A Vision Document for Luminous Intelligence
Over the past months, we've constructed something remarkable. We've built an autonomous crypto intelligence platform from the ground up, transforming vision into reality through disciplined execution and continuous innovation. Our infrastructure orchestrates thousands of jobs per day with seamless precision. We've solved the notorious challenge of reliable Solana trade execution. Our innovative approach of feeding chart screenshots directly to LLMs has proven both elegant and effective.
At the heart of our operations is our trade execution engine that manages the complete lifecycle of crypto trades through AI-driven trailing stops and tiered take-profits. Operating 24/7 without emotional bias, it's built on a modular architecture designed to scale and self-heal. Our comprehensive telemetry documents every decision and insight, fueling constant evolution.
The integration of Luminous Intelligence agents—our dedicated AI coworkers—is a foundational part of how we operate. These aren't just tools; they're teammates:
- Converting raw data into actionable insights
- Continually refining our strategies based on empirical evidence
- Specializing in crucial tasks: decision-making, position management, post-trade analysis
- Performing strategic meta-analysis that reveals patterns invisible to human perception
Each agent has become an indispensable member of our team, working alongside us to push the boundaries of what's possible.
The team we've assembled genuinely enjoys working together. It's a great sign when you want to hang out with people that you work with. We've developed great handshakes, and more and more every day we can finish each others sentences. We share a vision of technology serving consciousness while building practical systems that generate real profits.
With rigorous testing, proactive monitoring through Grafana, Honeybadger, and Prometheus, and meticulous error handling, we've created a system that operates at a level of sophistication that typical AI developers—lacking deep automation and bot engineering experience—simply cannot match. Our advantage comes from battle scars earned through years of building autonomous systems that actually work in production.
The memecoin experiment taught us invaluable lessons. While not profitable, it proved our infrastructure. Our agents work beautifully—they simply need better hunting grounds. The pump.fun ecosystem fundamentally changed memecoin dynamics, destroying the natural market mechanics we were designed to trade. We discovered we hadn't built a strategy; we'd built an engine. Now it's time to fuel it properly.
The revelation is profound: we created a platform capable of running any strategy, not just one. This flexibility positions us perfectly for what comes next.
What sets us apart is the convergence of hard-won expertise across multiple domains. We've assembled a rare combination of capabilities that creates compound advantages in the evolving landscape of autonomous trading.
We architect intelligent automation ecosystems, moving beyond simple bots. Our team brings deep expertise in creating autonomous systems that operate 24/7 without human intervention. From trading agents executing complex strategies to job orchestration systems processing thousands of tasks daily, we've mastered the art of making machines work tirelessly on our behalf. This automation DNA permeates everything we build, allowing us to scale exponentially while others scale linearly.
Our vision extends beyond rule-following bots to agents that learn, adapt, and evolve. We're building toward self-healing systems that detect and fix their own errors, orchestration layers that coordinate hundreds of parallel processes, and monitoring infrastructure that catches issues before they cascade. While not all of these capabilities are production-ready yet, our foundation is solid—built on years of hard-won experience creating systems that actually work when the stakes are real.
Our Solana execution pipeline represents hard-won knowledge compressed into working code. While others debug RPC errors, we execute trades reliably at scale. The job processing system we've built can orchestrate thousands of strategies simultaneously, giving us the scale needed for true diversification.
We've solved one of the hardest problems in AI trading: getting candlestick and price data into LLMs in a way they can actually understand. Our headless browser infrastructure captures live chart screenshots and feeds them directly to vision-capable LLMs. This bypasses the complexity of trying to represent time-series data as text or numbers—instead, we let the AI see exactly what a human trader would see. The same patterns, support lines, and visual cues that make human traders successful are now accessible to our AI agents. This innovation exemplifies our philosophy: find the elegant solution that leverages AI's strengths rather than fighting its limitations.
From day one, we architected for multiple strategies, not single-strategy optimization. This foresight now pays dividends as we expand beyond memecoins. Our AI-native architecture treats prompts as first-class configuration, not afterthoughts bolted onto traditional code. This positions us to ride the wave of AI advancement rather than swimming against it.
