# Tag Info

27

A quick google search retrieves the syllabus for the Stanford STATS 242 class. You can find it here. Just in case it's taken down at some point I'll copy-paste the source material. Keep in mind that I have no idea if this material is good or bad -- I didn't make this list. Also keep in mind that it contains treatments of what does and does not work. With ...

18

Accounting is a vital skill if you end up in a managerial position, and unless your career goal is to always be a cog in someone else's clockwork, then you will eventually find yourself in a managerial/senior partnership position even through quant research. I still play a critical role in my firm's quant strategies team, but here's a few things I've had to ...

15

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, ...

15

Windham Capital Management is using hidden markov models for their Risk Regime Strategies. Mark Kritzman, who is also CEO, has published an article about the general outline of the strategy (with source code so you can replicate the results!): Regime Shifts: Implications for Dynamic Strategies (corrected August 2012) by M. Kritzman, S. Page, D. Turkington]...

15

There are two key concerns (which in practice, may be difficult to distinguish): Previous research overestimated an effect. The effect shrinks over time. 1. Problems with reproducibility and replicability Previous research may have found an effect, but was the effect really there? There may be problems with: Reproducing results using the same data. ...

15

If you do this, you would destroy the value of the statistical tests that you performed on the backtest. You had a hypothesis that the strategy would make money, but the hypothesis was rejected. You cannot say "I will accept the hypothesis that the opposite strategy is successful"; no statistician would agree with this conclusion. In that case, you might as ...

14

I would say that most ML methods risk overfitting and it depends very much on the asset class. The only area where more sophisticated ML methods such as deep learning appear to make a major difference is in cash equities, where the feature space is very rich (NLP, news and announcements, corporate earnings, other financials) and the data is relatively good, ...

12

Here are some general directions: Alternative Risk Premia The ARP, or "smart beta," space has gained a lot of tractions over the past few years. These are rule-based strategies that provide systematic exposures to risk factors that have historically generated positive excess returns. Some of the best-known factors are, of course, trend, value, carry, etc. ...

12

Sniffing (or stalking) algo indeed detects other algorithms. How does that work in practice? Imagine the order book for a particular equity is: Bid 1 = 99 (size 10,000), Bid 2 = 98 (size 25,000), Bid 3 = 97 (size 30,000), Offer 1 = 101 (size 10,000), Offer 2 = 102 (size 25,000), Offer 3 = 103 (size 30,000). So in the example above, the bids and offers are ...

11

A public order book gives traders information not only on the current price of a security, but also the volume and structure of the entire supply and demand schedule. Such information can be used for arbitrage and market manipulation strategies in various ways: Spoofing: Inserting a large limit order as an apparent buy or sell signal which is canceled any ...

11

1) Why would you trade the error on the residual instead of creating a long/short factor model and trade expected returns? I would posit that the biggest reason people do this is for orthogonality of return. There are about 2,000 incredibly mature firms trading value, momentum, vol, etc. You would be competing with the likes of AQR, LSV Asset Management, ...

11

Is there a typical "half-life" of a strategy? This is a really subjective question, and I don't think any singular answer will generalize well. That being said, I will give some examples from personal experience. I have made hundreds of trading models in my career. I have only deployed 9 into live trading in the last ~25 years. Of those 9, 2 of ...

10

I am not sure Dark Pools (DP) have been created to avoid "market manipulation". They have been created by firms because they found an advantage to create them (see Market Microstructure in Practice, L and Laruelle Eds.). The main reasons have been: spare market fees, for DP created by brokers (like UBS MTF); spare market impact, for block pools (like ITG/...

9

It seems logical to me to have a Financial accounting course in a quant program. Quants can have a lot of different occupations, from derivative pricing to quant analyst in a "research" (i.e. analysis) dept. of a broker, a risk dept., a fund (as an analyst or as a potfolio manager), or quant execution trader (the list is far longer). In the case of being ...

9

A Sharpe ratio of at least 1 in backtesting is a promising start, but that is just one of many statistics of interest. The Sharpe ratio measures return per unit volatility, i.e., return per unit risk. Some other important Sharpe-like measures with different definitions of risk include: Return per unit turnover (aka yield): A high yielding strategy is more ...

9

If your strategy truly has no directional bias, then the benchmark should be cash (ie whatever you would earn using the capital in your trading account and taking no risk).

