Algorithmic Trading

Algorithmic trading, also known as algo trading or black-box trading, refers to the use of AI-based computer algorithms to automate the process of buying and selling financial instruments in global markets. These algorithms are programmed to execute trades based on predefined conditions, such as price fluctuations, volume, or other market indicators.

Use Case
AI Technology
Fintech and Banking

Conventional methods of algorithmic trading often rely on static algorithms that lack the predictive capabilities required to anticipate market movements accurately, leading to missed opportunities or increased risk exposure. The complexity and speed of today's financial markets require adaptive, real-time analysis that conventional algorithmic trading systems may not provide.


Algorithmic trading that implements AI technologies can examine enormous amounts of data, use complex algorithms, and make data-driven decisions. As a result, it offers a myriad of advantages, such as:

  • Scan Multiple Charts: As a human trader, you can only restrict yourself to a few geographies, and scan only a few price charts on any given day. However, AI bots and algorithms can help traders find the best trading opportunities across multiple geographies and exchanges by scanning multiple price charts within a few minutes.
  • Instant Trading: Algorithms and AI trading bots can help with high-frequency trading (HFT) by scanning multiple charts and identifying favourable conditions. Then, when the algorithm detects a favourable condition, it can place huge trading orders and execute them within seconds.
  • Eliminates Human Error: Suppose you are a trader using your trading app on your phone to make transactions. At some point in your trading journey, your emotions influenced your decision making. The emotions of fear and loss may have prevented you from making buying and selling transactions. AI-Based algorithmic trading solves these problems, as AI can help the computer adjust instructions and execute the most suitable strategies for different market environments.
  • Enables Market Adaptability: The AI models in algorithmic trading can adapt to changing market conditions and learn from historical mistakes, improving their predictive accuracy over time.
  • Getting Accurate Market Insights: An AI-based algorithmic trading system can gather data to identify market fluctuations using sentiment analysis, which is a method of gathering text and linguistics and utilising natural language processing (NLP) to identify accurate patterns within arbitrary information.

The result of integrating AI techniques in algorithmic trading systems provides several key benefits, including:

  • Enhanced Trading Accuracy and Efficiency: AI-driven trading systems can process and analyze large volumes of data in real-time, leading to more precise trading decisions. By reducing human involvement, algorithmic trading powered by AI significantly improves speed and efficiency, thereby reducing trading costs and minimizing human biases.
  • Enhanced Risk Management: AI algorithms can identify potential risks and adapt trading strategies accordingly, reducing exposure to market volatility. Risk management techniques, such as stop-loss and profit-taking algorithms, can be integrated into AI-based systems to automatically trigger trades based on predefined risk levels.
  • Improved Trade Execution: AI technology enables faster order execution, reducing price slippage and improving trade efficiency. Algorithmic trading systems can rapidly respond to market changes, detect liquidity opportunities, and execute trades at optimal prices, resulting in enhanced profitability.
  • Edge in the Market: AI in an algo trading system can be leveraged to automate some of the laborious repetitive processes that were previously done manually. Additionally, AI systems have the ability to continuously watch the stock market around the clock. As a result of their wider reach, traders gain an added advantage.

Techstacks Used

Technologies and Tools
NestJS, Hardhat, Redux, OpenZeppelin, ReactJS, NodeJS ,Solidity, MongoDB, C++, PostgreSQL, EthersJS, ReactNative, AngularJS, Commo, GraphQL, TypeORM, NextJs, ETH, Redis, Metabase.

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