Victor when was Quant Hedge formed and what type of investment strategies was it designed to undertake?
Quant Hedge was initiated 3 years ago from a desire to share my investment strategies with other partners. I had been running systematic strategies for myself since 2005, but wanted to trade in a more professional way so started looking at fund management companies. At this time the Hedge fund industry was in pretty good condition and many HF and trading managers were attending Investment Forums and Seeding conferences all over the world. I first had the idea of Quant Hedge at GAIM Investment Forum in Monaco in 2007. It was an incredible ambiance of High level conference people and investment standards, melded with Hedge Fund and Money managers, family office firms, Institutional and central bankers; dealing with all kind of investment strategies in a relaxed way with some of the most brilliant traders in the world. I ran out of my 100 box business card on the first day and there were so many people that there was not enough room at the closing party session. During this conference, I met many Systematic fund managers from all over the world and although it looked quite difficult to raise money and manage regulatory purposes, I was willing to take the challenge of starting my electronic trading company.
Who are the key people involved in the firm and what are their main day to day responsibilities?
When I came back from Monaco, I talked to Christian and Frederic about the idea of a prop-shoptrading company and they were enthusiastic and willing to help. Frederic was a friend from 20 years and Christian is director of Flavon Informatique;offering consultancy as a financial market architect. I now manage the companies trading and business relationships. As sponsor of the project since early 2007, Frederic has no day to day involvement, he helps us in defining new strategies ideas and innovative trading technologies. Christian is our outsourcing partner giving operational information and technical support. I manage the strategies and computational models. We are working with internship guys from famous top level engineering schools in Paris (ECE, Epita, Centrale, Polytechnique)
The initial development program for the fund chose Spot FX as a suitable asset class to use. Why was that?
FX spot is one of the best markets to start trading. It is very liquid, has currency pair correlations, is impacted by economic data and is open 5/7 24h. This is very important for a systematic trader who trades over both short and long periods. Being able to open and close trades all day are comfortable parameters for your algos. You will see very low market impact and slicing the execution is also possible and if you are using high leverage risk management becomes a factor too. Market data and real time prices are available for free with many trading strategies, as well as information and trading advice. Most retail brokers are offering algo trading with FX, so it was a good start and we could change provider if we were not satisfied with the service.
What investment objectives did you decide upon?
Managing the funds was not easy going at the start. We had to decide upon what return we would expect from the strategies. We were aiming a +60% with a first set of algos, using only our own money as investment. Today’s main objective is stability in the portfolio, improvement of the algos, risk management and reinforced due diligence processes. If things go well, we will communicate with and target, private investors, venture capitalists and seeders for openings at the end of 2010 or 2011. We would like to raise several millions over the next two years to start real fund and regulatory management. We are now really open to business relationships, internships and investors to seed and grow the company.
What trading platform do you use and what factors influenced your choice of broker?
The most relevant platform we have used since now is Metatrader. We have had a close look at other software such as Ninjatrader, Trade Station, CQG., but we selected MT4 because it is well widely supported, with precise documentation and active user forum. For broker we were satisfied with Oanda and Interactive brokers but API and program trading was not free. So we chose Alpari, which has supported MT4 for a long time now and has a liquidity partnership with Currenex. We also considered effective tight spreads and execution. We have also recently been looking at electronic trading prime brokerage and clearing services and at professional trading platform X Trader among others, which are quite different from retail to professional brokers, because they are mostly dedicated to institutional and large funds.
How did you go about building your trading IT infrastructure and what steps did you take to improve the operational management of the business?
We have virtualized servers in raid5 working 24/7 plus 15 backtest machines located at each others homes and at Christian Flavon’s offices. We have one prod running the portfolio, several pre-prod running research portfolios and local backtest desktops and laptops. We have invested in powerful i7 multi-core backtest dedicated servers. Improvements were mainly about BCP (Business Continuity) such as accurate RfQ processing in the code for trade execution, redundancy of connectivity, real time performance and risk management reporting.
The firms trading performance is based around fully systematic momentum and trend following strategies. What research did you carry out to develop and validate the most appropriate algorithms for this?
For our strategies and research we focus on market observation. Whether it is quantitative or technical analysis or understanding market behaviour. Trend following applies to medium and long term investment strategies. You just have to be sure of the direction of the trade to avoid or benefit carry to impact your investment. In this case we worked with classical backtests and we add some specific rules such as reinforcement or external condition checking. For momentum and arbitrage, we based our research on specific periods and intraday market conditions. We set many more constraints and to avoid drawdown our algos have been passed through a cycle of parameter optimisation across several time horizons and specific events to be very reactive to market behaviour. Our research is based more on how to lose less than how to gain more.
What back-testing methodologies do you employ to confirm that a strategy is relevant to your long term trading goals and performance criteria?