8

The Strata project is the new pure Java market risk quant library from OpenGamma. For more information, see the documentation and GitHub. It is Apache v2 licensed. Strata takes the experience of the OG-Platform codebase referenced in the question and turns it into a library - no need for databases, servers or similar. Ease of use is a big focus and there ...

8

Kenetic Component Analysis If I am to summarize the work of the authors from a broader view than that which is taken in the abstract, essentially the price process is decomposed into position, velocity and acceleration reminiscent of projectile motion in classical mechanics. I added this as an answer so that if @Pierre wants to accept it he may.

8

You can find everything you want to know about this here (and in a very readable and easily reproducible form): How Students Can Backtest Madoff’s Claims by Michael J. Stutzer (2009) From the abstract: Markopolos’ writings neither described nor included any specific backtests of the strike conversion strategy. Fortunately, a backtest is relatively ...

8

Within the fixed income space, there's a lot of literature on PCA trading. The first 2-3 principal component factors (PCs) can typically explain 90-99% of the total variances in yield curve movement. It's also nice, because the first PC looks like a change in the overall level of the yield curve, the second PC looks like a slope change, while the third ...

8

The best explanation I have seen so far is the so-called Adaptive Market Hypothesis by Andrew Lo: The adaptive market hypothesis, as proposed by Andrew Lo, is an attempt to reconcile economic theories based on the efficient market hypothesis (which implies that markets are efficient) with behavioral economics, by applying the principles of ...

8

Let's say your cumulative return series is $\{R_i \mid i=0,1,...,N-1\}$ of length $N$ days. There's 3 conventional ways to do this at this stage. You may convert the cumulative dollar return curve into arithmetic returns: $\displaystyle{r_i}= \dfrac{R_i-R_{i-1}}{R_{i-1}}$ Or dollar returns: $\displaystyle{r_i=R_i-R_{i-1}}$ Then take the ratio: \$\...

7

The demo account sends simulated data, not delayed data. It is unusable for just about anything except to see if your connections are working. The paper account sends real time data if you subscribe to it and pay data fees. It has all the functionality of a real account except fills are simulated. I believe it's worst case fills as in you have to trade ...

7

I just made a Genetic Algorithms calculator you can try at http://www.gregthatcher.com/Stocks/GeneticAlgorithmCalculator.aspx I'm not a "quant expert" like all of you (I'm just a programmer), but here is what I've found. 1.) If you set the constraints up correctly, the results are amazing. e.g. you can get portfolios that have very high return and low risk....

7

Let me guess, you fell for one of the fake Quantquote reviews and decided to purchase their buggy data? The reason for the missing quotes is Quantquote data is more of a snap-shot of market activity. It will not record every quote the way TickData or CQG does. ActiveTick is not as expansive as TickData but is more comprehensive than Quantquote. Maybe this ...

7

This is an old post, but I thought I would offer the following facts: 1) QQ claims to be sited in the Empire State Building, Suite 2100. (https://quantquote.com/contact.php) That is false. They do not lease or sublease that address. 2) The VM message on their sales line is a standard carrier-provided one such as a private individual would have. It does ...

7

If you are market making equities or futures you tend to make your profits over the short term by flipping your inventory. So if I'm showing 3.00 bid at 3.01 ask on a stock I'm going to tend to flip it pretty quickly for 0.01 profit. The guys that bought and sold from me may make/lose money depending on the length of their holding period and market direction....

6

As with many machine learning technologies, you can run a separate training and testing phase before deploying it live for prediction. All it does is build a collection of decision trees based on the parameters you give it - if the output field is a factor, you get classification (a finite enumerated set of values); if it's numeric, you get prediction. One ...

6

You are right, these work use deterministic control. Framework using stochastic control exist: Bouchard, B., Dang, N.-M., Lehalle, C.-A., 2011. Optimal control of trading algorithms: a general impulse control approach. SIAM J. Financial Mathematics 2 (1), 404-438. URL http://epubs.siam.org/doi/abs/10.1137/090777293?af=R Kharroubi, I., Pham, H., Jun. Optimal ...

6

For a basic introduction, the three chapters in Hull's Options, Futures, and Other Derivatives on Binomial Trees, Wiener Processes and Ito's Lemma, and The Black-Scholes-Merton Model helped me start to understand the basic concepts within a broader context. After that, Shreve's two books seems to be pretty popular (see here and here). He explains things ...

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