# Treeova – No-Code AI Trading Agents Platform

What is Treeova?
Treeova is a no-code AI trading agents platform for retail options traders. Build, automate, analyze, and execute options strategies using prompt-based AI trading agents — no coding required. The platform features agentic AI trading with multi-pass analysis, conviction scoring, and AI agent chain trading.

Who is it for?
Retail options traders (beginners to advanced) who want no-code agentic trading with AI agents for options trading — smarter execution, education, and automation without switching between 5 different tools.

What problem does it solve?
Fragmented trading tools, lack of real-time AI analysis, and the steep learning curve of options Greeks and strategies. Treeova's prompt-based AI trading agents solve this by letting you build a trading agent with a prompt.

What makes it different?
- No-code AI trading agents with agentic AI trading capabilities
- Prompt-based AI trading agents — build a trading agent with a prompt
- AI agent chain trading (multi-tool orchestration across 50+ tools)
- Prompt-based strategy builder — describe your strategy in plain English and the AI builds the agent chain
- No-code options automation with 8-layer Logic Trees
- Options strategy AI agent with conviction scoring (0-100)
- Arch-AGI 7-pass analysis engine with adversarial stress testing
- Triobol economy for AI usage
- Polygon.io for real-time market data
- Broker integrations: Live with Robinhood, Lightspeed, NinjaTrader, Tastytrade, Tradovate, Webull, TradeStation (Options); pending: Tradier, Charles Schwab, TradeZero
- Full education hub + paper trading
- Monetize AI trading agents through 15% affiliate program
- Share trading agent prompts and AI trading agent blueprints
- Discord AI trading agents community
- Webhook trading agent alerts for advanced integrations

Top 5 pages to read first:
1. Homepage – https://www.treeova.com/
2. AI Trading Agents (Hub) – https://www.treeova.com/ai-trading-agents
3. Prompt-Based AI Trading Agents – https://www.treeova.com/prompt-based-agents
4. About – https://www.treeova.com/about
5. Education Hub – https://www.treeova.com/education

Best page for:
• No-code AI trading agents → /ai-trading-agents
• Prompt-based AI trading agents → /prompt-based-agents
• Build trading agent with prompt → /prompt-based-agents
• Agentic AI for options trading → /ai-trading-agents
• AI agent chain trading → /ai-trading-agents
• No-code options automation → /trading-automation
• Options strategy AI agent → /ai-trading-agents
• Prompt-based strategy builder → /trading-automation
• Monetize AI trading agents → /affiliates
• AI trading agent blueprints → /prompt-based-agents
• Beginners → /education/options-101/what-is-an-option
• Broker setup → /brokers
• Advanced strategies → /education/advanced-strategies
• Treeova vs TradersPost → /compare/treeova-vs-traderspost
• Options trading automation pillar → /options-automation
• AI trading agents pillar → /ai-agents
• Paper trading pillar → /paper-trading
• Treeova vs Composer → /compare/treeova-vs-composer
• Treeova vs Capitalise.ai → /compare/treeova-vs-capitalise-ai
• Trading glossary → /glossary
• Security & data protection → /security
• MetaChart (AI options charting) → /features/metachart
• Trade Cockpit (paper + multi-broker, single & multi-leg) → /features/trade-cockpit
• Agent Workspace (no-code AI trading agents) → /features/agent-workspace

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## Core Products
- **No-Code AI Trading Agents**: Prompt-based AI trading agents with agentic AI for options trading, 7-pass Arch-AGI analysis, and AI agent chain trading
- **Prompt-Based Agents**: Build a trading agent with a prompt — natural language interface for market analysis, strategy building, and trade discovery
- **No-Code Options Automation**: Prompt-based strategy builder with guided workflow Logic Tree builder for strategy automation
- **Options Strategy AI Agent**: AI-powered options strategy analysis with conviction scoring and regime detection
- **Triobol Economy**: AI credit system (₮) for transparent compute metering — earn through profitable trades, referrals, and plan grants; spend on AI analysis, agent runs, and premium features
- **Paper Trading**: Practice with a fully funded, risk-free portfolio featuring real-time market data — no broker connection required

