Alec  Nikolaus

Alec Nikolaus

1596333660

Step By Step: Pull Stock Order History With TD Ameritrade API and Python

There isn’t very good documentation out there on how to connect and use TD Ameritrade’s APIs. In this blog, I’ll be connecting to TD Ameritrade’s APIs with Python to automatically pull order history transactions and store it into a local MySQL database.


Getting Started

All my code is posted on my Github repo for reference. Please feel free to follow my instructional guide below and contact me for improvements or help.

Target Audience:

Non-Brogrammers. I’m a mechanical engineer by discipline so a lot of this stuff wasn’t intuitive to me at first. I believe this guide will serve as a tool for beginner programmers, like me, in getting started.


Instructions

Step 1: Set up a “TD Ameritrade for developers” account

One of the first steps is to create a TDA developer account. Once created, you’ll be able to create an “app.” This app will generate a consumer key in order to connect to their API’s. Below are the steps to get your consumer key:

Navigate to “My Apps” in the navigation bar and click on “Add a new App”

  • App Name: Name_of_your_choice
  • Callback URL: [http://localhost/test](http://localhost/test)

Once the new app is created, a consumer key is generated. We’ll need that later.

You can also go ahead and browse around the various API’s TD Ameritrade offers.

Image for post

Fig 1: Generating a consumer key from TDA Developer app

Step 2: Get Authentication Code

Before we can use some of the API’s we’ll need to verify our accounts by providing an authentication token. The security on these API’s use OAuth 2.0, which means getting the auth token will be very difficult and painful (not necessarily a bad thing; it just means it’s secure).

The only way to retrieve the auth token is to first get an auth code. As far as I know, the only way to get the auth code is by visiting a TDA web address and logging in. (Note: these tokens and codes are only temporarily active so you will need to retrieve them frequently).

In the next few steps, as seen in GIF 1, we’ll be automating the retrieval of the auth code and then use the TDA API to retrieve the auth token.

#trading #api #python #trading-journals #td-ameritrade

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Buddha Community

Step By Step: Pull Stock Order History With TD Ameritrade API and Python
Alec  Nikolaus

Alec Nikolaus

1596333660

Step By Step: Pull Stock Order History With TD Ameritrade API and Python

There isn’t very good documentation out there on how to connect and use TD Ameritrade’s APIs. In this blog, I’ll be connecting to TD Ameritrade’s APIs with Python to automatically pull order history transactions and store it into a local MySQL database.


Getting Started

All my code is posted on my Github repo for reference. Please feel free to follow my instructional guide below and contact me for improvements or help.

Target Audience:

Non-Brogrammers. I’m a mechanical engineer by discipline so a lot of this stuff wasn’t intuitive to me at first. I believe this guide will serve as a tool for beginner programmers, like me, in getting started.


Instructions

Step 1: Set up a “TD Ameritrade for developers” account

One of the first steps is to create a TDA developer account. Once created, you’ll be able to create an “app.” This app will generate a consumer key in order to connect to their API’s. Below are the steps to get your consumer key:

Navigate to “My Apps” in the navigation bar and click on “Add a new App”

  • App Name: Name_of_your_choice
  • Callback URL: [http://localhost/test](http://localhost/test)

Once the new app is created, a consumer key is generated. We’ll need that later.

You can also go ahead and browse around the various API’s TD Ameritrade offers.

Image for post

Fig 1: Generating a consumer key from TDA Developer app

Step 2: Get Authentication Code

Before we can use some of the API’s we’ll need to verify our accounts by providing an authentication token. The security on these API’s use OAuth 2.0, which means getting the auth token will be very difficult and painful (not necessarily a bad thing; it just means it’s secure).

The only way to retrieve the auth token is to first get an auth code. As far as I know, the only way to get the auth code is by visiting a TDA web address and logging in. (Note: these tokens and codes are only temporarily active so you will need to retrieve them frequently).

In the next few steps, as seen in GIF 1, we’ll be automating the retrieval of the auth code and then use the TDA API to retrieve the auth token.

