Simulation of limit order driven market -> paper (JOT), (check reference as well)

double auction simulation market -> paper, and this

Matlab code -> here

Complex Event Processing(CEP), with application to P2P network, senor network, exchange etc. Event Processing Industry

Harvard senor network Lab(robotBee, Mercury). There is really some interesting thoughts, hardware implementation of financial data processing application (analogue to Soundblast and GPU). and Tito Ingagiola point out this, apparently ICAP is using this. Tervala (switch technology, option MM case) is backed by GS, Sigma(database VC), Acartha(financial tech PE), Nothhill( financial tech VC).  Tervala using ( Arista switch technology(cisco, stanford cs guys), Celoxica( system design, UK based, former INSTINET, IB IT, HF Operation), Fix Flyer(process automation, courant math, nasa software engineer, IB op engineer), Portware (trading app design, former trader and IT), softModule(grid computing), Volante (sys integration).

on hardware side, it’s ASICs, FPGAs, network processors, GPU etc. etc.

even valuation and risk guys has interest in GPU, for monte carlos?

this two webinar about algo trading and infrastructure.

Yahoo Pipes, graphic programming. this is an awesome application!

one stock trading agent idea came out from Peter Stone(autonomous bidding agent, RL, robotCup).

Exchange simulator JessX

Option MM Baird

General direction: Liquidity – Arbitrage – Understand details.

Elwyn Berlekamp, i’m late to the story but Misha Malyshev(ex-citadal HFT, Teza technology LLC & the guy behind Aleynikov GS saga(similar story at UBS, and this interesting post)had similar track/background with a few authors in previous post (MIPT, Princton, hedgefund etc. etc.) Tactical (Turtle trend following fund The System incorporates mathematical market models that integrate key elements of modern portfolio theory, chaos theory, and proprietary money management concepts. sounds like snake oil, no?)

Detection of security fraud would be an interesting topic. e.g. how can madoff return being detect? not just suspicsion but quantifiable measurement of likelihood.

Poker Bot, RL  Go, U Alberta Grp,

bank algo: CS AES, GS GSET, JPM Kissel ’05, DB Mkt Impact ’08.

Ad Hoc inference, a lot of talk about boosting, bagging etc.

given a set of timing model space, how to separate over fit/snooping with genuine prediction, regime shift how? 1 2 3 4 5

Time Series shops ( inefficiency/prediction from time series):

AHL (ManGrp, oxford based, stats, time series),

Winston (ex AHL stats, trending follow, old people)

Lynx( Brumer grp Stockholm based, seems v good performance since 01)

Transtrend( Robeco grp. Netherland based systematic 7b AUM), QIM(2b AUM)

Amplitude (people, bunch of IBankers, investor relationship guy is SOM ’00)

Cantab Capital (ex GS quant strategy euro head, Cambridge based)

Crabel(Milwaukee based Toby Crabel had a short stint with Niederhoffer)

John Locke(french fix-income arbitrage)

Quantmatrix(nothingness)

DFarmer team up with Lillo working of order flow model, here is information content in Volumne data, from northwestern again 1.

Tower Research(Lime)

QuantLab v.s. Shi Jingpu (developer), Xu Yongzhong(worked in prediction). co-founder.

Two Sigma: D.E.Shaw math + Tudor IT

EWT: ex-nymex CEO

Jump Trading / Spot Trading(Robert Merrilee trading equity options)

Getco (Chi market making), Forbes on Getco, WSJ, Bloomberg story, the prophet is being fulfilled, and market kept on evolving. with different player join the arena, the natural question is: What’s NEXT?

Sun Trading(SnP option pit trader); Matlock Trading (market maker, system sold to GS in 99); Ronin Capital(prop, affiliation to exchange)

Ikos London based HF shop. they are not even hedge fund<what’s the point to be a fund?? dilute return??>

Allston Trading: interesting JD and requiremet

Chopper Trading, Chicago based, said nothing

Trade Link, very old.  option focused. TransMarket, again exchanged based, early arber.

World Quant, igor Tulchinsky, Upenn/Millennium stat arb.

Capstone, (organizer+ ex-pit trader+ finanical engineer+???)

Klipp’s saying was “You have to love to lose money, to take small losses, and hate to make money to be successful. There’s no other way.”

