TRADE. ON. AUTOPILOT
Agent M.
Agent M orchestrates multiple AI agents across ingestion, analysis, and execution — transforming raw market signals into autonomous trades, end to end.
// PARTNERS:008
UBS
Alpaca
SMU
let it cook
How you can use Agent M
From news ingestion to trade execution, Agent M orchestrates the entire trading workflow autonomously, within your guardrails.
Agent M trades on your behalf
Agent M continuously monitors market news, runs sentiment analysis, and executes optimised buy and sell orders through your broker — all without you lifting a finger.
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terminal.Agent M
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SENTIMENT_SCAN.dither320x240
news_analysis.metrics
000+News Analysed / day
0.00msAvg Latency (News Analysis)
00.00%Uptime
Yahoo Finance, TradingView, RedditNews Sources
Llama 0.0Model Used for News Analysis
news_analysis.status
We read the market.
You reap the rewards.
ProcessStatusReal-Time Latency
News Scraping——
Pre-processing——
Ticker Identification——
Event Identification——
Sentiment Analysis——
Vectorisation & Embedding——
Global Throughput0%
// HOW IT WORKS005
RENDER: AgentFlow.objLIVE
AgentFlow
See your agents' performance in real-time. Click on nodes to view stats, or use the tour to explore key components.
Online
Error
MODEL: llama3.3 / sonar / nomic-embed-text-v1.5RES: 2048x2048
MANIFEST.mdv3.1.0
Full Transparency
watch your agents work
We engineer the execution layer between raw market data and your brokerage account, turning unstructured news and social chatter into autonomous, risk‑aware trades.
No black boxes. No vague “AI did it". Just transparent agent workflows & reasonings, deterministic routing from signal to order, and millisecond‑latency decisions across every major market session.
UPTIME:0d 00h 00m 00s
Signal IntelligenceNews Analysis Agent
Autonomous Brokeragevia Alpaca
Technical AnalysisRSI, MACD, Bollinger
Risk ManagementTP / SL
Trading made effortless
We are on beta, but here's what our users are saying about Agent M
Agent M reads the news, weighs the sentiment, and just executes the trade — I check my dashboard in the morning and see exactly what it did and why. I don't have time to monitor markets all day so this is great.
Shawn N.
Retail Investor
Agent M only acts within the risk limits I set. It trades aggressively when I want it to, or conservatively when I dial it back.
Tim C.
Independent Trader
I used to spend hours reading financial news and Reddit threads. Now Agent M scrapes all of that and I only get pinged when something actually affects my holdings.
Joshua A.
Retail Trader
The moment breaking news drops that's relevant to my stocks, I get a notification instantly, not 20 minutes later. And right after, another alert confirms the trade was executed. The speed is the whole point.
Z Y.
Swing Trader
I connected my existing account from Alpaca and Agent M immediately connected and got to work. The process was so seamless.
Derrick L.
Long-term Investor
I'll type 'Why did you sell NVDA yesterday?' and it explains the exact news event and sentiment score that triggered it. It's crazy transparent for a retail investor.
J Y.
Quantitative Analyst
I was skeptical on social media news and it would hallucinate and make bad trades. But I'm amazed at how it fact-checks claims and only acts on credibility-weighted sentiment, it's held off on trades when news turned out to be unreliable.
Bryan C.
Student
Agent M reads the news, weighs the sentiment, and just executes the trade — I check my dashboard in the morning and see exactly what it did and why. I don't have time to monitor markets all day so this is great.
Shawn N.
Retail Investor
Agent M only acts within the risk limits I set. It trades aggressively when I want it to, or conservatively when I dial it back.
Tim C.
Independent Trader
I used to spend hours reading financial news and Reddit threads. Now Agent M scrapes all of that and I only get pinged when something actually affects my holdings.
Joshua A.
Retail Trader
The moment breaking news drops that's relevant to my stocks, I get a notification instantly, not 20 minutes later. And right after, another alert confirms the trade was executed. The speed is the whole point.
Z Y.
Swing Trader
I connected my existing account from Alpaca and Agent M immediately connected and got to work. The process was so seamless.
Derrick L.
Long-term Investor
I'll type 'Why did you sell NVDA yesterday?' and it explains the exact news event and sentiment score that triggered it. It's crazy transparent for a retail investor.
J Y.
Quantitative Analyst
I was skeptical on social media news and it would hallucinate and make bad trades. But I'm amazed at how it fact-checks claims and only acts on credibility-weighted sentiment, it's held off on trades when news turned out to be unreliable.
Bryan C.
Student
Frequently Asked Questions
Everything you need to know about Agent M and how it can transform your trading experience.
What is Agent M and who is it for?
Agent M aims to deliver a fully autonomous investment companion that continuously ingests real-time market data, financial news, and internet sentiment, then translates them into timely, personalised buy/sell decisions executed via external brokerage APIs on behalf of retail investors.
How does the system help retail investors in practice?
It addresses time delay and information overload by automatically scraping and analysing financial news, extracting investment-relevant events and sentiment, and then either answering user queries via a RAG chatbot or autonomously executing trades within user-defined risk limits.
What kind of analytics and dashboard features will users see?
Users get a trading dashboard that visualises real-time sentiment indicators per stock, profit and loss trends over time, current portfolio holdings, trade logs, and portfolio positions, allowing them to monitor performance and understand how news affects their investments.
How does the Trading Agent decide when to buy or sell?
The Trading Agent uses a pipeline where scraped news and social media posts are preprocessed, checked for credibility, analysed for sentiment, embedded, and retrieved; it then makes automated trading decisions using weighted sentiment and user-set risk guardrails before executing orders through broker APIs.
How do you ensure accuracy of the trade decisions?
The team will conduct multiple rounds of functional testing, data validation testing, and user acceptance testing across all modules, and has identified risks such as scraping failures, anti-bot blocking, unreliable data sources, hallucinations, and stakeholder misalignment, each with mitigation strategies like modular scrapers, use of official APIs, curated sources, RAG-based validation, and regular stakeholder communication.