Systematic Trading – What is it?
What is systematic trading and how does it streamline the decision-making process in finance? With its roots in algorithm-based trading rules, systematic trading minimizes emotional bias, ensuring a level of consistency often sought but seldom achieved in market trading. This article demystifies systematic trading for you, contrasting its structured strategy with the uncertainty of discretionary trading, and providing a foundation for those interested in this calculated approach to the financial markets.
Key Takeaways
- Systematic trading employs pre-defined rules and algorithms to execute trading strategies, minimizing emotional bias and enabling consistent decision-making compared to discretionary trading.
- Risk management in systematic trading is vital and might involve setting stop-loss and take-profit points, using proper position sizing, diversifying portfolios, having uncorrelated strategies, and regularly adjusting risk exposure.
- Quantitative methods are fundamental in systematic trading, leveraging mathematical and statistical models to identify patterns in historical and real-time market data with the goal of generating alpha and formulating robust trading strategies.
- We prove an example of a systematic trading strategy.
Unveiling Systematic Trading
Systematic trading is a fascinating mix of financial expertise, data science, and technology that strategizes disciplined trading decisions. It’s like a well-oiled machine, where each cog – be it the trading rules, algorithms, or the trader – has a defined role to play, working in together to execute trading strategies.
Unlike its counterpart, discretionary trading, where decisions depends on subjective judgment, systematic trading prides itself on its reliance on pre-defined rules and automated processes. This approach reduces the influence of emotional biases, paving the way for more consistent decision-making.
While quantitative methods are integral to systematic trading, they are not exclusive to it and can be utilized within discretionary trading frameworks as well.
You might be curious about what systematic trading is based on. To clarify that, we will explore its definition and core principles.
Definition and Core Principles
At the heart of systematic trading lies manual and automated methods, all designed to support investment and trading decisions. These methods aren’t put together out of thin air but are based on predefined rules and models that establish entry and exit points for trades. These rules aim to exclude the influence of human emotion and cognitive biases from the decision-making process. They are systematic.
Picture this: a chess player meticulously planning each move, analyzing the board, and leveraging strategies based on years of experience and study. That’s somewhat analogous to a systematic trader crafting strategies based on quantitative evaluation, historical market behavior, and various market indicators, all to remove emotional biases and leverage market inefficiencies.
Systematic vs. Discretionary Trading
Systematic vs. discretionary trading might described metaphorically like this:
Consider systematic trading as a seasoned chef following a well-tested recipe, whereas discretionary trading is like a freestyle chef who adjusts ingredients based on intuition. While the latter approach may yield a unique dish, it could also lead to inconsistent results.
Moreover, discretionary trading often lacks the structured risk control and backtesting capabilities inherent in systematic approaches, making it more susceptible to emotional influences like greed and fear.
On the other hand, with predefined rules and automated processes, systematic trading ensures consistency, allowing systematic traders to trade with greater confidence, especially when using individual systematic trading funds.
You can read more about discretionary vs systematic trading and mechanical strategies vs discretionary strategies.
Crafting Your Systematic Trading Strategy
Crafting a systematic trading strategy is a bit like building a house: it requires careful planning, selection of the right materials (or in our case, financial instruments), and solid execution.
Just as an architect sketches a blueprint before laying the first brick, a systematic trader establishes mechanisms for trade entry and exit, as well as rules for order sizes. And as every house needs a strong foundation, deciding on the time frame for trading forms a critical component of a systematic trading approach, affecting the frequency and holding period of trades.
Diversification is another key component of a robust trading strategy. Much like an investor diversifying their portfolio, a systematic trader spreads their trades across multiple markets and asset classes to enhance the stability and performance of the trading strategy.
In addition, market analysis based on technical indicators and price action is imperative for setting up trade triggers in a systematic trading strategy. Beyond that, incorporating a mixture of short-term, medium-term, and long-term trading rules can cater to different market conditions.
You want to trade many strategies that are uncorrelated, and by being systematic you can cover more because you automate.
Establishing Trading Rules
Establishing trading rules in systematic trading is like setting the rules of the game. These rules, based on quantitative analysis and historical market data, provide a structured approach to determine entry and exit points, investment sizing, and risk management techniques.
Imagine a chess player contemplating their next move, considering their opponent’s strategy, the position of the pieces on the board, and potential outcomes. Similarly, systematic traders employ methods like:
- Cross-validation
- Use of training and test data sets
- Testing strategies across different assets
- Minimizing the number of trading strategy parameters
These measures help ensure that the strategies developed are not merely effective in theory, but also in practice.
Backtesting for Confidence
Have you ever rehearsed a speech before presenting it? That’s similar to what backtesting does in systematic trading. Backtesting uses historical market data to simulate a trading strategy’s performance, providing traders with a “rehearsal” to evaluate the strategy’s effectiveness before its real debut – the live market. This process includes analyzing metrics like annualized returns and Sharpe ratio, which provide insight into the strategy’s risk-adjusted returns.