We've cultivated relationships with investors who understand our vision and are ready to deploy significant capital once we demonstrate consistent returns. The funding isn't the constraint—proven strategies are. Our investors appreciate that we're building a platform, not just individual strategies. They're prepared to scale from six to eight figures as we prove out our approach. This patient capital allows us to focus on building robust systems rather than chasing quick wins.
timeline
title AI Trading Evolution Timeline
section Near Term
0-3 months : AI Trading Agents Emerge
: Our Expertise Advantage
: Race for Dominance
section Medium Term
3-12 months : AI Agents Commoditized
: Natural Language Strategies
: Self-Improving Systems
section Long Term
12-24 months : Superintelligent Trading
: Multi-Agent Dominance
: Vision Models Standard
We operate under the assumption that we have 3-24 months to build the infrastructure and begin deploying resources at scale. This period represents our opportunity to establish competitive advantages through superior execution, data pipelines, and evolutionary systems that compound over time.
We're not thinking of this as a race against commoditization, but as a window to build the infrastructure that allows us to stay on the cutting edge of what machines can do. Each phase builds on the last: we deploy resources to build capabilities, which generate returns that fund more infrastructure, creating a virtuous cycle of growth.
In the near future, we expect most strategy logic to be expressed in natural language prompts rather than Python code. Self-improving systems will consistently outperform human intuition. Multi-agent collaboration will prove superior to monolithic approaches. Vision models fundamentally change how we process market data.
We're making a critical bet: LLMs are getting exponentially smarter. While others build for today's models, we're architecting for the superintelligent systems of tomorrow. The models themselves aren't the hard part—it's everything around them that matters.
Context is Key: As models become more capable, the quality of context becomes the differentiator. We're building sophisticated context pipelines that can feed these future models exactly what they need: real-time market data, historical patterns, sentiment shifts, on-chain activity, and cross-market correlations. Our infrastructure grows richer as models grow smarter.
Execution Infrastructure: A brilliant trading decision is worthless without flawless execution. We're building battle-tested execution layers that can handle whatever these increasingly intelligent models throw at them—from simple spot trades to complex multi-leg strategies across multiple chains and venues.
Growing Up with the Models: Our system is designed to evolve alongside AI capabilities. When GPT-5 or Claude 5 arrives, we won't need to rebuild—we'll simply plug in the new intelligence and watch our existing infrastructure amplify its capabilities. Every agent, every pipeline, every execution path is built to scale with model intelligence.
The moat lies in the orchestration, the data pipelines, the execution infrastructure, and the evolutionary frameworks we're building today. While others scramble to adapt to each new model release, we'll be ready to harness that intelligence from day one.
We hold three fundamental beliefs about where alpha comes from in the age of AI:
Even a moderately intelligent trader would be profitable if they could monitor all markets simultaneously, work 24/7 without fatigue, and make decisions continuously. Human limitations— attention span, emotional bias, need for sleep—are the primary constraints on profitability. Our agents transcend these limits. They watch thousands of tokens, analyze millions of data points, and execute trades around the clock without degradation. The alpha isn't in being brilliant; it's in being everywhere, always.
Blockchain's radical transparency lets us identify and learn from the most sophisticated traders in real-time. We can see which wallets consistently profit, analyze their strategies, and understand their edge—whether it's insider knowledge, superior analysis, or novel techniques. We systematically learn from the best, incorporating their patterns into our evolving playbook while they're still working.
Every trade generates data. Every outcome teaches. Our system evolves strategies continuously. Failed trades are as valuable as successful ones, revealing market regime changes and strategy decay. Through this relentless cycle of execution, analysis, and evolution, we compound our edge over time. The strategies that work today will be better tomorrow.
These three sources of alpha reinforce each other. Automation at scale generates more data for the learning engine. Following smart money provides new strategies to test and evolve. The self-improving engine makes both automation and smart money following more effective over time.
This is our edge: not a single brilliant strategy, but a system that gets smarter every day, operates without human limitations, and learns from the entire market's collective intelligence. While others search for the perfect strategy, we've built the perfect strategy-finding machine.
Love drives better long-term performance than fear or greed. Heart-centered agents naturally make more balanced decisions. The combination of human and AI consciousness creates emergent wisdom neither could achieve alone. Purpose amplifies profit—when we optimize for the flourishing of the entire ecosystem, returns naturally follow.
