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Quant hedge fund how to#
Quant hedge fund free#

This lists every security/asset that might be of interest in a single, non-duplicated database. The first step to building a securities database of this kind is to create what is known as a securities master list. Frequencies were predominantly end-of-day/OHLC (open, high, low, close) through to ten minute polls of other proprietary sources. We were primarily concerned with the following asset classes: Global equities, fixed income macro and derivatives data, forex spot data (and futures), commodities (futures and options) and indices such as S&P500, FTSE100, VIX etc. Our fund predominantly, but not exclusively, utilised an equity long/short model as a trading mechanism.

Quant hedge fund free#

Pricing consists of four main areas: Connecting to data sources and obtaining data, storing that data in a unified manner, cleaning the data so it is free of errors and presenting that data to quant researchers in a straightforward, easy-to-use way. Unfortunately I won't be talking about the exact algorithm that we used, because this article is not about divulging trading strategies! However, I will discuss the pricing aspects of being a quantitative developer. In addition, portfolio management and reconciliation tools need to be fully automated, with the ability to generate snapshot reports on demand. Once the execution algorithm has been designed, it is the job of the execution quantitative developer to build an interface to a prime broker that allows trades to be carried out. As with pricing, teams of PhDs often work on effective execution algorithms - in fact execution and signals have significant overlap.

Execution/Orders: Once a trading strategy has passed any necessary backtesting and has gained sufficient theoretical performance, it is the job of the execution quants to build models for effectively getting the trades to the exchange without incurring too much slippage or transaction costs.

This area is the job of the quantitative researcher or trader. All funds keep their cards extremely close to their chest so good strategies are rarely revealed. For systematic funds, they will often fall into the groups of trend-following, mean-reversion, statistical arbitrage or high frequency/market making. The strategies employed by hedge funds are extremely diverse.

Signals/Algorithm(s): This aspect involves performing statistical research on the obtained pricing data in order to identify trading opportunities.

This is the job of the pricing quantitative developer. The data needs to be retrieved, stored, cleaned and made available to quants in a unified manner. Hence an important part of quantitative research is obtaining excellent quality securities pricing information.

Pricing/Feeds: Quantitative researchers and traders need to develop their algorithms against security price time series.

In systematic funds there are three key areas that need to be implemented before a "trading pipeline" can be established.

Each aspect of systematic trading is highly interwoven and thus every individual is exposed to mathematics and algorithms.

All of those job titles are prefixed with "quant" because they all involve a significant degree of mathematics. Many systematic/quantitative hedge funds are structured as independent "intrapreneurial" units that consist of small groups of quant researchers, quant traders and quant developers. Since a lot of you are probably interested in programming as much as mathematics and finance, it makes sense for me to discuss what the role was actually like and what I was working on "day to day", in case you decide that this type of work is more suitable than a "purer" quant role.

Quant hedge fund how to#

I've written a lot on the site about how to become a financial engineer or a quant analyst, but I've not really delved into the role I actually had in a hedge fund, which was that of a pricing quantitative developer or what it involved.