Financial AI Projects
R&D PROJECTS
Two AI agents
for financial markets.
We are currently developing two projects with different operating models.
One automates the workflow from market monitoring through order execution and position management for Nikkei 225 futures. The other detects and analyzes market signals, primarily across U.S. equities, and delivers the findings for human review.
Both are being developed for MIF LLC’s in-house research and operation.
PROJECT 01
Nikkei 225 Futures
Automated Trading Agent
A continuously running agent for Nikkei 225 mini and micro futures that automates price monitoring, signal generation, order execution, and position management.
It processes short-term market data and carries out trading decisions and execution according to defined rules and models.
WORKFLOW
Monitor the market
and execute when conditions align.
The agent continuously collects price data and analyzes market conditions using rule-based logic and machine-learning models.
When the defined trading conditions are met, it sends orders through a brokerage API. After entry, it monitors the position and performs actions such as take-profit or stop-loss execution according to the configured rules.
Each step is recorded so that trading history and model behavior can be reviewed and evaluated.
- 01Collect market data
- 02Analyze with rules and models
- 03Generate trading signals
- 04Execute orders
- 05Manage positions
- 06Record and evaluate results
ANALYTICAL APPROACH
Combine time-series patterns
with market context.
The system analyzes short-term price behavior and market conditions using time-series data such as one-minute bars and tick data.
Trading signals are generated through a combination of quantitative rules and machine-learning models. Large language models are used to organize news and market context, explain analytical output, and produce reports.
Quantitative decision logic and text-based interpretation are handled separately, then combined where relevant to the project.
Key Functions
Technology & Data
PROJECT 02
U.S. Equities, Commodities & FX
Signal Agent
An agent that detects and analyzes market signals using price data, company fundamentals, news, and other sources, with a primary focus on the S&P 500 and Nasdaq-100 and additional coverage across commodities and FX.
The findings are delivered through Slack for human review. The agent does not place trades automatically.
WORKFLOW
Detect relevant changes
and deliver decision-ready context.
The agent collects price, volume, company fundamentals, news, earnings-calendar data, and other relevant information, then screens securities and markets according to defined conditions.
When a candidate is detected, it organizes technical indicators, fundamentals, and related news before sending the signal and its supporting context to Slack.
The final decision is made by a human after reviewing the information provided.
- 01Collect market and company data
- 02Screen against defined conditions
- 03Analyze technical and financial data
- 04Organize news and market context
- 05Send the signal to Slack
- 06Human review and final decision
ANALYTICAL APPROACH
Analyze the numbers
and the context behind them.
In addition to quantitative data such as price, volume, and company fundamentals, the agent collects news, earnings-calendar information, and corporate events.
Large language models summarize and organize text sources, identifying information that may be relevant to the observed market movement.
Quantitative conditions and market context are combined into a single alert containing the information needed to review the signal.
Screening Approaches Under Evaluation
Key Functions
Technology & Data
OPERATING MODELS
Automated execution
and decision-support alerts.
Nikkei 225 Futures
The system handles price monitoring, signal generation, order execution, and position management.
U.S. Equities, Commodities & FX
The agent detects and organizes market signals and supporting context, while the final decision remains with a human.
The role of AI and algorithmic processing is designed around the market, strategy, and operating objective of each project.
SHARED FOUNDATION
From data collection to records,
designed for evaluation.
Both projects follow a common structure: market-data collection, analysis, signal generation, output, and historical recordkeeping.
The systems are designed to retain analytical inputs and processing results so that model and rule behavior can be reviewed and evaluated over time.
Data Collection
Collect the price, volume, financial, news, and other data required by the project.
Analysis & Signal Generation
Combine rule-based logic, machine learning, and LLMs according to the project’s objective.
Alerts & Execution
Connect analytical results to Slack alerts, reports, order execution, or other required actions.
Records & Evaluation
Retain inputs, signals, and processing results for later review and evaluation.
Explore our broader
financial AI research.
Learn more about our financial AI research focus and our approach combining rule-based logic, machine learning, and large language models.
Back to Financial AI Overview →IMPORTANT NOTICE
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