S T R E E T OF W A L L S.
Types of Quantitative Hedge Fund Trading Strategies.
Quant Hedge Funds come in all shapes and sizes—from small firms with employees numbering in their teens, to international funds with a presence on three continents. A larger asset base does not necessarily correlate with a larger number of employees; instead, a Hedge Fund’s staff is likely to be a function of the number of strategies it employs. Quant Hedge Funds may focus on equities, fixed income or other asset classes, although rarely would a Quant Hedge Fund be involved in a long-only strategy of individual stock-picking on an unhedged basis. Many CTAs or “Commodity Trading Advisors” also would be considered Quant Hedge Funds, given their role in buying or selling futures contracts, options on futures, or retail off-exchange forex contracts (or counseling others to trade in these commodities).
The following table provides more detail about different types of investment strategies at Hedge Funds; it is important to note that both Quantitative and non-Quantitative versions of nearly all of these Hedge Fund investment styles can be built:
Relative Value Trading vs. Directional Trading.
Most Quantitative Hedge Fund trading/investment approaches fall into one of two categories: those that use Relative Value strategies, and those whose strategies would be characterized as Directional . Both strategies heavily utilize computer models and statistical software.
Relative Value strategies attempt to capitalize on predictable pricing relationships (often “mean-reverting” relationships) between multiple assets (for example, the relationship between short-dated US Treasury Bill yields vs. long-dated US Treasury Bond yields, or the relationship in the implied volatility in two different option contracts). Directional strategies, meanwhile, typically build on trend-following or other pattern-based paths suggestive of upward or downward momentum for a security or set of securities (for example, betting that long-dated US Treasury Bond yields will increase or that implied volatility will decline).
Relative Value Strategies.
Common examples of Relative Value strategies include placing relative bets (i. e., buying one asset and selling another) on assets whose prices are closely linked:
Government securities of two different countries Government securities of two different lengths to maturity Corporate vs. mortgage bond securities The differential in implied volatility between two derivatives Equity prices vs. bond prices for a corporate bond issuer Corporate bond yield spreads vs. Credit Default Swap (CDS) spreads.
The list of potential Relative Value strategies is very long; above are just a few examples. There are three very important and commonly used Relative Value strategies to be aware of, however:
Statistical Arbitrage: trading a mean-reverting trend of the values of similar baskets of assets based on historical trading relationships. One common form of Statistical Arbitrage, or “Stat Arb, ” trading, is known as Equity Market Neutral trading. In this strategy, two baskets of equities are chosen (one “long” basket and one “short” basket), with the goal that the relative weights of the two baskets leave the fund with zero net exposure to various risk factors (industry, geography, sector, etc.) Stat Arb also could involve the trading of an index against a similarly matched ETF, or an index versus a single company’s stock. Convertible Arbitrage: purchasing of convertible bonds issues by a company and simultaneously selling the same company’s common stock, with the idea being that should the stock of a given company decline, the profit from the short position will more than offset any loss on the convertible bond position, given the convertible bond’s value as a fixed-income instrument. В Similarly, in any upward price move of the common stock, the fund can profit from the conversion of its convertible bonds into stock, selling that stock at market value by an amount that exceeds any losses on its short position. Fixed Income Arbitrage: trading fixed income securities in developed bond markets to exploit perceived relative interest rate anomalies. Fixed Income Arbitrage positions can use government bonds, interest rate swaps, and interest rate futures. В One popular example of this style of trading in fixed income arbitrage is the “basis trade, ” in which one sells (buys) Treasury futures, and buys (sells) a corresponding amount of the potential deliverable bond. В В Here, one is taking a view on the difference between the spot price of a bond and the adjusted future’s contract price (futures price Г — conversion factor) and trading the pairs of assets accordingly.
Directional Strategies.
Directional trading strategies, meanwhile, typically build on trend-following or other pattern-based paths suggestive of upward or downward momentum for a security’ price.  Directional trading will often incorporate some aspect of Technical Analysis or “charting. ” This involves predicting the direction of prices through the study of past price and volume market data.  The “direction” being traded can be that of an asset itself (momentum in equity prices, for example, or the euro/U. S. dollar exchange rate) or a factor that directly affects the asset price itself (for example, implied volatility for options or interest rates for government bonds).
