Mechanism and definition: what “Swing Highs Lows” refers to
Swing highs and swing lows are chart features that describe turning points in price movement. A “swing high” is a local peak, and a “swing low” is a local trough, based on a rule set you choose (for example, requiring that nearby highs/lows are lower/higher than the candidate). The exact rule matters, because changing the rule can change which candles qualify as swings.
When people ask what costs can affect swing highs and lows, they usually mean this: the turning points you identify on a chart can differ from the turning points implied by the underlying market if the price you observe is influenced by trading costs or execution frictions.
Cost types that can shift observed swing points
Costs can be grouped into two broad categories: direct and indirect. Direct costs are explicit charges. Indirect costs are embedded in how transaction prices are formed or how positions are maintained.
Direct costs
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Commissions and per-trade fees These increase the cost of entering and exiting. Even if swings are identified from chart prices rather than trade outcomes, commissions can affect the net price reality behind your analysis workflow if you use trade records or account data.
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Account or platform fees Recurring fees can change what “effective results” look like, especially if someone mixes chart reasoning with performance evaluation.
Indirect costs
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Spread The bid–ask spread affects the difference between prices you can buy versus sell. If you are marking swings using prices derived from bid or ask (or from last-traded/aggregated data), a wider spread can change which levels look like peaks and troughs.
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Slippage (execution vs. intended price) Slippage is the gap between an expected execution price and the actual fill price. If swing points are tested using executed trades, slippage can cause the realized entry/exit to come from a different part of the move, which can change what you label as the swing boundary.
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Financing effects (carry/rollover) For leveraged positions, holding a position can introduce financing or rollover-related effects. These do not directly redefine the geometric chart turning points from the underlying market, but they can change account equity over time. If equity is later used to infer “what happened,” the cost component can bias conclusions.
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Data and pricing source conditions Even without “trading” costs, different vendors/platforms may produce different candle construction, timestamps, or price feeds. That is a data condition, not a market “cost,” but it can still change which prices appear to form local extremes.
Evidence and examples: how costs can be verified without assuming outcomes
A practical way to verify cost impact is to separate chart mechanics from variable conditions.
Assumption setup
- Assume you define swing highs/lows with the same rule across comparisons (same lookback/neighbor condition).
- Assume the chart is built from the same timeframe.
- Then vary one cost-related factor at a time (for example, a proxy for spread or an execution-quality assumption).
Example comparison (conceptual)
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Market price vs. tradeable price proxy If your analysis uses mid prices, but your executed prices depend on bid/ask, the “turning point” used for profit/loss can shift even if the mid-price swing stays similar.
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Backtest-style measurement vs. pure chart labeling If you only label swings on a chart without including execution, you test the chart feature itself. If you then measure returns using fills, you are testing how slippage and spread interact with those swing boundaries.
What to check in documentation
For verification, rely on stable, non-promotional documentation from the places that define how prices and costs are computed:
- Commission and fee schedules (direct costs)
- Spread definitions and pricing methodology (indirect costs)
- Execution quality and order handling descriptions (slippage drivers)
- Financing/rollover explanations for held positions (financing effects)
This approach keeps the conclusion tied to observable definitions rather than predictions.
Limitations and failure modes
- Swing labeling is rule-dependent Even with zero “cost” changes, different swing-identification rules can produce different highs/lows. Costs cannot be isolated if the swing rule changes.