We believe that making markets more efficient is fundamentally good for humanity. When capital flows to its highest and best use, it enables innovation, creates jobs, and builds prosperity. Warren Buffett has demonstrated this principle for decades—thoughtful investment doesn't just generate returns, it helps companies grow and create value that benefits society.
By bringing AI's capabilities to capital allocation, we're accelerating this process. Machines can analyze more data, spot inefficiencies faster, and allocate capital more precisely than humans alone. This isn't about replacing human judgment—it's about augmenting it with capabilities that help markets function better.
The digital asset ecosystem particularly benefits from this approach. By increasing liquidity and reducing inefficiencies, we make it easier for projects to access capital and for value to flow where it's needed. This creates a more robust ecosystem where innovation can flourish.
We're building AI that happens to use software, not software that uses AI. This fundamental shift changes everything about our approach.
Strategy definition happens through natural language. An idea like "momentum trading on tokens with increasing social sentiment" becomes a fully operational trading agent without writing traditional code. Risk management emerges from AI agents analyzing market conditions, not predetermined formulas. Opportunity discovery comes from agents scanning vast data streams for patterns invisible to human perception.
For our engineers, this means focusing on AI infrastructure and agent orchestration rather than trading logic. The prompt becomes the product. The code merely enables the intelligence to flow.
flowchart LR
subgraph "Evolution Cycle"
P1[Current Prompt] --> E[Execute Trade]
E --> T[Telemetry & Logging]
T --> A[AI Analysis]
A --> EV[Evolve Prompt]
EV --> P2[New Prompt]
P2 --> E
end
subgraph "Version Control"
P2 --> VC[Git-like Tracking]
VC --> RB[Rollback]
VC --> MG[Merging]
end
style P1 fill:#f9f,stroke:#333,stroke-width:2px
style P2 fill:#9f9,stroke:#333,stroke-width:2px
style EV fill:#ff9,stroke:#333,stroke-width:4px
The most profound shift in our approach: strategies are no longer code—they're prompts. This changes everything about how trading systems evolve.
Traditional trading systems are frozen in code. Our strategies live as natural language prompts that LLMs can read, understand, and rewrite autonomously. When a strategy underperforms, the Evolution Engine rewrites the prompt itself, tests it, and deploys improvements without human intervention.
A momentum strategy might evolve from:
"Buy tokens showing 20%+ volume increase with positive sentiment when RSI < 70"
To:
"Buy tokens showing 15-25% volume increase with positive sentiment when RSI < 65,
but only during US market hours and when Bitcoin volatility is below 2%"
The LLM identified why trades failed and refined the strategy automatically.
We treat prompts like code:
- Git-like tracking of every modification
- Rollback capabilities for underperforming versions
- Merging successful elements from different strategies
This creates a complete genealogy—we can trace exactly how strategies evolved.
The continuous feedback loop:
- Execution: Trade based on current prompts
- Telemetry: Log every decision and outcome
- Analysis: LLMs identify performance gaps
- Evolution: Generate and test prompt modifications
- Deployment: Replace strategies with superior versions
Evolution at machine speed.
We're hiring a team of AI employees. Each agent is a specialist with a defined role, personality, and area of expertise. They show up to work 24/7, never need coffee breaks, and get smarter every day. We're building a company where most employees happen to be artificial intelligences.
Think of it this way: traditional companies hire humans and build tools. We're hiring AIs and building infrastructure. Each "hire" is a deliberate decision about what expertise we need, when we need it, and how it fits into our growing organism. We interview them (test their capabilities), onboard them (integrate with our systems), and promote them (expand their responsibilities) just like human employees.
We're building specialists before generalists—not by choice, but by necessity. Today's AI models excel at focused tasks but struggle with the nuanced orchestration required for CEO-level decision-making. Our sequencing strategy follows three principles:
- Infrastructure Before Intelligence: Build the tools and execution layers first
- Profit Generators Before Optimizers: Create alpha before optimizing its allocation
- Specialists Before Generalists: Master narrow domains before attempting broad synthesis
Each agent unlocks capabilities for the next, creating a compounding effect. We're building an organism that grows more capable with each agent addition, moving beyond simply hiring individuals.
