- Introduction to Quantitative Trading
- Quantitative Trading - Advantages and Disadvantages
- Types of Quantitative Trading Strategies
- Momentum Strategies
- Mean Reversion Strategies
- Market Making Strategies and Day Trading Strategies
- How to Generate Trading Ideas
- Designing A Trading Strategy For Profit
- Backtesting a Trading Strategy - Considerations
- Risk Management of a Trading Strategy
- Risk Indicators - VIX Index and TED Spread
- Plotting the VIX Index and TED Spread in R
- Introduction to Quantmod in R
- Downloading Data Using Quantmod Package in R
- Creating Charts with Quantmod
- Data Analysis with Quantmod in R
- Measuring Overall ETFs Performance
- Quantstrat Example in R - EMA Crossover Strategy
- Quantstrat - EMA Crossover Strategy - Performance and Risk Metrics
- Quantstrat Example in R - RSI Strategy
- Quantstrat Case Study - Multiple Symbol Portfolio
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 are also used. Traders who develop these quant-based trading strategies and execute these strategies are called quant traders.
While the infrastructure to support quantitative and algorithmic trading is quite robust, the key to finding success is in identifying the right opportunities and building a solid trading strategy. Quants traders make use of programming tools such as R, Python, and Matlab to build and backtest their trading strategies before deploying them for real trade execution.
Who Uses Quantitative Trading?
Quantitative trading is used mostly used by financial institutions and hedge funds, though individuals are also known to engage in such strategy building. Once the trading strategy is built, the trades can be executed manually or automatically using those strategies. The key idea is to pick investments or build a trading strategy solely based on mathematical analysis.
Algorithmic trading is a subset of quantitative trading that makes use of a pre-programmed algorithm. The algorithm, using the quantitative models, decides on various important aspects of the trade such as the price, timing, and quantity, and executes the trades automatically without human intervention. The algorithmic trading process involves making use of powerful computers to run these complex mathematical models and execute the trade orders. This involves automating the full process including order generation, submission, and the order execution. Algorithmic trading is often used by large institutional investors such as pension funds, and mutual funds, to break large orders into several smaller pieces. Since the information is received electronically, algo trading is also used by players such as hedge funds to automatically make decisions to order before other human traders even receive the information, thereby providing them with a huge advantage.
What We Will Learn
In this course, we will focus on understanding the process of designing a successful trading strategy, and learning to use R to build and backtest a trading strategy. We will be making use of some popular R packages such as quantmod and quantstrat.
It is important to note that R can be used for analyzing data, building a strategy and backtest it. It is great for trading analytics. However, once you are ready to execute the strategy, i.e, ready to place orders based on strategy, you are going to do that in a real-time order execution system.
Note: Backtesting is one of the most important steps in building a successful quantitative trading strategy. Quants use their computational finance and programming skills to build complex trading strategies. However, before these strategies are executed in the live market, they are tested using historical data. Basically, the traders will feed the historical data into these algorithmic trading programs which will tell them how well their strategies performed on this historical data. This refers to backtesting and can help traders in finding flaws in their trading strategies and improvise them.