## Monte Carlo Simulation

On a nice day 2 years ago, when I was in the financial field. My boss sent our team an email. In this email, he would like to us propose some machine learning techniques to predict stock price.

So, after accepting the assignment from my manager, our team begin to research and apply some approaches for prediction. When we talk about Machine Learning, we often think of supervised and unsupervised learning. But one of the algorithms we applied is one that we forgot however equally highly effective algorithm: Monte Carlo Simulation.

# What is Monte Carlo simulation?

The Monte Carlo method is a technique that uses random numbers and probability to solve complex problems. The Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial sectors, project management, costs, and other forecasting machine learning models.[1]

Now let’s jump into python implementation to see how it applies,

# Python Implementation

In this task, we used data of DXG stock dataset from 2017/01/01 to 2018/08/24 and we would like to know what is stock price after 10 days, 1 month, and 3 months, respectively

We will simulate the return of stock and next price will be calculated by

P(t) = P(0) * (1+return_simulate(t))

Calculate mean and standard deviation of stock returns

```miu = np.mean(stock_returns, axis=0)
dev = np.std(stock_returns)```

Simulation process

```simulation_df = pd.DataFrame()
last_price = init_price
for x in range(mc_rep):
count = 0
daily_vol = dev
price_series = []
price = last_price * (1 + np.random.normal(miu, daily_vol))
price_series.append(price)
for y in range(train_days):
if count == train_days-1:
break
price = price_series[count] * (1 + np.random.normal(miu, daily_vol))
price_series.append(price)
count += 1
simulation_df[x] = price_series```

Visualization Monte Carlo Simulation

```fig = plt.figure()
fig.suptitle('Monte Carlo Simulation')
plt.plot(simulation_df)
plt.axhline(y = last_price, color = 'r', linestyle = '-')
plt.xlabel('Day')
plt.ylabel('Price')
plt.show()```

Now, let’s check with actual stock price after 10 days, 1 month and 3 months

```plt.hist(simulation_df.iloc[9,:],bins=15,label ='histogram')
plt.axvline(x = test_simulate.iloc[10], color = 'r', linestyle = '-',label ='Price at 10th')
plt.legend()
plt.title('Histogram simulation and last price of 10th day')
plt.show()```

We can see the most frequent occurrence price is pretty close to the actual price after 10th

If the forecast period is longer, the results are not good gradually

Simulation for next 1 month

After 3 months

# Conclusion

Monte Carlo simulation is used a lot in finance, although it has some weaknesses, hopefully through this article, you will have a new look at the simulation application for forecasting.

# Reference

[1] Pratik Shukla, Roberto Iriondo, “Monte Carlo Simulation An In-depth Tutorial with Python”, medium, https://medium.com/towards-artificial-intelligence/monte-carlo-simulation-an-in-depth-tutorial-with-python-bcf6eb7856c8

Please also check Gaussian Samples and N-gram language models,
Bayesian Statistics for more statistics knowledge.

# Hiring Data Scientist / Engineer

We are looking for Data Scientist and Engineer.

# Vietnam AI / Data Science Lab

Please also visit Vietnam AI Lab

## Hiring- Data Scientist (Algorithm Theory)

 Job Title Data Scientist (Algorithm Theory) Location Ho Chi Minh Contact recruitment　 @　 mti-tech.vn Employment Fulltime Level Middle/Senior Report to Line Manager

## If you want to join in exciting and challenging projects, MTI Tech could be the next destination for your career.

MTI Technology specializes in creating smart mobile contents and services that transform and transcend customers’ lives. We design and develop our products using agile methods bringing the best deliverable results to the table in the shortest amount of time. MTI stands for an attitude: seeking a balance in excellence, pragmatism and convenience for customers. With the original members of 20 people, we grow our members up to more than 100 bright talents and continue to grow more. Looking for a place to grow your talents and be awesome? This is the place!

### The Job

We are looking for Data Scientists who would like to participate in the project to use existing various data to apply to AI, moreover, combine with other data to create new value.

Currently, we are looking for candidates with experiences in Algorithms, Natural Language Processing (NLP), but any other fields of AI will be considered too.

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Currently, development is mainly in Python. It is good to understand object thinking programming in Java etc. It is also good if you have parallel processing experience in the server-side language (Golang, etc.).

In addition, engineers who can use functional languages (Haskell, Erlang / Elixir) are treasures of talented people. Such people are interested in various programming languages, have mathematical curiosity, and many of them study by themselves. Although we do not have many opportunities to use these languages in actual development, we welcome such engineers as well.

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• ## Have experiences in research and study about mathematics.