Technical trading may also comprise the use of moving averages, bands around the historical standard deviation of prices, support and resistance levels, and rates of change.   Typically, technical indicators would not constitute the sole basis for a Quantitative Hedge Fund’s investment strategy; Quant Hedge Funds employ many additional factors over and above historical price and volume information.  In other words, Quantitative Hedge Funds that employ Directional trading strategies generally have overall quantitative strategies that are much more sophisticated than general Technical Analysis.
This is not to suggest that day traders may not be able to profit from Technical Analysis—on the contrary, many momentum-based trading strategies can be profitable. Thus for the purposes of this training module, references to Quant Hedge Fund trading strategies will not include Technical Analysis-based strategies only.
Other Quantitative Strategies.
Other quantitative trading approaches that are not easily categorized as either Relative Value strategies or Directional strategies include:
High-Frequency Trading , where traders attempt to take advantage of pricing discrepancies among multiple platforms with many trades throughout the day Managed Volatility strategies use futures and forward contracts to focus on generating low, but stable, LIBOR-plus absolute returns, increasing or decreasing the number of contracts dynamically as the underlying volatilities of the stock, bond and other markets shift. В Managed Volatility Strategies have gained in popularity in recent years due to the recent instability of both stock and bond markets. ←What is a Quantitative Hedge Fund? Top Quantitative Hedge Funds→
Quantitative Strategies vs. The Idiocies of Human Nature: Using One to Help The Other.
I had some friends come by to pay me a visit not too long ago and to much of their surprise, found my discretionary systems alongside another that runs strictly on basic velocity and acceleration principles. It's my little bit of Newtonian impetus that runs alongside my day to day business model that many of you know fairly well by now.
A while back, I had a very common issue with trading. In the past now, it is something with which most forex traders struggle and is far from unusual: the old issue of price running like a freight train, and never seeming to stop, despite any catalyst telling it to do otherwise.
I will admit that my velocity and acceleration system did indeed help me get over a period of destroying endless amounts of time, waiting for price to simple get to a level before I even had any opportunity to take another trade. If anything, it served as a simple wake-up call as to how often certain behaviors occur. Since then, I have also identified several price patterns (continuation thrust, price symmetry principles) that speak strictly to the tone of expecting, at some point, stops getting blasted and price making a parabolic shove further.
Three Horrible Traits.
There are 3 main, horrible traits that forex traders tend to exhibit that take homegrown money and turn it into Wall Street money. They are:
1. Letting losers run but snapping at profits in seconds,
2. Have a terrible time adapting to a newly formed trends, always seeking to chase price. And when they do, it is usually about to turn on them once again,
3. Don't exclude the possibility of a trend to keep moving or failing, and enter prematurely, or not soon enough.
These three things alone are the cause of most destruction in this business, with which end results are mostly related to trade management, not entry.
My velocity and acceleration system is as simple as it gets. Velocity moves in one direction, acceleration in the other = price is slowing down. Velocity and acceleration both moving in the same direction = price is speeding up. Simple laws of physics. And it is nothing more than computerized confirmation of what I can already see on my bare chart.
Quantifying a strategy has several benefits, but can also have major pitfalls. The worst pitfall, of course, is the system's inability to adapt to varying market conditions. Big system or small system, this is a very common flaw that is commonly improperly implemented. Those that thrive with quantified strategies do a great deal of continual optimization. I have seen quant strategies fall apart that trade literally, hundreds of millions of dollars, as I have seen them fall apart for small retail accounts. More about this issue is discussed below.
The better systems I have seen exploit cycles and momentum at all costs. They forget about bottom or top catching, and just simply trade with relative trends. They don't care about nailing price “to the pip” or anything of that matter. They go in, get what they need, and exit the market.
Additionally, they do not, by any means, mirror the behavior of common “loser” pitfalls. They manage trades well and provide good, risk-adjusted returns. Trading systems that mimic any of the three traits listed above are not trading systems. They are money vacuums.
