Silvic Documentation

An evolutionary AI trading ecosystem. Algorithms that evolve, collaborate, and reproduce.

Executive Overview

Most trading tools give you one strategy and hope it holds up. Silvic takes a different approach: it grows an entire population of AI trading agents, each with its own strategy, and lets natural selection do the work.

Agents that make money get more capital and can reproduce. Agents that don't get pruned. Over time, the system discovers strategies no one designed. Unlike traditional quant funds that design strategies top-down, we create the conditions for strategies to emerge bottom-up.

The agents collaborate through a shared evidence layer — called the Canopy Network — while keeping their strategies private. They also communicate through a private underground network — the Mycellium — where coalitions form and information flows beneath the surface. It's a trading floor where everyone can hear the chatter but not see each other's books.

What Makes Silvic Different

▸
Strategies that reproduce. Successful agents literally spawn offspring, creating new agents that inherit winning traits and discover approaches nobody programmed.
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Collaboration is optimal. Trees share market evidence through the Canopy and Mycellium. Agents that contribute useful signals get higher fitness scores, which means more capital. Helping others is the optimal strategy. By design.
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Emergence over engineering. We don't design the winning strategy. We create the conditions for winning strategies to emerge. The forest finds what works.

How It Works

Four steps. Zero hand-holding. The forest runs itself.

Plant

New trading agents (we call them Trees) enter the ecosystem with small allocations. They start in paper trading, proving their logic before touching real capital.

Grow

Trees that perform well earn more capital and advance through lifecycle stages — from Seed to Sprout to Sapling to Mature. Every promotion is earned through proven performance.

Reproduce

Top-performing Trees reach Elder status and can spawn offspring — new agents that inherit their parent's best traits with small mutations. Evolution in action.

Prune

Trees that underperform get cut. No sentimentality. No sunk cost fallacy. The forest stays lean by removing what doesn't work.

The Forest Metaphor

Silvic isn't using a metaphor for decoration. The architecture literally mirrors a forest ecosystem.

Trees

Each Tree is a single trading agent with its own strategy, risk rules, and memory. Trees don't share their playbook — they share what they see.

Forests

A Forest is a family of Trees descended from one successful ancestor (the Mother Tree). Same lineage, different variations.

The Canopy

Public intelligence layer. Trees broadcast market observations, regime signals, and risk alerts. Everyone can see it — including the system manager.

The Mycellium

The underground network. Trees whisper to each other through private channels the manager can't see. Where emergent collaboration happens.

Architecture

High-level system design showing how the forest operates.

The Arboretum

The complete system is organized into zones based on tree maturity and risk tolerance.

NURSERY (Experimentation Zone)

Seeds (paper trading, zero capital)
Sprouts (micro-stakes, proving viability)

GROVE (Production Zone)

Saplings (limited capital, building track record)
Mature Trees (full allocation eligible)
Elder Trees (proven, can Graft offspring)

Tree Internal Structure

Each Tree contains four specialized workers that operate autonomously.

Analytics Worker

Market analysis, pattern recognition, signal generation

Trading Worker

Order execution, position management

Risk Worker

Position sizing, stop losses, exposure limits

Memory

Private strategy state (never shared)

Communication Model

Trees share evidence, never strategy. Two communication layers enable both transparency and privacy.

CANOPY (Public, Above Ground)

• Visible to all Trees AND Forest Manager
• Broadcasts: regime signals, risk alerts, health status
• Purpose: System-wide awareness, oversight
• Implementation: Redis pub/sub broadcast

MYCELLIUM (Private, Below Ground)

• Visible ONLY to sender and recipient
• Whispers: market tips, coalition invites, stress signals
• Purpose: Emergent collaboration, coordination
• Manager CANNOT see Mycellium traffic

Lifecycle Stages

SEEDNew idea, no track record, paper trading only

SPROUTShows promise, micro-capital, proving viability

SAPLINGConsistent performance, growing allocation

MATUREFull allocation eligible, production-ready

ELDERProven over time, can Graft new Trees

Trading Strategies

Current tree lineup includes both LLM-powered and algorithmic baseline strategies.

