Stock Market Analysis & Markowitz Efficient Frontier on Python

In Portfolio (or mean-variance) theory, the efficient frontier is a set of points (or portfolios) in which no other point achieves higher return given a certain risk. It is a spectrum that is closely related to the Markowitz Portfolio Optimization problem. In this lecture, and in an attempt of achieving the optimal portfolio in two different ways, we show how to plot the efficient frontier on a scatter plot containing different portfolios. This lecture is outlined as follows:

⏲Outline⏲
00:00​ Highlights
00:23​ Introduction
01:41​ Setting Jupyter Lab
02:11​ Pandas Datareader
04:42​ Reading Stocks
06:04​ Dataframe Concatenation
07:25​ Returns
08:15​ Log Returns
09:18​ Sharpe Ratio
11:49​ Log Asset Returns
12:14​ Volatility Per Portfolio
13:02​ Return vs Volatility Scatter Plot
18:34​ Sharpe Ratio Maximization (1st way)
19:28​ Scatter Plot: Returns vs Volatility
22:01​ Optimal Weights by Markowitz Portfolio Optimization (2nd way)
28:00​ Efficient Markowitz Frontier
32:30​ Important Message
33:36​ Outro

Instructor: Dr. Ahmad Bazzi

🏗️Material

Browser: https://www.google.com/chrome/ ​

Jupyter: https://jupyter.org/ ​

Google: https://www.google.com/ ​

Pandas: https://pandas.pydata.org/ ​

DataReaders: https://pandas-datareader.readthedocs… ​

MATPLOTLIB: https://matplotlib.org/ ​

datetime: https://docs.python.org/3/library/dat… ​

SciPy: https://www.scipy.org/ ​

NumPy: https://numpy.or/gj ​

Subscribe: https://www.youtube.com/channel/UCgC1d4JZ1Fz4t8MWLJD464w

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What is GEEK

Buddha Community

top 30 Python Tips and Tricks for Beginners

Welcome to my Blog , In this article, you are going to learn the top 10 python tips and tricks.

1) swap two numbers.

2) Reversing a string in Python.

3) Create a single string from all the elements in list.

4) Chaining Of Comparison Operators.

5) Print The File Path Of Imported Modules.

6) Return Multiple Values From Functions.

7) Find The Most Frequent Value In A List.

8) Check The Memory Usage Of An Object.

…

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Lambda, Map, Filter functions in python

Welcome to my Blog, In this article, we will learn python lambda function, Map function, and filter function.

Lambda function in python: Lambda is a one line anonymous function and lambda takes any number of arguments but can only have one expression and python lambda syntax is

Syntax: x = lambda arguments : expression

Now i will show you some python lambda function examples:

#python #anonymous function python #filter function in python #lambda #lambda python 3 #map python #python filter #python filter lambda #python lambda #python lambda examples #python map

Stock Market Betting Software Development | Stock Market Betting Solutions

Mobiweb Technologies is considered as an expert stock market betting software development company that offers an extensive and wide coverage betting software to empower the bettors. Our Team of top level management and expert sports betting software developers design unbeatable software development strategies that compete with other betting software in the market. People who believe in investment consider gambling as a source of profit generation and for this, Mobiweb’s betting software offers one touch betting facility to place the wager. Most importantly, it offers best-in-class user experience and provides a user engaging atmosphere. If you are looking for developing stock market betting software then you don’t need to hesitate, give this opportunity to Mobiweb and get on time delivery of your project under minimum possible cost.

#stock market betting software development #stock market betting software #stock market betting app development #stock market betting app developers #stock market betting software providers

Stock Fundamental Analysis: EDA of SEC’s quarterly data summary

Many investors consider fundamental analysis as their secret weapon to beat the stock market. You can perform it using many methods, but one thing they have in common. They all need data about companies’ financial statements.

Luckily all stocks traded on US stock markets must quarterly report to the Securities and Exchange Commission (SEC). Every quarter SEC prepares a comfortable CSV package to help all the investors in their quest for the investment opportunity. Let’s explore how to get valuable insights from these .csv files.

