Strategy analysis example

Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data. The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.

Setup

```python from pathlib import Path from freqtrade.configuration import Configuration

Customize these according to your needs.

Initialize empty configuration object

config = Configuration.from_files([])

Optionally, use existing configuration file

config = Configuration.from_files(["config.json"])

Define some constants

config["timeframe"] = "5m"

Name of the strategy class

config["strategy"] = "SampleStrategy"

Location of the data

data_location = Path(config['user_data_dir'], 'data', 'binance')

Pair to analyze - Only use one pair here

pair = "BTC/USDT" ```

```python

Load data using values set above

from freqtrade.data.history import load_pair_history

candles = load_pair_history(datadir=data_location, timeframe=config["timeframe"], pair=pair, data_format = "hdf5", )

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

```python

Load strategy using values set above

from freqtrade.resolvers import StrategyResolver strategy = StrategyResolver.load_strategy(config)

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.

```python

Report results

print(f"Generated {df['buy'].sum()} buy signals") data = df.set_index('date', drop=False) data.tail() ```

Load existing objects into a Jupyter notebook

The following cells assume that you have already generated data using the cli.
They will allow you to drill deeper into your results, and perform analysis which otherwise would make the output very difficult to digest due to information overload.

Load backtest results to pandas dataframe

Analyze a trades dataframe (also used below for plotting)

```python from freqtrade.data.btanalysis import load_backtest_data, load_backtest_stats

if backtest_dir points to a directory, it'll automatically load the last backtest file.

backtest_dir = config["user_data_dir"] / "backtest_results"

backtest_dir can also point to a specific file

backtest_dir = config["user_data_dir"] / "backtest_results/backtest-result-2020-07-01_20-04-22.json"

```

```python

You can get the full backtest statistics by using the following command.

This contains all information used to generate the backtest result.

stats = load_backtest_stats(backtest_dir)

strategy = 'SampleStrategy'

All statistics are available per strategy, so if --strategy-list was used during backtest, this will be reflected here as well.

Example usages:

print(stats['strategy'][strategy]['results_per_pair'])

Get pairlist used for this backtest

print(stats['strategy'][strategy]['pairlist'])

Get market change (average change of all pairs from start to end of the backtest period)

print(stats['strategy'][strategy]['market_change'])

Maximum drawdown ()

print(stats['strategy'][strategy]['max_drawdown'])

Maximum drawdown start and end

print(stats['strategy'][strategy]['drawdown_start']) print(stats['strategy'][strategy]['drawdown_end'])

Get strategy comparison (only relevant if multiple strategies were compared)

print(stats['strategy_comparison'])

```

```python

Load backtested trades as dataframe

trades = load_backtest_data(backtest_dir)

Show value-counts per pair

trades.groupby("pair")["sell_reason"].value_counts() ```

Load live trading results into a pandas dataframe

In case you did already some trading and want to analyze your performance

```python from freqtrade.data.btanalysis import load_trades_from_db

Fetch trades from database

trades = load_trades_from_db("sqlite:///tradesv3.sqlite")

Display results

trades.groupby("pair")["sell_reason"].value_counts() ```

Analyze the loaded trades for trade parallelism

This can be useful to find the best max_open_trades parameter, when used with backtesting in conjunction with --disable-max-market-positions.

analyze_trade_parallelism() returns a timeseries dataframe with an "open_trades" column, specifying the number of open trades for each candle.

```python from freqtrade.data.btanalysis import analyze_trade_parallelism

Analyze the above

parallel_trades = analyze_trade_parallelism(trades, '5m')

parallel_trades.plot() ```

Plot results

Freqtrade offers interactive plotting capabilities based on plotly.

```python from freqtrade.plot.plotting import generate_candlestick_graph

Limit graph period to keep plotly quick and reactive

Filter trades to one pair

trades_red = trades.loc[trades['pair'] == pair]

data_red = data['2019-06-01':'2019-06-10']

Generate candlestick graph

graph = generate_candlestick_graph(pair=pair, data=data_red, trades=trades_red, indicators1=['sma20', 'ema50', 'ema55'], indicators2=['rsi', 'macd', 'macdsignal', 'macdhist'] )

```

```python

Show graph inline

graph.show()

Render graph in a seperate window

graph.show(renderer="browser")

```

Plot average profit per trade as distribution graph

```python import plotly.figure_factory as ff

hist_data = [trades.profit_ratio] group_labels = ['profit_ratio'] # name of the dataset

fig = ff.create_distplot(hist_data, group_labels,bin_size=0.01) fig.show()

```

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.