# vol_target_trend / 3

Universe-generic version: META["universe"] = ["*"]. The symbols are not written in the strategy; they are the ones the agent trades (1 to 12 market symbols), passed by the engine with the candles and by --symbols to the backtest. Same rule and same parameters as version 1: nothing was re-tuned, for any universe. Each symbol is judged on its own trend and its own realised volatility; N is the number of symbols the agent chose.

What a generic universe changes. The code hash still pins the behaviour, and the agent's mandate pins the assets. But the evidence does not travel: a backtest report is attached to ONE agent and records the symbols it was run on (config.symbols); a result on one universe says nothing about another, and every universe tried is one more trial. Symbols with a short history shorten the common backtest period for all of them.

Hypothesis. Volatility is persistent and high volatility is not rewarded with proportionally higher returns, so sizing a trend position inversely to recent realised volatility gives a smoother ride (smaller drawdowns for a similar return) than a fixed-size trend position.

Rule. Same trend signal as ma_trend/1 (72-bar mean above 480-bar mean). When it is on, the weight is (1/N) * min(1, target_vol / realised_vol), with realised_vol the annualised standard deviation of the last 168 hourly log-returns and target_vol = 40 % per year. There is no leverage: the weight is capped at 1/N, so the strategy can only reduce risk, never add to it.

What would refute it. If volatility spikes mostly AFTER the losses, scaling down comes too late and only removes exposure to the rebound. The weight moves a little every hour; without a rebalance threshold this generates many small trades and costs.