Optimization.
The common Metatrader platform uses a technique called brute force in order to find the best input parameters for a given system. It essentially tests all potential variables for a system and does this over and over again until the outcome of all possible combinations are found within a specified range, and over a specified amount of data.
If you want to know why many trading systems lose money, it is for one, major, overlooked flaw that human nature accepts but systems do not:
There is no statistical mean for the performance of the system in all market environments. Just because one set of parameters is able to achieve certain results on sample data does not (and rarely does) mean that it will continue to do so in the future. This is a flaw of human nature, NOT the system. The system is doing exactly what you told it to do: curve fit the results by forcing a very specific set of parameters upon it.
In other words, a system must be able to perform within a wide range of parameters on out-of-sample data and over an extended period of time. Gaining positive results within a wide range allows the user to derive the mean performance and conclude whether or not it is satisfactory in terms of his or her target outcome.
Additionally, and more frightening, the creator of the system will force common trader pitfalls on the code, such as exercising extremely poor risk / reward. The math doesn't lie, nor does common sense. Risk adjusted returns are simply the best way to go.
How this Relates to Discretionary Trading.
Now compare this to price pattern trading. We are told from the get-go that price patterns are going to occur and a certain result will follow afterwards. This, as I have explained many times in the past, is simply flawed logic. I made a short presentation a while back that explains why this is the case here: paracurve/2010/07/video-identifying-entry-points-within. html.
Attached to every price pattern is the conventional wisdom that price will display certain characteristics after it occurs, which may or may not necessarily be the case. We have seen this is recent weeks with the notorious “gravestone doji” on EUR/USD, which essentially “signified” to go short, only to have that bar swallowed up and price trade higher. Or how about the recent head and shoulders pattern on EURUSD? Wasn't price supposed to keep trading lower? By book definition: yes. In reality: NO.
This brings me to the ultimate point: management. As I mentioned earlier, trade management is perhaps the killer of all killers when it comes to anyone losing money in this business. One look at a retail order book will show the following:
Open positions shown as net. Blue represents negative P&L, Orange represents positive P&L.
Source: Oanda fxtrade. oanda/analysis/forex-order-book.
As you can see, losers are left to wane, but compared to the open winners, the situation is horrendously bleak. If looking at this chart is not a wake - up call then I don't know what is. This, perhaps, is one of the strongest suits of a quantified strategy. Human intervention is traditionally the result of less patience and more illogical action.
Moreover, we have no statistical proof that these price patterns pay off over the long haul. The people demonstrating them rarely ever mention potential signs of defeat, which in truth, happens on a very consistent basis. Bottom line, they don't know, and are writing in an irresponsible and uneducated manner.
Take, for instance, the descending triangle study by Tom Bulkowski, which I mentioned in the video link above. Descending triangles work in a manner contingent on the leg of the trend in which they are found. Tom's study showed that up breakouts on descending triangles do indeed have much better performance, and the logical explanation for this is that new trends develop when they are found at the base of a previous trend.
Tom has done a tremendous amount of automated testing on common price patterns, most of which busts conventional wisdom into pieces. You can find them all here: thepatternsite/rank. html.
In regards to this example, and as I mentioned in the video linked above, trade management is key. A trader might short into a descending triangle, and another play the upside break long. Both might have positive results, depending on the desired target. The better risk / reward, however, comes with the upside breakout. So it all simply depends.
A properly devised trading system is going to see where the better returns are going to lie. These systems rarely trade in a manner that a discretionary forex trader would, simply due to the common human element that will likely never go away.
When it comes to tackling the pitfalls above, step 1 is simply seeing human nature for what it really is. Plain and simple, it makes us do simply illogical and stupid things when it comes to trade management. By shifting your focus on not resolving these “issues” or seeing them as a problem, but rather how to exploit them for opportunity, you are better able to start working in a more constructive manner.
As far as logistics are concerned, there are a number of different things that can help you out. I am a huge proponent of common sense in no way does anything listed above advise against it. Quant strategies can be helpful in simply “turning on the light bulb” by observing positive traits typicaly overlooked by discretionary traders. There is a great deal of information posted on this site by now which speaks to the tone of following simple, logical trading behavior and sifting out the noise, which fixed trading systems regularly do.
