All Questions

-Until recently I believed that classes were 'molds' that were used to create objects; however, this question ( Why can't you delete methods in python? ) made me question a few things. What is said in the most voted answer is that, in short, you can delete methods since, just like anything else, methods (without parentheses) are basically pointers that store the reference ('point') to the method code that loads into RAM. In other words: they are attributes of all life except that, in this case, they point to code and not to normal data, therefore when using the reserved worddelwhat we do is eliminate the references that pointer saves, which translates into 'cutting' the 'link' that pointer has with the variable(s) to which it points (from what I also understand can save multiple references, it would be the same as what would happen with lists for example, at least that's how it works in Cand I guess it will be the same as what happens in python (if not, please comment) therefore we can no longer access nothing through it, since it no longer points anywhere...

• So far so good, the question begins when it is mentioned that objects inherit from the classes from which they are instantiated, it is for the same reason that a method cannot be eliminated through an object, since that method is not yours but of your class , this makes a lot of sense, since (as you can see in the post) starting from that premise everything works.

-With which, the question would be the following: Are the classes really ' molds '? What actually happens when we create and instantiate an object from a class? (removing all the abstraction layers, which is what actually happens in RAM)

PLOT

-I understood the following...

class persona:
    def __init__(self, nombre, apellido, edad, sexo) -> None:
        self.nombre = nombre
        self.apellido = apellido
        self.edad = edad
        self.sexo = sexo

persona_nueva = persona('Jhon','Doe', 33, 'h')

-In this case, it persona_nuevawould then be a pointer that would store the reference to an object (in this case we can understand an object as a set of pointers), therefore, execute ...

print(persona_nueva.nombre)

-It would be like saying to persona_nueva(apart from the participation of _ _ str _ _): 'pass printthe value of the place where the pointer you are pointing to points to, that nombre' (this would be more or less the structure that is shape in my head)

                ____-nombre -> 'Jhon'
               /    -apellido -> 'Doe'
 persona_nueva      -edad -> 33
                    -sexo -> 'h'

 

already persona_nueva agreed to nombre...

persona_nueva.nombre -> print

It's for the same reason that it del persona_nueva.nombreworks (at least on my machine), as it would be equivalent to...

                ____-nombre -> 'Jhon'
               \    -apellido -> 'Doe'
 persona_nueva      -edad -> 33
                    -sexo -> 'h'

and therefore ...

                    -apellido -> 'Doe'
 persona_nueva      -edad -> 33
                    -sexo -> 'h'

-Well, the case is that if it were a method (that is, if the name, for example, were a method) and therefore...

                    -nombre -> nombre()
                    -apellido -> 'Doe'
 persona_nueva      -edad -> 33
                    -sexo -> 'h'
 

In this case, it could not be executed anymore del persona_nueva.nombre... This is basically what I asked in (Why can't you delete methods in python?), in response I was told that it is because technically, the attributes are not of persona_nueva(object ) but of its class persona, but if this is the case, why can I remove attributes (use del) from persona_nuevait and not methods, these having the same behavior. Most likely it's because I don't know what a class is, hence the question...

I am trying to create an Efficient Frontier based simulated portfolio optimization on 50 stocks, which you can find in csv here . Now I know how to get the best portfolio but it keeps the free weights and I'm looking for an optimization that keeps this variable positive.

I get my efficient frontier by getting weights with a fixed network of volatiles σ _{p 1} ,... σ _{p n} . So for each σ _{p i} , I maximize the expected returns with the constraint that the volatility is not greater than σ _{p i} , to obtain μ _{p i} . Then σ _{p i} , μ _{p i} are n points on the efficient frontier.

In other words, we can maximize the returns r that are equal to the weights of the individual results of each action RW . This leads to the following optimization problem (I reduced it to two variables for simplicity):

Which, in matrix form, should be more or less:

Where COV is the covariance matrix between all assets.

However, I was only able to program it with no weight restrictions W .