The hard and stiff will be broken, the soft and supple will prevail.

In July 2009, Spitznagel opened a fund betting on hyperinflation

Alpha Clone

Clone of low turn over value focused fund actually make sense(?)

Kaching: another Clone.

SharePost: private company research and exchange.

Second Market: illiquid asset market place(?)

Portfolio Solution, diversified(?) index fund ETF portfolio charging 0.25% management fee.

IASG: many listed CTA, the strategy description could be interesting.

Global Debt problem:  Forbes article,  Hayman advisor letters,  Bloomberg story about mortgage

An intersting presentation tools: prezi, Vimeo’s top 25 video of 2009. blublu wall painting

Investment outlook for 2010: 立此存照

Buffet letters, bridgewater, firstQ(currency)

Swensen, Ihcan, Schwarzman, Redleaf lecture.

Mebane Faber’s idea for 2009, all seems reasonably good idea with hindersight.

ETF related resources: T Herzfeld, Close End Fund Center (CEF), Claymore

Bill Harts’ company

Chapman University Economics Science Institute: Vernon Smith/Experimental economics -> here is an intro

Paul Rowady join Tabb Group (Tabb CEO here, who’s a regular contributor to WS&Tech) associated with this type of bogus

• What time series analysis is trying to model here is f(x_t|F_t-1), so what we are trying to understand here is how mean and variance related to the past, through ACF.
• portmanteau Test Q(m) (Ljung-Box stats for testing sufficiency of model) i.e. residue of dynamics, i.e. no series correlation no conditional heteroscedasticity). Q(m) ~ X^2(m) link to p stats to reject null hypothesis( no series corr in residue, no CH in residue square etc.)
• model identification( PACF or Akaike Information Criteria) ->  parameter estimation (OLS with significant level of parameter estimated)-> model checking ( check if residue series is close to white noise, using Ljung-box Q(m) ~X^2(m-order(AR model used))->conditional forcast with model(i.e. error in model is not taken into account).
• unit-root nonstationary, long memory, ARIFIMA(d), ARIMA(1), dicky fuller test for unit root. Ljung-box test, look at ACF, same effect.
• seasonality model, ACF contains information for AR and seasonality. (i.e. a tool to detect seasonality & predict shape of futures curve).
• nonlinearity test( Q, BDS, F, Threshold Test), parametric( TAR, Markov switch model), nonparametric( NN, kernel, MCMC etc.
• nonsynchronize trading, var(r_o) v.s. var(r). bid ask bounce, and high frequency dynamics(negative lag-1 AR)
• order prohibit model(lo et. al.),A Decomposition Model(ADS)* ( using partition I(i) indicative function and MLE to estimate parameters) ; duration model( using combination of quadratic function to remove duriual effect), using ACR model with exponential or generalized gamma innovation; non-linear duration model with two regime. bivariate model for both price change and duration( with method similar to ADS, this is close to what altreva is doing, i.e. simulate the market)
• cross-correlation matrix contains linear dependency information( couple, direct, independent) .
• VAR model able to absorb all dynamic dependency and concurrent dependency information into transition matrix, with cholesky decomposition. stationary condition, how to test for cointegration in real applicaton, Tasy mentioned about difficulty. And erro correction form( still fuzzy about this concept).
• PCA, FA,  MCMC

under which ‘there is no spot at all where the mosquito is safe’ and ‘don’t stop there. You can adjust the system to destroy all irritating elements in your surroundings. For instance the neighbor’s cat. Or the neighbor’s house. Or undesirable planets.’  here is the wired article.

Here is the force behind this new invention: Intellectual Venture Lab, the pilot project, The Venture firm behind it, and seems it is expanding. HBS idea cast: re-inventing invention.

here is the high speed camera the project was using: up to 1,000,000 frame/sec !! here is the method used to detect mosquito: Particle Image Velocimetry.

there was once a young man who dreamed to reducing the world to pure logic. Because he was a very cleaver young man, he managed to do it. When he finished his work he stood back and admired it. It was beautiful, a world purged of imperfection and indeterminacy, countless acres of gleaming ice stretch to the horizon. So the cleaver young looked around the world he created and decided to explore it. He took one step forward and fell flat on his back. You see, he forgot about friction, the ice was smooth and level and stainless, but you couldn’t walk there. So the cleaver young man set down and wept bitter tears. As he grew into a wise old man, he started to understand that roughens and ambiguity aren’t imperfections, they are what make the world turns. He want to run and dance, on the words and things that scattered upon the ground, all battered and tarnished and ambiguous. The wise old man saw that is the way things were. But something in him still hope for the ice, where everything is radiant and absolute and relentless. Though he come to like the idea of rough ground, he could live there. So now he is trapped between earth and ice, at home of neither. And this is the cause of all his grieve.

wiki, movie.

