From English to Autonomous Agent
Describe your strategy in plain language. YorN generates inspectable code, validates it, and lets you test before deployment.
Your Words Become Working Code
Start with a natural-language description of your strategy. YorN parses your intent, maps it to market data sources and signal logic, then generates a complete Python strategy class that plugs directly into the execution engine.
The generation pipeline handles everything: importing the right market data feeds, building evaluation logic, sizing positions with Kelly criterion, and wiring up risk management -- all from a single prompt.
- Supports any prediction market category
- Automatically selects relevant data sources
- Generates position sizing and risk limits
- Full code review available before deployment
# Strategy: Swing State Momentum
class SwingStateMomentum(BaseAgent):
name = "swing_state_momentum"
version = "1.0.0"
category = "politics"
def configure(self):
self.lookback = 7 # days
self.threshold = 3.0 # points
self.max_position = 0.05
def evaluate(self, market):
polls = self.fetch_polls(
state=market.state,
days=self.lookback
)
shift = self.momentum(polls)
if shift >= self.threshold:
return Signal(
side="YES",
size=self.kelly(shift),
confidence=shift / 10
)
AST Safety Validation
Every generated strategy passes through a rigorous Abstract Syntax Tree validation layer before it can execute. The AST walker inspects every node of the generated code, verifying that it only calls approved functions, accesses approved modules, and stays within the sandboxed execution environment.
Dangerous operations like file system access, network calls outside the approved API list, dynamic code evaluation, and process spawning are automatically detected and blocked. You get the power of AI-generated code with the safety of a locked-down sandbox.
- Every AST node inspected before execution
- Blocked: eval, exec, os, subprocess, network
- Allowlisted API calls only
- Transparent validation report for every build
Deploy to Paper Without Build Work
Once validated, your agent can enter paper-trading mode from the product UI. No build pipelines, no infrastructure provisioning, no DevOps tickets. Click a button and your agent starts scanning markets and generating signals.
Every agent can start with simulated funds. Set your own review criteria -- minimum number of trades, drawdown limits, or metric thresholds -- then decide when live trading should be enabled on a supported paid tier.
- Instant paper-trading deployment
- Custom graduation thresholds for live trading
- Automatic or manual promotion pipeline
- Real-time performance monitoring from day one
Strategy Lab: Describe It, AI Builds It
The Strategy Lab is a conversational AI workspace with 9 specialized tools. Describe your trading idea in plain English
and the AI generates a complete Python DirectionalStrategy subclass with signal logic,
position sizing, and risk rules. Backtest it instantly, run crowd simulations, compare strategies — then deploy as a live agent directly from the chat.
Momentum
Poll-driven momentum signals
Sentiment
News and social sentiment analysis
Mean Reversion
Contracts that overshoot fair value
Arbitrage
Cross-market price discrepancies
Custom
Build anything from scratch
Contrarian
Fade extreme market positions
A Conversational AI Workspace
The Strategy Lab is more than a chat. It's an AI workspace with 9 specialized tools that the AI can call autonomously during your conversation — backtesting strategies, running crowd simulations, generating Python code, comparing performance, and deploying agents. All from a single chat interface.
- generate_strategy_code — AI writes a full Python DirectionalStrategy subclass
- backtest_custom_code — Run the generated code through the backtest engine instantly
- run_crowd_simulation — Simulate 10K market participants on any event
- deploy_as_agent — Turn an approved strategy into a managed agent
- compare_strategies — Head-to-head performance comparison with metrics
that buys underdogs when they're down 10+ points
[tool_call] generate_strategy_code
Writing DirectionalStrategy subclass...
[tool_call] backtest_custom_code
Sharpe: 1.42 | Win Rate: 63% | Return: +18.5%
AI: Strategy backtested successfully. 63% win rate
with a 1.42 Sharpe. Want me to deploy it?
[tool_call] deploy_as_agent
Agent "NBA Underdog Momentum" deployed.
Describe a Strategy. Deploy an Agent.
No coding skills required. Your first agent is free to build and paper trade.