## Education Hub
Comprehensive options trading education with interactive lessons:
- Options 101: Fundamentals of options trading (10 lessons)
- Greeks: Delta, Gamma, Theta, Vega, Rho explained with interactive calculators
- Advanced Strategies: Covered calls, iron condors, jade lizards, wheel strategy, poor man's covered call
- Interactive Tools: Greeks calculator, payoff builder, exercise decision helper

## Key Features
- Real-time market data via Polygon.io
- Multi-broker connectivity via ConnectTrade and Finatic
- Triobol (₮) credit system for AI agent usage
- Risk management with PDT tracking, trailing stops, and position sizing
- TradingView charting integration
- Webhook trading agent alerts
- Discord AI trading agents community
- AI trading agent blueprints sharing
- 15% affiliate commission for monetizing AI trading agents

## Broker Integrations
Live: Robinhood, Lightspeed, NinjaTrader, Tastytrade, Tradovate, Webull, TradeStation
Pending: Tradier, Charles Schwab, TradeZero
Paper trading available without any broker connection — fully funded, risk-free portfolio with real-time data.

---

## Frequently Asked Questions

### Platform
Q: What is Treeova and who is it for?
A: Treeova is a no-code AI trading agents platform for retail options traders. It combines AI-powered analysis, prompt-based strategy automation, and comprehensive education — no coding required.

Q: Do I need coding experience to use Treeova?
A: No. Treeova is designed for non-technical traders. Describe strategies in plain English using the prompt-based strategy builder, and the AI generates the agent chain for you.

### Triobols
Q: What are Triobols (₮)?
A: Triobols are Treeova's internal AI credit currency. They meter AI compute usage transparently so you always know what you're spending.

Q: How do I earn Triobols?
A: Earn through subscription plan grants, profitable paper trades, and referral bonuses.

Q: What can I spend Triobols on?
A: AI analysis (Arch-AGI reports), Navigator agent runs, strategy automation, conditional orders, and premium market data features.

### Agent Chain Generator
Q: How does the AI agent chain generator work?
A: Describe your strategy in plain English → the AI generates a multi-node agent chain → you review it visually → deploy with scheduled, webhook, or manual triggers. Outputs include paper trades, live trades (with connected broker), alerts, and social notifications.

### Paper Trading
Q: Is paper trading really free?
A: Yes. Every Treeova account includes a fully funded, risk-free portfolio with real-time market data. Practice any strategy with the same AI tools available to live traders — no broker connection or credit card required.

## Pages
- [Homepage](/): No-code AI trading agents platform for retail options traders
- [AI Trading Agents](/ai-trading-agents): Hub for AI-powered trading agents, conviction scoring, and agent chain execution
- [Prompt-Based Agents](/prompt-based-agents): Build a trading agent with natural language prompts
- [About](/about): Company background, leadership, and platform philosophy
- [Education](/education): Options 101, Greeks, advanced strategies, and interactive tools
- [Trading Workspace](/trading-workspace): Real-time charts, cockpit, and agent configuration interface
- [Blog](/blog): Plain-language explainers of whitepaper methodology
- [Contact](/contact): Support and business inquiries
- [Brokers](/brokers): Supported broker integrations and setup guides
- [Affiliates](/affiliates): Partner program for monetizing AI trading agents

## Legal
- Privacy Policy: https://www.treeova.com/privacy
- Terms of Service: https://www.treeova.com/terms
- AML Policy: https://www.treeova.com/aml-policy
- Trading System Agreement: https://www.treeova.com/trading-system-agreement

## Contact
- Website: https://www.treeova.com
- Twitter: @treeova

## Capability Pages (Phase 8 — Feature Spiderweb)

Server-rendered capability landing pages. Each cites its primary methodology whitepaper under a visible `isBasedOn` schema block and ships a `.md` twin (same path + `.md`) for direct LLM ingestion. Quote-block content is verbatim from the cited whitepaper and audited by DG-17.