Here, the minimization of a quadratic objective under linear constraints can be obtained by solving a linear system. Which is really easy to calculate and not that hard to understand. To get the best acuity ratio give the following:

def efficient_portfolios(returns, risk_free_rate, sigma, mu, e):
    weights_record = []
    volatilities = []
    results = np.zeros((3,len(returns)))
    i = 0
    for portfolio_return in returns:
        A = np.block([[2*sigma, mu, e], [mu.T, 0, 0], [e.T, 0, 0]])
        b = np.zeros(n+2)
        b[n] = portfolio_return
        b[n+1] = 1
        w = np.linalg.solve(A, b)[:n]
        weights_record.append(w)
        portfolio_std_dev = np.sqrt( w.T @ sigma @ w )
        volatilities.append(portfolio_std_dev)
        results[0,i] = portfolio_std_dev
        results[1,i] = portfolio_return
        results[2,i] = (portfolio_return - risk_free_rate) / portfolio_std_dev
        i+=1
    return results, weights_record, volatilities

def display_simulated_ef_with_random(mean_returns, risk_free_rate, sigma, mu, e, df):
    results, weights, volatilities = efficient_portfolios(mean_returns,risk_free_rate, sigma, mu, e)
    max_sharpe_idx = np.argmax(results[2])
    sdp, rp = results[0,max_sharpe_idx], results[1,max_sharpe_idx]
    max_sharpe_allocation = pd.DataFrame(weights[max_sharpe_idx],index=df.columns,columns=['allocation'])
    max_sharpe_allocation.allocation = [round(i*100,2)for i in max_sharpe_allocation.allocation]
    max_sharpe_allocation = max_sharpe_allocation.T

    min_vol_idx = np.argmin(results[0])
    sdp_min, rp_min = results[0,min_vol_idx], results[1,min_vol_idx]
    min_vol_allocation = pd.DataFrame(weights[min_vol_idx],index=df.columns,columns=['allocation'])
    min_vol_allocation.allocation = [round(i*100,2)for i in min_vol_allocation.allocation]
    min_vol_allocation = min_vol_allocation.T

    print("-"*80)
    print("Maximum Sharpe Ratio Portfolio Allocation\n")
    print("Annualised Return:", round(rp,2))
    print("Annualised Volatility:", round(sdp,2))
    print("\n")
    print(max_sharpe_allocation)
    print("-"*80)
    print("Minimum Volatility Portfolio Allocation\n")
    print("Annualised Return:", round(rp_min,2))
    print("Annualised Volatility:", round(sdp_min,2))
    print("\n")
    print(min_vol_allocation)

    plt.figure(figsize=(10, 7))
    plt.scatter(results[0,:],results[1,:],c=results[2,:],cmap='YlGnBu', marker='o', s=10, alpha=0.3)
    plt.colorbar()
    plt.scatter(sdp,rp,marker='*',color='r',s=500, label='Maximum Sharpe ratio')
    plt.scatter(sdp_min,rp_min,marker='*',color='g',s=500, label='Minimum volatility')
    plt.title('Simulated Portfolio Optimization based on Efficient Frontier')
    plt.xlabel('annualised volatility')
    plt.ylabel('annualised returns')
    plt.legend(labelspacing=0.8)

    return max_sharpe_allocation, min_vol_allocation

stock_prices = pd.read_csv('stocks.csv', index_col=0)
returns = stock_prices.pct_change()
mu = 252 * returns.mean().values
sigma = 252 * returns.cov().values
n = mu.shape[0]

# add risk free asset to mu/sigma
risk_free_rate = 0.0178
z = np.zeros((n,1))
#mu = np.block([mu, risk_free_rate])
#sigma = np.block([[sigma, z], [z.T, 0]])
#n = mu.shape[0]

# solve minimize w'∑w subject to μ'w = r, e'w=1 for varying r
mu = np.expand_dims(mu, axis=1)
e = np.ones((n,1))
returns = np.linspace(risk_free_rate, np.max(mu))


max_sharpe_al, min_vol_al = display_simulated_ef_with_random(returns, risk_free_rate, sigma, mu, e, stock_prices)

And gives the following plot and portfolio:

--------------------------------------------------------------------------------
Maximum Sharpe Ratio Portfolio Allocation

Annualised Return: 0.63
Annualised Volatility: 0.23


               DD  ADBE  ATVI   APD    NVS      A   ADI    AVB    AYI   AAN  \
allocation -19.33  0.03 -0.32  29.3  12.65 -14.57  2.85 -25.28 -13.17  2.77   

            ...   SWKS    NOV  KMT   MDT   RIO   PSA   STE  POWI  VALE   TX  
allocation  ... -15.61 -10.08 -7.2 -3.16  7.57 -9.39  7.93  5.13  1.07  8.4  

[1 rows x 51 columns]
--------------------------------------------------------------------------------
Minimum Volatility Portfolio Allocation

Annualised Return: 0.03
Annualised Volatility: 0.13


             DD  ADBE  ATVI   APD   NVS     A   ADI   AVB   AYI   AAN  ...  \
allocation -0.6 -7.11  5.36  3.81  22.9 -3.69  7.37 -1.27 -1.13 -0.16  ...   

