📊 Full opportunity report: Introducing Forezai · TradingAgents — a committee of LLMs decides paper-trades on ThorstenMeyerAI.com — validation score, market gap, and execution plan.
TL;DR
Forezai has launched TradingAgents, a framework where multiple LLMs form a committee to simulate trading decisions. The system aims to test if AI can outperform random choices in paper trading, with operational tools added for research purposes.
Forezai has introduced TradingAgents, a system where a committee of large language models (LLMs) collaboratively make paper-trades based on structured analysis. This development aims to explore whether AI-driven, multi-agent decision-making can produce trading outcomes at least no worse than random chance, with potential implications for AI research in financial decision-making.
The TradingAgents framework, originally developed by TauricResearch, involves multiple specialized LLM roles—including analysts, debate agents, and risk teams—that argue and synthesize trading insights. Forezai’s fork enhances this setup by adding operational features such as an autonomous scheduler, paper-trading interfaces, position management, and a web dashboard, allowing researchers to run, monitor, and evaluate the system in real-time without risking actual capital. The system is designed to force explicit reasoning from the models, avoiding reliance on raw data recall, and aims to test the hypothesis that structured AI committees can outperform random decision-making in simulated markets. The launch marks a step toward more sophisticated AI research tools in trading, emphasizing transparency, auditability, and controlled experimentation.AGENTS
FOREZAI · 03
TRADINGAGENTS · LAUNCH
Research Series · Companion to Polybot Week 1-2 · 2026-05-17
Introducing Forezai · TradingAgents.
A committee of LLMs
decides paper-trades.
After two weeks of finding out most parametric strategies don’t work, the obvious next research question: can multi-agent LLM judgment do any better?
A fork of the open-source TradingAgents framework (TauricResearch): thirteen LLM agents in four stages — four parallel analysts · a bull-bear debate with research-manager arbitration · a three-voice risk team · a two-layer trader + portfolio-manager decision. The fork keeps the agent graph intact and adds the operational layer the upstream doesn’t ship: an autonomous loop · a multi-broker abstraction · a local web dashboard · Codex OAuth · MCP plug-ins · 520+ unit tests. The question is narrower than “do LLMs predict the market” — that prior is “no, with high confidence.” The narrower question is: when LLMs are structured into specialised adversarial roles, does the committee produce decisions at least no worse than a coin flip after fees? Honest priors before running: it might fail too. If it appears to work, the most likely explanation is variance.
This is not financial advice. Nothing in this announcement should be used to inform real trading decisions. The software described trades simulated money by default. If you reconfigure it to trade real money, you should expect to lose that money — regardless of how clever any individual agent’s reasoning looks. Algorithmic trading is zero-sum after fees and structurally hostile to part-time retail strategies.
13 agents
Specialised roles in four stages
Analysts · Debate · Risk · Decision
Analysts · Debate · Risk · Decision
78% / -33%
Polybot prior: fleet win rate
combined with -33% bankroll
combined with -33% bankroll
520+
Passing unit tests across engine,
services, HTTP routes (starting baseline)
services, HTTP routes (starting baseline)
€0 floor
LLM cost on Codex OAuth
(falls back to public API per token)
(falls back to public API per token)
FOREZAI / TRADINGAGENTS·
APACHE 2.0 FORK·
UPSTREAM TAURIC RESEARCH·
LANGGRAPH·
13 AGENTS / 4 STAGES·
4 PARALLEL ANALYSTS·
BULL-BEAR DEBATE·
3-VOICE RISK TEAM·
TRADER + PORTFOLIO MANAGER·
5-TIER FINAL RATING·
ALPACA PAPER + LOCAL + SHADOW·
LIVE ENDPOINTS HARD-REFUSED·
FASTAPI + REACT VIA CDN·
