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Can a $10,000 investment yield $1,000,000 in a year? In five years? If so, what is th
Clearly we have a combination of very smart, witty and opinionated folks here carefully crafting posts to this thread! Nonetheless my initial inquiry is still not resolved. Mr III opened an age-old topic regarding how much money can be made in the market. So let's see what other tools Mr III is looking to use to chip away toward his $1 million goal other than Hurst Divergences and Fourier analysis.
With all due respect to the well written and articulate posts by Artesimo, I thought along with "Apocalypse Now" (only mentioned because of my title), that "Good Will Hunting" was a very enjoyable movie, especially for those of us from New England. And I don't think most movie goers actually believe college math professors carry on in real life as depicted in the movie, any more than any other profession Hollywood portrays.
Can you help answer these questions from other members on NexusFi?
I would like to say thank you all for your contribution.
My idea to post a thread in first place was with the intend to create something productive.
In my opinion the issue with the thread is obviously related with the fact that you can generate a large amount of money over a determinate period of time. - By controlling your risk! (no one paid attention to this bit!)
@artisimo, Dear Friend, I could spot miles away the that you were a pure mathematician or just a pure nag (as per this blog you could be very well the one being a total fake!, the ones that I deal on a regular basis, although I don't agree an inch with your comments on Fourier Analyses, Hurst, etc... In order to defend your superiority (a clear illusion of yours!) you had to waive with your mathematics Society Foundation thing (like if I or anyone in my opinion would care about such!) - Isn't that a violation in first place of the your proclaimed principle - reason why you are trying to beat me up so badly? Shame on you, asking the moderator of this forum to ban someone with a different vision than yours. How can you contribute to progression with an attitude like that!
In mathematics, a Fourier series decomposes periodic functions or periodic signals into the sum of a (possibly infinite) set of simple oscillating functions, namely sines and cosines (or complex exponential). The study of Fourier series is a branch of Fourier analysis.
The Fourier series is named in honour of Joseph Fourier (1768–1830), who made important contributions to the study of trigonometric series, after preliminary investigations by Leonhard Euler, Jean le Rond d'Alembert, and Daniel Bernoulli. Fourier introduced the series for the purpose of solving the heat equation in a metal plate, publishing his initial results in his 1807 Mémoire sur la propagation de la chaleur dans les corps solides (Treatise on the propagation of heat in solid bodies), and publishing his Théorie analytique de la chaleur in 1822. Early ideas of decomposing a periodic function into the sum of simple oscillating functions date back to the 3rd century BC, when ancient astronomers proposed an empiric model of planetary motions, based on deferents and epicycles.
The heat equation is a partial differential equation. Prior to Fourier's work, no solution to the heat equation was known in the general case, although particular solutions were known if the heat source behaved in a simple way, in particular, if the heat source was a sine or cosine wave. These simple solutions are now sometimes called eigensolutions. Fourier's idea was to model a complicated heat source as a superposition (or linear combination) of simple sine and cosine waves, and to write the solution as a superposition of the corresponding eigensolutions. This superposition or linear combination is called the Fourier series.
From a modern point of view, Fourier's results are somewhat informal, due to the lack of a precise notion of function and integral in the early nineteenth century. Later, Dirichlet and Riemann expressed Fourier's results with greater precision and formality.
Although the original motivation was to solve the heat equation, it later became obvious that the same techniques could be applied to a wide array of mathematical and physical problems, and especially those involving linear differential equations with constant coefficients, for which the eigensolutions are sinusoids. The Fourier series has many such applications in electrical engineering, vibration analysis, acoustics, optics, signal processing, image processing, quantum mechanics, econometrics, thin-walled shell theory, etc.
Since Fourier's time, many different approaches to defining and understanding the concept of Fourier series have been discovered, all of which are consistent with one another, but each of which emphasizes different aspects of the topic. Some of the more powerful and elegant approaches are based on mathematical ideas and tools that were not available at the time Fourier completed his original work. Fourier originally defined the Fourier series for real-valued functions of real arguments, and using the sine and cosine functions as the basis set for the decomposition.
