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17

There is a very good reason why the ratio $$\frac {mean(HIGH-LOW)}{mean(|CLOSE-OPEN|)} \approx 2$$ on various financial series. If the price of a security evolves according to a Wiener process beginning at the opening bell and throughout the day, and the drift is negligible for that period of time, i.e.$\mu=0$, then the denominator of the above ratio ...


14

Clark, This is one of the popular questions we have on our community when someone new to the field come in and ask where they should start. We point them all to the list we have gathered which is now one of the most comprehensive list for quant finance http://www.quantnet.com/master-reading-list-for-quants/


11

There was a proxy called the ECU. You should be able to use the weights on the Wikipedia page to get a time series back to 1979. Alternatively, the St. Louis FRED also provides this time series.


9

There is certainly much more to quantitative finance than technical analysis, and a previous question does a decent job of outlining the different areas, as does the wikipedia on "quantitative analyst". Even for what wikipedia terms an "algorithmic trading quant" or what Mark Joshi terms a "statistical arbitrage quant", technical analysis is just one tool ...


7

The term "Walk Forward Analysis" typically comes from technical analysis schemes. If that's the case here, I would be careful with whatever you're considering (or reading). Even if you tune your model parameters (and I'm not talking about any TA scheme) with 80% of the data and then "check" your model with the remaining 20%, you're still using that last ...


5

Hi Quantitative Finance has in my opinion two main streams. The first is about of valuation of some derivative contracts in a consistent way. This is a theory and once paradigms accepted it is coherent, it can considered as science at the same level as economy can pretend to this kind of terminology. The second is about making (or trying to) prediction(s) ...


4

If you look at the ladder, you might have some insight, but it's mainly speculation. The only way to be really "sure" in my opinion would be to have some insight from a broker. Otherwise, what I'd try to look for is to recognize execution schemes, but again you have to know the algorithms of all the participants in order to determine "who" it was. In my ...


4

I found a very good process for running a walk forward analysis in The Encyclopedia of Technical Market Indicators, Second Edition: http://www.amazon.com/Encyclopedia-Technical-Market-Indicators-Second/dp/0070120579 . The approach in the book helps mitigate the problem described above of assemble/test/retest. When you finally implement a trading system, it ...


4

FWIW, here's the approach I used. I keep the dates as an integer in YYYYMMDD form and merge the calls and puts in to a data frame both. Then I use ddply to operate on each matched call and put to find the future SPX close and call/put bid-offer average boa. library(plyr) both <- merge(calls, puts[, c("date", "exdate", "strike", "boa", "delta", "vega")], ...


4

One approach would be to rescale these metrics so that they are approximately normally distributed with unit variance under the null hypothesis that the stock's price is an unbiased geometric random walk (equivalently that the log returns are zero mean). This rescaling is effectively going to 'downweight' the statistics with a large amount of variance. Once ...


4

Quant in trading creates system that can be backtested, has a certain risk valuation. It is more like playing chess when you need to calculate multistep strategy. Let say certain instrument moves 1% a day. Our goal is to create strategy for one year (250 step strategy). If we use stock + options we get 50 or more entries a day into our system for analysis. ...


3

If you just want to run some simplistic technical analysis on quotes, then select the last quote for each unique timestamp. That will ensure that you don't have duplicate timestamps. If you must have it evenly spaced (i.e. no gaps from one second to another), then you can reuse the previous quote to fill-in the missing value.


3

This could be very difficult to determine in practice, because the axe (who controls the supply and demand) wants to hide his tracks. Also consider the axe's aliases. I mention this because you would need to take into account the axe disguising his trades through another market maker (for example, Goldman trading through ARCA, or even showing sales between ...


3

John C. Hull's "Options, Futures, and Other Derivatives" is the mostly widely recognized introductory book for derivatives valuation.


3

I like Statistics and Data Analysis for Financial Engineering by David Ruppert (http://www.amazon.com/gp/product/1441977864/ref=oss_product)


3

One that I found via google that seems promising (for beginners though) is. Numerical methods in finance and Economics


3

Options, Futures, and Other Derivatives Analysis of Financial Time Series Inside the Black Box: The Simple Truth About Quantitative Trading Trading and Exchanges: Market Microstructure for Practitioners


3

A multi-alpha trading model ranks each asset according to the individual signals. For example, if I have two metrics and three stocks, I could just create this reverse-sorted table: Rank| PNL W2L ----| --------- 3 | AAPL AAPL 2 | MSFT YHOO 1 | YHOO MSFT Because this ranking/sorting method is non-parametric, I can just average each metric's rank by ...


3

I haven't seen a framework for options specifically, however... The way I have done this in the past is to essentially setup a timeseries(xts or zoo) for each option(underlying,type,strike,expiry). Obviously doing this via code is important because it is intensely error prone. We use a build function to put those into the workspace. It is still difficult ...


3

Unfortunately, I cannot answer fully your question. Though I'll give you my partial answer. First of all, using entire price history of an index (from Yahoo), this is what I got: Daily Weekly Monthly DJIA 2.91 2.37 2.33 NASDAQ 100 1.74 1.94 1.91 NYSE Composite 1.61 1.59 1.85 S&P 100 ...


2

Returns-based analysis cannot calculate the expected return of a trading system. It yields nonsensical results and is not suited to this particular calculation. Consider a game where every time you play, you win 25% twice and lose 40% once. There are basically three permutations of this game. Represented in R vectors: first <- c(.25, .25, -.4) second ...


2

You can't add returns. You must multiply them. In your example above where daily returns are 25%, 25%, and -40% To compute expected return from a return series, simply use this formula: return = product( 1+return); in the case of you example this yields: return = (1.25 * 1.25 * .6) = .9375 To get the expected daily return use the geometric mean: ...


2

My type is "An introduction to the mathematics of financial derivatives" by Salih N. Neftci. Though it's definitely harder to digest than Hull.


2

I like the following book (though have only very briefly skimmed it): Optimization methods in finance


2

This may be too basic a book for what you're hungering for. In preparation for the Financial Engineering actuarial exam, I'm studying from Derivative Markets by McDonald. It's very technical, but gives a great introduction to the mathematics behind pricing options and even goes into depth on Brownian motion. Check it out here: http://amzn.to/g3QOES.


2

cost of leverage for equity only long/short investing is a function of the margin deal you can negotiate with your broker, if you have a large amount of capital. If you don't have significant capital to start with, then it's likely you'll only be able to get 2x leverage with a loan rate between 4% and 10% (retail reg-t margin rates at most brokers) This ...


2

I would create separate estimates for volume and choice of debt instrument. There are tools to estimate these simultaneously but I do not see a compelling advantage here. I assume the volume is conditional on the choice of debt issuance so you might start by predicting choice of debt issuance and use this as an input to the volume model. The volume model ...


2

I'd put this down as a comment, but don't have the reputation to do so. There is (or at least used to be) a two part MOOC course over at Coursera by one of the developers of QuantSoftware Toolkit. This is not an endorsement of the course or the software, just a statement of fact (for the record, I did do a part of the course, but found it too simplistic and ...


1

To help you understand why you need to follow recipes (like chrisaycock's) just have a look at your tick data. You will find ticks clustered at some points in time while they seem scarce at others. If you proceed with your recipe 2, you will lose those clusters of activity and stretch them out. In periods of low activity you will condense the market. ...


1

Despite the rather unconventional terminology used I would say you are pretty much spot on with what you are doing and what you try to achieve. I would, however use log returns in order to get an identical percentage no matter whether you measure the distance from 100 -> 90 or 90 -> 100, for example. You can also standardize the value you capture by ...



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