Jim Simons Trading Secrets 1.1 MARKOV Process
Summary
TLDRCe script vidéo explore les stratégies de trading quant de Jim Simons et son fonds Medallion, qui a réalisé un rendement de 39% net des frais sur trois décennies. Il introduit le processus de Markov et son application dans la modélisation des marchés financiers, en se concentrant sur la prédiction de la probabilité des états futurs en se basant sur l'état actuel. Le script propose également une démonstration pratique de la façon de coder et d'appliquer ces concepts dans la stratégie de trading, en utilisant des exemples concrets et des analyses de données historiques pour établir des probabilités de performance sur le marché.
Takeaways
- 🧠 Jim Simons et son fonds Medallion sont considérés comme l'un des plus grands succès de la trading quantitatif, montrant que la stratégie basée sur les quants peut être extrêmement lucrative.
- 💡 La stratégie de trading de Simons est secrète, mais certaines idées peuvent être déduites à travers la lecture et l'analyse de livres pertinents.
- 📚 Le livre mentionné dans le script a été une source d'inspiration pour les stratégies de trading personnelles et les cours enseignés.
- 🔢 L'importance de la chaîne de Markov est soulignée, où les modèles stochastiques basés sur cette chaîne peuvent prédire l'avenir avec une certaine précision.
- 🔄 La stratégie de inversion de la moyenne, basée sur l'idée que les prix ont tendance à revenir à la moyenne après un mouvement initial, est un élément clé des stratégies enseignées.
- 📉 Les performances exceptionnelles du fonds de Simons, en particulier pendant les périodes de récession, montrent l'efficacité des stratégies de trading quantitatifs en environnements de forte volatilité.
- 🤖 Le script mentionne l'utilisation de modèles de machine learning pour améliorer la prédiction des marchés, une méthode qui a été employée par les traders de haut niveau.
- 📈 L'analyse des performances de la stratégie de inversion de la moyenne sur le SPY démontre son efficacité, en particulier pendant les périodes de récession et de volatilité.
- 📝 L'importance de la matrice de transition dans le cadre du processus de Markov est expliquée, servant à calculer les probabilités de changement d'état du marché.
- 💻 Un exemple concret d'application du processus de Markov à la trading est présenté, en utilisant des données historiques pour établir des probabilités de performance future.
- 🌐 L'idée finale est que les outils et les compétences pour traiter les données, comme le processus de Markov, sont essentiels pour créer des stratégies de trading performantes.
Q & A
Quel est le rendement net des frais du fonds Medallion de Jim Simons sur trois décennies ?
-Le fonds Medallion de Jim Simons a réalisé un rendement net de 39% sur trois décennies.
Quel est le rôle d'Ax en tant que membre du fonds Medallion ?
-Ax était un employé du fonds Medallion, faisant partie de l'équipe de Jim Simons, et était reconnu pour être un génie mathématicien ayant écrit de nombreux articles académiques impressionnants.
Quelle est la signification d'un processus de Markov dans le contexte du trading quantitatif ?
-Un processus de Markov est une séquence aléatoire d'événements où les probabilités futures dépendent de l'état actuel, pas de l'état précédent, ce qui est utilisé pour prédire avec une certaine précision les mouvements du marché à partir d'un modèle capable.
Quel est le lien entre le processus de Markov et les stratégies de trading de Jim Simons ?
-Bien que le processus de Markov soit utilisé dans de nombreux domaines, il est impliqué dans les stratégies de trading du fonds Medallion de Jim Simons, bien que les détails spécifiques de son utilisation soient extrêmement secrets.
Quel est le concept de stratégie de réversion à la moyenne mentionné dans le script ?
-La stratégie de réversion à la moyenne est basée sur l'idée que les prix ont tendance à revenir vers une moyenne après un mouvement initial vers le haut ou le bas. Cette stratégie consiste à acheter après une baisse anormalement faible ou à vendre après une hausse.
