Data quality: how much do you trust your historical data?

Backtests are only as good as the data: gaps, bad ticks, wrong session times and broker-specific prices all distort results. Traders who check data quality discover their tests were testing artefacts.

Data quality: how much do you trust your historical data? — risk-reward diagram
A risk-reward ratio of 1 to 2

What's your data routine?

  • how you source and validate history
  • the data problems you've found
  • how they changed your results
Data quality: how much do you trust your historical data? — trading sessions clock diagram
The four forex trading sessions across a 24-hour day

The expectancy guide assumes clean data — worth ensuring.

Background: Risk-reward ratio, win rate and expectancy: the maths behind a trading edge

A high win rate can still lose money. See how win rate and risk-reward combine into expectancy, with break-even win rates and worked examples.

What is a good risk-reward ratio in forex?

There isn't one right ratio. What matters is expectancy, the win rate and ratio together. A 1:2 ratio breaks even at about 33% winners before costs, while a 1:1 ratio needs 50%.

How do you calculate trading expectancy?

Multiply the win rate by the average win and subtract the loss rate multiplied by the average loss. Measuring wins and losses in multiples of the amount risked (R) makes the result easy to compare.

Read the full guide

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