How is a mechanical system designed and tested without building a physical model?

I am new to mechanical engineering, although I have a scientific background (postgrad in Mathematics), and I (mostly) code for a living. I have an idea about creating a mechanical device; I envisage it will entail gears, linkages and actuators. I have a rough idea where things will fit but I would like to be able to test and tweak the design in software before building the actual device. As a point of clarification, when I say "test," I mean view via animation, for example, whether two items will collide when in motion, or if there is sufficient leeway between them as they move past each other. This allows me to do the design and simulation testing of the parts, before finalising the design and then building the physical system from the design. The stages are: Build the 3D design in software Run simulation to see if it "works," if not fix design and iterate Build physical system from design that "works" I have figured out that the system consists of three subsystems working together. So, I would like to design and test each sub-component before integrating them into the complete system. My question then is this: Is this how design is done in the real world? What are the pros and cons of the scheme I have planned? I am intending to use FreeCAD to do the design and testing.

In the video you've linked to, the spheres are seen on both the leading and trailing edges of the propeller: I expect they are intentional - there are a number of ways to attach a propeller without having to disturb that surface, or using flush caps. Cavitation is caused by a drop in pressure. This would be seen on the trailing edges of the propeller, and is worse on the outer edge of the propeller which is moving faster through water than the inner area. I doubt these spheres have any effect on cavitation at all. It is either a...

Answer: Yes , this is exactly how it is done in the real world. What you have described is what I do in my job to check systems in CAD. Since you indicated you would like me to step through my design process, I have detailed it below. Note that most of this does not involve CAD. CAD is invaluable, but only if you are prepared to get out the pencil and paper first. Also note that this is just my design process, it is by no means the only way to go about things. Preparation When I begin any...

Nothing is rigid, the raceways and the ball in a ball-bearing are no exception. The contact area deflects and accommodates the ball in a small contact surface, not a point. Also, the balls take the load in groups. The digrams are from SKF ball-bearings. '

As others stated before, induction loops are the primary - most reliable method: the coils (usually just several loops of wire) embedded in the road; fed given frequency from a generator, in presence of metal the frequency of the LC circuit changes and the sensor circuitry detects the change of frequency, producing a presence signal. In some cases these may fail to detect bicycles, but they are by far most common as they aren't affected by weather (or more precisely, the detection circuit tunes in to slow changes of frequency caused by weather) and are immune to accidental false positives....


I am making a device for measurements. I would like to measure the distance within accuracy of 1 mm. Range could be 2 cm to 15 cm . I looked at Proximity Sensors but the readings displayed by these sensors are not steady. I wish to measure the thickness of the plate (carbon steel). The two sensors will be mounted on a structure. The sensors will give me the distance of the surface from the sensor. Then I will calculate the thickness of the plate. What are possible types of low cost sensors I can use?

mechanical-engineering measurements sensors tolerance

Answering the question: What are possible types of low cost sensors I can use? There are several types of sensors that can provide millimeter level accuracy. "Low cost" is a very relative term, so you'll need to do some shopping around based on your specific budget. Optical sensors- Included here are those of the type you listed, though it's a very cheap sensor meant more to provide a "yeah something is in front of me, about yay-far-away". There are also laser sensor systems which can provide millimeter level accuracy. Ultrasonic Sensors- Most ultrasonic sensors have relatively low accuracy; in the...

My first thought is that it might be intended to be a wing nut driver of some sort, but those are usually hollow cylinders with slots for the wings. Ah ... sure enough, it's described as such in this Ebay ad :

Water meets the low compressibility requirement, but there are many other considerations in the design of a hydraulic system: Boiling point/vapor pressure: If the system warms up during operation, the fluid may boil, which results in high compressibility and thus decreased effectiveness of the hydraulic system. Hydraulic fluid has a higher boiling point than water to help combat this. Related to this is the concept of vapor pressure. Hydraulic systems often involve small orifices, which can cause cavitation (localized boiling). This cavitation has the same effects as boiling and can cause pitting damage to the components near the cavitated region....

In Ye olden days DC generators were brushed commutated devices. They had a one or more stator windings and an armature winding. Field wound DC generators as well as motors were commonly connected in one of three methods: Series, Shunt and Compound. Without getting into details, each had its own set of strengths and weaknesses. But you only have to remember these two things: the voltage of a DC motor is dependent on its input shaft speed. Current is a function of torque. More voltage means more RPM's and more amps means more newton-meters (or foot-pounds). So with all that,...


How much clearance does a car need when turning a corner?

I am contemplating buying a new car. However, the approach to the underground garage in my apartment has a 90 degree frustrating turn. Given the dimensions of the approach and car, what is the max turn circle for the car to fit the garage and turn? given Ackerman steering and the overhanging front part of the car I believe you can use Pythagoreas' theorem to get R min and R max. delta R should be less than the shortest path in the pathway, ie 2.5m. unfortunately the result does not seem plausible. feedback would be greatly appreciated.

Please note : I'm not a building designer by trade, but I have had to investigate related questions for other reasons. I'll let you in on a dirty secret about sanitation lines within buildings - The biggest concern is not about how fast things are flying, it's about maintaining air pressure and providing adequate ventilation. This guide presents a bit of a historical background to tall building design with respect to sanitation system design. This presentation provides some more recent perspective and goes into some of the research that is refining current building standards. The traditional viewpoint is that waste...

Those are counterweights . They work exactly the same as those lead counterbalance weights on the wheels of your automobile. If they left those out, then those connecting rods and bearings would create an out-of-balance condition, and the wheels would vibrate at higher speeds. That could very well damage the wheels. But as a couple of others have nicely pointed out, the violent shaking could derail the train .

It depends on how steep the hill is. On a slight hill, the energy added by gravity is still not enough to overcome rolling friction and air resistance, so the car still needs power to maintain speed. On a steeper hill, the two may balance out, so no power is used, and no power is generated. On a hill that's steep enough to require braking to control the speed, the car recovers energy. It's called regenerative braking. If the car is going too fast, applying the brakes turns the motor into a generator and charges the battery.

To slightly generalize I'll reform the question slightly. A ridged 2-D body (car) has a line $l$ that moves with it. The car can be linearly transformed as long as the instantaneous center of rotation lies along $l$ at least distance $R$ away from a point $c$ that also moves with the car. In this case point $c$ lies in the center of the rear axle and $l$ lies on the rear axle. Now imagine the car's domain is limited to a quarter plane with edges $A$ and $B$. It initially is placed against $A$, far from $B$ with $l$...


How do you ensure physical network interfaces always get the same interface name across reboots on an embedded Linux system?

For an embedded Linux system, if I have two or more network interfaces, how do I ensure that they always get the same interface names every boot In other words, I want, for example, eth0 to always map to one physical Ethernet port, eth1 to the next, etc. My Linux "distribution" is home-grown, and I use devtmpfs for populating /dev. I use busybox for init (and most everything else), along with custom init scripts for system startup and shutdown. I do not need hotplug facilities of mdev or udev -- I'm referring to "fixed" Ethernet ports.

It is more efficient to transmit DC using about the same infrastructure. This is because of several effects: Skin effect experienced with AC. There is no skin effect with DC. Higher voltage allowed with DC for the same transmission lines. The lines have to withstand the peak voltage. With AC, that is 1.4 times higher than the RMS. With DC, the RMS and peak voltages are the same. However, the power transmitted is the current times the RMS, not peak, voltage. No radiation loss with DC. Long transmission lines act as antennas and do radiate some power. That can only...

This works for me with Linux 3.9.0 on an x86_64 architecture. #!/bin/sh # This assumes the interfaces come up with default names of eth*. # The interface names may not be correct at this point, however. # This is just a way to get the PCI addresses of all the active # interfaces. PCIADDRLIST= for dir in /sys/class/net/eth* ; do [ -e $dir/device ] && { PCIADDRLIST="`readlink -f $dir/device` ${PCIADDRLIST}" } done # Now assign the interface names from an ordered list that maps # to the PCI addresses of each interface. # IFNAMES could come from some config file....

When a reactor is shut down the core produces much less heat, but they do still produce heat through a mechanism known as decay heat . The fact that the core is producing less heat means that the coolant temperature is going to drop, but how far that temperature drops depends on the decay heat generation rate. This in turn is based on operating history, or the power at which the plant was operating before shutdown. This can be large for commercial plants, because they typically operate at or very near capacity and the power companies bring coal or natural...

I will expand on DKNguyen answer, because to my knowledge also the two reasons are: reduce contact/bearing stresses (having a significant effect on thin finishes live galvanisation) change the joint tightening characteristics (see joint diagram). reduce contact stresses on surfaces. The basic idea is that since contact stress is defined as: $$\sigma = \frac{F}{A}$$ Obviously the larger the area the less the stress. Also, like Nuclear Hoagie pointed out the area changes with a square law of the diameter. UPDATE 2 - Calculation for M6 bolt (thanks to BenC) *The question gathered enough interest for me to carry out a...


What are left justified and right justified ADC results?

The TI MSP430F20XX series has a 12-bit internal ADC output, which is right-justified. What is the difference between a left-justified output and a right-justified output? What are their pros and cons?

If you consider only the static forces then indeed the thickness might seem over-engineered. However, engine blocks are not statically loaded. They operate in the range of a few hundred to a few thousand rpm (Revolution Per Minute), so there are dynamic considerations here. Fatigue When materials are subjected to cyclic loading they exhibit a reduction in the allowable stresses. See below for an example: In general, BCC (body-centered cubic) materials (like steel) show a marked drop in strength (close to 50% or more depending on the steel). For these material there is stress (called the endurance limit), for which...

On this processor, the register that holds the conversion result is 16 bits wide. A right-justified result means that bits [( N -1):0] (where N is the number of bits of precision) of the register contain the ADC value and the most-significant bits of the register are set to zero. A left-justified result means that bits [15:(16- N )] of the register hold the result, and bits [(15- N ):0] are set to zero. For example, if your actual conversion result is 0x123, it would be read as 0x0123 if the register was right-justified, and as 0x1230 if it was...

At the beginning of my career (1979) as a design engineer at HP, the mechanical engineer created the part design and then rendered it in pencil on paper, and then transferred it onto sheets of vellum paper held onto a huge flat tilting table called a drafting board which had a precision sliding arm on it with which parallel and right angle lines could be drawn anywhere on the sheet. Clever graphical rules were applied to the resulting drawings which permitted auxiliary views of the part to be generated so it could be viewed from any desired angle, as an...

The loops are known as expansion loops. They need to be placed in pipelines to enable the pipelines to contend with thermal expansion and contraction and other forces that can affect the pipeline. They are typically placed in gas pipelines, irrespective of when the gas is hot or cold - natural gas or steam. The following quote is from Pipeline Design . It's near the end of the page under Pipe Expansion and Supports Steel piping systems are subject to movement because of thermal expansion/contraction and mechanical forces. Piping systems subjected to temperature changes greater than 50°F or temperature changes...


From an engineering standpoint, what is the purpose of the indent in a coffee lid?

I've see the same lids every day but I never really thought about their construction. There is an indent in a "Solo Travel Lid" for coffee cups that is just above the hole that you drink the coffee from. You can see the crescent shaped indent in the image. What is it for? Does it somehow increase fluid flow? Is it just a good spot for your upper lip? If so, why is there not a matching one for the nose? I tried googling this but I got no answer.

If the receiver does not detect the sub-carrier for the "colour burst" signal which is transmitted during the horizontal blanking period the receiver switches on the "colour-killer" circuit so the set reverts to black and white mode. The colour-burst signal - 8 to 10 cycles of 3.85 MHz - is unlikely to be generated by random noise. Figure 1. The colorburst signal is transmitted on the "back porch" between the horizontal blanking pulse and the start of that line's luminance signal. The colorburst signal is used to synchronise the QAM (quadrature amplitude modulation) oscillator which can hold its frequency accurately...

The current limit specification for a wire is limited by the heat that current will produce, and how much heat the wire can dissipate before getting too hot. "Too hot" depends on the circumstances. You will see higher current ratings for the same type of wire in chassis wiring applications than the electrical code allows for home installations, for example. This is mostly due to how hot too hot is. The ultimate limit for extreme applications is that the conductor not melt. Temperatures anywhere near that would be unsafe running along wooden supports inside a wall in a house. As...

I'm going to set aside the potential legal liability aspects of your question. In part because you didn't ask about them, but also because liability will vary based upon jurisdiction. Obviously, consult an attorney familiar with the relevant law. Ethically, I think you've done everything that you're obligated to do. You have contacted the customer and notified them of an unsafe situation or configuration of the product. And you have (strongly) advised them that they need to discontinue using the older product in the unsafe configuration. If the customer remains insistent about using the product in an unsafe manner, you...

From the "Solo Traveler" patent : A more specific object of the present invention is to provide a lid having an opening formed therethrough to enable drinking, and having a recess formed in the lid adjacent the opening to accommodate the upper lip of one drinking from the cup This is also illustrated in the patent diagrams. I came across this interesting article on the history, use, design, and styles of disposable lids. The Solo Traveler is prominently featured for its excellent aesthetics and design . The article reiterates that: the Solo Traveler lid was designed to accommodate the nose...


What is this Y-shaped screwdriver bit, and what is its purpose?

I found this strange looking bit in a bit set of uncommon bits, including Security Torx, hexalobular, tri-wing, spanner head, 12-point flange, Torq-set and others. It's the only one that I could not identify, what is it for?

This is to make sure they know what the foundation is made of. For all they knew there may have been an old tunnel underneath that would have collapsed when the new building is put on top. London is built on top of an old marsh, this type of soil is very prone to sinking and uneven settling, digging down and reinforcing the foundation alleviates that. It also ensures the foundation is uniform under the building to avoid a new tower of Pisa. Given the age of the city it may have been to scour the land for potential archaeological...

The turbulence model can make a big difference in your simulation . There are many turbulence models around. It becomes a tough job to select one out of them. There is no perfect turbulence model. It all depends on several parameters like Reynold's number, whether the flow is separated, pressure gradients, boundary layer thikness and so on. In this answer, brief information about a few popular models is given along with pros and cons and potential applications. However, interested users can see this excellent NASA website and references therein to know more about turbulence modeling. A) ONE EQUATION MODEL: 1....

My first thought is that it might be intended to be a wing nut driver of some sort, but those are usually hollow cylinders with slots for the wings. Ah ... sure enough, it's described as such in this Ebay ad :

OP injection molding tag is correct. OBall uses injection molding and plastic welding. The OBall is the invention of David E. Silverglate. Toy Ball Apparatus with Reduced Part Count Reduced image from Kids II . It consists of four identical, flat, injection molded, pentagon and hexagon shapes with circular (or elipitical) holes, which are shaped and plastically welded into spheres. Pentagon and hexagon edges are the same size and individual connected circles are only connected along one edge. The four shapes are clearly shown in colors above and from the patent. Solid lines on each part are hard connections, while...


What is the difference between the Polar Moment of Inertia, $ I_P $ and the torsional constant, $ J_T $ of a cross section?

This question is so fundamentally basic that I am almost embarrassed to ask but it came up at work the other day and and nearly no one in the office could give me a good answer. I was calculating the shear stress in a member using the equation, $\frac{Tr}{J_T}$ and noticed, that for a shaft with a circular cross section, $J_T = I_P$. Both $I_P$ and $J_T$ are used to describe an object's ability to resist torsion. $I_P$ is defined as, $ \int_{A} \rho^2 dA $ where $\rho$ = the radial distance to the axis about which $I_P$ is being calculated. But $J_T$ has no exact analytical equations and is calculated largely with approximate equations that no reference I looked at really elaborated on. So my question is, what is the difference between the Polar Moment of Inertia, $ I_P $, and the torsional constant, $ J_T $? Not only mathematically, but practically. What physical or geometric property is each a representation of? Why is $J_T$ so hard to calculate?

mechanical-engineering structural-engineering applied-mechanics stresses

The torsion constant $J_T$ relates the angle of twist to applied torque via the equation: $$ \phi = \frac{TL}{J_T G} $$ where $T$ is the applied torque, $L$ is the length of the member, $G$ is modulus of elasticity in shear, and $J_T$ is the torsional constant. The polar moment of inertia on the other hand, is a measure of the resistance of a cross section to torsion with invariant cross section and no significant warping . The case of a circular rod under torsion is special because of circular symmetry, which means that it does not warp and it's...

As with all good things, it depends. If you can assume that your supports are totally stiff and that the loading on the shelf will be approximately uniform, then you basically have the following structure: A rectangular cross-section (such as a plank) will behave equally under positive or negative bending moment, so your objective should be to balance both. To do so, you want your main span to be $2\sqrt2 \approx 2.83$ times the cantilevers. This is found by calculating the cantilever required to offset half of the bending moment due to a uniform load along a simply supported beam:...

Some ideas: Wheel Load Distribution : The load is greater on the rear wheels providing the power; more force on the front ones bring no benefit and would provide less traction. Better manoeuvrability from having a shorter wheelbase. Better Ground Clearance in some conditions, especially for bumps and or up a increasing slope for instance. Better Driving : The front wheels now turn around a point closer to the C.G. than with the rear wheels. Not good with vehicle dynamics but this appears better than the rear end 'trailing' behind. Structural : As some people have pointed out, it's better...


This is a toy for infants. My son was playing with it, and I started wondering how you could manufacture this. I can't think of any reasonably cheap way it could be done. I believe it's made of plastic and not silicone. It's flexible, but not elastic. My first thought would be injection molding, but I can't see any way to get parts of the mold in and out of the sphere. My wife suggested that perhaps the mold had no interior surface and was rotated like they do with chocolate, but I don't think that's the case, because if you look carefully you'll see that the inside surface is rounded and not flat. Anyway I'm stumped. How do you think they did it?

OP injection molding tag is correct. OBall uses injection molding and plastic welding. The OBall is the invention of David E. Silverglate. Toy Ball Apparatus with Reduced Part Count Reduced image from Kids II . It consists of four identical, flat, injection molded, pentagon and hexagon shapes with circular (or elipitical) holes, which are shaped and plastically welded into spheres. Pentagon and hexagon edges are the same size and individual connected circles are only connected along one edge. The four shapes are clearly shown in colors above and from the patent. Solid lines on each part are hard connections, while...

After the 2014 ICCT report revealed that these light-duty passenger diesel vehicles were emitting too much NOx and US regulators confronted VW about the results, VW did some testing and proposed a voluntary software recall to recalibrate the various emissions control devices on the affected vehicles. From the California Air Resources Board's (CARB's) in-use compliance letter to Volkwagen AG, 2015-09-18 , following that recall: To have a more controlled evaluation of the high NOx observed over the road, CARB developed a special dynamometer cycle which consisted of driving the Phase 2 portion of the FTP repeatedly. This special cycle revealed...

I'm going to set aside the potential legal liability aspects of your question. In part because you didn't ask about them, but also because liability will vary based upon jurisdiction. Obviously, consult an attorney familiar with the relevant law. Ethically, I think you've done everything that you're obligated to do. You have contacted the customer and notified them of an unsafe situation or configuration of the product. And you have (strongly) advised them that they need to discontinue using the older product in the unsafe configuration. If the customer remains insistent about using the product in an unsafe manner, you...


How can I tell if I got counterfeit aluminum?

I recently received an order of extruded aluminum strip 3/32" thick that supposed to be 6061 hardened to specification ASTM B221. However, when we tried to bend it, it broke and the interior looked like cast zinc coated with aluminum. Is 6061 supposed to look that way and be unable to bend 90-degrees? How can I verify that I have solid aluminum and not some fake alloy with zinc in it?

