sense / sense_tick

sense(candles, vol_period=20)

Convert a window of OHLCV candles into STRATA semantic signals.

Args:

Param	Type	Default	Description
candles	list[dict]	—	OHLCV dicts, oldest→newest. Min 3. Optional: bid, ask keys.
vol_period	int	20	Lookback for volume/volatility normalisation

Returns: dict

Key	Range	Description
trend	[-1, 1]	EMA crossover direction, ATR-normalised
vol	[0, 1]	Volatility intensity (ATR / median price)
liquidity_above	[0, 1]	Spread z-score (if bid/ask) or volume spike
break_structure	[0, 1]	Close beyond prior N-bar high/low
source	str	"spread" | "volume" | "none"

Example:

from strata import sense

# Volume-based (reactive)
signals = sense(candles)
# {"trend": 0.12, "vol": 0.03, "liquidity_above": 0.15, "break_structure": 0.0, "source": "volume"}

# Spread-based (anticipatory) — add bid/ask
candles[-1]["bid"] = 150.18
candles[-1]["ask"] = 150.22
signals = sense(candles)
# {"trend": 0.12, "vol": 0.03, "liquidity_above": 0.61, "break_structure": 0.0, "source": "spread"}

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sense_tick(candles, vol_period=20)

Extended sense for full tick-level data. Adds spread_pressure and side_imbalance.

Requires: bid, ask fields per candle. Optional: bid_size, ask_size.

Returns: All sense() outputs plus:

Key	Range	Description
spread_pressure	[0, 1]	Current spread vs historical norm
side_imbalance	[-1, 1]	+1 = ask-heavy (sell), -1 = bid-heavy (buy)

Example:

from strata import sense_tick

candle = {
    "open": 150.0, "high": 150.5, "low": 149.8, "close": 150.2,
    "volume": 1_000_000,
    "bid": 150.18, "ask": 150.22,
    "bid_size": 2500, "ask_size": 800,
}
signals = sense_tick([...window..., candle])
# signals["spread_pressure"] = 0.12
# signals["side_imbalance"]  = -0.51   (bid-heavy = buy pressure)