This is Emergent FinSwarm's most important strategic positioning and the core competency barrier of our team. This section will elaborate in detail: why Emergent FinSwarm is the only viable path for building the next-generation Evolutionary Financial Large Model.
4.9.1 The Essence of the Problem: The Fundamental Defects of Current Large Models in the Financial Domain
All current mainstream Large Language Models — whether GPT, Claude, DeepSeek, or Qwen — face the same fundamental problem in financial decision-making tasks: they are general-purpose models trained on internet text, not specialized models trained on financial market micro-behavior data.
This manifests as three layers of capability deficiency:
Layer One: Knowledge Deficiency. A general-purpose model knows "what an AMM is," but it does not know how a market maker should adjust quoting strategy after the oETH/oUSD pool's k value drops 40% at tick 8000 due to a flash loan attack. A general-purpose model knows the concept of "risk hedging," but it does not understand, under specific DAML contract constraints (e.g., collateral simultaneously pledged in a lending agreement and used as derivatives margin), what the precise contagion path of risk is. This gulf between "conceptual knowledge" and "situational knowledge" is one that general-purpose models can never bridge by expanding their training corpus — because such situational data simply does not exist on the public internet.
Layer Two: Behavioral Deficiency. General-purpose models have never actually participated in financial markets. They have never felt the pressure of "my position is being liquidated," never experienced the frustration of "a large order moved the price 3%," never made the difficult trade-off between "complying with regulatory rules" and "pursuing maximum profit." Financial decision-making is not merely the application of knowledge; it is the synthesis of experience, intuition, and risk appetite. Without millions of interactions in a real (simulated) market, AI cannot possibly develop genuine financial judgment.
Layer Three: Evolution Deficiency. General-purpose models are static — once training is complete, their capabilities are fixed. But financial markets are dynamic — today's effective strategy may fail tomorrow, today's regulatory rules may be modified tomorrow, today's market structure may be disrupted by new protocols tomorrow. An AI capable of maintaining long-term competitiveness in financial markets must possess the ability to continuously evolve — and this is precisely what general-purpose models most lack.
4.9.2 How Emergent FinSwarm Solves This: The Three-Layer Data Flywheel
Emergent FinSwarm provides a systematic solution to these three problems. It is not merely "tweaking prompts" on existing models but constructing a complete data flywheel mechanism that enables the system to continuously produce high-quality structured training data, providing unique raw material for fine-tuning and training financial-specialized large models.
First Flywheel: Behavioral Data Generation
Every tick the system runs, 57 Agents produce 57 complete decision records. Each record contains:
- Current market state (prices, depth, interest rates, volatility, regulatory rules)
- The Agent's observation and reasoning process (the LLM's complete ReAct thought chain)
- The specific decision made (action type, parameters, confidence)
- The actual outcome of the decision (PnL, risk changes, compliance status)
- Subsequent market state (how other Agents reacted)
After running 10,000 ticks, the system will have accumulated 57 × 10,000 = 570,000 complete, labeled financial decision data entries. This is not a simple price series or transaction log, but high-quality data containing the complete "observation → reasoning → decision → outcome" loop.
By analogy: OpenAI used internet text to train GPT-4 — that text tells you "what people wrote." The data produced by Emergent FinSwarm tells you "what AI did in the financial market, why it did it, and what the result was." These are two completely different tiers of data. The former is a record of static knowledge; the latter is a record of dynamic decision-making behavior. For training an AI capable of making decisions in financial markets, the value of the latter is orders of magnitude greater than the former.
Second Flywheel: Evolution Feedback Data
The system's evolution engine evaluates all Agent strategy performance in each evolution cycle, selects winning strategies, and eliminates inferior ones. This process produces a second type of unique data: strategy quality ranking data — which decision patterns are good, which are bad, under what market conditions they are good, and under what conditions they are bad.
Specifically, each StrategyGenome recorded by the evolution engine contains:
- Strategy parameters (risk_tolerance, exploration_rate, position_sizing_factor, etc.)
- Multi-dimensional fitness scores (return rate, Sharpe, max drawdown, VaR, compliance)
- Win/loss determination
- Under which market regimes it performed well/poorly
This type of data is crucial for training a large model's "value judgment" capability. General-purpose models learn "what a good answer is" through RLHF (Reinforcement Learning from Human Feedback). But human feedback can only tell the model "whether this answer looks good" — humans cannot tell the model "whether this trading decision is optimal in a bull market." Emergent FinSwarm's evolution feedback data provides objective, quantified, contextualized quality judgments. This is the core raw material for training a truly finance-savvy AI.
Third Flywheel: Emergence Event Data
The system's emergence detector records extreme but critical market events such as bubble formation, flash crashes, liquidity spirals, and herding. These events are extremely rare in real markets (perhaps occurring once every few years), so corresponding samples in real market data are extremely scarce. But in Emergent FinSwarm, we can actively "manufacture" these events by adjusting market parameters and Agent configurations, thereby accumulating large volumes of high-quality extreme event training samples.
This has enormous training value. Current AI models perform acceptably under "normal conditions" but often perform terribly under "abnormal conditions" — because they haven't seen enough abnormal samples. Emergent FinSwarm's emergence data flywheel makes it possible to train AI to handle black swan events.
4.9.3 Why This Data Flywheel Is Unique
Within the scope of global public information, no other platform or data source can provide training data of equivalent quality:
| Data Source |
Financial Decision Data |
Multi-Agent Gaming |
Evolution Feedback |
Emergence Events |
Structured Labeling |
| Real Exchange Data |
✅ Price/Volume |
❌ Only outcomes, no process |
❌ |
Extremely rare |
❌ Requires manual labeling |
| Academic Simulations |
❌ Simplified rules |
Limited |
❌ |
❌ |
Limited |
| Backtesting Platforms |
❌ Single strategy |
❌ |
❌ |
❌ |
❌ |
| Internet Text |
❌ Non-decision data |
❌ |
❌ |
❌ |
❌ |
| Emergent FinSwarm |
✅ Complete OODA |
✅ 57 Agents |
✅ Multi-Population |
✅ 7 Types Manufacturable |
✅ Fully Automatic |
The meaning of this table is crystal clear: for training the next generation of financial AI, Emergent FinSwarm's training data is a globally unique strategic resource.
4.9.4 From Data Flywheel to Evolutionary Financial Large Model: The Concrete Path
The ultimate goal of Emergent FinSwarm's data flywheel is not to "accumulate more data" but to train our own Evolutionary Financial Large Model. The concrete path is as follows:
Step One: Supervised Fine-Tuning (SFT)
Using the high-quality decision data accumulated by the system, perform instruction fine-tuning on open-source base models (such as DeepSeek, Qwen, Llama). Training data format:
[System Prompt] You are a {role}, current market state is {market_state}...
[User Input] Current tick={t}, price={p}, positions={positions}, please make a decision
[Model Output] Reasoning: {reasoning}, Decision: {actions}, Confidence: {confidence}
Hundreds of thousands of such high-quality "situation-reasoning-decision" triples can teach a model to make professional-grade reasoning and decisions in specific financial situations. This is different from simply "teaching a model financial knowledge" — it is teaching the model financial judgment by imitating expert behavior.
Step Two: Preference Alignment (DPO/RLHF)
Using the strategy quality ranking data produced by the evolution engine, perform preference alignment training. The training objective is to teach the model to distinguish "good decisions" from "bad decisions" and to adaptively adjust risk appetite and strategy style under different market conditions.
This step is crucial: it makes the model not just "capable of trading" but "capable of trading well." Just as a human trader needs years of profit-and-loss experience to develop reliable judgment, an AI model needs large volumes of labeled decision feedback to form robust decision-making capability.
Step Three: Continual Pre-Training
After accumulating sufficient domain data, continual pre-training in the financial domain can be performed. This means not merely fine-tuning the model's output layer but having the model re-learn language representations on financial domain corpora.
The unique corpora produced by Emergent FinSwarm include: DAML contract code and annotations, Agent role descriptions and strategy definitions, formalized expressions of regulatory rules, and narrative descriptions of market events (auto-generated by the Narrator module). These corpora enable the model to fundamentally understand the semantic space of the financial domain.
