📘 Smart Use of Leverage! Complete Freqtrade Leverage Strategy Guide

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'

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🔍 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