strategy-framework
Strategy Framework provides a standardized template for documenting trading strategies with precise, machine-testable rules. It enforces discipline through structured sections covering edge hypotheses, entry/exit conditions, position sizing, risk guardrails, and performance thresholds, enabling reproducible backtesting and live trading validation.
Strategy Framework lets you define trading strategies with testable entry/exit rules and risk parameters.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-06-24
Strategy Framework lets you define trading strategies with testable entry/exit rules and risk parameters. Strategy Framework provides a standardized template for documenting trading strategies with precise, machine-testable rules. It enforces discipline through structured sections covering edge hypotheses, entry/exit conditions, position sizing, risk guardrails, and performance thresholds, enabling reproducible backtesting and live trading validation.
Use it when
- Strategy Framework requires you to document entry and exit conditions as explicit, machine-testable rules rather than vague guidelines.
- Strategy Framework establishes portfolio-level risk controls and position sizing rules as core components.
Verify before relying
Read SKILL.md below before installing (5 files). Open directory: indexed for reading, not audited.
Install
agiprolabs/claude-trading-skills/strategy-framework · repository language: Python
Open directory. Skills are indexed for reading, not audited. Review a skill's body before installing it.
Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I create a trading strategy template with strategy-framework?
Strategy Framework provides a standardized template for documenting trading strategies with precise, machine-testable rules. The template enforces discipline through structured sections covering edge hypotheses, entry/exit conditions, position sizing, risk guardrails, and performance thresholds. This enables reproducible backtesting and live trading validation by ensuring every strategy component is explicitly defined and testable before deployment.
How can I define entry and exit rules for trading with this framework?
Strategy Framework requires you to document entry and exit conditions as explicit, machine-testable rules rather than vague guidelines. You specify the exact price levels, indicators, or market conditions that trigger entry, along with corresponding exit rules including stop-loss and take-profit thresholds. This precision enables automated backtesting and eliminates ambiguity during live trading execution.
What position sizing and risk management framework does strategy-framework offer?
Strategy Framework establishes portfolio-level risk controls and position sizing rules as core components. You define position size based on account risk, volatility, or fixed percentages, then set guardrails for maximum drawdown, correlation limits, and aggregate exposure. These rules ensure disciplined capital allocation and prevent catastrophic losses across your strategy portfolio.
How do I backtest a trading strategy with historical data using this tool?
Strategy Framework enables backtesting by enforcing documented entry/exit rules and position sizing that can be applied to historical data. Your strategy's testable rules feed directly into validation workflows, allowing you to measure performance metrics and walk-forward validation before live trading. This reproducible approach reveals curve-fitting risks and confirms edge validity.
What is the strategy lifecycle from hypothesis to retirement in strategy-framework?
Strategy Framework implements a disciplined lifecycle: start with a testable hypothesis about market inefficiency, document entry/exit rules and risk controls, backtest against historical data, validate with paper trading, deploy to live trading with monitoring thresholds, and finally retire the strategy when performance degrades or market regime shifts. Each stage has defined gates and performance criteria.
How does strategy-framework help me avoid curve fitting in backtesting?
Strategy Framework enforces walk-forward validation and out-of-sample testing by structuring your strategy documentation upfront, before optimization. By separating hypothesis definition from parameter tuning, and requiring explicit performance thresholds, the framework discourages over-optimization to historical data. Strategy retirement rules also trigger when live performance diverges from backtest results, signaling regime change or overfitting.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
Strategy Framework
A standardized system for defining, documenting, testing, and managing trading strategies. This skill provides templates and tools that enforce discipline, enable reproducibility, and make strategies testable.
Why a Strategy Framework Matters
Trading without a written strategy framework leads to: - Inconsistency: ad-hoc decisions driven by emotion rather than rules - Untestability: vague ideas that cannot be backtested or evaluated - Scope creep: strategies that drift without version-controlled definitions - Unmanaged risk: missing stop losses, position limits, or drawdown halts
A strategy framework forces you to: 1. State a falsifiable hypothesis about a market inefficiency 2. Define precise, machine-testable entry and exit rules 3. Specify position sizing and risk parameters before trading 4. Set minimum performance criteria for continuation or retirement 5. Track changes through versioned strategy documents
Strategy Definition Template
Every
(truncated - see the full file via the links below)
File tree — 5 files
skills/strategy-framework/SKILL.md
skills/strategy-framework/references/strategy_template.md
skills/strategy-framework/references/strategy_types.md
skills/strategy-framework/scripts/define_strategy.py
skills/strategy-framework/scripts/strategy_scorecard.py
Let your AI agent find skills like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 56,283 agent skills by what they can do, searchable in plain language.
wish › “Define and document a trading strategy with testable entry/exit rules”
Give your agent the search over MCP, or paste the wish link into any chat. No install? Search from any chat →
Related skills
Validate trading strategies against historical market data before deploying live. This skill runs backtests on crypto perpetual and Polymarket prediction strategies, returning key performance metrics like Sharpe ratio, maximum drawdown, win rate, and profit factor. Execution is fast (20-60 seconds) and inexpensive ($0.001 per test).
Deploy Live Trading launches your backtested strategies into live trading on Hyperliquid, handling deployment creation, monitoring, and control across EOA wallets or professional vaults. The skill manages real capital with strict safeguards: it requires thorough backtesting (6+ months, Sharpe >1.0, drawdown <20%), enforces pre-deployment checklists, and provides tools to start, stop, and track active trades. Essential for traders ready to move from simulation to production.
Test trading strategies against historical data with built-in performance metrics including Sharpe, Sortino, and max drawdown calculations. Includes 8 pre-built strategies and parameter optimization via grid search to find the best-performing configurations.
Validate trading strategies using historical market data before deploying real capital. This skill includes eight built-in strategies and calculates key performance metrics like Sharpe ratio, Sortino ratio, and maximum drawdown alongside trade-by-trade analysis and equity curve visualization. Optimize strategy parameters through grid search to find the best-performing configurations.
Browse Robonet Data provides instant access to trading resources through eight lightweight exploration tools. Query available trading pairs, 170+ technical indicators, Allora ML prediction topics, existing strategies, and backtest performance—all executing in under 1 second with minimal cost. Start here to understand what data and tools are available before designing or testing strategies.
Crypto Backtest transforms natural language trading ideas into validated strategies with professional backtesting and detailed reports. It combines multiple indicators—RSI, moving averages, Bollinger Bands, and more—to build robust entry and exit conditions for spot trading only. Users describe their strategy, confirm the generated plan, and receive comprehensive backtest results.
More skills position-sizing (MIT) · risk-management (MIT) · portfolio-analytics (MIT) · Design Trading Strategies (unlicensed)