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Analyzing bot data

You can analyze the results of backtests and trading history easily using Jupyter notebooks. A sample notebook is located at user_data/notebooks/analysis_example.ipynb. For usage instructions, see jupyter.org.

Pro tip - Don't forget to start a jupyter notbook server from within your conda or venv environment or use nb_conda_kernels

Example snippets

Load backtest results into a pandas dataframe

from freqtrade.data.btanalysis import load_backtest_data
# Load backtest results
df = load_backtest_data("user_data/backtest_data/backtest-result.json")

# Show value-counts per pair
df.groupby("pair")["sell_reason"].value_counts()

This will allow you to drill deeper into your backtest results, and perform analysis which otherwise would make the regular backtest-output very difficult to digest due to information overload.

Load live trading results into a pandas dataframe

from freqtrade.data.btanalysis import load_trades_from_db

# Fetch trades from database
df = load_trades_from_db("sqlite:///tradesv3.sqlite")

# Display results
df.groupby("pair")["sell_reason"].value_counts()

Load multiple configuration files

This option can be usefull to inspect the results of passing in multiple configs in case of problems

from freqtrade.configuration import Configuration
config = Configuration.from_files(["config1.json", "config2.json"])
print(config)

Strategy debugging example

Debugging a strategy can be time-consuming. FreqTrade offers helper functions to visualize raw data.

Import requirements and define variables used in analyses

# Imports
from pathlib import Path
import os
from freqtrade.data.history import load_pair_history
from freqtrade.resolvers import StrategyResolver

# You can override strategy settings as demonstrated below.
# Customize these according to your needs.

# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategy_name = 'AwesomeStrategy'
# Path to user data
user_data_dir = 'user_data'
# Location of the strategy
strategy_location = Path(user_data_dir, 'strategies')
# Location of the data
data_location = Path(user_data_dir, 'data', 'binance')
# Pair to analyze 
# Only use one pair here
pair = "BTC_USDT"

Load exchange data

# Load data using values set above
bt_data = load_pair_history(datadir=Path(data_location),
                            ticker_interval=ticker_interval,
                            pair=pair)

# Confirm success
print(f"Loaded {len(bt_data)} rows of data for {pair} from {data_location}")

Load and run strategy

Rerun each time the strategy file is changed

# Load strategy using values set above
strategy = StrategyResolver({'strategy': strategy_name,
                            'user_data_dir': user_data_dir,
                            'strategy_path': strategy_location}).strategy

# Generate buy/sell signals using strategy
df = strategy.analyze_ticker(bt_data, {'pair': pair})

Display the trade details

Note that using data.head() would also work, however most indicators have some "startup" data at the top of the dataframe.

Some possible problems

Columns with NaN values at the end of the dataframe
Columns used in crossed*() functions with completely different units

Comparison with full backtest

having 200 buy signals as output for one pair from analyze_ticker() does not necessarily mean that 200 trades will be made during backtesting.

Assuming you use only one condition such as, df['rsi'] < 30 as buy condition, this will generate multiple "buy" signals for each pair in sequence (until rsi returns > 29). The bot will only buy on the first of these signals (and also only if a trade-slot ("max_open_trades") is still available), or on one of the middle signals, as soon as a "slot" becomes available.

# Report results
print(f"Generated {df['buy'].sum()} buy signals")
data = df.set_index('date', drop=True)
data.tail()

Feel free to submit an issue or Pull Request enhancing this document if you would like to share ideas on how to best analyze the data.

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