            SWKS   NOV   KMT   MDT   RIO    PSA   STE  POWI  VALE    TX  
allocation  -6.4 -0.25 -9.24  6.15  4.41  19.86 -1.31 -0.23 -2.99  6.05  

But how would you do if we needed to always be positive? I know that the solution cannot be obtained simply by solving a linear system. You would need to use a quadratic optimization solver .

I had a question about what exactly is the class or object objectin Python.

As far as I know, in Python everything is created by the metaclass type(but from what I will explain next it makes me doubt):

>>> str.__class__
<class 'type'>
>>> int.__class__
<class 'type'>
>>> bool.__class__
<class 'type'>
>>> float.__class__
<class 'type'>
>>> class A:
...     pass
... 
>>> A.__class__
<class 'type'>
>>> ClassName = type('ClassName', (), {})
>>> ClassName.__class__
<class 'type'>

On the other hand objectit is an instance of type:

>>> object.__class__
<class 'type'>
>>> isinstance(object, type)
True

What doesn't work for me at all is this:

>>> isinstance(type, object)
True

How can it possibly typebe an instance of objectif it's supposed to objectbe an instance of type? Has no sense. Looks like Python is contradicting itself...

In addition to this, it objectis a base class of type:

>>> type.__bases__
(<class 'object'>,)

But according to Python objectit is an instance of typeand according to that, how is it possible for a class ( type) to inherit from an instance ( object) of said class?

So based on this: What exactly is typeand object? Which of the two is the "true" metaclass or "the class that creates classes" so to speak? As I understood it was type:

type('MyClass', (), {})

And out of curiosity to know more about object, it seems that all classes inherit or descend from it:

>>> int.__bases__
(<class 'object'>,)
>>> bool.__bases__
(<class 'int'>,)
>>> str.__bases__
(<class 'object'>,)
>>> float.__bases__
(<class 'object'>,)
>>> class A:
...     pass
... 
>>> A.__bases__
(<class 'object'>,)

Why is it like this?

I have two <input type="text">and I need both to be centered as if they were one, let me explain:

There are two fields, one for name and one for last name, if I introduce for example "JUAN DIAZ", this text (which would be two input) is visually centered in the yellow container, it is not exact, but it gives the appearance.

The problem is when the first name or last name do not have the same length, as I show in this image:

Is there a way to do it only with CSS, to center the text of both inputs as if they were one? You could also opt for JS. The only thing is that there must always be two (2) inputcentered. It would be something like Excel's "combine and center" function, just to give an example.

This is the problem code:

input {
  border: none;
  background: transparent;
  text-transform: uppercase;
  font-weight: bold;
  width: 150px;
}

.contenedor {
  background-color: gray;
  width: 350px;
  padding: 4rem;
}

.centrar {
  background-color: yellow;
  padding: 1rem;
  margin: 0 auto;
  text-align: center
}

.nombre {
  text-align: right;
}

.apellido {
  text-align: left;
}

<div class="contenedor">

  <div class="centrar">
    <input class="nombre" type="text" /> <input class="apellido" type="text" />

  </div>
 <div>

Thank you for your comments.

Turns out I have this simple login, I handle Overhang.js to display a message in case the credentials are wrong. With this same plugin I could show a message when the access is correct, but among all its options, none of them completely convinces me (UI/UX issues).

For this case, when the access is correct, I would like to handle, for example, a change in the message of the inputtype submitthat goes from "Enter" to "Entering".

Or it could also be a fade effect like the Bootstrap modals that darkens the screen.

