AI for Algorithmic Trading
Use artificial intelligence to improve algorithmic trading speed and accuracy, and increase portfolio returns for your clients
Learn how you can use AI for algorithmic trading to automate the process of keeping up with second-by-second market moves in securities, treasuries, futures, and other investment vehicles. The world of high-frequency trading demands real-time decision-making and split-second transaction capabilities that only artificial intelligence can deliver.
What is algorithmic trading?
Algorithmic trading uses the power of artificial intelligence and machine learning to automatically and accurately extract real-time investment insights from vast quantities of trade and market movement data.
Maximizing returns for institutional investors
Balancing risk and return in hedge fund management
Benefiting from currency fluctuations in international fintech
How does AI enable algorithmic trading?
AI-enabled algorithmic trading—whether in securities, commodities/futures, currency, or more arcane derivative investment vehicles—is a critical contributor to successful portfolio management, particularly in an investment landscape characterized by millions of second-by-second transactions and instantly-changing external factors.
Not only can AI-based trading solutions deliver the speed and accuracy your portfolio needs by taking into account all of these factors, AI-driven trading solutions are also fully autonomous, completing transactions without human intervention (or possible errors) as externalities change, positioning you to maximize the returns your clients demand. And, while AI solutions automate the previously manual legwork of trading analysis, the professional trader is nevertheless in charge at all times with fully explainable AI. The accuracy and productivity delivered by AI-based algorithmic trading solutions offer early adopters a significant advantage over their competitors.
Execute faster, more accurate transactions in a millisecond-by-millisecond trading world
Base investment decisions on real-time transaction data, without the potential for human error
Traditional investment approaches rely on labor-intensive deep dives into historical price data and market factors, followed by endless spreadsheet work and mathematical modeling. AI-powered algorithmic trading, by contrast, incorporates data from millions of transactions, delivering results with no labor-intensive processes at all.
Automatically extract insights from unstructured financial performance data
AI-powered algorithmic trading across industries
Algorithmic trading in the hedge fund industry
- Identify high-leverage opportunities.
- Assess and mitigate risk.
- Maximize portfolio returns.
Algorithmic trading in pensions, annuities, IRAs, and 401ks
Balance risk and return to ensure fund guideline compliance while also consistently delivering the objective return rate for your institutional clients (pensions, annuities, etc.). Enhance your decision-making with automated up-to-the-second insights.
- Deliver investment return goals.
- Ensure compliance with fund risk guidelines.
- Manage client expectations.
Algorithmic trading in international currency trading
- Stay up to the second on currency exchange swings.
- Determine and stick to your currency risk profile.
- Set stop-loss thresholds accurately.
How SparkCognition delivers AI-powered algorithmic trading
SparkCognition’s AI for algorithmic trading applies our patented machine learning and natural language processing algorithms to the vast universe of investment transaction data and analyst/business reporting documents.
Our AI-based algorithmic trading methodology is straightforward and effective, led throughout by SparkCognition AI and domain experts, with minimal time requirements from your staff.
Step 1: Real-time data ingestion
To ingest and analyze historical and real-time security/commodity prices, volumes, and ranges, SparkCognition employs a number of powerful ML-based data validation techniques that enable transaction models to then be used to conduct trades quickly and automatically.
Step 2: Unstructured information analysis
Natural language processing models read and digest vast quantities of written investment-related documents, e.g., 10K/Qs, ARs, and analyst reports.
Step 3: Transaction model development
Models are constructed to trade quickly and without human subjectivity or emotion, seizing upon opportunities that may last for only milliseconds.