Quantitative Trading Strategies in R

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 […]

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 […]

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 […]

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 […]

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. […]

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 […]

As a quantitative trader, it is a good practice to establish a strategy pipeline that will provide with a stream of ongoing trading ideas. We can have a framework to backtest trading ideas that can be based on any open source projects such as quantstrat from R package. Idea Sources There are plenty of text-books, […]

The first step in the process of designing a trading strategy is to choose which type of strategy will be used and a timeframe to trade.  As stated earlier, the strategy can seek different goals and should be according to each person’s preferences and trading style. The different strategies are designed to take advantage of […]

Backtesting a trading strategy refers to testing the strategy with historical data and observe their metrics, results and performance. Commonly, backtesting has some pitfalls that should be considered by any trader before they put a strategy in production.  Data Scientist and developers should take into account issues such as Optimization Bias, Survivorship Bias and Market Impact when backtesting their trading […]

Lastly we should have rules to manage the risk of the strategy. Risk and money management are the most important aspect in a trading strategy. This field focuses on drawdowns, leverage and volatility. It is important that any trading plan contains a risk management approach to minimize drawdowns, by the use of stop loss or […]