In this tutorial, we will use python’s pandas library which ideal for parsing CSV files, and we will learn how to:

• explore the **structure **of the quarterly dump
• identify the records are we **interested **in
• get an idea of how many submission forms exist
• Delve into the content of 10-K, 8-K or 40-F forms
• Analyze if every company report only a single file each quarter
• Checks if the submission meet the SEC’s deadlines

We will process the data and:

• explore files in the SEC dump
• review each column of these files and talk about the most relevant
• remove **duplicated **data grouped by a key column or multiple columns
• visualize the data to support our exploration using interactive Plotly charts
• and much more

As usual, you can follow the code in the notebook shared on GitHub.

vaclavdekanovsky/data-analysis-in-examples

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SEC Quarterly data

There doesn’t seem to be any problem. You simply download the quarterly package from the SEC dataset page, you sort the values from the financial statements in descending order and pick the stocks on the top. The reality isn’t that straightforward. Let’s have a look and explore 45.55MB big zip file with all SEC filings for the first quarter of 2020.

The package for every quarter contains 5 files. Here’s an example of 2020 Q1:

• readme.htm — describes the structure of the files
• **sub.txt **— master information about the submissions including company identifiers and type of the filing
• **num.txt **— numeric data for each financial statement and other documents
• tag.txt — standard taxonomy tags
• pre.txt — information about how the data from num.txt is displayed in the online presentation

Unzipped files in the SEC quarterly data dump

This article will only deal with the submission master because it contains more than enough information for one article. Follow-up story will examine the data in more detail. Let’s begin.

2020Q1 Submission files

In the first quarter of 2020, the companies have submitted 13560 files and the sub.txt gathers 36 columns about them.

# load the .csv file into pandas
sub = pd.read_csv(os.path.join(folder,"sub.txt"), sep="\t", dtype={"cik":str})

# explore number of rows and columns
sub.shape
[Out]: (13560, 36)

I always start with a simple function that reviews each column of the data frame, checks the percentage of empty values, and how many unique values appear in the columns.

Explore the sub.txt file to see what data each column contain

Let me highlight a few important columns in the SEC submission master.

Example of the quick file overview in pandas

• adsh — EDGAR accession number uniquely identifies each report. This value is **never duplicated **in the sub.txt. Example 0001353283–20–000008 is the code for 10-K (yearly filing) of Splunk.
• cik — Central Index Key, unique key identifying each SEC registrant. E.g. 0001353283 for Splunk. As you can see the first part of the adsh is the cik.
• name — the name of the company submitting the quarterly financial data
• form — the type of the report being submitted

Form s— submissions types delivered to SEC

Based on the analysis, we see that the 2020Q1 submission contains 23 unique types of financial reports. Investors’ primary interest lies in the 10-K report, which covers the annual performance of the publically traded company. Because this report is expectedly delivered only once a year, important is also 10-Q report showing quarterly changes in the company’s financials.

• 10-K Annual report of US-based company
• 10-Q Quarterly report and maybe
• 20-F Annual Reports of a foreign company
• 40-F Annual Reports of a foreign company (Canadian)

Let’s see which forms are the most common in the dataset. Plotting of the form types in the 2020Q1 will show this picture:

Using Plotly’s low level API to produce bar and pie subplots

Different submission types reported by the companies in 2020Q1 using visualization in Plotly

The dataset contains over 7000 8-K reports notifying about important events like agreements, layoffs, usage of material, modification of shareholder rights, change in the senior positions, and more (see SEC’s guideline). Since they are the most common we should spend some time exploring them.

#stocks #exploratory-data-analysis #python #data-analysis #stock-market #data analysis

3 Python Pandas Tricks for Efficient Data Analysis

Explained with examples.

Pandas is one of the predominant data analysis tools which is highly appreciated among data scientists. It provides numerous flexible and versatile functions to perform efficient data analysis.

In this article, we will go over 3 pandas tricks that I think will make you a more happy pandas user. It is better to explain these tricks with some examples. Thus, we start by creating a data frame to wok on.

The data frame contains daily sales quantities of 3 different stores. We first create a period of 10 days using the date_range function of pandas.

import numpy as np
import pandas as pd

days = pd.date_range("2020-01-01", periods=10, freq="D")

The days variable will be used as a column. We also need a sales quantity column which can be generated by the randint function of numpy. Then, we create a data frame with 3 columns for each store.

#machine-learning #data-science #python #python pandas tricks #efficient data analysis #python pandas tricks for efficient data analysis