#trading #api #python #trading-journals #td-ameritrade

Dylan  Iqbal

Dylan Iqbal

1561523460

Matplotlib Cheat Sheet: Plotting in Python

This Matplotlib cheat sheet introduces you to the basics that you need to plot your data with Python and includes code samples.

Data visualization and storytelling with your data are essential skills that every data scientist needs to communicate insights gained from analyses effectively to any audience out there. 

For most beginners, the first package that they use to get in touch with data visualization and storytelling is, naturally, Matplotlib: it is a Python 2D plotting library that enables users to make publication-quality figures. But, what might be even more convincing is the fact that other packages, such as Pandas, intend to build more plotting integration with Matplotlib as time goes on.

However, what might slow down beginners is the fact that this package is pretty extensive. There is so much that you can do with it and it might be hard to still keep a structure when you're learning how to work with Matplotlib.   

DataCamp has created a Matplotlib cheat sheet for those who might already know how to use the package to their advantage to make beautiful plots in Python, but that still want to keep a one-page reference handy. Of course, for those who don't know how to work with Matplotlib, this might be the extra push be convinced and to finally get started with data visualization in Python. 

You'll see that this cheat sheet presents you with the six basic steps that you can go through to make beautiful plots. 

Check out the infographic by clicking on the button below:

Python Matplotlib cheat sheet

With this handy reference, you'll familiarize yourself in no time with the basics of Matplotlib: you'll learn how you can prepare your data, create a new plot, use some basic plotting routines to your advantage, add customizations to your plots, and save, show and close the plots that you make.

What might have looked difficult before will definitely be more clear once you start using this cheat sheet! Use it in combination with the Matplotlib Gallery, the documentation.

Matplotlib 

Matplotlib is a Python 2D plotting library which produces publication-quality figures in a variety of hardcopy formats and interactive environments across platforms.

Prepare the Data 

1D Data 

>>> import numpy as np
>>> x = np.linspace(0, 10, 100)
>>> y = np.cos(x)
>>> z = np.sin(x)

2D Data or Images 

>>> data = 2 * np.random.random((10, 10))
>>> data2 = 3 * np.random.random((10, 10))
>>> Y, X = np.mgrid[-3:3:100j, -3:3:100j]
>>> U = 1 X** 2 + Y
>>> V = 1 + X Y**2
>>> from matplotlib.cbook import get_sample_data
>>> img = np.load(get_sample_data('axes_grid/bivariate_normal.npy'))

Create Plot

>>> import matplotlib.pyplot as plt

Figure 

>>> fig = plt.figure()
>>> fig2 = plt.figure(figsize=plt.figaspect(2.0))

Axes 

>>> fig.add_axes()
>>> ax1 = fig.add_subplot(221) #row-col-num
>>> ax3 = fig.add_subplot(212)
>>> fig3, axes = plt.subplots(nrows=2,ncols=2)
>>> fig4, axes2 = plt.subplots(ncols=3)

Save Plot 

>>> plt.savefig('foo.png') #Save figures
>>> plt.savefig('foo.png',  transparent=True) #Save transparent figures

Show Plot

>>> plt.show()

Plotting Routines 

1D Data 

>>> fig, ax = plt.subplots()
>>> lines = ax.plot(x,y) #Draw points with lines or markers connecting them
>>> ax.scatter(x,y) #Draw unconnected points, scaled or colored
>>> axes[0,0].bar([1,2,3],[3,4,5]) #Plot vertical rectangles (constant width)
>>> axes[1,0].barh([0.5,1,2.5],[0,1,2]) #Plot horiontal rectangles (constant height)
>>> axes[1,1].axhline(0.45) #Draw a horizontal line across axes
>>> axes[0,1].axvline(0.65) #Draw a vertical line across axes
>>> ax.fill(x,y,color='blue') #Draw filled polygons
>>> ax.fill_between(x,y,color='yellow') #Fill between y values and 0