Quels sont les avantages d'une stratégie de réversion à la moyenne pendant une période de récession ?
-Pendant une période de récession, où la volatilité est extrêmement élevée, les stratégies de réversion à la moyenne ont tendance à bien se comporter, comme le démontre les performances exceptionnelles du fonds Medallion en 2007 et 2008.
Quelle est la différence entre une stratégie basée sur le processus de Markov et une stratégie de réversion à la moyenne ?
-Une stratégie basée sur le processus de Markov utilise des modèles stochastiques pour prédire les mouvements futurs du marché en se basant sur l'état actuel, tandis qu'une stratégie de réversion à la moyenne s'appuie sur l'idée que les prix reviennent vers une moyenne après un mouvement significatif.
Comment les stratégies de trading basées sur les modèles de Markov peuvent-elles être codées et testées ?
-On peut coder et tester des stratégies basées sur les modèles de Markov en utilisant des langages de programmation comme Python, en calculant des matrices de transition et en utilisant ces matrices pour prédire les probabilités de performance du marché à court terme.
Quels sont les outils et les bibliothèques Python nécessaires pour implémenter un modèle de Markov dans le trading ?
-Pour implémenter un modèle de Markov, on peut utiliser des bibliothèques Python telles que YFinance pour télécharger des données de marché, Pandas et Numpy pour manipuler et analyser les données, et Pine Editor pour tester des stratégies de trading.
Comment les modèles de Markov peuvent-ils être améliorés avec l'apprentissage automatique ?
-Les modèles de Markov peuvent être améliorés en utilisant des techniques d'apprentissage automatique pour ajuster les paramètres du modèle et prédire les probabilités de transition plus efficacement, en s'appuyant sur de grandes quantités de données historiques.
Outlines
😎 Explication du fonds Medallion de Jim Simons et de la stratégie basée sur les quants
Ce paragraphe aborde la performance exceptionnelle du fonds Medallion de Jim Simons, qui a généré un rendement de 39% nets des frais sur trois décennies. Il souligne l'intelligence et la richesse de Simons, reconnu comme l'un des meilleurs traders de tous les temps, dépassant même des légendes comme Warren Buffett ou Charlie Munger. La stratégie du fonds est secrète, mais on peut tirer des idées de son approche quantitative à travers un livre consulté par le narrateur. Ce livre inspire également les stratégies personnelles du narrateur. L'objectif de la vidéo est d'explorer certaines de ces idées, notamment la chaîne de Markov et la stratégie de réversion à la moyenne, et de les coder pour analyser les résultats.
📚 Introduction à la chaîne de Markov et à la stratégie de réversion à la moyenne
Le narrateur explique le concept de chaîne de Markov, où les événements futurs sont prévisibles avec une certaine précision en se basant sur l'état actuel plutôt que le passé. Il cite Ax, un ancien employé de Jim Simons et génie des mathématiques, qui a utilisé cette théorie pour créer des équations stochastiques. Ensuite, il mentionne le travail d'un autre mathématicien, Lo, qui a développé une stratégie de réversion à la moyenne, profitant de la tendance des prix à revenir à leur niveau moyen après un mouvement initial. Le narrateur observe également la performance de cette stratégie pendant les périodes de récession, en particulier en 2007 et 2008, où elle a généré des rendements élevés malgré la hausse de la volatilité.
🔢 Application de la chaîne de Markov et de la réversion à la moyenne dans les stratégies de trading
Le narrateur entreprend de calculer les probabilités de performance du marché à l'aide de la chaîne de Markov, en utilisant des exemples simples pour illustrer comment les probabilités sont basées sur l'état actuel plutôt que sur les événements précédents. Il présente ensuite une matrice de transition, qui est un outil pour modéliser les probabilités de changement d'état du marché. Cette matrice est utilisée pour prédire les mouvements du marché en se basant sur des données historiques, et le narrateur propose également de recourir à des modèles de machine learning pour améliorer ces prédictions.
