence the fair picture in case of off-beat events like splits and dividends. Using close price instead of adj close price provides unrealistic and false values of metrics like returns, which could generate false signals in your ML model.


1

Pretty much agree with what everyone is saying above. Just want to add one more comment. The sad truth of not advocating a lot on the usage of ML in Asset Management is the difficulty to marketing it. Most of the pitches on the quant portfolios are trying to make a systematic fundamental (these days called quantamenal) story. ML methods are apparently not ...



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