However, creating great solutions requires much more than the ability to write code. It begins with a clear understanding of requirements and goals, and continues through design, development, testing, and refinement of the solution. The common thread throughout is regular two-way communication to insure that we create the right solution for you. My goal is to create lasting relationships with clients. Relationships built on trust.

Relationships that deliver value. And most importantly, relationships that are mutually beneficial. Matt Radtke has spent over 30 years as a software engineer, including 10 years designing, implementing and testing custom software solutions for banks, insurance companies, trading outfits and other Fortune firms. These customers require applications implemented to their exact specifications, with zero tolerance for errors. He has also actively traded stocks, ETFs, and options since , with occasional forays into futures and forex.

Matt is an avid traveler and loves scuba diving. Skip to content.

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The Process is Simple. Getting in Touch. An Agile Approach. Project Management. Should You Automate Your Trading? For HFT strategies it is necessary to create a fully automated execution mechanism, which will often be tightly coupled with the trade generator due to the interdependence of strategy and technology. The key considerations when creating an execution system are the interface to the brokerage , minimisation of transaction costs including commission, slippage and the spread and divergence of performance of the live system from backtested performance.

There are many ways to interface to a brokerage.

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They range from calling up your broker on the telephone right through to a fully-automated high-performance Application Programming Interface API. Ideally you want to automate the execution of your trades as much as possible. This frees you up to concentrate on further research, as well as allow you to run multiple strategies or even strategies of higher frequency in fact, HFT is essentially impossible without automated execution.

As an anecdote, in the fund I used to be employed at, we had a 10 minute "trading loop" where we would download new market data every 10 minutes and then execute trades based on that information in the same time frame. This was using an optimised Python script. In a larger fund it is often not the domain of the quant trader to optimise execution. Bear that in mind if you wish to be employed by a fund. Your programming skills will be as important, if not more so, than your statistics and econometrics talents!

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Another major issue which falls under the banner of execution is that of transaction cost minimisation. Note that the spread is NOT constant and is dependent upon the current liquidity i. Transaction costs can make the difference between an extremely profitable strategy with a good Sharpe ratio and an extremely unprofitable strategy with a terrible Sharpe ratio. It can be a challenge to correctly predict transaction costs from a backtest.

Entire teams of quants are dedicated to optimisation of execution in the larger funds, for these reasons. Consider the scenario where a fund needs to offload a substantial quantity of trades of which the reasons to do so are many and varied! By "dumping" so many shares onto the market, they will rapidly depress the price and may not obtain optimal execution. Hence algorithms which "drip feed" orders onto the market exist, although then the fund runs the risk of slippage. Further to that, other strategies "prey" on these necessities and can exploit the inefficiencies.

This is the domain of fund structure arbitrage. The final major issue for execution systems concerns divergence of strategy performance from backtested performance. This can happen for a number of reasons. We've already discussed look-ahead bias and optimisation bias in depth, when considering backtests. However, some strategies do not make it easy to test for these biases prior to deployment. This occurs in HFT most predominantly. There may be bugs in the execution system as well as the trading strategy itself that do not show up on a backtest but DO show up in live trading. The market may have been subject to a regime change subsequent to the deployment of your strategy.

New regulatory environments, changing investor sentiment and macroeconomic phenomena can all lead to divergences in how the market behaves and thus the profitability of your strategy. The final piece to the quantitative trading puzzle is the process of risk management. It includes technology risk, such as servers co-located at the exchange suddenly developing a hard disk malfunction. It includes brokerage risk, such as the broker becoming bankrupt not as crazy as it sounds, given the recent scare with MF Global!

In short it covers nearly everything that could possibly interfere with the trading implementation, of which there are many sources. Whole books are devoted to risk management for quantitative strategies so I wont't attempt to elucidate on all possible sources of risk here. Risk management also encompasses what is known as optimal capital allocation , which is a branch of portfolio theory.

This is the means by which capital is allocated to a set of different strategies and to the trades within those strategies.

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It is a complex area and relies on some non-trivial mathematics. The industry standard by which optimal capital allocation and leverage of the strategies are related is called the Kelly criterion. Since this is an introductory article, I won't dwell on its calculation.

The Kelly criterion makes some assumptions about the statistical nature of returns, which do not often hold true in financial markets, so traders are often conservative when it comes to the implementation. Another key component of risk management is in dealing with one's own psychological profile. There are many cognitive biases that can creep in to trading.

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Although this is admittedly less problematic with algorithmic trading if the strategy is left alone! A common bias is that of loss aversion where a losing position will not be closed out due to the pain of having to realise a loss. Similarly, profits can be taken too early because the fear of losing an already gained profit can be too great. Another common bias is known as recency bias.

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This manifests itself when traders put too much emphasis on recent events and not on the longer term. Then of course there are the classic pair of emotional biases - fear and greed. These can often lead to under- or over-leveraging, which can cause blow-up i. As can be seen, quantitative trading is an extremely complex, albeit very interesting, area of quantitative finance.

I have literally scratched the surface of the topic in this article and it is already getting rather long! Whole books and papers have been written about issues which I have only given a sentence or two towards.

For that reason, before applying for quantitative fund trading jobs, it is necessary to carry out a significant amount of groundwork study. At the very least you will need an extensive background in statistics and econometrics, with a lot of experience in implementation, via a programming language such as MATLAB, Python or R. If you are interested in trying to create your own algorithmic trading strategies, my first suggestion would be to get good at programming.

My preference is to build as much of the data grabber, strategy backtester and execution system by yourself as possible. If your own capital is on the line, wouldn't you sleep better at night knowing that you have fully tested your system and are aware of its pitfalls and particular issues? Outsourcing this to a vendor, while potentially saving time in the short term, could be extremely expensive in the long-term. Join the QSAlpha research platform that helps fill your strategy research pipeline, diversifies your portfolio and improves your risk-adjusted returns for increased profitability.

Join the Quantcademy membership portal that caters to the rapidly-growing retail quant trader community and learn how to increase your strategy profitability. How to find new trading strategy ideas and objectively assess them for your portfolio using a Python-based backtesting engine. How to implement advanced trading strategies using time series analysis, machine learning and Bayesian statistics with R and Python. Strategy Identification All quantitative trading processes begin with an initial period of research. Here is a small list of places to begin looking for strategy ideas: Social Science Research Network - www.

Strategy Backtesting The goal of backtesting is to provide evidence that the strategy identified via the above process is profitable when applied to both historical and out-of-sample data. Errors can sometimes be easy to identify, such as with a spike filter , which will pick out incorrect "spikes" in time series data and correct for them. At other times they can be very difficult to spot.

It is often necessary to have two or more providers and then check all of their data against each other. Survivorship bias is often a "feature" of free or cheap datasets.