📉 Exemple concret avec les données historiques du S&P 500 (SPY)
Le narrateur passe à une démonstration实务的, en utilisant les données historiques du S&P 500 (SPY) pour calculer les probabilités de performance à l'aide de la chaîne de Markov. Il explique comment télécharger et manipuler les données avec Python, en utilisant des bibliothèques comme Pandas et Numpy. Il calcule les retours quotidiens et les états du marché (haut ou bas), puis crée une matrice de transition pour déterminer les probabilités de continuation ou de changement des tendances. Il met en évidence les performances de la stratégie de réversion à la moyenne après une séquence de jours à la baisse.
🚀 Optimisation de la stratégie de trading avec le processus de Markov
Le narrateur propose d'optimiser la stratégie de trading en utilisant le processus de Markov pour déterminer les conditions d'entrée et de sortie plus efficaces. Il illustre cela avec un exemple de stratégie basée sur une séquence de six jours à la baisse, qui a une probabilité élevée de suivre un jour à la hausse. Il backteste cette hypothèse et montre que, même avec une condition simple, la stratégie peut être performante. Il encourage les téléspectateurs à explorer davantage en combinant plusieurs conditions et en appliquant la stratégie à de multiples actions, ce qui peut conduire à une réduction significative des baisses de portefeuille.
👋 Conclusion de la vidéo et invitation à la discussion
Le narrateur conclut la vidéo en remerciant les téléspectateurs et en les invitant à poser des questions ou à demander des éclaircissements dans les commentaires. Il exprime son désir de les aider dans leur parcours de trading quantitatif et espère qu'ils ont apprécié la vidéo. Il souhaite une bonne journée à tous et prend congé.
Mindmap
Keywords
💡Quants
💡Markov chain
💡Stratégie de réversion à la moyenne
💡Volatilité
💡Jim Simons
💡Récessions
💡Modèle stochastique
💡Transition Matrix
💡Anaconda Notebook
💡Y Finance
💡Pine Editor
Highlights
Jim Simons' Medallion fund has achieved a 39% net return over three decades, demonstrating the effectiveness of a quant-based strategy.
Simons is considered one of the greatest traders, surpassing even Warren Buffett or Charlie Munger in performance.
The secretive nature of Simons' fund operations has inspired the exploration of quant strategies inspired by his approach.
Ax, a former employee of Jim Simons and a mathematical genius, focused on Markov chains for predicting market movements.
Markov chains are used to create stochastic equations for trading strategies based on the predictability of future steps from the current state.
Lo, another employee, implemented mean-reverting strategies, capitalizing on the tendency of prices to revert after significant moves.
During the 2007-2008 recession, Simons' fund achieved exceptionally high returns, highlighting the effectiveness of diverging strategies in volatile markets.
Mean-reverting strategies taught in the course have shown significant performance during recessions and the past two years.
The video will demonstrate coding and applying Markov processes to create trading strategies, inspired by Simons' success.
A Markov process is defined as a sequence of events where future probabilities depend only on the current state, not the past.
The Markov process is applicable in various fields, including weather forecasting and quantitative trading.
A simple example of a Markov process is given using the daily activities of a human and a Markov chain model.
Transition matrices are used to calculate the probabilities of market movements based on Markov processes.
Historical data and machine learning models can be utilized to determine the probabilities within the Markov model.
The video includes a practical example of calculating Markov process probabilities using Python, pandas, and numpy.
The calculated probabilities can be used to create entry and exit conditions for trading strategies, enhancing performance.
The Q5 strategy from the course, inspired by Markov models, has shown excellent performance even during market downturns.
The video concludes by emphasizing the importance of using data and tools like the Markov process to create effective trading strategies.
