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Whale Trading

Risk-Reward & R-Multiples: A Common Language for Trade Outcomes

This is the first step in the
risk-management series:
understanding risk-reward and R-multiples.

The same “+100 USD” profit can mean:

  • +1% on one account,
  • +0.1% on another,
  • or a lucky escape from an oversized leveraged bet.

Because of this, professional traders care less about:

“How many dollars did I make on this trade?”

and more about:

“How many R did I make or lose on this trade?”

1. Why think in R instead of raw dollars?

Raw PnL has two big problems:

  • its meaning changes completely with account size, and
  • it hides how much risk you actually took
    (wide vs tight stops, leverage, etc.).

Example:

  • Trader A: 10,000 USD account, risks 1% (100 USD) per trade.
  • Trader B: 100,000 USD account, risks 0.25% (250 USD) per trade.

If both make +500 USD:

  • A: +5R (+500 / 100)
  • B: +2R (+500 / 250)

The strategy quality behind those results is not the same.

By using R-multiples:

  • you can compare strategies
    across different account sizes and currencies,
  • you get a common language for risk and outcome.

2. Defining 1R: setting account-based risk

First, we define 1R like this:

1R = the maximum loss you allow
on a single trade

Example:

  • Account: 10,000 USD
  • Max risk per trade: 1% of account

→ 1R = 100 USD

For each trade, you then choose:

  • a stop distance on the chart, and
  • a position size so that
    if the stop is hit, you lose exactly 1R.

(We go deeper into this in position-sizing.)

Key idea:

  • 1R is not a strategy parameter;
    it’s a safety standard for your account.
  • You can change strategies or markets,
    but your basic rule of
    “I’m OK with risking this much per trade”
    shouldn’t swing wildly.

3. Example: expressing stops and targets in R

Let’s use a simple long example.

  • Account: 10,000 USD
  • 1R: 100 USD (1% of account)
  • BTC entry: 20,000 USD
  • Stop: 19,800 USD (−200 USD per BTC)

Here:

  • risk per coin: 200 USD
  • to keep risk at 100 USD (1R),
    → you take a position of 0.5 BTC.

If the stop is hit:

  • loss = 200 × 0.5 = 100 USD = −1R

Now set targets:

  • Target 1: 20,400 USD (+400 per BTC)
    • profit = 400 × 0.5 = 200 USD = +2R
  • Target 2: 20,600 USD (+600 per BTC)
    • profit = 600 × 0.5 = 300 USD = +3R

So this trade is:

  • −1R at the stop,
  • +2R at target 1,
  • +3R at target 2.

Once trades are expressed in R:

  • you can ask:
    “Is this risk-reward structure reasonable?”
    “What win rate does this need
    to make sense over time?”

4. Logging strategy performance in R

When you keep a trading journal,
it’s very useful to always record:

  1. Entry, stop, and target prices
  2. Actual PnL in currency
  3. Outcome in R (e.g., −1R, +2R, +0.7R)
  4. Whether you followed your rules or not

Example: results for 10 trades:

  • −1R, −1R, +2R, +0.5R, −0.8R, +1.5R, +3R, −1R, +0.2R, +1R

Sum:

  • (+2 + 0.5 − 0.8 + 1.5 + 3 − 1 + 0.2 + 1 − 1 − 1)R
    = +4.4R

If 1R = 100 USD → +440 USD.

Later, if your account grows to 20,000 USD,
1R might become 200 USD,
but the system is still:

“roughly +4.4R per 10 trades on average”

You can compare strategies on a normalized scale,
not just in raw dollars.

5. Understanding win rate and R/R together

Most traders ask:

“What win rate should I aim for?”

But win rate alone is not enough.

Example:

  • Strategy A: win rate 70%, avg win +1R, avg loss −1R
  • Strategy B: win rate 40%, avg win +3R, avg loss −1R

Over 10 trades:

  • A: (7 × +1R) + (3 × −1R) = +4R
  • B: (4 × +3R) + (6 × −1R) = +6R

On win rate alone, A looks better.
Once you include risk-reward,
B can have the higher expected value.

In real trading you want to consider:

  • win rate,
  • average R (your R/R structure),
  • and whether that combo fits your psychology.

Your tolerance for losing streaks
connects directly to
loss-psychology
and drawdown.

6. Common real-world traps

6-1. Small profits, large losses

A classic pattern:

  • cut profits quickly (+0.3R, +0.5R),
  • but let losses grow to −3R, −5R.

If you add this up:

  • 5 wins × +0.5R = +2.5R
  • 1 loss × −5R = −5R

→ net = −2.5R (account shrinks).

Traders with this pattern often say:

  • “My win rate is high,
    but my account doesn’t grow.”

The core issue is that
risk-reward is upside down.

6-2. Inconsistent 1R from trade to trade

Another common issue:

  • some trades risk 0.5% of the account,
  • some trades risk 5% or more.

So “−1R” means different things each time
in terms of account damage.

Better:

  • define a clear rule in
    position-sizing
    for “risk per trade = x% of account”,
  • keep 1R consistent across trades.

6-3. Judging strategies by “feel” instead of R

Without R-based records, it’s easy to say:

  • “This strategy doesn’t feel good lately.”
  • “That signal feels strong.”

But that often means
you’re reacting to just a handful of recent trades.

By logging in R, you can see:

  • total R over 50–100 trades,
  • average R,
  • worst losing streak in R,

and use those numbers when designing:

7. Two small exercises after reading this

If you want to make this concrete,
try these two steps:

  1. Define your personal 1R in numbers

    • “What % of my current account
      am I willing to risk per trade?”
    • Convert that into dollars:
      “My 1R is X USD.”

  2. Rewrite your last 20 trades in R

    • use entry, stop, and position size
      to compute each trade’s R outcome,
    • calculate your average R,
      largest loss in R, and largest win in R.

Once you think in R and risk-reward:

you shift from
“How much did I make on this trade?”
to
“Is my strategy structure healthy?”

In the next articles:

we’ll connect this R framework
to practical rules for stops, targets,
and position sizing in your day-to-day trading.