CODEX OAUTH·
MCP PLUG-IN REGISTRY·
520+ UNIT TESTS·
POLYBOT WEEK 1: 21 EXPERIMENTS·
WEEK 2: -33% BANKROLL·
78% FLEET WIN RATE·
HONEST RESEARCH, NOT EDGE·
FOREZAI / TRADINGAGENTS·
APACHE 2.0 FORK·
UPSTREAM TAURIC RESEARCH·
LANGGRAPH·
13 AGENTS / 4 STAGES·
4 PARALLEL ANALYSTS·
BULL-BEAR DEBATE·
3-VOICE RISK TEAM·
TRADER + PORTFOLIO MANAGER·
5-TIER FINAL RATING·
ALPACA PAPER + LOCAL + SHADOW·
LIVE ENDPOINTS HARD-REFUSED·
FASTAPI + REACT VIA CDN·
CODEX OAUTH·
MCP PLUG-IN REGISTRY·
520+ UNIT TESTS·
POLYBOT WEEK 1: 21 EXPERIMENTS·
WEEK 2: -33% BANKROLL·
78% FLEET WIN RATE·
HONEST RESEARCH, NOT EDGE·
FIG. 01 — THE 13-AGENT COMMITTEE
Thirteen specialised roles · four stages · biases made to argue in public
The architecture forces the system to articulate its reasoning rather than relying on what a single context window happens to recall
Stage 1 · Four analysts in parallel4 agents
Market
Structure, ranges, regime indicators
News + Insider
News flow, filings, insider activity
Fundamentals
Balance sheet, earnings, ratios
Social Sentiment
Social-media tone, retail signal
↓
Stage 2 · Bull-bear debate + research-manager arbitration3 agents
Bull researcher
Argues upside thesis from analyst reports
Bear researcher
Argues downside thesis from same reports
Research manager
Arbitrates · writes single synthesis
↓
Stage 3 · Three-voice risk team3 agents
Aggressive
Looks for upside · accepts variance
Conservative
Looks for downside · protects capital
Neutral
Balances · forces downside articulation
↓
Stage 4 · Two-layer decision2 agents
Trader
Three-tier proposal · buy / hold / sell
Portfolio manager
Five-tier rating + price target + horizon · sees arguments only, never raw data
The portfolio manager only sees the arguments, never the raw data — which forces the committee to make its reasoning explicit rather than relying on a single context window’s recall. The upstream framework ships the agent graph; it does not ship the operational machinery to run that graph on autopilot, observe its results honestly, store them for later inspection, or prevent the operator from accidentally trading real money. That gap is what the Forezai fork fills.
FIG. 02 — THE POLYBOT PRIOR · WHY THIS IS A DIFFERENT BET
Two weeks of paper-trading prediction markets · the trap underneath the headline numbers
25 experiments · 78% fleet-wide win rate · -33% bankroll · most parametric strategies are structurally negative-expectation when measured honestly
The flattering number
78%
Fleet-wide win rate · week 2
“You can win four out of five trades and still go broke, because the one loss is bigger than the four wins put together.” Win rate without P&L context is a mechanical illusion.
The honest number
−33%
Fleet bankroll · week 2 close
The strongest possible demonstration of the trap. A parametric trading strategy that looks compelling in a backtest will almost always fail to survive a fresh sample. Most “edges” are mechanical artefacts.
Week 1: 21 parallel strategy experiments · early winners mostly mechanical illusions · exactly one strategy (a fair-value taker on BTC) showed the mathematical signature of real edge over a few hundred settled trades. Week 2: same fair-value strategy with more data collapsed. A separate mid-week hypothesis (market-making) also failed cleanly. Fleet ended week 2 at roughly negative thirty-three percent of bankroll. The honest research finding wasn’t on the winning side — it was on the losing side. Adding more parameters to Polybot wouldn’t change that. TradingAgents is asking a separable question.
FIG. 03 — WHAT THE FORK ADDS · THE OPERATIONAL LAYER
Six layers the upstream framework doesn’t ship
Same agent graph, intact. The fork makes it a research instrument rather than a tech demo.
01 · Loop
An autonomous loop
Scheduler · watchlist · auto-trader maps ratings to paper orders · allow-list filtering · per-ticker cooldowns · sector caps · cash checks · position manager evaluates open positions every 60s for TP / SL / max-hold. Append-only audit logs.
