Systematic Trading: Concepts, Strategies, Steps, and Implementations

19 min read

By Chainika Thakar

Imagine stepping into the world of finance where technology and data-driven insights blend seamlessly. This is where systematic trading comes into play. Systematic trading isn't just about buying and selling stocks or assets; it's a method that harnesses the power of algorithmic strategies and quantitative analysis. Systematic trading, hence, helps to guide smart and disciplined trading choices.

Think of systematic trading as a finely tuned combination of financial expertise, data science, and cutting-edge technology.

Over recent years, it has caught the spotlight because of its ability to make trading more efficient and also to reduce the impact of emotions on decision-making.

In this guide, we're about to embark on a journey through the core of systematic trading. We'll uncover its core principles, strategies, tools, and the essential skills that make it all work. So, whether you're new to trading or looking to up your game, get ready for an in-depth exploration of this fascinating world.

But before we dive into the specifics, let's take a moment to understand what algorithmic trading, algorithmic trading strategies, and quantitative analysis really mean.

Algorithmic Trading: Imagine having a computer program that can automatically execute your trading decisions. That's algorithmic trading. It's like having a trading assistant that follows a set of predefined rules to buy or sell assets without you having to do it manually.

Algorithmic Trading Strategies: These are the rules that guide the computer program in making those buy or sell decisions. These strategies can be based on patterns, mathematical models, or even a combination of various indicators to predict market movements.

Quantitative Analysis: This is where the data magic happens. Quantitative analysis involves crunching large amounts of financial data to identify trends, patterns, and anomalies. It's like using data to uncover hidden insights that can help in making better trading decisions.

Now that we have learnt about these concepts, let's dig deeper into systematic trading – a method that brings all of this together in an organised and effective way.

Some of the concepts covered in this blog are taken from this Quantra course on Getting Started with Algorithmic trading. You can take a Free Preview of the courses by clicking on the green-coloured Free Preview button.

This blog covers:

What is systematic trading?

Think of systematic trading as a machine equipped to follow a set of rules derived from quantitative analysis, historical data, and market indicators. These rules dictate when to enter or exit trades, how much to invest in each trade, and how to manage risks.

By taking human emotions out of the equation and relying on systematic processes, traders aim to achieve consistency, efficiency, and a more calculated approach to gaining from market movements.

In a nutshell, systematic trading is about using technology, data, and algorithms to make trading decisions, fostering a more disciplined and strategic way of capitalising on opportunities in the financial markets.

Key concepts in systematic trading

Now, let us find out the fundamental concepts underpinning systematic trading. Moreover, we will unveil the role of each concept in refining trading strategies.

Key concepts
Key concepts

Quantitative analysis and modelling

Quantitative analysis and modelling form the analytical backbone of systematic trading. These processes involve using mathematical and statistical techniques to analyse historical and real-time market data, uncover patterns, and develop predictive models.

Let us find out what each means and how are they the key concepts.

Quantitative analysis

The concept of quantitative analysis revolves around processing and interpreting numerical data to derive meaningful insights. In systematic trading, it involves examining vast amounts of historical and current market data to identify patterns, trends, correlations, and anomalies that might not be apparent through traditional methods. By quantifying market behaviour, systematic traders gain a more objective understanding of how assets move and interact with each other.


Modelling in systematic trading refers to creating mathematical representations of market behaviour. These models are built using historical data and various mathematical techniques to predict future price movements or assess risk.

Common models include time series models, regression models, and machine learning algorithms. These models can help traders make more informed decisions by providing a structured framework for understanding market dynamics.

Role of quantitative analysis and modelling in systematic trading

Quantitative analysis and modelling are integral to systematic trading for several reasons:

Role of quantitative analysis and modelling
Role of quantitative analysis and modelling
  • Data-Driven Insights: By analysing historical price data, trading volumes, volatility, and other market variables, quantitative analysis helps uncover patterns that might repeat in the future.
  • Strategy Development: Quantitative models aid in designing trading strategies. For instance, a mean reversion strategy might be based on identifying situations where an asset's price deviates from its historical average, with the expectation that it will eventually revert to the mean.
  • Risk Assessment: Quantitative models can assess the potential risks associated with a trading strategy. They can help determine the likelihood of specific scenarios playing out and their potential impact on portfolio returns.
  • Backtesting: Historical data is used to test the performance of trading strategies. This allows traders to evaluate how the strategy would have performed in the past and make informed decisions about its future use.
  • Optimization: Quantitative models can be fine-tuned and optimised to improve trading strategy performance. Parameters within the models can be adjusted to enhance returns and minimise risk.
  • Adaptability: Quantitative analysis enables traders to adjust strategies based on changing market conditions. Models can be updated as new data becomes available, ensuring that strategies remain relevant and effective.

