📘Smart Use of Leverage! Complete Freqtrade Leverage Strategy Guide

This article was produced by the Quantitative Trading Lab at https://www.itrade.icu. Visit for more benefits. In Freqtrade, when trading with leverage (especially on futures markets like Binance Futures), you can flexibly control leverage via configuration or strategy code.

This guide covers:

• leverage in the config file

• leverage() function in the strategy

• Practical usage and examples

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⚙️ 1. Config Option leverage (Global Setting)

In config.json, you can set a global leverage for all trades:

"leverage": 3

✅ Suitable Scenarios:

• Use the same leverage for all trades

• Futures mode trading (e.g., Binance Futures)

⚠️ Notes:

• Spot exchanges (e.g., Binance Spot) cannot use leverage

• Ensure the account has leverage permissions if using futures

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🧠 2. leverage() Function (Dynamic Control in Strategy)

You can define a leverage() function in your strategy to dynamically specify leverage per trade. This is only active in Futures mode.

🔧 Function Definition:

def leverage(
    self,
    pair: str,
    current_time: datetime,
    current_rate: float,
    proposed_leverage: float,
    max_leverage: float,
    entry_tag: Optional[str],
    side: str,
    **kwargs,
) -> float:
    ...

Parameter	Meaning
pair	Trading pair name
current_time	Current datetime
current_rate	Current price
proposed_leverage	Default suggested leverage
max_leverage	Maximum allowed leverage for pair
entry_tag	Optional signal tag
side	'long' or 'short'

---

🔍 Example 1: Fixed Leverage by Pair

def leverage(self, pair: str, **kwargs) -> float:
    if "BTC" in pair:
        return 3.0
    elif "ETH" in pair:
        return 4.0
    else:
        return 2.0

---

📈 Example 2: Higher Leverage When Trend Strong

def leverage(self, pair: str, current_time: datetime, current_rate: float,
            proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
            side: str, **kwargs) -> float:
    df = self.dp.get_analyzed_dataframe(pair, self.timeframe)[0]
    df['ma20'] = df['close'].rolling(20).mean()
    ratio = current_rate / df['ma20'].iloc[-1]

    if ratio > 1.02:
        return min(5.0, max_leverage)
    elif ratio < 0.98:
        return 1.0
    else:
        return proposed_leverage

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✅ Recommended Practices

Item	Recommendation
Global leverage	Quick and simple, same leverage for all trades
leverage() function	Flexible, adjust leverage per pair, signal, or trend
Backtesting	Leverage not enforced in backtests; assess risk accordingly
Combine with stoploss	Leverage increases risk; always set stoploss

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🎯 Practical Example Strategy

from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime
from typing import Optional

class LeverageStrategy(IStrategy):
    timeframe = '5m'
    leverage = 3  # default leverage for backtesting

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['ma20'] = dataframe['close'].rolling(20).mean()
        return dataframe

    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df['enter_long'] = df['close'] > df['ma20']
        return df

    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
        df['exit_long'] = df['close'] < df['ma20']
        return df

    def leverage(self, pair: str, current_time: datetime, current_rate: float,
                 proposed_leverage: float, max_leverage: float, entry_tag: Optional[str],
                 side: str, **kwargs) -> float:
        df = self.dp.get_analyzed_dataframe(pair, self.timeframe)[0]
        ma = df['ma20'].iloc[-1]
        ratio = current_rate / ma

        if ratio > 1.02:
            return min(5.0, max_leverage)
        elif ratio < 0.98:
            return 1.0
        else:
            return 2.0

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🧠 Summary

• Leverage can be defined in config or strategy for flexible control

• Dynamic function supports allocation per market, pair, signal, or trend

• Always set a stoploss; proper leverage usage is key to stable profits