To ensure accuracy and operational efficiency, backtesting should be conducted using point-in-time historical data with no look-ahead biases and may involve a live test period.
Live Trading and Adjustments
Systematic traders must continuously monitor live trading performance and adjust strategies to adapt to real-time market conditions. This could include refining algorithms and adjusting strategy parameters to remain competitive.
However, change is the only constant, and this applies to financial markets as well. Therefore, continuous learning and the adaptation of strategies to market evolution are critical for maintaining effective systematic trading strategies.
Keep in mind that no trading system stays profitable forever. Therefore, systematic traders must be ready to adapt or halt a strategy if market changes deem it ineffective.
Systematic trading example
Let’s show you a simple example of systematic trading.
You have discovered that markets tend to trend, and you have heard about the 200-day moving average. How you trade the 200-day moving average systematically?
That’s simple. We make the following trading rules:
- When the close crosses above the 200-day moving average, you go long S&P 500; and
- You sell when the close crosses below.
How has this systematic trading strategy performed? Let’s backtest and find out:
Since 1960 until today it has performed pretty well:
The 199 trades generated an annual return of 6.5% (not including reinvested dividends), which is just below buy and hold. The benefit of the strategy is that you avoided three severe drawdowns along the way. The max drawdown is 28% versus almost 60% for buy and hold.
Algorithmic Trading and Its Impact
Trading and investing systems, particularly algorithmic trading, has significantly evolved since the introduction of computers in the 1970s, opening the doors to complex strategies like:
- statistical arbitrage
- mean reversion
- high-frequency trading
- trend following
- passive index tracking
This evolution has brought about various systematic trading strategies.
This wide spectrum of strategies underlines the versatility of systematic trading, offering a smorgasbord of options for traders to choose from, including specific trading strategies.
Understanding Trading Algorithms
Understanding trading algorithms is like the secret sauce that spices up the systematic trading strategy. These pre-programmed strategies execute trades based on specific rules and conditions, ranging from simple to highly complex systems. Executing trading strategies efficiently, they are the backstage artists that enable rapid, precise, and efficient execution of trades, benefiting from advancements in computing to calculate complex trading strategies.
Therefore, systematic trading becomes a mix of financial expertise, data science, and sophisticated algorithms, developing strategies that autonomously learn and adjust according to data patterns identified by quantitative analysts.
Benefits of Automated Trading
The benefits of automated trading are like having a skilled assistant who tirelessly executes tasks accurately and efficiently. It minimizes latency in trade execution, allowing traders to seize market opportunities and manage risk more effectively. Moreover, quicker trade execution helps avoid missed opportunities that may arise from hesitation or second-guessing.
Automated systems execute orders efficiently by considering factors such as price, timing, and volume, and can strategically divide large orders to minimize their market impact. By rigidly following the trading plan, automated trading systems uphold discipline, thereby mitigating the influence of emotional decision-making during trade execution.
Risk Management Techniques in Systematic Trading
Risk management techniques in systematic trading act like a safety net. They aim to shield against substantial trading losses, ensuring that you don’t lose your trading capital.
Here are some key strategies for risk management in systematic trading:
- Plan trades in advance
- Backtest
- Perhaps use stop-loss and take-profit points
- Use proper position sizing
- Diversify your portfolio of strategies
- Monitor and adjust your risk exposure regularly
But risk management doesn’t stop at planning. It extends to portfolio risk and position sizing, and the implementation of risk controls.
Portfolio Risk and Position Sizing
Portfolio risk and position sizing guide traders to determine the optimal size for each trade, considering the overall account size and risk tolerance. Portfolio risk, on the other hand, can be managed through diversification across various industry sectors, market capitalizations, and geographic regions. This is like spreading your eggs across multiple baskets, ensuring that if one basket falls, obviously, the eggs in the other baskets remain safe.
Expanding diversification to include different asset classes, markets, or timeframes furthers capital stability and diminishes the risk of significant losses.
Implementing Risk Controls
Implementing risk controls in systematic trading acts like brakes in a car – they help manage the speed and prevent accidents. Stop-loss orders, for instance, are employed to automatically sell a security when it drops to a certain price, serving to cap the potential losses on a trade.
Maximum drawdown limits are set to define the maximum expected decline in a portfolio’s value, helping to mitigate the risk of excessive capital being allocated to high-risk trades.
Position and exposure limits, too, play a key role in controlling the size of investments and capping the exposure to any single investment or sector.
The Role of Quantitative Finance in Systematic Trading
The role of quantitative finance in systematic trading is to employ mathematical and statistical methods for assessing financial assets and devising sophisticated trading algorithms.
