We begin 2018 with so much to look forward to. With definitely the best yet to come, let’s take a look at what was fabulous in the year 2017. Here’s a collation of our ten most popularly read blogs from last year. Offering a summary of the trending topics from last year which is followed by a category-wise collection of the best reads from last year.
- Best 10 Blogs
- Algo Trading Basics
- Algo Trading Strategies and Indicators
- Tools and Platforms
- Career Advice
Best 10 Blog
This blog summarises why has Machine Learning become such a buzz word lately. The author gives you different scenarios where a computer programme comes across as a more befitting resource than a human mind. Machine Learning is being employed for long. In 1763, Thomas Bayes published a work ‘An Essay towards solving a Problem in the Doctrine of Chances’ which lead to ‘Bayes Rule’, one of the important algorithms used in Machine Learning. Today applications of Machine Learning are everywhere, this blog elaborates on the implementation of strategies like Linear Regression.
This blog is a step by step guide on how to implement machine learning classification algorithm on S&P500 using Support Vector Classifier (SVC). SVCs are supervised learning classification models. The article will take you through the linear process of implementing the machine learning classification strategy in Python, which begins from importing the libraries, to fetching data and determining the target variable. The next step is the creation of variables to test and train dataset split and create the machine learning classification model using the train dataset.
The advent of algorithmic trading has rewritten the rules of traditional broking. With significant volumes on the exchanges now being traded with the help of sophisticated algorithms, it is imperative that traders should be fully aware of the trading platforms that would enable them to implement their strategies and remain competitive. This write-up makes note of the top trading platforms and tools: Omnesys NEST, Presto ATS, ODIN, FLEXTRADE, AlgoNomics, MetaTrader, AmiBroker, NinjaTrader.
The article covers 9 Best Cryptocurrency Exchanges: eToro, Kraken, Poloniex, BitFinex, HitBTC, Bittrex, BitMEX, Coinbase and Localbitcoins. Cryptocurrency trading has gained substantial popularity owing to many logical aspects. The concept of Cryptocurrency is based on knowledge-sharing on a distributed platform. The entire transaction is for everyone to see. The data entered cannot be altered, nor can it be removed, enabling a system of complete transparency and trust. The entire money flow for the working model is beyond the traditional practices and hence the rising interest in the subject. Read on to know how to be a part of the bandwagon.
After having learnt the basics of Algo Trading, acquiring the knowledge of trading strategies is the secondary level of education. An algorithm is just a set of instructions or rules. These set of rules are then used on a stock exchange to automate the execution of orders without human intervention. This concept is called Algorithmic Trading. The article further elaborates on some of the trading strategies.
Even after the dramatic shift in the technological sphere, finance jobs are as much in-demand as roles in technology sector or other domains. MBA graduates in Finance are proving that they can make a difference as leaders in many different industries. This article lists top courses after MBA finance that students can take up to enhance their finance career.
Technical Indicator is essentially a mathematical representation based on data sets such as price (high, low, open, close, etc.) or volume of a security to forecast price trends. There are several kinds of technical indicators that are used to analyze and detect the direction of movement of the price. This blog shall take you through a thorough description of the various indicators like EVM, Moving Average (MA), Rate of Change (ROC), Bollinger Bands, Force Index. Traders use them to study the short-term price movement since they do not prove very useful for long-term investors, read the full article to learn how to utilize the same for your own trades.
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. Within a decade, it is sure to be the most common way of trading in the developed markets. This article shall help you learn how to utilize algorithmics to trade markets profitably.
Numerous machine learning models like Linear/Logistic regression, Support Vector Machines, Neural Networks, Tree-based models etc. are being tried and applied in an attempt to analyze and forecast the markets. Researchers have found that some models have more success rate compared to other machine learning models. eXtreme Gradient Boosting also called XGBoost is one such machine learning model that has received rave from the machine learning practitioners. In this post, we covered the basics of XGBoost, a winning model for many kaggle competitions and attempted to develop an XGBoost stock forecasting model using the “xgboost” package in R programming.
A good starting point for an aspiring trader would be to pick up a good book, immerse oneself, and absorb all that the book has to offer. This post pens down core focus areas for aspiring quants and covers some of the good reads in each of those categories. The post also shares a comprehensive list of books considered must-reads for aspiring algo-traders.
Algo Trading Basics
This article elaborates on how the accuracy of machines serves a miraculous purpose for High-Frequency Trading and why it is a smart move to adopt machines to take your financial decisions.
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. Read the full blog to acquire a step by step understanding of Algorithmic Trading.
Domain knowledge, skilled resources, technology & infrastructure in the form of hardware and software are the basic requirements for setting up any business or start-up. This blog gives you an overview of the requirements for setting up an algorithmic trading desk or firm.
If you’re keen on getting your strategy funded by someone, you’ll need to have at least 2 year’s worth of consistent profitable track record. Read on to know the perfect roadmap to get your trading strategy funded.
