Example notebooks for building and testing strategies.

Shows one signal becoming one strategy, one verified backtest, and one paper-tracked run.

• How one signal becomes a concrete strategy in code.
• How a local draft differs from the verified run that becomes canonical.

Combines signals into a market-neutral strategy, verifies the backtest, then starts paper tracking.

• Neutralisation against broad crypto beta and venue concentration.
• Turnover controls and portfolio caps for fast reversion signals.

Uses a regime signal as a live risk gate around a slower carry strategy.

• How a context signal changes sizing and exposure rather than picking trades directly.
• Where carry does and does not survive in crypto perps.

Shows how a customer-owned momentum signal can use Alphora construction, risk controls, and paper tracking.

• How to combine Alphora signals with a customer-owned score.
• How to target realised risk without leaking future data.

Shows how Alphora's universe tools preserve point-in-time correctness before anything reaches a verified backtest.

• How to request as-of correct universe snapshots.
• How to avoid look-ahead through symbol remapping and liquidity filters.

A short reference for getting an LLM or agent to compose the library, submit verified backtests, and inspect paper results safely.

• How to constrain the lower-level API to point-in-time semantics.
• Which fields an agent should verify before composing a downstream strategy.