I figured out a simple test. Since the density of 6061 is 2.5 g/cm3 and the density of zinc is over 7 g/cm3, all I had to do was measure the density of the material. This showed that indeed it was aluminum. So, its brittleness is probably just because it was hardened.

The picture, below, of the exaggerated long section of the Channel Tunnel was taken from Wikipedia . Full-sized image here . Some of the limiting factors for the Channel Tunnel are: Railways don't like steep gradients The tunnels comprising the Channel Tunnel were excavated using tunnel boring machines (TBMs). Like railways, they cannot tolerate steep gradients. The tunnel was excavated in chalk marl (green coloured material in the picture). This was due to its depth (not being too shallow and not being too deep) and its ability to be easily dug but also it would cause major support issues for...

Please note : I'm not a building designer by trade, but I have had to investigate related questions for other reasons. I'll let you in on a dirty secret about sanitation lines within buildings - The biggest concern is not about how fast things are flying, it's about maintaining air pressure and providing adequate ventilation. This guide presents a bit of a historical background to tall building design with respect to sanitation system design. This presentation provides some more recent perspective and goes into some of the research that is refining current building standards. The traditional viewpoint is that waste...


What is the process of transferring an engineering license from the United States to Germany?

The United States have different rules about exactly how one obtains an engineering license, but the general process is the FE exam, a few years of work, and the PE exam. Suppose one then wishes to practice engineering in Germany. What are the legal requirements for doing so?

Unlike within the United States and the PE license , it doesn't appear that it's possible to receive an EUR ING license by direct comity . EU Engineering licensing is handled by FEANI , and on their EUR ING page they state: Application is open only to individuals if they are members of an engineering association represented in FEANI through a National Member But it may be possible to join one of the German FEANI members and see if they can support a reciprocity or comity process. 1 1 There are other member organization in other countries, but your question...

Electricity production optimization is a very complex subject. It is also affected by many parameters , which I will try to outline below. TL;DR: Demand is constantly monitored and supply is constantly adjusted TL;DR 2: Lshaver's excellent post is a suggested reading after reading this, because it expands and explains what happens at timescales ranging from the micro-second to minutes timescales. Changes in energy demand during the day First of all, the energy demand during the day, week, month of year can change quite a lot. Below is a typical day in New England. Fluctuation in power energy consumption a...

As other have pointed out, the main utility is to allow easier cleaning of the tank cars from sediment (solid precipitates). Usually, there is a drain in the lowest point. The reason that it is in the middle is that in this way, you can allow double the incline compared to if you had the drain at one of the ends for the same height. For example, if the allowed height difference is about 15 cm, and about 15 m length, then if you had the drain in: the edge, the angle would be $\arctan\left(\frac{0.15}{15}\right)=0.57^\circ$ or 1%. the middle ,...

After the 2014 ICCT report revealed that these light-duty passenger diesel vehicles were emitting too much NOx and US regulators confronted VW about the results, VW did some testing and proposed a voluntary software recall to recalibrate the various emissions control devices on the affected vehicles. From the California Air Resources Board's (CARB's) in-use compliance letter to Volkwagen AG, 2015-09-18 , following that recall: To have a more controlled evaluation of the high NOx observed over the road, CARB developed a special dynamometer cycle which consisted of driving the Phase 2 portion of the FTP repeatedly. This special cycle revealed...


How are spindle speeds calculated for a drill bit? I've seen dozens of charts that highlight the rpm that should be used for specific drill bit types, bit diameter, and material. However, what if my chart doesn't have the particular type of material or bit that I am using? I'd also like to have some intuition to know if the chart looks right or wrong. Upon some quick research, it appears that the "cutting speed" is what is ultimately needed for a particular material. I assume the cutting speed for each material must be looked up? Is there a standard or "go-to" place to find these? Then, information about the drill bit can be used to determine spindle speed. Again, what if I'm using a big hole saw or circle cutter, and it's not listed? How do I model the bit to use the cutting speed to determine rpm (for a given material of course)? I'd also like to know to calculate feed speeds for a drill or mill, but there are presumably more variables. It is probably better answered in another question.

For long freight trains and those that will be climbing to stations at higher altitudes, an extra or two locomotives are attached to the front. I've always wondered why. As usual there are multiple issues. Most important is landscape. If more power than a single locomotive can provide is not needed for the whole trip, but only a short stretch, like climbing or crossing a mountain range, then it would be a huge waste of resources to attach them the whole trip. For example, take a 10 hour trip time, where all, except for a 1 hour stretch, can be...

You are correct that the cutting speed of the material is what determines the rpm for your drill-bit. This actually makes the calculation very simple. $$ \text{Spindle speed (RPM)} = \frac{\text{Cutting speed}}{\text{Circumference}} = \frac{\text{Cutting speed}}{π \cdot \text{Diameter}} $$ The thing you need to be careful of is the units of cutting speed and diameter. For example: Metric: If your cutting speed is in $m/min$ and your diameter is in $mm$ then you need to multiply your cutting speed by 1000 so that it is in $mm/min$ Imperial: If your cutting speed is in $ft/min$ and your diameter is in $inches$...

Biggest advantage is that when connecting to trailers, there are no bleeding issues. Imagine having to connect hydraulic pipes and remove the air bubbles... As for the amount of air - the compressor and receiving tanks are designed for normal use. However if you wish to accompany AC/DC with the air brakes you will run out. Disc brakes already exist in an airbrake version for trucks - came out over 10 years ago IIRC. But one big difference is that if you lose hydraulics on a car braking system then you lose stopping power (except for handbrake etc) but with...

When a reactor is shut down the core produces much less heat, but they do still produce heat through a mechanism known as decay heat . The fact that the core is producing less heat means that the coolant temperature is going to drop, but how far that temperature drops depends on the decay heat generation rate. This in turn is based on operating history, or the power at which the plant was operating before shutdown. This can be large for commercial plants, because they typically operate at or very near capacity and the power companies bring coal or natural...


What causes rechargeable batteries to age? What can be done to extend life of these batteries?

These days most of modern electronics use rechargeable batteries as a power source. Also, these days most modern rechargeable batteries are Lithium Ion or Lithium Polymer based. Like any other devices, over time these rechargeable batteries lose the ability to recharge, retain and discharge energy thus users have to replace the devices or rechargeable batteries. It is my understanding that the rise in battery internal resistance is the primary cause the rechargeable battery aging. Is this accurate? If so what can be done to lower or eliminate the internal resistance in rechargeable batteries. If my understanding is inaccurate what is the cause for rechargeable battery aging? If the causes for battery aging are understood, how can electronic engineers design charging and discharging circuits to extend the rechargeable battery life? References: Battery University All About Batteries, Part 1: Introduction All About Batteries, Part 2: Specifications & Terminology All About Batteries, Part 7: Lithium Thionyl Chloride

energy electrical-engineering energy-storage chemical-engineering battery

The picture, below, of the exaggerated long section of the Channel Tunnel was taken from Wikipedia . Full-sized image here . Some of the limiting factors for the Channel Tunnel are: Railways don't like steep gradients The tunnels comprising the Channel Tunnel were excavated using tunnel boring machines (TBMs). Like railways, they cannot tolerate steep gradients. The tunnel was excavated in chalk marl (green coloured material in the picture). This was due to its depth (not being too shallow and not being too deep) and its ability to be easily dug but also it would cause major support issues for...

It is more efficient to transmit DC using about the same infrastructure. This is because of several effects: Skin effect experienced with AC. There is no skin effect with DC. Higher voltage allowed with DC for the same transmission lines. The lines have to withstand the peak voltage. With AC, that is 1.4 times higher than the RMS. With DC, the RMS and peak voltages are the same. However, the power transmitted is the current times the RMS, not peak, voltage. No radiation loss with DC. Long transmission lines act as antennas and do radiate some power. That can only...

I will expand on DKNguyen answer, because to my knowledge also the two reasons are: reduce contact/bearing stresses (having a significant effect on thin finishes live galvanisation) change the joint tightening characteristics (see joint diagram). reduce contact stresses on surfaces. The basic idea is that since contact stress is defined as: $$\sigma = \frac{F}{A}$$ Obviously the larger the area the less the stress. Also, like Nuclear Hoagie pointed out the area changes with a square law of the diameter. UPDATE 2 - Calculation for M6 bolt (thanks to BenC) *The question gathered enough interest for me to carry out a...

One of the problems that plagued older rechargeable batteries (e.g. Nickel Cadmium ($\text{NiCad}$) and Nickel Metal Hydride ($\text{NiMH}$)) was the memory effect . The memory effect occurs when a rechargeable battery is not fully discharged. It then "forgets" that it has a greater capacity than it thinks it has, and so in the future it discharges less. A good example is a water bottle. Initially, water bottles have a certain capacity for water. Let's say that I drink most of the water in a water bottle during one usage. If the memory effect affected water bottles, I would not be...


