A new book on market microstructure is available: "Market Microstructure in Practice (Lehalle and Laruelle Eds)"any comment is welcome.

Sounds interesting -- maybe you can post some excerpts or links to same ...

I guess the publisher will put an extract on amazon or on its web site soon.if you can't wait here is the table of contents emacs produces from the tex sources: 1 Monitoring the fragmentation at any scale 1.1 Fluctuations of market shares: a first graph on liquidity 1.1.1 The market share: a not so obvious liquidity metric 1.1.2 Phase 1: first attempts of fragmentation 1.1.2.1 Best execution 1.1.2.2 Types of trading venues 1.1.2.3 Pre-trade transparency 1.1.2.4 Post-trade transparency 1.1.2.5 Two failures in the MiFID early years 1.1.2.5.1 Turquoise trading: from Investment Banks to the London Stock Exchange. 1.1.2.5.2 NASDAQ-OMX Europe: when technology is not enough. 1.1.3 Phase 2 : convergence towards an European offer 1.1.3.1 Entropy of the market microstructure 1.1.3.2 The Fragmentation Efficiency Index : a metric summarizing fragmentation 1.1.3.2.1 Examples: 1.1.3.3 Fragmentation of what? 1.1.4 Phase 3: Apparition of Broker Crossing Networks and Dark Pools 1.1.4.1 How HFT activity promoted trading outside of visible pools 1.1.4.2 Small and mid-caps : catching up main indexes 1.1.4.3 From division to union 1.2 Smart Order Routing, a structural component of European Price Formation Process 1.2.1 How to route orders in a fragmented market ? 1.2.1.1 Focus on atomic orders 1.2.1.2 Using a Smart Order Router (SOR) 1.2.1.3 Aggregate liquidity to minimize the impact of each order 1.2.1.3.1 Smart Order Routers or Smart Fee Savers? 1.2.1.4 Beware of duplicate liquidity 1.2.2 Fragmentation is a consequence of primary markets' variance 1.3 Still looking for the optimal tick size 1.3.1 Why does tick size matter ? 1.3.2 How tick size affects market quality 1.3.2.1 Decreasing the tick size lowers spreads when tick size is a constraint 1.3.2.2 Smaller and faster liquidity, does this means more unstable? 1.3.2.3 Cumulative depth and order exposure incentive 1.3.2.4 Queue jumping and the profitability of limit orders relative to marketable ones 1.3.3 How can tick size be used by trading venue to earn \inda{Market Share}{market share} ? 1.3.3.1 Tick size war in the US 1.3.3.2 Tick size war in Europe 1.3.4 How tick size change the profitability of the various participants in the market ? 1.3.5 The value of a quote 1.4 Can we see in the dark ? 1.4.1 Mechanism of dark liquidity pools 1.4.2 In-depth analysis of dark liquidity 2 Understanding the stakes and the roots of fragmentation 2.1 From intraday market share to \inda{Volume Curve}{volume curves}: some stationarity issues 2.1.1 Inventory-driven investors need fixing auctions 2.1.1.1 Fixing auctions: what's at stake 2.1.1.2 Basic matching rules during call auctions 2.1.1.3 Pre-fixing dynamics demystified 2.1.1.3.1 Theoretical matching curves. 2.1.1.3.2 The opaque German order book is tested. 2.1.1.3.3 Informational peaks. 2.1.2 Timing is money: investors need to trade accordingly 2.1.2.1 Market design and information flow timing imply liquidity patterns 2.1.2.2 Examples of mixed effects 2.1.3 Fragmentation and the evolution of intraday volume patterns 2.2 Does more liquidity guarantee a better market share ? A little story about the European Bid-Ask spre 2.2.1 The bid-ask spread and volatility move accordingly 2.2.2 Bid-ask spread and \inda{Market Share}{market share} are deeply linked 2.2.2.0.1 Turquoise's change of behavior came after market makers stopped their agreements. A drastic fall in 2.2.3 Exchanges need to show volatility-resistance 2.3 The agenda of High Frequency Traders: how do they extend their universe ? 2.3.1 Metrics for the balance in liquidity among indexes 2.3.2 A history of coverage 2.3.3 \inda{HFT (High Frequency Trader)}{High-frequency traders} do not impact all investors equally 2.3.3.1 How could HFT lower the \inda{Spread}{spread} without changing execution costs ? 2.3.3.2 Bid-ask spread: cost and uncertainty for investors 2.4 The link between fragmentation and systemic risk 2.4.1 The Spanish experiment 2.4.2 Volatility, \inda{Correlation}{cross-stock correlation}, intraday, extraday 2.4.2.1 The volatility of an index depends on the correlations among its components 2.4.2.2 The cross-stock correlation and the volatility at short time scales 2.4.2.3 Stylized fact: High correlation, high volatility 2.4.2.4 High correlations during summer 2010 2.4.2.5 Increasing \inda{Correlation!intraday}{intraday correlation} 2.4.2.6 \inda{Correlation!overnight}{Overnight} versus \inda{Correlation!overday}{overday} correlation 2.4.3 The \inda{Flash Crash}{Flash Crash} (May 6th, 2010) in NY: how far are we from systemic risk ? 