Again, I stress the importance of trade management and how you are navigating your daily routine. Ultimately, when it comes to trade entry, people will follow book definition 100%. When it comes to trade management, people avoid the books at all costs . Flip this scenario on its head and you have yourself a profitable outcome.
Related Posts.
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Good post, that chart in particular was enlightening (though maybe not surprising…).
I agree 100% about entries – entries are the least important element of a trading strategy IMHO.
“Those that thrive with quantified strategies do a great deal of.
continual optimization.” I disagree. If a quant strategy needs continual optimization, it’s garbage and likely the result of faulty creation (forced curve). A thriving strategy requires little to no maintenance or optimization (over years).
“I have seen quant strategies fall apart…” So have I–when I was developing them in 2004 without understanding the rules that govern successful strategy creation. Regardless of the amount traded (although there may be capacity issues), a strategy that “falls apart” was likely bad to begin with. All strategies should expect some short-term deterioration (over the course of several weeks or even months). However, a good strategy will, obviously, bounce back from such events (while we may fret or even write it off as garbage).
My strategies have outperformed the S&P 500 for over five years now. They require little to no maintenance or optimization. They’re easy to trade and liquid.
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Quant Strategies - Are They For You?
Quantitative investment strategies have evolved into very complex tools with the advent of modern computers, but the strategies' roots go back over 70 years. They are typically run by highly educated teams and use proprietary models to increase their ability to beat the market. There are even off-the-shelf programs that are plug-and-play for those seeking simplicity. Quant models always work well when back tested, but their actual applications and success rate are debatable. While they seem to work well in bull markets, when markets go haywire, quant strategies are subjected to the same risks as any other strategy.
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One of the founding fathers of the study of quantitative theory applied to finance was Robert Merton. You can only imagine how difficult and time-consuming the process was before the use of computers. Other theories in finance also evolved from some of the first quantitative studies, including the basis of portfolio diversification based on modern portfolio theory. The use of both quantitative finance and calculus led to many other common tools including one of the most famous, the Black-Scholes option pricing formula, which not only helps investors price options and develop strategies, but helps keep the markets in check with liquidity.
When applied directly to portfolio management, the goal is like any other investment strategy: to add value, alpha or excess returns. Quants, as the developers are called, compose complex mathematical models to detect investment opportunities. There are as many models out there as quants who develop them, and all claim to be the best. One of a quant investment strategy's best-selling points is that the model, and ultimately the computer, makes the actual buy/sell decision, not a human. This tends to remove any emotional response that a person may experience when buying or selling investments.
Quant strategies are now accepted in the investment community and run by mutual funds, hedge funds and institutional investors. They typically go by the name alpha generators, or alpha gens.
Just like in "The Wizard of Oz," someone is behind the curtain driving the process. As with any model, it's only as good as the human who develops the program. While there is no specific requirement for becoming a quant, most firms running quant models combine the skills of investment analysts, statisticians and the programmers who code the process into the computers. Due to the complex nature of the mathematical and statistical models, it's common to see credentials like graduate degrees and doctorates in finance, economics, math and engineering.
Historically, these team members worked in the back offices, but as quant models became more commonplace, the back office is moving to the front office.
Benefits of Quant Strategies.
While the overall success rate is debatable, the reason some quant strategies work is that they are based on discipline. If the model is right, the discipline keeps the strategy working with lightning-speed computers to exploit inefficiencies in the markets based on quantitative data. The models themselves can be based on as little as a few ratios like P/E, debt to equity and earnings growth, or use thousands of inputs working together at the same time.
Successful strategies can pick up on trends in their early stages as the computers constantly run scenarios to locate inefficiencies before others do. The models are capable of analyzing a very large group of investments simultaneously, where the traditional analyst may be looking at only a few at a time. The screening process can rate the universe by grade levels like 1-5 or A-F depending on the model. This makes the actual trading process very straightforward by investing in the highly rated investments and selling the low-rated ones.
