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Michael Halls-Moore, founder of QuantStart. If you would like to watch the video of Michael's presentation, you can here. The post is suitable for those who are beginning quantitative trading as well as options research trading strategies module mock test who have had some experience with options research trading strategies module mock test area.

The post discusses the common pitfalls of backtesting, as well as some uncommon ones! It also looks at the different sorts of backtesting mechanisms as well as the software landscape that implements these approaches. Then we discuss whether it is worth building your own backtester, even with the prevalence of open source tools available today. Finally, we discuss the ins-and-outs of an event-driven backtesting system, a topic that I've covered frequently on QuantStart in prior posts. That is, if we define a set of options research trading strategies module mock test for entry and exit into a portfolio of assets, and apply those rules to historical pricing data of those assets, we can attempt to understand the performance of this "trading strategy" that might have been attained in the past.

It was once said that "All models are wrong, but some are useful". The same is true of backtests. So what purpose do they serve?

Backtests ultimately help us decide whether it is worth live-trading a set of strategy rules. It provides us with an idea of how a strategy might have performed in the past. Essentially it allows us to filter out bad strategy rules before we allocate any real capital.

It is easy to generate backtests. Unfortunately backtest results are not live trading results. They are instead a model of reality. A model that usually contains many assumptions. There options research trading strategies module mock test two main types of software backtest - the "for-loop" and the "event-driven" systems. When designing backtesting software there is always a trade-off between accuracy and implementation complexity.

The above two backtesting types represent either end of the spectrum for this tradeoff. There are many pitfalls associated with backtesting. They all concern the fact that a backtest is just a model of reality. Some of the more common pitfalls include:. There are some more subtle issues with backtesting that are not discusssed as often, but are still incredibly important to consider. Much has been written about the problems with backtesting.

Tucker Balch and Ernie Chan both consider the issues at length. A For-Loop Backtester is the most straightforward type of backtesting system and the variant most often seen in quant blog posts, purely for its simplicity and transparency. Essentially the For-Loop system iterates over every trading day or OHLC barperforms some calculation related to the price s of the asset ssuch as a Moving Average of the close, and then goes long or short a particular asset often on the same closing price, but sometimes the day after.

The iteration then continues. All the while the total equity is being tracked and stored to later produce an equity curve. As you can see the design of such a sytem is incredibly simple.

This makes it attractive for getting a "first look" at the performance of a particular strategy ruleset. For-Loop backtesters are straightforward to implement in nearly any programming language and are very fast to execute. The latter advantage means that many parameter combinations can be tested in order to optimise the trading setup. The main disadvantage with For-Loop backtesters is that they are quite unrealistic. They often have no transaction cost capability unless specifically added.

Usually orders are filled immediately "at market" with the midpoint price. As such there is often no accounting for spread. Options research trading strategies module mock test is minimal code re-use between the backtesting system and the live-trading system.

This means that code often options research trading strategies module mock test to be written twice, introducing the possibility of more bugs. For-Loop backtesters are prone to Look-Ahead Bias, due to bugs with indexing. For-Loop backtesters should really be utilised solely as a filtration mechanism. You can use them to eliminate the obviously bad strategies, but you should remain skeptical of strong performance.

Further research is often required. Strategies rarely perform better in live trading than they do in backtests! Event-Driven Backtesters lie at the other end of the spectrum. They are much more akin to live-trading infrastructure implementations. As such, they are often more realistic in the difference between backtested and live trading performance. Such systems are run in a large "while" loop that continually looks for "events" of differing types in the "event queue".

When a particular event is identified it is routed to the appropriate module s in the infrastructure, which handles the event and then potentially generates new events which go back to the queue.

As you can see there is a heavy reliance on the portfolio handler module. Such a module is the "heart" of an Event-Driven backtesting system as we will see below. While the advantages are clear, there are also some strong disadvantages to using such a complex system:. In this section we will consider software both open source and commercial that exists for both For-Loop and Event-Driven systems. There are plenty of code snippets to be found on quant blogs.

A great list of such blogs can be found on Quantocracy. The expensive commercial offerings include Deltix and QuantHouse. They are often found in quant hedge funds, family offices and prop trading firms.

Cloud-based backtesting and live trading systems are relatively new. Quantopian is an example options research trading strategies module mock test a mature web-based setup for both backtesting and live trading. Institutional quants often also build their own in house software.