Market data and historical analysis

Market data and historical analysis play a crucial role in systematic trading by providing the foundation for informed decision-making and strategy development.

Let's explore how market data and historical analysis contribute to the effectiveness of systematic trading below.

Market Data

The market data encompasses a wide range of information about financial instruments, including stocks, currencies, commodities, and more. This data includes details such as price quotes, trading volumes, bid and ask sizes, order book depth, and transaction history. In systematic trading, the access to accurate and timely market data is essential for understanding market dynamics and making well-informed trading decisions.

Historical Analysis

In general, historical analysis involves studying past market data to identify patterns, trends, and anomalies. By analysing historical price movements and other relevant data, traders can gain insights into how assets have behaved under various market conditions.

This historical context provides a basis for creating trading strategies that aim to capitalise on repeatable patterns or deviations from historical norms.

Role of market data and historical analysis in Systematic Trading

Market data and historical analysis serve several critical functions in systematic trading:

Role of market data and historical analysis
Role of market data and historical analysis
  • Pattern Recognition: Historical analysis helps identify recurring price patterns and trends. For instance, recognising that a particular asset tends to rise during certain market conditions and fall during others can guide trading decisions.
  • Strategy Development: By studying historical data, traders can develop systematic trading strategies that capitalise on historical patterns. These strategies can be backtested to determine their effectiveness over different market conditions.
  • Indicator Creation: Historical data is used to create technical indicators, such as moving averages, Bollinger Bands, and relative strength indexes (RSIs). These indicators help identify potential entry and exit points for trades.
  • Risk Assessment: Historical analysis allows traders to assess the risks associated with specific trading strategies. By understanding how a strategy would have performed during past market downturns or periods of volatility, traders can manage risk more effectively.
  • Backtesting: Historical data is used to test trading strategies against past market conditions. This process helps traders evaluate the strategy's potential performance and refine its parameters.
  • Strategy Adaptation: Historical analysis can reveal how strategies have performed over time. This information enables traders to adapt strategies to changing market conditions or modify parameters to optimise performance.
  • Behavioural Insights: Studying historical data can provide insights into market behaviour and investor sentiment. This understanding can be used to anticipate market movements and align trading strategies accordingly.

Backtesting and performance evaluation

In systematic trading, the combination of backtesting (This course on backtesting trading strategies by Quantra is just what you need to get the best out of your trading) and performance evaluation empowers traders to make data-driven decisions and objectively assess the strengths and weaknesses of their strategies. This process enhances the likelihood of implementing strategies that are not only historically effective but also well-suited to navigate the complexities of dynamic financial markets.

Backtesting and performance evaluation are critical components of systematic trading that allow traders to assess the viability and effectiveness of their trading strategies before deploying them in real markets. Let's delve into how these processes work and their significance in systematic trading:


Backtesting involves testing a trading strategy using historical market data to simulate how it would have performed in the past. Traders use historical data to feed the strategy's rules and parameters and then analyse how the strategy would have executed trades, generated profits or losses, and responded to various market conditions. The goal is to evaluate whether the strategy's historical performance aligns with its intended goals and to identify potential weaknesses or flaws.

Performance Evaluation

Performance evaluation involves assessing the effectiveness of a trading strategy using various metrics and performance measures. These measures can include metrics like annualised returns, Sharpe ratio (risk-adjusted return), maximum drawdown (largest peak-to-trough decline), and win-loss ratios. By analysing these metrics, traders gain insights into the strategy's risk-reward profile, its ability to generate consistent returns, and its overall robustness.

