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High frequency trading strategy example

High frequency trading strategies

High frequency trading strategy example. Examples of quant strategies that make use of algorithms. ▫ Index and ETF arbitrage. ▫ Statistical arbitrage (``Stat Arb''). ▫ Liquidity providing (``Market making''). ▫ Volume providing (``High-frequency, selective, market-making''). ▫ High frequency trading and price forecasting.

High frequency trading strategy example


In HFT, are strategies e. From my experience, high-frequency trading strategies require speed, mathematical modeling and a lot of gaming. In HFT, technology is key, almost by definition. Proximity matters because, as you can imagine, the closer you are to the exchange, the faster you see and react to its activity. This is due to the speed of light being finite.

Hardware matters a lot, and tends to be a highly intricate problem facing HFTs. FPGAs are a type of hardware with the trade off that you need to encode simple logic, but it can execute on that logic very quickly.

Rather than sending signals through underground fiber cables, the signals are sent from tower to tower, and packets travel faster through air than fiber most of the time. It also matters that operational speed is a function of raw machine temperature, among other things. The efficiency of algorithms is a deep problem.

Further, each operation adds up, which means you want your models to be extremely simple. Note that the complex math very often involves research and creating a strategy. Financial data is extremely noisy, and validation requires extreme care in preparing and running any analysis. Often, machine learning in HFT means running intricate statical fits on linear models.

SVMs and neural networks are usually more difficult to apply, in large part because of market noise. Next trade, or ten trades? End of day auction trade price? All of these will likely lead to qualitatively different fits. Objective functions are the functions used for optimization.

In classical OLS, the objective function is simply:. Objective functions are highly nontrivial in how they fit, select, over and under fit. However there are significant difficulties around how to handle outliers and highly correlated variables. Another interesting mathematical component involves risk management. Risk management can often be decomposed as a linear algebra problem. One useful strategy is hedging your portfolio with broad market indices. Many players aim to manipulate markets using a large variety of techniques.

More intricate gamesmanship involve exotic order types offered by exchanges and gaming the quirks of exchange microstructure. HFT strategies incorporate to some level speed, mathematical modeling and market gaming. Some strategies are highly focused speed trades, while others take advantage of market quirks, but in general successful HFT shops are highly versed in each of these facets.

This question originally appeared on Quora - the place to gain and share knowledge, empowering people to learn from others and better understand the world. Tap here to turn on desktop notifications to get the news sent straight to you. In classical OLS, the objective function is simply: Shifting my career to Algorithmic trading, where should I start?

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