In terms of open source software, there options research trading strategies module mock test many libraries available. One of the most important aspects, however, is that no matter which piece of software you ultimately use, it must be paired with an equally solid source of financial data. Otherwise you will be in a situation of "garbage in, garbage out" and your live trading results will differ substantially from your backtests.

While software takes care of the details for us, it hides us from many implementation details that are options research trading strategies module mock test crucial when we wish to expand our trading strategy complexity. At some point it is often necessary to write our own systems and the first question that arises is "Which programming language should I use?

Despite having a background as a quantitative software developer I am not personally interested in "language wars". There are only so many hours in the day and, as quants, we need to get things done - not spend time arguing language design on internet forums!

Python is an extremely easy to learn programming language and is often the first language individuals come into contact with when they decide to learn programming. It has a standard library of tools that can read in nearly any form of data imaginable and talk to any other "service" very easily. While it is great for ML and general data science, it does suffer a bit for more extensive classical statistical methods and time series analysis.

It is great for building both For-Loop and Event-Driven backtesting systems. In fact, it is perhaps one of the only languages that straightforwardly permits end-to-end research, backtesting, deployment, live trading, reporting and monitoring.

However, work is being carried out to improve this problem and over time Python is becoming faster. R is a statistical programming environment, rather than a full-fledged "first class programming language" although some might argue otherwise! It is widely used for For-Loop backtesting, often via the quantmod library, but is not particularly well suited to Event-Driven systems or live trading. It does however excel at strategy options research trading strategies module mock test. This is its primary advantage.

Unfortunately it is painful for carrying out strategy research. Due to being statically-typed it is quite tricky to easily load, read and format data compared to Python or R. You may also wish to take a look at Java, Scala, CJulia and many of the functional languages. It is a great learning experience to write your own Event-Driven backtesting system. Firstly, it forces you to consider all aspects of your trading infrastructure, not just spend hours tinkering on a particular strategy. Even if you don't end up using the system for live trading, it will provide you with a huge number of questions that you should be asking of your commercial or FOSS backtesting vendors.

While Event-Driven systems are not quick or easy to write, the experience will pay huge educational dividends later on in your quant trading career. They are all written in Python due to the reasons I outlined above and thankfully Python is very much like reading pseudo-code. That is, it is very easy to follow. I've also written many articles on Event-Driven backtest design, which you can find herethat guide you through the development of each module of the system. Rob Carver, at Investment Idiocy also lays out his approach to building such systems to trade options research trading strategies module mock test.

Remember that you don't have to be an expert on day 1. You can take it slowly, day-by-day, module-by-module. If you need help, you can always contact me or other willing quant bloggers. See the end of the article for my contact email. I'll now discuss the modules that are often found in many Event-Driven backtesting systems.

While not an exhaustive list, it should give you a "flavour" of how such systems are designed. This is where all of the historical pricing data is stored, along with your trading options research trading strategies module mock test, once live.

Ideally, we want to obtain and store tick-level data as it gives us an idea of trading spreads. It also means we can construct our own OHLC bars, at lower frequencies, if desired.

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Derivatives are known to be among the most powerful financial instruments. The Indian equity derivatives market has seen tremendous growth since the year when equity derivatives were introduced in India. This module provides insights into different types of equity derivatives, their trading, clearing and settlement and the regulatory framework.

Why should one take this course? Investment Analysis and Portfolio Management is a growing field in the area of finance. Fundamental analysis is a stock valuation methodology arrived at by performing security analysis.

An appropriate security analysis forms the basis of successful investment decisions. This module aims at providing a basic insight about fundamental analysis and various valuation methodologies used. There are vast arrays of strategies available for trading options. This module discusses the objectives of these strategies and the conditions under which they are successful. It is advisable to take the NCFM Derivatives Markets Dealers Module test which would make you familiar with the basic concepts of the options market, before attempting this module.

Every business operates in an internal and external environment which has embedded hazards for business operations referred to as inherent risks of doing business. Operational risk management is a methodology which helps in risk assessment, risk decision making, and implementation of risk controls, which results in acceptance, mitigation, or avoidance of risk which is highly integral to businesses. Businesses need funds for establishment, growth and development.