• Have a great ambition and ability to study the most leading-edge research by yourself and apply them to your own development.
• Have technical skills and creativity to build new technologies from scratch by yourself if it is necessary but does not exist yet.
• Adapt yourself to our working culture in a team such as discussion or sharing together. Personality is preferred. Excellent person has a variety of personalities. However, being able to work only on your own becomes a problem.
• Have experiences in research and study related to Statistical Mathematic such as Regression analysis, SVM or Information theory.
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• English skill: Be able to use your English reading skill to gain information related to AI.

### More on MTI – what is it like to work in MTI?

At MTI Technology, our goal is to empower every individual to learn, discover, be able to communicate openly and honestly to create the best services based on effective teamwork.

## Bayesian estimator of the Bernoulli parameter

In this post, I will explain how to calculate a Bayesian estimator. The taken example is very simple: estimate the parameter θ of a Bernoulli distribution.

A random variable X which has the Bernoulli distribution is defined as

with

In this case, we can write

.

In reality, the simplest way to estimate θ is to sample X, count how many times the event occurs, then estimate the probability of occurring events. This is exactly what the frequentists do.

In this post, I will show how do the Bayesian statisticians estimate θ. Although this doesn’t have a meaningful application, it helps to understand how do Bayesian statistics work. Let’s start.

# The posterior distribution of θ

Denote Y as the observation of the event. Given the parameter θ, if we sample the event n time, then the probability that the event occurs k time is (this is called the probability density function of Bernoulli )

In Bayesian statistics, we would like to calculate

$p(\theta|Y=y)$

By using the Bayesian formula, we have

With the prior distribution of theta as a Uniform distribution, p(θ) = 1, and it is easy to prove that

where Γ is the Gamma distribution. Hence, the posterior distribution is

Fortunately, this is the density function of the Beta distribution:

We use the following properties for evaluating the posterior mean and variance of theta.

If , then

# Simulation

In summary, the Bayesian estimator of theta is the Beta distribution with the  mean and variance as above. Here are the Python codes for simulating data and estimating theta

```def bayes_estimator_bernoulli(data, a_prior=1, b_prior=1, alpha=0.05):
'''Input:
data is a numpy array with binary value, which has the distribution B(1,theta)    a_prior, b_prior: parameters of prior distribution Beta(a_prior, b_prior)    alpha: significant level of the posterior confidence interval for parameter    Model:
for estimating the parameter theta of a Bernoulli distribution    the prior distribution for theta is Beta(1,1)=Uniform[0,1]    Output:
a,b: two parameters of the posterior distribution Beta(a,b)
pos_mean: posterior estimation for the mean of theta
pos_var: posterior estimation for the var of theta'''
n = len(data)
k = sum(data)
a = k+1
b = n-k+1
pos_mean = 1.*a/(a+b)
pos_var = 1.*(a*b)/((a+b+1)*(a+b)**2)
## Posterior Confidence Interval
theta_inf, theta_sup = beta.interval(1-alpha,a,b)
print('Prior distribution: Beta(%3d, %3d)' %(a_prior,b_prior))
print('Number of trials: %d, number of successes: %d' %(n,k))
print('Posterior distribution: Beta(%3d,%3d)' %(a,b))
print('Posterior mean: %5.4f' %pos_mean)
print('Posterior variance: %5.8f' %pos_var)
print('Posterior std: %5.8f' %(np.sqrt(pos_var)))
print('Posterior Confidence Interval (%2.2f): [%5.4f, %5.4f]' %(1-alpha, theta_inf, theta_sup))
return a, b, pos_mean, pos_var

# Example
n = 129 # sample size
data = np.random.binomial(size=n, n=1, p=0.6)
a, b, pos_mean, pos_var = bayes_estimator_bernoulli(data)```

And the result is

```Prior distribution: Beta(  1,   1)
Number of trials: 129, number of successes: 76
Posterior distribution: Beta( 77, 54)
Posterior mean: 0.5878
Posterior variance: 0.00183556
Posterior std: 0.04284341
Posterior Confidence Interval (0.95): [0.5027, 0.6703]
```
In the simulation, we simulated 129 data from the Bernoulli distribution with θ=0.6. And the Bayesian estimation of θ is the posterior mean which is 0.5878.
This is a very simple example of Bayesian estimation. In reality, it is usually tricky to determine a closed-form solution of the posterior distribution from the given prior distribution. In that case, the Monte Carlo technique is one of the solutions to approximate the posterior distribution.
Please also check Gaussian Samples and N-gram language models for more statistics knowledge.

# Hiring Data Scientist / Engineer

We are looking for Data Scientist and Engineer.