Quantitative trading, or quant trading, is an approach to financial investment using multiple large datasets in conjunction with advanced mathematical techniques and statistical modeling. Computer algorithms are commonly used in order to sift through data, quickly iterating through hundreds of thousands of data points. In contrast to algorithmic trading, quant trading involves a highly computational mathematical approach when deciding appropriate trading conditions whereas algorithmic trading uses traditional technical analysis (i.e. using past data). Quantitative trading has garnered a lot of attention especially in recent years, and constitutes a significant portion of many hedge fund and investment bank’s assets alike.
As mentioned briefly, the main goal of quant trading is to identify and capitalize on appropriate trading conditions to generate consistent profits. In general, these conditions arise from market inefficiencies coming from imperfect information about an asset's true price; in other words, certain assets may be under or overvalued, which can later prove valuable when liquidating a stock holding. To give a more concrete example, when a stock is undervalued, it would be appropriate to buy. When certain conditions are met—which is up to the trader to decide—an asset is bought, sold, or more commonly, nothing is done. Other examples of appropriate trading conditions can be determined using technical indicators covered in more detail here.
While this important aspect of quantitative trading may seem trivial due to its similarity to regular trading, it should. However, there is a significant difference between a quantitative trader and a day trader: the use of algorithms to execute trades. At a very basic level and without delving too far into the technical nature of coding, an algorithm makes comparisons between various computer-stored values. In trading specifically, we are able to use this to our advantage by automating the aforementioned comparisons previously left to human judgment and emotion. This is useful, as little mental effort is required to complete these calculations, allowing for more time to be spent determining the conditions of interest and even developing more algorithms. Further, as a consequence of using algorithms, we increase the volume of trades completed in a day by an astronomical amount, as data about markets can sometimes come in as little as microsecond divisions.
It is also worth mentioning the importance of Artificial Intelligence (AI) and Machine Learning (ML) currently being used in quantitative trading. In a world so rich with data—and more importantly, big data—it would be unfavorable not to use the tools available to us when generating potential profits. AI and ML in quant trading is currently being employed to provide insight into certain alternative data events, portfolio management, and even fraud detection. While we will not cover any AI/ML techniques here, it is definitely important to keep in mind.
Another major aspect to the strategy behind quantitative trading is backtesting. The idea behind backtesting is simple; using data from the past to test an algorithm. To backtest, an algorithmic trading strategy must have already been written. Once the trading strategy is translated to code, it is given data from the past allowing for the code to make a trading decision as if the market conditions it was fed were current. When given enough data over a long period of time, the backtesting engine will either generate profits or loss. It is this distinction which provides quant traders with the information they need to decide whether or not to implement a strategy. When executing high volume trades like quant traders, it is incredibly important to test your algorithm so you don’t lose your money at the drop of a hat.
Not only is backtesting generally used to test the profitability of an algorithm, but it is also used to determine the correct parameters by which to run an algorithm at. For example, a Simple Moving Average (SMA) algorithm depends on parameters such as the ticker(s) of interest and the lookback window by which to calculate the average. It is very possible for an SMA strategy to generate next to no profits with one ticker and a surplus of profit in another, or even with different length lookback windows. Backtesting is the method by which to vary these parameters in order to find the optimal run conditions.
In addition to backtesting, there is also a testing strategy used by quant traders called forward performance testing. As the name suggests, forward performance testing uses real time data and paper accounts (theoretical money) to test an algorithm. To briefly compare the benefits of the two testing strategies: backtesting is useful in the sense that it is fast whereas forward performance testing is useful because it uses real-time data. In actuality, it can never hurt to use both testing schemas, as more confirmation that the algorithm will run as intended can never hurt. For a more detailed comparison of the two testing methods look here.
The software that MachineTrader.io is built upon allows for both backtesting and forward performance testing. Specifically, the paper account feature itself is an implementation of forward performance testing. We have set up the system infrastructure so that when a paper account is selected, by default, you are conducting forward performance testing. From the node red terminal this information is then reported to the front end MachineTrader website for more elegant reporting. The MachineTrader infrastructure also allows for backtesting using the correct Polygon.io date range formatting. By testing an algorithm in a paper account using a date range from the past dating back to as far as 2004, we are able to successfully backtest the algorithm. Further, our chief quant, Jerzy Pawlowski, is in the process of developing an engine specific to backtesting, automating the process even more.
