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Learn how to create an automated trading bot using Python with this comprehensive course. Master Python basics, understand trading fundamentals, build and integrate the bot with a broker API, and run it effectively. Learning Outcomes: Gain proficiency in Python programming for trading purposes. Understand the fundamental concepts of trading and market dynamics. Build a structured trading bot using Python and Github version control. Integrate the bot with a broker API for real-time trading functionality. Successfully run and manage the automated trading bot for efficient execution. Why buy this Making Automated Trading Bot Using Python? Unlimited access to the course for forever Digital Certificate, Transcript, student ID all included in the price Absolutely no hidden fees Directly receive CPD accredited qualifications after course completion Receive one to one assistance on every weekday from professionals Immediately receive the PDF certificate after passing Receive the original copies of your certificate and transcript on the next working day Easily learn the skills and knowledge from the comfort of your home Certification After studying the course materials of the Making Automated Trading Bot Using Python there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £5.99. Original Hard Copy certificates need to be ordered at an additional cost of £9.60. Who is this course for? This Making Automated Trading Bot Using Python course is ideal for Students Recent graduates Job Seekers Anyone interested in this topic People already working in the relevant fields and want to polish their knowledge and skill. Prerequisites This Making Automated Trading Bot Using Python does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Making Automated Trading Bot Using Python was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Career path As this course comes with multiple courses included as bonus, you will be able to pursue multiple occupations. This Making Automated Trading Bot Using Python is a great way for you to gain multiple skills from the comfort of your home. Course Curriculum Section 01: Introduction About the course structure 00:05:00 Why working is important? 00:04:00 The free and perfect tools 00:07:00 Our editor: Atom 00:04:00 Version control: Github 00:07:00 Python download (Mac) 00:05:00 Python download (Windows) 00:02:00 Section 02: Python Basics for Trading Introduction 00:03:00 Python Libraries 00:05:00 Iterators: for 00:08:00 Iterators: while 00:08:00 Conditionals: if & else 00:10:00 Logic gates: and & or 00:09:00 Error handling: try & except 00:09:00 Functions and calls to libraries 00:13:00 Objects and classes (1) 00:10:00 Objects and classes (2) 00:07:00 Debugging the code 00:12:00 Closing and wrap up 00:01:00 Section 03: Trading Basics Introduction 00:03:00 Fundamental vs Technical Analysis 00:04:00 Stocks vs CFDs 00:05:00 Long and Short positions 00:04:00 Takeprofit and Stoploss 00:03:00 Setting a real Stoploss 00:08:00 Limit and Market orders 00:10:00 Don't forget the spread 00:04:00 Stock data visualisation: candles 00:08:00 Technical Indicators: about 00:05:00 Exponential Moving Average 00:08:00 EMA use and interpretation 00:06:00 Relative Strength Index 00:07:00 Stochastic Oscillator 00:09:00 Closing and wrap up 00:01:00 Section 04: Bot Code General Structure Introduction 00:02:00 Overview 00:08:00 The Entry Strategy 00:10:00 About Tradingview 00:12:00 When to enter (1) 00:08:00 When to enter (2) 00:08:00 Open and hold a position 00:12:00 Closing a position 00:08:00 Review (1) 00:06:00 Review (2) 00:13:00 Closing 00:02:00 Section 05: Github Basics Introduction 00:04:00 Download and install Github 00:01:00 Create a repo 00:10:00 Working with branches 00:13:00 Section 06: Building Your Bot Introduction 00:05:00 Create the first bot file 00:07:00 Building the bot scheme 00:08:00 Complete your code scheme (1) 00:11:00 Complete your code scheme (2) 00:11:00 Complete your code scheme (3) 00:18:00 A logger to remember (1) 00:14:00 A logger to remember (2) 00:14:00 Organising your code 00:07:00 Main function: run bot 00:23:00 Link the