2D Data 

>>> fig, ax = plt.subplots()
>>> im = ax.imshow(img, #Colormapped or RGB arrays
      cmap= 'gist_earth', 
      interpolation= 'nearest',
      vmin=-2,
      vmax=2)
>>> axes2[0].pcolor(data2) #Pseudocolor plot of 2D array
>>> axes2[0].pcolormesh(data) #Pseudocolor plot of 2D array
>>> CS = plt.contour(Y,X,U) #Plot contours
>>> axes2[2].contourf(data1) #Plot filled contours
>>> axes2[2]= ax.clabel(CS) #Label a contour plot

Vector Fields 

>>> axes[0,1].arrow(0,0,0.5,0.5) #Add an arrow to the axes
>>> axes[1,1].quiver(y,z) #Plot a 2D field of arrows
>>> axes[0,1].streamplot(X,Y,U,V) #Plot a 2D field of arrows

Data Distributions 

>>> ax1.hist(y) #Plot a histogram
>>> ax3.boxplot(y) #Make a box and whisker plot
>>> ax3.violinplot(z)  #Make a violin plot

Plot Anatomy & Workflow 

Plot Anatomy 

 y-axis      

                           x-axis 

Workflow 

The basic steps to creating plots with matplotlib are:

1 Prepare Data
2 Create Plot
3 Plot
4 Customized Plot
5 Save Plot
6 Show Plot

>>> import matplotlib.pyplot as plt
>>> x = [1,2,3,4]  #Step 1
>>> y = [10,20,25,30] 
>>> fig = plt.figure() #Step 2
>>> ax = fig.add_subplot(111) #Step 3
>>> ax.plot(x, y, color= 'lightblue', linewidth=3)  #Step 3, 4
>>> ax.scatter([2,4,6],
          [5,15,25],
          color= 'darkgreen',
          marker= '^' )
>>> ax.set_xlim(1, 6.5)
>>> plt.savefig('foo.png' ) #Step 5
>>> plt.show() #Step 6

Close and Clear 

>>> plt.cla()  #Clear an axis
>>> plt.clf(). #Clear the entire figure
>>> plt.close(). #Close a window

Plotting Customize Plot 

Colors, Color Bars & Color Maps 

>>> plt.plot(x, x, x, x**2, x, x** 3)
>>> ax.plot(x, y, alpha = 0.4)
>>> ax.plot(x, y, c= 'k')
>>> fig.colorbar(im, orientation= 'horizontal')
>>> im = ax.imshow(img,
            cmap= 'seismic' )

Markers 

>>> fig, ax = plt.subplots()
>>> ax.scatter(x,y,marker= ".")
>>> ax.plot(x,y,marker= "o")

Linestyles 

>>> plt.plot(x,y,linewidth=4.0)
>>> plt.plot(x,y,ls= 'solid') 
>>> plt.plot(x,y,ls= '--') 
>>> plt.plot(x,y,'--' ,x**2,y**2,'-.' ) 
>>> plt.setp(lines,color= 'r',linewidth=4.0)

Text & Annotations 

>>> ax.text(1,
           -2.1, 
           'Example Graph', 
            style= 'italic' )
>>> ax.annotate("Sine", 
xy=(8, 0),
xycoords= 'data', 
xytext=(10.5, 0),
textcoords= 'data', 
arrowprops=dict(arrowstyle= "->", 
connectionstyle="arc3"),)

Mathtext 

>>> plt.title(r '$sigma_i=15$', fontsize=20)

Limits, Legends and Layouts 

Limits & Autoscaling 

>>> ax.margins(x=0.0,y=0.1) #Add padding to a plot
>>> ax.axis('equal')  #Set the aspect ratio of the plot to 1
>>> ax.set(xlim=[0,10.5],ylim=[-1.5,1.5])  #Set limits for x-and y-axis
>>> ax.set_xlim(0,10.5) #Set limits for x-axis

Legends 

>>> ax.set(title= 'An Example Axes',  #Set a title and x-and y-axis labels
            ylabel= 'Y-Axis', 
            xlabel= 'X-Axis')
>>> ax.legend(loc= 'best')  #No overlapping plot elements

