Algorithmic Trading Strategies: Basic to Advance Algo Overview (2024)

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Trading continues to evolve at a rapid pace. Where once manual trades dominated financial markets, increasingly, the space is shifting towards rules-based automation that leverages powerful computers and advanced mathematics.

At the heart of this transformation is algorithmic trading,or trading executed using pre-set instructions. Using the latest technology, trades can be completed at speeds and frequencies impossible for mere mortals.

Interested in exploring algorithmic trading strategies? Whether you’re a curious novice trader or a seasoned expert looking to refine your toolset with advanced techniques, this article’s got you covered.

Over the next few minutes, we’ll unravel the mysteries of these seemingly complex strategies, delving deep into their building blocks and exploring the tools that make them possible.

At a Glance: Best Resources for Algorithmic Trading

The right tools are crucial if you want to explore algorithmic trading. Here are some of the best resources out there — we’ll do a deeper dive on each of the platforms and resources below later on in the post.

What is an Algorithmic Trading Strategy?

Algorithmic trading strategies are systemic and computer-automated methods used to execute trades, like buying and selling stocks. At the core of every strategy is the algorithm. Algorithms are simply a set of defined instructions to make trade decisions based on specific criteria, like the price of a security.

To develop an algo trading strategy or automated trading strategy, you need to identify a set of rules that a trading platform can follow without human intervention. For example, a simple algo trading strategy might be to “buy 100 shares of Apple whenever the 50-day moving average crosses above its 200-day moving average.” You might also add conditions like “do not trade more than 500 shares in any 24-hour period.”

Algorithms are designed to capitalize on market inefficiencies, reduce human errors, and ultimately generate profits at a speed and frequency that are impossible for humans to achieve.

Algorithmic trading strategies include the following components:

• Signal generation: This is the process of identifying possible trading opportunities. Algorithms, or algos, look for these opportunities based on quantitative or technical criteria.

  For example, a signal might be when a stock price’s 50-day moving average crosses above its 200-day moving average. This event might indicate a further potential upward momentum is likely. As a result, the algo trading strategy may trigger a buy of additional shares based on its predefined rules.

• Risk management: Algo trading strategies don’t necessarily dump all their capital into the first opportunity they see. Instead, positions are sized appropriately based on predefined risk criteria.

  For example, this might include setting stop-loss orders on existing positions. Algo trading strategies will also include optimal amounts to invest in each trade. They may also have criteria around diversification, ensuring they don’t overconcentratein a particular name, sector, or asset class.

• Portfolio and order management: Following trade execution, the algo will monitor its performance. If required, the position can be resized based on the ongoing monitoring. This might include increasing exposure to one security while exiting exposure to another.
  
• Execution: This final component is the actual trade order execution of the strategy. Algorithms are typically built to obtain efficient execution. To this end, they analyze market liquidity and other factors in real-time. If required, an algo trading strategy may prompt trade blocks to be broken into smaller chunks to lessen market impact, for example.

Advantages of Algorithmic Trading Strategies

• Speed and Efficiency: Modern computers can process vast amounts of data and rapidly execute trades. This allows for high-frequency trading strategies that can capitalize on minor price discrepancies.
• Emotionless Trading: Trading is characterized by numerous cognitive biases. Even the best traders in the world are susceptible to these biases. Fortunately, automated trading strategies operate without emotions. This ensures they don’t suffer from the same psychological pitfalls that human traders encounter, like fear or greed.
• Consistency: Algorithms don’t show up to work in a bad mood after a challenging commute to the office. Instead, once defined, they deliver consistent execution without deviation.
• Backtesting: Algorithms can backtest to help understand how they would have performed in a live market. This enables its creators to tweak and refine the rules based on the feedback.

Algorithm Challenges and Concerns

• Market Impact and Slippage: When improperly designed, high-frequency or large-volume trades can substantially impact the market. For example, selling too many shares of a company relative to the outstanding quantity can drive the price down as you exit, hurting profits.
• Overfitting: If an automated trading strategy is too finely tuned to historical data, it looks great when backtesting, but a slight price shift can render the strategy ineffective.
• Technological Failures: Reliance on technology means algorithms are at risk when they fail. For example, glitches or unforeseen bugs can drive significant losses.
• Regulatory Scrutiny: Automated trading strategies have sometimes generated a bad rap because of their potential for severe market disruption. As a result, regulators often heavily scrutinize the strategies.

