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
Since the announcement of a book about the Renaissance
Technology founder, It is a week about Jim Simons. The thing is that he is
easily the most successful trader or investor of our time. Someone went out and
wrote about some of the lessons learned from this book.
Out of all the articles I have seen over the last few weeks,
this is easily the most useful.
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:
I have put together some articles on what is happening with
Goldman Sachs as more people are leaving. Here are some reasons from the first
article:
Junior traders have been complaining of a new approach at Goldman Sachs. As the firm
focuses on developing stable streams of income from ‘platform’
businesses, volatile trading revenues have gone out of fashion. Insiders
complain there’s less interest in generating alpha and more of a focus on pure
market making, meaning alpha-generating traders feel they’re better off on the
buy-side.
Many seem to be going to Bluecrest but never ask me why.
Here are the other articles leading into Goldman is moving into platform based
businesses which make sense:
I am still looking for feedback from people on this new physical
book I am thinking of self publishing. As said, a darft is in place but I am
not willing to move forward unless people comment on what more they would like
to see in it. This book proposes to speak about 4 main topics:
Your retirement and future taxes
How to hire a coder
Rise of Bitcoin and crypto currency
It generally will speak of why the benefits of algo trading
can help secure your unclear future.
As said, let me know by commenting or responding about this.
I will simply quash the idea if I just see clicks and social media likes.
You can find the article on Quora which is pretty valid as I explain in this video. Also, I hint at how Ren Tech uses these alternative data sources for trading edge for impressive annual returns
I learned these 2 items that I should be logging and considering for future trading logic in my crypto and forex algo trading bots:
Standard deviation
Derivative of raw closing price could be fed into the machine learning as input
Neat. I learned this from someone on my private chat server. Now I learned even more someone in today’s webinar. This video playback will be part of my ELITE membership as well. This is only place you can watch it on demand now.
AS YOU KNOW THIS CHEAPEST GOING UP REALLY REALLY SOON!
