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Quantitative trading involves backtesting which is a pivotal process that subjects strategies to historical data to determine their ability to perform. Given its powerful analytical capabilities and large number of financial packages, R, an adaptable statistical programming language is one of the popularly used tools for backtesting. This article will focus on using R in
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Market making is a technique involving traders or firms that place buy (bid) and sell (ask) orders for a given financial instrument with the aim of adding liquidity to the market. The objective is to benefit from the bid-ask spread while minimizing inventory risk. These scenarios are implemented through automated systems in algo trading whereby
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The introduction to this blog will explore the use of machine learning in quantitative trading. Understanding Machine Learning in Trading Machine Learning Definition: Machine learning refers to algorithms that can learn from data and improve their performance without being explicitly coded. In the context of trading, these algorithms have been used to anticipate price movements,
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Algorithmic trading system is a process of using codes for placing and managing trades, which are provided by rules and strategies. In financial markets, these are used to conduct high speed and precise trade execution. Below are important components of an effective algorithmic trading: Data Acquisition and Processing Purpose: Collect real time and past market
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Quantitative trading is a technique that uses math models, algorithms and data analysis procedures to identify trading opportunities and improve the strategies. Given the fact that quantitative trading is now more accessible, open-source projects have become invaluable learning and experimentation tools. Algorithmic Trading, Data Analysis, Backtesting are some of these projects which provide hands-on experience
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Introduction: Financial markets have changed significantly with the advent of algorithmic trading (algo trading) through which highly quantifiable strategies that maximize profitability can be executed at high speeds. Such strategies are based on complicated mathematical models and hugedatasets, hence they need enormous computing power andstorage. This is why cloud computing has become an important tool
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Decisions can be influenced by human emotions in a way that often results in biases which favor personal feelings or social influences. Financial markets, business decisions, hiring practices and even healthcare are industries with such biases leading to negative outcomes, inefficient decision making and unfair practices; however, if algorithms are designed well enough they can
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Pairs trading is a market-neutral strategy in which two related assets (for instance; stocks, commodities etc.) are identified and positions are taken against them when their relative prices break apart. It aims to exploit the mean-reverting property of the price disparity between these two assets with an anticipation that such spread will eventually revert back
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The algorithms are taking over technology and that means it is affecting people’s lives. Like hiring, loan approvals or even judicial sentencing. They are quite effective and accurate but they also present moral dilemmas. Ensuring that there is an ethical design of algorithm should be the goal for fairness to prevail, discriminatory practices avoided and
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Geolocation data consists of information about the geographical position of an item, such as a human, mobile phone or firm based on its IP address, GPS signals or Wi-Fi network. In trading, this data has become significant because it resorts to more informed decisions. Below are some uses of geolocation data in trading models: Consumer