Thermoelectric Technology to Harvest Energy from Internal Combustions Engines

Background: In an automobile, only 1 / 3 of the potential energy in the fuel is converted into mechanical energy and significant portion of the energy is lost as heat. There have been previous attempts to recuperate this lost energy. In the early 1990's, Porsche developed automotive thermoelectric generators (ATEG) which didn't go past prototyping stage. Currently, Porsche Motorsports is testing a thermal energy harvesting system in their LeMans series Race car. In addition to Porsche's research, GM is in collaboration with Future Tech, LLC. to explore the idea of using themoelectric technology to harvest energy from internal combustion engines. Other automotive manufactures, such as BMW, are also exploring this technology. Currently the power usage in a Small car is approximately 150 W Full size truck is approximately 500 W If this technology can successfully be implemented, then components such as the radiator, water pump, and alternator could effectively have reduced workload or removed from the system, thus reducing the load to the internal combustion engine. Question: With the growing interest in green technology, are there technology barriers beside efficiency that are preventing the implementation of energy harvesting from internal combustion engines using thermoelectric technology? References: Which one must be used matched output voltage or open circuit voltage? Benefits of Thermoelectric Technology for the Automobile The Promise and Problems of Thermoelectric Generators Modeling of an Automotive ThermoElectricGenerator (ATEG) Thermoelectrics to replace car alternators and improve MPG Thermo-Electric Generator in Turbocharged Diesel Engine Kettering University researchers are working with General Dynamics to convert the unused heat energy of their propulsion systems to useful and clean energy Porsche 919 Hybrid LeMans Racer Goes After The Two Thirds of Gasoline’s Energy That’s Wasted As Heat Germans trying to replace Alternator with Thermoelectric Generators or TEGs Porsche 919 Hybrid LMP1 Le Mans prototype... Footnote The suggested duplicate is related, but still distinctly different. The order of magnitude of energy available to recover from an internal combustion engine is significantly greater than within the GPU of a video card. As such, the economies of scale are different and different solutions are therefore possible.

As with all good things, it depends. If you can assume that your supports are totally stiff and that the loading on the shelf will be approximately uniform, then you basically have the following structure: A rectangular cross-section (such as a plank) will behave equally under positive or negative bending moment, so your objective should be to balance both. To do so, you want your main span to be $2\sqrt2 \approx 2.83$ times the cantilevers. This is found by calculating the cantilever required to offset half of the bending moment due to a uniform load along a simply supported beam:...

If you consider only the static forces then indeed the thickness might seem over-engineered. However, engine blocks are not statically loaded. They operate in the range of a few hundred to a few thousand rpm (Revolution Per Minute), so there are dynamic considerations here. Fatigue When materials are subjected to cyclic loading they exhibit a reduction in the allowable stresses. See below for an example: In general, BCC (body-centered cubic) materials (like steel) show a marked drop in strength (close to 50% or more depending on the steel). For these material there is stress (called the endurance limit), for which...

As with any new technology, the cost is the big driver here. In addition, these devices produce electricity which is a form of energy that a typical internal combustion automobile can only utilize for ancillary equipment. This would effectively improve fuel efficiency but the gain would be relatively minor. Engineers are generally reluctant to use expensive new technologies that are relatively untested when existing methods are sufficient for achieving the goal. In this case, most automobile manufacturers strive to produce a cost-competitive product. The people who are most concerned with fuel efficiency will tend to consider a hybrid (or all-electric)...


I've seen it in several data sheets - it is a measure of error of some kind, of course. The problem is I dont know the exact meaning. I've seen it in the context of repeatability, accuracy and linearity. An example is the following data sheet: smc data sheet (On page 3)

I will expand on DKNguyen answer, because to my knowledge also the two reasons are: reduce contact/bearing stresses (having a significant effect on thin finishes live galvanisation) change the joint tightening characteristics (see joint diagram). reduce contact stresses on surfaces. The basic idea is that since contact stress is defined as: $$\sigma = \frac{F}{A}$$ Obviously the larger the area the less the stress. Also, like Nuclear Hoagie pointed out the area changes with a square law of the diameter. UPDATE 2 - Calculation for M6 bolt (thanks to BenC) *The question gathered enough interest for me to carry out a...

Electricity production optimization is a very complex subject. It is also affected by many parameters , which I will try to outline below. TL;DR: Demand is constantly monitored and supply is constantly adjusted TL;DR 2: Lshaver's excellent post is a suggested reading after reading this, because it expands and explains what happens at timescales ranging from the micro-second to minutes timescales. Changes in energy demand during the day First of all, the energy demand during the day, week, month of year can change quite a lot. Below is a typical day in New England. Fluctuation in power energy consumption a...

FS = FULL SCALE = maximum reading. It means that the accuracy is such that the reading is probably within + or - 0.5% of the FULL SCALE reading. This is a very important and easily overlooked qualification of the result. If I have a reading of 1 Volt and the accuracy is +/- 0.5% it means that the actual result should lie in the range 1 - 0.5% x 1 to 1 + 0.5% of 1 = 0.995V to 1.005 V However - if I measure the result on the 10V range then 0.5% of 10V = 0.5% of...


At what point does an I-beam becomes a H-beam?

According to BS5950, a beam section can be classified as plastic, semi-compact, compact or slender. For the same section area, a H-beam can take axial compression (without buckling) better than an I-beam, and as such, uses a different strut curve in the code: Now, I understand that a H-beam has a wider flange compared to an I-beam, but at what point, precisely, does this transition from I- to H- occurs? For example, is a 400x300 (depth x width) beam considered a H- or an I-beam? Update: Extracted from BS5950 guide, the following table shows H-beams (also known as universal columns, some of which with depth greater than width. This is the reason why I don't believe the differentiation is so straight forward.

I'm going to set aside the potential legal liability aspects of your question. In part because you didn't ask about them, but also because liability will vary based upon jurisdiction. Obviously, consult an attorney familiar with the relevant law. Ethically, I think you've done everything that you're obligated to do. You have contacted the customer and notified them of an unsafe situation or configuration of the product. And you have (strongly) advised them that they need to discontinue using the older product in the unsafe configuration. If the customer remains insistent about using the product in an unsafe manner, you...

If you consider only the static forces then indeed the thickness might seem over-engineered. However, engine blocks are not statically loaded. They operate in the range of a few hundred to a few thousand rpm (Revolution Per Minute), so there are dynamic considerations here. Fatigue When materials are subjected to cyclic loading they exhibit a reduction in the allowable stresses. See below for an example: In general, BCC (body-centered cubic) materials (like steel) show a marked drop in strength (close to 50% or more depending on the steel). For these material there is stress (called the endurance limit), for which...

BS5950-1:2000 Clause 1.3.23 defines an H-section as having " an overall depth not greater than 1.2 times its overall width ", and Clause 1.3.25 defines an I section as having " an overall depth greater than 1.2 times its overall width ". Note that at exactly a ratio of 1.2, it would be an H section not an I section.

So there's an incorrect assumption underlying your question. In an ideal world the lifting capacity required of the jacks would be the self-weight of the bridge divided by the number of jacks (+ allowances for wind/snow, etc.). And the assumption there is that the lifting capacity is equivalent only to the weight of the bridge. The problem is that if anything goes wrong, you're likely to see a catastrophic failure of some sort which could lead to irreparable damage. Real world lifts don't operate in that "ideal" manner, and instead rely upon a safety factor in order to make sure...


Why are most standard bolt threads single start?

When looking at thread descriptions, one of the basic properties is always the number of thread starts. As far as I could tell, all of the major standard bolt threads are single-start. This includes: Unified Standard (UNC, etc.) National Pipe Thread (NPT, NPS) British Standard I only found one standard thread that can also come in a multiple-starts: ACME . What are the reasons why single-start threads are so common and multiple-start threads are rare? I am specifically interested in bolts and other fasteners.

Biggest advantage is that when connecting to trailers, there are no bleeding issues. Imagine having to connect hydraulic pipes and remove the air bubbles... As for the amount of air - the compressor and receiving tanks are designed for normal use. However if you wish to accompany AC/DC with the air brakes you will run out. Disc brakes already exist in an airbrake version for trucks - came out over 10 years ago IIRC. But one big difference is that if you lose hydraulics on a car braking system then you lose stopping power (except for handbrake etc) but with...

As Dave Tweed points out, the ratio of torque to tension is lower the lower the lead angle is. Since the important measure of bolt tightness is generally the tension in the bolt, we want to achieve that minimum pretension with the least effort possible. Assuming we have to maintain a certain shear area of the thread (so the the threads are stronger than the bolt when fully engaged) having two starts means we double the lead angle and greatly increase the amount of force required in the wrench to tighten the fastener appropriately. On its own though, this isn't...

The problem is that the formula $P=I\ V$ is correct when dealing with DC circuits or with AC circuits where there is no lag between the current and the voltage. When dealing with realistic AC circuits, the power is given by $$ P=I\ V\ \cos(\phi), $$ where $\phi$ is the phase difference between the current and the voltage. The unit kVA is a unit of what is called 'apparent power' whereas W is a unit of 'real power'. Apparent power is the maximum possible power attainable when the current and voltage are in phase and real power is the actual...


Is interference between aircraft an issue for fly-by-wireless technology?

I was reading up on fly-by-wire development, and I saw a short section about fly-by-wireless technology. It seems like a great idea, with the potential to lower costs, weight and complexity. I can see a possible scenario where it could be an issue, though: Two aircraft are very close together (e.g. on a runway or flying in formation). One pilot transmits commands through the aircraft's fly-by-wire system to other parts of the aircraft. The other aircraft accidentally receives the signal because it is so close. Things get very bad very quickly. The thing is, I haven't been able to find any technical specifications regarding fly-by-wireless systems, and I have no idea if the transmission would be powerful enough to reach the other craft, nor if it would then be interpreted as actual data sent from that aircraft's pilot. Is this cross-interference between fly-by-wireless systems possible? If so, how can it be mitigated?

electrical-engineering aerospace-engineering emc telecommunication product-testing

Cross-interference between aircraft is a high unlikely event because all commercial aircraft designs have to pass DO-160 environmental testing requirements. Among the DO-160 testing specification is EMI/EMC Testing. These tests include Radiated Emission, Interference and Immunity Testing. Part of these tests are to answer " Two aircraft are very close together " and " The other aircraft accidentally receives the signal because it is so close. " Below is a picture of an anechoic chamber used to test aircraft. Fly-by-wire allows the aircraft control system to access and command monitoring and control systems. The monitoring and control systems have a...

