How do you build a systematic trading strategy?

Start with a hypothesis about why a market behavior should exist. Translate it into measurable inputs, define portfolio and risk rules, test the idea under realistic costs and constraints, then decide whether the evidence is strong enough to trade or continue researching.

A good strategy begins as a falsifiable claim, not a vague chart pattern. For example: extreme funding plus stable liquidity may predict short-term carry opportunity in a crypto perps basket.

The rules should specify the universe, signal calculation, rebalance cadence, position sizing, exposure caps, transaction cost model, and stop conditions. If two researchers cannot reproduce the same positions from the same data, the strategy is not yet systematic.

Validation means checking more than the final return. Look at drawdowns, turnover, capacity, slippage sensitivity, regime dependence, parameter stability, and whether the strategy survives out-of-sample periods.

Suppose the hypothesis is that rich funding and a stretched perp basis create a carry opportunity. The research path should begin with the economic reason the edge might exist, then move into data availability, point-in-time alignment, hedge design, execution assumptions, and portfolio limits.

Only after those pieces are explicit should the trader compare performance against a simple baseline, such as a static carry screen or an equal-weighted market-neutral basket.

A strategy is not ready for automation simply because it has a strong historical chart. It needs a written specification, known failure modes, monitoring thresholds, and a plan for what happens when live behavior diverges from research.

Alphora is designed around reusable signals, explicit strategy specs, validation artifacts, and controlled paths from research toward trading. That makes the workflow easier to inspect than a one-off notebook or prompt transcript.