📘 Chapter 6: "Show and Tell! plot-dataframe Chart Visualization Tutorial"

During strategy development, backtesting results are often presented in tabular form, making it difficult to quickly identify issues. The plot-dataframe command can visualize buy/sell points, indicator lines, and price movements in charts, helping you instantly see strategy performance.

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🎯 1. Basic Usage: Plotting Charts

freqtrade plot-dataframe \
  --config user_data/config.json \
  --strategy MyStrategy \
  --timerange 20230101-20230201

After execution, an .html file will be generated under user_data/plot/, which can be opened directly in a browser. It includes:

• Candlestick price movements
• Buy/Sell points (arrows)
• Technical indicators (e.g., EMA, MACD)

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🧾 2. Parameter Details

Parameter	Description
--config	Path to the config file, including trading pairs and timeframes
--strategy	Strategy class name (e.g., MyStrategy)
--timerange	Specify the plotting period, format like 20230101-20230201
--indicators1	Indicators plotted on the main chart (e.g., EMA, close)
--indicators2	Sub-chart indicators (e.g., RSI, MACD)
--exportfilename	Export file path (supports .html or .png)
--userdir	Custom user_data path (default is fine)

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📐 3. Adding Custom Indicators

You can add extra indicators to the chart to validate signal logic:

Example code:

def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
    dataframe['ema'] = ta.EMA(dataframe['close'], timeperiod=20)
    dataframe['fast_ema'] = ta.EMA(dataframe['close'], timeperiod=10)
    dataframe['slow_ema'] = ta.EMA(dataframe['close'], timeperiod=50)
    dataframe['rsi'] = ta.RSI(dataframe['close'], timeperiod=14)
    macd, macdsignal, macdhist = ta.MACD(dataframe['close'])
    dataframe['macd'] = macd
    return dataframe

• --indicators1 will be plotted on the main chart (price chart), e.g., EMA lines.
• --indicators2 will be plotted on sub-charts, e.g., RSI, MACD.

Full example:

freqtrade plot-dataframe \
  --config user_data/config.json \
  --strategy MyStrategy \
  --timerange 20230101-20230201 \
  --indicators1 close ema fast_ema slow_ema \
  --indicators2 rsi macd

❗ Note:

These indicator names must match the column names in your strategy class’s populate_indicators() method (added to the DataFrame).

• Names must exactly match your dataframe column names.
• Otherwise, no error will occur, but the chart will not display the desired lines.
• Indicators must be defined in populate_indicators() to take effect.

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💾 4. Export as HTML / PNG

By default, output is an HTML file. To specify:

--exportfilename user_data/plot/myplot.html

To export as PNG (static image):

--exportfilename user_data/plot/myplot.png

📌 Note: Exporting PNG requires additional tools like Puppeteer or headless Chrome. Beginners are recommended to use HTML format.

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🐳 5. Running in Docker

Example command in Docker:

docker compose run --rm freqtrade plot-dataframe \
  --config /quants/freqtrade/user_data/config.json \
  --strategy MyStrategy \
  --timerange 20230101-20230201

Make sure the /quants/freqtrade/user_data directory is correctly mounted in docker-compose.yml.

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✅ 6. Usage Suggestions

Purpose	Method
Check strategy logic	See if buy/sell points are at appropriate positions
Assist debugging	Compare indicators with signal relationships
Share strategy	Export chart as HTML for easy presentation
Evaluate indicator performance	Plot multiple indicators to check redundancy

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📌 Summary

plot-dataframe is a powerful visualization tool provided by Freqtrade, especially useful for debugging complex strategies and validating buy/sell logic.

Recommended Workflow:

1. Backtest the strategy:

freqtrade backtesting \
  --config user_data/config.json \
  --strategy MyStrategy \
  --timeframe 15m \
  --timerange 20220101-20230101

2. Plot the chart:

freqtrade plot-dataframe \
  --config user_data/config.json \
  --strategy MyStrategy \
  --timerange 20250601-20250626

3. Analyze the chart:

• Are strategy buys too early or too late?
• Are there frequent false signals?
• Are the indicators effective?

Mastering plot-dataframe provides a solid foundation for strategy optimization and more efficient tuning!