bot and the library 00:08:00 Traderlib control functions (1) 00:12:00 Traderlib control functions (2) 00:13:00 Check if tradable function 00:06:00 Set stoploss function 00:10:00 Set takeprofit function 00:04:00 Load historical data function 00:01:00 Get open positions function 00:04:00 Submit and cancel order functions 00:04:00 Check positions function 00:09:00 The Tulipy libraries 00:07:00 Importing all the libraries 00:03:00 First filter: get general trend 00:19:00 Second filter: get instant trend 00:14:00 Third filter: RSI 00:08:00 Fourth filter: Stochastic Oscillator 00:14:00 Enter position (1) 00:13:00 Enter position (2) 00:11:00 Enter position (3) 00:15:00 Enter position (4) 00:08:00 Last check before opening 00:13:00 Exit position and get out 00:10:00 Linking everything (1) 00:12:00 Linking everything (2) 00:12:00 Linking everything (3) 00:15:00 Fixing a mistake: going Short 00:05:00 Handling all your variables 00:18:00 Closing and wrap up 00:01:00 Run function scheme clarification and rebuild 00:13:00 Section 07: Integrating the Broker API Introduction 00:03:00 The Alpaca Python API Wrapper 00:07:00 Initialising the REST API 00:09:00 Running the program (crash!) 00:06:00 Integration with check account (1) 00:08:00 Integration with check account (2) 00:05:00 Clean open orders function 00:10:00 Importing the trading library 00:04:00 Running the main 00:05:00 Check position function 00:09:00 Check if asset exists function 00:08:00 Fetching barset data (theory) 00:07:00 Fetching barset data (practice) 00:12:00 Updating the code for the Alpaca API V2 (explanation) 00:03:00 Updating the code for the Alpaca API V2 (implementation) 00:08:00 Organizing data with Pandas 00:06:00 Get general trend function (1) 00:08:00 Reframing the timeframe with Pandas 00:23:00 Get general trend function (2) 00:13:00 Get instant trend function 00:08:00 Get RSI function 00:06:00 Get Stochastic function 00:10:00 Get current price function 00:05:00 Finishing get shares amount 00:09:00 Opening a position (1) 00:12:00 Opening a position (2) 00:09:00 Check the open position 00:07:00 Cancelling the order (1) 00:20:00 Cancelling the order (2) 00:08:00 Making sure we cancelled 00:03:00 Get average entry price function 00:10:00 Fixing bugs when getting price 00:18:00 Check Stochastic crossing 00:02:00 Holding an open position 00:11:00 Submitting the exit order 00:08:00 Closing position and out (1) 00:08:00 Closing position and out (2) 00:10:00 Closing and wrap up 00:01:00 Section 08: Running the Trading Bot Introduction 00:03:00 Filtering asset by price and volume 00:07:00 Get the bot ready to trade 00:04:00 Running the Trading Bot with AAPL 00:09:00 A real open position 00:08:00 Debugging and bug fixing 00:12:00 Fixing one (last) bug 00:02:00 Running the bot with TSLA 00:10:00 Discussing EMA implementations 00:12:00 Closing and wrap up 00:02:00

Overview This comprehensive course on Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 will deepen your understanding on this topic. After successful completion of this course you can acquire the required skills in this sector. This Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 comes with accredited certification from CPD, which will enhance your CV and make you worthy in the job market. So enrol in this course today to fast track your career ladder. How will I get my certificate? After successfully completing the course you will be able to order your certificate, these are included in the price. Who is This course for? There is no experience or previous qualifications required for enrolment on this Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4. It is available to all students, of all academic backgrounds. Requirements Our Web Scraping and Mapping Dam Levels in Python and Leaflet Level 4 is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G. There is no time limit for completing this course, it can be studied in your own time at your own pace. Career Path Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc. Course Curriculum 8 sections • 22 lectures • 04:20:00 total length •Introduction: 00:16:00 •Installing PostgreSQL and PostGIS: 00:08:00 •Creating the