Many other Fourier-related transforms have since been defined, extending the initial idea to other applications. This general area of inquiry is now sometimes called harmonic analysis. A Fourier series, however, can be used only for periodic functions, or for functions on a bounded (compact) interval.
The importance of harmonic Analyses in Financial Markets and Related Fields
Harmonic analysis is a branch of mathematics concerned with the representation of functions or signals as the superposition of basic waves, and the study of and generalization of the notions of Fourier series and Fourier transforms. In the past two centuries, it has become a vast subject with applications in areas as diverse as signal processing, quantum mechanics, and neuroscience.
The term "harmonics" originated in physical eigenvalue problems, to mean waves whose frequencies are integer multiples of one another, as are the frequencies of the harmonics on stringed musical instruments, but the term has been generalized beyond its original meaning.
The classical Fourier transform on Rn is still an area of ongoing research, particularly concerning Fourier transformation on more general objects such as tempered distributions. For instance, if we impose some requirements on a distribution f, we can attempt to translate these requirements in terms of the Fourier transform of f. The Paley–Wiener theorem is an example of this. The Paley–Wiener theorem immediately implies that if f is a nonzero distribution of compact support (these include functions of compact support), then its Fourier transform is never compactly supported. This is a very elementary form of an uncertainty principle in a harmonic analysis setting. If you would like to consult further, the topic is: Convergence of Fourier series.
Fourier series can be conveniently studied in the context of Hilbert spaces, which provides a connection between harmonic analysis and functional analysis.
A Touch of Chaos
Chaos brings to mind images of complete randomness, of disorder and anarchy. It is a messy room, a mob rushing down a city street and a swarm of bees. In 1986, at a conference on mathematical chaos held by the Royal Society in London , mathematicians were asked to define the ``chaos'' that had become the buzzword for their hot research area. After much deliberation, they offered the following:
Stochastic behavior occurring in a deterministic system.
As definitions go, this one is particularly constipated and quite far from fostering any intuition about the subject. In Stewart's Does God Play Dice?, he claims knowledge of the etymology of stochastic in the statement, ``The Greek word stochastikos means `skillful in aiming' and thus conveys the idea of using the laws of chance for personal benefit.''
According to Stewart, stochastic behavior is probabilistic behavior. Probability is that ``other branch'' of mathematics - the one that can't give any specific answers to the outcomes of systems, but can predict how likely a particular outcome is. A single die roll is a probabilistic system: there's a one in six chance that the roll will end with the four face up. We can't predict the outcome of the die roll, but we can assign some numbers to how often certain events will happen.
By placing both stochastic and deterministic in the same definition, the mathematicians have formed a bridge between the two sciences - two sciences that were regarded as mutually exclusive until then. Chaos is the study of deterministic systems that are so sensitive to measurement that their output appears random.
Edward Lorenz found out all of that the hard way. In 1961, he had managed to create a skeleton of a weather system from a handful of differential equations. He kept a continuous simulation running on an extremely primitive computer that would output a day's progress in the simulation every minute as a line of text on a roll of paper. Evidently, the whole system was very successful at producing ``weather-like'' output - nothing ever happened the same way twice, but there was an underlying order that delighted Lorenz and his associates.
Sensitivity to initial conditions (please note the similarity and inter-operation)
Sensitivity to initial conditions means that each point in such a system is arbitrarily closely approximated by other points with significantly different future trajectories. Thus, an arbitrarily small perturbation of the current trajectory may lead to significantly different future behaviour. However, it has been shown that the last two properties in the list above actually imply sensitivity to initial conditions and if attention is restricted to intervals, the second property implies the other two (an alternative, and in general weaker, definition of chaos uses only the first two properties in the above list). It is interesting that the most practically significant condition, that of sensitivity to initial conditions, is actually redundant in the definition, being implied by two (or for intervals, one) purely topological conditions, which are therefore of greater interest to mathematicians.