Role of backtesting and performance evaluation in systematic trading

Backtesting and performance evaluation serve several crucial roles in systematic trading:

Role of backtesting and performance evaluation
Role of backtesting and performance evaluation
  • Strategy Validation: Backtesting provides a way to validate trading strategies before deploying real capital. By testing strategies against historical data, traders can gauge whether the strategy aligns with their expectations.
  • Risk Assessment: Backtesting allows traders to assess how a strategy performs during different market conditions and economic cycles. This insight helps in identifying potential risks and vulnerabilities.
  • Parameter Optimization: Backtesting enables traders to fine-tune strategy parameters for optimal performance. It helps identify parameter values that maximise returns while minimising risks.
  • Decision-Making: Backtesting results guide traders in making informed decisions about whether to use, modify, or discard a particular strategy.
  • Performance Benchmarking: Performance evaluation metrics help traders compare different strategies and select the ones that align with their risk appetite and financial goals.
  • Realistic Expectations: Backtesting provides traders with a realistic view of a strategy's historical performance, helping them set realistic expectations for potential future results.
  • Continuous Improvement: Performance evaluation helps traders continuously refine and improve their strategies based on historical performance insights.

Risk management and position sizing

Risk management and position sizing are fundamental aspects of systematic trading that focus on preserving capital, minimising losses, and optimising portfolio returns.

In systematic trading, risk management and position sizing are essential tools for achieving consistent and sustainable results. By maintaining control over risk exposure, traders can navigate market uncertainties with greater confidence and discipline, ultimately enhancing the longevity and success of their trading strategies.

Let's explore how these elements contribute to the success of systematic trading strategies:

Risk Management

Risk management in systematic trading involves the implementation of strategies and techniques to protect capital and manage potential losses. The goal is to limit the impact of adverse market movements on the overall portfolio. Effective risk management ensures that a series of losses doesn't lead to catastrophic portfolio depletion.

Position Sizing

Position sizing refers to determining the appropriate amount of capital to allocate to a particular trade or investment. It's a critical component of risk management as it helps control exposure to individual trades and manage the overall portfolio's risk level. Learn more about how position sizing in trading can help enhance your skills.

Role in Systematic Trading

Risk management and position sizing play essential roles in systematic trading:

  • Capital Preservation: By setting limits on the amount of capital allocated to each trade, position sizing helps prevent excessive losses that could significantly impact the overall portfolio.
  • Diversification: Proper position sizing ensures that a portfolio is diversified across multiple trades and assets. This diversification reduces the impact of a single trade's poor performance on the entire portfolio.
  • Risk-Reward Balance: Risk management techniques help traders find a balance between the potential reward of a trade and the associated risk. This balance ensures that the potential for profit justifies the potential loss.
  • Volatility Management: Systematic traders often adjust position sizes based on the volatility of the asset. Highly volatile assets may require smaller positions to mitigate potential large swings.
  • Portfolio Optimisation: Position sizing allows traders to optimise their portfolios for better risk-adjusted returns. Assets with higher expected returns can be allocated more capital, while maintaining a sensible level of risk.
  • Adaptation to Market Conditions: As market conditions change, risk management strategies and position sizing rules can be adjusted to reflect evolving market dynamics.
  • Controlling Emotions: By defining position sizes in advance, traders can stick to their strategy even during periods of market turbulence, avoiding impulsive decisions driven by emotions.
  • Preserving Long-Term Viability: Effective risk management and position sizing help ensure that a series of losses doesn't deplete the trading capital, allowing the trader to participate in future market opportunities.

What are systematic trading strategies?

Systematic trading strategies are predefined sets of rules (algorithms) given by the traders to the algorithmic trading system in order to guide the trading decisions. These sets of rules are devised on the basis of quantitative analysis, historical data, and market indicators.

These strategies aim to remove emotional biases from trading and rely on data-driven approaches to capitalise on market inefficiencies, trends, and patterns.

Here are a few common types of systematic trading strategies:

Systematic trading strategies
Systematic trading strategies

Trend Following Strategies

Trend following strategies aim to identify and capitalise on sustained price trends in the market. These strategies involve buying assets that are in an uptrend (rising prices) or short-selling assets that are in a downtrend (falling prices). Trend followers believe that markets have a tendency to continue existing trends for a certain period, and they aim to profit from these trends.

Mean Reversion Strategies

Mean reversion strategies are based on the idea that prices tend to revert to their historical averages over time. These strategies involve buying assets that are currently trading below their historical averages and selling assets that are trading above their averages. Mean reversion traders believe that price deviations from the average will eventually correct themselves.