These quantitative methods scrutinize historical and real-time market data to discern patterns that aid in formulating strategies targeting alpha – the surplus returns over the market benchmark.
Mathematical Models and Market Analysis
Mathematical models serve as architect’s drawings, supplying a blueprint of trading rules to predict future price movements.
They are the language of finance, converting complex market data into understandable and actionable trading rules. With the use of extensive market data, quantitative analysis uncovers trends and anomalies not immediately apparent through conventional discretionary analysis.
In fact, the power of these models is further amplified by the use of advanced analytical tools such as machine learning, artificial intelligence, and big data analytics, which are instrumental in formulating and improving trading strategies.
Generating Alpha Through Quantitative Methods
Generating alpha through quantitative methods is the ultimate ambition for most investors, as it represents extra returns over the market benchmark. Quantitative methods in systematic trading analyze historical and real-time market data to identify patterns that assist in developing strategies for achieving alpha.
Common Pitfalls and How to Avoid Them
Common pitfalls and how to avoid them might be done like this:
- Data mining bias
- Survivorship bias
- Curve fitting
- Selection bias
All of these biases can severely distort trading strategies and their outcomes.
Each challenge presents a chance for learning and growth:
Overfitting and Data Mining Bias
Overfitting and atamining bias refer to the mistake of creating a strategy that appears effective but is actually attuned to random market noise rather than genuine, repeatable market behavior.
Data mining bias, on the other hand, contributes to the development of strategies based on spurious correlations, which are unlikely to be reliable when applied to future market conditions. Therefore, it’s crucial to employ the following methods to prevent overfitting and data mining bias:
- Cross-validation
- Use of training and test data sets
- Testing strategies across different assets
- Minimizing the number of trading strategy parameters
- Put strategies in incubation for many months before you start trading them.
By implementing these methods, you can ensure your strategies are more robust and reliable.
Taking Excessive Risks
Taking excessive risks in systematic trading is like betting all your money on a single roll of dice in a game of chance. Don’t do that – you want a margin of safety to avoid risk of ruin.
Establishing a maximum risk level per trade and sticking to it consistently helps manage risk exposure and maintain financial stability. Diverging from the pre-established risk level for the promise of higher returns can lead to disproportionately large losses when trades go awry.
You might also trade many strategies in different assets, employ different types of strategies, and trade different market directions.
Real Systematic Trading Success Stories
They say success is the best teacher, and in the context of systematic trading, there’ are success stories that can offer valuable lessons. Systematic trading strategies have yielded successful outcomes for a variety of trading entities, from quantitative hedge funds to individual retail traders.
That said, most who try fail. There are a few winners, bit don’t be fooled – systematic trading is not easy and requires a lot of work.
From Theory to Practice
Proprietary trading firms and individual retail traders have successfully translated theoretical financial models into practical trading outcomes. We at Quantified Strategies day traded systematically (and profitably) for 18 years via prop firms.
In the case of systematic trading, the stone is the raw market data, and the envisioned form is the successful trading strategy.
Performance Measures and In-Depth Research
Performance metrics such as the Sharpe ratio, Sortino ratio, and Calmar ratio are crucial for evaluating the risk-adjusted returns of a trading strategy. In addition, benchmarking against market indexes like the S&P 500 allows traders to compare their strategy’s performance to a standard reference.
Summary
Systematic trading offers a structured, disciplined, and data-driven method for making investment decisions. It combines experience, data science, technology, and quantitative finance to develop robust trading strategies.
With risk management techniques and performance measures, along with continuous learning and adaptation, systematic trading can be a powerful tool for both individual and institutional traders.
Frequently Asked Questions
What is the difference between systematic and algorithmic trading?
The difference between systematic and algorithmic trading is that systematic trading involves making investment decisions based on predefined rules, while algorithmic trading uses computer algorithms to execute trades automatically based on specific rules.
Both are related approaches to trading, with algorithmic trading encompassing quantitative trading and high-frequency trading.
What is a systematic strategy?
A systematic strategy is a predefined set of rules given to an algorithmic trading system by traders, based on quantitative analysis, historical data, and market indicators, to guide trading decisions.
How to become a systematic trader?
To become a systematic trader, essential skills include math and programming expertise, an understanding of market structure, and knowledge of different asset classes and their tradeable instruments. Combining these abilities is critical for success in this field.
However, at the end of the day, experience and learning by trial and error are the most important factors for success.
Is systematic trading better?
Yes, systematic trading is better because it removes emotional decision-making from the trading process and ensures discipline in executing trades based on objective rules. This can help avoid impulsive decisions that could lead to losses.
How does backtesting contribute to systematic trading?
Backtesting contributes to systematic trading by allowing traders to evaluate a trading strategy’s effectiveness using historical market data before implementing it in live markets. This helps in assessing the strategy’s performance and potential success.