Sensitivity to initial conditions is popularly known as the "butterfly effect", so called because of the title of a paper given by Edward Lorenz in 1972 to the American Association for the Advancement of Science in Washington, D.C. entitled Predictability: Does the Flap of a Butterfly’s Wings in Brazil set off a Tornado in Texas? The flapping wing represents a small change in the initial condition of the system, which causes a chain of events leading to large-scale phenomena. Had the butterfly not flapped its wings, the trajectory of the system might have been vastly different.
A consequence of sensitivity to initial conditions is that if we start with only a finite amount of information about the system (as is usually the case in practice), then beyond a certain time the system will no longer be predictable. This is most familiar in the case of weather, which is generally predictable only about a week ahead.
Most of the users of this forum use in a daily basis Elliott wave, W. D. Gan, Hilbert Transform, Fibonacci Projections, Hurst, Elhers, etc... based indicators or strategies. All those are based in some degree on the studies of the cyclical behavior of the market.
Fast Fourier Transformation applied to the pattern recognition.
For the skeptics and critics (less @Zondor) here it is an indicator (because it seems that you like to see to believe) that helps recognise repeated waves on a chart. The regular assumption for for designing the indicator is that price changes are cyclic. Since there are a a great deal of “random” changes in the price we cannot just match or adjust one of the periods to another. We need somehow to filter the small changes and keep only the main price trend. Fast Fourier transformation (FFT) have been used to get the main trend of price and filer the random noise. A great example how "trivial" mathematics can achieve something complex and actually functional.
As well it was added to the indicator an Harmonic Selector, this way a Single Harmonic can be Displayed.
I know that you will immediately will mention that Fourier Analyses is not suitable for real market condition, that's where I beg to differ and set forward to demonstrate (it's a simple concept with a twist!).
Start of Code
--Initial statement
local DEBUG = false
-- Indicator profile initialization routine
-- Defines indicator profile properties and indicator parameters
function Init()
indicator:name("Fourier signal transformation");
indicator:description("Transforms price by FFT and then draws the signal");
indicator:requiredSource(core.Tick);
indicator:type(core.Indicator);
indicator.parameters:addGroup("Calculation");
indicator.parameters:addInteger("N", "N Periods to calc", "How many periods to calculate", 256, 128, 2048);
local i;
for i = 7, 11 do
local Num = math.pow(2, i);
indicator.parameters:addIntegerAlternative("N", Num, "", Num);
end
indicator.parameters:addInteger("Shift", "N Periods of shift", "Shift future and past by N periods", 0, 0, 2048);
indicator.parameters:addInteger("MaxH", "Maximum Harmonic", "Maximum harmonic to display", 4, 2, 11);
indicator.parameters:addGroup("Harmonic Selector");
indicator.parameters:addBoolean("Single", "Display Single Harmonic ", "", false);
indicator.parameters:addInteger("Number", "Single Harmonic Number","", 2, 2, 11);
--indicator.parameters:addBoolean("Smooth", "Draw transition line", "Draw transition line between past, future and current waves", true);
indicator.parameters:addGroup("Style");
indicator.parameters:addColor("color_past", "Color of past", "Color of past", core.rgb(128, 0, 0));
indicator.parameters:addColor("color_curr", "Color of current", "Color of current", core.rgb(0, 128, 0));