Momentum Strategies

Momentum strategies focus on identifying assets that are showing strong price momentum, either upward or downward. Momentum traders believe that assets that have performed well in the recent past will continue to perform well in the short term due to the "momentum effect." These strategies can involve trend continuation (riding the momentum) or trend reversal (anticipating a reversal after an extreme move).

Statistical and Quantitative Strategies

Statistical and quantitative trading strategies utilise advanced mathematical and statistical models to identify trading opportunities. These strategies often involve complex quantitative analysis, machine learning, and algorithmic execution. They can include strategies like statistical arbitrage, pairs trading, news based trading and factor-based investing, where relationships between assets are exploited based on historical data.

You can explore this webinar below to find out about quantitative strategies in detail.

Also, you should read through the Primer on Quant Strategies for a better understanding.

Example of a systematic trading strategy with Python

Let us now see an example of a moving average crossover strategy applied to Apple Inc. (AAPL) stock using Python.

In this strategy, we'll use a shorter-term moving average and a longer-term moving average to generate buy and sell signals based on their crossovers.

This example strategy falls under the category of trend-following strategies.


Price and MA
Price and MA
Moving Average crossover strategy
Moving Average crossover strategy
  • The output graphs above can be interpreted in the following manner as discussed below.

AAPL Price and Moving Averages:

  • The first graph is the price and moving averages graph. Let us see what all the graph shows with the points below.
  • The graph shows the historical closing prices of AAPL stock along with the 20-day Simple Moving Average (SMA) and the 100-day Exponential Moving Average (EMA).
  • The blue line represents the daily closing prices of AAPL stock.
  • The orange line represents the 20-day SMA, which provides insight into the shorter-term trend.
  • The green line represents the 100-day EMA, offering a smoother representation of the longer-term trend.
  • Crossovers between the SMA and EMA can indicate potential changes in trend direction.

Moving Average Crossover graph for AAPL (Performance of the portfolio):

  • The second graph portrays the performance of the strategy or the portfolio value. Below you can see a few observations.
  • The graph shows the performance of the moving average crossover strategy applied to AAPL stock in terms of USD or U.S. Dollars.
  • The blue line represents the portfolio value over time based on the strategy.
  • The initial portfolio value is 100 shares, meaning that it will hold 100 shares of stock when the strategy begins and that number of 100 shares is multiplied by the initial closing price.
  • Buy and sell signals based on moving average crossovers determine the portfolio's performance.
  • Peaks and valleys in the portfolio value line indicate periods of gains and losses based on strategy trades.

Now that you have seen an example of the strategy, you can experiment with different combinations of moving average lengths to find the parameters that yield better performance for the strategy.

Keep in mind that while parameter adjustments can improve performance, backtesting and evaluation of various parameter settings are essential to ensure the strategy's viability across different market conditions.

Role of a systematic trader

The role of a systematic trader involves designing, implementing, and executing trading strategies using systematic and data-driven approaches.

Systematic traders use quantitative analysis, algorithms, and technology to make informed and disciplined trading decisions.

Their role can encompass various responsibilities:

  • Strategy development: Systematic traders create, refine, and optimise trading strategies. They identify patterns, signals, or edges in historical market data and develop algorithms that can exploit these patterns to make profitable trades.
  • Quantitative analysis: Systematic traders conduct in-depth quantitative analysis to understand market dynamics, develop predictive models, and identify opportunities for generating alpha (market outperformance). They often use statistical and mathematical techniques to analyse data and backtest strategies.
  • Algorithm implementation: Systematic traders code and implement algorithms that automate trading decisions. This involves programming skills to translate trading strategies into executable code that interfaces with trading platforms or APIs.
  • Data management: Gathering and managing market data is crucial for systematic trading. Traders need to ensure accurate, clean, and timely data is available for analysis and strategy execution.
  • Backtesting: Systematic traders test their strategies using historical data to assess how they would have performed in past market conditions. Backtesting helps to validate the viability of a strategy and identify potential shortcomings.
  • Risk management: Effective risk management is paramount in systematic trading. Traders define risk parameters, position sizing rules, and measures to control drawdowns to ensure their strategies don't expose them to excessive losses.
  • Execution and automation: Systematic traders automate the execution of their strategies using trading algorithms. They monitor real-time market data and manage the execution of trades to ensure they align with the strategy's parameters.
  • Performance monitoring: Systematic traders continuously monitor the performance of their strategies in live markets. They track portfolio returns, risk-adjusted metrics, and other performance indicators to assess strategy effectiveness.
  • Adaptation and improvement: As markets evolve, systematic traders need to adapt their strategies to changing conditions. This might involve refining algorithms, adjusting parameters, or developing new strategies to remain competitive.
  • Technology utilisation: Systematic traders leverage advanced technology, such as cloud computing, machine learning, and high-frequency trading infrastructure, to enhance strategy development, execution, and analysis.
  • Compliance and regulation: Traders must adhere to legal and regulatory requirements in their trading activities. They work closely with compliance teams to ensure their strategies comply with relevant laws and regulations.