02 · Brokers
Multi-broker abstraction
Three modes: local Python broker (yfinance fills, JSON-persisted) · Alpaca paper-trading adapter · “shadow” mode running both in parallel with divergence view. Real Alpaca live endpoints are hard-refused at multiple layers.
03 · Dashboard
A local web dashboard
FastAPI backend · React via CDN, no Node toolchain · SVG equity curve · rolling-peak drawdown · win-rate by rating / ticker / model · exit-reason breakdown · LLM cost vs realised P&L joined by run ID. Runs locally; nothing sent to a cloud service.
04 · Codex
Codex OAuth
Runs the engine on a ChatGPT Pro subscription via the Codex backend. LLM cost floor effectively zero if you already have ChatGPT Pro. Token stored encrypted locally. Falls back to the regular OpenAI API if you’d rather pay per token.
05 · Alerts
Multi-channel alerts
Slack · Discord · SMTP email · configurable filter on rating events and order fills · append-only history kept locally. Webhook URLs masked in API responses so a screenshot can’t accidentally leak credentials.
06 · MCP
MCP plug-ins
Registry for adding Anthropic Model Context Protocol servers (Kensho · Aiera · FactSet · Morningstar · LSEG) as analyst tools. Plug-ins advertise category (fundamentals · news · market data · social) · probe endpoint tests credentials.
Honest-by-design touches: every generated report prepends “Research, not advice” and appends a footer with version, commit, provider, models used, run ID, and cost. Closed trades carry the same metadata. 520+ passing unit tests across engine, services, and HTTP routes. The intent: when the system loses money, the journal makes it impossible to pretend it didn’t.
FIG. 04 — HONEST PRIORS · BEFORE RUNNING THIS IN ANGER
Three priors stated before the data starts arriving
The bias of the project: when the data says no, the dashboard says no, the article says no
1
It might fail too. LLMs are not oracles, and a sophisticated framework around language-model outputs does not change the underlying error rate of the model. Sample is still everything. The framework’s outputs are subject to the same statistical noise as any prediction system over small samples.
Highest likelihood
2
If it appears to work, the most likely explanation is variance. The same trap that caught the first article’s candidate edge applies here. A high win rate over fifty trades means much less than it looks. Without out-of-sample confirmation, a flattering early sample tells you almost nothing about whether the system has real edge.
Second-most likely
3
If it appears to work for the right reasons — empirical win rate matches stated confidence, and alpha-versus-benchmark persists across non-overlapping samples — that would be a meaningful research finding. Whether that happens, I don’t know. The point of putting it in the open is that the data will say.
Genuinely open
This is explicitly not a launch announcement for a product anyone should connect a real brokerage account to. The Alpaca live endpoints are hard-refused at multiple layers in the code, and the design choice is deliberate. The right next step is data, not deployment. The bias of the whole project is straightforward: when the data says no, the dashboard says no, the article says no, and no one tries to retroactively rescue the thesis. That’s the contribution.
FIG. 05 — WEEK THREE · WHAT THE METHODOLOGY WILL MEASURE
Four concrete measurements before publishing findings
The hope: write the week-three article from a position of “here’s what the data says”. The fear: another candidate falsified at higher sample. Both outcomes are publishable.
M1 · Sample discipline
Small watchlist for a few weeks before publishing
A handful of tickers across two or three sectors. Long enough to gather sample, narrow enough to keep attention on what’s actually happening per agent. Avoid the noise of a 65-ticker autonomous loop until the smaller version has been read carefully.
M2 · Calibration view
Stated confidence vs. realised win rate
When the system says “75% confident”, do the trades actually win 75% of the time? Same measurement applied to Polybot’s fair-value model. If the model is systematically over-confident, that bias dominates everything downstream.
M3 · Cost accounting
Cost per ticker · per rating · per profitable trade
With Codex OAuth the marginal LLM cost is effectively zero. With the public OpenAI API, each run is hundreds of agent turns. The honest question: does this scale economically if you ever did run it at real cost?