Transcripts
what do you think of quants Jim Simon's
Medallion fund has done 39 net of fees
for three decades which proves that it
works they were very very smart yes they
got very rich very very smart very smart
and very rich yeah and and very high
grade by the way yeah Jim Simons Jim
cement is considered to be one of the
greatest traders of all time who has
Beats and the likes of Warren Buffett or
Charlie Munger and his strategy as being
purely a quan based strategy
um what he does in his fund is extremely
secretive but there are certain ideas
and there are certain concept that we
could get from what he does through this
book that I've been reading and most of
my uh strategies that I've come across
which I do in my personal life has also
been inspired from this book so what
we're going to do today is we're going
to take some of the information that we
can find in this book and start coding
and trying to see the results and try to
figure out what Jim cement has been
doing in this fund so one of the pages
is in the book here it's about ax ax
used to work for Jim Simmons he was part
of the fund and he's also some kind of a
mathematical genius I think he's got
like amazing Papers written by him and
if you can see in this paragraph he
focuses on a thing called Markov chain
so in a Markov chain each step along the
way is impossible to predict with
certainty but future steps can be
predicted with some degree of accuracy
if one relies on a capable model and
they go on to create a stochastic
equation based on this Markov chain
another important thing which is just a
few pages prior to this was this one
loafer again another mathematical genius
working for Siemens and they did more of
a mean inverting strategy so here the
strategies were often based on the idea
that prices tend to revert after an
initial move higher or lower and they
would buy if you just gone right if they
opened at unusually low prices so that
is a typical example of a mean reverting
strategies so at the end of the book one
of the things that I noticed was his uh
trading result and if you can see in
2008 2007 which was basically the
recessionary time frame uh he went on to
make 152 return 136 return that's
substantially higher than any of those
years and you've got to understand that
during recessionary periods the
volatility is extremely high and being
diverting strategies perform extremely
well so even the strategies that we do
in our course especially Q3 and Q5
worked tremendously well during the
2008-2007 recession and also the past
two years so this is one of the
strategies that we teach in the course
Q5 and it's performed very well the past
two years and also in the 2008 recession
this is a mean inverting strategy so if
I can zoom into some of the strategies I
can hear short hair close position there
long here closer position the next day
uh long hair closer position there so
you're we're always going to see lots of
good trades session environment in the
past two years has been really good for
a main reverting strategy so this is the
trading result of that mean inverting
strategy on the spy and if I can look
into that Buy and Hold equity line this
period you see here that was the 2008
recession you can literally see the blue
line which is the S P 500 Buy and Hold
has crashed almost 50 percent but the
main reverse strategy performed
extremely well now if you can go back to
the 2001-2002 period it's literally an X
the Blue Line went down considerably
while our strategy performed extremely
well same thing can be seen the past two
years because 2001 to 2000 2021 2022 and
including now the market still hasn't
recovered you can see from the peak it's
been going down and still isn't like a
consolidation doesn't recover at the
highs but the strategy is performed
extremely well and the reason why is
because of the recessionary environment
recession your volatility based High
wealthily based environment gives great
results for mean inverting strategies so
what we're going to do so if you guys
want to check out this course us feel
free to visit our website at one program
and this strategy comes in the corn
program Prometheus which includes 10
strategies and it also includes many
other important strategies along with
Trend following and momentum based and
also Monte Carlo simulation portfolio
optimization forward testing and all the
other important Quant trading tools
necessary so what we're going to do in
this video is good we're going to
discuss what the Markov process is
because Marco process is what's what we
saw from the book and what is a markup
process and how we can create trading
strategies from the Marco process
so to start off with a markup process is
basically a random sequence of events
