Market Making Strategies Market making strategies are called execution strategies or sell-side methods which are designed to capture spreads, otherwise known as the difference in price between buys and sells. Market makers provide liquidity to the order book of a certain asset and are constantly updating the price based on the supply and demand in the market. In […]

## Mean Reversion Strategies

Mean reversion strategies, also called pairs trading, tend to capture market anomalies or inefficiencies between prices of stocks, ETFs or commodities with similar behavior. These assets usually pertain to the same industry and are affected by the same supply and demand dynamics. So, in normal conditions, it is expected that both assets have a similar path. […]

## Momentum Strategies

Even though we classify momentum with a longer time frame than a day, it is necessary to point out that momentum can also exist within the day. Traders can find momentum during the day, as well as for longer time frames. The strategies that we analyze below have long-time horizon momentum. A momentum strategy is based on […]

## Types of Quantitative Trading Strategies

There are different types of trading strategies which differ in terms of their time horizon, risk profiles, capital requirements, as well as liquidity and volatility needed for a correct execution. These algorithmic trading strategies can be classified into the following types: Momentum Strategies Mean Reversion Strategies Market Making Strategies Intra-day Momentum or Day Trading Strategies […]

## Quantitative Trading – Advantages and Disadvantages

Advantages Quantitative trading has many advantages over the discretionary approach of trading. The performance of a quantitative strategy can be tested with historical market data. This process is known as backtesting where we test the strategy using historical data to help us determine if the strategy is likely to be profitable in the future. The […]

## Introduction to Quantitative Trading

Quantitative trading involves developing and executing trading strategies based on quantitative research. The quants traders start with a hypothesis and then conduct extensive data crunching and mathematical computations to identify profitable trading opportunities in the market. The most common inputs to these mathematical models are the price and the volume data, though other data inputs […]

## Financial Time Series in R – Course Conclusion

This course provided an overview of the fundamentals of time series analysis and how we can perform time series analysis in R. We reviewed some of the most important concepts of time series analysis and looked at the process involved in modeling a time series using the ARIMA models. Time series analysis is a complex […]

## Automatic Identification of Model Using auto.arima() Function in R

auto.arima() Function R also has a package called forecast, which contains many forecasting functions for time series and linear models. It also contains a very useful function called auto.arima, which returns the best ARIMA model according to either AIC, AICc or BIC value. The function conducts a search over possible models within the order constraints provided. […]

## Forecasting with ARIMA Modeling in R – Case Study

In this lesson, we will take a new dataset (stock prices) and use all that we have learned to create a forecast using the ARIMA Models. We will take the closing prices of Facebook stock for this example. Step 1: Load the Data We will load Facebook daily closing prices for the past 3 years […]

## ARIMA Modelling – Identify Model for a Time Series

The first step is to identify a possible model for a given time series. To do so, we need three things: a time series plot of the data, ACF plot and the ACF plot. Analysis of these three plots can help us fairly identify the suitable model. Observing the Time Series Plot The very first […]