Definition first: what “Double Top Bottom” refers to
A “Double Top Bottom” description can mean two related chart ideas: a double top (two nearby peaks) followed by a breakdown, or a double bottom (two nearby troughs) followed by a rise. In both cases, the visual structure relies on price making two similar extremes within a chosen time window.
Because the concept depends on what you count as the “two highs” (or “two lows”), anything that changes executed prices versus displayed or observed prices can affect how clearly the structure appears and how it would have behaved in practice.
What costs can affect the idea: direct vs indirect
When people say “costs affect Double Top Bottom,” they usually mean costs that change the economics of trading and the accuracy of price levels used to evaluate the pattern.
Direct costs
Direct costs are typically explicit or directly measurable from your execution record:
- Spread impact: In practice, you transact at bid for selling and at ask for buying. The spread can widen the effective distance between where the pattern appears on charts and where orders fill.
- Commissions and platform fees: Fixed or percentage-based fees per trade reduce net outcomes relative to a “gross” chart move.
- Financing/rollover charges (where applicable): If you hold positions across sessions, carry costs can accumulate and change the net payoff compared with a same-session or cash-like assumption.
Assumption for examples: If a chart “move” assumes you enter and exit at the same reference price, any spread, commission, or carry that shifts your actual fill prices changes the net result.
Indirect costs
Indirect costs are not always a line item in the fee schedule, but they still change realized execution and the observed structure:
- Slippage: When market orders or fast-moving prices cause fills away from the intended level, your actual entry/exit points differ from the pattern’s measured extremes.
- Order latency and partial fills: Delays can cause executions that do not match the moment you thought the pattern confirmed.
- Bid-ask effects on highs and lows: Charts typically display a single price series (for example, midpoint/last/trade). Your fill occurs on bid or ask, so measured “two peaks” can be less aligned with executed levels.
Evidence or example: how to verify cost effects without relying on predictions
You can independently verify whether costs mattered by comparing three things: (1) the pattern’s reference levels, (2) your actual executed prices, and (3) the fee and cost inputs you used.
Example method (no live data required)
- Record the pattern reference levels you used (the two peaks for a double top, or two troughs for a double bottom) and the time window.
- Compare to executed prices from your statements: entry price, exit price, and any intermediate fills.
- Break down net vs gross:
- Compute a gross move using chart reference prices.
- Compute a net move using your executed prices and documented fees/charges.
- Check sensitivity to assumptions:
- Repeat the net/gross comparison under reasonable spread assumptions (using the average spread you observed in your account, not a random web value).
- If you held positions, include only the carry/financing figures shown in your statement for that period.
If net results change materially when you vary spread or include slippage, then costs are plausibly affecting the practical meaning of the pattern. If net results barely change, costs may be less influential than timing or execution choices.
Limitations and failure modes
At least one material limitation is built into the concept itself: the pattern is defined visually and time-window dependent. That creates several failure modes related to costs:
- Misalignment between chart reference and execution: The “two extremes” on a chart can occur at prices that do not match the prices where orders fill.
- Confirmation timing: If your evaluation assumes the breakdown/breakout occurred instantly, delays and slower fills can turn a theoretically “clean” structure into a worse execution.
- Changing market conditions: Volatility and liquidity often change around extremes. Those changes can increase spreads and slippage, making costs highly time-dependent.
Also, historical relationships do not guarantee future results, so cost impact seen in one period may not repeat.