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The last down candle before a strong rally is often marked as an order block. That candle alone cannot identify who placed orders or how much they held.
The basic candidate
A bullish candidate is the last opposing candle before an upward move; a bearish candidate is the counterpart before a downward move. What qualifies as a sufficiently strong move changes the candidate set.
OHLC records open, high, low, and close. Those values cannot establish a particular institution’s orders, inventory, or hidden liquidity.
A fictional boundary example
For a candidate with open 105, close 102, high 106, and low 100, the full range is 100–106 and the body is 102–105. A later price of 101 is inside the full range but outside the body; whether price traded through the body requires the intervening path.
Choose boundaries in advance. Mixing full-range and body definitions changes revisit counts.
A simple candidate rule
When a completed bar closes beyond an already confirmed swing, store the last opposing candle before that break as a candidate. Preserve both its original timestamp and the later confirmation time. Do not backdate the confirmed candidate into an earlier trade signal.
An executable study must also fix swing confirmation, search length, reuse, and expiry. This article is a concept guide, not a complete trading system.
Measure the return separately
Record first revisit time, penetration depth, and subsequent favorable and adverse movement over fixed horizons. Strong-move and FVG flags can be tested separately.
A return to the candidate does not prove support or guarantee profit. Revisit statistics are not strategy results without entry, stop, and cost rules.
Continue with common foundations
Position Sizing: Convert a Risk Budget into Order Quantity · Trading Expectancy: Calculate Average Profit and Loss (scheduled) · Open Interest: What an Increase in Contracts Means · Python Price Data Checks: Duplicates, Timestamps, and Returns (scheduled)
Research methods and reporting standards
For education and research. Numerical examples are teaching assumptions unless explicitly identified otherwise. Historical results do not guarantee future performance.