Would
you be interested in an online learning LIVE workshop that will show MotiveWave
live trading and Python trading bots. This will cover both forex and crypto asset
classes. It will be an expansion of the DVD I have put out:
Please
let me know as soon as possible because I want to launch it live
as soon as possible. I will also include weekly live chat sessions to ask me
all the questions you want as well as part of the learning.
A few weeks ago, it seems that we saw quite a few articles with Jim Simons of Rennaissance Technologies. Here is a recent interview with author of the biography put. Here is some highlights from the video:
Josh here – we had Greg Zuckerman of the Wall Street Journal up at the Compound to talk about his new book, The Man Who Solved the Market – the first ever deep dive into Jim Simons and his legendary firm Renaissance Technology. Renaissance has pulled over $100 billion in trading profits out of the markets since its founding decades ago, in a highly secretive approach that combines mathematical equations with cutting edge technology.
Zuckerman conducted over 400 interviews with current and former employees of the firm and even spent a few days with Simons himself – despite the fact that he didn’t want this story told or the book written.
Check it out here.
Thanks Bryan
P.S. Don’t forget the big 25% sale of MotiveWave starts Dec
26 until Jan 1.
My questions is why would Jim Simons talk to a Wall Street
Journal reporter for so long? Then he begs him not to a book. I don’t get it.
Can some one explain this?
Here is the video response:
…we had Greg Zuckerman of the Wall Street Journal up at the
Compound to talk about his new book, The Man Who Solved the Market – the first
ever deep dive into Jim Simons and his legendary firm Renaissance Technology.
Renaissance has pulled over $100 billion in trading profits out of the markets
since its founding decades ago, in a highly secretive approach that combines
mathematical equations with cutting edge technology. Zuckerman conducted over
400 interviews with current and former employees of the firm and even spent a
few days with Simons himself – despite the fact that he didn’t want this story
told or the book written….
If you want the want extra 2 months ($200) for a 3 month
subscription, today is the last chance to get it! I will going back to the
normal pricing tomorrow. Get the 3 month subscription:
Another mathematician gone trader success story. This ones involves who built a net worth to $700 in short order. The firm is called XTX out of London with math and programming whizzes who do market making. The other usual story is they have quadrupled in size since 2015. See the article here.
Also, I am hoping to get new Quant Analytics launched this
week with partial signals working from MotiveWave. This will include live
trading with forex and crypto starting in a few days. This is crucial to start rebuilding
a track record with this new platform I have introduced to you all in the last
few weeks.
You better jump on the initial Quant Analytics now before
the price jumps up in coming weeks. I might even start promoting later in the
week as well.
A little while ago, Ray Dalio said the world was mad. He
also goes into further detail in this video
About: Raymond Dalio is an American billionaire investor,
hedge fund manager, and philanthropist. Dalio is the founder, Co-Chairman and Co-Chief
Investment Officer of investment firm Bridgewater Associates, one of the
world’s largest hedge funds. Bloomberg ranked him as the world’s 58th
wealthiest person in June 2019.
Click here for the Ray Dalio interview video
See below for the latest analysis in my cryptocurrency trading bot.
Question: Are you willing to pay for discounted long term Analytic subscription terms? Let me know.
If
you have not been reading these emails, I have done revamped the store
to my old reactivated Shopify store. I am sure you are not interested in
the benefits, so I will just post the the highlighted links:
It seems the initial 5 models are based on Bayes Theorem for
probability analysis. They use a series
of analysis to compare one against another (.e.g French vs English) to find
patterns to see how connection relate with each other. This is for translation
purposes.
P
Pg 7: We generally follow the common
convention of using uppercase letters to denote random variables and the
corresponding lowercase letters to denote specific values that the random
variables may take. We have already used I and m to represent the lengths of
the strings e and L and so we use L and M to denote the corresponding random
variables.
We need to understand the corresponding random variables.
There is mention of Lagrange multipliers and normalizing predictive data.
Auxiliary functions are used as well to generate desirable
parameters using extrema/maxima analysis. Conditional probabilities are used as
well. On pg 276 there are translation, distortion and fertility probabilities.
Page 282: Model 5 is a powerful but unwieldy ally in the battle to align
translations. It must be led to the battlefield by its weaker but more agile
brethren Models 2, 3, and 4. In fact, this is the raison d’etre of these
models. To keep them aware of the lay of the land, we adjust their parameters
as we carry out iterations of the EM algorithm for Model 5. That is, we collect
counts for Models 2, 3, and 4 by summing over alignments as determined by the
abbreviated S described above, using Model 5 to compute Pr(ale, f). Although
this appears to increase the storage necessary for maintaining counts as we
proceed through the training data, the extra burden is small because the
overwhelming majority of the storage is devoted to counts for t(fle ), and these
are the same for Models 2, 3, 4, and 5.
Page 283 shows the number of translations done which goes
into the millions to determine a small subset of useful words. EM algo used
with maximum likelihood.
Pg 283 Although the entire t array has 2,437, 020,096
entries, and we need to store it twice, once as probabilities and once as
counts, it is clear from the preceeding remarks that we need never deal with
more than about 25 million counts or about 12 million probabilities. We store
these two arrays using standard sparse matrix techniques. We 283 Computational
Linguistics Volume 19, Number 2 keep counts as pairs of bytes, but allow for
overflow into 4 bytes if necessary. In this way, it is possible to run the
training program in less than 100 megabytes of memory. While this number would
have seemed extravag…
Page 293 speaks of Viterbi algo training:
We have already used this algorithm successfully as a part
of a system to assign senses to English and French words on the basis of the
context in which they appear (…
Page 297 table of notation
Appendix B has summary of models. Note especially Log-Likelihood
Objective Function.
Note page 300 iterative improvement.
In order to apply these algorithms, we need to solve the
maximization problems of Steps 2 and 4. For the models that we consider, we can
do this explicitly. T
Page 301: Parameter Reestimation Formulae: In order to apply
these algorithms, we need to solve the maximization problems of Steps 2 and 4.
For the models that we consider, we can do this explicitly.
Equation (73) is useful in computations since it involves only O(lm) arithmetic operations, whereas the original sum over alignments (72) involves 0(I m) operations.
Download entire collection including some old interview with Jim Simons and rare audio speech of Peter Brown and Bob Mercer