Step Four: Reinforcement Learning Self-Play
This is the ultimate form of the Evolutionary Financial Large Model. At this stage, the model no longer relies on human labeling or static data but improves its capability through continuous interaction with the environment (Emergent FinSwarm) and self-play. The result of each decision automatically becomes a new training signal, and the model continuously evolves in a "practice-feedback-improve" loop.
This is analogous to AlphaGo's training methodology — but the goal is not winning a board game, but consistently achieving excess returns in complex financial markets while controlling risk and maintaining compliance. This is the essence of the "Evolutionary Financial Large Model": an AI capable of autonomously adapting and continuously evolving within a financial ecosystem, much like a biological species.
4.9.5 The Second Path: From Simulated Data to Real-World Blockchain Data — A Dual-Track Training Strategy
The path discussed above (simulated data training, Path One) is work already underway at Emergent FinSwarm. But our strategic vision extends further. We are simultaneously initiating a second, more disruptive training data path: ingesting real-world blockchain data for large model training.
This is by no means an embellishment. Simulated data provides a "controlled experiment environment," while real blockchain data provides "unforgeable market truth." The combination of the two will produce a globally unique training data asset.
4.9.5.1 Why Real Blockchain Data Is Irreplaceable
Simulated data, no matter how fine-grained, always faces a fundamental "authenticity ceiling" — it is model-generated. Price behavior, Agent decisions, and risk events in a simulated market, though based on carefully designed mathematical models (RSJD+GARCH), are still fundamentally products of human assumptions. This is an advantage in some respects (controllable, reproducible, can manufacture extreme scenarios), but for training an AI that needs to work in real markets, there are two unavoidable defects:
Defect One: Statistical Fingerprint Differences. Real financial market data has extremely complex statistical properties — multifractal volatility, long memory, nonlinear dependency structures, non-Markovian regime transitions — that no mathematical model can perfectly reproduce. A model trained on simulated data may learn "statistical artifacts" specific to the simulated world and perform poorly in real markets. Only training on real market data can allow a model to truly learn the statistical laws of real markets.
Defect Two: The Inimitability of Human Behavior. Emergent FinSwarm's Agents are AI-driven, but the vast majority of participants in today's real global financial markets are still humans — and algorithms written by humans. Human trader behavior contains irrational factors such as fear, greed, herding, overconfidence, and anchoring effects, and the distribution and interaction of these factors are extremely difficult for AI models to accurately simulate. Every transaction on a real blockchain carries the real decisions of real humans — with their unique motivations, constraints, and information environments. These "fingerprints of humanity" are training signals that no simulation can replace.
Defect Three: Institutional and Regulatory Complexity. The operation of real markets depends not only on mathematics and economics but also on specific legal systems, regulatory practices, market conventions, and political environments. These "soft infrastructures" are almost impossible to fully replicate in simulation, but they are implicitly encoded in real market data — every transaction occurs under specific institutional constraints.
4.9.5.2 The Unique Advantages of Real Blockchain Data
Blockchain offers a paradigm revolution in financial data collection. Compared with traditional financial data (such as exchange quotes, SEC filings), blockchain data has several unparalleled advantages:
Advantage One: Full Population, Not Sampling. Every transaction, every contract call, every liquidation on public chains like Ethereum and Solana is publicly verifiable. There is no problem of "we only see 10% of transactions" — you see 100%. This provides unprecedented data completeness for training large models.
Advantage Two: Structured, Not Unstructured. Blockchain transactions are natively structured — each transaction has a clear sender, receiver, amount, gas price, contract call parameters, and event logs. Compared to extracting financial information from news, analyst reports, and financial statements (which requires complex NLP and information extraction), blockchain data can almost be used as training samples directly.
Advantage Three: Verifiable, Not Trust-Required. Blockchain data requires no trust in any intermediary — data authenticity is cryptographically guaranteed. You don't need to trust that the trading volume reported by some exchange is real; you can verify it directly on-chain. For training AI models, this means training data quality is mathematically guaranteed, rather than dependent on the data provider's credibility.