Ticks 

>>> ax.xaxis.set(ticks=range(1,5),  #Manually set x-ticks
             ticklabels=[3,100, 12,"foo" ])
>>> ax.tick_params(axis= 'y', #Make y-ticks longer and go in and out
             direction= 'inout', 
              length=10)

Subplot Spacing 

>>> fig3.subplots_adjust(wspace=0.5,   #Adjust the spacing between subplots
             hspace=0.3,
             left=0.125,
             right=0.9,
             top=0.9,
             bottom=0.1)
>>> fig.tight_layout() #Fit subplot(s) in to the figure area

Axis Spines 

>>> ax1.spines[ 'top'].set_visible(False) #Make the top axis line for a plot invisible
>>> ax1.spines['bottom' ].set_position(( 'outward',10))  #Move the bottom axis line outward

Have this Cheat Sheet at your fingertips

Original article source at https://www.datacamp.com

#matplotlib #cheatsheet #python

Ray  Patel

Ray Patel

1619510796

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

John  Smith

John Smith

1657107416

Find the Best Restaurant Mobile App Development Company in Abu Dhbai

The era of mobile app development has completely changed the scenario for businesses in regions like Abu Dhabi. Restaurants and food delivery businesses are experiencing huge benefits via smart business applications. The invention and development of the food ordering app have helped all-scale businesses reach new customers and boost sales and profit. 

As a result, many business owners are searching for the best restaurant mobile app development company in Abu Dhabi. If you are also searching for the same, this article is helpful for you. It will let you know the step-by-step process to hire the right team of restaurant mobile app developers. 

Step-by-Step Process to Find the Best Restaurant App Development Company

Searching for the top mobile app development company in Abu Dhabi? Don't know the best way to search for professionals? Don't panic! Here is the step-by-step process to hire the best professionals. 

#Step 1 – Know the Company's Culture

Knowing the organization's culture is very crucial before finalizing a food ordering app development company in Abu Dhabi. An organization's personality is shaped by its common beliefs, goals, practices, or company culture. So, digging into the company culture reveals the core beliefs of the organization, its objectives, and its development team. 

Now, you might be wondering, how will you identify the company's culture? Well, you can take reference from the following sources – 

  • Social media posts 
  • App development process
  • About us Page
  • Client testimonials

#Step 2 - Refer to Clients' Reviews

Another best way to choose the On-demand app development firm for your restaurant business is to refer to the clients' reviews. Reviews are frequently available on the organization's website with a tag of "Reviews" or "Testimonials." It's important to read the reviews as they will help you determine how happy customers are with the company's app development process. 

You can also assess a company's abilities through reviews and customer testimonials. They can let you know if the mobile app developers create a valuable app or not. 

#Step 3 – Analyze the App Development Process

Regardless of the company's size or scope, adhering to the restaurant delivery app development process will ensure the success of your business application. Knowing the processes an app developer follows in designing and producing a top-notch app will help you know the working process. Organizations follow different app development approaches, so getting well-versed in the process is essential before finalizing any mobile app development company. 

#Step 4 – Consider Previous Experience

Besides considering other factors, considering the previous experience of the developers is a must. You can obtain a broad sense of the developer's capacity to assist you in creating a unique mobile application for a restaurant business.

You can also find out if the developers' have contributed to the creation of other successful applications or not. It will help you know the working capacity of a particular developer or organization. Prior experience is essential to evaluating their work. For instance, whether they haven't previously produced an app similar to yours or not. 

#Step 5 – Check for Their Technical Support

As you expect a working and successful restaurant mobile app for your business, checking on this factor is a must. A well-established organization is nothing without a good technical support team. So, ensure whatever restaurant mobile app development company you choose they must be well-equipped with a team of dedicated developers, designers, and testers. 

Strong tech support from your mobile app developers will help you identify new bugs and fix them bugs on time. All this will ensure the application's success. 