The role of a systematic trader requires a blend of skills in finance, programming, quantitative analysis, and risk management. Successful systematic traders are disciplined, analytical, adaptable, and continuously strive to improve their strategies and performance.

Essential steps for becoming a systematic trader

Let us now find out the essential steps to become a systematic trader for an individual right from the beginning.

These steps include:

Essential steps for becoming a systematic trader
Essential steps for becoming a systematic trader

Step1: Acquire the right qualification

A systematic trader needs to have a bachelor's degree in one of the following:

  • Science,
  • Technology,
  • Engineering or
  • Mathematics

Step 2: Equip yourself with the skillset as needed

  • Quantitative research perception
  • Ability to deal with uncertainty and tweak the actions accordingly
  • Keen interest in trading and algorithmic approach to trading
  • Problem-solving attitude and strategic decision making

Step 3: Learn programming and data analysis

  • Develop proficiency in programming languages commonly used in quantitative finance, such as Python, R, or MATLAB. Python is particularly popular due to its versatility and extensive libraries.
  • Gain skills in data manipulation, analysis, and visualisation using programming languages.

Step 4: Study quantitative finance

  • Dive into quantitative finance concepts, including risk management, portfolio optimisation, asset pricing models, and time series analysis.
  • Understand different types of financial instruments, market microstructure, and trading strategies.

Step 5: Learn algorithmic trading

  • Study algorithmic trading concepts, order types, execution algorithms, and market impact.
  • Explore algorithmic trading strategies like market making, arbitrage, and execution-based strategies.

Step 6: Understand systematic trading

  • Learn about systematic trading strategies, including trend following, mean reversion, momentum, statistical arbitrage, and more.
  • Understand the principles of backtesting, risk management, and position sizing.

How to learn systematic trading?

But how to learn systematic trading?

For learning systematic trading,  you should invest your time and efforts in the following:

  • Training
  • Books
  • Trading knowledge
  • Understanding the workflow


For the training purpose, you can join an organisation as a trainee or an intern so as to get familiarised with the work process and ethics.

You can also opt for online courses. You can opt from the Algo Trading courses to gain the in-depth knowledge.


Books play an essential role since they can be your best guide for starting with algorithmic trading. Read the algorithmic trading books and gather the essential knowledge you may need in your wonderful endeavour.

Trading knowledge

Also, you must have trading knowledge. For starting with algorithmic trading, you must have the knowledge of:

  • types of trading instruments (stocks, options, currencies etc.),
  • types of strategies (Trend Following, Mean Reversal etc.),
  • arbitrage opportunities, and
  • risk management

Understanding the workflow

The practical workflow of systematic trading involves the following:

  • Strategy formulation
  • Coding the strategy in a computer language such as Python
  • Backtesting the strategy
  • Making the strategy live

In order to become a systematic trader, you can also learn systematic trading strategies from our Algo Trading Course.

Steps to do systematic trading

Now, let us see the steps of performing systematic trading as a beginner.

Steps of performing systematic trading
Steps of performing systematic trading 

Step 1: Develop a trading strategy

  • Select a trading strategy that aligns with your strengths and interests. Start with a simple strategy and gradually work your way to more complex ones.
  • Develop clear rules for generating buy and sell signals based on your chosen strategy.

Step 2: Practice on simulated platform

Use simulated trading platforms to practise implementing and testing your strategies without risking real capital. This helps you gain experience and refine your strategies.

Step 3: Backtest and refine the strategy

  • Backtest your strategy using historical data to assess its performance under various market conditions.
  • Refine your strategy based on the insights gained from backtesting. Adjust parameters, rules, and risk management to improve results.