With all of this information in mind, the process of quantitative trading is just a matter of executing these steps: market research, identifying a mathematical model, building an algorithm, testing (and testing again) the algorithm, and finally running the algorithm in real time. While it may seem simple and a relatively fast process, both the research step and the testing step can take an exhaustive amount of time if guaranteed profits are desired. However, in the case where profits are reaped, the benefits of algorithmic trading far outweigh the costs. For instance, once an algorithm is proven to be profitable, the scalability is incredibly high. For a lot of major financial institutions, it is this factor that makes quantitative trading such a lucrative aspect of their portfolio.
Following the current trend, quantitative trading will continue to garner attention as long as the practice still guarantees consistent returns on investment. While many institutions have already implemented certain features such as AI/ML and alternative data, as the digital world continues to advance, there will be a greater and greater emphasis and dependence on these currently underappreciated tools in quantitative trading. The future of financial technology lies in quantitative trading.
Trading activity in the new Bitcoin ETFs fell rapidly yesterday. While several of the ETFs made it into the top 5 most heavily traded ETFs on Friday, interest on Tuesday, when trading resumed after the MLK holiday, the bloom was off the rose.
The long-awaited Bitcoin ETFs debuted in yesterday's trading, making quite a splash. Three Bitcoin ETFs were among the five most actively traded ETFs: GBTC, IBIT, and FBTC, all trading more than 10 million shares.
Day trading is a trading strategy where traders buy and sell securities within the same trading day taking advantage of short-term price fluctuations in various assets to make quick profits. Day traders aim to capitalize on small price movements by entering and exiting positions multiple times throughout the day.
Day traders employ a variety of strategies in the quest to make profitable trades. This is a list of the more common ones:
Quantitative trading, also known as algorithmic trading or algo trading, is a method of trading financial assets using advanced mathematical and statistical models. It involves the use of computer algorithms to execute trades based on predefined rules and strategies. This approach to trading has gained significant popularity in recent years, with many institutional investors and hedge funds adopting it as an integral part of their investment strategies.
Swing trading is a popular trading strategy that involves taking advantage of short-term price movements or "swings" within a larger trend. Swing traders aim to capture gains by entering and exiting positions over a period of days or weeks, rather than holding positions for months or years like long-term investors.
The main objective of algorithmic trading is to maximize profits by taking advantage of short-term market inefficiencies. It aims to exploit price discrepancies, liquidity imbalances, and other temporary market conditions that might arise within fractions of a second. By automating the trading process, algorithmic trading eliminates human emotions and biases, enabling faster and more efficient execution of trades.
This strategy creates a portfolio of QQQ (Invesco Trust Series 1) that is traded in correspondence with logic defined by the Moving Average Convergence/Divergence (MACD) lagging indicator. The MACD strategy is a commonly employed algorithmic trading strategy, and can be calculated using Exponential Moving Averages (EMAs) by hand using the following formulas.
This strategy creates a portfolio of IWB (iShares Russell 1000 ETF) that is bought based on the recent news sentiments. The state of the news being reported has an incredible effect on the stock market, as popular reporting agencies can directly increase/decrease people’s optimism about the economy, leading to a snowball effect, of sorts.
Building custom ETFs using MachineTrader™ visual programming toolkit is easy to do and often an effective way to enhance your portfolio. While technically these aren’t algorithmic trading strategies, you can easily add flows that will optimize the ETF based on performance of the individual assets.
The latest significant change in the investment world is the rise of what we call, "independent trading platforms" (ITPs). These are software platforms whose user-friendly web and mobile applications provide all of the functionality of trading platforms, but pass the actual trade executions off, through an API, to the registered broker-dealer of the user's choice.
Monitoring minute-by-minute (or second-by-second) changes in time series charts of prices is a pretty tedious task. While a number of trading platforms offer off-the-shelf algorithms or bots for automating strategies, we've found that it's hard to make money using an algo that other traders are employing, or worse, other traders have programmed to work against.
You're a newbie to trading and you open your Robinhood account and begin to actively trade fractional shares of Tesla (TSLA). Your first dozen trades of the day work great as you pick the best moments to buy and sell. All of a sudden your account is flagged with a 'pattern day trader' warning.
