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.

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 the script

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

# Define some constants
ticker_interval = "1m"
# Name of the strategy class
strategy_name = 'NewStrategy'
# Path to user data
user_data_dir = 'user_data'
# Location of the strategy
strategy_location = os.path.join(user_data_dir, 'strategies')
# Location of the data
data_location = os.path.join(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("Loaded " + str(len(bt_data)) + f" rows of data for {pair} from {data_location}")

Load and run strategy

Rerun each time the strategy file is changed
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

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

# Run strategy (just like in backtesting)
df = strategy.analyze_ticker(bt_data, {'pair': pair})

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

Load backtest results into a pandas dataframe

# 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()

Load live trading results into a pandas dataframe

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

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

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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