Backtesting, Slippage, Transaction Costs

Learn why including slippage and transaction costs in backtesting is crucial for accurate profitability projections, especially in futures trading. Avoid common

Published · Updated · Methodology: Technical Indicators

Part of: Algorithmic & Automated Trading

  • Methodology: Technical Indicators
  • Content type: educational
  • Markets: Coffee futures

Source video

Decoded from: Trading Backtest Mistake: Don't Ignore Slippage & Costs! #shorts by Algo Trading With Kevin Davey — watch the original

Key timestamps:

  • 0:00 - Introduction to backtesting mistake
  • 0:05 - Explanation of transaction costs (slippage, commission)
  • 0:09 - Issue with assuming limit order fills
  • 0:15 - Example with coffee futures and slippage

Strategy overview

Slippage and transaction costs are the gap between the price a backtest assumes and the price the market actually gives you — and this entry is not a setup at all, but the accounting layer that sits underneath every setup. The source is a short-form clip from Kevin Davey's channel, "Trading Backtest Mistake: Don't Ignore Slippage & Costs! #shorts", and its subject is a validation error rather than an entry signal: the argument that an equity curve built on optimistic fill assumptions is describing a market that does not exist.

The clip's specific target, per its own index, is the limit-order fill assumption — the habit of treating a limit order as filled simply because price traded at that level. In live execution, a touch is not a fill: the order has to work through the queue at that price, and the trades that do fill are disproportionately the ones the market was about to move against. The clip closes on a futures example to make the point concrete, which is a deliberate choice of venue: in thinner contracts the round-turn cost of spread plus commission is large enough relative to a typical move that it stops being a rounding error in the results and starts being the results.

The practical consequence is that this cost line scales with trade count, not with conviction — the same per-trade assumption that barely dents a system taking a few positions a month can erase one taking several a day, which is why two backtests with identical logic and different fill models can disagree completely. This is a roughly twenty-second warning flag rather than a methodology, and no rules or parameters were extracted from it, so treat it as a check to run against the other strategies in this catalog rather than as something to trade on its own.

Topics

backtesting · trading strategy · transaction costs · slippage · futures trading · trading psychology · trading mistakes · strategy performance · commission · technical indicators

Frequently asked questions

What is slippage in a trading backtest?

Slippage is the difference between the price your backtest assumes you traded at and the price you actually get in the live market. It comes from spread, queue position, and order size relative to available liquidity, and it applies on both entry and exit.

Why can't a backtest assume a limit order is filled when price touches the level?

Because a touch only means the market traded at that price, not that your specific order reached the front of the queue. This is the core issue the source clip raises: backtest engines commonly fill limit orders on contact, which credits the strategy with executions it would not have received.

Which strategies are most affected by transaction costs?

High-frequency ones. Costs are charged per round turn, so the total drag is roughly the per-trade cost multiplied by the number of trades — a fixed assumption that is harmless for a low-frequency system can consume the entire expectancy of a scalping system with the same logic.

How should I apply this when testing a decoded strategy?

Re-run any backtest with explicitly configured commission and slippage rather than the platform defaults, and check how far the assumption can move before the results break down. Strategy Decoder extracts the structure of strategies from video sources so you can evaluate them on TradingView under your own cost settings.

About this strategy page

This trading strategy was decoded by Strategy Decoder's AI from a public YouTube trading video and turned into a structured, reviewable specification. In the interactive app this page shows the full entry and exit logic, risk management settings, the indicators involved with their parameters, AlgoWizard-compatible logic and a Pine Script export ready for TradingView backtesting — plus an automated backtest verdict when one has been computed for this strategy.

Strategy Decoder catalogs 2,229 decoded strategies. Each one is extracted with confidence scoring, cross-linked to the indicators it uses, and kept up to date as new videos are processed daily. Load this page with JavaScript enabled to use the interactive tools, or start from the strategy explorer to filter by methodology, market and timeframe.

Other versions of this strategy

More decoded strategies