No matter how much time, effort, and money you put into an investment, if you don’t have a predetermined exit strategy, everything can be gone. For this reason, investment guru never invests without k

Institutional asset managers specialize in a particular asset class, style, sector, or geography, based on their expertise or domain knowledge. This is reflected in the investment products they offer

In this article we are going to consider our first intraday trading strategy. It will be using a classic trading idea, that of “trading pairs”. In this instance we are going to be making use of two Ex

In this article we will make use of the machinery we introduced to carry out research on an actual strategy, namely the Moving Average Crossover on AAPL. Moving Average Crossover Strategy The Movi

In this article I want to introduce you to the methods by which I myself identify profitable algorithmic trading strategies. Our goal today is to understand in detail how to find, evaluate and select

It’s been a while since we’ve considered the event-driven backtester, which we began discussing in this article. In Part VI I described how to code a stand-in ExecutionHandler model that worked for a

In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy. In this article we are going to discuss how to assess the performance of a strategy post-bac

This article continues the discussion of event-driven backtesters in Python. In the previous article we considered a portfolio class hierarchy that handled current positions, generated trading orders

In the previous article on event-driven backtesting we considered how to construct a Strategy class hierarchy. Strategies, as defined here, are used to generate signals, which are used by a portfolio

The discussion of the event-driven backtesting implementation has previously considered the event-loop, the event class hierarchy and the data handling component. In this article a Strategy class hier

In the previous two articles of the series we discussed what an event-driven backtesting system is and the class hierarchy for the Event object. In this article we are going to consider how market dat

In the last article we described the concept of an event-driven backtester. The remainder of this series of articles will concentrate on each of the separate class hierarchies that make up the overall

We’ve spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas. The vectorised nature of pandas ensures that certain operations on large dataset

In the first article on successful backtesting we discussed statistical and behavioural biases that affect our backtest performance. We also discussed software packages for backtesting, including Exce

This article continues the series on quantitative trading, which started with the Beginner’s Guide and Strategy Identification. Both of these longer, more involved articles have been very popular so I

Value at Risk (VaR) for Algorithmic Trading Risk Management Estimating the risk of loss to an algorithmic trading strategy, or portfolio of strategies, is of extreme importance for long-term capital

About This Post The post is suitable for those who are beginning quantitative trading as well as those who have had some experience with the area. The post discusses the common pitfalls of backtest

Money Management via the Kelly Criterion Risk and money management are absolutely critical topics in quantitative trading. We have yet to explore these concepts in any reasonable amount of detail bey

When carrying out an algorithmic trading strategy it is tempting to consider the annualised return as the most useful performance metric. However, there are many flaws with using this measure in isola

Brief Overview of Futures Contracts Futures are a form of contract drawn up between two parties for the purchase or sale of a quantity of an underlying asset at a specified date in the future. This

Backtesting is the research process of applying a trading strategy idea to historical data in order to ascertain past performance. In particular, a backtester makes no guarantee about the future perfo

Foreword I’m certainly not a great programmer, but writing this project taught me a lot (and kept me occupied). Most of my code were done on FMZ.COM, and if I were to refactor the python code I wou

Algorithmic trading is usually perceived as a complex area for beginners to get to grips with. It covers a wide range of disciplines, with certain aspects requiring a significant degree of mathematica

It is common, as a beginning algorithmic trader practising at retail level, to question whether it is still possible to compete with the large institutional quant funds. In this article I would like t

Lucrative salaries, hefty bonuses and creativity on the job have resulted in quantitative trading becoming an attractive career option. Quantitative traders, or quants for short, use mathematical mode

Wouldn’t it be great to have a robot trade on your behalf and earn guaranteed profits? It’s a dream of many to find the perfect computerised trading system for automated trading that guarantees profit

Traders and investors can turn precise entry, exit and money management rules into automated trading systems that allow computers to execute and monitor the trades. One of the biggest attractions of s

With the boom in technological advancements in trading and financial market applications, algorithmic trading and high-frequency trading is being welcomed and accepted by exchanges all over the world.

Intro To Algo Trading I discussed the different types of trading which includes algorithmic, discretionary, and hybrid trading. Let’s say you are intrigued by algorithmic trading. What is it exactl

Basics of Algorithmic Trading: Concepts and Examples Algorithmic trading (also called automated trading, black-box trading, or algo-trading) uses a computer program that follows a defined set of in