As it happens, I just recently went through that calculation myself for a different site. Given the following facts from a quick web search, it isn't difficult to work out the numbers. The maximum efficiency of a (large) windmill is about 40%. The density of air is 1.225 kg/m 3 You need about 50 mW (10 mA at 5V) to light up an LED First, we'll need about 50 mW / 0.40 = 125 mW of air power flowing through the windmill to create the electricity we need (ignoring other factors such as the actual efficiency of a small windmill...

I think you are talking about roundabouts, not traffic circles. It is baffling to those of us in the UK that Americans think roundabouts are a new idea. In the UK we have so many variants, from mini-roundabouts all the way up to full motorway junctions (a giant roundabout above or below the motorway). So do roundabouts take up more space? Not necessarily, this is a mini roundabout: It's nothing more than a slightly domed area of paint on the road, no lights are necessary, you can actually drive straight over the top of it rather than around it, its...


How to calculate lever force when lever has uniformed distributed load?

We have a simple class 1 lever: $$\begin {array}{c} \text {5,000 kg} \\ \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \, \downarrow \\ ==================== \\ \hphantom {==} \triangle \hphantom {==============} \\ \vdash \text{1 m} \dashv \vdash \hphantom {======} \text{4 m} \hphantom {======} \dashv \\ \end {array} $$ The lever ($===$) is 5 m long. The fulcrum ($\triangle$) is 1 m from one end of the lever. The lever has a object sitting uniformly upon it weighing 5,000 kg. How do I compute the upward force that needs to be exerted at the end of the 1 m side of the lever to keep the lever stationary? $F = (W \times X)/L$ is simple when the weight is applied at the very end of the lever. But what happens if the weight is distributed along the lever? Our final goal is to tether the free end (on the 1m side) to keep the lever level and we need to know how strong the tether should be.

Answer: Yes , this is exactly how it is done in the real world. What you have described is what I do in my job to check systems in CAD. Since you indicated you would like me to step through my design process, I have detailed it below. Note that most of this does not involve CAD. CAD is invaluable, but only if you are prepared to get out the pencil and paper first. Also note that this is just my design process, it is by no means the only way to go about things. Preparation When I begin any...

Since the mass is 5k kg and the lever is 5m, this makes it quite easy to simplify because it is exactly 1k kg per m. The leftmost 2k kg (2m) of the mass has its center of mass exactly above the fulcrum so can be ignored as it provides no contribution to the moment. This leaves 3k kg (3m) spread from 1m to 4m on the right side. The center of mass will therefore be at 2.5m. Now it's super-simple, assuming you want the moment when the lever is level (i.e. when gravity is pulling straight down, perpendicular to...

OP injection molding tag is correct. OBall uses injection molding and plastic welding. The OBall is the invention of David E. Silverglate. Toy Ball Apparatus with Reduced Part Count Reduced image from Kids II . It consists of four identical, flat, injection molded, pentagon and hexagon shapes with circular (or elipitical) holes, which are shaped and plastically welded into spheres. Pentagon and hexagon edges are the same size and individual connected circles are only connected along one edge. The four shapes are clearly shown in colors above and from the patent. Solid lines on each part are hard connections, while...

In the video you've linked to, the spheres are seen on both the leading and trailing edges of the propeller: I expect they are intentional - there are a number of ways to attach a propeller without having to disturb that surface, or using flush caps. Cavitation is caused by a drop in pressure. This would be seen on the trailing edges of the propeller, and is worse on the outer edge of the propeller which is moving faster through water than the inner area. I doubt these spheres have any effect on cavitation at all. It is either a...


Will more electricity be generated by using a lens to focus sunlight onto solar cells?

I have been wondering about this question for quite some time. Assuming an ideal case, the energy from photons hitting solar cells is converted into electric energy as described by the equation: $RI^2t=W\equiv E=\hbar\nu$ where $\nu$ is the frequency of photons. Using a lens won't increase the frequency of photons, thus no extra electricity is generated. Am I correct in thinking that no extra electricity will be generated by solar cells when a lens is used to focus light onto them?

electrical-engineering optics photovoltaics solar-energy

Your equation is partly correct. You've calculated the energy per photon ($\hbar \nu$), but you've neglected the number of photons. That's why the units don't match (power is energy per unit time, while you've only got energy for each photon). The ideal power (energy per unit time) depends on the area of the solar panel, $A_p$, the number of photons striking it per unit time ($\Phi$) and the energy of each photon, $E$, such that $W_{Ideal} =A_p \cdot \Phi \cdot E$. A lens or mirror can focus light (a flux of photons) onto a small area. Under really ideal conditions,...

As others stated before, induction loops are the primary - most reliable method: the coils (usually just several loops of wire) embedded in the road; fed given frequency from a generator, in presence of metal the frequency of the LC circuit changes and the sensor circuitry detects the change of frequency, producing a presence signal. In some cases these may fail to detect bicycles, but they are by far most common as they aren't affected by weather (or more precisely, the detection circuit tunes in to slow changes of frequency caused by weather) and are immune to accidental false positives....

On this processor, the register that holds the conversion result is 16 bits wide. A right-justified result means that bits [( N -1):0] (where N is the number of bits of precision) of the register contain the ADC value and the most-significant bits of the register are set to zero. A left-justified result means that bits [15:(16- N )] of the register hold the result, and bits [(15- N ):0] are set to zero. For example, if your actual conversion result is 0x123, it would be read as 0x0123 if the register was right-justified, and as 0x1230 if it was...


How long does it take for dust to settle out of the air?

In order to make this a manageable question, let's add a few simplifications. The dust particles can be well described as uniform spheres of radius $R$ and density $\rho$. The space is enclosed and there is no bulk flow, i.e the air is still in a macroscopic sense. The air is at the standard temperature and pressure (STP) ; $T=20\ ^\circ\mathrm{C}$ and $P=1\ \mathrm{atm}$. Under these conditions, what is the settling time for dust particles? At what size/density does Brownian motion of the air become important?

Solid particle settling time in air depends mainly on the size of the particle. Different forces become significant depending on what size range you're talking about, so it's hard to give an answer that's both concise and accurate. I'll do my best to synthesize the important points rather than parrot a reference; that said, where practical applications in the field of air quality are concerned, the text I recommend is Air Pollution Control by Cooper & Alley . In particular, I'm going to pull many of the details for this answer from Section 3.3: Particulate Behavior in Fluids. Gravitational Settling...

The picture, below, of the exaggerated long section of the Channel Tunnel was taken from Wikipedia . Full-sized image here . Some of the limiting factors for the Channel Tunnel are: Railways don't like steep gradients The tunnels comprising the Channel Tunnel were excavated using tunnel boring machines (TBMs). Like railways, they cannot tolerate steep gradients. The tunnel was excavated in chalk marl (green coloured material in the picture). This was due to its depth (not being too shallow and not being too deep) and its ability to be easily dug but also it would cause major support issues for...


How can I go about applying machine learning algorithms to stock markets?

Can anyone share their experience and basic pointers about how to go about it or at least start applying it to see results from data sets? How ambitious is this? Also, mention standard algorithms that should be investigated.

There seems to be a basic fallacy that someone can come along and learn some machine learning or AI algorithms, set them up as a black box, hit go, and sit back while they retire. My advice to you: Learn statistics and machine learning first, then worry about how to apply them to a given problem. There is no free lunch here. Data analysis is hard work . Read "The Elements of Statistical Learning" (the pdf is available for free on the website), and don't start trying to build a model until you understand at least the first 8 chapters....

1. Determine Factors Economically, the use of factor models can be either motivated using the ICAPM or the APT . Although there are some theoretical differences between the model, for empirical and practical work these differences are irrelevant. In the end, both models stipulate that returns and expected returns are linear functions of the factors: $$ r_{i,t} = \alpha_i + \sum_j \beta_{i,j} F_{j,t} + \epsilon_{i,t} \quad (1)$$ $$ \mathbb{E}[ r_{i,t}] = \lambda_o + \sum_j \beta_{i,j} \lambda_j \quad\quad\quad(2)$$ where $F_{j,t}$ is the factor surprise of factor $j$ at time $t$ and $\lambda_j $ is the factor risk premium of factor $j$....

The volatiltiy surface is just a representation of European option prices as a function of strike and maturity in a different "unit" - namely implied volatility (while the term implied volatility has to be made precise by the model used to convert prices (quotes) into implied volatilities - for example: we may consider log-normal vols and normal vols). Volatility is often preferred over prices, e.g., when considering interpolations of European option prices (although this may introduce difficulties like arbitrage violations, see, e.g., http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1964634 ). A local volatility model can generate a perfect fit to the implied volatility surface via Dupire's...

Wavelets are just one form of "basis decomposition". Wavelets in particular decompose in both frequency and time and thus are more useful than fourier or other purely-frequency based decompositions. There are other time-freq decompositions (for instance the HHT) which should be explored as well. Decomposition of a price series is useful in understanding the primary movement within a series. In general with a decomposition, the original signal is the sum its basis components (potentially with some scaling multiplier). The components range from the lowest frequency (a straight-line through the sample) to the highest frequency, a curve that oscillates with a...


How are correlation and cointegration related?

In what ways (and under what circumstances) are correlation and cointegration related, if at all? One difference is that one usually thinks of correlation in terms of returns and cointegration in terms of price. Another issue is the different measures of correlation (Pearson, Spearman, distance/Brownian) and cointegration (Engle/Granger and Phillips/Ouliaris).

time-series statistics correlation cointegration

This isn't really an answer, but it's too long to add as a comment. I've always had a real problem with the correlation/covariance of price . To me, it means nothing. I realize that it gets used (abused) in many contexts, but I just don't get anything out of it (over time, price has to generally go up, go down, or go sideways, so aren't all prices "correlated"?). On the flip side, correlation/covariance of returns makes sense. You're dealing with random series, not integrated random series. For example, below is the code required to generate two price series that have...

