📘Profit Machine! Three Steps to Master Freqtrade Core Buy/Sell Signals – A Step-by-Step Guide to Writ

This article was produced by the Quantitative Trading Lab at https://www.itrade.icu. Visit for more benefits. In the Freqtrade strategy framework, populate_indicators, populate_entry_trend, and populate_exit_trend are the three most basic and commonly used functions. They are responsible for:

• ✍️ Calculating technical indicators (e.g., RSI, MACD, moving averages)

• 📈 Determining buy signals

• 📉 Determining sell signals

This article will explain the purpose, usage, code examples, and tips for these three functions, helping you build your own quantitative trading strategy from scratch.

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1️⃣ populate_indicators: Core Function for Calculating Technical Indicators

✅ Purpose

This function calculates and fills technical indicators in the DataFrame, which are used for subsequent buy/sell signal decisions.

It takes historical OHLCV data as input and returns a DataFrame with additional indicator columns, such as RSI, MACD, SMA, EMA, etc.

🧠 Backtesting vs Live Trading

• Backtesting: populate_indicators is called once for the entire historical dataset.

• Live Trading: Called every time a new candle is generated (e.g., every minute or hour).

In live trading, you can add real-time processing here; in backtesting, you may iterate over rows with for row in dataframe.iterrows().

💡 Example Code

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    # Calculate 14-period RSI
    dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)

    # Optional: row-wise processing for backtesting
    for index, row in dataframe.iterrows():
        if row['rsi'] < 30:
            # Logging or tagging
            pass

    return dataframe

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2️⃣ populate_entry_trend: Buy Signal Logic

✅ Purpose

populate_entry_trend defines when to enter a trade. It generates a buy column in the DataFrame:

• buy = 1 means this candle is a buy signal

• buy = 0 (default) means no action

Freqtrade will execute a trade with a market or limit order when the condition is met.

🛠 Usage

You can use .loc to filter the DataFrame and set buy signals:

dataframe.loc[condition_expression, 'buy'] = 1

💡 Example Code

def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['buy'] = 0  # Initialize as no buy

    # RSI below 30 triggers buy signal
    dataframe.loc[dataframe['rsi'] < 30, 'buy'] = 1

    return dataframe

📌 Tips

• Combine multiple conditions using (cond1) & (cond2).

• Incorporate Bollinger Bands, moving averages, or other indicators to refine entries.

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3️⃣ populate_exit_trend: Sell Signal Logic

✅ Purpose

populate_exit_trend defines when to exit a trade. It works similarly to populate_entry_trend, but sets the sell column:

• sell = 1 triggers a sell

• sell = 0 means no action

Freqtrade will sell when sell=1.

💡 Example Code

def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['sell'] = 0  # Initialize as no sell

    # RSI above 70 triggers sell signal
    dataframe.loc[dataframe['rsi'] > 70, 'sell'] = 1

    return dataframe

🧠 Tips

• Add volume or price pattern logic to improve exit accuracy.

• Combine with ROI or trailing stop to create a complete exit mechanism.

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🧩 Example Strategy: Minimal RSI Strategy

A complete strategy class combining the three functions. Logic:

• Buy when RSI < 30 (oversold)

• Sell when RSI > 70 (overbought)

from freqtrade.strategy import IStrategy
from pandas import DataFrame
import talib.abstract as ta

class RsiExampleStrategy(IStrategy):
    timeframe = '5m'
    stake_amount = 100
    minimal_roi = {"0": 0.1}
    stoploss = -0.2
    use_exit_signal = True

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['buy'] = 0
        dataframe.loc[(dataframe['rsi'] < 30), 'buy'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['sell'] = 0
        dataframe.loc[(dataframe['rsi'] > 70), 'sell'] = 1
        return dataframe

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🧾 Summary

Function	Purpose	Returns
populate_indicators	Calculate indicators	DataFrame with new indicators
populate_entry_trend	Set buy signals	DataFrame with buy=1
populate_exit_trend	Set sell signals	DataFrame with sell=1

These three functions are the core “trio” of a strategy. You can further optimize and combine them to build your own trading system.

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Next article will cover custom_exit vs custom_exit_price and how to use them together. Stay tuned!