Signals and Features

This page covers model-side feature engineering and signal composition before framework allocation.

Primary signals

Signal	Weight	Meaning
MVRV value	70%	Lower MVRV increases buy intensity
Price vs MA	20%	Below 200-day MA increases buy intensity
4-year percentile	10%	Cycle-context signal

Feature construction

200-day moving average

price_ma = price.rolling(200, min_periods=100).mean()
price_vs_ma = (price / price_ma) - 1

MVRV Z-score and gradient

mvrv_zscore = (mvrv - rolling_mean(365)) / rolling_std(365)
mvrv_gradient = tanh(mvrv_zscore.diff(30).ewm(span=30).mean() * 2)

4-year percentile and volatility

mvrv_percentile = rolling_percentile(mvrv, window=1461)
mvrv_volatility = percentile_rank(mvrv_zscore.rolling(90).std())

Leakage prevention

Signal columns are shifted by one day so weight for day t uses only information available through t-1.

signal_cols = [
    "price_vs_ma",
    "mvrv_zscore",
    "mvrv_gradient",
    "mvrv_percentile",
    "mvrv_acceleration",
    "mvrv_zone",
    "mvrv_volatility",
]
features[signal_cols] = features[signal_cols].shift(1)

Dynamic multiplier structure

combined = value_signal * 0.70 + ma_signal * 0.20 + pct_signal * 0.10
combined *= acceleration_modifier
combined *= confidence_boost
combined *= volatility_dampening
dynamic = exp(clip(combined * DYNAMIC_STRENGTH, -5, 100))

See also

• Allocation Kernel
• Backtest Runtime