The term has a different meaning to different people. to econometricians, microstructure noise is a disturbance that makes high frequency estimates of some parameters (e.g. realized volatility) very unstable. Generally this strand of the literature professes agnosticism as to the its origin; to market microstructure researchers, microstructure noise is a deviation from fundamental value that is induced by the characteristics of the market under consideration, e.g. bid-ask bounce, the discreteness of price change, latency, and asymmetric information of traders. The last example is frequently cited but I don't think it is accurate. Asymmetric information does not have to be a...

Just to be painfully clear, it only seems to make sense to consider the logarithm of returns, i.e. $X=\log (1+\frac r{100})$ for a simple return of $r\%$ in an arbitrary period because this is what sums when returns are temporally aggregated. A basic property of cumulants is that cumulants of all orders are additive under convolution, for which a proof can be found here here . So if $X_1$, $X_2$, ... $X_n$ are i.i.d. , then all the cumulants of $$Y_n = \sum_{i=1}^nX_i$$ scale linearly with $n$, i.e. $$\kappa_k(Y_n)=n\kappa_k(Y_1).$$ However, I suspect that you are normalizing this sum so that...

In general there are two basic ways to make money out of your option pricing models: Sell side (market maker, risk neutral): You use these models to calculate your greeks to hedge your portfolio, so that you live on the spread. Buy side (market/risk taker): You use your model to find mispriced options in the market and buy/sell accordingly. (A third possibility would be to write fancy books and papers about these models and get rich and/or tenure this way ;-)


How useful is the genetic algorithm for financial market forecasting?

There is a large body of literature on the "success" of the application of evolutionary algorithms in general, and the genetic algorithm in particular, to the financial markets. However, I feel uncomfortable whenever reading this literature. Genetic algorithms can over-fit the existing data. With so many combinations, it is easy to come up with a few rules that work. It may not be robust and it doesn't have a consistent explanation of why this rule works and those rules don't beyond the mere (circular) argument that "it works because the testing shows it works". What is the current consensus on the application of the genetic algorithm in finance?

I have worked on this topic extensively (pricing and calculating IV in production) and believe can offer an informed opinion. First of all Mathworks - the company that creates Matlab is not a trading firm so you should probably not rely on their advice so much. There are few closed form options pricing models, and all have practical shortcomings. Barone-Adesi and Whaley (please correct my spelling of last names as I'm typing from memory) model is simple approximation for American options but is unfortunately not very accurate, and does not deal with dividends. Roll-Geske-Whaley deals with dividends, but not very...

The standard story (also told by @vonjd) is of "The Drunk and Her Dog". This is based on "A Drunk and Her Dog: An Illustration of Cointegration and Error Correction" (1994). The story is itself based on the standard illustration for a random walk known as the "drunkard's walk". The Dickey-Fuller test is used to check for a unit root . It can be used as part of the general Engle-Granger two-step method (although it isn't the only option). In this case, while the two assets themselves are not stationary, you are able to test if the residuals between a...

By "cryptography" you mean information theory. Information theory is useful for portfolio optimization and for optimally allocating capital between trading strategies (a problem which is not well addressed by other theoretical frameworks.) See: --- J. L. Kelly, Jr., "A New Interpretation of Information Rate," Bell System Technical Journal, Vol. 35, July 1956, pp. 917-26 --- E. T. Jaynes, Probability Theory: The Logic of Science http://amzn.to/dtcySD --- http://en.wikipedia.org/wiki/Gambling_and_information_theory http://en.wikipedia.org/wiki/Kelly_criterion In the simple case, you would use "The Kelly Rule". More complicated information theory based strategies for allocating capital between trading strategies take into account correlations between the performance of trading strategies...

I've worked at a hedge fund that allowed GA-derived strategies. For safety, it required that all models be submitted long before production to make sure that they still worked in the backtests. So there could be a delay of up to several months before a model would be allowed to run. It's also helpful to separate the sample universe; use a random half of the possible stocks for GA analysis and the other half for confirmation backtests.


Why Drifts are not in the Black Scholes Formula

This question has puzzled me for a while. We all know geometric brownian motions have drifts $\mu$: $dS / S = \mu dt + \sigma dW$ and different stocks have different drifts of $\mu$. Why would the drifts go away in Black Scholes? Intuitively, everything else being equal, if a stock has higher drift, shouldn't it have higher probability of finishing in-the-money (and higher probability of having higher payoff), and the call option should be worth more? Is there an intuitive and easy-to-understand answer? thanks.

options option-pricing black-scholes risk-neutral-measure

In general there are two basic ways to make money out of your option pricing models: Sell side (market maker, risk neutral): You use these models to calculate your greeks to hedge your portfolio, so that you live on the spread. Buy side (market/risk taker): You use your model to find mispriced options in the market and buy/sell accordingly. (A third possibility would be to write fancy books and papers about these models and get rich and/or tenure this way ;-)

Here couple pointers that may make it clearer: Drift can be replaced by the risk-free rate through a mathematical construct called risk-neutral probability pricing. Why can we get away with that without introducing errors? The reason lies in the ability to setup a hedge portfolio, thus the market will not compensate us for the drift above and beyond the risk free rate under risk-neutral probability pricing. As long as such hedge exists and couple other conditions are met (please look up Girsanov's Theorem) we can introduce a risk-neutral measure so that when applying it to the differential equation and through...

A general model (with continuous paths) can be written $$ \frac{dS_t}{S_t} = r_t dt + \sigma_t dW_t^S $$ where the short rate $r_t$ and spot volatility $\sigma_t$ are stochastic processes. In the Black-Scholes model both $r$ and $\sigma$ are deterministic functions of time (even constant in the original model). This produces a flat smile for any expiry $T$. And we have the closed form formula for option prices $$ C(t,S;T,K) = BS(S,T-t,K;\Sigma(T,K)) $$ where $BS$ is the BS formula and $\Sigma(T,K) = \sqrt{\frac{1}{T-t}\int_t^T \sigma(s)^2 ds}$. This is not consistent with the smile observed on the market. In order to match...

I can only talk about quantitative trading. As a rule of thumb, the lower frequency you work in, the more econometrics is important, whereas for a higher frequency, the more econometrics becomes useless . (I would still recommend a top econometrician for HFT since they have what it takes to succeed, it's just the models aren't out-of-the-box applicable.) But if I was interviewing someone who was educated in econometrics for a quantitative research position, I would hope for (given the relevance to financial time-series): I have tried to put in a legend, ^ is something you should learn later and...


Which approach dominates? Mathematical modeling or data mining?

According to my current understanding, there is a clear difference between data mining and mathematical modeling . Data mining methods treat systems (e.g., financial markets) as a "black box". The focus is on the observed variables (e.g., stock prices). The methods do not try to explain the observed phenomena by proposing underlying mechanisms that cause the phenomena (i.e., what happens in the black box). Instead, the methods try to find some features, patterns, or regularities in the data in order to predict future behavior. Mathematical modeling , in contrast, tries to propose a model for what happens inside the black box. Which approach dominates in quantitative finance? Do people try to use more and more fancy data mining techniques or do people try to construct better and better mathematical models?

The minimum variance solution loads up on securities that have low variances and co-variances. Theoretically you are correct that this should have a low expected return profile. However, it turns out - in contradiction to modern portfolio theory - that securities that have low-volatility or low-beta experience higher returns than high-volatility or high-beta stocks. This is well-documented in the literature as the low-volatility anomaly . As a result, many funds and ETFs have been launched in recent years to exploit this phenomenon. There are a couple arguments as to why the anomaly exists. The paper I cite above argues that...

One starts with the Black-Scholes equation $$\frac{\partial C}{\partial t}+\frac{1}{2}\sigma^2S^2\frac{\partial^2 C}{\partial S^2}+ rS\frac{\partial C}{\partial S}-rC=0,\qquad\qquad\qquad\qquad\qquad(1)$$ supplemented with the terminal and boundary conditions (in the case of a European call) $$C(S,T)=\max(S-K,0),\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad(2)$$ $$C(0,t)=0,\qquad C(S,t)\sim S\ \mbox{ as } S\to\infty.\qquad\qquad\qquad\qquad\qquad\qquad$$ The option value $C(S,t)$ is defined over the domain $0 Step 1. The equation can be rewritten in the equivalent form $$\frac{\partial C}{\partial t}+\frac{1}{2}\sigma^2\left(S\frac{\partial }{\partial S}\right)^2C+\left(r-\frac{1}{2}\sigma^2\right)S\frac{\partial C}{\partial S}-rC=0.$$ The change of independent variables $$S=e^y,\qquad t=T-\tau$$ results in $$S\frac{\partial }{\partial S}\to\frac{\partial}{\partial y},\qquad \frac{\partial}{\partial t}\to - \frac{\partial}{\partial \tau},$$ so one gets the constant coefficient equation $$\frac{\partial C}{\partial \tau}-\frac{1}{2}\sigma^2\frac{\partial^2 C}{\partial y^2}-\left(r-\frac{1}{2}\sigma^2\right)\frac{\partial C}{\partial y}+rC=0.\qquad\qquad\qquad(3)$$ Step 2. If we...

I would offer the distinctions are i) pure statistical approach, ii) equilibrium based approach, and iii) empirical approach. The statistical approach includes data mining. Its techniques originate in statistics and machine learning. In its extreme there is no a priori theoretical structure imposed on asset returns. Factor structure might be identified thru Principal Components, for example. The goal here is to maximize predictive accuracy at the expense of intuition and explanatory power. This approach increasingly dominates at very short frequencies in modeling market microstructure, market making algorithms, volatility modeling, etc. However, even in high-frequency trading one can impose a factor...