The blog offers an introduction to the basic functionalities of the market and market makers who are agents who stand ready to buy and sell securities in the financial markets. The rest of the market participants are therefore always guaranteed counterparty for their transactions. Explore the article to know more about the subject.
Markets microstructure deals with issues of market structure and design, price formation, price discovery, transaction and timing cost, information & disclosure, and investor behavior. It is the functional setup of a market functioning under a given set of rules & deals.
Algorithmic trading is amongst the most talked about technologies in the recent years. It has given trading Firms more power in the rapidly evolving markets by eliminating human errors and changing the way Financial markets are interlinked today.
If you are trading a strategy which is profitable for you, you need to be able to increase the number of profitable trades to earn more. In trading, the losses and wins happen together. You come out profitable only when your wins compensate your losses enough so as to account for your efforts and costs. Algorithmic trading is a way to do the same.
- Raining Data – Cloud Computing Solutions for Retail Traders
- Can I Be A Quant In My 40s?
- Overcome the Fear of Programming
- Overview of Machine Learning in Trading
Algo Trading Strategies and Indicators
Technical Indicator is essentially a mathematical representation based on data sets such as price (high, low, open, close, etc.) or volume of a security to forecast price trends. There are several kinds of technical indicators that are used to analyze and detect the direction of movement of the price. Traders use them to study the short-term price movement since they do not prove very useful for long-term investors. They are employed primarily to predict the future price levels
In recent years, machine learning has been generating a lot of curiosity for its profitable application to trading. Numerous machine learning models like Linear/Logistic regression, Support Vector Machines, Neural Networks, Tree-based models etc. are being tried and applied in an attempt to analyze and forecast the markets. Researchers have found that some models have more success rate compared to other machine learning models. eXtreme Gradient Boosting also called XGBoost is one such machine learning model that has received rave from the machine learning practitioners.
This blog has been divided into the following segments:
- Getting the data and making it usable.
- Creating Hyper-parameters.
- Splitting the data into test and train sets.
- Getting the best-fit parameters to create a new function.
- Making the predictions and checking the performance.
- Finally, some food for thought.
- Use Decision Trees in Machine Learning to Predict Stock Movements
- Options Pricing Models – Black Scholes, Derman Kani & Heston Model
- Earnings Announcement Strategies: Extracting Earnings Dates
- Trading Using Machine Learning In Python Part-2
- Statistical Arbitrage: Pair Trading In The Mexican Stock Market
- Trading Using Machine Learning In Python – SVM (Support Vector Machine)
- Mean Reversion in Time Series
- Fundamental Analysis With Algorithmic Trading
- Forecasting Stock Returns using ARIMA model
Tools and Platforms
With significant volumes on the exchanges now being traded with the help of sophisticated algorithms, it is imperative that traders should be fully aware of the trading platforms. This has also created a need for software, tools, and platforms, which are being accessed by traders to perform the financial maneuverings. Offering a detailed description of the various tools and platforms avail for your perusal.
This blog entails an overview of the Interactive Brokers API architecture and an explanation of the underlying structure of the IBrokers package. Interactive Brokers provides its API program which can be run on Windows, Linux, and MacOS. The API makes a connection to the IB TWS. The TWS, in turn, is connected to the IB data centers and thus, all the communication is routed via the TWS.
For the hardworking and enterprising ones, starting their own business seems to be the most acceptable and logical step in career. The only concern is how? The basic questions that you need to answer are who else and how did they make it happen. A situational analysis helps in putting the best foot forward. Here’s a story that might interest you.
Prerequisites of an algo-trader are understanding of the functionalities of the market framework, the workflow at the exchange and knowledge of what effects the long-standing positions of the listed firms. This blog shall take you a step further and help you understand how to formulate a trading strategy and shall provide you a technical understanding of what is needed to become a quant.
Quants are often called as ‘Rocket Scientists of Wall Street’ and there is a reason for it. Modern financial instruments are so complex that it takes a genius to understand them fully and as a result people who do understand them, get paid well. This article is a comparative understanding of the pay scales in different countries. It provides an idea of the average salary in the industry, based on the experience level and overall performance of the firm
As financial securities become increasingly complex, it is still interesting to note that it is the people who understand the trading strategies and are responsible for incorporating the same in algorithms. Complex mathematical and financial models are drafted, interpreted and put to use by computerized mechanisms. There has been a steady growth in demand for people who not only understand the complex mathematical models that price these securities, but who can enhance them to generate profits and reduce risk. The article enriches suitable candidates with sufficient knowledge on how to take their career to the next level.
An MBA Finance degree can help develop skills that can be used in a number of situations. In a dramatic shift versus a decade ago, finance jobs are as much in-demand as roles in technology sector or other domains. MBA graduates in Finance are proving that they can make a difference as leaders in many different industries. This article lists top courses after MBA finance that students can take up to enhance their finance career.
If our blogs motivate you to wish to learn various aspects of algorithmic trading, check out the Executive Programme in Algorithmic Trading (EPAT™). The course covers training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading. EPAT™ equips you with the required skill sets to be a successful trader. Enroll now!