TIB — Tick Imbalance Bars Indicator
Detect Market Information Before Price Moves
Limited-time introductory pricing for early adopters - secure your copy now at a reduced rate before the price increases to standard retail pricing once the initial release period ends.
Version 1.0
Initial Release Version
Formulas and Methods - Correct and Complete adapted for cTrader
Updates and Guides Coming Soon
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Available for Video Session with Setup Guide after Purchase
Tick Imbalance Bars Indicator brings institutional-grade market microstructure analysis to cTrader. Based on the groundbreaking research of Marcos López de Prado, as detailed in his seminal work Advances in Financial Machine Learning, this indicator samples price data not by time or volume — but by information arrival.
The Core Insight
Traditional bars (time, tick, volume) sample markets uniformly, missing critical moments when informed traders act. Tick Imbalance Bars solve this by detecting when buying or selling pressure exceeds expected levels — signaling the presence of informed traders and potential price movement.
How It Works
The indicator applies the tick rule to classify each trade as buying (+1) or selling (-1) pressure. It then accumulates these signed ticks until the cumulative imbalance exceeds a dynamic threshold calculated using an Exponentially Weighted Moving Average (EWMA). When the threshold is breached, a new TIB bar is created — each bar containing approximately equal amounts of market information.
Key Features
- Real-time visualization of cumulative imbalance vs. dynamic thresholds
- Chart candle coloring by TIB membership
- Developing TIB display shows current bar formation live
- Min Ticks Filter to display only significant bars
- Fully configurable Expected Bar Size and EWMA parameters
Why Use Tick Imbalance Bars Indicator ?
- Sample more frequently during high-information periods
- Detect informed trading activity before price equilibrium
- Reduce noise from uninformed market participants
- Apply proven quantitative finance methodology
Based on research by Marcos López de Prado — Advances in Financial Machine Learning (2018)
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