PGPFE course helped me in moving to Semi Quant team internally. Lectures are very informative and covered wide range of complex topics building from fundamentals to very advanced levels. Very refreshing experience. Both Simulated Trading Lab as well as Live Trading Lab fully equipped with advanced algorithmic trading platforms and statistical analysis systems. Lorem ipsum dolor sit amet consectetur adipisicing elit.

Et doloribus, ipsa, beatae tempora eos magni, esse architecto quo unde quae provident ipsum? Soluta harum fugit quo temporibus error maxime quas! We are very happy to help you progress to greater heights in your career in every way possible. Easy EMI plans available. Encourages the full time students to enter this domain, benefits, if you are still pursuing formal education. Proficiency in spoken and written English.

Basic knowledge of Statistics. Working knowledge of Excel Computer and an internet connection. We teach in Windows based platform. Mac users will need to Install Windows in Parallels Desktop for Mac. Lot of people say, particularly those who do not have a programming background that it is extremely difficult to learn algorithmic trading.

There is no denying the fact that if someone is technically sound then it is imperative that he will learn the techniques of algo trading much faster and they have a higher chance of succeeding in this field. While the truth is that though this is something not very easy to learn particularly for non-programmers, however if taught in a right way and in right context it is not impossible as well provided the learner is serious enough and given the modern tools that are available at ones disposal nowadays.

What is required is serious and sincere effort and a zeal to learn. For participants who do not have a programming background, they will need to attend a primer module on basic Python Programming. For registered participants of the course, the access to the lecture recordings for the Python Primer module will be provided free of cost. The Python primer module is designed for people who do not have any kind of prior programming background and want to learn programming for developing applications related to finance.

The aim of this program is to teach python in an easy, lucid and structured way so that people coming from even no-technical or non-programming background can learn and use the python language. While any prior knowledge is always useful, however the course has been designed in a way that even people having no knowledge of finance can attend and learn from this program.

The program includes modules on relevant areas of basic finance like theories of technical analysis, charting, financial markets, derivatives, option strategies etc. theories are brushed-up before we go into the advanced areas. The course is taught in an easy, lucid and structured way. We teach from basic to advanced levels in a way where people with limited background can also pick-up the skills.

If you are someone who is interested in making a career as a trader, whether you wish to take-up a job or you wish to trade on your own, then learning Algorithmic Trading is no longer a matter of choice it is almost a compulsion now. We have built this course with aim to teach Algo Trading to participants of all kinds of background, the only criteria are that the participant should have a zeal to learn.

Whatever is your background, if you are someone who wants to learn Algo Trading and either work in an International Bank, Hedge Fund or Prop Desks or you wish to trade on your own or you wish set-up your own trading desk, we offer you the right course to fulfil all your requirements. Also, students from Engineering, Mathematics, Statistics, Economics, Finance, Commerce etc. background who aspires to work in International Banks, Hedge Funds, Prop Desks etc.

should do this course. This program is conducted as a comprehensive online course offered via online live interactive lecture sessions on weekends. All lectures are recorded also and participants gets access to view the lecture recordings as well.

All aspects of Algo Trading, starting from strategy development, extensive back-testing, optimization, order management, risk management, error handling and integration with a trading platform. You also get to learn Quantitative Algorithmic Strategies and Machine Learning for Quantitative Trading Using Python. Training is provided on industry leading algorithmic trading platform.

The course has a very advanced curriculum designed by Traders and Quant practitioners from top Wall Street Investment Banks and financial institutions and industry experts to prepare job-ready professionals. The course is taught by Top-notch Traders, Quant Practitioners and Industry Experts from International Banks and Hedge Funds. We believe in imparting complete learning and we offer support even after the course gets over. You get whole-hearted support not only during the course but after the completion of the course as well.

Our faculty team goes out of their way to extend all possible help to serious learners. You can email us with your doubts and queries and you will be connected to the appropriate faculty for solving your doubts and queries.