We have several testing phases. The first is operational validation; it is most important to me to ensure the algos operate properly regarding the strategy definition. You don’t want your algo to go wild and turn to “Rogue Algos” as it happened at GS Global Alpha fund in summer 2007. After this validation we have short term backtest phases to approximate parameter ranges and then we run several long term backtests to corroborate best short term and long term parameters. We also expect to run research on prediction price methodologies using quantitative models and differential approximation. There are always new ideas and models we would like to develop and improve; it looks like an endless work in progress.
What do you consider to be the key strengths and weaknesses of your investment strategy?
Our strength is our capability to manage the relationship between trading opportunities and technology delivery, designing decisional algorithms and putting intelligence into autonomous agents. Long term strategies have low deviation in performance but engage more capital. Short term and intraday are more statistical bets on the markets. It can deliver more return but they are more volatile and need better parameters definition. The good point is that diversification will bring stronger results in term of risk/return management. Another point is that our strategies are very scalable and adaptive. The main weakness is the strategy correlation, we don’t want all strategies to fail on the same signals or trend, so we designed a portfolio analyser to improve cross-algo trades, but like every fund we are exposed to extreme and unpredictable events impacting today’s business and operations.
What key risk management frameworks have you put in place and how do you adjust your risk capital allocation?
Technology is the first element you have to fix, this is the main risk to assess in this activity including connectivity loss, inefficient RfQ, data snooping, ineffective parameters. Market risk is anticipated based on the historical maximum losses of each algo or the stop loss range. We set a daily maximal accepted deviation and dispatch it between the strategies so we know the amount of money we may lose in one day. Capital allocation is done based on risk/return Sharpe ratios of algos and their expected drawdowns. We have a global reporting framework so that we can track performance on a daily, weekly and monthly basis.
How diversified do you consider your investment approach to be?
We are not as diversified as we would like. As we are just starting the company, we focus our investment and expertise on the FX market and we invest only a small amount of cash and research in indexes, futures and other asset classes. Today we have single market diversification strategies and time horizon diversification. As soon as we can pool enough cash from investors and venture capitalists, we will develop new investments programs to build specific algos for other markets such as arbitrage or option trading.
Your algorithms were launched live in May last year and delivered high returns until the summer when they experienced a drop. Why did the strategies need re-adjusting?
We had built a portfolio of algos based on my experience and new backtest developed to fit our +60% expectations. The portfolio was running in demo from March 2009 and we were quite happy to see it was also going live very well until July 2009. During the summer 2009 some new patterns appeared due to less liquidity and late events and news releases. This was a hard time and we reassessed the algos to better fit to these new market conditions. We believed our strategies were great and were proved right because the algos returned to positive performance at the end of the summer. Faith has a role to play in algo-trading, you have to believe in the algo decision you have designed and sometimes accept to lose money to benefit future superior performance, knowing markets always return to fundamentals. If you were to manage the algos by hand, you may expose the portfolio to wrong decisions with confused trading rules.
The firm has now had over a year of live trading conditions. Has your performance met all of your expectations?
We saw changes in the performance delivery. Spreads are lower since last year because many new bank players came to FX leverage market to benefit volatility opportunities. Where we assumed 15 to 20% real performance less than backtested in the past, we are more up to 10 or 15% less in real performance today. The Market has become cheaper with more liquidity and fragmentation. All this has increased execution quality. We only got 30% performance when we expected 60%. But this is good for us because we have proved we were able to manage risk and investment in fast changing conditions. We are now focusing much more on risk deviation and we are working on new portfolio models and reporting systems to improve the performance.
Looking ahead, what research and development will you be undertaking to explore new investment opportunities and what new strategies are you considering employing?
We are improving our strategies with more dedicated functions such as reinforcements, hedging, market condition testing, execution slicing. We wish to deploy several versions of a carry trade algo on which we are working on but need to be qualified. In terms of markets, we will look at futures, indexes, commodities and if we have enough research capabilities the options markets. But the more complex the traded product is, the harder it is for the backtest result to be implemented in a profitable algo. All our research has been related to electronic trading with very liquid assets. This was to build cash and performance management skills within the company. I wanted this in order to be able to manage other less liquid asset classes and specific portfolios with a running bottom line investment offering easy in and out cash entry.
You share your work with a passion for Contemporary Art. Do you see any similarities and synergies between currency investment and cultural asset management?
Cultural assets are at the opposite of OTC FX e-trading. If you want to invest in fine arts you will consider lots of external factors for the pricing and price data will only be a part of it. It looks more like credit derivative rating than systematic investment. That is the challenge for me to understand this illiquid asset pricing where human and business relationship is primary in the negotiation. In FX trading the real asset is the “exchange rate” not the currency in itself. In cultural assets you will refer to basic markets references such as liquidity, market demand, volatility but also to subjective valuation such as beauty and taste of the object, fame and preservation state. The interest is that by collecting these assets with intelligence you will increase unit value as part of a relevant collection, the quality of the portfolio reinforcing the price of each asset. This is also the opportunity to meet incredible people, atypical investors and artists who are much more related to entrepreneurship than one may think. Money and culture is true wealth.