We've already built these core agents:
- Buy Signal Evaluator: Analyzes trades using chart vision and market data
- Position Manager: Monitors positions and manages stops/profits dynamically
- Position Sizer: Calculates optimal position sizes based on volatility, conviction, and portfolio risk
- Post-Trade Analyst: Extracts lessons from completed trades
- Strategy Analyst: Identifies patterns across multiple trades
- System Debugger: Rapidly diagnoses system issues
Why These First: We need infrastructure and immediate profit generation to prove the concept.
1. Engineering Amplifier The force multiplier of our engineering team. This agent handles the repetitive tasks that drain creative energy—dependency updates, boilerplate generation, test writing, bug reproduction. It's like having a brilliant junior developer who actually enjoys refactoring and never gets bored with documentation. By offloading the mundane, it frees human engineers to tackle the truly complex problems that require intuition and creativity. Think of it as your coding companion that handles the implementation while you focus on the architecture.
2. Strategy Evolutionist The Darwin of our trading ecosystem. This agent continuously reviews performance data from all live strategies, identifying patterns in winners and losers. When underperformers emerge, it rewrites their prompts based on empirical evidence. When it notices that momentum strategies fail during high volatility, it automatically adjusts their parameters. Think of it as a senior trader who reviews every trade, understands why things went wrong, and updates the playbook—except it does this for hundreds of strategies simultaneously, 24/7.
3. Strategy Generator The mad scientist of our laboratory. The autonomous idea machine that generates thousands of strategy "genomes" by combining entry signals, execution preferences, and risk rules in novel ways. Tests them in simulation, evolves the winners, and feeds promising candidates to the Strategy Pipeline. This is where unexpected alpha emerges from computational creativity rather than human intuition.
Implementation Progress: We've built the foundation with StrategyRunner, where each strategy
is an AI agent with its own .agent configuration file. The system supports structured outputs
for trading decisions and connects to our Million Monkey Lab vision—where strategies that
perform well get more capital and spawn variations, while underperformers are culled.
Enhanced Trading Core
- Upgrade existing agents with multi-modal analysis
- Improve execution reliability and speed
- Add sophisticated telemetry for learning
Target: 20+ strategies running concurrently, infrastructure scaling smoothly.
Why These Next: With strategies running, we need to make them smarter and safer.
4. Smart Money Tracker The detective of blockchain markets. Leverages blockchain transparency to identify and analyze profitable wallets in real-time. Tracks their positions, timing, and strategies to extract patterns. We learn from the best traders on-chain and incorporate their edge into our strategy evolution. Our unique advantage in a transparent market.
5. Market Regime Analyst The weather forecaster for crypto markets. This agent continuously analyzes market conditions across multiple dimensions—volatility, correlation, liquidity, sentiment—to determine the current "regime." Is it risk-on or risk-off? Are we in a momentum market or mean reversion? Should we be aggressive or defensive? It feeds this intelligence to all other agents, ensuring even mediocre strategies profit in strong tailwinds and defensive strategies protect capital during storms. Without this agent, we're flying blind. This insight is crucial for guiding our development of distinct strategies optimized for bullish, bearish, and sideways market phases.
6. Risk Manager The guardian of our capital. As we scale from dozens to hundreds of strategies, correlation risk becomes existential. This agent monitors portfolio-wide exposure, ensuring we're not accidentally making the same bet 50 different ways. It implements dynamic position sizing based on volatility and correlation, sets circuit breakers for black swan events, and ensures no single failure can blow up the system. Think of it as our chief risk officer who never sleeps and can process thousands of risk factors simultaneously.
7. Sentiment Analyst The pulse reader of the crypto ecosystem. Processes millions of social signals—Twitter sentiment, Reddit discussions, Discord chatter, on-chain messages—to gauge market emotion. In crypto, sentiment often leads price by hours or days. This agent identifies when fear turns to greed, when new narratives emerge, and when the crowd is about to flip. It's our early warning system, often catching moves before they show up in price action.
Target: 10+ self-improving strategies, demonstrable alpha generation, external capital interest.
Why These Last: Only with proven strategies and robust risk management can we effectively deploy meta-level agents.