Application Database: 00:03:00 •Installing Django in a Python Virtual Environment: 00:08:00 •Installing the ATOM IDE: 00:09:00 •Creating the Django Base Project: 00:08:00 •Adding the Database Configuration to the settings.py File: 00:09:00 •Creating a Model in the models.py File: 00:07:00 •Extracting Data from the Web: 00:24:00 •Cleaning and Transforming the Data Part 1: 00:18:00 •Cleaning and Transforming the Data Part 2: 00:10:00 •Loading the Data into the Model: 00:12:00 •Adding the Web Map Tile Service Link in settings.py: 00:08:00 •Reading from the Model and Creating a GeoJSON Dataset: 00:12:00 •Adding Template Files (HTML): 00:10:00 •Adding a Layout and the Base Map: 00:25:00 •Plotting Circle Markers: 00:16:00 •Creating a Sliding Sidebar: 00:14:00 •Creating a Doughnut Chart: 00:19:00 •Creating a Multi-Bar Bar Chart: 00:12:00 •Creating a Multi-Bar Bar Chart: 00:12:00 •Resource: 00:00:00

Diploma in C++ and Python Programming is one of our best selling and most popular course. The Diploma in C++ and Python Programming is organized into 64 modules and includes everything you need to become successful in this profession. To make this course more accessible for you, we have designed it for both part-time and full-time students. You can study at your own pace or become an expert in just 17 hours! If you require support, our experienced tutors are always available to help you throughout the comprehensive syllabus of this course and answer all your queries through email. Why choose this course Earn an e-certificate upon successful completion. Accessible, informative modules taught by expert instructors Study in your own time, at your own pace, through your computer tablet or mobile device Benefit from instant feedback through mock exams and multiple-choice assessments Get 24/7 help or advice from our email and live chat teams Full Tutor Support on Weekdays Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Mock exams Multiple-choice assessment Certification After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post. Who is this course for? Diploma in C++ and Python Programming is suitable for anyone who want to gain extensive knowledge, potential experience, and professional skills in the related field.

Network automation training course description This course concentrates on the technical side of tools and languages for network DevOps rather than the soft skills. These tools include Python, Ansible, Git and NAPALM By the end of the course delegates should be able to recognise the tools that they can use to automate their networks and be able to use the knowledge gained to feel confident approaching network automation. What will you learn Describe network DevOps. Choose network automation tools. Explain the role of various network automation technologies including: Python Ansible Git NAPALM Network automation training course details Who will benefit: Those wishing to learn about the tools of network automation. Prerequisites: Introduction to data communications. Duration 1 day Network automation training course contents What is DevOps and network automation Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Unit testing. Hype vs reality. Benefits and features. Network monitoring and troubleshooting Traditional methods, SNMP. Netflow and xflow. Traditional automation. Streaming telemetry. Event driven automation. gRPC, Protocol buffers. Configuration management Catch 22 and initial configuration. ZTP, POAP. Traditional automation. TFTP. Ansible vs the rest (chef, salt, puppet). Jinja2 and templating. How ansible works. Network programmability Programming languages. Linux, shell scripting. Python vs the rest. Off box vs on box automation. Python network libraries Sockets pysnmp, ncclient, paramiko, netmiko, pyez, NAPALM. APIs Proprietary APIs, CLI, NETCONF, RETCONF. YANG, XML, YAML, JSON. Other tools Git, GitHub, Jenkins, JIRA and others.

Want to become an expert NLP engineer and a data scientist? Then this is the right course for you. In this course, we will be covering complex theory, algorithms, and coding libraries in a very simple way that can be easily grasped by any beginner as well.

