algorithmic-trading
Build algorithmic trading systems: backtesting, strategy design, order execution, risk management, and market data pipelines. Use for quantitative finance.
Target: Design & content creation. Automatically collected · unverified.
Excerpt from the author’s documentation
Extracted from a fixed version of SKILL.md in its original language. This is the author’s documentation; installation, compatibility and runtime results have not been verified by this site.
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Topics in the source
- Algorithmic Trading
- Quick Start
- When to Use
- Step-by-Step
- 1. Market Data Pipeline
- 2. Strategy Design
- 3. Backtesting Engine
- 4. Risk Management
Building algorithmic trading systems with backtesting, strategies, and risk management. Building automated trading systems for stocks, crypto, or futures Backtesting trading strategies before live deployment When manual trading is too slow or emotional Need for systematic risk management Always backtest before live trading Include slippage and commission in backtests Use walk-forward analysis to avoid overfitting Implement circuit breakers for extreme market conditions Log everything — trades, signals, errors Start with paper trading before real money Overfitting to historical data (curve fitting) Ignoring transaction costs and slippage Look-ahead bias in backtests Not handling market data gaps or errors Running without risk limits
- Source repository
- ssrjkk/agent-skills
- Source file
- .claude/skills/finance/algorithmic-trading/SKILL.md
- Fixed revision
- 868975614072b553f929cb32c52c5e7dc4ba8734
Sources and evidence
This record was collected automatically. Its documentation, suitability and runtime behavior have not been manually verified.
Author: ssrjkk/agent-skills · original repository metadata. Documentation review: Not reviewed. Last source check: 2026-11-11T03:01:42.632Z.
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MIT. Third-party content and copyright remain with their authors. Refer to the original license and terms.
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