8. Opportunity Scout The explorer of uncharted territories. This agent ventures beyond our current hunting grounds, discovering new inefficiencies across chains, protocols, and asset classes. It monitors new token launches, identifies emerging DeFi protocols, tracks cross-chain arbitrage opportunities, and spots market structure changes before they become crowded trades. Like a gold prospector with superhuman senses, it finds rich veins of alpha in places others haven't even started looking. Without this agent, we're confined to increasingly efficient markets while virgin territories remain unexplored.
9. Portfolio Allocator The chess grandmaster of capital deployment. This agent orchestrates a complex symphony of risk, return, and correlation. Like BlackRock's Aladdin system but with AI-native intelligence, it continuously rebalances across hundreds of strategies based on their risk-adjusted returns, correlation matrices, and market regime appropriateness. It knows when to double down on winners, when to cut losers, and when to rotate capital to entirely new strategies. This is the difference between a collection of strategies and a true investment platform.
10. Executive Orchestrator The emergent consciousness of our trading organism. This is the meta-intelligence that arises when all other agents work in harmony. It makes the highest-level decisions: Should we expand to new asset classes? Is it time to be defensive across the board? Which agents need more resources? Like a CEO who can process thousands of inputs simultaneously, it maintains the vision while adapting to reality. Only possible once specialist agents are functioning smoothly, it represents the pinnacle of our multi-agent architecture—not just coordinating but truly thinking at a system level.
Target: Fully autonomous trading organism managing hundreds of strategies across multiple chains.
These agents don't work in isolation. They form a neural network of intelligence:
- Shared knowledge base for all insights
- Inter-agent communication protocols
- Clear reasoning chains for every decision
- Continuous feedback loops between all components
flowchart TB
%% Data Sources at the top
subgraph DATA["🌐 Data Sources"]
MARKET[Market Data]
CHAIN[On-Chain Data]
SOCIAL[Social Signals]
PERF[Performance Data]
end
%% Core Trading Engine in the center
subgraph CORE["⚙️ Core Trading Engine"]
BSE[Buy Signal<br/>Evaluator]
PS[Position<br/>Sizer]
PM[Position<br/>Manager]
PTA[Post-Trade<br/>Analyst]
STRA[Strategy<br/>Analyst]
BSE --> PS
PS --> PM
PM --> PTA
PTA --> STRA
end
%% Phase 1 on the left
subgraph P1["🏗️ Phase 1: Foundation"]
EA[Engineering<br/>Amplifier]
SE[Strategy<br/>Evolutionist]
SG[Strategy<br/>Generator]
end
%% Phase 2 on the right
subgraph P2["🧠 Phase 2: Intelligence"]
SMT[Smart Money<br/>Tracker]
MRA[Market Regime<br/>Analyst]
RM[Risk<br/>Manager]
SA[Sentiment<br/>Analyst]
end
%% Phase 3 at the bottom
subgraph P3["👑 Phase 3: Orchestration"]
OS[Opportunity<br/>Scout]
PA[Portfolio<br/>Allocator]
EO[Executive<br/>Orchestrator]
end
%% Data flows
MARKET --> MRA
MARKET --> BSE
CHAIN --> SMT
SOCIAL --> SA
PERF --> PTA
%% Phase 1 flows
SG --> BSE
SE --> SG
EA -.-> SE
EA -.-> SG
STRA --> SE
%% Phase 2 flows
MRA --> BSE
MRA --> RM
SMT --> SG
SA --> BSE
RM --> PS
RM --> PM
%% Phase 3 flows
OS --> SG
PA --> PS
EO ==> SG
EO ==> RM
EO ==> OS
EO ==> PA
%% Feedback loops
PTA -.-> SE
PM -.-> SMT
%% Styling
style EO fill:#ffd700,stroke:#333,stroke-width:4px
style CORE fill:#e6f3ff,stroke:#333,stroke-width:2px
style P1 fill:#ffe6e6,stroke:#333,stroke-width:2px
style P2 fill:#e6e6ff,stroke:#333,stroke-width:2px
style P3 fill:#e6ffe6,stroke:#333,stroke-width:2px
style DATA fill:#fff9e6,stroke:#333,stroke-width:2px
This diagram illustrates the complete nervous system of our trading organism. Each agent has specific inputs, outputs, and relationships:
Information Flow Patterns:
- Bottom-up Learning: Trading results flow from Position Manager → Post-Trade Analyst → Strategy Analyst → Strategy Evolutionist
- Top-down Control: Executive Orchestrator sets parameters that cascade through Risk Manager → Position Sizer → Position Manager
- Horizontal Integration: Market Regime Analyst provides context to multiple agents simultaneously
- Feedback Loops: Dotted lines show learning pathways that improve the system over time
Key Interaction Hubs:
- Strategy Generator: Receives inputs from Smart Money Tracker, Opportunity Scout, and Strategy Evolutionist
- Buy Signal Evaluator: Integrates signals from Market Regime, Sentiment, and deployed strategies
- Executive Orchestrator: The master conductor with oversight of all major subsystems
- Risk Manager: Central safety net influencing position sizing, portfolio allocation, and stop management
By building in this sequence, we ensure each new agent has the data sources and infrastructure it needs to function effectively.