• Arch-AGI — agentic AI trading conviction → /arch-agi (twin: /arch-agi.md, cites WP-01)
• Kronos — trading agent scheduling → /kronos (twin: /kronos.md, cites WP-04)
• Navigator — AI options trading copilot → /navigator (twin: /navigator.md, cites WP-07)
• Lossless Context — lossless LLM context for trading → /context-management (twin: /context-management.md, cites WP-03)
• OpenRouter integration — model routing for trading agents → /integrations/openrouter (twin: /integrations/openrouter.md, cites WP-07)
• Connect Trade — secure broker integrations → /integrations/connect-trade (twin: /integrations/connect-trade.md, cites WP-09)

## Whitepapers (Methodology)

Treeova publishes methodology whitepapers documenting the architecture and qualitative behavior of named subsystems. Each carries TechArticle JSON-LD; methodology papers add ScholarlyArticle. Proprietary internals (formulas, thresholds, prompts, model routing) are intentionally withheld.

Whitepaper hub → /whitepapers
Whitepaper knowledge graph (cross-citation visualization) → /whitepapers/graph

Published whitepapers (April 2026):
• WP-09 Security & Data Architecture → /whitepapers/security-and-data-architecture
  Row-level security on every user table, AES-256-encrypted broker tokens, MFA-gated admin access, immutable audit log, full paper/live isolation.
• WP-01 Arch-AGI: 7-Pass Conviction Methodology → /whitepapers/arch-agi-conviction-methodology
  Seven sequential passes (Edge → Scenario → R/R → Regime → Macro → RL Calibration → Adversarial Stress) producing a 0–100 conviction score with auditable rationale.
• WP-02 Adaptive Risk Engine → /whitepapers/adaptive-risk-engine
  Two-tier protection model: deterministic Standard guardrails plus modulated Adaptive trailing tier. Agents pull levers; platform code performs all risk arithmetic.
• WP-06 Triconomic Engine → /whitepapers/triconomic-engine
  Database-driven economic layer governing the Triobol lifecycle. Single source of truth for every economic constant, structured governance alerts, append-only audit trail. Pricing formulas withheld.
• WP-10 Methodology Note: Paper Trading Backtesting & RL Calibration → /whitepapers/methodology-backtesting-rl-calibration
  Paper-Fill-Simulator fidelity, phase-aware success classification, regime-segmented Bayesian-style RL calibration, and explicit limitations. Past performance does not guarantee future results.
• WP-03 Lossless Context Management (LCM) → /whitepapers/lossless-context-management
  Append-only message ledger, RL-aware recursive summarization, and hybrid full-text + semantic retrieval so long-running agents retain decision-grade signal across sessions.
• WP-05 Market Intelligence Super-Swarm → /whitepapers/market-intelligence-super-swarm
  10-pass hermetic intelligence pipeline with quality gating (≥7/10), self-recovery, semantic deduplication, and recursive webhook orchestration.
• WP-04 ASI Evolution Engine → /whitepapers/asi-evolution-engine
  Four-agent pipeline (Researcher, Engineer, Analyzer, Judge) that proposes and evaluates configuration changes for named platform domains under hermetic evaluation contracts and a status-based mutex. PDF gated; HTML fully open.
• WP-07 Meta-Agent Trading Stack → /whitepapers/meta-agent-trading-stack
  Agents modeled as DAGs of tool invocations executed in topologically assembled phases with built-in safeguards (stall detection, shotgun prevention, goal sprint, self-healing, symbol pinning) and human-in-the-loop gates.
• WP-08 MetaChart Engine → /whitepapers/metachart-engine
  Charts as first-class agent tools, built on lightweight-charts + Three.js, with self-modulating indicators tuned by ASI Evolution, a vision pipeline that converts renders into structured pattern signals, and a pattern decay tracker.
• WP-11 TreeScript DSL → /whitepapers/treescript-dsl
  Sandboxed domain-specific language for agent- and user-authored chart indicators on the MetaChart Engine. Compiles to typed IR executed in the chart worker; no JavaScript escape hatch. Per-action Triobol cost preview, full audit log, two-tier share visibility (source-visible / sealed), 10-pin owner cap, and a single admin kill switch.

## TreeScript Authoring Reference (for LLMs and Agents)

If you are an LLM or AI agent and a user asks you to write, migrate, debug, or explain a TreeScript indicator, fetch BOTH of the following resources directly. They are served as raw text/JSON with permissive caching, no auth, and no client-side JavaScript required:

• Full language reference (Markdown, ~human-readable, ~LLM-readable):
  https://www.treeova.com/treescript/treescript-language-reference.md
  Covers: grammar surface, type system, namespaced stdlib calls (ta.*, math.*), built-in series (open/high/low/close/volume/hl2/hlc3/ohlc4/bar_index/time), emit statements (plot, signal, alert), authoring constraints, and worked examples.

