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Merge pull request #6147 from freqtrade/plot_parallel

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Plot parallel and underwater
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Matthias 2022-01-01 19:27:42 +01:00 committed by GitHub
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6 changed files with 111 additions and 15 deletions
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docs/assets/plot-profit.png
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4
docs/plotting.md
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@ -283,6 +283,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
* The summarized profit made by backtesting.
Note that this is not the real-world profit, but more of an estimate.
* Profit for each individual pair.
* Parallelism of trades.
* Underwater (Periods of drawdown).
The first graph is good to get a grip of how the overall market progresses.
@ -292,6 +294,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
Possible options for the `freqtrade plot-profit` subcommand:
```

35
freqtrade/data/btanalysis.py
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@ -361,6 +361,36 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
return df
def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
) -> pd.DataFrame:
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
max_drawdown_df['date'] = profit_results.loc[:, date_col]
return max_drawdown_df
def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
):
"""
Calculate max drawdown and the corresponding close dates
:param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
:param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
:param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
:return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
high and low time and high and low value.
:raise: ValueError if trade-dataframe was found empty.
"""
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
return max_drawdown_df
def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
value_col: str = 'profit_ratio'
) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
@ -376,10 +406,7 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
if len(trades) == 0:
raise ValueError("Trade dataframe empty.")
profit_results = trades.sort_values(date_col).reset_index(drop=True)
max_drawdown_df = pd.DataFrame()
max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
idxmin = max_drawdown_df['drawdown'].idxmin()
if idxmin == 0:

61
freqtrade/plot/plotting.py
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@ -5,7 +5,8 @@ from typing import Any, Dict, List
import pandas as pd
from freqtrade.configuration import TimeRange
from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframes_with_mean,
from freqtrade.data.btanalysis import (analyze_trade_parallelism, calculate_max_drawdown,
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit, extract_trades_of_period, load_trades)
from freqtrade.data.converter import trim_dataframe
from freqtrade.data.dataprovider import DataProvider
@ -185,6 +186,48 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
return fig
def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots:
"""
Add underwater plot
"""
try:
underwater = calculate_underwater(trades, value_col="profit_abs")
underwater = go.Scatter(
x=underwater['date'],
y=underwater['drawdown'],
name="Underwater Plot",
fill='tozeroy',
fillcolor='#cc362b',
line={'color': '#cc362b'},
)
fig.add_trace(underwater, row, 1)
except ValueError:
logger.warning("No trades found - not plotting underwater plot")
return fig
def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
"""
Add Chart showing trade parallelism
"""
try:
result = analyze_trade_parallelism(trades, timeframe)
drawdown = go.Scatter(
x=result.index,
y=result['open_trades'],
name="Parallel trades",
fill='tozeroy',
fillcolor='#242222',
line={'color': '#242222'},
)
fig.add_trace(drawdown, row, 1)
except ValueError:
logger.warning("No trades found - not plotting Parallelism.")
return fig
def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
"""
Add trades to "fig"
@ -482,20 +525,30 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
name='Avg close price',
)
fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
row_width=[1, 1, 1],
fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
row_heights=[1, 1, 1, 0.5, 1],
vertical_spacing=0.05,
subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
subplot_titles=[
"AVG Close Price",
"Combined Profit",
"Profit per pair",
"Parallelism",
"Underwater",
])
fig['layout'].update(title="Freqtrade Profit plot")
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
fig['layout']['yaxis4'].update(title='Trade count')
fig['layout']['yaxis5'].update(title='Underwater Plot')
fig['layout']['xaxis']['rangeslider'].update(visible=False)
fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
fig.add_trace(avgclose, 1, 1)
fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
fig = add_parallelism(fig, 4, trades, timeframe)
fig = add_underwater(fig, 5, trades)
for pair in pairs:
profit_col = f'cum_profit_{pair}'

15
tests/data/test_btanalysis.py
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@ -11,10 +11,10 @@ from freqtrade.constants import LAST_BT_RESULT_FN
from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
analyze_trade_parallelism, calculate_csum,
calculate_market_change, calculate_max_drawdown,
combine_dataframes_with_mean, create_cum_profit,
extract_trades_of_period, get_latest_backtest_filename,
get_latest_hyperopt_file, load_backtest_data, load_trades,
load_trades_from_db)
calculate_underwater, combine_dataframes_with_mean,
create_cum_profit, extract_trades_of_period,
get_latest_backtest_filename, get_latest_hyperopt_file,
load_backtest_data, load_trades, load_trades_from_db)
from freqtrade.data.history import load_data, load_pair_history
from tests.conftest import create_mock_trades
from tests.conftest_trades import MOCK_TRADE_COUNT
@ -291,9 +291,16 @@ def test_calculate_max_drawdown(testdatadir):
assert isinstance(lval, float)
assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
underwater = calculate_underwater(bt_data)
assert isinstance(underwater, DataFrame)
with pytest.raises(ValueError, match='Trade dataframe empty.'):
drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
with pytest.raises(ValueError, match='Trade dataframe empty.'):
calculate_underwater(DataFrame())
def test_calculate_csum(testdatadir):
filename = testdatadir / "backtest-result_test.json"

11
tests/test_plotting.py
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@ -336,15 +336,20 @@ def test_generate_profit_graph(testdatadir):
assert fig.layout.yaxis3.title.text == "Profit BTC"
figure = fig.layout.figure
assert len(figure.data) == 5
assert len(figure.data) == 7
avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
assert isinstance(avgclose, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Profit")
assert isinstance(profit, go.Scatter)
profit = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
assert isinstance(profit, go.Scatter)
drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
assert isinstance(drawdown, go.Scatter)
parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
assert isinstance(parallel, go.Scatter)
underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
assert isinstance(underwater, go.Scatter)
for pair in pairs:
profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")