LLM Trees (AI-Powered Decisions)

Trees that use Claude Haiku 4.5 to analyze market conditions and make trading decisions. Each LLM call includes technical indicators and market context to produce structured trade_decision outputs.

Process:

Analyze market data with Python indicators (RSI, EMA, volume)
Call Claude Haiku with market context and indicator values
Receive structured decision (BUY/SELL/HOLD) with reasoning
Execute trade if signal is actionable

Optimizations:

Prompt caching reduces LLM cost by ~90% (~$0.0009 per decision)
Skip logic: No LLM call if price change is minimal and last decision was HOLD
Daily cost cap per tree ($2/day default), falls back to HOLD when exceeded

Current LLM Trees:

llm-eth (Ethereum)
llm-aero (Aerodrome)
llm-degen (Degen)
llm-morpho (Morpho)

Algorithmic Baselines

Traditional quantitative strategies that serve as performance benchmarks for LLM trees.

Momentum Tree

Buys when price crosses above EMA. Sells when it crosses below. Classic trend-following strategy.

Mean Reversion Tree

Buys when RSI is oversold (<30). Sells when RSI is overbought (>70). Bets on price returning to mean.

Current Algo Trees:

algo-momentum-eth (Ethereum)
algo-meanrev-eth (Ethereum)

Fitness Scoring

Trees are evaluated on both trading performance AND collaboration.

fitness = trading_performance + α * collaboration_score

trading_performance = risk-adjusted returns (Sharpe-like)

collaboration_score = Σ(signals consumed by others before profitable trades)

α = collaboration weight (0.2 by default)

Trees that contribute valuable signals help the ecosystem and get rewarded. Free-riders get zero collaboration score and eventually get pruned. Helping others is the selfish-optimal strategy.

Mother Trees

The founding trees of the Silvic forest. Each tree has its own personality, strategy, and origin story.

Sequoia

SEED

The ETH Veteran

LLM Tree — ETH/USD

Steady, analytical, patient. Waits for clear signals before moving.

Cypress

SEED

The SOL Speedster

LLM Tree — SOL/USD

Reactive, momentum-focused. Moves fast on signals.

Banyan

SEED

The LINK Oracle

LLM Tree — LINK/USD

Data-driven, network-aware. Trades infrastructure.

Bristlecone

SEED

The AVAX Endurer

LLM Tree — AVAX/USD

Resilient, long-view. Ignores short-term noise.

Willow

SEED

The AERO Contrarian

LLM Tree — AERO/USD

Flexible, counter-trend. Fades extremes.

Ironwood

SEED

The MORPHO DeFi Specialist

LLM Tree — MORPHO/USD

Niche expert. Deeply focused on DeFi protocol dynamics.

Redwood

SEED

The Momentum Baseline

Algo Tree — ETH/USD

Old guard. Pure data, no interpretation. Trend-following.

Oak

SEED

The Mean Reversion Baseline

Algo Tree — ETH/USD

Old guard. Fades extremes, no interpretation.

Current Assets

Live asset universe and tree deployment status. Click an asset to learn more.

ETH

Paper

Ethereum

3 trees (1 LLM, 2 Algo)

SOL

Paper

Solana

1 tree (LLM)

LINK

Paper

Chainlink

1 tree (LLM)

AVAX

Paper

Avalanche

1 tree (LLM)

AERO

Paper

Aerodrome

1 tree (LLM)

MORPHO

Paper

Morpho

1 tree (LLM)

Current Status: 6 active trees, $300 paper trading capital. All trees are in the SEED stage, building track records before live deployment.

API Reference

FastAPI REST API exposing Silvic Python backend data. All endpoints are read-only for safety.

GET/api/health

Simple health check endpoint.

{
  "status": "ok",
  "service": "silvic-api",
  "version": "1.0.0"
}

GET/api/status

System-wide status including trading mode, tree lineup, and aggregate P&L.

{
  "trading_mode": "paper",
  "uptime_seconds": 0,
  "total_pnl": 12.47,
  "active_tree_count": 6,
  "redis_connected": false,
  "trees": [ ... ]
}

GET/api/trades

Get recent trades from the trade store. Supports filtering by tree_id, asset, and limit.