where the probabilities of the future is
based on the current state
okay it's not based on the past so
tomorrow's probabilities depends upon
today it's not dependent on yesterday so
for instance if I have to predict the
weather
the weather prediction for tomorrow is
based on today and not yesterday so
Marco process used in many different
fields just not in the quantitative
trading field it's also used in weather
forecasting and many other fields so I
hope you guys understood the definition
of markup process now I'm going to the
example of it so you guys get an idea in
simple terms so let's take two scenarios
so one is a markup guy and the other is
a human being
so let's take the case of a human being
let's take myself as a condition so I
wake up in the morning I wake up at home
and then I go to the shop to buy some
stuff and I buy the stuff and then I go
to work so when I reach the shop I know
I came from home so there's no reason
for me to go back to home so I can go
straight to work so Marco on the other
hand he goes from home and goes straight
to the shop and now he's in the current
state right so in the current state
he can go either home or to work because
he doesn't know what happened the
previous instance as compared to human
being
so the whole Mark of probabilities is
based on this
um
this current state and future State
because the shop is where the corner
state is and once Markov is in that
currency of the shop he can go either to
home or to work but when Markov is at
work he has nowhere else to go so he
goes straight to shop same thing goes
when he's at home he has nowhere else to
go and then he goes straight to the shop
so if you're calculating the
probabilities of the Markov that's when
things get slightly not complicated but
the numbers start to come into play so
when he is at home there's only one
place for him to go and that is to the
shop so there's a hundred percent
probability that he will go to the shop
so then we write one now on the other
scenario when he's at the shop
as I said before he doesn't know where
they where he came back from so he can
go either to home or to work so now
there's a 50 chance for him to go either
home or work
now once Marco reaches work again he has
nowhere else to go so he's got 100
probability that he will go to the shop
so
this is how simple the marker
probability is now if you are putting
this into trading perspective so let's
let's take a trading example into
consideration so forget the thing that's
going on here let's just focus on this
one here so these numbers are
hypothetical numbers so I'm going to
explain to you what it's all about so
you see this positive percentage and
negative percentage so whenever you see
the news you always see the market when
that five percent of the market went
down two percent and things like that so
that's a percentage move for the
specific day so in this percentage
positive percentage move the 0.7 depicts
the probability of the next day being a
positive percentage so if today is a
positive percentage close if today is an
up day
uh then the next days probability is 0.7
now the 0.7 is just a hypothetical
number so don't don't go deep into it as
of now
um so this positive percentage for the
next day to be positive percentage is
0.7 so what will be a negative
percentage it's pretty simple it's 1
minus 0.7 that is 0.3 so you can see the
arrow here that's minus percentage so
similarly when it's today is negative
percentage what is the probability that
the next day will be negative well here
I put in 0.2 so what's the probability
that it will be a positive it's 1 minus
0.2 which is 0.8 now how did I come
across all these numbers well you can
calculate in many ways you can calculate
just based on historical data you can we
can calculate the number of updates the
number of down days and divided by the
uh update and the total number of down
days and we'll get the probability of
the up days and down days and then there
are machine learning models as well so
if you can go through that book one of
the things that they have done is even
off before many years you're talking 30
years or so they've been using machine
learning models but now these days you
can use machine learning models with
just a few lines of code so I hope you
guys understood the whole idea of this
thing of this markup probabilities these
numbers are just hypothetical but now
you can put this into a matrix right
this is called transition Matrix so
you've got the positive percentage
you've got the negative percentage
you've got the positive percentage
you've got the negative percentage here
as well in the columns so a positive
percentage and the next day is a
positive percentage is 0.7 as you saw
here
