# Tag Info

21

As mentioned elsewhere on this site, Lo, Mamaysky, and Wang (2000) do exactly what you're talking about, namely algorithmic detection of head and shoulders patterns. Their definition: Head-and-shoulders (HS) and inverted head-and-shoulders (IHS) patterns are characterized by a sequence of five consecutive local extrema $E_1,...,E_5$ such that  HS ...

12

I would recommend that you read "Evidence-Based Technical Analysis" by David Aronson. Firstly, I am mentioning it because it is a highly worthwhile book. Secondly, on pp151--161 he attempts to "objectify subjective TA", using the head-and-shoulders pattern as an example.

10

Most contemporary NN systems are just made to use the raw price time series for input (maybe with some kind of simple normalization), but for my thesis I wrote a system which traded equities with an ANN with technical indicator inputs (MAs, MACD, even pattern matching for stuff like Head-Shoulders, support levels, etc.). So at least conceptually it's ...

8

The predictor variables would consist of the input layer to the neural network. The output layer would consist of your target. You need to specify the hidden layer, number of nodes per layer, the learning algorithm, and the learning algorithm stopping criteria. Typically inputs are normalized (first-differenced, z-scored, etc.) before inputting into the ...

6

Yes, there are. For pure technical indicator libraries I would first check out: http://www.ta-lib.org/ Its open source and they provide APIs for both C# and Java among others. Let me know if you look for commercial ones but this one is definitely the most comprehensive in terms of open source code.

6

The issue for any technique is, does it consistently work as expected in the future? If not, then it's worthless. The idea behind mean reversion is that you have a "mean" that means something (it's not arbitrary), and a deviation from that mean that reverts in some consistent way. A pair trade is a common form of a "mean reversion" trade. Below is a ...

6

I think of mean reversion as more of a single stock phenomenon. In aggregate, these ididosyncratic mean reversions should offset one another and make the market smoother than its component stocks. There is a lot of work on mean reversion at the single stock level. The best entry is Jegadeesh's 1990 paper on what became known as "short run reversal" -- the ...

5

Dynamic Time Warping, recursive, time-delayed feedforward neural networks, wavelets, empirical mode decomposition, ..., there's plenty of it. BUT If you want my advice, don't go this way, I wasted too much time doing things like that. Neither big nor small players (profitably and consistently) trade this way and for a good reason. Technical analysis is a ...

5

Remember that there is almost no point in predicting market movements if you cannot use it to trade and generate P&L. Thus, backtesting a stat arb strategy based on the indicator is best option. Don't let yourself fooled by correlation or even directional forecast percentage accuracy as a few wrong predictions can blow your capital. You will need a ...

5

I will break up your question in to some parts to make answering easier. "people use various economic indicators with their networks (moving average, MACD, etc.) However, how do these come into play in a NN context?"--the 'indicators' MA, MACD etc. come from the data. They are measures of the data capturing some aspect. You could try to capture/replicate ...

3

There is so much finance literature on this topic, I don't even know where to begin. Specifically on momentum, some of the earlier foundational papers are Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency Momentum Strategies Price Momentum and Trading Volume International Momentum Strategies Momentum has an entire ...

3

You might want to check out the book Evidence Based Technical Analysis by David Aronson. In it he applies statistical techniques to determine whether certain technical analysis indicators and ensembles have any predictive power. It's an interesting read and should equip you with some ideas on how you might perform a similar analysis.

3

There is considerable literature on the role of sentiment in predicting stock market returns. Sentiment is often used as the proxy variable to explain Risk Aversion. I would check out the following for details: Neal & Wheatley - Do measures of investor sentiment predict stock market returns Stambaugh - The Short of it: Investor sentiment and stock ...

3

Post-crisis there has been some research that uses return series for financial institutions to predict downturns. I think the major ones are CoVaR (Adrian and Brunnermeier) and CATFIN (Allen, Bali, and Tang). These lit reviews in these papers should provide a lot of background.

3

I am not aware of a research specifically on integrating Macro view, but I'll give a shot at your question, hopefully it helps. I believe the answers depends on the initial trading strategies and on the macroeconomic indicators. From the way you formulate your question, I imagine that your trading strategy in based on quantitative asset allocation ...

3

It seems that this is the key difference between OBV and TSV: "Time segmented volume is the way to get consistent volume data and eliminate all the volume distortions that we discussed above. Here's the key to why time segmented volume works: Let's start with volume on a 5 minute chart and for this example, look at the 10:15 bar. Now take the average of ...

3

There is, unfortunately, no broad agreement on this point. In fact, put-call ratios may be constructed from volume as well as open interest, and they can even be constructed from certain subsets of the options chain (e.g., only certain strikes or tenors). I have usually used your option #2, because option #1 has a tendency to be extremely high or even ...

2

CBOE defines Put Call ratio as PCR = OIputs / OIcalls and I have always seen it defined this way. You should express it in a decimal way, a fraction doesn't really make sense here if you have 9999 in OIcall and 9998 in OIput for instance.

1

In the United States, the Federal Reserve is always late to adjust to rising inflation with an extreme outlier in the mid-1990s. Inflation always leads the flattening of the yield curve since the Fed raising interest rates which flattens the yield curve is usually in response to rising inflation. Poorly managed currencies or even the US in the 1970s will ...

1

You might have a look into the CRAN's "Empirical Finance" task view. It lists a whole bunch of R packages for time-series analysis and construction of automatic trading rules. Link: http://cran.r-project.org/web/views/Finance.html

1

Well pattern recognition and image processing is so developed these days. This is cutting edge in CS now and if we could identify cancer or brain tumor on a hazy image or a suspect face on an industry cam then recognizing head and shoulders on a chart is really really easy. Support Vector Machines or entropy come to mind but there is a myriad of ...

1

Regarding trading, it depends upon one's style and temperament. Don't rely solely on Aronson's book and his views and a phrase quoted by Andrew Lo in his study. The formula posted by Tal Fishman of Head and Shoulders as quoted by Lo, Mamaysky and Wang (2000) is not exhaustive. There is a lot of scope for further improvement. However, there are many studies ...

1

An excellent example is the Federal Reserve Bank of Chicago’s National Financial Conditions Index (NFCI): http://research.stlouisfed.org/fred2/series/NFCI CXO Advisory Group just published a report which came to the following conclusion: [...] evidence from simple tests suggests that the Federal Reserve Bank of Chicago’s NFCI may be a useful indicator ...

1

Renowned CXO Advisory Group have created a research compendium exclusively on momentum investing. This is the most exhaustive treatment of the topic I have ever seen: The Momentum Investing Research Compendium With \$25 the price is reasonable.

1

Cliff Asness's PhD thesis was based on Momentum and Value. AQR has a lot of interesting research. http://www.aqrindex.com/AQR_Momentum_Indices/Momentum_Research/Content/default.fs http://aqr.com/Research/ByTopic.aspx Jegadeesh and Titman (Returns to Buying Winners...- first paper linked in the above answer ) seems to be the standard reference.

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