2.4.3.1 Example of the Flash Crash impact: Procter \& Gamble 2.4.3.2 A large order initiated by a fundamental trader lighted the Flash Crash 2.4.3.3 The package of measures under examination or adopted by the SEC in the Flash Crash aftermath 2.4.3.4 \inda{Outage}{Outages} in Europe 3 Optimal organisations for optimal trading 3.1 Organising a trading structure to answer to a fragmented landscape 3.1.1 Main inputs of trading tools 3.1.1.1 The market data 3.1.1.2 The connection to venues 3.1.1.3 Historical data 3.1.1.4 Models 3.1.2 Components of trading algorithms 3.1.3 Main Outputs of an automated trading system 3.1.3.1 Pre-trade analytics 3.1.3.2 Monitoring indicators 3.1.3.2.1 Performance indicators. 3.1.3.2.2 What if scenarios ? 3.1.3.2.3 Where to produce real time indicators ? 3.1.3.3 Post trade analysis. 3.1.3.4 TCA (Transaction Cost Analysis) 3.2 Market Impact measurements: understanding the price formation process from the viewpoint of one inve 3.2.1 Better understanding what impacts the price 3.2.2 Market impact over the trading period 3.2.3 Market impact on a longer horizon: Different patterns for different investment styles 3.2.4 Dependence between investment style and market impact on a monthly horizon 3.3 Optimal trading methods 3.3.1 Algorithmic trading: adapting trading style to investors' needs 3.3.1.1 Each trading feature has its own benchmark 3.3.1.2 Customization offers multi-feature trading styles 3.3.2 Liquidity seeking algorithms are no longer a nice to have 3.3.2.1 From Smart Order Routing to liquidity seeking 3.3.2.1.1 A typical example of smart routing. 3.3.2.2 Seeking an optimal liquidity capturing scheme 3.3.2.2.1 Example of a passive split. 3.3.2.2.2 Building a liquidity seeker A Quantitative appendix A.1 From entropy to (FEI) Fragmentation Efficiency Index A.2 Information seeking and price discovery A.3 A simple model explaining the natural fragmentation of market microstructure A.3.1 A toy model of SOR dynamics A.3.2 A toy model of the impact of SOR activity on the market shares A.3.3 A coupled model of SOR-market shares dynamics A.3.4 Simulations A.3.5 Qualitative analysis A.4 A toy model of the flash crash A.4.1 A market depth-oriented model A.4.2 Impact of the flash crash on our model A.5 Harris Model: underlying continuous spread discretized by tick A.5.0.0.1 Motivation and notations A.5.0.0.2 Numerical application A.6 Optimal trade scheduling A.6.1 The trading model A.6.2 Towards a mean-variance optimal trade scheduling A.6.2.1 A minimum expectation criteria A.6.2.2 A pure risk adverse trade schedule A.6.2.3 Combining market impact and risk: the mean-variance criterion A.7 Estimation of proportion and its confidence intervals A.7.0.0.1 Remark. A.7.1 Application to the estimation of the \inda{Market Share}{market share} of venues on an asset A.7.2 Aggregation or application to the market share on an index A.7.2.0.1 Examples. A.7.3 Comparison of the estimators A.8 Gini coefficient and Kolmogorov-Smirnov test A.8.1 Gini coefficient A.8.2 Kolmogorov-Smirnov test A.8.2.0.1 Two-sample Kolmogorov-Smirnov test. A.8.3 Practical implementation A.9 Simple Linear Regression Model A.9.1 Model presentation A.9.1.0.1 Estimator properties. A.9.1.0.2 Distribution of $\hat{a}_0$ and $\hat{a}_1$. A.9.1.0.3 Fitting quality and variance analysis. A.9.2 Application to relation between spread and volatility A.10 Time series and seasonalities A.10.1 Introduction to \inda{Time Series}{time series} A.10.1.1 Application to deseasonality A.10.1.1.1 Additive model. A.10.1.1.2 Multiplicative model. A.10.2 Example of volume model A.10.2.0.1 Illustration of daily and yearly seasonality on real data. A.11 Clusters of Liquidity A.11.1 Introduction to Point Processes A.11.1.0.1 Example: Homogeneous Poisson Process. A.11.1.0.2 Stochastic Time Change. A.11.2 One-dimensional Hawkes processes A.11.2.0.1 Stationarity. A.11.2.0.2 Example of calibration of Hawkes process on real data. A.12 Signature plot and Epps effect A.12.1 \inda{Volatility}{Volatility} and \inda{Signature Plot}{signature plot} A.12.1.0.1 Observations and microstructure. A.12.2 \inda{Correlation}{Correlation} and \inda{Epps Effect}{Epps effect} A.12.2.0.1 Epps effect. A.13 Averaging effect A.13.1 Mean versus path A.13.2 Regression of average quantities versus mean of the regressions A.13.2.0.1 Numerical example with the DAX. B Glossary B.0.0.0.1 Price Formation Process. B.0.0.0.2 Regulation. B.0.0.0.3 Trading venues. B.0.0.0.4 Execution costs and market depth measurements.

The book is very nice and usefull. Especially the comparison of the intraday volume accross all countries.Do you mind to tell us what do you use for organizing the data? How do you draw the pictures? Do you use Matlab, R or Mathematica?Kim

We mainly used Matlab, and python (matplotlib) for few of them.