indicator.parameters:addColor("color_future", "Color of future", "Color of future", core.rgb(0, 0, 128));
end
local first;
local source = nil;
local Single;
-- Streams block
local stream = {};
local NPeriods = nil;
local Shift = nil;
local MaxH = nil;
local Number;
local Smooth = false
local aFFT = nil;
local logN = nil;
local calcDate = nil;
-- Routine
function Prepare()
source = instance.source;
first = source:first();
Number=instance.parameters.Number;
Single=instance.parameters.Single;
local name = profile:id() .. "(" .. source:name() .. ")";
instance:name(name);
NPeriods = instance.parameters.N;
Shift = instance.parameters.Shift;
Smooth = instance.parameters.Smooth;
logN = math.floor(math.log(NPeriods)/math.log(2));
MaxH = math.min(logN,instance.parameters.MaxH);
local i;
for i = 1, MaxH do
stream[i] = {};
end
bookmarks = instance:addInternalStream(first);
if DEBUG then core.host:trace("Prepare"); end
core.host:execute("addCommand", 1, "Set FFT base point", "Sets the point from which FFT analysis is started");
core.host:execute("addCommand", 2, "Set FFT shift", "Sets the point where past wave is drawn");
end
local pattern = "([^;]*);([^;]*)";
function AsyncOperationFinished(cookie, success, message)
-- check that this is our "Set FFT base point" command
if cookie == 1 then
-- retrieve the coordinates of the point to which the user clicked
local date;
local level;
local pos;
level, date = string.match(message, pattern, pos);
--local date2 = core.dateToTable(date);
--local label = level .. ";" .. string.format("%d/%d/%d %d:%d:%d EST", date2.year, date2.month, date2.day, date2.hour, date2.min, date2.sec);
-- draw a label
--core.host:execute("drawLabel", 1, tonumber(date), tonumber(level), label);
-- saving the date
calcDate = date;
-- recalcing the FFT
CalcFFT(getPeriodId());
for i = 2, MaxH do
DrawFFT(i);
end
end
-- check that this is our "Set FFT shift" command
if cookie == 2 then
-- retrieve the coordinates of the point to which the user clicked
local date;
local level;
local pos;
level, date = string.match(message, pattern, pos);
local period = findDateFast(source, date, false);
local basePeriodId = getPeriodId();
local newshift = basePeriodId - period - NPeriods;
if newshift < 0 then
newshift = 0;
end
if newshift > NPeriods then
newshift = NPeriods
end
-- saving the new shift
Shift = newshift;
-- recalcing the FFT
CalcFFT(getPeriodId());
for i = 2, MaxH do
DrawFFT(i);
end
end
end
-- Indicator calculation routine
-- TODO: Add your code for calculation output values
function Update(period)
if period == source:size()-1 then
if aFFT == nil then
CalcFFT(source:size()-1);
end
for i = 2, MaxH do
DrawFFT(i);
end
end
end
function ClearStream(stream)
local i;
for i = 0, stream:size() - 1 do
stream[i] = nil;
end
end
function DrawFFT(NHarm)
if aFFT == nil then
return;
end
if Single and Number ~= NHarm then
return;
end
local last = getPeriodId();
local harm1 = DupArray(aFFT);
local i;
for i = math.floor(math.exp(math.log(2)*NHarm)), harm1:size() - 1 do
harm1[i] = 0;
end
if DEBUG then traceArray("FFT_h"..NHarm, harm1); end
core.ifft(harm1);
if DEBUG then traceArray("IFFT_h"..NHarm, harm1); end
for i = 0, NPeriods - 1 do
stream[NHarm].curr[last - i] = harm1[i];
end
for i = 0, NPeriods - 1 do
stream[NHarm].future[last + i + 1 + Shift] = harm1[NPeriods - i - 1];
end
for i = 0, NPeriods - 1 do
stream[NHarm].past[math.max(last - NPeriods - i - Shift, first)] = harm1[i];
end
if Smooth then
-- cutExtremums
local firstExt, lastExt;
firstExt, lastExt = Extremums(harm1, 0, NPeriods - 1);
if DEBUG then
core.host:trace("firstExt: "..firstExt);
core.host:trace("lastExt: "..lastExt);
end
local P0 = {};
local P3 = {};
local P01 = {};
local P31 = {};
P0.x = last - lastExt;
P0.y = stream[NHarm].curr[P0.x];
for i = 0, firstExt - 1 do
