stable/docs/strategy_analysis_example.md

Strategy debugging example

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

Setup

# Change directory
# Modify this cell to insure that the output shows the correct path.
import os
from pathlib import Path

# Define all paths relative to the project root shown in the cell output
project_root = "somedir/freqtrade"
i=0
try:
    os.chdirdir(project_root)
    assert Path('LICENSE').is_file()
except:
    while i<4 and (not Path('LICENSE').is_file()):
        os.chdir(Path(Path.cwd(), '../'))
        i+=1
    project_root = Path.cwd()
print(Path.cwd())

# Customize these according to your needs.

# Define some constants
ticker_interval = "5m"
# Name of the strategy class
strategy_name = 'SampleStrategy'
# 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 data using values set above
from pathlib import Path
from freqtrade.data.history import load_pair_history

candles = load_pair_history(datadir=data_location,
                            ticker_interval=ticker_interval,
                            pair=pair)

# Confirm success
print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
candles.head()

Load and run strategy

• Rerun each time the strategy file is changed

# Load strategy using values set above
from freqtrade.resolvers import StrategyResolver
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(candles, {'pair': pair})
df.tail()

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.