- 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
Quantitative Trading - Advantages and Disadvantages
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 automation of the strategy is more efficient as there is no need for an individual or a team to constantly monitor market prices or news.
- Traders and investors have gained more computational power to perform tasks that require high volumes of data and also allow them to have more accurate calculations of position sizes and risk management.
- This computation power allows execution of certain types of strategies that would be impossible for humans to execute by themselves such as high-frequency trading strategies that triggers orders in less than one second using order book information.
- All these things are not possible in a discretionary world where traders are unable to continuously compute risk and monitor the market.
- The quantitative approach to trading also gets rid of emotions that can alter the course of transactions.
Despite these advantages, the quantitative approach to trading also has some drawbacks.
- The main constraint of quantitative trading is that it generally requires a far larger capital base than what is utilized in retail discretionary trading.
- Another drawback is that there are many datasets that are expensive for the retail trader. Specifically intraday data feeds are costly.
- In the cryptocurrency market, there are traders and algorithmic trading firms which locate servers in the same cloud, and using the same domain as the exchange. This practice allows them to send orders and match the exchange engine much faster than traditional traders. This also increases costs.
In the last few years, the quantitative trading field has grown significantly and there are many tools and open source platforms that allow the retail traders to build and backtest their quantitative strategies.
Free Guides - Getting Started with R and Python
Enter your name and email address below and we will email you the guides for R programming and Python.