# Tag Info

21

Definitely check out Quantopian and Zipline. Quantopian provides a free research environment, backtester, and live trading rig (algos can be hooked up to Interactive Brokers). The algorithm development environment includes really handy collaboration tools and an open source debugger. They provide tons of data (even Morningstar fundamentals!) free of charge. ...

17

Flow trading is in spirit very similar to market making - such firms make a profit by earning a spread. There are 3 common ways this is done. Suppose a client wants to buy 100k shares of XYZ, which is publicly quoted at 1M@10.01 bid, 1M@10.03 ask. For sake of simplification, assume sub-penny pricing is not accepted in the jurisdiction where XYZ is listed. ...

15

An alternative approach is to size your bet to maximize your expected utility, which is assumed to be given by a function $u(w)$ of your total wealth $w$. This could be a better approach than using the Kelly criterion, because the Kelly fraction gives the amount to bet if you want to maximize your long-term growth rate, assuming that you will bet a large ...

13

Of course it is fast enough. But what is fast enough? I know guys who trade off Excel sheets and they make millions, but those guys are clearly not active in high frequency space. So, it entirely depends on your trading frequency and average holding period. I also know of shops that run live trading systems by calling R functions, so, obviously Matlab ...

13

I can think of an application in options pricing. I came across the following paper a long time ago but think it explains FT very eloquently as applied to pricing options under BS: http://maxmatsuda.com/Papers/2004/Matsuda%20Intro%20FT%20Pricing.pdf The fun starts on page 112 but it relies on the 1998 paper by Madan and Carr. What I like about the paper ...

13

Here's a way to think about it: imagine you can do something in an ASIC (i.e. directly in hardware). However, the process of fabrication is in itself expensive, and you get a design that you cannot change afterwards. ASICs make sense for predefined tasks such as Bitcoin mining, well-known data processing algorithms, etc. On the other hand we have ordinary ...

13

QuantConnect provides an open source, community driven project called Lean. The project has thousands of engineers using it to create event driven strategies, on any resolution data, any market or asset class. Our system models margin leverage and margin calls, cash limitations, transaction costs. We maintain a full cashbook of your currencies. Its about as ...

12

"quote spam", "book colouring", "quote stuffing", etc encompass any mechanism to modify the shape of the orderbook by a market participant who does not intend to really buy or sell shares thanks to these orders. It means that someone fills the bid side of the book with 10,000 shares at different levels of price and does not want to buy at all, or only 100 ...

12

At the top of this list I still recommend you to seek employment in order to learn from others in QF space. Could you possible work in a quant team within an investment bank where you currently reside? Start to reach out to the quant finance community so you are connected once you decide to locate to where you can practice this discipline.reach out to alumni,...

12

Edit: Freddy's answer is good -- we wrote concurrently. He rightly points out that QF is a broad field, and that it is among other things a community. Here, I describe a practical, down-to-earth path for getting your feet wet in one key piece of it -- software and model development for derivatives analysis, starting with vanilla options. Your best bet ...

12

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, ...

11

Among matching rule, do not forget "auction calls", in most markets, you have one at the open and one at the close. To give you the main reasons to use one matching engine rather than another: Auction calls (i.e. fixings) are good to digest a lot of orders in a very short amount of time. It is why after a trading suspension, the trading starts with an ...

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 ...

10

I found this solid overview of different trading algorithms by Deutsche Bank Research: Trade execution algorithms Designed to minimise the price impact of executing trades of large volumes by ‘shredding’ orders into smaller parcels and slowly releasing these into the market. Strategy implementation algorithms Designed to read real-time market data and ...

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

First we have to clarify what we mean by profits: I think your question can only address the fact that some human traders beat the market (because you also make profit by just buying the market, e.g. through an ETF). I think there are two, perhaps even three main sources: Randomness, luck (as @PerAlexandersson) correctly pointed out - financial markets are ...

