A Framework for Finding and Evaluating Algorithmic Trading Strategies
Summary
The article lays out a process for finding, screening, and preparing algorithmic trading ideas for backtesting. It begins with personal constraints: temperament, available time, capital, programming ability, and whether the trader needs regular income or can pursue longer-term gains. These factors influence suitable strategy frequency and the infrastructure a trader can manage. It also stresses ongoing research and the discipline to avoid interfering with a system during drawdowns.
For sourcing ideas, it recommends building a repeatable pipeline from textbooks, blogs, forums, and academic papers, while screening claims with data rather than preference or reputation. Before testing, researchers should check data availability, quality, cost, and implementation details, then account for fees, spread, and slippage. The article notes that published studies may rely on stale or expensive data, illiquid markets, or incomplete trading assumptions. It offers general guidance rather than a specific tested strategy, and its claims about capital needs and strategy frequency are contextual opinions, not universal rules.
Key ideas
- Choose strategy frequency and complexity to fit your temperament, time, capital, and income needs.
- Treat idea sourcing as a repeatable pipeline and screen strategies objectively.
- Replicate published methods and include realistic transaction costs before judging performance.
- Historical data quality, cost, and trading assumptions can make an otherwise interesting strategy impractical.
- Ongoing research and discipline are needed to sustain an algorithmic trading process.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.