Will certificate be awarded on completion of the program? What are the certification criteria? The participant becomes eligible to get the certificate on completion of a capstone project that is given at the end of the program, participants who attend and follow all the lectures should be able to complete the project. So, to get the certificate you will also have to complete and submit the project. We have dedicated placement team who provides strong support to all successful participants for getting relevant jobs in International Banks, Hedge Funds and Trading Desks of other financial institutions.

You may work as Quant Trader, Algo Trader, Consultant, Domain expert and lead Quant and Trading Teams of International Banks, top Hedge Funds, Proprietary Trading Desks, Fund Managers and or fortune Financial Consulting Firms and Leading IT Companies.

This program is entirely taught using Python. It provides complete hands-on training in Programming Algo-trading strategies in Python. Need Help? Enquire Email Whatsapp. Bookmark Share This Course Facebook Twitter LinkedIn Email. Post Graduate Program in Algorithmic Trading PGPAT Build your career in Quants Live Online Instructor-led Weekend Program. FinoQ Executive Program Indian Institute of Quantitative Finance Book a guidance call Quick Facts Program Duration Program Schedule Program Timing Program Start Date.

PGPAT Course Highlights. Highly qualified industry practitioner faculty Advanced Curriculum Thoroughly hands-on training in programming algorithmic trading strategies in Python Training on industry leading algorithmic trading platforms Training in Simulation Lab Live Trading experience in real market Course Structure. Post Graduate Program in Algorithmic Trading Course Online. Enquire Now. Course Calendar Students gets free access to recordings of all 4 Primers on course registration.

Download Brochure Register Now. Batch Start Date Fee Mode Time. Who should attend. Fresh Graduates Management Students Finance Professionals Dealers Prop Traders Arbitrageurs Retail Traders Enquire Now. Abhijeet Vaze Working as a Quant in a leading MNC. Sahil Puri Quantitative Analyst. Shubhaditya Dutta Quantitative Research Team, Deloitte. Rohan Deodhar, FRM Analyst, Nomura. Shyam Nayma Nomura. Karthik suriyanarayanan Specialist Business Analysis,BNY Mellon. Anand Kumar Deputy Manager , Aptiva.

Yatish Borole Fresher. Lalitha Duru Manager, Morgan Stanley. View More Enquire Now. Brief PGPAT Course Outline. Part 1 Module What is "Algorithmic" Trading? Market Structures Evolution: Algorithmic Trading trends and their impact on the markets Types of Algorithmic Trading Strategies Lifecycle of Algorithmic Trading Market Microstructure and Concepts Order Book Dynamics Bid-Ask Spread Bid-Ask Bounce Latency Introduction to jupyter notebook Introduction to IntelliJ IDE Installing intelliJ Basics of IntelliJ Read stock data with IntelliJ and basic functionality.

Module Overview of Systematic Trading indicators in Technical Analysis 2. Trend following Strategies 3. Momentum based Strategies 4. Exploring strategies on stock price data 5.

Exploring such strategies on bitcoin data. Ideation and Strategy Creation 2. Architecture of a back-testing System 3. Common Pitfalls Look-ahead bias, survivorship bias etc. Implementing a back-tester 5. Strategy Module 6. Performance Measurement Statistics 7.

Parameter Optimization 8. Transaction Cost Analysis. Optimal Capital Allocation 2. Risk Management. Algorithm Trading Mechanics 2. Architectural design 3. Basic platform design and architectural setup 4.

Operational considerations and pitfalls. Implementing Strategies 2. Order Management 3. Risk Management 4. Error Handling 5. API Integration. Options Pricing 2. Options Greeks 3. Options Trading Strategies a. Market Neutral Strategies b. Bullish Strategies c. Bearish Strategies d. Arbitrage Strategies i. Cash Future Arbitrage ii. Introduction to Machine Learning 2. Regression Models a. Simple Linear Regression i. Example with stock data and why linear regression not a good fit b.