M4 · Non-overlapping windows
Alpha vs benchmark · out-of-sample
Not within-sample alpha — trivially inflatable. Hold out one period entirely, run the system on the next, then check whether the held-out result matches the in-sample stats. If they diverge sharply, the in-sample was curve-fit.
Open under Apache-2.0 with upstream cited from every relevant surface. Not open: the operator’s running results, the specific watchlist, the per-agent prompt customisations, the alert channels, the trade journals — kept local for the same reason Polybot’s per-experiment data is kept local. Publishing exact configurations encourages people to copy them with real money, which is the opposite of what an honest research project should do. Summary findings will be published. Recipes will not.
The bet is on a different mechanism, not a different parameter setting. The point is not to find a money-printing AI. The point is to put honest measurements of these systems into the public record — so the next person looking at the space starts a step further along than the last.Thorsten Meyer AI · Introducing Forezai · TradingAgents · § 03
Implications for AI-Driven Market Research
The launch of Forezai’s TradingAgents system could influence future research on AI decision-making in trading environments. By demonstrating how multi-LLM committees can be operationalized for systematic testing, it offers a new approach to evaluating AI’s potential in financial analysis without risking real money. While the system does not aim to predict markets accurately, its structured reasoning process and operational tools could inform the development of more transparent and accountable AI trading research frameworks. This development is relevant for AI researchers, quantitative analysts, and anyone interested in the intersection of AI and financial markets, emphasizing the importance of rigorous testing and auditability in AI-driven decision systems.
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Background of Multi-Agent AI Trading Research
The concept of using AI models, particularly large language models, to assist or automate trading decisions has been explored for several years, with early efforts focusing on rule-based or backtested strategies. Recent research by TauricResearch introduced a multi-agent framework where different LLM roles analyze market data, debate, and synthesize recommendations, aiming to overcome the limitations of single-model predictions. Prior experiments with parametric strategies revealed that many apparent edges fail under real-world testing, prompting interest in less rule-bound AI approaches. Forezai’s fork builds on this foundation, adding operational tools to facilitate rigorous research and simulation, without risking actual funds. The project emphasizes transparency, explicit reasoning, and structured decision-making, reflecting ongoing efforts to understand AI’s capabilities and limitations in financial decision environments.“This system aims to test whether a committee of LLMs can produce trading decisions that are at least as good as random, with the added benefit of explicit reasoning and auditability.”
— Thorsten Meyer, Forezai
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Unconfirmed Efficacy of AI Committee Decisions
It remains unclear whether the AI committee’s decisions will outperform random or baseline strategies in live or extended testing. While initial experiments focus on simulated data and paper trading, real-world applicability and consistency over time are still unproven. The system’s ability to generate meaningful insights versus noise, and its robustness against market volatility, are still under evaluation.
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Next Steps for Testing and Validation
Forezai plans to conduct extended testing of the TradingAgents system, including longer simulation periods and varied market conditions. Future updates may include more sophisticated decision layers, integration with live paper-trading accounts, and comparative analysis against traditional models. Researchers and developers will monitor performance metrics, reasoning transparency, and system stability to assess AI’s potential in structured trading decision frameworks.
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Key Questions
Can this system trade with real money?
No, the current setup is designed for paper trading only. Forezai’s implementation explicitly prevents risking real capital unless operators override safety measures, which is not advised at this stage.
Does the AI committee predict market movements?
No, the system does not aim to predict markets. Instead, it evaluates data through structured debate and reasoning, producing buy/hold/sell recommendations that are then tested for effectiveness.
How does the system ensure transparency?
Each layer of decision-making writes to audit logs, and the reasoning process is explicit, with arguments from different agents visible and contestable. The web dashboard provides detailed insights into the decision process and performance metrics.
What are the limitations of this approach?
The primary limitation is that the system’s effectiveness in real markets remains unproven. Its reliance on structured reasoning does not guarantee profitable outcomes, and models may still generate noise or biased assessments.
Source: ThorstenMeyerAI.com
Nothing in this article is financial or investment advice. Cryptocurrency and precious-metal investments carry significant risk — do your own research and consider a licensed advisor.