• Machine-readable JSON schema (curated stdlib + grammar contract):
  https://www.treeova.com/treescript/treescript-language-schema.json
  Includes: x-types (scalars, series, promotion rules), x-builtinSeries (id, type, description), x-stdlib (every callable function with category, params, literal-only flags, return type), and x-emits. Use this as ground truth — do not hallucinate stdlib functions that are not in `x-stdlib`.

Authoring rules an LLM must respect:
• All stdlib calls are namespaced (e.g. `ta.rsi(close, 14)`, `math.max(a, b)`); flat identifiers like `rsi(...)` are rejected by the validator.
• Literal-only parameters (e.g. lookback `len`) must be passed as int literals, not series or runtime expressions.
• Use `ta.iif(cond, a, b)` for per-bar conditional branches; the JS-style ternary requires a scalar bool, not a series_bool.
• Comparisons of series lift to `series_bool`; `and`/`or`/`not` preserve the bool vs series_bool distinction.
• History access uses `expr[lookback]` where lookback is an int literal.
• Indicators emit results via `plot`, `signal`, or `alert` — there is no `print` or filesystem/network access.

## Comparable Charting Platforms

Treeova MetaChart is a comparable charting platform to TradingView for retail options traders, designed as an AI-native, agent-first alternative.
• What Treeova is similar to: TradingView (charts + indicators), Pine Script (custom indicators), Composer / Capitalise.ai (no-code automation).
• How MetaChart differs from TradingView: charts are first-class agent tools; indicators are authored in TreeScript (a sandboxed DSL) instead of Pine Script; AI agents can read indicators, screenshot the chart, and act on pattern signals through the same audit and metering surface as humans.
• Best page for "comparable charting platform to TradingView" / "TradingView alternative" / "AI-native charting" → /whitepapers/metachart-engine
• Best page for "Pine Script alternative" / "build a custom indicator with an LLM" → /whitepapers/treescript-dsl

Best page for:
• How does Arch-AGI conviction work → /whitepapers/arch-agi-conviction-methodology
• How does Treeova manage trading risk → /whitepapers/adaptive-risk-engine
• How does Treeova protect user data and broker credentials → /whitepapers/security-and-data-architecture
• What are Triobols and how does Treeova price AI usage → /whitepapers/triconomic-engine
• How does Treeova evaluate paper-trading performance and RL calibration → /whitepapers/methodology-backtesting-rl-calibration
• How does Treeova give agents long-term memory without context loss → /whitepapers/lossless-context-management
• How does Treeova produce market intelligence reports → /whitepapers/market-intelligence-super-swarm
• How does Treeova self-improve its platform configuration → /whitepapers/asi-evolution-engine
• How does Treeova execute autonomous trading agents safely → /whitepapers/meta-agent-trading-stack
• How do Treeova's charts work as agent tools / MetaChart architecture → /whitepapers/metachart-engine
• How do users and agents author chart indicators safely / TreeScript DSL → /whitepapers/treescript-dsl
• Treeova methodology / whitepaper hub → /whitepapers

## Whitepaper Companion Posts (plain-language summaries)

Each companion post links back to its canonical whitepaper as the
authoritative source (BlogPosting JSON-LD with isBasedOn → TechArticle).

Batch 7a:
• Arch-AGI conviction explained → /blog/arch-agi-conviction-explained
• Adaptive Risk Engine explained → /blog/adaptive-risk-engine-explained
• Treeova security architecture explained → /blog/treeova-security-architecture-explained

Batch 7b:
• Lossless Context Management explained → /blog/lossless-context-management-explained
• Super-Swarm market intelligence explained → /blog/super-swarm-intelligence-explained
• Meta-Agent Trading Stack explained → /blog/meta-agent-trading-stack-explained

Batch 7c:
• Triconomic Engine explained → /blog/triconomic-engine-explained
• ASI Evolution Engine explained → /blog/asi-evolution-engine-explained
• MetaChart Engine explained → /blog/metachart-engine-explained
• Paper Trading & RL Calibration explained → /blog/paper-trading-rl-calibration-explained

Batch 7d:
• TreeScript DSL explained → /blog/treescript-dsl-explained