A chart scanner should identify reproducible market conditions, not decorate hindsight. Define the candle source, reference level, timeframe, confirmation rule, volume test, volatility context, benchmark, and execution gate before the current move. Then preserve the full signal snapshot so successful and failed patterns can be evaluated together.
Keep each data family traceable so one attractive metric cannot silently override missing identity, liquidity, supply, or control information.
Use consistent intervals, close times, quote currencies, venue methodology, and missing-data treatment across the scan universe.
Specify the lookback, reference level, close requirement, and tolerance before the event instead of selecting resistance afterward.
Compare volume with a robust historical baseline and determine whether activity is broad or concentrated in one venue.
Measure compression and expansion relative to the token’s own history so a move can be compared with its normal behavior.
Compare the token with bitcoin, ether, the chain asset, or a sector basket over the same timeframe.
Require acceptable spread, pool depth, order-book depth, and price impact so the chart corresponds to a practical market.
A useful scan is a documented sequence with clear failure states, not a mysterious score that always produces a candidate.
Use a higher timeframe and benchmark to label trend, range, recovery, or countertrend conditions.
Apply a predefined close, range, momentum, or volatility rule on the primary timeframe.
Check volume persistence, venue breadth, liquidity, and known token-specific events.
Save the exact chart inputs and evaluate future outcomes at consistent intervals without rewriting the original signal.
Scanner results become more trustworthy when their blind spots are displayed beside the output.
A small trade can create a dramatic candle when liquidity is shallow or the reference venue is stale.
Different candle boundaries and aggregate methods can change indicator values and breakout classifications.
False breakouts are normal. Confirmation can reduce some cases but cannot remove uncertainty or sudden events.
Move from the page-level framework into a detailed, source-linked research workflow.
A chart-scanning framework for separating durable breakouts from thin spikes using OHLC data, volume, volatility, relative strength, liquidity, and multi-timeframe confirmation.
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A step-by-step method for turning a broad token universe into a focused research list using liquidity, market structure, supply, onchain, and risk filters.
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A research framework for separating durable attention from short-lived hype using search interest, volume, liquidity, catalysts, persistence, and cross-chain context.
Read the guide →The timeframe should match the decision horizon. Use one context timeframe, one trigger timeframe, and a lower timeframe only when it serves execution or diagnostics.
No. Volume should be compared with history and paired with liquidity, venue breadth, price structure, and persistence.
Choose the benchmark before scanning: bitcoin, ether, the token’s chain asset, or a relevant sector basket. The right choice depends on the thesis.
Without failures, evaluation suffers from selection and survivorship bias. The complete alert history shows whether the scanner improved attention allocation.
Start with a defined universe, combine independent signals, and record why each candidate survives or fails the scan.