Banks remain as the main pillar for financing business activities. There is an increased need for qualified individuals who possess requisite skills and significant knowledge in banking in these fast moving, globalized financial markets. This module aims at providing a basic insight about banking operations and to acquaint the learners with various banking related services. Insurance serves a number of valuable economic functions that are largely distinct from other types of financial intermediation.

The insurance market has witnessed dynamic changes, which includes presence of a fairly large number of insurers both in life and non-life segment.

There is an increased need for qualified individuals who possess requisite skills and significant knowledge in insurance in these fast moving and globalised financial markets.

Understanding of Economics is a key to discern how the financial markets operate. There are intricate linkages between various economic factors and financial variables which can have both direct and indirect impact on the financial markets. An economic perspective facilitates identification of the causes of different economic developments as well as anticipation of the possible impact of changes in policies.

This module aims at providing a basic understanding of various macroeconomic concepts and a glimpse of macroeconomic behavior. An efficient depository is critical to the efficient functioning of the capital market. This module provides deep insight into the functioning of the depository and outlines the various operational issues. It has been mandated by the National Securities Depository Limited NSDL which is one of the depositories in India , that all branches of depository participants must have at least one person qualified in this certification programme.

The aim of this module is to provide beginners as well as the dealers with both theoretical and applied knowledge pertaining to commodities trading. The module is beneficial for those who wish to pursue careers in brokerage firms dealing in commodity derivatives.

Effective surveillance is the sine qua non for a well functioning capital market. The module provides insights into the surveillance issues in the stock market. To build confidence among investors, it is imperative to adopt the best corporate governance policies and practices. Recognizing this need, this module endeavors to impart knowledge about the evolution of the corporate governance in India. It also discusses important concepts related to corporate governance and the regulatory framework governing it.

Compliance officers of any company needs to have adequate knowledge of the legal and regulatory requirements for carrying out the business of that company. A sound knowledge of these helps the organization adhere to the required compliance standards. The Compliance Officers Corporates module tests the candidates on their knowledge of the relevant rules, regulations and guidelines governing the corporates such as the Companies Act. Please note that no study material is provided for this module.

Information security is of vital importance in the corporate environment where a vast amount of information is processed by organizations on a day to day basis. An information security audit is one of the best ways to determine the security of an organization's information.

This module has been developed for those involved with or interested to know about information security related issues in the financial markets. On successful completion of both the parts of this module, candidates are provided with a 'Certified Information Security Auditor for Financial Markets' certification. This module has been prepared with a view to provide a comprehensive and in depth knowledge about technical analysis. This module has been prepared with a view to provide a comprehensive and in depth knowledge about mergers and acquisitions.

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In recent years, the relevance of small and medium enterprises SMEs for economic development has become particularly noteworthy. However, often these firms are more financially constrained than larger firms. In such scenarios, venture capital and private equity play a vital role in constituting a valuable resource for a firms' growth, especially for more innovative SMEs, so that they can support economic development and innovation in the economy.

Private equity is considered to be one of the elements of a good entrepreneurial eco-system. In the last couple of decades, it has emerged as a serious source of finance for start-ups, growing companies and takeover transactions. The objective of this module is to help a student get an overview of the various segments within the financial world and get basic understanding of core concepts in finance.

The program will help candidates to: The NSE Certified Quality Analyst NCQA is a finance professional who has knowledge of quality tools and their uses and is involved in quality improvement projects, but doesn't necessarily come from a traditional quality area. NCQA modules have been prepared with a view to provide candidates the required application level knowledge and skills on quality thinking and quality tools such as Lean, 7 Steps of Problem Solving, Data Analysis Tools, and basics of Six Sigma in their everyday work.

On successful completion of the course, the candidate should gain proficiency in executing small quality improvement projects, including collecting and analyzing voice of customer, gathering and analyzing data and re-engineering processes with a view to improving their efficiency and effectiveness. Just follow these simple steps: If a security warning pops up, just continue.

If you need help registering, click here. You can skip this step if you already have an NCFM registration number. You need to pay for the SSA training first, at the time of purchase of the course. You will be directed to pay for the NCQA certification exam thereafter, at the time of booking the date. List the important milestone in the process of quality evolution. Quality in current Business scenario. Outline the various factors that affect quality in an organisation.

Continuous improvement and Problem solving. AIWMI primarily focuses on broader and strategic role of developing a more robust and forward-looking training infrastructure for the financial services sector and to promote more active industry involvement and collaboration in training and continuing education matters.

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