#Step 6 – Analyze Design Standards

Besides focusing on an organization's development, testing, and technical support, you should check the design standards. An appealing design is crucial in attracting new users and keeping the existing ones stick to your services. So, spend some time analyzing the design standards of an organization. Now, you might be wondering, how will you do it? Simple! By looking at the organization's portfolio. 

Whether hiring an iPhone app development company or any other, these steps apply to all. So, don't miss these steps. 

#Step 7 – Know Their Location

Finally, the last yet very crucial factor that will not only help you finalize the right person for your restaurant mobile app development but will also decide the mobile app development cost. So, you have to choose the location of the developers wisely, as it is a crucial factor in defining the cost. 

Summing Up!!!

Restaurant mobile applications have taken the food industry to heights none have ever considered. As a result, the demand for restaurant mobile app development companies has risen greatly, which is why businesses find it difficult to finalize the right person. But, we hope that after referring to this article, it will now be easier to hire dedicated developers under the desired budget. So, begin the hiring process now and get a well-craft food ordering app in hand. 

Top 10 API Security Threats Every API Team Should Know

As more and more data is exposed via APIs either as API-first companies or for the explosion of single page apps/JAMStack, API security can no longer be an afterthought. The hard part about APIs is that it provides direct access to large amounts of data while bypassing browser precautions. Instead of worrying about SQL injection and XSS issues, you should be concerned about the bad actor who was able to paginate through all your customer records and their data.

Typical prevention mechanisms like Captchas and browser fingerprinting won’t work since APIs by design need to handle a very large number of API accesses even by a single customer. So where do you start? The first thing is to put yourself in the shoes of a hacker and then instrument your APIs to detect and block common attacks along with unknown unknowns for zero-day exploits. Some of these are on the OWASP Security API list, but not all.

Insecure pagination and resource limits

Most APIs provide access to resources that are lists of entities such as /users or /widgets. A client such as a browser would typically filter and paginate through this list to limit the number items returned to a client like so:

First Call: GET /items?skip=0&take=10 
Second Call: GET /items?skip=10&take=10

However, if that entity has any PII or other information, then a hacker could scrape that endpoint to get a dump of all entities in your database. This could be most dangerous if those entities accidently exposed PII or other sensitive information, but could also be dangerous in providing competitors or others with adoption and usage stats for your business or provide scammers with a way to get large email lists. See how Venmo data was scraped

A naive protection mechanism would be to check the take count and throw an error if greater than 100 or 1000. The problem with this is two-fold:

  1. For data APIs, legitimate customers may need to fetch and sync a large number of records such as via cron jobs. Artificially small pagination limits can force your API to be very chatty decreasing overall throughput. Max limits are to ensure memory and scalability requirements are met (and prevent certain DDoS attacks), not to guarantee security.
  2. This offers zero protection to a hacker that writes a simple script that sleeps a random delay between repeated accesses.
skip = 0
while True:    response = requests.post('https://api.acmeinc.com/widgets?take=10&skip=' + skip),                      headers={'Authorization': 'Bearer' + ' ' + sys.argv[1]})    print("Fetched 10 items")    sleep(randint(100,1000))    skip += 10

How to secure against pagination attacks

To secure against pagination attacks, you should track how many items of a single resource are accessed within a certain time period for each user or API key rather than just at the request level. By tracking API resource access at the user level, you can block a user or API key once they hit a threshold such as “touched 1,000,000 items in a one hour period”. This is dependent on your API use case and can even be dependent on their subscription with you. Like a Captcha, this can slow down the speed that a hacker can exploit your API, like a Captcha if they have to create a new user account manually to create a new API key.

Insecure API key generation

Most APIs are protected by some sort of API key or JWT (JSON Web Token). This provides a natural way to track and protect your API as API security tools can detect abnormal API behavior and block access to an API key automatically. However, hackers will want to outsmart these mechanisms by generating and using a large pool of API keys from a large number of users just like a web hacker would use a large pool of IP addresses to circumvent DDoS protection.