Quant models also open up variations of strategies like long, short and long/short. Successful quant funds keep a keen eye on risk control due to the nature of their models. Most strategies start with a universe or benchmark and use sector and industry weightings in their models. This allows the funds to control the diversification to a certain extent without compromising the model itself. Quant funds typically run on a lower cost basis because they don't need as many traditional analysts and portfolio managers to run them.
Disadvantages of Quant Strategies.
There are reasons why so many investors do not fully embrace the concept of letting a black box run their investments. For all the successful quant funds out there, just as many seem to be unsuccessful. Unfortunately for the quants' reputation, when they fail, they fail big time.
Long-Term Capital Management was one of the most famous quant hedge funds, as it was run by some of the most respected academic leaders and two Nobel Memorial Prize-winning economists Myron S. Scholes and Robert C. Merton. During the 1990s, their team generated above-average returns and attracted capital from all types of investors. They were famous for not only exploiting inefficiencies, but using easy access to capital to create enormous leveraged bets on market directions.
The disciplined nature of their strategy actually created the weakness that led to their collapse. Long-Term Capital Management was liquidated and dissolved in early 2000. Its models did not include the possibility that the Russian government could default on some of its own debt. This one event triggered events and a chain reaction magnified by leverage-created havoc. LTCM was so heavily involved with other investment operations that its collapse affected the world markets, triggering dramatic events. In the long run, the Federal Reserve stepped in to help, and other banks and investment funds supported LTCM to prevent any further damage. This is one of the reasons quant funds can fail, as they are based on historical events that may not include future events.
While a strong quant team will be constantly adding new aspects to the models to predict future events, it's impossible to predict the future every time. Quant funds can also become overwhelmed when the economy and markets are experiencing greater-than-average volatility. The buy and sell signals can come so quickly that the high turnover can create high commissions and taxable events. Quant funds can also pose a danger when they are marketed as bear-proof or are based on short strategies. Predicting downturns, using derivatives and combining leverage can be dangerous. One wrong turn can lead to implosions, which often make the news.
Quantitative investment strategies have evolved from back office black boxes to mainstream investment tools. They are designed to utilize the best minds in the business and the fastest computers to both exploit inefficiencies and use leverage to make market bets. They can be very successful if the models have included all the right inputs and are nimble enough to predict abnormal market events. On the flip side, while quant funds are rigorously back tested until they work, their weakness is that they rely on historical data for their success. While quant-style investing has its place in the market, it's important to be aware of its shortcomings and risks. To be consistent with diversification strategies, it's a good idea to treat quant strategies as an investing style and combine it with traditional strategies to achieve proper diversification.
S T R E E T OF W A L L S.
Types of Quantitative Hedge Fund Trading Strategies.
Quant Hedge Funds come in all shapes and sizes—from small firms with employees numbering in their teens, to international funds with a presence on three continents. A larger asset base does not necessarily correlate with a larger number of employees; instead, a Hedge Fund’s staff is likely to be a function of the number of strategies it employs. Quant Hedge Funds may focus on equities, fixed income or other asset classes, although rarely would a Quant Hedge Fund be involved in a long-only strategy of individual stock-picking on an unhedged basis. Many CTAs or “Commodity Trading Advisors” also would be considered Quant Hedge Funds, given their role in buying or selling futures contracts, options on futures, or retail off-exchange forex contracts (or counseling others to trade in these commodities).
The following table provides more detail about different types of investment strategies at Hedge Funds; it is important to note that both Quantitative and non-Quantitative versions of nearly all of these Hedge Fund investment styles can be built:
Relative Value Trading vs. Directional Trading.
Most Quantitative Hedge Fund trading/investment approaches fall into one of two categories: those that use Relative Value strategies, and those whose strategies would be characterized as Directional . Both strategies heavily utilize computer models and statistical software.
Relative Value strategies attempt to capitalize on predictable pricing relationships (often “mean-reverting” relationships) between multiple assets (for example, the relationship between short-dated US Treasury Bill yields vs. long-dated US Treasury Bond yields, or the relationship in the implied volatility in two different option contracts). Directional strategies, meanwhile, typically build on trend-following or other pattern-based paths suggestive of upward or downward momentum for a security or set of securities (for example, betting that long-dated US Treasury Bond yields will increase or that implied volatility will decline).