Step 4: Risk management

  • Develop robust risk management rules to control position sizes, define stop-loss levels, and manage overall portfolio risk.
  • Understand the concept of drawdowns and how to mitigate their impact on your trading capital.

Step 5: Transition to live trading

  • Start trading with a small amount of capital to test your strategy in a real-market environment with real emotions.
  • Gradually increase your capital as you gain confidence and achieve consistent results.

Step 6: Continuous learning

  • Stay updated on the latest developments in trading technology, quantitative analysis, and market trends.
  • Attend workshops, seminars, webinars, and conferences related to systematic trading.

Step 7: Adapt and improve

  • Regularly review your strategy's performance and adapt it to changing market conditions.
  • Keep experimenting with different ideas and approaches to find what works best for you.

Step 8: Networking

Connect with professionals in quantitative finance and algorithmic trading. Networking can provide insights, mentorship, and potential career opportunities.

For getting started with systematic trading, you can try Blueshift.

A usual day for a systematic trader

Moreover, a lot of traders prefer to live close to the workplace (within a 30–40 min commute), as they have to be at their desks early. For example, a trader based in London trading the European markets, needs to be at the desk at least at 7- 7:30 AM, before the European open at 8 AM UK time.

This gives enough time to do your preparation for the rest of the day, which we discuss next.

Preparation for the day ahead includes all the research and analysis about what had happened in the world overnight and if there are going to be any key data release that day.

For example, if you are an interest rates trader, then you need to be on top of any developments or statements by the central banks and any key data release like the non-farm payrolls (US jobs data) in the day ahead. Apart from this you need to take account of your overnight positions and how you plan to add, cut down or hedge them. Quant traders might have to see how they need to tweak the parameters of the models.

Then comes the opening session which is a fairly busy time and might last for two-three hours (say from 8–12 AM) depending on the market. There are some good opportunities to build some new positions or cut down on some existing ones.

Again the markets get busy during the closing time and you and/or your algos are on it! During all the sessions you keep an eye on the PnL/risk and make sure that you trade within your limits and the mandate of the firm/desk. At the end of the day, you again take account of your positions again and hedge your exposures if required.

Challenges and risk management with systematic trading

Below you can see a table outlining some of the challenges and risk management considerations associated with systematic trading.


Risk Management Strategies

Data Quality and Integrity

Data validation and cleansing before analysis. Regular monitoring and adjustment of data sources.

Overfitting and Data Snooping

Rigorous backtesting with out-of-sample data. Use cross-validation techniques. Avoid excessively complex models.

Market Impact and Liquidity Issues

Implement trading algorithms that consider market impact. Avoid large orders that can move the market.

Technological Failures and Glitches

Robust infrastructure, redundancy, and disaster recovery plans. Continuous monitoring of trading systems.

Model Drift and Changing Market Dynamics

Regularly update and adapt strategies to evolving market conditions. Re-optimize when necessary.

Regulatory and Compliance Challenges

Adhere to legal and regulatory requirements. Work closely with compliance teams.

Psychological Biases

Implement trading rules to minimise emotional decision-making. Stick to the strategy's rules.

Execution Slippage

Use algorithms with intelligent order routing and execution. Be aware of slippage and minimise it.

Black Swan Events

Diversification and risk-limiting strategies. Options for hedging extreme market moves.

Data Delays and Latency

Use real-time data sources and minimise latency in trading systems.

High-Frequency Trading Competition

Focus on niche markets or unique strategies. Consider low-latency infrastructure if necessary.



This article aimed to take you through the important topics related to systematic trading. We discussed several aspects of systematic trading starting from the what and why. Systematic trading is a profession that is meant to make trading automated for the ease of the investors. There are some easy steps to begin systematic trading with Quantra’s course and Blueshift helps once you are ready to start with systematic trading.

If you wish to learn more about systematic trading, you can explore our learning track on Getting Started with Algorithmic trading. This course builds a foundation in Algorithmic Trading and is perfect for those who want to get a complete picture of the domain. It includes the what, how, and why of algorithmic trading. Moreover, you can learn about the regulations as well as the strategies to start with algorithmic trading on your own.

Note: The original post has been revamped on 31st October 2023 for accuracy, and recentness.

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.

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