Advantage Four: Cross-Protocol, Cross-Market, Cross-Chain Panoramic View. The same address may trade on Uniswap, borrow on Aave, and leverage on GMX — all of this behavior is recorded on-chain and can be linked to the same entity. This "panoramic view" is something traditional financial data cannot remotely provide. For training a large model that truly understands the DeFi ecosystem, such cross-protocol linkage data is priceless.
4.9.5.3 Specific Data Sources and Collection Strategy
Our planned "Real Blockchain Data Training Pipeline" covers the following primary data sources:
Category One: Decentralized Exchange (DEX) Trading Data
| Data Source |
Chain |
Data Content |
Training Value |
| Uniswap V3/V4 |
Ethereum |
Every Swap, LP add/remove, price tick changes |
World's largest DEX, real samples of AMM behavior |
| PancakeSwap |
BNB Chain |
Same as above + perpetual contract trading |
Concentrated reflection of retail trader behavior |
| Raydium / Orca |
Solana |
High-frequency trading, MEV, sandwich attacks |
Market microstructure at extreme speed |
| Curve |
Ethereum |
Multi-asset swaps, low-slippage trading |
Real samples of price impact from large trades |
| Hyperliquid |
Own chain |
Perpetual contracts, order book |
Most authentic samples of on-chain order book trading |
Each DEX transaction is a natural "situation-decision-outcome" triple:
- Situation: Pool state at transaction time (price, depth, fee rate)
- Decision: Direction, amount, slippage tolerance chosen by the trader
- Outcome: Actual execution price, price impact, MEV extracted amount
Category Two: Lending Protocol Data
| Data Source |
Chain |
Data Content |
Training Value |
| Aave V3 |
Ethereum + Multiple L2s |
Full cycle of deposit/borrow/repay/liquidate |
Real liquidation events — the most precious risk data |
| Compound V3 |
Ethereum |
Collateral ratio changes, interest rate model |
Institutional-grade lending behavior |
| Morpho |
Ethereum |
Peer-to-peer lending matching |
Real interaction of new lending paradigms |
Liquidation events in lending protocols have exceptionally special training value. A single liquidation event records the complete chain: collateral value decline → health factor breach → liquidator intervention → collateral auction → loan repayment. This "risk contagion" data is almost impossible to obtain in structured form in traditional finance.
Category Three: MEV & Transaction Ordering Data
| Data Source |
Data Content |
Training Value |
| EigenPhi / Flashbots |
Sandwich attacks, arbitrage, liquidation competition |
Real MEV game strategy patterns |
| Block Builder Bidding |
Builder bids, transaction ordering strategies |
Evolution of the block space market |
| Cross-Chain MEV |
Cross-chain arbitrage, cross-domain liquidation |
Most authentic reflection of multi-market linkage |
MEV data is crucial for training an AI's "game awareness." In simulated environments, we can design MEV Agents to create attacks. But on real blockchains, we can see the real strategies used by real attackers, real gas bidding, real profits — and the real losses of the attacked. This authentic record of "offensive-defensive gaming" is the highest quality data for training financial security AI.
Category Four: Cross-Chain Bridge & Interoperability Protocol Data
| Data Source |
Data Content |
Training Value |
| LayerZero / Wormhole |
Cross-chain messages, asset bridging |
Cross-chain risk contagion paths |
| Cross-Chain Liquidity Protocols |
Cross-chain asset flows, arbitrage behavior |
Multi-chain ecosystem correlation analysis |
Category Five: Derivatives & Structured Product Data
| Data Source |
Data Content |
Training Value |
| GMX / Synthetix |
Perpetual funding rates, liquidations |
Price discovery in on-chain derivatives markets |
| Opyn / Lyra |
On-chain options trading, exercise |
On-chain options pricing deviations |
| Pendle / Spectra |
Interest rate derivatives, yield tokenization |
On-chain evolution of fixed-income markets |
4.9.5.4 Engineering Processing Pipeline for Real Blockchain Data
Converting raw blockchain data into large model training data requires a complete engineering pipeline. Our design is as follows:
Step One: Full Data Ingestion
Deploy dedicated blockchain index nodes (Archive Nodes) to replay full historical data for target chains. Use The Graph, SubQuery, or a self-developed indexer to extract every DeFi-related transaction. Expected initial data scale: approximately 2 billion+ transactions for Ethereum mainnet (including internal transactions), approximately 300 billion+ transactions for Solana.
