How to develop an algorithmic trading strategy

A strategy should begin with a question about market behaviour, not with a search for the perfect indicator. For example: do pullbacks in a defined trend behave differently from pullbacks against that trend, after realistic trading costs? A question gives the research a boundary.

Write down the instrument, timeframe, holding period, trade direction, entry condition, exit condition, and risk rule before running a large sweep. If the rules cannot be explained in plain language, they will be difficult to audit later.

A first test should contain the fewest rules needed to answer the question. Adding filters until the equity curve looks smooth can create a strategy that describes the past rather than a behaviour that may persist.

Use a small number of economically or structurally motivated variables. Technical indicators can be useful measurements, but they are not explanations on their own.

After a candidate looks promising, stop changing it and test it on data that did not influence the rules. Walk-forward analysis, parameter perturbation, and execution stress tests are useful because they challenge the assumptions that made the candidate attractive.

A strategy that only works after repeated tuning is not ready for live money. The goal is not to make every historical period profitable; it is to understand where the logic works, where it fails, and whether the risk is acceptable.

A validated backtest is a reason to run a controlled demo or very small live test, not a reason to jump to maximum risk. Compare fills, spreads, timing, missed signals, and platform behaviour with the assumptions in the research report.

If live execution differs materially from the test, pause and investigate the difference before changing the strategy. Do not use larger risk to compensate for weak evidence.