Consider the standard error , and in particular the distance between the upper and lower limits: \begin{equation} \Delta = (\bar{x} + SE \cdot \alpha) - (\bar{x} - SE \cdot \alpha) = 2 \cdot SE \cdot \alpha \end{equation} Using the formula for standard error, we can solve for sample size: \begin{equation} n = \left(\frac{2 \cdot s \cdot \alpha}{\Delta}\right)^{2} \end{equation} where $s$ is the measured standard deviation, which you already have from your IR calculation. High-frequency Example I was testing a market-making model recently that was expected to return a couple basis points for each trade and I wanted to be confident...


How do I graphically represent the evolution of a covariance matrix over time?

I am working with a set of covariance matrices evaluated at various points in time over some history. Each covariance matrix is $N\times N$ for $N$ financial time-series over $T$ periods. I would like to explore some of the properties of this matrix's evolution over time, particularly whether correlation as a whole is increasing or decreasing, and whether certain series become more or less correlated with the whole. I am looking for suggestions as to the kinds of analysis to perform on this data-set, and particularly graphical/pictorial analysis. Ideally, I would like to avoid having to look in depth into each series as $N$ is rather large. Update The following graphs were generated based on the accepted answer from @Quant-Guy. PC = principal component = eigenvector. The analysis was done on correlations rather than covariances in order to account for vastly different variances of the $N$ series.

Representing time series (esp. tick data) using elaborate data structures may be not the best idea. You may want to try to use two arrays of the same length to store your time series. The first array stores values (e.g. price) and the second array stores time. Note that the second series is monotonically increasing (or at least non-decreasing), i.e. it's sorted. This property enables you to search it using the binary search algorithm. Once you get an index of a time of interest in the second array you also have the index of the relevant entry in the first...

It is hard to find a stable non-trivial dependence structure in financial data. Usually when such is found it is hard to rationalize. One of my favorite (although I am sure there are others) is the so called "Presidential Puzzle". This is an old finding by Santa-Clara and Valkanov (2003) They find that " Excess return in the stock market is higher under Democratic than Republican presidencies: 9 percent for the value‐weighted and 16 percent for the equal‐weighted portfolio. At the time the finding was very robust and did not seem to be explained by anything else. What is more...

The minimum variance solution loads up on securities that have low variances and co-variances. Theoretically you are correct that this should have a low expected return profile. However, it turns out - in contradiction to modern portfolio theory - that securities that have low-volatility or low-beta experience higher returns than high-volatility or high-beta stocks. This is well-documented in the literature as the low-volatility anomaly . As a result, many funds and ETFs have been launched in recent years to exploit this phenomenon. There are a couple arguments as to why the anomaly exists. The paper I cite above argues that...

I would consider a motion chart that plots the eigenvalues of the covariance matrix over time. For a static view you can create a table: rows represent dates, and columns represent eigenvectors. The entries of the table represent changes in the angle of the eigenvector from the previous row. This will show how stable your covariance structure is. You can also create a second table this time with eigenvalues as the columns sorted from high to low (and the corresponding values below for each date). This shows the variance described by each eigenvector so you can see whether correlation as...


How much data is needed to validate a short-horizon trading strategy?

Suppose one has an idea for a short-horizon trading strategy, which we will define as having an average holding period of under 1 week and a required latency between signal calculation and execution of under 1 minute. This category includes much more than just high-frequency market-making strategies. It also includes statistical arbitrage, news-based trading, trading earnings or economics releases, cross-market arbitrage, short-term reversal/momentum, etc. Before even thinking about trading such a strategy, one would obviously want to backtest it on a sufficiently long data sample. How much data does one need to acquire in order to be confident that the strategy "works" and is not a statistical fluke? I don't mean confident enough to bet the ranch, but confident enough to assign significant additional resources to forward testing or trading a relatively small amount of capital. Acquiring data (and not just market price data) could be very expensive or impossible for some signals, such as those based on newer economic or financial time-series. As such, this question is important both for deciding what strategies to investigate and how much to expect to invest on data acquisition. A complete answer should depend on the expected Information Ratio of the strategy, as a low IR strategy would take a much longer sample to distinguish from noise.

data time-series backtesting quant-trading-strategies arbitrage

Strictly speaking, data snooping is not the same as in-sample vs out-of-sample model selection and testing, but has to deal with sequential or multiple tests of hypothesis based on the same data set. To quote Halbert White: Data snooping occurs when a given set of data is used more than once for purposes of inference or model selection. When such data reuse occurs, there is always the possibility that any satisfactory results obtained may simply be due to chance rather than to any merit inherent in the methody yielding the results. Let me provide an example. Suppose that you have...

Consider the standard error , and in particular the distance between the upper and lower limits: \begin{equation} \Delta = (\bar{x} + SE \cdot \alpha) - (\bar{x} - SE \cdot \alpha) = 2 \cdot SE \cdot \alpha \end{equation} Using the formula for standard error, we can solve for sample size: \begin{equation} n = \left(\frac{2 \cdot s \cdot \alpha}{\Delta}\right)^{2} \end{equation} where $s$ is the measured standard deviation, which you already have from your IR calculation. High-frequency Example I was testing a market-making model recently that was expected to return a couple basis points for each trade and I wanted to be confident...


Are there any new Option pricing models?

Back in the mid 90's I used the Black-Scholes Model and the Cox-Ross-Rubenstein (Binomial) Model's to price Options. That was nearly 15 years ago and I was wondering if there are any new models being used to price Options?

Black-Scholes itself didn't change a lot but we can now adjust it to deal with a lot more complicated factors to price more complicated contracts: stochastic volatility (Heston, Gatheral) stochastic rates (Hull) credit risk dividends Other methods (computing intensive) have also evolved to deal with various types of contracts where BS is not very appropriate choice (e.g. Monte Carlo simulation for path-dependant options).

The volatiltiy surface is just a representation of European option prices as a function of strike and maturity in a different "unit" - namely implied volatility (while the term implied volatility has to be made precise by the model used to convert prices (quotes) into implied volatilities - for example: we may consider log-normal vols and normal vols). Volatility is often preferred over prices, e.g., when considering interpolations of European option prices (although this may introduce difficulties like arbitrage violations, see, e.g., http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1964634 ). A local volatility model can generate a perfect fit to the implied volatility surface via Dupire's...

The way you do it in the first place is a discretization of the Geometric Brownian Motion (GBM) process. This method is most useful when you want to compute the path between $S_0$ and $S_t$, i.e. you want to know all the intermediary points $S_i$ for $0 \leq i \leq t$. The second equation is a closed form solution for the GBM given $S_0$. A simple mathematical proof showed that, if you know the initial point $S_0$ (which is $a$ in your equation), then the value of the process at time $t$ is given by your equation (which contains $W_t$,...


How can we reverse engineer a market-making algorithm (HFT)?

Consider a market participant $A$ who is mechanically following an automated liquidity providing algorithm (HFT) in a number of large cap stocks on a specific exchange. Assume furthermore that we are able to observe all orders placed by $A$ and that we know that the algorithm used by $A$ takes only public market data as input. $A$ starts and ends all trading days with zero inventory. We want to reverse engineer the algorithm used by $A$. Let's call this algorithm $f(...)$. The first step in reverse engineering the algorithm $f(...)$ would be to collect potential input variables to the algorithm that can later be used to infer the exact form of $f(...)$. The first problem we face is which input variables we should collect in order to be able to reverse engineer $f(...)$. To have a starting point we can consider the input variables used in Avellaneda & Stoikov (2008) . In Avellaneda & Stoikov (2008) the authors derive how a rational market maker (non-specialist) should set his bid and ask quotes in a limit order book market. The results are obviously contingent on the assumptions and model choices made in the paper. The optimal bid (or ask) in Avellaneda & Stoikov (2008) is a function of the following inputs: The trader's reservation price, which is a function of the security price ($S$), the market maker's current inventory ($q$) and time left until terminal holding time ($T-t$) The relative risk aversion of the trader ($\gamma$) (obviously hard to observe!) The frequency of new bid and ask quotes ($\lambda_{bid}$ and $\lambda_{ask}$) The latest change in frequency of new bid and ask quotes ($\delta\lambda_{bid}$ and $\delta\lambda_{ask}$) What potential input variables should we collect in order to be able to reverse engineer $f(...)$?

trading algorithmic-trading high-frequency market-microstructure

I'll take a stab at it, but this is a really broad question. A direct answer: Bayesian models often use "probability that the counter-party is informed." Indirect answers: I think your assumption is that the algorithm operates on each stock individually, and has no knowledge of what it's doing in any other stock. But, it is likely that the algorithm is doing some hedging that you don't see yet. You should look at similar products (or build synthetic baskets) and see if your algorithm is changing it's quote sizes/prices when other products' quote sizes/prices change. (It is also possible that...

The risk-neutral measure $\mathbb{Q}$ is a mathematical construct which stems from the law of one price , also known as the principle of no riskless arbitrage and which you may already have heard of in the following terms: "there is no free lunch in financial markets". This law is at the heart of securities' relative valuation , see this very nice paper by Emmanuel Derman ("Metaphors, Models & Theories", 2011) and some part of this discussion. In what follows, assume for the sake of simplicity existence of a risk-free asset ; deterministic and constant rates, with risk-free rate $r$ ;...

There seems to be a basic fallacy that someone can come along and learn some machine learning or AI algorithms, set them up as a black box, hit go, and sit back while they retire. My advice to you: Learn statistics and machine learning first, then worry about how to apply them to a given problem. There is no free lunch here. Data analysis is hard work . Read "The Elements of Statistical Learning" (the pdf is available for free on the website), and don't start trying to build a model until you understand at least the first 8 chapters....

The lead paper in the January 2011 Journal of Finance ( Hendershott, Jones, and Menkveld ) addresses algorithmic trading (AT). In short, they find that AT improves liquidity as measured by bid-offer spreads. Taking the econometrics as correct (it is in the Journal of Finance) the next question is if bid-offer spreads are a sufficient statistic for measuring liquidity (or any other benefits). It is a difficult question to answer because, given current market structure, AT may improve liquidity (as measured by bid-offer spreads), but without data on other market structures, it is hard to say that we wouldn't better...