How to secure against API key pools

The easiest way to secure against these types of attacks is by requiring a human to sign up for your service and generate API keys. Bot traffic can be prevented with things like Captcha and 2-Factor Authentication. Unless there is a legitimate business case, new users who sign up for your service should not have the ability to generate API keys programmatically. Instead, only trusted customers should have the ability to generate API keys programmatically. Go one step further and ensure any anomaly detection for abnormal behavior is done at the user and account level, not just for each API key.

Accidental key exposure

APIs are used in a way that increases the probability credentials are leaked:

  1. APIs are expected to be accessed over indefinite time periods, which increases the probability that a hacker obtains a valid API key that’s not expired. You save that API key in a server environment variable and forget about it. This is a drastic contrast to a user logging into an interactive website where the session expires after a short duration.
  2. The consumer of an API has direct access to the credentials such as when debugging via Postman or CURL. It only takes a single developer to accidently copy/pastes the CURL command containing the API key into a public forum like in GitHub Issues or Stack Overflow.
  3. API keys are usually bearer tokens without requiring any other identifying information. APIs cannot leverage things like one-time use tokens or 2-factor authentication.

If a key is exposed due to user error, one may think you as the API provider has any blame. However, security is all about reducing surface area and risk. Treat your customer data as if it’s your own and help them by adding guards that prevent accidental key exposure.

How to prevent accidental key exposure

The easiest way to prevent key exposure is by leveraging two tokens rather than one. A refresh token is stored as an environment variable and can only be used to generate short lived access tokens. Unlike the refresh token, these short lived tokens can access the resources, but are time limited such as in hours or days.

The customer will store the refresh token with other API keys. Then your SDK will generate access tokens on SDK init or when the last access token expires. If a CURL command gets pasted into a GitHub issue, then a hacker would need to use it within hours reducing the attack vector (unless it was the actual refresh token which is low probability)

Exposure to DDoS attacks

APIs open up entirely new business models where customers can access your API platform programmatically. However, this can make DDoS protection tricky. Most DDoS protection is designed to absorb and reject a large number of requests from bad actors during DDoS attacks but still need to let the good ones through. This requires fingerprinting the HTTP requests to check against what looks like bot traffic. This is much harder for API products as all traffic looks like bot traffic and is not coming from a browser where things like cookies are present.

Stopping DDoS attacks

The magical part about APIs is almost every access requires an API Key. If a request doesn’t have an API key, you can automatically reject it which is lightweight on your servers (Ensure authentication is short circuited very early before later middleware like request JSON parsing). So then how do you handle authenticated requests? The easiest is to leverage rate limit counters for each API key such as to handle X requests per minute and reject those above the threshold with a 429 HTTP response. There are a variety of algorithms to do this such as leaky bucket and fixed window counters.

Incorrect server security

APIs are no different than web servers when it comes to good server hygiene. Data can be leaked due to misconfigured SSL certificate or allowing non-HTTPS traffic. For modern applications, there is very little reason to accept non-HTTPS requests, but a customer could mistakenly issue a non HTTP request from their application or CURL exposing the API key. APIs do not have the protection of a browser so things like HSTS or redirect to HTTPS offer no protection.

How to ensure proper SSL

Test your SSL implementation over at Qualys SSL Test or similar tool. You should also block all non-HTTP requests which can be done within your load balancer. You should also remove any HTTP headers scrub any error messages that leak implementation details. If your API is used only by your own apps or can only be accessed server-side, then review Authoritative guide to Cross-Origin Resource Sharing for REST APIs

Incorrect caching headers

APIs provide access to dynamic data that’s scoped to each API key. Any caching implementation should have the ability to scope to an API key to prevent cross-pollution. Even if you don’t cache anything in your infrastructure, you could expose your customers to security holes. If a customer with a proxy server was using multiple API keys such as one for development and one for production, then they could see cross-pollinated data.

#api management #api security #api best practices #api providers #security analytics #api management policies #api access tokens #api access #api security risks #api access keys