Duration 5 Days 30 CPD hours This course is intended for This introductory-level Python course is geared for experienced web developers new to Python who want to use Python and Django for full stack web development projects. Overview Working in a hands-on learning environment, guided by our expert team, attendees will learn to: Develop full-stack web sites based on content stored in an RDMS Use python data types appropriately Define data models Understand the architecture of a Django-based web site Create Django templates for easy-to-modify views Map views to URLs Take advantage of the built-in Admin interface Provide HTML form processing Geared for experienced web developers new to Python, Introduction to Full Stack Web Development with Python and Django is a five-day hands-on course that teaches students how to develop Web applications using the Django framework. Students will explore the basics of creating basic applications using the MVC (model-view-controller) design pattern, as well as more advanced topics such as administration, session management, authentication, and automated testing. This comprehensive, practical course provides an in-depth exploration of working with the programming language, not an academic overview of syntax and grammar. Students will immediately be able to use Python to complete tasks in the real world. The Python Environment Starting Python Using the interpreter Running a Python script Getting help Editors and IDEs Getting Started Using variables Built in functions Strings Numbers Converting among types Writing to the screen Command line parameters Flow Control About flow control Conditional expressions Relational and Boolean operators while loops Lists and Tuples About sequences Lists and list methods Tuples Indexing and slicing Iterating through a sequence Sequence functions, keywords, and operators List comprehensions Working with Files File overview The with statement Opening a file Reading/writing files Dictionaries and Sets About dictionaries Creating and using dictionaries About sets Creating and using sets Functions Returning values Function parameters Variable Scope Sorting with functions Errors and Exception Handling Exception overview Using try/catch/else/finally Handling multiple exceptions Ignoring exceptions Modules and Packages Creating Modules The import statement Module search path Creating packages Classes About OO programming Defining classes Constructors Properties Instance methods and data Class/static methods and data Inheritance Django Architecture Django overview Sites and apps Shared configuration Minimal Django layout Built in flexibility Configuring a Project Executing manage.py Starting the project Generating app files App configuration Database setup The development server Using cookiecutter Creating models Defining models Related objects SQL Migration Simplel model access Login for Nothing and Admin for Free Setting up the admin user Using the admin interface Views What is a view HttpResponse URL route configuration Shortcut: get_object_or_404() Class-based views Templates About templates Variable lookups The url tag Shortcut: render() Querying Models QuerySets Field lookups Chaining filters Slicing QuerySets Related fields Q objects Advanced Templates Use Comments Inheritance Filters Escaping HTML Custom filters Forms Forms overview GET and POST The Form class Processing the form Widgets Validation Forms in templates Automated Testing Why create tests? When to create tests Using Django's test framework Using the test client Running tests Checking code coverage

The course is crafted to reflect the most in-demand workplace skills. It will help you understand all the essential concepts and methodologies with regards to PySpark. This course provides a detailed compilation of all the basics, which will motivate you to make quick progress and experience much more than what you have learned.

A beginner-level course that will help you learn all you need to know about building applications using Python 3, FAST API, MongoDB, and NoSQL as well as front-end technologies such as HTML, CSS, JSX, and REACT JS with live demonstrations. You need to know the basics of HTML, CSS, and JavaScript to get started

Register on the Create Smart Maps in Python and Leaflet today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career. The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials. Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get an e-certificate as proof of your course completion. The Create Smart Maps in Python and Leaflet is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones. The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly! What You Get With The Create Smart Maps in Python and Leaflet Receive a e-certificate upon successful completion of the course Get taught by experienced, professional instructors Study at a time and pace that suits your learning style Get instant feedback on assessments 24/7 help and advice via email or live chat Get full tutor support on weekdays (Monday to Friday) Course Design The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace. You are taught through a combination of Video lessons Online study materials Certification Upon successful completion of the course, you will be able to obtain your course completion e-certificate free of cost. Print copy by post is also available at an additional cost