What is the best way to "fix" a covariance matrix that is not positive semi-definite?

I have a sample covariance matrix of S&P 500 security returns where the smallest k-th eigenvalues are negative and quite small (reflecting noise and some high correlations in the matrix). I am performing some operations on the covariance matrix and this matrix must be positive definite. What is the best way to "fix" the covariance matrix? (For what it's worth, I intend to take the inverse of the covariance matrix.) One approach proposed by Rebonato (1999) is to decompose the covariance matrix into its eigenvectors and eigenvalues, set the negative eigenvalues to 0 or (0+epsilon), and then rebuild the covariance matrix. The issue I have with this method is that: the trace of the original matrix is not preserved, and the method ignores the idea of level repulsion in random matrices (i.e. that eigenvalues are not close to each other). Higham (2001) uses an optimization procedure to find the nearest correlation matrix that is positive semi-definite. Grubisic and Pietersz (2003) have a geometric method they claim outperforms the Higham technique. Incidentally, some more recent twists on Rebonato's paper are Kercheval (2009) and Rapisardo (2006) who build off of Rebonato with a geometric approach. A critical point is that the resulting matrix may not be singular (which can be the case when using optimization methods). What is the best way to transform a covariance matrix into a positive definite covariance matrix? UPDATE: Perhaps another angle of attack is to test whether a security is linearly dependent on a combination of securities and removing the offender.

risk statistics research correlation covariance

Nick Higham's specialty is algorithms to find the nearest correlation matrix. His older work involved increased performance (in order-of-convergence terms) of techniques that successively projected a nearly-positive-semi-definite matrix onto the positive semidefinite space. Perhaps even more interesting, from the practitioner point of view, is his extension to the case of correlation matrices with factor model structures. The best place to look for this work is probably the PhD thesis paper by his doctoral student Ruediger Borsdorf. Higham's blog entry covers his work up to 2013 pretty well.

I'm just providing a global answer to the question, as I think it can be interesting for some beginners in quant finance. The properties given by TheBridge: Normalize $\rho (\emptyset)=0$ This means you have no risk in taking no position. Sub-addiitivity $\rho(A_1+A_2) \leq \rho(A_1)+\rho(A_2)$ Having a position in two different can only decrease the risk of the portfolio (diversification) Positive homogeneity $\rho(\lambda A) = \lambda \rho(A)$ Doubling a position in an asset A doubles your risk. And finally, Translation invariance $\rho(A + x) = \rho(A)-x$ That is, adding cash to a portfolio only diminishes the risk. So a risk-measure is...

There are some cases where you can blend your portfolios using weights directly. One case involves corner portfolios . In this case a linear combination of weights is also efficient. Another case is where you can treat the two separate weights you have produced each as distinct portfolio under the assumption that the correlation between these portfolios is relatively stable. In this scenario, the problem reduces to a two-asset portfolio optimization problem (each asset is simply the linear combination of weights produced via your two methods). The other class of methods involves blending via the expected returns. If you arrived...


Lévy alpha-stable distribution and modelling of stock prices.

Since Mandelbrot, Fama and others have performed seminal work on the topic, it has been suspected that stock price fluctuations can be more appropriately modeled using Lévy alpha-stable distrbutions other than the normal distribution law. Yet, the subject is somewhat controversial, there is a lot of literature in defense of the normal law and criticizing distributions without bounded variation. Moreover, precisely because of the the unbounded variation, the whole standard framework of quantitative analysis can not be simply copy/pasted to deal with these more "exotic" distributions. Yet, I think there should be something to say about how to value risk of fluctuations. After all, the approaches using the variance are just shortcuts, what one really has in mind is the probability of a fluctuation of a certain size. So I was wondering if there is any literature investigating that in particular. In other words: what is the current status of financial theories based on Lévy alpha-stable distributions? What are good review papers of the field?

I recently read "Modeling financial data with stable distributions" (Nolan 2005) which gives a survey of this area and might be of interest (I believe it was contained in "Handbook of Heavy Tailed Distributions in Finance" ). Another more recent reference is "Alpha-Stable Paradigm in Financial Markets" (2008). I'm not aware of anything covering "risk of fluctuations" and this is still certainly not at the center of the field (i.e. most theory still includes some version of Gaussian or mixture of Gaussians). Would also be interested in other references.

From what I remember, there is no real relation between Markov and Martingale, and my intuition was confirmed by this post . Basically, it says that you can say neither of the following: If A is Markov, then A is a martingale. If A is a martingale, then A is Markov. further down the post, you can find two counter examples: $dX_t = a dt + \sigma dW_t$ is Markov but not a martingale and $dX_t = (\int_0^t X_s ds) dW_t$ is a Martingale but is not Markov. As for the assumption of these properties being true, I think it...

There are some cases where you can blend your portfolios using weights directly. One case involves corner portfolios . In this case a linear combination of weights is also efficient. Another case is where you can treat the two separate weights you have produced each as distinct portfolio under the assumption that the correlation between these portfolios is relatively stable. In this scenario, the problem reduces to a two-asset portfolio optimization problem (each asset is simply the linear combination of weights produced via your two methods). The other class of methods involves blending via the expected returns. If you arrived...


Machine Learning vs Regression and/or Why still use the latter?

I come from a different field (Machine learning/AI/data science), but aim to ask a philosophical question with the utmost respect: Why do quantitative financial analysts (analysts/traders/etc.) prefer (or at least seem) traditional statistical methods (traditional = frequentist/regression/normal correlation methods/ts analysis) over newer AI/machine learning methods? I've read a million models, but it seems biased? Background: I recently joined a 1B AUM (I know it's not a ton) asset management firm. I was asked to build a new model for a sector rotation strategy (basically predicting which SP 500 sector would do the best over 6 months-- chose to use forward rolling 6 month returns) they employ and my first inclination was to combine ARIMA (traditional) with random forest (feature selection) and a categorical (based on normal distribution standard deviation) gradient boosted classifier for ETFs in each sector. Not to be rude, but I beat the ValuLine timeliness for each sector. I used the above mentioned returns as my indicator and pretty much threw everthing at the wall for predictors initially (basically just combing FRED), then used randomForest to select features. I ended up combining EMA and percent change to create a pretty solid model that, like I said, beat ValuLine. I've read a lot of literature, and I haven't seen anyone do anything like this. Any help in terms of pointing me in the right direction for literature? Or any answers to the overarching idea of why isn't there more machine learning in equity markets (forgetting social/news analysis)? EDIT: For clarification, I'm really interested in long-term predictions (I think Shiller was right) based on macro predictors. Thanks PS- I've been lurking for a while. Thanks for all the awesome questions, answers, and discussions.

In order to answer your question (for you) you would need something to compare to . You would need numbers to know if it is slower/faster, how much, and if it will impact your system overall. Also knowing your performance goals could narrow down the options. My advice is to take a look at your overall architecture of the sytem you have or intend to build. To just look at QuickFIX is rather meaningless without the whole chain involved in processing information and reacting to it . As an example, say QuickFIX is 100 times faster than some part (in...

In general there are two basic ways to make money out of your option pricing models: Sell side (market maker, risk neutral): You use these models to calculate your greeks to hedge your portfolio, so that you live on the spread. Buy side (market/risk taker): You use your model to find mispriced options in the market and buy/sell accordingly. (A third possibility would be to write fancy books and papers about these models and get rich and/or tenure this way ;-)

Because of: The (extreme) dominance of noise over signal The prevalence of non-repeating patterns (many of which we know are not going to repeat) A pathetic sample size for cross-validation Regime changes due to exogenous events. These are typically in the cross-val window which makes it even worse. (GFC, financial integration, trade law changes, interest rate adjustments by central banks, some idiot in a bank was hiding trades and loses 5 billions dollars, etc). It is well known that non-linear relationships are generally just artefacts of the in sample dataset There is also the following: Much price changes are driven...


Why does the minimum variance portfolio provide good returns?

I've been a researching minimum variance portfolios (from this link ) and find that by building MVPs adding constraints on portfolio weights and a few other tweaks to the methods outlined I get generally positive returns over a six-month to one year time scale. I am looking to build some portfolios that are low risk, but have good long term (yearly) expected returns. MVP (as in minimum variance NOT mean variance) seems promising from backtests but I don't have a good intuition for why this works. I understand the optimization procedure is primarily looking to optimize for reducing variance, and I see that this works in the backtest (very low standard deviation of returns). What I don't have an intuitive feel for is why optimizing variance alone (with no regards to optimizing returns, i.e. no mean in the optimization as in traditional mean-variance optimization) gives generally positive returns. Any explanations?

portfolio-management optimization modern-portfolio-theory covariance

Short of having a 'reasonable' predictive model for expected returns and the covariance matrix, there are a couple lines of attack. Shrinkage estimators (via Bayesian inference or Stein-class of estimators) Robust portfolio optimization Michaud's Resampled Efficient Frontier Imposing norm constraints on portfolio weights Naively, shrinkage methods 'shrink' (of course,no?) your estimates (arrived at using historical data), toward some global mean or some target. Within the mean-variance framework, you can use the shrinkage estimators, for both, the expected returns vector, as well as the covariance matrix. Jorion introduced application of a 'Bayes-Stein estimator' to portfolio analysis. Bradley & Efron have a...

There are some cases where you can blend your portfolios using weights directly. One case involves corner portfolios . In this case a linear combination of weights is also efficient. Another case is where you can treat the two separate weights you have produced each as distinct portfolio under the assumption that the correlation between these portfolios is relatively stable. In this scenario, the problem reduces to a two-asset portfolio optimization problem (each asset is simply the linear combination of weights produced via your two methods). The other class of methods involves blending via the expected returns. If you arrived...