High frequency trading - page 4

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High Frequency Trading And Endof Day Manipulation : high_frequency_trading_and_endof_day_manipulation.pdf

We examine the impact of high frequency trading on the frequency and severity of suspected end of day price dislocation cases in 22 stock exchanges around the world over the period January 2003 – June 2011. Controlling for country, market, legal and other differences across exchanges and over time, and using a variety of robustness checks, we show that the presence of high frequency trading in some markets has significantly mitigated the frequency and severity of end-of-day manipulation, counter to recent concerns expressed in the media. The effect of HFT is more pronounced than the role of trading rules, surveillance, enforcement and legal conditions in curtailing the frequency and severity of end-of-day manipulation. We show our findings are robust to different measures of end-of-day manipulation, including but not limited to option expiry dates, among other things.
 

Real-Time Trading Models and the Statistical Properties of Foreign Exchange Rates : real-time_trading_models_and_the_statistical_properties_of_foreign_exchange_rates.pdf

Real-time trading models use high frequency live data feeds and their recommendations are transmitted to the traders through data feed lines instantaneously. The contributions of this paper are twofold. First, the performance of a widely used commercial real-time trading model is compared with the performance of systematic currency traders. Second, the real-time trading model is used to evaluate the statistical properties of foreign exchange rates. The out-of-sample test period is seven years of high frequency data on three major foreign exchange rates against the US Dollar and one cross rate. The trading model yields positive annualized returns (net of transaction costs) in all cases. Performance is measured by the annualized return, two measures of risk corrected annualized return, deal frequency and maximum drawdown. Their simulated probability distributions are calculated with the four well-known processes, the random walk, GARCH, AR-GARCH and AR-HARCH. The null hypothesis of whether the real-time performances of the foreign exchange series are consistent with these traditional processes is tested under the probability distributions of the performance measures. The results from the real-time trading model are not consistent with these processes.
 

High-frequency trading (HFT) practices in the global financial markets : high-frequency_trading_hft_practices_in_the_global_financial_markets.pdf

High-frequency trading (HFT) practices in the global financial markets involve the use of information and communication technologies (ICT), especially the capabilities of high-speed networks, rapid computation, and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds. HFT practices exist because a variety of new technologies have made them possible, and because financial market infrastructure capabilities have also been changing so rapidly. The U.S. markets, such

as the National Association for Securities Dealers Automated Quote (NASDAQ) market and the New York Stock Exchange (NYSE), have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable. In this article, we explore the technological, institutional and market developments in leading financial markets around the world that have embraced HFT trading. From these examples, we will distill a number of common characteristics that seem to be in operation, and then assess the

extent to which HFT practices have begun to be observed in Asian regional financial markets, and what will be their likely impacts. We also discuss a number of theoretical and empirical research directions of interest.
 

Optimal Strategies of High Frequency Traders : optimal_strategies_of_high_frequency_traders.pdf

This paper develops a continuous-time model of the optimal strategies of high-frequency traders (HFTs) to rationalize their pinging activities. Pinging, or the most aggressive fleeting orders, is defined as limit orders submitted inside the bid-ask spread that are cancelled shortly thereafter. The current worry is that HFTs utilize their speed advantage to ping inside the spread to manipulate the market. In contrast, the HFT in my model uses pinging to control inventory or to chase short-term price momentum without any learning or manipulative motives. I use historical message data to reconstruct limit order books, and characterize the HFT’s optimal strategies under the viscosity solution to my model. Implications on pinging activities from the model are then gauged against data. The result confirms that pinging is not necessarily manipulative and is rationalizable as part of the dynamic trading strategies of HFTs.
 

Aspects of Algorithmic and High-Frequency Trading : aspects_of_algorithmic_and_high-frequency_trading.pdf

High-Frequency Trading - Market Making

Refers to the subset of High-Frequency Trading strategies, which are characterised by their reliance on speed di erences relative to other traders to make pro ts based on short-term predictions and also

(objective/consequence) to hold essentially no asset inventories for more than a very short period of time.
 

High-Frequency Trading and the Execution Costs of Institutional Investors : high-frequency_trading_and_the_execution_costs_of_institutional_investors.pdf

This paper studies whether high-frequency trading (HFT) increases the execution costs of institutional investors.We use technology upgrades that lower the latency of the London Stock Exchange to obtain variation in the level of HFT over time. Following upgrades, the level of HFT increases. Around these shocks to HFT institutional traders’ costs remain unchanged.We find no clear evidence that HFT impacts institutional execution costs.
 

Statistical Arbitrage in High Frequency Trading Based on Limit Order Book Dynamics : download the book

Classic asset pricing theory assumes prices will eventually adjust to and re°ect the fair value,the route and speed of transition is not speci¯ed. Market Microstructure studies how prices adjust to re°ect new information. Recent years have seen the widely available high frequency data enabled by the rapid advance in information technology. Using high frequency data, it's interesting to study the roles played by the informed traders and noise traders and how the prices are adjusted to re°ect information °ow. It's also interesting to study whether returns are more predictable in the high frequency setting and whether one could exploit limit order book dynamics in trading.

Broadly speaking, the traditional approach to statistical arbitrage is through attempting to bet on the temporal convergence and divergence of price movements of pairs and baskets of assets, using statistical methods. A more academic de¯nition of statistical arbitrage is to spread the risk among thousands to millions of trades in very short holding time, hoping to gain pro¯t in expectation through the law of large numbers. Following this line, recently, a model based approach has been proposed by Rama Cont and coauthors [1], based on a simple birth-death markov chain model. After the model is calibrated to the order book data, various types of odds can be computed. For example, if a trader could estimate the probability of mid-price uptick movement conditional on the current orderbook status and if the odds are in his/her favor, the trader could submit an order to capitalize the odds. When the trade is carefully executed with a judicious stop-loss, the trader should be able to make

pro¯t in expectation.

In this project, we adopted a data-driven approach. We ¯rst built an "simulated" exchange order matching engine which allows us to reconstruct the orderbook. Therefore, in theory, we've built an exchange system which allows us to not only back-test our trading strategies but also evaluate the price impacts of trading. And we then implemented, calibrated and tested the Rama Cont model on both simulated data and real data. We also implemented, calibrated and tested an extended model. Based on these models and based on the orderbook dynamics, we explored a few high frequency trading strategies.
 

Is there any book with code examples for HFT?

 
morro:
Is there any book with code examples for HFT?

Some books at this thread are explaining that too

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