• microstructure/order book/agent based modeling/market maker/double auction/gaming/liquidity-> largestly heuristic but more pragmatic
• dynamic system approach/econophysics, distribution, complexity, much on theoretical level
• hardware/latency/routing/optimal execution, institution dominates with fancy word such as ‘dark pool’
• HMM-Kalman filter/BDNetwork ML(EM etc.)/Classifier (VM, NN, TREE, BOOSTING etc) ->advanced technical analysis, advanced classifier
• RLearning, GA, evolutionary market hypothesis -> capture market dynamics(?), prediction power(?)
• pair trading, Stats Arb, –> technically simple yet basic, BREAD AND BUTTER OF algo trading.
• last, most basic and fundamental is money management and risk management. control of order flow, system monitoring.

information theory address both problem, estimation(model)/inference(observation) and optimal betting/money management(kelly/drawdown/portfolio) together on the fundamental level. MPT address the second problem for multivariate case.

let start with most basic and most fundamental proble: stat arb

• modelling dynamics of stat arb [kalman filter application, with time dependency, long winding?]-> paper
• stat arb in US equity market -> paper
• pair trading, with EM algo, model mean reversion with state space model, [clear and succinct, kalman for estimation, EM, smooth KF for model validation, interesting idea is using Ornstein-Uhlenbeck stop time as exit strategy]05-> paper
• flexible least square for data mining and stat arb apply on ETF, imperial math/bluecrest, Tesfatsion’s FLS page -> paper [ this paper discussed the implementation of an online  FLS algo, and online SVD for priciple component]
• data mining for algorithmic asset management on Futures, Montana 09 -> paper
• FLS and stat arb in layman’s term ->[the is a waste of time, so much so for B-school professors]  paper
• Review of Statistical Arbitrage, Cointegration, and Multivariate Ornstein-Uhlenbeck [clearly written, intuitive, my favorite so far.], Attilio Meucci BB alpha head-> link
• Engle Granger, erro correction representation and cointegral -> paper
• Engle Granger 87 -> paper did this one won them nobel? repost again for emphasis.
• UCDavis Econ time series course with all the reference and list of topics -> website
• pair trading, performance of relative arbitrage rule [utility sector, average crossing, bid-ask bounce, trading cost, question left open is: a) how to choose the threshold? b) within trading period till when before next calibration you should not trade? ]-> paper, 06 IFC working paper
• here is an paper describe all SA strategy [wast of time] -> paper
• an intelligent SA system [a good outline of strategy, (moving window to estimate B, and NN-Garch for interval estimation and corresponding trading strategy]-> paper
• high frequency pairs trading with u.s. treasury securities[touched implementation issue e.g. repo financing cost in rates market ]-> paper
• statistical analysis of cointegral vector(strucuture change) -> paper
• THIS google search would yield interesting result, example here, here ( Alexandre 08), here(alexandra investment related to AP Alexandra?)here, here cointegration alpha: index tracking(carol alex 02), here (carol alex 03), here( carol alex JPM 05), here( this one came from SMU), optimal hedge cointegration (carol alex)|| here(fractional cointegration ”09), copper market efficiency, metal future efficiency
• an VERY good tutorial: NYU Courant Stat Arb course, let’s call it Dyna arb? the author Farshid Maghami Asl is a VP in GS FICC.  other courant people(robert almgre, algo 08, marco avellaneda present some course presentation here), there is a good portion dedicated to vol trading and stoch vol estimation using KF, but not enough details is given out. Chpt 14 start to touch some interesting general topics as financial system, robust control, bayesian optimal control and control of balance sheet, again just touched, not enough details.
• pair trading, quantitative method and analysis -> book
• pair trading, capture profit and hedge risk with SA strategy -> book
• Tsay’s course website for Time series analysis -> 1 2