- 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

# Market Making Strategies and Day Trading Strategies

## 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 a ** market making strategy**, signals can be generated by extracting information from the order book and real time transactions. One common signal in this type of strategy is the tick imbalance bar that has the goal of detecting how informed traders are playing in the market. The imbalance bar is produced when there is an imbalance between bid and ask ticks that exceed an expectation.

These strategies are based on high frequency data and demand high storage requirements, execution optimization and quantitative models to grasp market regime. Generally, algorithms designed for these strategies are fed with tick level transaction data and a real time copy of the order book of the exchange.

### Day Trading Strategies or Intraday Momentum

A big advantage of ** day trading strategies**, when compared to strategies with longer time periods, is that they do not suffer big drawdowns such as those occurring during a financial crisis event. They also have higher

**compared to**

*Sharpe ratios***. These strategies try to gain profits from short-term opportunities during the day.**

*inter-day momentum strategies*A well-know ** day trading strategy** is the

**. This setup is originated when a stock or an ETF starts the new trading session with an open price having a big gap compared to the previous market close. The gap can be in either direction.**

*Opening Gap Strategy*Trading strategies can be automated in order to find stocks and ETFs that have great gaps between the previous close and current open and define entry points based on the price action within one hour after the markets open. Exit rules can also be defined by the algorithm.

In case the gap has bullish direction, and the price makes a new high after the up-gap, the stop loss can be placed at the close level of the previous day.

Other great sources for ** day trading strategies** are

**. Market news are momentum drivers which can extend along the whole trading session. An**

*news sentiments***and other corporate events will move stock prices, and this movement would persist for some time after the announcement. Many trading strategies have developed around**

*Earning Announcement***in order to profit from stocks moves. This kind of strategy is called the**

*Earnings Announcement***. For an implementation and backtesting of this strategy, it is necessary to get the dates of the Earning Announcement and define rules to enter and exit the position.**

*Post Earnings Announcement Drift (PEAD)*Day traders also use ** scalping strategies** to make profits from the market. These strategies run over the shortest time frames such as one and five minutes. A

**can be built around overbought and oversold signals generated by technical indicators such as the**

*scalping strategy***(RSI) and the**

*Relative Strength Index***.**

*Stochastic*#### Related Downloads

## Data Science in Finance: 9-Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

### What's Included:

- Getting Started with R
- R Programming for Data Science
- Data Visualization with R
- Financial Time Series Analysis with R
- Quantitative Trading Strategies with R
- Derivatives with R
- Credit Risk Modelling With R
- Python for Data Science
- Machine Learning in Finance using Python

Each book includes PDFs, explanations, instructions, data files, and R code for all examples.

Get the Bundle for $39 (Regular $57)## 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.