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22

I'll take a stab at it, but this is a really broad question. A direct answer: Bayesian models often use "probability that the counter-party is informed." Indirect answers: I think your assumption is that the algorithm operates on each stock individually, and has no knowledge of what it's doing in any other stock. But, it is likely that the algorithm is ...


20

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. ...


16

This is a very interesting question. I believe it is getting a lot of up-votes from people who have wondered the same thing and don't know where to begin, whereas you have at least laid out a reasonable-sounding plan. I commend you for that. However, it is not clear to me what you're trying to learn by posting this question. In my opinion, the plan you ...


16

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

One simple method, based on the principles of mean-variance optimization, is to set the weights proportional to the product of the inverse of the covariance matrix and a vector of standard deviations. This implicitly assumes that the normalized expected return of each stock is equal. If you wish, you can take only the top 5 weights and set the others to zero....


15

Great question! I think the most useful starting point is Stock Return Characteristics, Skew Laws, and the Differential Pricing of Individual Equity Options by Bakshi, Kapadia and Madan (2003). Their paper proposes a definition of model-free implied skewness (they originally called it risk-neutral skewness, but MFIS is more accurate), which they prove will ...


15

Yes. First, it is much easier to proceed if you standardize the output of your forecast so they are in the same units (returns, for example, or probabilities of an event/condition occurring). After you have done this, there are 3 general approaches: Signal weighting: Then you need to define a weighting scheme for your factors. Richard Grinold has an one ...


13

For "maximum pessimism" you should calculate thus: for longs - enter at the bar high and exit at the bar low on bar following signal bar for shorts - enter at the bar low and exit at the bar high on bar following signal bar I had previously heard this approach to back testing called the "torture test."


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

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 ...


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 ...


11

Market makers place quotes on both sides (ie, the bid and the ask). Depending on the market, the MM might even be contractually obligated to provide liquidity within some threshold. NYSE's designated market makers (who replaced the specialists a few years back) are an example. Even when there is no explicit requirement, the MM will quote both sides and ...


11

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 ...


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

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 ...


10

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 ...


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

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 ...


9

Slippage is multi-facetted, however, I think the main element to slippage is going to depend on the sophistication of your execution approach. Also, in your case there are 2 types of slippage: execution slippage (i.e. cost above mid to get your fill) tracking slippage (how much price difference between actual close and your fill price) Execution ...


9

The problem of the selecting the best portfolio (according to some risk measure) with a limited number of assets can be formulated as a mixed integer linear or quadratic program and is reviewed in the recent paper "Portfolio selection problems in practice: a comparison between linear and quadratic optimization models". It can be solved for reasonable sizes ...


9

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

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 ...


9

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, ...


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

The very good description of specialized hardware in finance can be found at Cisco.com - Algo Speed High Frequency Trading Solution section. Their High-Performance Trading Architecture (pdf) poster is just great to find out used hardware for different purposes and there are also some presentations, white papers and videos about Cisco's solutions for ...


8

The paper "Do option markets correctly price the probabilities of movement of the underlying asset? " by Yacine Aït-Sahalia, Yubo Wang, and Francis Yared should in my opinion provide many very usefull elements for this question (look in particular at section 3). Regards


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 ...


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