Introduction

Risk management is the single most important skill separating profitable crypto traders from those who blow up their accounts. In this lesson you will learn three pillars of risk control: position sizing, stop loss placement, and risk-reward ratio. These are coded as concrete rules so you can backtest or run them as a bot.

Position Sizing

The goal of position sizing is simple: never risk more than a fixed percentage of your total capital on a single trade. The most common rule is the 1-2% rule — risk no more than 1% to 2% of account equity per trade.

The formula to compute position size is:

position_size = (account_balance * risk_percent) / (entry_price - stop_loss_price)

For example, with a $10,000 account, 1% risk, entry at $50,000 (BTC) and stop loss at $49,000, the math looks like this:

account_balance = 10000
risk_percent = 0.01
entry_price = 50000
stop_loss_price = 49000

risk_amount = account_balance * risk_percent  # = 100 USD
price_diff = entry_price - stop_loss_price    # = 1000 USD
position_size = risk_amount / price_diff      # = 0.1 BTC

You are risking exactly $100 even though you hold a $5,000 position. That is how the percentage rule keeps losses predictable.

Stop Loss Placement

A stop loss is not a guess — it is placed where your trade idea is invalidated. There are three reliable techniques crypto traders use:

Percentage-based stop: place the stop a fixed percentage below entry (commonly 1-3% for BTC/ETH). Structure-based stop: place the stop just below a key support level, recent swing low, or the low of the candle that triggered entry. ATR-based stop: place the stop at a multiple of the Average True Range (ATR) to adapt to current volatility.

The ATR-based method adapts to market conditions automatically:

import pandas as pd

def atr_stop(df, atr_period=14, multiplier=2.0):
    df["tr"] = df[["high", "low", "close"]].apply(
        lambda row: max(row["high"] - row["low"],
                        abs(row["high"] - row["close"]),
                        abs(row["low"]  - row["close"])), axis=1)
    df["atr"] = df["tr"].rolling(atr_period).mean()
    return df["close"] - df["atr"] * multiplier

# last_row_stop = atr_stop(df).iloc[-1]

In a quiet market ATR is small, so the stop sits close to price. In a volatile market ATR expands, so the stop widens and you avoid being shaken out by noise.

Risk-Reward Ratio

Risk-reward ratio (RRR) measures how much you stand to gain versus how much you stand to lose. It is calculated as:

risk_reward_ratio = (take_profit_price - entry_price) / (entry_price - stop_loss_price)

A 1:2 RRR means you risk $1 to make $2. A 1:3 RRR means you risk $1 to make $3. With a 1:2 ratio you can be wrong 60% of the time and still break even after fees. With 1:3 you only need to win roughly 35-40% of trades to be profitable long-term.

Most professional crypto traders refuse to enter a trade below 1:2 RRR.

Summary

Capital protection comes before profits. Size every position so a loss can never exceed 1-2% of account equity. Place stops where your thesis is invalidated — using fixed percentage, structure, or ATR — and only take trades where reward is at least 2-3 times the risk. These three rules, executed without emotion, are what keep traders in the game long enough to win.