9

The Kelly criterion gives the fraction, $f$, of the current bankroll to bet in order to maximize the longterm growth. The criterion is given by $$f = \frac{bp-q}{b},$$ where $b$ is the winnings received on \$1 bet,$p$is the probability of winning, and$q=1-p$is the probability of losing the bet of \$1. In your case $b=2$, $p=q=0.5$ so the optimal ...

9

Pete's seven year old answer is just as relevant now as it was in 2011. None of the limiting factors of their API has changed since then, so this is essentially an extensive reiteration. The Interactive Brokers API is not suitable for high frequency trading execution. However the main reason that this is the case is not necessarily what would come to mind ...

8

There is a paper of mine answering To this question: Dealing with the Inventory Risk. A solution to the market making problem by Olivier Guéant, Charles-Albert Lehalle, Joaquin Fernandez Tapia.

8

Skew "arbitrage" is a pretty broad term. When you are trading the skew, there are 3 principal risks (or sources of P&L, if you will): (a) the actual change in the slope of the skew in the implied space. e.g. if you are trading 95% strike against 105% strike and your underlying stays in place, all of your instantaneous P&L would be due to the changes ...

8

You can have a look at rgarch. It's quite versatile. From what I remember, you have to get it explicitly from R-Forge, as it's not available from CRAN. See the rgarch website for more details. Last time I checked, usage was something like this: spec.gjrGARCH = ugarchspec(variance.model=list(model="gjrGARCH", garchOrder=c(1,1)), mean.model=list(armaOrder=c(...

8

The "correct" way is the way best suited to your trading. Regardless as to your data structure of choice, you have to maintain a list of all orders active on your book. That's because subsequent messages reference Order ID and you have to look up the corresponding order to determine the price level being acted upon. Given that you have to maintain a ...

8

To answer your questions we have to take a look to what it does. PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system, such that news vectors are orthogonals and explain the main part of the variance of the first set. It took an N x M matrice as input, N represents the differents ...

8

Since the stock is listed on NASDAQ, you have access to fairly standard 10Q and 10K financial statements. So you can apply the analysis pioneered by Ed Altman in his Z-score paper - compare this company's fundamental ratios with those of other companies, and see how many of them went bankrupt historically. For example, Moody's KMV uses this approach to ...

7

I think one of the best (and very current) articles about how to break into QF (for any kind of background) is: "On becoming a Quant" by Mark Joshi For your special background in mathematics see this excerpt from section 9: The main challenge for a pure mathematician is to be able to get one’s hands dirty and learning to be more focussed on getting ...

7

The other answers are useful and sensible. I have worked full time in equity research for nearly two decades, so very much a "qualitative" rather than a quantitative approach. However, all the firms for which I have worked had quants and because of my casual interest in the area I've spent a lot of time talking to quant teams over the years, often over a ...

7

The main application I know of is in option pricing. Peter Carr has done some research here. For an introductory article see this one: Option valuation using the fast Fourier transform by Peter Carr and Dilip B. Madan: In this paper the authors show how the fast Fourier transform may be used to value options when the characteristic function of the ...

7

Your first definition is wrong; I'm not sure where you got that from. Your second definition is correct: the ISO alerts the exchange that the submitting party has taken responsibility for RegNMS and requests a fill at only that venue's price; there is no routing away. Obviously, there is a huge red-tape burden to get permission to do this.

7

Of course, optimal control is at the core of math finance. Take few applications: Option Pricing: you have an exposure to a time dependent combination of market factors; you have some knowledge of their dynamics. They are partly deterministic, partly stochastic (i.e. random). At each "time step" you can adjust your portfolio at a given cost. Your goal is to ...

7

As the problem is currently formulated, you have a binary decision (whether to buy the cards or not) and a single state variable (your current wealth). I'm assuming the deck is reshuffled every play. A policy function will be a function $f: \mathbb{R} \rightarrow \{0, 1\}$ that will say whether to buy or not buy the cards as a function of your wealth. How ...

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