FINANCIAL AI
Interpreting
financial markets
with AI.
We research and develop algorithms and AI agents for market monitoring, signal detection, alerts, and execution across equities, futures, commodities, and currencies.
Our approach combines rule-based logic, machine learning, and large language models to work with both quantitative data and broader market context.
RESEARCH FOCUS
From monitoring to execution—
designed around the objective.
The role of financial AI varies according to the market, strategy, and operating model.
A system may continuously monitor market conditions, detect signals, issue alerts, or execute orders where appropriate. We define the role of each algorithm and AI agent around the objective of the project.
Our current work includes one agent that automates the workflow from monitoring through execution, and another that delivers analysis and alerts to support human decision-making.
Monitor the Market
Continuously collect and organize relevant market data, including prices, volume, financial information, and news.
Generate Signals
Use quantitative rules and machine-learning models to identify relevant changes and patterns.
Connect to Alerts or Execution
Depending on the project, detected signals may be delivered through alerts and reports or connected to order execution and position management.
PROJECTS IN DEVELOPMENT
Two projects,
built for different roles.
Both projects are being developed for MIF LLC’s in-house research and operation.
One automates the workflow from market monitoring through execution. The other detects and reports signals, leaving the final decision to a human.
Nikkei 225 Futures Automated Trading Agent
A continuously running agent for Nikkei 225 mini and micro futures that automates price monitoring, signal generation, order placement, and position management.
U.S. Equities, Commodities & Currencies Signal Agent
Centered on the S&P 500 and Nasdaq-100, with coverage extending to commodities and currencies, the agent detects signals from market data, company fundamentals, and news, then sends alerts to Slack.
It does not place trades automatically; the analysis is used to support human decision-making.
TECHNICAL APPROACH
Rules, machine learning, and LLMs—
combining quantitative signals with context.
Rule-based logic and machine-learning models generate candidate signals from quantitative inputs such as price, volume, and financial data.
Large language models help organize and interpret text sources including news, earnings, and broader economic context. This adds information that may not be visible in numerical data alone, with the resulting output connected to alerts, reports, or execution logic according to the project.
Generate Signals from Quantitative Data
Analyze time-series data such as price and volume using rule-based logic and machine-learning models.
Interpret News and Market Context
Use large language models to organize news, earnings, economic indicators, and other information relevant to a signal.
Connect Analysis to the Required Action
Connect the analysis to the alerts, reports, order execution, or other processing required by each project.
Explore the two
R&D projects.
Learn more about the systems and technology behind our Nikkei 225 futures automated trading agent and our signal agent for U.S. equities, commodities, and currencies.
View the Projects →IMPORTANT NOTICE
本ページに記載のプロダクトは、MIF LLCによる研究開発・自社運用を目的としたものです。 特定の金融商品の購入・売却を推奨するものではなく、投資成果を保証するものでもありません。 投資判断はご自身の責任において行ってください。