📘Mastering Smart Order Pricing in Freqtrade: Full Guide to adjust_entry_price & adjust_order_price

This article was produced by the Quantitative Trading Lab at https://www.itrade.icu. Visit for more benefits. In Freqtrade, controlling order prices is crucial for minimizing slippage and improving strategy efficiency. The bot provides two core hooks:

• adjust_entry_price — Adjusts initial buy orders only (first-time entries).

• adjust_order_price — Adjusts all orders (buy, sell, add, reduce).

Using them properly allows you to implement smarter limit orders, staggered entries, and better profit control.

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🎯 Function Overview

Function	Purpose	When to Use
adjust_entry_price	Modify price for first-time entry orders	✅ First Buy Only
adjust_order_price	Adjust price for all orders (entry/exit)	✅ Every Order Call

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🧠 adjust_entry_price – First-Time Entry Only

This hook is called once when a strategy generates a first-time buy signal. Use it to tweak the entry price—e.g., avoid chasing a rising market.

✅ Example Use Cases

• Lower the first buy slightly to prevent buying at a peak.

• Fine-tune price relative to the order book.

🧪 Example Code

def adjust_entry_price(self, pair: str, order_type: str, current_rate: float, proposed_rate: float, **kwargs) -> float:
    # Lower first entry price by 0.5%
    adjusted_price = proposed_rate * 0.995
    print(f"[adjust_entry_price] Original: {proposed_rate}, Adjusted: {adjusted_price}")
    return adjusted_price

⚠️ Notes

• Only triggered for first-time entries, not for add-ons.

• Must return the desired limit price.

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🛠 adjust_order_price – Global Order Control

This hook affects all orders, including buy, sell, add-on, or reduce.

✅ Example Use Cases

• Apply consistent slippage adjustments across all orders:

  • Buy slightly lower

  • Sell slightly higher

• Dynamically adjust exit prices based on the current market.

🧪 Example Code

def adjust_order_price(self, pair: str, is_buy: bool, current_price: float, proposed_price: float, **kwargs) -> float:
    if is_buy:
        # Buy: reduce price 0.3%
        return proposed_price * 0.997
    else:
        # Sell: increase price 0.3%
        return proposed_price * 1.003

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🔍 Key Differences

Aspect	adjust_entry_price	adjust_order_price
Trigger	First-time buy only	All orders (buy/sell/add/reduce)
Control Direction	Buy only	Buy & Sell
Control Scope	First entry price	All order prices
Recommended Usage	Fine-tune initial entry	Unified price adjustment for all orders

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📌 Edge Cases

Scenario	adjust_entry_price called?	Recommended Approach
First-time buy	✅ Yes	Adjust initial limit price
Add-on buy (existing position)	❌ No	Use adjust_trade_position
Sell (profit/stoploss)	❌ No	Use custom_exit_price or adjust_order_price

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✅ Best Practices

• Use adjust_entry_price only for first entries

• Use adjust_order_price for all subsequent orders

• Combine with custom_exit_price for precise exit pricing

• Never try to handle add-ons in adjust_entry_price—it won’t be called

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📘 Simple Momentum Strategy Example

Buy Signal: Current close > previous close Sell Signal: Current close < previous close

Order Price Adjustments:

• adjust_entry_price: -0.5% for first entry

• adjust_order_price: buy -0.3%, sell +0.3%

• custom_exit_price: sell +0.2% above current price

from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame

class ExampleStrategy(IStrategy):
    timeframe = '5m'

    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'stoploss': 'market',
    }

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['enter_long'] = dataframe['close'] > dataframe['close'].shift(1)
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['exit_long'] = dataframe['close'] < dataframe['close'].shift(1)
        return dataframe

    def adjust_entry_price(self, pair, order_type, current_rate, proposed_rate, **kwargs):
        return proposed_rate * 0.995  # first entry -0.5%

    def adjust_order_price(self, pair, is_buy, current_price, proposed_price, **kwargs):
        return proposed_price * (0.997 if is_buy else 1.003)  # buy -0.3%, sell +0.3%

    def custom_exit_price(self, pair, trade, current_time, current_rate, **kwargs):
        return current_rate * 1.002  # sell +0.2%

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🧾 Summary

• adjust_entry_price: fine-tune first entry price

• adjust_order_price: control all orders, buy & sell

• custom_exit_price: precise exit pricing

By combining these three, you can:

• Avoid chasing high prices

• Protect against slippage

• Stagger orders intelligently

• Maximize profit on exits

Mastering these hooks is a key step toward professional-level limit order strategies in Freqtrade.