Relative Value Strategies.
Common examples of Relative Value strategies include placing relative bets (i. e., buying one asset and selling another) on assets whose prices are closely linked:
Government securities of two different countries Government securities of two different lengths to maturity Corporate vs. mortgage bond securities The differential in implied volatility between two derivatives Equity prices vs. bond prices for a corporate bond issuer Corporate bond yield spreads vs. Credit Default Swap (CDS) spreads.
The list of potential Relative Value strategies is very long; above are just a few examples. There are three very important and commonly used Relative Value strategies to be aware of, however:
Statistical Arbitrage: trading a mean-reverting trend of the values of similar baskets of assets based on historical trading relationships. One common form of Statistical Arbitrage, or “Stat Arb, ” trading, is known as Equity Market Neutral trading. In this strategy, two baskets of equities are chosen (one “long” basket and one “short” basket), with the goal that the relative weights of the two baskets leave the fund with zero net exposure to various risk factors (industry, geography, sector, etc.) Stat Arb also could involve the trading of an index against a similarly matched ETF, or an index versus a single company’s stock. Convertible Arbitrage: purchasing of convertible bonds issues by a company and simultaneously selling the same company’s common stock, with the idea being that should the stock of a given company decline, the profit from the short position will more than offset any loss on the convertible bond position, given the convertible bond’s value as a fixed-income instrument. В Similarly, in any upward price move of the common stock, the fund can profit from the conversion of its convertible bonds into stock, selling that stock at market value by an amount that exceeds any losses on its short position. Fixed Income Arbitrage: trading fixed income securities in developed bond markets to exploit perceived relative interest rate anomalies. Fixed Income Arbitrage positions can use government bonds, interest rate swaps, and interest rate futures. В One popular example of this style of trading in fixed income arbitrage is the “basis trade, ” in which one sells (buys) Treasury futures, and buys (sells) a corresponding amount of the potential deliverable bond. В В Here, one is taking a view on the difference between the spot price of a bond and the adjusted future’s contract price (futures price Г — conversion factor) and trading the pairs of assets accordingly.
Directional Strategies.
Directional trading strategies, meanwhile, typically build on trend-following or other pattern-based paths suggestive of upward or downward momentum for a security’ price.  Directional trading will often incorporate some aspect of Technical Analysis or “charting. ” This involves predicting the direction of prices through the study of past price and volume market data.  The “direction” being traded can be that of an asset itself (momentum in equity prices, for example, or the euro/U. S. dollar exchange rate) or a factor that directly affects the asset price itself (for example, implied volatility for options or interest rates for government bonds).
Technical trading may also comprise the use of moving averages, bands around the historical standard deviation of prices, support and resistance levels, and rates of change.   Typically, technical indicators would not constitute the sole basis for a Quantitative Hedge Fund’s investment strategy; Quant Hedge Funds employ many additional factors over and above historical price and volume information.  In other words, Quantitative Hedge Funds that employ Directional trading strategies generally have overall quantitative strategies that are much more sophisticated than general Technical Analysis.
This is not to suggest that day traders may not be able to profit from Technical Analysis—on the contrary, many momentum-based trading strategies can be profitable. Thus for the purposes of this training module, references to Quant Hedge Fund trading strategies will not include Technical Analysis-based strategies only.
Other Quantitative Strategies.
Other quantitative trading approaches that are not easily categorized as either Relative Value strategies or Directional strategies include:
High-Frequency Trading , where traders attempt to take advantage of pricing discrepancies among multiple platforms with many trades throughout the day Managed Volatility strategies use futures and forward contracts to focus on generating low, but stable, LIBOR-plus absolute returns, increasing or decreasing the number of contracts dynamically as the underlying volatilities of the stock, bond and other markets shift. В Managed Volatility Strategies have gained in popularity in recent years due to the recent instability of both stock and bond markets. ←What is a Quantitative Hedge Fund? Top Quantitative Hedge Funds→
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