Multiple Linear Regression i. Example with stock data c. Logistic Regression d. Decision Tree Regression e. Random Forest Regression 3. Classification Models a. Decision Tree Classification b. Random Forest Classification 4.

Few examples on what not do fit to stock data. Analytical vs Numerical Optimization 2. Any delay could make or break your algorithmic trading venture.

One needs to keep this latency to the lowest possible level to ensure that you get the most up-to-date and accurate information without a time gap. Latency has been reduced to microseconds, and every attempt should be made to keep it as low as possible in the trading system.

A few measures to improve latency include having direct connectivity to the exchange to get data faster by eliminating the vendor in between; improving the trading algorithm so that it takes less than 0.

Most algorithmic trading software offers standard built-in trade algorithms, such as those based on a crossover of the day moving average MA with the day MA. A trader may like to experiment by switching to the day MA with the day MA. Unless the software offers such customization of parameters, the trader may be constrained by the built-in fixed functionality.

Whether buying or building, the trading software should have a high degree of customization and configurability. Most trading software sold by third-party vendors offers the ability to write your own custom programs within it. This allows a trader to experiment and try any trading concept. Software that offers coding in the programming language of your choice is obviously preferred. Backtesting simulation involves testing a trading strategy on historical data.

This mandatory feature also needs to be accompanied by the availability of historical data on which the backtesting can be performed. Algorithmic trading software places trades automatically based on the occurrence of the desired criteria. The software should have the necessary connectivity to the broker s network for placing the trade or a direct connectivity to the exchange to send the trade orders.

Understanding fees and transaction costs with various brokers is important in the planning process, especially if the trading approach uses frequent trades to attain profitability. Depending upon individual needs, the algorithmic trading software should have easy plug-and-play integration and available APIs across such commonly used trading tools.

This ensures scalability , as well as integration. A few programming languages need dedicated platforms. While building or buying trading software, preference should be given to trading software that is platform -independent and supports platform-independent languages. You never know how your trading will evolve a few months down the line. It is the trader who should understand what is going on under the hood. While buying trading software, one should ask for and take the time to go through the detailed documentation that shows the underlying logic of a particular algorithmic trading software.

Avoid any trading software that is a complete black box , and that claims to be a secret moneymaking machine. While building software, be realistic about what you are implementing and be clear about the scenarios where it can fail. Thoroughly backtest the approach before using real money.

Ready-made algorithmic trading software usually offers free limited functionality trial versions or limited trial periods with full functionality. Explore them in full during these trials before buying anything. Do not forget to go through the available documentation in detail.

Algorithmic trading software is costly to purchase and difficult to build on your own. Purchasing ready-made software offers quick and timely access, and building your own allows full flexibility to customize it to your needs.

Before venturing into algorithmic trading with real money, however, you must fully understand the core functionality of the trading software. Failure to do so may result in big losses. Automated Investing. Trading Skills. Company News Markets News Cryptocurrency News Personal Finance News Economic News Government News. Your Money. Personal Finance. Your Practice. Popular Courses. FinTech Automated Investing. Key Takeaways Picking the correct software is essential in developing an algorithmic trading system.

A trading algorithm is a step-by-step set of instructions that will guide buy and sell orders. Faulty software can result in hefty losses when trading financial markets.

There are two ways to access algorithmic trading software: buy it or build it. Ready-made algorithmic trading software usually offers free trial versions with limited functionality.

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Primer for this course is very helpful for beginners as a prerequisite. This expert advisor have been trading for the past 3 weeks and the account balance have increased exponentially from USD to USD 10, A Bloomberg terminal is a computer system offering access to Bloomberg's investment data service, news feeds, messaging, and trade execution services. Understanding fees and transaction costs with various brokers is important in the planning process, especially if the trading approach uses frequent trades to attain profitability. How much do I need to launch a live trading robot?

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