Volume Weighted Trend (VWT)

Warmup Window

Minimum bars needed: vwma_length bars (default params: 34 bars (vwma_length=34))

The VWMA needs vwma_length bars of price and volume data. The ATR band also uses the same period. After warmup, the indicator updates in real-time.

✅ After the warmup window is filled, this indicator produces a new value on every incoming bar in real-time.

Lagging Indicator

The VWMA center line and trend signals lag behind price reversals.

Event	Lag	Detail
VWMA center line reacts to price reversal	≈ `vwma_length / 2` bars	VWMA has lag ≈ vwma_length/2
Upper/lower bands react	≈ `vwma_length / 2` bars	Bands use ATR with same period as VWMA
Trend flips bullish ↔ bearish	≈ `vwma_length / 2` bars	Trend determined by close vs VWMA
Buy / sell signal fires	≈ `vwma_length / 2` bars	Signal fires on the bar the trend flips

Formula for custom params: lag ≈ vwma_length / 2

The Volume Weighted Trend indicator uses a Volume Weighted Moving Average (VWMA) with ATR-based volatility bands to determine trend direction. Based on the "Volume Weighted Trend [QuantAlgo]" concept. The VWMA serves as the trend baseline, while upper and lower bands (VWMA +/- ATR * multiplier) define breakout thresholds. The trend flips bullish when price closes above the upper band and bearish when price closes below the lower band.

def volume_weighted_trend(
    df: Union[PdDataFrame, PlDataFrame],
    vwma_length: int = 34,
    atr_multiplier: float = 1.5,
    high_column: str = "High",
    low_column: str = "Low",
    close_column: str = "Close",
    volume_column: str = "Volume",
) -> Union[PdDataFrame, PlDataFrame]:

Returns the following columns:

vwt_vwma: Volume Weighted Moving Average
vwt_atr: Average True Range
vwt_upper: Upper volatility band (VWMA + ATR * multiplier)
vwt_lower: Lower volatility band (VWMA - ATR * multiplier)
vwt_trend: Trend direction (+1 bullish, -1 bearish, 0 undefined)
vwt_trend_changed: 1 on bars where trend flipped, 0 otherwise
vwt_signal: +1 on bullish flip, -1 on bearish flip, 0 otherwise

Example

from investing_algorithm_framework import download

from pyindicators import volume_weighted_trend, get_volume_weighted_trend_stats

pd_df = download(
    symbol="btc/eur",
    market="bitvavo",
    time_frame="4h",
    start_date="2024-01-01",
    end_date="2024-04-01",
    pandas=True,
)

# Calculate Volume Weighted Trend
pd_df = volume_weighted_trend(pd_df, vwma_length=34, atr_multiplier=1.5)

# Get summary statistics
stats = get_volume_weighted_trend_stats(pd_df)
print(stats)

pd_df[["Close", "vwt_vwma", "vwt_upper", "vwt_lower", "vwt_trend", "vwt_signal"]].tail(10)

VOLUME_WEIGHTED_TREND

Momentum and Oscillators

Indicators that measure the strength and speed of price movements rather than the direction.

Chart Parameters

The image above uses the following parameters:

Parameter	Value
`vwma_length`	`34`
`atr_multiplier`	`1.5`

Momentum and Oscillators​

Momentum and Oscillators