One idea is Dynamic time warping (DTW).
There is an R package for that: dtw
Here is the vignette:Computing and Visualizing Dynamic Time Warping
Alignments in R: The dtw Packageby Toni Giorgino
And here is an example from Systematic Investor with full code:
Time Series Matching with Dynamic Time Warping
As can be seen from this example from Yahoo!Finance this should not happen (click on "+ The adjusted close"):
Another more complete example can be found here:
So my explanation is that this is a glitch in ...
Caveat - I am a finance person only recently exposed to theory of computation and complexity so this will be more finance and less CS.
The computational difficulty of option pricing is relatively well understood: If you buy the assumptions of the Black, Scholes, and Merton model, then we have a closed form expression for the European option price which has ...
S&R levels can be obtained quite easily. However, you will not find this data in most references. Professional day traders use value areas from the previous day as well a forming value areas in the current session. There are several crucial levels gathered from the previous GLOBEX session. These can be derivied from any market. These support/resistance ...
A different approach to the more usual algo approach here, Bollinger Bands are a measure of the volatility, differences between buyers and sellers. So as either buyers or sellers are bought/sold out the bollinger bands squeeze beyond the average as a precursor to a sharp trend movement. I use a Bollinger Band Squeeze to highlight an imminent price movement
This depends on what ML you are using. For example, random forest is extremely resilient and doesn't require much normalisation of inputs to produce good outputs. However, RNN is extremely sensitive to input normalisation.
What I do for RNN is to divide each sample input by 1 standard deviation of the entire sample set, and that works quite nicely. But make ...
Phil Newton's answer:
Most modern charting packages can do this.
Naoya Yamaguchi's answer:
And Ninja trader is free. You can also program more indicators into it, using C#. This means a great collection of .NET assemblies, including Deep Learning ...
In conventional parlance, fundamental values would refer to data about the stock's financials, i.e. the data contained in the financial statements. Examples could be the cash flow, assets, liabilities and so on. Technical values would refer to data obtained from the price history of the stock, for example a 50 day moving average of the price, or the ...
You want to stay away from this as a student for a project, even if you are a doctoral student. If you want to tackle this a doctoral student, then I would love to completely consume every moment of your time working on this because I do have a problem that I want to reduce to deterministic polynomial time. Trust me, you don't want to talk to me because ...
I did the search by myself some time ago and couldn't find much that is readily available (in the sense that you can directly use it in a Python program, say). The closest I found find is the following library:
TA-Lib : Technical Analysis Library
It's a bit dated, however, with the latest release being published in 2007.
There are a couple of good papers on this but my favorite are Predicting stock market index using fusion of machine learning techniques and Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques.
Do not concur with the paradigm behind this thinking:
"The problem is, a random process will consistently generate lots and lots of S&R levels, and you can be 100% sure that those S&R levels mean absolutely nothing. Think about it, how can a random process NOT turn and go the other way?"
"Random" means 50/50. Whoever believe the markets are random ...