Quantitative trading is a method of using mathematical models and algorithms to analyze financial data and make predictions about the market. It is a highly technical field, and understanding key terms and concepts are essential for success. Here are some of the most important terms that every quantitative trader should be familiar with:

  1. Backtesting: This is the process of evaluating a trading strategy by simulating how it would have performed in the past using historical data. Backtesting allows portfolio managers/traders to assess the effectiveness of a strategy and identify any potential weaknesses before putting it into practice.
  2. Alternative data: Non traditional sources of information that could be used to model a security or asset class’s performance or forward returns. Satellite imagery, credit card data, mobility trends are examples of alternative datasets that could be used.
  3. In sample/out of sample: In every backtest, the set of data that is used to train the model on (training data) is referred to as in-sample data. The data on which the logic is then tested (test data) is referred to as out-of-sample data.
  4. Backtest optimization: Backtest optimization is the process of adjusting a trading strategy to improve its historical performance. It’s important to note that backtest optimization can lead to overfitting, which is when a strategy is too closely fitted to past data and performs poorly on new data.
  5. Algorithm: An algorithm is a set of instructions that a computer follows to perform a specific task. In quantitative trading, algorithms are used to analyze financial data and take positions based on the output.
  6. Execution algo: A specific type of algorithm which focuses on the logic and rules for executing a given order in the market based on multiple different parameters and constraints. These could be used either by sell side or buy side institutions.
  7. Volatility: Volatility is a measure of the amount of variation in the price of a security over time. In trading and investing, volatility is an important factor to consider when assessing the risk of an investment.
  8. Sharpe ratio: A risk adjusted measure for judging returns or performance of a given strategy, asset class or investment portfolio.
  9. Slippage: For any systematic strategy this would be the difference in execution price between the backtest/simulation (theoretical price) and the price at which the order is actually executed (execution price). For most active strategies estimating this before taking it live is important to estimate its success.
  10. Trend following: One of the most well-known styles of investing/trading in the systematic trading world, this style essentially assumes a continuation of prices or returns or any other tradable metric in the same direction as its immediate history.
  11. Statistical Arbitrage: Statistical arbitrage is a type of quantitative strategy that uses statistical techniques to identify and exploit pricing inefficiencies in the market.
  12. Correlation: A statistical measure used to identify the strength and magnitude of co-movement between two variables such as returns. 

Understanding these (and others) concepts is essential for the success of quantitative traders. It’s also important to keep in mind that even the best models and strategies cannot predict the future with certainty, so it’s always important to have a risk management plan in place and conduct continuous research and analysis.

Categories : Quantitative Investing
Tags : alpha mineAlphamineQuantitative InvestingQuantitative TradingQuantitative Trading Success