of £9.99 and PDF Certificate at £4.99. Who Is This Course For: The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge. Requirements: The online training is open to all students and has no formal entry requirements. To study the Create Smart Maps in Python and Leaflet, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16. Course Content Section 01: Introduction Introduction 00:08:00 Section 02: Building a Spatial Database using PostgreSQL and PostGIS Installing PostgreSQL and PostGIS Part1 00:10:00 Installing PostgreSQL and PostGIS Part2 00:10:00 Section 03: Building a GeoDjango Application Installing Python Django in a Virtual Environment 00:10:00 Installing and Configuring Atom IDE Part1 00:10:00 Installing and Configuring Atom IDE Part2 00:03:00 Creating a GeoDjango Application Skeleton 00:10:00 Section 04: Writing the GeoDjango Back-end Code Adding a Spatial Database to our Django Backend 00:09:00 Updating our django models file 00:08:00 Registering our model in the admin file Part1 00:09:00 Registering our model in the admin file Part2 00:10:00 Registering our model in the admin file Part3 00:10:00 Section 05: Building the Front-End using Leaflet.js Updating the settings file 00:07:00 Creating the layout page Part 1 00:09:00 Creating the layout page Part 2 00:10:00 Creating the layout page Part 3 00:07:00 Creating the index page Part 1 00:10:00 Creating the index page Part 2 00:07:00 Updating the index page 00:07:00 Section 06: Adding the Data Creating datasets 00:10:00 Displaying data on the map Part 1 00:10:00 Displaying data on the map Part 2 00:02:00 Creating a legend 00:10:00 Creating the barchart legend 00:06:00 Creating the barchart Part 1 00:10:00 Creating the barchart Part 2 00:09:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Duration 4 Days 24 CPD hours This course is intended for This course is designed for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. Overview Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Learn how to operate machine learning solutions at cloud scale using Azure Machine Learning. This course teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring with Azure Machine Learning and MLflow. Prerequisites Creating cloud resources in Microsoft Azure. Using Python to explore and visualize data. Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow. Working with containers AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience. 1 - Design a data ingestion strategy for machine learning projects Identify your data source and format Choose how to serve data to machine learning workflows Design a data ingestion solution 2 - Design a machine learning model training solution Identify machine learning tasks Choose a service to train a machine learning model Decide between compute options 3 - Design a model deployment solution Understand how model will be consumed Decide on real-time or batch deployment 4 - Design a machine learning operations solution Explore an MLOps architecture Design for monitoring Design for retraining 5 - Explore Azure Machine Learning workspace resources and assets Create an Azure Machine Learning workspace Identify Azure Machine Learning resources Identify Azure Machine Learning assets Train models in the workspace 6 - Explore developer tools for workspace interaction Explore the studio Explore the Python SDK Explore the CLI 7 - Make data available in Azure Machine Learning Understand URIs Create a datastore Create a data asset 8 - Work with compute targets in Azure Machine Learning Choose the appropriate compute target Create and use a compute instance Create and use a compute cluster 9 - Work with environments in Azure Machine Learning Understand environments Explore and use curated environments Create and use custom environments 10 - Find the best classification model with Automated Machine Learning Preprocess data and configure featurization Run an Automated Machine Learning experiment Evaluate and compare models 11 - Track model training in Jupyter notebooks with MLflow Configure MLflow for model tracking in notebooks Train and track models in notebooks 12 - Run a training script as a command job in Azure Machine Learning Convert a notebook to a script Run a script as a command job Use parameters in a command job 13 - Track model training with MLflow in jobs Track metrics with MLflow View metrics and evaluate models 14 - Perform hyperparameter tuning with Azure Machine Learning Define a search space Configure a sampling method Configure early termination Use a sweep job for hyperparameter tuning 15 - Run pipelines in Azure Machine Learning Create components Create a pipeline Run a pipeline job 16 - Register an MLflow model in Azure Machine Learning Log models with MLflow Understand the MLflow model format Register an MLflow model 17 - Create and explore the Responsible AI dashboard for a model in Azure Machine Learning Understand Responsible AI Create the Responsible AI dashboard Evaluate the Responsible AI dashboard 18 - Deploy a model to a managed online endpoint Explore managed online endpoints Deploy your MLflow model to a managed online endpoint Deploy a model to a managed online endpoint Test managed online endpoints 19 - Deploy a model to a batch endpoint Understand and create batch endpoints Deploy your MLflow model to a batch endpoint Deploy a custom model to a batch endpoint Invoke and troubleshoot batch endpoints