similarly a positive percentage and the
next day is a negative percentage is 0.3
again negative percentage day and the
next day is a positive percentage day is
0.8 as you can see here and a negative
percentage
followed by the next day a negative
percentage is 0.2 so if you can observe
something 0.7 plus 0.3 is 1 and 0.8 plus
0.2 is again one so this is basically a
transition Matrix so here we have just
taken two days in a row so we can
actually have more rows and more columns
where you can have uh plus plus minus
minus a plus plus minus minus minus you
can have many kinds of permutations and
combinations in this but these is
basically a mark of trading and this is
how we calculate the trading property so
now what we're going to do is we're
going to go to a real world example on
spy and we're going to calculate the
probabilities of the Spy getting a
positive percentage on the next day
following the previous day being the
positive percentage and similarly
negative and negative and negative and
positive so this is the Anaconda
notebook where we'll be calculating the
Marco process and transition
probabilities
um and if you don't know anything about
python
then I would suggest you to go to our
video in our Channel algorithmic trading
in Python so you get the basics of how
to do python so it'll be really
beneficial for you in your Quant trading
Journey you can also do the trading
Viewpoint script if you fancy as well so
now going to the Anaconda notebook so
first thing we do is basically we
download the Y Finance library and the
pandas in the numpy which is necessary
for us to calculate many things then we
download the data so we download the
data for spy from 2010 to 2022 you can
download more data or you can keep the
data smaller so you can access different
time periods so for example if you want
to just assess a recessionary time
period you can just do the 2008 or the
2001.com Bob request so you get the
recessionary environment data as well so
it's up to you really so I've just
randomly chosen 2010 to 2022. and then
we've actually downloaded the data
um and basically you can see the Open
high low close and the just close and
the volume so we need to get up on the
daily return so we're going to take the
adjuster close and Dot percentage change
function and that will give us the
percentage difference between yesterday
and today and also the states so
basically state is where the daily
return is greater or equal to zero we
have got up so we've got the num uh
numpy pandas numpy SNP so that's why we
use NB here so daily routine is greater
than is equal to zero then it's an
update else it's a down day and then
we've stored it in data of state so then
here is the data frame of the data and
you can see the daily return here and
whether it's an up percentage closed or
a down percentage close so
you can see whenever there's a positive
one it's up and whenever there's a
negative one it's down negative here
again it's down uh negative here it's
again down as well so basically uh we're
using uh just pure map to find out the
probabilities as compared to using
machine learning models however uh in
the book they've talked about machine
learning models and that was years ago
so now you can do a machine learning
models just with a few lines of code so
if you guys are stoked about doing this
making this more efficient then go ahead
with the machine learning model as well
but as of now we're just going to make
it simple so you guys can understand the
process so we've got the up counts and
the down counts so up counts is
basically you take the length of the
data of the state where it's up so how
many days has there been up and then
similarly down counts give you the
length of how many days it's been down
so if you can get that information then
we can calculate the probabilities we're
not going to use these two lines of
codes anyway but it's just created to
give you an understanding on how to
calculate the probabilities
so up to up is like two consecutive
positive percentage close uh down to up
the negative day followed by a positive
day and up to down similarly and down to
down two consecutive uh down days
so we calculate the length of the how
many times the days has been like
consecutive updates and then we divided
by the update and that will give us the
probability of a two consecutive updates
similarly uh down to up up to down and
down to down and then we'll do a
transition Matrix where we've got like a
pandas data frame and we're going to put
all these results into like a matrix
kind of a fancy kind of a way and then
we'll print the transition Matrix and
we've got the information so you can see
up to up is 54 percent
up to down is 45 percent
down to up is 57 and down to down is 42.
