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Trading analytics

Trading analytics: the metrics that show whether you have an edge

Trading analytics turns a list of trades into a few numbers you can act on. Here is what to measure, what each metric means and how to read them together.

By Reid KashUpdated How we calculate

What trading analytics is

Trading analytics is the measurement of trading results: win rate, average win and loss, expectancy, drawdown and how each changes over time. It answers whether your method makes money, how much risk it takes to do so and whether the result is repeatable or luck.

Retail vs institutional trading analytics

Search for "trading analytics" and most results are enterprise products: market data, transaction cost analysis and execution quality tools sold to banks and funds. That is analytics about the market and the order flow. Retail trading analytics is about you: your trades, your sizing, your habits. The question is not how the market behaved but whether you traded it well, and an imported trade history is all it needs.

The trading analytics metrics that matter

Expectancy

The average result per trade: win rate × average win − loss rate × average loss. Positive expectancy means the method pays over many trades. Check yours with the expectancy calculator.

Maximum drawdown

The deepest fall from an equity peak to the next low, in dollars or percent. It is the cost of the return. Two accounts with the same profit and different drawdowns are not equally good.

Profit factor

Gross profit divided by gross loss. Above 1 the account makes money. It is useful for comparing setups, but it ignores how many trades are behind it.

R-multiple

A trade's result divided by the amount risked on it. A +2R trade made twice what it risked. R-multiples let you compare trades of different sizes and markets on one scale.

Win rate and reward-to-risk

Read these as a pair. A 40% win rate is fine when the average win is twice the average loss, and a 70% win rate fails when losers are three times the size of winners. The win rate calculator and risk-reward calculator work through both.

From metrics to a trading analytics routine

  1. Import your trades so every figure comes from fills, not memory.
  2. Check expectancy and drawdown first. If expectancy is negative, nothing else matters yet.
  3. Split the results by setup, market, day and hour to find where the edge sits.
  4. Change one thing, then give it at least 30 more trades before judging.

The trading dashboard shows the headline numbers on one screen. The methodology page lists every formula. This page is educational, not financial advice; see the risk disclosure.

Trading analytics questions

What is trading analytics?

Trading analytics is the measurement of your own trading results: how often you win, how big your wins and losses are, how deep your losing periods run and whether the method pays over many trades. For an individual trader it starts with an imported trade history and a handful of metrics.

What is the difference between trading analytics and a trading dashboard?

Analytics is the set of measurements. A dashboard is the screen that shows the most important of them at once. You open the dashboard daily to see where you stand, and the deeper analytics when a number on it moves and you need to know why.

Which trading analytics matter most?

Expectancy, maximum drawdown, profit factor, average win against average loss and trade count. Expectancy says whether the method pays; drawdown says what it costs; the rest explain why.

How many trades do I need before the numbers mean anything?

At least 30 trades gives a rough read, and 100 or more is better. With fewer trades a single outsized win or loss can swing every metric.

Trading analytics

Run these metrics on your own trades

Import one CSV and see expectancy, drawdown, profit factor and more, from your own fills.

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