Quantitative Trading in a Nutshell

1. Quantitative Trading in a Nutshell. Quantitative trading is basically the implementation of trading techniques and strategies in a very disciplined and systematic manner. In other words, it can be simply termed as designing the trading strategies in a streamlined manner according to a fixed plan and these trading strategies are developed through rigorous research and mathematical computations. But what do these trading strategies involve? When you implement a trading masterplan, it includes the application of scientific methods in choosing the securities, filtering and selecting the data, and also analyzing the data in order to trade those securities. This complete analysis is termed as quantitative analysis and those who perform this analysis are called as quant traders. The quant traders are supposed to analyze various aspects of their strategy like measurement of risk exposure, while following a disciplined approach. The influence of quant traders in the trading market can be estimated from the fact that in 2013, about 70 percent of the orders to buy or sell on the Wall Street were placed by quants with the help of software programs. Quantitative Trading generally includes: Strategy Back Testing It involves testing the strategy on the historical data for various scenarios and then optimizing it by removing various biases that can impact the trading. Strategy Identification It is called as the research phase of discovering a strategy that corresponds to your portfolio, and then selecting the trading frequency and checking whether the strategy gets fit into your portfolio of your other strategies. Risk Management It involves a continuous process of scrutinizing, and checking for all the possible biases and risks that can have a negative impact on the profitability of the portfolio.

2. Trading Platforms It is the implementation phase of a trade which encompasses connections to broker, execution of trade, and trying to diminish the transaction cost such that the trade execution turns out to be profitable. Growth and Future of Algorithm Trading When we ponder over the primitive times, when fire was the greatest achievement of mankind, who could have thought about what we as humans have achieved today? Today, Algorithm Trading is amongst the most hyped technologies in the past few years. With Algorithm Trading, trading firms are empowered by the elimination of human errors and changing the ways financial markets are interlinked today. Why to Choose Algorithm Trading? Algorithm Trading has witnessed an impeccable upswing and some of the best performing hedge funds account for their success. The algorithm trading implements the trading commands instantly and accurately because it is uninfluenced by human emotions, technology oriented and fast paced, and the repelling latency. Currently, trading takes place in a matter of microseconds and is marching to nanoseconds, with one millisecond corresponding to millions in terms of revenue fetched per year from the market trades. If the market does not favors your trading strategy rules, the self-learning algorithms of the system would automatically adjust the trading to different patterns and change the rules so as to match the market conditions. Which is the Best Programming Language for Algorithm Trading Systems? Though C++ is often used for real time transaction applications, as it is really fast, but you might spend a considerable amount of time writing your trading system with it and to maintain. However, Java and Python prefer to have less complex code to deal with and they can manage real time data without any problems, and hence they are much more preferable. Python is a great tool for data analysis and for research, and it has great packages such as pandas, numpy and the scikit learn for Machine Learning. The analytical traders should consider learning programming and building systems on their own, so that they can be confident enough about developing the right strategies in a full proof manner.