If you have read our previous article on Market Making and its working methodology, you must know how market makers maintain adequate liquidity. In that article, we also mentioned that a reliable market-making algorithm is necessary for an efficient market-making process.  

Here is a real-world example of the effectiveness of market-making algorithms.

In 2019, the average bid-ask spread for S&P 500 stocks was around 0.01%. This tight spread was primarily due to the presence of market-making algorithms that ensured consistent liquidity & efficient pricing. Besides maintaining standard liquidity in the market, these algorithms strive to generate profits.  

Today, let’s explore what these algorithms are and how they function and help traders & markets alike.

Why Market Making Matters

Market makers’ invisible hand is necessary for financial markets to function smoothly. They provide liquidity by consistently quoting bids and asking prices, making it simple for investors to purchase and sell assets. Absorbing excess supply or demand lowers transaction costs and stabilises prices by reducing the bid-ask spread. The price discovery process increases investor confidence, making the market more lively. The critical role that market makers play in preserving market stability and efficiency is further highlighted by data analysis of variables such as spreads, volumes, and volatility.

Understanding Market Making Algorithms

Earlier, when technology usage was limited, market-making was carried out by human traders. However, when technologies started to penetrate the market, and market-making algorithms were introduced, the chances of error fell drastically. These algorithms rely on complex mathematical models and real-time data to automate the process, offering numerous advantages over their human counterparts.  

You are at a bustling farmer’s market. Buyers are looking for fresh produce, and sellers are eager to offload their goods. However, transactions would be chaotic and inefficient without someone bridging the supply and demand gap. Financial markets often need an intermediary to balance liquidity and spread, and these set the stage for market makers. It makes sure that traders can always find someone to trade with.

Algorithms Automating Market Making
‍

• Avellaneda-Stoikov Model
  Developed by Marco Avellaneda and Sasha Stoikov, this model is a cornerstone of high-frequency trading. The Avellaneda-Stoikov model uses stochastic control to determine the optimal bid and ask quotes, balancing the trade-off between profit and inventory risk.  
  
  
  • Mathematical Foundation: The model employs stochastic differential equations to model the price dynamics and the trader’s inventory.
                                             dSt​=μSt​dt+σSt​dWt​
    
    
  • Risk Management: By factoring in inventory risk, the model adjusts quotes to manage exposure, ensuring that the market maker doesn’t accumulate excessive inventory that could lead to significant losses.
    
    
  • Optimal Spread Calculation: The model dynamically adjusts the bid-ask spread based on market volatility and the current inventory, ensuring that the quotes are always competitive yet profitable.
    
    
  • Implementation in High-Frequency Trading: Due to its dynamic nature, the Avellaneda-Stoikov model is well-suited for high-frequency trading environments where rapid adjustments are crucial.
    
    
    • Data Acquisition: Real-time market data, including asset prices, order book information, and trade volumes, is continuously fed into the algorithm.
      
      
    • Quote Adjustment: The algorithm calculates the optimal bid and ask prices based on the current inventory, market volatility, and order arrival rates.
      
      
    • Order Execution: Orders are placed at the calculated bid and ask prices. The algorithm monitors executions and adjusts quotes to maintain optimal inventory levels.
      
      
    • Continuous Monitoring: The algorithm monitors market conditions and adjusts its parameters to adapt to changing market dynamics.
      
      
  • Real-World Applications
    
    Market makers and quantitative trading businesses have widely used the Avellaneda-Stoikov model because of its strong performance across various market situations. It is an essential instrument for guaranteeing liquidity and stability in financial markets since it offers a framework that balances profitability and risk management.
    ‍
    

• Statistical Arbitrage
  
  Commonly known as StatArb, Statistical arbitrage is a class of trading strategies that employ statistical methods to exploit pricing inefficiencies between related financial instruments. This approach is grounded in quantitative analysis and often involves trading pairs of correlated assets to capitalise on mean-reverting behaviours.
  
  
  • Mathematical Foundation
    
    • Z-Score
      
      Z=(X−μ)/σ𝒁=(𝑿−𝝁)/𝝈
      
      Where X is the current price spread, μ is the mean spread, and σ is the standard deviation of the spread.
      
      
      • Linear Regression
        
        Y=α+βX+ϵ
        
        Y and X are the prices of the two assets, α is the intercept, β is the slope (indicating the relationship), and ϵ is the error term.
        
        
    • Pairs Trading: The algorithm identifies pairs of correlated assets, such as two stocks in the same industry, and trades on short-term price divergences.
      
      
    • Mean Reversion: Statistical arbitrage assumes that prices of correlated assets will revert to their mean relationship over time.
      
      
    • Execution: When the price of one asset in the pair diverges from its expected value relative to the other, the algorithm executes trades to profit from the eventual convergence.
      
      
    • Risk Management: The algorithm continuously monitors the correlation and divergence patterns to manage risk and adjust positions as needed.
      
      
    • Real-World Example: Pairs Trading
      
      
      • You have two stocks, Stock A and Stock B, that usually move together in the market. This means that if Stock A goes up, Stock B typically increases too, and vice versa. Over time, you notice that the prices of these two stocks stay close, but sometimes, one stock’s price goes higher or lower than the other by an unusual amount.
        
        
        • Identify the Pair: Stock A and Stock B have a strong relationship, often moving up or down.
          
          
        • Monitor the Price Difference: You keep track of the difference in their prices. Usually, this difference stays within a specific range.
          
          
        • Detect an Opportunity: One day, you notice that Stock A’s price is significantly higher than Stock B’s, more than what is typical. This unusual difference suggests that Stock A might be overvalued (priced too high) and Stock B might be undervalued (priced too low).
          
          
        • Place Trades: To take advantage of this situation, you sell Stock A (since it’s likely to come down in price) and buy Stock B (since it’s expected to go up in price). This way, you bet that the prices will eventually return to their usual relationship.
          
          
        • Close the Position: After some time, if the prices of Stock A and Stock B move back towards their typical difference, you close your trades. This means you buy back Stock A and sell Stock B, ideally at a profit.
          ‍

• Reinforcement Learning Algorithms
  
  Reinforcement learning (RL) algorithms are a state-of-the-art approach to trading that uses machine learning techniques to design and refine trading strategies based on market conditions. In contrast to conventional algorithms, RL algorithms are not limited by pre-established rules; instead, they constantly learn from market interactions and evolve.
  
  
  • Fundamental Concept
    
    
    • Reinforcement learning involves training an agent to make decisions by rewarding it for desirable actions and penalising it for undesirable ones. The agent’s objective is to maximise the cumulative reward over time.
      
      
      • Agent: The decision-maker, which in this context is the trading algorithm.
        
        
      • Environment: The market where the agent operates.
        
        
      • Actions: The possible trades or strategies the agent can execute, such as buying, selling, or holding an asset.
        ‍
        
      • State: The current situation or set of conditions in the market that the agent observes.
        
        
      • Reward: The feedback the agent receives after taking an action, often related to its profitability or success.
        
        
  • Learning from Experience: The algorithm receives feedback from the market and adjusts its strategies accordingly, much like a human learning from experience.
    
    
  • Optimisation: Over time, reinforcement learning algorithms optimise their strategies to maximise cumulative rewards, such as profit or reduced risk.
    
    
  • Adaptability: These algorithms can adapt to changing market conditions, making them robust in dynamic and volatile markets.
    
    
  • Complex Decision-Making: Reinforcement learning algorithms can make sophisticated trading decisions by considering various factors and possible actions.
    
    
• Mean Reversion
  
  Mean reversion is a widely used concept in finance that assumes asset prices and returns eventually move back towards their historical average or mean. Mean reversion algorithms leverage this principle to generate trading strategies by identifying when prices deviate significantly from their long-term averages and capitalising on the expected reversion to the mean.
  
  
  • Identifying Deviations: The algorithm identifies when an asset’s price deviates significantly from its historical average or mean.
    
    
  • Trade Execution: When a deviation is detected, the algorithm places trades to profit from the expected reversion to the mean.
    
    
  • Risk Management: Mean reversion strategies include risk management mechanisms to handle scenarios where prices do not revert as expected.
    
    
  • Suitability: This strategy is particularly effective in markets with apparent mean-reverting behaviour, such as specific equity and commodity markets.
    
    
  • Real-World Example: Stock Trading
    
    When trading Stock X, a mean reversion approach is implemented. To determine the average price (mean), the algorithm first examines the prices from the previous year. Usually, utilising standard deviations establishes thresholds based on how far the current price deviates from this average. The algorithm believes there is a buying opportunity when the price of Stock X drops sharply below the average and predicts that the price will rebound towards the mean.  On the other hand, if the price increases much above average, it suggests that you should sell with the expectation that the price will fall. The algorithm constantly checks prices and modifies trades to exploit these mean reversion possibilities.
    
    
• Order Book Dynamics
  
  Order book dynamics algorithms analyse the order book to predict short-term price movements. Understanding the supply and demand dynamics within the order book allows these algorithms to place strategic bids and ask to capture small price movements.
  
  
  • Order Book Analysis: The algorithm continuously monitors the order book, looking for patterns and imbalances between buy and sell orders.
    
    
  • Predictive Modelling: The algorithm uses historical data and real-time analysis to predict short-term price movements based on order flow and liquidity.
    
    
  • Strategic Placement: The algorithm strategically places bids and asks to capitalise on predicted price movements, often executing trades with minimal market impact.
    
    
  • High-Frequency Trading: Order book dynamics algorithms are well-suited for high-frequency trading environments due to their reliance on real-time data and rapid execution.
    
    
  • Real-World Use Case: High-Frequency Trading with Order Book Dynamics
    
    Think about a high-frequency trading company that trades Apple Inc. (AAPL) stock using an order book dynamics algorithm. The programme continuously analyses real-time order book data to observe a recurrent pattern of significant buy interest below the current market price.  
    
    The algorithm positions buy orders marginally above this support level and sell orders marginally above the current market price, anticipating that these buy orders would drive the price upward once they are filled. With this calculated placement, the company can benefit from the expected price increase while continuously adjusting for the best possible execution in real-time market movements.

Takeaway

These sophisticated algorithms make the market-making process smooth and reliable. Liquidity here always refers to the ease with which traders can conveniently buy and sell an asset; market makers are the intermediaries facilitating these trades. Market-making algorithms are indispensable tools that just automate these tedious jobs. You can automate these jobs with advanced market-making algorithms and tools. Contact our experts today at info@webmobsoft.com to learn how we can help.