Signal and portfolio design

What is premium reversion in crypto perps?

Premium reversion is a short-horizon idea that fades stretched perp pricing when leverage, funding pressure, and local fair-value dislocations start to mean revert.

What is a regime filter in systematic trading?

A regime filter is a context signal that decides when a strategy should trade, size down, or step aside because the environment has changed.

What is funding rate in crypto perps?

Funding rate is the periodic payment mechanism that helps keep perpetual futures anchored to spot. It also acts as a live read on crowding, leverage pressure, and when carry is rich enough to matter.

What is a market-neutral strategy in crypto?

A market-neutral strategy tries to remove broad market direction so returns come from spread behavior, relative value, or feature selection instead of simply being long or short crypto beta.

What is position sizing in systematic trading?

Position sizing is the rule that decides how much risk a strategy is allowed to put behind an idea. It often matters as much as the signal itself because bad sizing can turn a decent edge into an untradeable equity path.

How do binary signals differ from continuous signals in systematic trading?

A binary signal outputs a discrete action or state like trade or do not trade. A continuous signal outputs a score, rank, probability, or target exposure. That difference is about signal representation, not about whether the instrument itself is a binary option or event market.

Can you build a binary signal from continuous indicators?

Yes. A binary signal often sits downstream of continuous indicators like z-scores, spreads, forecasts, or probabilities. The key is deciding where the threshold should live and whether the conversion destroys useful information.

How do you backtest a binary signal strategy?

Backtesting a binary signal strategy means testing the trigger rule, holding logic, re-entry policy, and trading costs together. If you only measure whether the signal would have been directionally right, you usually miss the real strategy behavior.

What metrics matter most for binary signal strategies?

For binary signal strategies, hit rate alone is not enough. You need to know base rate, calibration, average edge after costs, opportunity frequency, turnover, and whether the trigger is adding value beyond a simpler continuous-score policy.

Why can a 60 percent signal still be a bad trade?

Because probability is not expected value. A 60 percent signal can still be bad if the payoff is poor, the model is miscalibrated, the costs are too high, or the trigger only appears in the noisiest parts of the market.

When should a continuous score become a binary trade trigger?

When should a continuous score become a binary trade trigger is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

How does conviction sizing differ from equal weighting?

conviction sizing differ from equal weighting and signal and portfolio design solve related but different problems. The practical difference shows up in what each one controls, what evidence it needs, and how it changes the downstream trading rule.

How does rebalancing differ for binary and continuous signals?

Binary signals often produce state changes like in or out, while continuous signals naturally suggest proportional resizing. The key difference is not that one must rebalance on a fixed schedule and the other must not. It is that the rebalance rule should match signal decay, trading cost, and the information lost or kept by the signal format.

What are signal thresholds?

signal thresholds is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Why do signal thresholds matter in systematic trading?

signal thresholds is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

What is conviction sizing?

conviction sizing is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Why does conviction sizing matter in systematic trading?

conviction sizing is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

What are exposure caps?

exposure caps is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Why do exposure caps matter in systematic trading?

exposure caps is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

What are portfolio sleeves?

portfolio sleeves is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

Why do portfolio sleeves matter in systematic trading?

portfolio sleeves is one of the core ideas inside signal and portfolio design. It matters because it changes how a researcher turns a clean intuition into a repeatable rule about selection, sizing, timing, or validation.

How do you turn a raw score into a position size?

You turn a raw score into a position size by turning the idea into a repeatable decision rule, attaching realistic turnover and risk constraints, and checking whether the workflow still holds up once the flattering assumptions are removed.

What should you validate before you turn a raw score into a position size?

The most useful ways to evaluate turn a raw score into a position size are the ones that show whether it still works after costs, timing, turnover, and portfolio context are included, not just whether one chart looks cleaner in hindsight.

How do you combine multiple signals in one portfolio?

You combine multiple signals in one portfolio by turning the idea into a repeatable decision rule, attaching realistic turnover and risk constraints, and checking whether the workflow still holds up once the flattering assumptions are removed.

What should you validate before you combine multiple signals in one portfolio?

The most useful ways to evaluate combine multiple signals in one portfolio are the ones that show whether it still works after costs, timing, turnover, and portfolio context are included, not just whether one chart looks cleaner in hindsight.