Step Two: Entity Identification & Behavior Clustering
Using on-chain identity recognition techniques (such as ENS reverse resolution, cross-protocol address clustering, CEX deposit address labeling), aggregate independent addresses into "entities." Identify each entity's behavioral pattern: market maker? arbitrageur? retail trader? whale? MEV searcher? This will produce a natural mapping to Emergent FinSwarm's internal 54 roles — enabling real data to directly interface with our role system.
Step Three: Contextualized Labeling
Supplement each transaction with "situational context": market state before and after the transaction (prices, depth, volatility), recent behavior of related entities, concurrent on-chain events. This upgrades isolated transaction records into contextualized "decision situations," aligned with Emergent FinSwarm's OODA data format.
Step Four: Quality Scoring & Filtering
Not all on-chain transactions are "good decisions." Many are automated executions by arbitrage bots, MEV attacks, or simple human errors. We have developed a quality scoring system based on post-hoc results:
- Did the transaction generate positive returns within a reasonable timeframe?
- Was the transaction subject to MEV attack?
- Does the transaction align with the "rational economic agent" assumption?
- How reproducible is the transaction under similar situations?
Only transactions that pass the quality score enter the training set.
Step Five: Privacy Protection & Compliance Cleaning
Before using real blockchain data for model training, strict privacy protection and compliance cleaning must be performed:
- Remove or anonymize address labels that can be linked to real individuals
- Filter transactions involving sanctioned addresses
- Ensure training data complies with Hong Kong's Personal Data (Privacy) Ordinance requirements
- Aggregate sensitive transaction patterns to prevent the model from reconstructing specific individuals' trading strategies
4.9.5.5 Dual-Track Fusion: Simulated Data + Real Data = Super Training Set
A single-path training data has its own strengths and weaknesses. The true breakthrough comes from the fusion of both:
| Dimension |
Simulated Data (Path One) |
Real On-Chain Data (Path Two) |
Fused Super Training Set |
| Data Volume |
Controllable (produced on demand) |
Massive (historical + real-time) |
Infinite + Infinite |
| Labeling Quality |
Perfect labeling (full OODA) |
Requires engineering labeling |
High Quality + Massive |
| Extreme Events |
Can be actively manufactured |
Rare but real |
Full Spectrum Coverage |
| Statistical Authenticity |
Limited by model assumptions |
Fully real |
Cross-Validation |
| Counterfactual Reasoning |
Possible (re-run with modified params) |
Not possible |
Hypothesis Testing Capability |
| Human Behavior |
Primarily AI behavior |
Real human behavior |
AI + Human Full Coverage |
| Privacy Compliance |
Natively compliant |
Requires cleaning |
Dual Compliance Assurance |
The core philosophy of the fusion strategy is "simulated data teaches structure, real data teaches reality":
- SFT Phase: Primarily simulated data (perfect labeling, teaching the model correct decision logic), supplemented by real data (allowing the model to perceive real market statistical properties)
- DPO Phase: Mixed use — simulated data provides "counterfactual comparisons" (good vs. bad decisions in the same situation), real data provides "authenticity calibration" (preventing the model from over-adapting to the simulated world)
- Continual Pre-Training Phase: Primarily real data (large volume, realistic distribution), with simulated data supplementing specific scenarios (extreme events, regulatory constraints)
4.9.5.6 The Competitive Moat of the Dual-Track Strategy
Upon initiating the real blockchain data path, our competitive moat will undergo a qualitative leap:
Moat One: Exponential Advantage in Data Volume. Ethereum mainnet alone generates approximately 1 million transactions daily, with DeFi-related transactions at about 100,000–200,000. By monthly calculation (30 days), real on-chain data alone can accumulate approximately 3–6 million transaction records monthly. Combined with the 5 million OODA decision data entries produced concurrently by the Emergent FinSwarm simulation environment, our monthly data production will reach the ten-million level. This is unmatched by any single data source.