$(document).ready(function() {
  $('.background-image').on('webkitAnimationEnd', function(e) {
    $(this).addClass('visible');
  });
});

/* ---------- GENERAL ---------- */

* {
  box-sizing: inherit;
}

html {
  box-sizing: border-box;
  height: 100%;
}

body {
  background-color: #ebebeb;
  font-family: 'Open Sans', sans-serif;
  line-height: 1.5;
  margin: 0;
  min-height: 100%;
}

input {
  background-image: none;
  border: none;
  font: inherit;
  margin: 0;
  padding: 0;
  transition: all .3s;
}

/* ---------- ALIGN ---------- */

.align {
  align-items: center;
  display: flex;
  flex-direction: column;
  justify-content: center;
}

/* ---------- GRID ---------- */

.grid {
  margin-left: auto;
  margin-right: auto;
  max-width: 500px;
  max-height: 500px;
  width: 80%;
}

/* ---------- LOGIN ---------- */

#login h2 {
  background: #0088a3;
  border-radius: 15px 15px 0 0;
  color: #fff;
  font-size: 28px;
  padding: 20px 26px;
  height: 100%;
}

#login h2 span[class*="fontawesome-"] {
  margin-right: 14px;
}

#login fieldset {
  background: #fff;
  border-radius: 0 0 10px 10px;
  padding: 20px 26px;
}

#login fieldset p {
  color: #777;
  margin-bottom: 14px;
}

#login fieldset p:last-child {
  margin-bottom: 0;
}

#login fieldset input {
  border-radius: 3px;
}

#login fieldset input[type="text"],
#login fieldset input[type="password"] {
  background: #eee;
  color: #777;
  padding: 4px 10px;
  width: 100%;
}

#login fieldset input[type="submit"] {
  background: #0088a3;
  color: #fff;
  display: block;
  margin: 0 auto;
  padding: 4px 0;
  width: 100px;
}

#login fieldset input[type="submit"]:hover {
  background: #0088a3;
  border-radius: 15px 2px;
  cursor: pointer;
}

#login fieldset input[type="submit"]:active {
  background: #0088a3;
  border-radius: 2px 15px;
  cursor: wait;
}

@import "compass/css3";
@-webkit-keyframes fade-in {
  0% {
    opacity: 0;
  }
  100% {
    opacity: 1;
  }
}

.background-image {
  background: url('../images/bg.jpg') no-repeat center center fixed;
  -webkit-background-size: cover;
  -moz-background-size: cover;
  -o-background-size: cover;
  background-size: cover;
  position: absolute;
  top: 0;
  right: 0;
  bottom: 0;
  left: 0;
  opacity: 0;
  -webkit-animation-name: fade-in;
  -webkit-animation-duration: .5s;
  -webkit-animation-timing-function: ease-in;
  -webkit-animation-iteration-count: 1;
  -webkit-animation-delay: 1s;
}

.background-image.visible {
  opacity: 1;
}

<body class="align">

  <div class="align background-image">

    <div class="grid">

      <div id="login">

        <h2><img src="images/banner-wh.png" alt="Logo" style="max-width: 100%; width: auto;"></h2>

        <form id="loginform" action="validarcode.php" method="POST" autocomplete="off">

          <fieldset>

            <p><label for="usuario">Usuario</label></p>
            <p><input type="text" name="txtusuario" id="usuario" autofocus></p>

            <p><label for="password">Contraseña</label></p>
            <p><input type="password" name="txtpassword" id="password"></p>

            <br><br>

            <p><input class="with-arrow" type="submit" name="Enviar" value="Ingresar"> <i class="icon-arrow-right"></i> </p>

            <br>

            <div style="font-size: 14px;color: #444;">¿Olvidó su contraseña?</div>
            <h1 style="font-size: 12px;color: #555;">Escríbanos a <a href="mailto:[email protected]?subject=Restablecer%20Contraseña">[email protected]</a></h1>

          </fieldset>

        </form>

      </div>

    </div>

  </div>

  <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.1.4/jquery.min.js"></script>
  <script type="text/javascript" src="//ajax.googleapis.com/ajax/libs/jqueryui/1.10.4/jquery-ui.min.js"></script>
  <script src="assets/overhang.js"></script>
  <script src="assets/app.js"></script>

</body>

Long story short, I know that with JS I can display this alertby pressing the submit, but how could I call an animation in CSS instead?

function exito() {
  alert("Ingresando...");
  return true;
}

<input class="with-arrow" type="submit" name="Enviar" value="Ingresar" onclick="return exito();">