- 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
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 identifying and following a price trend in the market. It is based on the premise that an asset price that is moving strongly in a direction will continue to move in that direction until the price trend loses strength. The momentum is determined based on the trading volume and rate of price change. Momentum can be classified into time-series momentum and cross-sectional momentum.
Time series momentum means that past returns are correlated with future returns.
In order to design a time-series momentum strategy, researchers calculate the correlation coefficient of the returns using a statistical approach, where the null hypothesis represents no correlation between returns.
The correlation coefficient of returns can vary across lags and sometimes the most positive correlation is between returns of different lags. For example, we can find that the correlation coefficient of returns in one day is negative, and the correlation coefficient of returns in a twenty day window is positive. So, while using this approach, it is necessary to take into account the different timeframes of the returns correlations.
Cross-sectional momentum is based on the relative performance of two prices series, where one price series outperforms the other. The hypothesis in this type of strategy support is that if one price series outperforms the other in the present, it is likely to keep doing so in the future.
Working with Momentum Strategies
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