stream[NHarm].curr[last - i] = nil;
stream[NHarm].past[last - (NPeriods + Shift + i)] = nil;
end
for i = lastExt, NPeriods - 1 do
stream[NHarm].curr[last - i] = nil;
stream[NHarm].future[last + Shift + (NPeriods - i)] = nil;
end
end
end
function CalcFFT(last)
-- TODO: add check with last period involved
if source:size() < NPeriods then
return;
end
if DEBUG then core.host:trace("NPeriods type:"..type(NPeriods)); end
local harmonycs = core.makeArray(NPeriods);
local i;
-- saving of calcDate
calcDate = source:date(last);
for i = 0, NPeriods - 1 do
harmonycs[i] = source[last - i];
end
core.fft(harmonycs);
if DEBUG then traceArray("FFT", harmonycs); end
aFFT = harmonycs;
end
function DupArray(src)
local target = core.makeArray(src:size());
local i;
for i = 0, src:size() - 1 do
target[i] = src[i];
end
return target;
end
function traceArray(title, array)
local desc = title..": ";
for i = 0, array:size() - 1 do
desc = desc .. string.format(" %.5f\n", array[i]);
end
core.host:trace(desc);
end
--
--
function DrawBezier(output, P0, P1, P2, P3)
local B;
local t, x, y;
local desc = "t:";
for t = 0, 1, 1/(5*(math.max(P0.x,P3.x) - math.min(P0.x,P3.x))) do
x = math.floor(
P0.x*(1-t)^3
+ 3*P1.x*(1-t)^2*t
+ 3*P2.x*(1-t)^1*t^2
+ P3.x*t^3
);
output[x] =
P0.y*(1-t)^3
+ 3*P1.y*(1-t)^2*t
+ 3*P2.y*(1-t)^1*t^2
+ P3.y*t^3
;
desc = desc..string.format(" %.2f %.2f %.2f\n", t, x, output[x]);
end
if DEBUG then core.host:trace(desc); end
end
function DrawBezier2P(output, P0, P3)
local P1 = {};
local P2 = {};
if DEBUG then
core.host:trace(string.format("P0 %.5f %.5f\n", P0.x, P0.y));
core.host:trace(string.format("P3 %.5f %.5f\n", P3.x, P3.y));
end
local diff = (math.max(P0.x,P3.x) - math.min(P0.x,P3.x));
local dev = math.floor(diff/2);
function Extremums(stream, startOffs, endOffs)
local ext = nil;
local oldValue = stream[startOffs];
local newValue;
local i;
local currValue = stream[startOffs + 1];
local min, max;
minIdx, maxIdx = minmax(stream, startOffs, endOffs);
if minIdx > maxIdx then
return maxIdx, minIdx;
else
return minIdx, maxIdx;
end
return nil;
end
function FirstExtremum(stream, startOffs, endOffs)
local ext = nil;
local oldValue = stream[startOffs];
local newValue;
local i;
local currValue = stream[startOffs + 1];
for i = (startOffs + 2), endOffs do
newValue = stream[i];
if ((currValue > oldValue)
and (currValue > newValue))
or
((currValue < oldValue)
and (currValue < newValue))
then
-- found extremum
return i - 1;
end
oldValue = currValue;
currValue = newValue;
newValue = nil;
end
return nil;
end
function LastExtremum(stream, startOffs, endOffs)
local ext = nil;
local oldValue = stream[endOffs];
local newValue;
local i;
local currValue = stream[endOffs - 1];
for i = (endOffs - 2), startOffs, -1 do
newValue = stream[i];
if ((currValue > oldValue)
and (currValue > newValue))
or
((currValue < oldValue)
and (currValue < newValue))
then
-- found extremum
return i + 1;
end
oldValue = currValue;
currValue = newValue;
newValue = nil;
end
return nil;
end
function minmax(arr, startOffs, endOffs)
local min, max;
min = arr[startOffs];
max = arr[startOffs];
minIdx = startOffs;
maxIdx = startOffs;
for i = startOffs + 1, endOffs do
if arr[i] < min then
minIdx = i;
min = arr[i];
end
if arr[i] > max then
maxIdx = i;
max = arr[i];
end
end
return minIdx, maxIdx;
end
function getPeriodId()
local period;
period = findDateFast(source, calcDate, false);
-- if no period found return 0
if (period < 0) then
return 0;
else
return period;
end
end
-- ------------------------------------------------------------------------------
-- Find the specified date in the specified stream
--
-- The function uses the binary search algorithm, so it requires only O(n) = log2(n) operations
-- to find (or to do not find) the value.