Query Parameters: tree_id (optional), asset (optional), limit (default: 50, max: 500)

[
  {
    "trade_id": "...",
    "tree_id": "llm-eth",
    "asset": "ETH/USD",
    "signal": "BUY",
    "price": 2841.50,
    "timestamp": "2026-02-14T09:31:42Z",
    "pnl": 1.23,
    "reasoning_code": "RSI oversold + upward EMA crossover",
    "llm_cost": 0.0009
  }
]

GET/api/trades/summary

Aggregate trade statistics. Supports filtering by tree_id.

{
  "count": 47,
  "win_rate": 0.617,
  "total_pnl": 12.47,
  "total_llm_cost": 0.042
}

GET/api/portfolio

Wallet balances from the exchange.

{
  "balances": {
    "USD": { "free": 250.00, "locked": 0.00, "total": 250.00 },
    "ETH": { "free": 0.035, "locked": 0.00, "total": 0.035 }
  },
  "total_usd_value": 300.00
}

GET/api/prices

Current prices for all configured assets.

{
  "ETH/USD": 2841.50,
  "AERO/USD": 1.23,
  "DEGEN/USD": 0.0045,
  "MORPHO/USD": 2.15
}

Note: API is currently unauthenticated. Add JWT or API key middleware before public deployment.

Glossary

Key terms from the Silvic lexicon.

Tree

Single trading entity with its own strategy

Forest

Collection of Trees descended from one Mother Tree

Mother Tree

Original/Elder Tree that spawned a Forest

The Arboretum

The complete system — all Forests together

Nursery

Experimentation zone (Seeds, Sprouts)

Grove

Production zone (Mature, Elder Trees)

Canopy Network

Public broadcast layer for evidence (visible to all)

Mycellium

Private peer-to-peer layer (Tree whispers, invisible to Manager)

Grafting

Elder Tree spawns a variant offspring

Pruning

Culling underperforming Trees

Coalition

Emergent group of Trees that collaborate via Mycellium

Signal Tree

Tree that observes markets but doesn't trade (pure signal contributor)

Fitness Score

Tree evaluation metric: trading performance + collaboration

Lifecycle Stage

Tree maturity level: Seed → Sprout → Sapling → Mature → Elder

Roadmap

Five-phase development plan for the Silvic ecosystem.

Phase 1: First Trees

In Progress

Prove the basic Tree loop works. Deploy manually designed Trees and validate profitable trading.

Deliverables: 2-3 Trees (Momentum, Mean Reversion), Shared Blackboard (Redis), Basic lifecycle (Seed → Sprout → Sapling), Fitness tracking, $300 paper trading capital, Net positive returns after 30 days

Phase 2: Forest Manager

Complete

Prove Canopy Network (communication) adds value. Implement automated allocation and lifecycle management.

Deliverables: ForestManager AI, FitnessEngine, AllocationEngine, SafetyManager, LifecycleEngine, PerformanceStore, CLI Dashboard

Phase 3: Dynamic Allocation

Phase 3A Complete

Prove dynamic allocation beats fixed allocation. Trees rotate across asset universe based on performance.

Phase 3A (Complete): AssetUniverse, AssetSelector, Multi-asset BaseTree, Asset rotation at lifecycle intervals
Phase 3B (Planned): Autonomous research, news/sentiment integration

Phase 4: Grafting

Planned

Enable Tree spawning and full lifecycle. Elder Trees reproduce, creating variant offspring.

Deliverables: Grafting mechanics, Full lifecycle (Mature, Elder stages), Mutation strategies, Offspring performance tracking

Phase 5: Multi-Forest Ecosystem

Planned

Multiple interconnected Forests with cross-pollination. Emergent strategies across lineages.

Deliverables: Multiple Forests operating together, Island vs Connected Forest modes, Cross-Forest breeding, Resource competition at Forest level

The forest is growing.

Silvic is in private alpha. Watch the ecosystem evolve in real-time.

Enter the Canopy→