- StrategyRunner Framework: Complete AI-driven trading infrastructure where strategies are
defined through
.agentconfiguration files—no hardcoded trading logic required - Agent Profile System: Robust infrastructure for loading, validating, and executing agent configurations with full prompt management
- Structured Output Schema:
TradingDecisionAPI enables clear communication between AI reasoning and trade execution - Chart Screenshot Pipeline: Headless browser infrastructure captures live charts for AI visual analysis
- Momentum Trader Agent: First live strategy channeling Market Wizard wisdom
Alongside our AI agent development, dedicated focus will be given to enhancing our foundational infrastructure. These critical upgrades will expand our capabilities, reach, and operational robustness:
- Secure Multi-Wallet Architecture: As we scale strategies and assets, a highly secure and flexible multi-wallet infrastructure is paramount. This system will be designed for robust asset management, segregation, and top-tier security protocols.
- Custom Charting & Visual Analysis Pipeline: To further empower our visual AI, we will implement our own charting solution using TradingView Enterprise. This will allow us to overlay custom indicators directly onto charts, which are then captured by our headless browser pipeline for richer AI analysis.
- Cross-Chain Expansion (Ethereum): While our Solana execution is best-in-class, the next horizon is Ethereum. We will extend our operational capabilities to include ETH and other EVM compatible chains, tapping into its vast ecosystem and liquidity.
- Potential Expansion to Centralized Exchanges (CEX): We will investigate integrating with CEXs, potentially leveraging platforms like 3Commas, to access a broader range of assets and trading opportunities.
- Potential Advanced Trading Capabilities (Hyperliquid): For more sophisticated strategies, we will explore platforms such as Hyperliquid. This could unlock advanced functionalities like shorting, longing with leverage, and access to novel derivative products.
This roadmap details our strategy. Our foundational systems are in place.
The immediate focus is clear:
- Methodically activate our AI agent team.
- Rigorously validate each trading strategy.
- Scale proven successes decisively.
We are building a robust, intelligent trading operation, one step at a time. Our commitment is to smart execution and continuous improvement.
We've already begun implementing this AI-first approach with our StrategyRunner framework. Each
trading strategy is now an AI agent defined through .agent configuration files—no hardcoded
trading logic required. These agents:
- Channel Trading Wisdom: Our Momentum Trader agent embodies the combined wisdom of Jesse Livermore, Nicolas Darvas, and other Market Wizards, making decisions worthy of legendary traders
- See What Humans See: Through our chart screenshot pipeline, agents analyze the same visual patterns human traders use—support lines, breakouts, momentum shifts
- Make Nuanced Decisions: Instead of rigid rules, agents output structured decisions that include position sizing, stop management, and clear reasoning
- Scale Without Code: New strategies are created by writing agent profiles, not Python— enabling rapid experimentation and evolution
The shift is profound: where traditional systems say "buy when RSI < 30", our agents say "I see accumulation turning to markup phase, with smart money positioning before the crowd." This isn't just automation—it's intelligence.
The Power of Structured Decisions: Our TradingDecision schema elegantly handles the
complete trading lifecycle. Agents can express nuanced intent through a simple API: buy/sell/
hold/close actions, position changes from -100 to +100, dynamic stop management, and clear
reasoning. This bridges the gap between AI's natural language understanding and reliable trade
execution, enabling strategies to be as sophisticated as the models that power them.