--
-- The function compares the date and time rounded to the whole seconds.
--
-- Parameters:
-- stream The price stream to find the date in
-- date Date and time value to be found
-- precise The search mode
-- In case the value of the parameter is true, the function
-- Searches for the the exact date and returns not found in the
-- date is not found.
-- In case the value of the parameter is false, the function
-- returns the period with the biggest time value which is smaller
-- than the value of the date parameter.
-- Returns:
-- < 0 The value is not found
-- >= 0 The index of the the period in the stream
-- ----------------------------------------------------------------------------------
function findDateFast(stream, date, precise)
local datesec = nil;
local periodsec = nil;
local min, max, mid;
datesec = math.floor(date * 86400 + 0.5)
min = 0;
max = stream:size() - 1;
while true do
mid = math.floor((min + max) / 2);
periodsec = math.floor(stream:date(mid) * 86400 + 0.5);
if datesec == periodsec then
return mid;
elseif datesec > periodsec then
min = mid + 1;
else
max = mid - 1;
end
if min > max then
if precise then
return -1;
else
return min - 1;
end
end
end
end
End of Code - Parse it to C# and you should be pleased with the results of this indicator. I'm not supporting any further query's regarding the code, it should be fairly easy to understand, or other wise I'm quite sure that fellow @artisimo will be keen to help, as it's actually basic stuff.
Indicator Interpretation
If the price will not follow the blue wave it will be just safer to exit the market and try to predict/identify another wave.
If one cannot recognize any pattern on the chart, it suggested to change the analyses parameters - simply changing the number of periods in the indicator settings (usually the bigger the period better results, fewer results but more accurate). Also it may indicate that the market is not predictable at the moment (e.g. on news events), that's one of the reason why a succession of Hurst Exponents have been add to filter such market conditions[?].
There is a fundamental distinction between Applied Mathematics and Pure Mathematics. Seems that @artimiso is a Pure Mathematician (I don't question if he is or not, but in the case of being and belonging to the Mathematics Society - Kind of the Inquisition of the World of Mathematics , of course this year carnival come way ahead, and of course I still don't care!)
In fact I don't really care what ever you do as individuals or group or associations, or how much money you have or make in a daily basis (good for you!) as long as the debate can be useful and can bring ideas to the table I welcome everyone.
Once more I do apologise to all if any of my comments and/or remarks have been rude or inappropriate, I can guarantee that was never my intention. As well I would like to clarify that some of my functions are actually related with teaching and once more my Dear Fellow @artimiso just assumed that I don't teach Mathematics (or anithing for the matter), he decided to pick up on my english in first place followed by the mathematics. Like I said previously in a post to this thread it should be total transparent to the topic of the thread if I'm a Mathematician, a Baker and part time Clown, a Surfer a Millionaire or all of the above.
As well I have not a clue what relation Optimus Futures have with the thread, we probably have anther mathematical problem to solve, but I'm passing on that one! Another assumption from my Dear Fellow @artisimo with out any foundation, some Fellows live the conspiracy theory quite vividly!
The start - one question in the start of the thread, the question is simple yet quite complicated to reply, never the less, immediately I made available the answer.
As posted previously, please be so kind to read the book: Profit Magic of Stock Transaction Timing, Prentice Hall Trade (January 1973)
The fact that most of you passed through the answer without even paying attention, proved my point and the matter in first place (one thinks to fast in regards to what one wants to achieve in first place!), further could be decomposed from here. I am totally sure that I placed the thread under the right category "Psychology and Money Management".
