As we know, 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. There are many advantages of quantitative trading such as faster processing as the algorithms are run using powerful computers that can process millions of data points in milliseconds. It also makes the decision process emotionless and the execution faster.

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.

## Types of Quantitative Trading Strategies

Quantitative trading strategies can generally be classified into one of the following types:

• Momentum: 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.
• Trend Following: Trend following strategies are similar to momentum strategies. In this, the traders use algorithms to find specific price movement patterns and then execute trades based on these patterns. For example, executing a buy order when the price breaks resistance. The idea is to ride the trend, i.e. buy when the price is going up and sell when the price starts falling.
• Statistical Arbitrage: Statistical arbitrage strategies involve simultaneous buying and selling of securities based on pre-defined statistical models in order to take advantage of small pricing inefficiencies. These strategies are highly compute-intensive and time-sensitive and are mostly used by high-frequency traders and hedge funds.
• Market Making: Since the role of a market maker is to provide liquidity both buy and sell-side of securities, the trading algorithms based on market making are designed to make a profit from the bid-ask spreads.
• Sentiment Analysis: These strategies take advantage of the large amounts of unstructured data such as social media, news articles, blog posts, research reports, etc in order to identify the crowd sentiment and make a profit from short-term price changes based on these sentiments.

Apart from these, there are also other forms of trading strategies such as factor-based, scalping, and mean reversion, among others.

## Building A Quantitative Trading Strategy

So, how does one go about building their own quantitative strategy or trading system? Let’s look at the important steps in building a trading strategy.

### 1. Identify Strategy

The first step of the process is to identify a trading strategy. The quants trader will typically start with an idea or a hypothesis that is based on some economic condition, a market trend or some market anomaly. For example, you may ask a question such as: “Is there a relationship between SPY ETF and TLT ETF?”. Based on this, a hypothesis can be formed such as “Does the spread between SPY ETF and TLT ETF follow a mean-reverting behavior?”. Based on this hypothesis, we can then say that the null hypothesis is that there is no mean-reverting behavior and the price spread follows a random pattern. The idea or hypothesis is then supported by market research. Using research and quantitative analysis, the quants trader can then prove that the null hypothesis is false and that the spread does exhibit a mean-reverting behavior.

This involves identifying factors for our model, identifying and collecting data required to build these factors, analyzing these factors and then building a model of a trading strategy. The model is a mathematical representation of the trading strategy. It’s a combination of the factors selected for the model and how these factors are combined. Everything from research, factor selection, and the model building should be driven by your original idea or hypothesis.

To become an expert at identifying profitable trading opportunities and building strategies around them will take time and practice. To begin with, you should look for academic research papers, read quant blogs as well as trade journals. But most of all, it is important to build a sound conceptual understanding of the quantitative methods underlying it.

### 2. Backtest Strategy

Once your strategy is ready, the next step is to backtest the strategy. The goal of backtesting is to make sure that the strategy built is indeed profitable when applied to historical data. This provides some proof and confidence that the strategy actually works, however, backtesting, is in no way a guarantee of success, as the real-markets can behave differently for various reasons. The traders must develop their trading strategies in good faith and avoid any biases (such as survivorship bias, lookahead bias, etc) while conducting backtesting. This is important to achieve dependable backtesting results. It is also important to use clean historical data, include all transaction costs, and use a solid backtesting platform. Only if backtesting provides satisfactory results, a trader should implement the strategy in reality. If the results are not satisfactory, the trader should reject or alter the strategy and backtest again. R is a popular programming language for building as well as backtesting trading strategies.

### 3. Execute / Implement Strategy

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