Basic Strategies: The Building Blocks

Simple Moving Average (SMA)

• Concept: Moving averages smooth out price data to create a single flowing line. It makes it easier to identify the direction of a potential trend by removing short-term “noise.”
  
• Strategy: The Simple Moving Average (SMA) is calculated by taking the average price of a security over a specific number of periods.

  For example, it’s not uncommon to see 50-day SMAs compared to 200-day SMAs. An algorithm might generate a buy signal when the shorter SMA (50-day) crosses above the 200-day SMA. Alternatively, it may trigger a sell order if the 50-day SMA crosses below the 200-day SMA.

Volume Weighted Average Price (VWAP)

• Concept: The VWAP provides an average price of a security that accounts for both the price as well as the volume traded throughout a trading day. Periods with higher volume assign a higher weight to the price at that time.
  
• Strategy: VWAP is commonly used by institutional traders to help ensure they aren’t paying too much or receiving too little on a sale. When the price of a security is above the VWAP, it may indicate a good buy at a value price. Conversely, it might be considered overpriced when the price is below the VWAP.

Stochastic Oscillator

• Concept: The stochastic oscillator is a method that compares a stock’s close price to its historical price range over a specified period.
  
• Strategy: When used, the oscillator processes values that lie between 0 and 100. Usually, a 14-day period is used as part of the assessment.
  • Overbought Condition: A security is typically considered overbought when the stochastic oscillator exceeds 80.
  • Oversold Condition: A security is considered undersold when the value is below 20.

Momentum Trading

• Concept: This approach entails buying and selling securities based on the strength of recent price trends. The stronger the signal, the more conviction in the decision, and the bigger the position can be sized.
  
• Strategy: Traders leveraging momentum seek out securities moving significantly in one direction on high volume. They will typically take a position in that direction.

  For example, a strong movement downwards would entail a short position, while a strong movement upwards would trigger a long position. The idea is a position can be taken temporarily before profitably exiting. Several momentum trading strategies exist, like the Relative Strength Index (RSI) or Moving Average Convergence Divergence (MACD).

Intermediate Strategies: Adding Sophistication

News Trading

• Concept: This strategy works off the premise that news events can significantly impact markets and security prices.
  
• Strategy: News trading algorithmic strategies aim to capitalize on market movements that result from the release of a major news event.

  For example, an announcement from the Federal Reserve that aggressive interest rate tightening could be forthcoming might send stock prices lower. Automated systems can be designed to identify these stories and rapidly place trades to profit from the event. This strategy, in particular, relies heavily on the support of powerful computers that can parse events and execute trades as quickly as possible.

Pairs Trading

• Concept: Pairs trading is a market-neutral strategy combining a long position with a short one in two historically correlated names.
  
• Strategy: This strategy attempts to benefit from a breakdown in historical correlation. If the correlation between the two securities weakens (i.e., their price movements deviate more than average), a trader can short the outperforming stock and purchase the underperforming one. This is done in anticipation of the stocks reverting toward their historical norm.

Delta Neutral

• Concept: These strategies seek to construct positions so that the overall delta measure (sensitivity to price movements) is practically nonexistent.
  
• Strategy: This approach uses a combination of stock positions along with options contracts. Essentially, the main objective is to offset positive and negative delays so that the overall delta equals zero. Traders can then profit from other specific factors, like volatility, without risk of exposure to underlying price movements. Think of it this way: This strategy eliminates the risk of a loss due to price movements while still allowing profit to be generated from other factors.

Grid Trading

• Concept: Grid Trading entails placing orders at consistent intervals. This creates a ‘grid’ of orders at increasingly higher or lower prices.
  
• Strategy: Trades are executed as the stock price crosses these order thresholds. This strategy seeks to benefit from a securitytrading in a particular range. As a result, it will generate both buy and sell trades.

Advanced Strategies: High Complexity

Statistical Arbitrage

• Concept: Statistical Arbitrage or StatArb uses quantitative models to exploit minor price deviations from the mean.
  
• Strategy: StatArb usually involves complex mathematical models and ultra-powerful computer algorithms. Combined, they’re used to identify and exploit market inefficiencies. Typically, positions are short-lived, benefiting from windows of opportunity that shut rapidly.

High-Frequency Trading (HFT)

• Concept: High-frequency trading is another algorithmic trading strategy that relies on powerful hardware to execute a large number of orders in rapid succession.
  
• Strategy: These strategies are usually held for extremely short periods. Some holding periods are measured in milliseconds! Essentially, HFT strategies attempt to profit off very short-term price movements resulting from fleeting arbitrage opportunities, for example.
  

Smart Order Routing (SOR)

• Concept: Smart Order Routing attempts to route orders to various trading venues to optimize execution.
  
• Strategy: A trade’s exact route might depend on the security’s price, liquidity, or trading costs.

Market-Making

• Concept: This approach attempts to profit off the difference between bids and offers. It requires quoting both a buy and a sell price for a particular security, hoping to profit off the spread between the two.
  
• Strategy: Financial markets rely on market makers to generate liquidity. Automated algorithm market-making strategies entail continuous price quotes for both buying and selling. Trades are executed to realize the profit when a spread opportunity is identified.

Tools and Software for Algo Trading

Looking to get started with algorithmic trading? The right tools can help you on your journey. Here are some of our top picks:

TradeStation — Best Overall Algo Trading Platform

TradeStationis one of the best platforms to help traders implement complex and profitable algorithms. It offers straightforward yet powerful tools suitable for a wide range of traders.

With over three decades in the market, TradeStation isn’t a new and flashy platform. It enjoys a strong history and reputation from its loyal users.

The platform sticks out for its hundreds of customizable apps allowing advanced traders with coding experience to create their own trading programs. If that weren’t enough, TradeStation offers competitive commissions and access to a vast library of educational materials and research.

Finviz — Best Platform for Backtesting and Advanced Visualizations

When it comes to testing your algorithm on historical, real-world data, no better platform comes to mind than Finviz.

Finviz is not a trading platform — but it’s one of the beststock screening and backtesting platforms out there for algo traders.

With Finviz Pro, users gain access to a vast set of tools, including:

• Backtesting that can recognize 102 unique chart patterns up to two years back using multiple combinations.
• 67 stock screening metrics.
• Integrated news aggregation.

Finviz also offers fast heatmaps that provide valuable sector and industry visualizations.

Radical X13 EZ Trading Computer – Best Algo Trading Hardware

The right software is only one-half of the equation. Without powerful hardware support, your algo won’t be able to operate optimally.

If you’re looking for power, one of the best options in the market at the moment is the Radical X13 EZ Trading Computer.

Here’s why we love it.

• It includes a liquid-cooled Intel Core i9-13900KF 24 CORE Processor @ 5.8 Ghz In Turbo Boost Mode.
• Option to include 64 GB of RAM.
• Available 1 TB solid state drive (SSD).
• Ability to display up to 4 monitors.

If these powerful specs weren’t enough, the Radical X13 also comes with an impressive 5-year warranty and lifetime tech support!

Final Word:

The dramatic evolution trading has undertaken in recent years can’t be overstated. The old era characterized by manual trading is fading. In its place, sophisticated, technologically driven automated solutions are emerging.

Central to this seismic shift is algorithmic trading. These mathematical models offer the ability to parse vast volumes of data rapidly. Not only is the research and subsequent trading faster, but it’s also less prone to error and emotional bias.

Algorithms also allow for backtesting on historical data. This permits traders and analysts to refine and iterate their algo before deploying it with actual capital.

Of course, algorithmic trading isn’t perfect; it’s not without its challenges. Algos can negatively impact the market when calibrated incorrectly, generating substantial price disruptions. They can also be overfitted to past data, driving underperformance when matched against real-world scenarios.

Algos also rely on technology. As with any technology, there’s always the risk of failure. Algos require an uninterrupted power supply and reliable internet access. Even a brief failure in these conditions can prove cataclysmic.

Before embarking on your own algorithmic trading journey, take the time to understand the worst-case scenarios and implications of incorrect assumptions. Thoroughly backtest your model and keep a close eye on it during the initial phase. While they can be lucrative, algos possess substantial risk that needs to be appreciated.

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