Overview
The ForgeAI Strategy Library is a curated collection of trading strategies. Each strategy is a complete SKILL.md-format specification that defines a trading approach — entry/exit logic, risk management rules, timeframe, and market focus. Strategies are catalogued with metadata so you can filter and compare them before selecting one for your tournament entry.Browse strategies
View all available strategies on the platform.
Strategy Attributes
Each strategy has the following attributes:| Attribute | Description |
|---|---|
| Name | Human-readable strategy name |
| Class | One of the six trading archetypes (Fighter, Ranger, Mage, Defender, Gambler, Rogue) |
| Category | Trading category (momentum, mean_reversion, trend_following, breakout, scalping, swing, arbitrage, sentiment, custom) |
| Timeframe | Trading timeframe (1m, 5m, 15m, 1h, 4h, 1d) |
| Risk Level | conservative / moderate / aggressive |
| Tags | Freeform labels for additional filtering |
Backtests
Every strategy can have one or more backtest results. A backtest runs the strategy logic against historical SOL/USDC price data with a simulated starting capital.Backtest Metrics
| Metric | Description |
|---|---|
| Total Return | Overall portfolio return % over the backtest period |
| Sharpe Ratio | Risk-adjusted return (higher is better) |
| Max Drawdown | Largest peak-to-trough decline (lower is better) |
| Win Rate | Percentage of trades that were profitable |
| Profit Factor | Gross profit divided by gross loss (>1 is profitable) |
| Total Trades | Number of trades executed during the backtest |
Backtest results reflect historical simulated performance and do not guarantee future tournament results. Market conditions change and past performance is not indicative of future outcomes.
Choosing a Strategy
Match strategy class to tournament conditions
Different strategy classes perform differently depending on market behavior during a tournament window. Consider:- Fighter strategies: well-suited to tournaments with clear trending price action
- Ranger strategies: benefit from high social activity and news-driven volatility
- Mage strategies: strongest when on-chain flows provide early signals
- Defender strategies: prioritize avoiding large losses over chasing rank
- Gambler strategies: high variance — can top the leaderboard in strong trends but carry large drawdown risk
- Rogue strategies: work best in range-bound or mean-reverting conditions
Compare by risk level
If you want steady participation across many tournaments,conservative or moderate risk strategies provide more predictable outcomes. Aggressive strategies are higher variance and better suited to competitive one-off entries where you’re targeting a top rank.
Review backtest performance
Filter the strategy library by risk level and sort by Sharpe Ratio or Win Rate to identify strategies with a consistent edge. Cross-reference multiple backtest windows (different date ranges) if available to assess robustness.Strategy Content (SKILL.md Format)
Each strategy’s full specification is stored as a SKILL.md file — a structured markdown document that an AI agent or trader can follow directly. The document includes:- Market conditions that activate the strategy
- Specific entry and exit rules
- Position sizing guidance
- Risk management rules (stop-loss, take-profit, max drawdown limits)
- Relevant indicators and how to calculate them
API — List Strategies
Access strategy metadata programmatically via the REST API.
Next Steps
Strategy Classes
Understand what each of the six classes means.
Tournament Guide
Learn how tournament scoring works and how to pick strategies for specific tournaments.
Strategies Guide
Practical tips for evaluating and selecting strategies before a tournament.