so the best performing is an update
after a down day so that is 57 so if I'm
going to take any bet in all these four
conditions my bet will be to go uh for
an update after a down day because
there's a 50 57 chance for that to work
out uh now down to down is significantly
lower so it's just 42 so there is
nothing significant for us to uh you
know make a trade in so in all these
numbers these numbers are not that
significant it's on the 50 40 area so I
want something more effective
so let's do another one let's do what's
the probability of update if there is
five consecutive down days so down day
down day down day down day down day and
then we divide it by the length
tier again length of the uh five or six
down days and what is the probability of
that so that probability is 66 percent
now that is a pretty good probability
six to six percent is something that I
can work with so what I'm going to do is
I'm going to take this information that
I've got like five to six days of down
days and the probability of the next day
being an update is pretty good so I'm
going to take this information and I'm
going to back test it so nine times out
of ten I would back test in Army broker
but now just to make things simple I'm
just going to go into Pine editor and
I'm going to do a condition where close
is less than one close to one is less
than close with two so basically
yesterday's close is lower than the
prior day so we've got that condition
for five to six days
and that is our entry if that condition
is met and then we are going to close
our position
if the next day close is higher than uh
today's scores so it's pretty simple and
you can create a markup model for the
exit condition as well so what is the
probability of us having a greater
return if the close is tomorrow or two
days later so then we can calculate a
better probability and create even
better strategy so when you run this
strategy you would see the result as 46
with a drawdown of just five percent so
this is not at all significant when you
look at simple terms but when you look
in the overall perspective it's pretty
good because you can see there's only 20
trays placed if that's from 1994. and
it's only based on one condition so
imagine if you create multiple
conditions or Marco models so here we
did six days of consecutive close below
so what about five days what about four
days what about a combination like up
down up followed by an update or down
down up followed by an updates then you
can add up more and more conditions uh
if the probability is suiting you will
like 60 up and then your net profit
starts clamping up and up and up again
and because you've got more conditions
the drawdowns start to uh be better as
well because if you're basing it on just
one strategy then the drawdowns won't be
that significant so you need to have
more strategy so we did the video uh
just the prior video the gpt4 where we
did a strategy wherein we had a strategy
applied to 25 stocks and also 90 stocks
and how we were able to reduce the
drawdown considerably just by doing the
strategy in multiple stocks similarly if
you can add more conditions to this this
will be spectacular so the whole idea of
this video is for you guys to understand
what a Markov process is to code it in
Python and then apply it in your trading
goal so for example the Q5 strategy that
we did in our course if you can look at
it
um because it's a mean inverting
strategy and also the fact that it's
kind of inspired from the markovia model
you can see how amazingly it performs so
this is the 2001 dot combo where the
market is crashed considerably well and
you can see it's literally a diagonal uh
you know literally across uh similarly
here again 2008 crash where it went down
54 and look at the results of that
strategy again recently past two years
uh the market went down and this
strategy has outperformed the market and
the drawdown is quite minimal as well so
this is the advantage of Mercury model
so in this strategy I took the markovi
model and I effectively did I don't want
to give out much information about it
but the people who know the course uh
know how I combine two different things
I've chosen the days similar to what we
saw here
uh the close is less than one kind of
thing but then I use the exit condition
to be slightly tweaked
um so these are some of the things that
you can efficiently do in creating good
strategies great strategies based on
good probabilities so in this case we've
got a 66 probability and that why it
kind of worked so even if I go to the
ETF of QQQ again you will see a 21
return and Microsoft and one button and
the list just goes on so you will
basically see just start bit just based
on one condition right so imagine having
multiple conditions and applying this to
multiple stocks and this is pretty much
what Jim Smith is doing and we don't
know exactly what he's doing but all we
can do is to get information from the
book information from any kind of
interviews he does you know combined
together and kind of improve but
regardless when you're in a Quant
trading Journey you're trying to get the
probabilities in your favor so any tool
including the markovian process and
calculate the transition properties is
highly efficient so in this case we
actually
did you know just based on historical
data probabilities now you can actually
tweak that to use a machine learning
model to calculate the probabilities of
these you can go a step further create a
for Loop and change these down datas and
you know down and up to different
combinations and calculate more
properties create a massive Matrix not
just up up down like you know like 10 10
rows here 10 10 columns there and then
uh you can tweak the data points you
know instead of this you can actually do
the recession environment so you can
create a recessionary based environment
strategy or Trend following strategy so
the sky is the limit when you have data
and when you have the tools and the
skills to process it so I hope you guys
like this video if you have any queries
any uh any doubts or clarifications feel
free to leave a comment and I'll be more
than happy to help you guys out so hope
you guys enjoy this video have a great
great day bye-bye
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