Sadly I'm abandoning the thread without achieving any valid participation on the topic, I acknowledge that my dear fellows rather prefer to attack, attack... and attack again... after the lat attack, repeat in the same order!
As per the original post, "Can a $10,000 investment yield $1,000,000 in a year? In five years? If so, what is the risk involved?" followed by "Many people still don't understand what I do nor they understand the nature of my research. They compare it to already beaten channel analysis or moving average analysis. As an example, it isn't what Hurst used, it is how and why he used it that is important. Hurst was the first to point the relation of proportionality of time/price swings with the periodicity of indicators as well as synchronicity of time cycles and he laid out mathematical foundation for computerized measurements (some of what I teach). This requires some technical/programming skill and what's more important good sense of proportion."
I actually must confess that over the years I become quite fed-up with personalities such as @artimiso, although I can understand that the nature of my work, opinions and demonstration very rarely please such personalities. Well that's the basic structure of debate, one proposes, one opposes, them it's a matter of demonstration.
I really would like that @artimiso could show the decomposition and demonstration of a "simple Fourier formulation" as I asked, but I suppose that such is never going to happen, first because I'm not replying any further into this matter publicly or either believe that @artimiso can pick up the presented two parts and deduct the missing ones, simple like that!
Please note, that I'm not an individual that decided to come to a nice forum to super impose my ideas or trying to call all of you something not nice, I'm not trying to demolish egos, quite contrarily - as I learn every single day from my experiences, inter-actions and others opinions, if anything I live reduced every single day to humbleness as I'm constantly challenged by the nature of my work for years, when I think that something major have been accomplished, just to learn that the journey carries on!
Couldn't agree more with @wldman, I believe that every single one of us have a motivation to join blogs like the one presented by Big Mike. I wish I could say the same about making money out of the markets on a daily basis, there are days that insist in not going as planned, I believe that is also part of ones journeys through the markets.
We are all in total agreement , that there are traders of all levels, intellects of all sizes and incredible egos that can't take any standard deviation from the "holly norm"at all, actually quite sad!
Well seems that cinema was use as vehicle to make ridiculous of my person, I have to say in that regard the following.
Adapting to Intelligence
After reading in this forum about such “disorders” as the one that @artimiso seems to suffer from, I am not sure if I should be removing my two favorite signs from the wall of my office:
Gone crazy. Be back shortly.
Anything worth doing is worth overdoing.
People like to say there is a thin line between genius and psychosis. And there are many famous cases where mathematicians fell over the line (Go see the movie “A beautiful mind”. And to get a clearer picture of how “hollywood” sees us make sure you also see: “Proof”, “Pi” and “Good WIll Hunting”. But remember that this is fiction and represents just how others see us.) The problem is that mathematics did not make us “legally” insane, or we could walk away and take care of it. Rather, it was precisely these characteristics which drove us into an area which finds all our strange behaviors as completely normal – even desirable.
Employers like us because we question everything – even those things they have held sacred forever. This gives them a chance of making real needed changes in their institution. But these same qualities can alienate those around us who don’t like having someone questioning everything. Their worlds are comfortable precisely because they don’t constantly question their surroundings.
As if things are not bad enough, it is almost impossible to tell a non-mathematician what we are doing. They don’t have the patience or blackboard space to contain the 12 definitions we need to begin the discussion. And if we really try to explain ourselves, we just look even more abnormal to someone who can not comprehend why anyone in this universe – or any parallel universe – could possibly derive excitement from this.
As the mathematician Janet Tremain puts it:
Intelligence is maladaptive.
Please accept my apologies as a final word and enjoy the money making process by protecting your principal in first place.
, mathematicians were asked to define the ``chaos'' that had become the buzzword for their hot research area. After much deliberation, they offered the following:
