What is Algorithmic Trading and How It Works

Today, algorithmic trading owns a substantial portion of the trading volume. It enhances market liquidity, reduces the impact of large trades, and increases the efficiency of price discovery. It is crucial for institutional investors, hedge funds, and brokers.

Basics Algorithmic Trading

Algorithmic trading, often called algo-trading, automates the trading process using computer programs. It executes trades based on predefined criteria without human intervention. 

Algorithms use multiple defined rules or instructions to make trading decisions. These rules are based on timing, price, quantity, or mathematical models.

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Workflow of Algorithmic Trading

Here is how Algorithmic Trading works:

Strategy Formulation

Traders and programmers create a trading strategy by selecting specific assets, defining entry and exit criteria, and determining key performance metrics. This process is foundational to developing an effective algorithm.

Algorithm Development

The strategy is then coded into an algorithm using programming languages such as Python. This involves translating the strategy’s decision-making process into precise code that a computer can execute.


Before live deployment, algorithms undergo backtesting, testing against historical data to evaluate their effectiveness. This step is crucial to ensure the algorithm performs as expected under various market conditions.


After backtesting, the algorithm is optimized. This involves tweaking parameters based on the backtesting results to improve performance and adaptability to market conditions. This step reduces potential risks and enhances potential gains.

Live Deployment

Once optimized, the algorithm is set up with real-time market data feeds and linked to brokerage accounts for live trading. This phase requires rigorous monitoring to ensure seamless execution.


Continuous monitoring addresses any anomalies or inefficiencies. Algorithms may need to be adjusted in response to new market data or unexpected market conditions.

Risk Management

Effective risk management strategies mitigate losses. These include setting stop-loss orders, adjusting trade sizes, and diversifying investments to manage exposure and protect against significant losses

Advantages of Algorithmic Trading

Algorithmic Trading offers multiple benefits, including:

Efficiency and Speed

It executes transactions in milliseconds. This rapid execution allows traders to capitalize on market opportunities that would be too fast for manual trading processes.

Emotion-Free Trading

Algorithms eliminate emotional influences from trading decisions. By strictly following predefined rules, they ensure decisions are consistent and objective, removing biases like fear or greed.

Consistent Application

Algorithms apply trading strategies uniformly and without deviation. This consistent adherence to rules maintains discipline across all trades, reducing the risk of errors and ensuring strategies are executed as planned.

Risks of Algorithmic Trading

Despite all the benefits, Algorithmic Trading has several risks:

Technical Risks

Algorithmic trading systems are susceptible to technical failures like glitches and connectivity issues. It can cause trading disruptions or unintended trades. Continuous system checks and maintenance are crucial to mitigate these risks.

Market Risks

Algorithms rely on historical data and patterns. They are vulnerable to unexpected market events or changes that they have not been programmed to anticipate. Such market volatility can adversely affect algorithm performance and results.

Regulatory and Ethical Considerations

Algorithmic trading raises concerns about market fairness and potential manipulation. Regulatory bodies continuously scrutinize algorithmic activities to prevent abuses, such as creating artificial price movements or flash crashes.


Staying informed through continuous education and exploration is crucial to success in algorithmic trading. If implemented successfully, traders and brokers can gain significantly.

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