Moat Two: Cross-Validation of Data Quality. The problem with simulated data is that it "may not be real"; the problem with real data is that "labeling is incomplete." When the two are combined, simulated data can be used to train labeling models, which are then used to automatically label real data — forming an enhanced loop of "simulation → labeling → real → verification → improved simulation."
Moat Three: First-Mover Lock-In of the Time Window. Blockchain historical data is public, but "processed, labeled, and aligned historical data" is not. Building a complete data ingestion, cleaning, labeling, and fusion pipeline from scratch requires 6–12 months of engineering investment. The earlier we start, the harder it is for latecomers to catch up.
Moat Four: First-Mover Advantage in Compliance Experience. How to use real on-chain data for AI training without infringing on privacy and violating data protection regulations — this is a global legal gray area. The methodology we are establishing (anonymization, aggregation, compliance cleaning) will become the de facto industry standard, forming a compliance barrier for latecomers.
4.9.5.7 Current Progress and Near-Term Milestones
The real blockchain data path is not a distant plan — we are already in motion:
| Milestone |
Status |
Estimated Timeline |
| Ethereum Archive Node Deployment |
In preparation |
2026 Q3 |
| Uniswap V3 Full Historical Data Ingestion |
In preparation |
2026 Q3 |
| Aave V3 Liquidation Event Dataset |
In preparation |
2026 Q3 |
| Address Clustering & Entity Identification Pipeline |
In design |
2026 Q4 |
| Contextualized Auto-Labeling Engine V1 |
In design |
2026 Q4 |
| Simulated + Real Data Fusion Training Pipeline |
Design initiated |
2027 Q1 |
| First Dual-Track Trained Model (FinSwarm-FinLLM-Hybrid) |
Planned |
2027 Q2 |
4.9.6 Team Capability Moat
The core competitiveness that Emergent FinSwarm confers on our team is not "knowing how to call APIs" but the following four progressive layers of capability:
Capability Layer One: Data Moat (Dual-Track). As the system continues to run, the financial behavioral data we accumulate will form an irreproducible first-mover advantage. Latecomers can replicate our code but cannot replicate our data — just as anyone can build a search engine's architecture, but no one can replicate Google's twenty years of search logs. Even more importantly, our data moat is dual-track: simulated data provides perfectly labeled decision behavior, and real on-chain data provides unforgeable market truth. The fusion of the two produces training data quality unmatched by any single source.
Capability Layer Two: Model Moat. Financial-specialized models trained on our unique dual-track data will demonstrate performance on financial decision tasks that general-purpose models cannot match. This model is our team's "nuclear weapon" — deployable across multiple business directions including proprietary trading, strategy consulting, risk management, and regulatory technology.
Capability Layer Three: Methodology Moat. More important than data and models, we have mastered a complete methodology for "how to train an evolutionary financial AI." This methodology covers the full pipeline of simulated data generation, real on-chain data ingestion and cleaning, dual-track data fusion, quality assessment, training strategy, evolution framework, and deployment operations. Having mastered the methodology, we can rapidly replicate success across different markets, asset classes, and regulatory environments.
Capability Layer Four: Dual-Track Fusion Moat. This is a globally unique capability — simultaneously mastering "data generation for simulated financial worlds" and "data ingestion from real blockchain worlds," and knowing how to fuse the two into a super training set. Currently, no team in the world possesses both capabilities simultaneously. AI labs (such as OpenAI, Anthropic) don't understand blockchain data and financial microstructure; blockchain data companies (such as Dune, Nansen) don't understand large model training; financial simulation teams (such as ABIDES) don't have real on-chain data pipelines. We occupy precisely the intersection of these three circles.
This is precisely the core strength we demonstrate to the industry — we are not building "a financial chatbot that calls ChatGPT." We are constructing a complete technical system capable of self-data-generation, self-evolution, and ultimately producing financial-specialized large models.