Taker Intensity Guide

One of Blave's core alpha indicators — what it measures, why it matters, and three ways to build strategies with it.

What is Taker Intensity?

Taker Intensity (TI) is a Blave proprietary indicator that measures whether futures traders are aggressively going long or aggressively going short, compared with the coin's usual level. "Aggressive" means the side that does not wait with a resting order but takes the price to fill right away.

No upper or lower bound: around 0 is the normal level, and the larger the absolute value, the more unusual. Beyond ±2 is rare and beyond ±3 extreme, on the same scale for every coin. Scan your thresholds with a backtest. The same buying pressure might produce only TI +0.5 in an active market but +2.5 in a quiet one. Blave offers TI on six timeframes: 15min, 1h, 4h, 8h, 24h, 3d. Longer timeframes are smoother and suit position strategies; shorter ones react faster but carry more noise.

The critical nuance: high TI ≠ always bullish

This is where most people misread the indicator. Aggressive buying has two sources:

1. Genuine buyers — traders who want to go long and are willing to pay the ask.
2. Short liquidations (forced closes) — when a short position is force-closed, the exchange buys it back. That forced buying also pushes TI up, but nobody is actually bullish.

Three ways to use Taker Intensity

1. Threshold (most common)

Enter long when TI exceeds an entry threshold; exit when TI falls below an exit threshold. The dead zone between the two thresholds prevents the strategy from flip-flopping when TI hovers near a single level.

ENTRY_TH = 1.686
EXIT_TH  = -0.473

def compute_signals(df, entry_th=None, exit_th=None):
    eth = entry_th if entry_th is not None else ENTRY_TH
    xth = exit_th  if exit_th  is not None else EXIT_TH
    ti  = df['TI']
    signal = pd.Series(np.nan, index=df.index)
    signal[ti > eth] = 1.0   # enter long
    signal[ti < xth] = 0.0   # go flat
    return signal

This is exactly the logic in the btc_ti_5min example — TI on a 24h window, checked every 5 minutes. The ENTRY_TH = 1.686 and EXIT_TH = -0.473 values were found by parameter scanning, not guessed.

A separate independent backtest (2022/01–2025/12, long above TI 1.8 / short below -1.8 — a different threshold from btc_ti_5min) posted +1000.3% total return, -27.74% max drawdown, and a 1.682 Sharpe ratio across both bull and bear regimes. The two parameter sets differ but point the same direction — evidence the TI momentum filter holds up across thresholds, not a guarantee any threshold works. Always scan and verify your own.

2. Crossover (simpler, often noisier)

Treat the zero line as the signal threshold. Long when TI crosses above zero, flat when it crosses below. This is intuitive but tends to generate many short-lived trades on 15min or 1h timeframes, running up fees. Works better on 8h or 24h windows where crossovers are more meaningful.

signal[ti > 0] = 1.0
signal[ti < 0] = 0.0

3. Regime filter (most powerful)

Use TI as a gate for another primary signal. Only take entries when TI is positive (net buying pressure exists). This prevents trend strategies from entering into heavy selling environments.

primary = compute_primary_signals(df)  # e.g. SMA cross
ti_positive = (df['TI'] > 0)
signal = primary.copy()
signal[~ti_positive] = 0.0  # suppress entries when TI is negative

Regime filtering typically reduces trade count and fees while improving Sharpe, at the cost of missing some trades. It works best with TI on longer timeframes (8h, 24h) as a macro filter on shorter-interval strategies.

Choosing the right timeframe

In btc_ti_5min, the strategy runs every 5 minutes but uses 24h TI. This is intentional: you want the speed of a 5-minute check (react quickly to TI crossing a threshold) without the noise of a 5-minute TI window.

Combining TI with other indicators

TI in isolation is useful. TI combined with Blave's other alpha indicators is powerful: