Add test and formatting to drawdown

docs/plotting.md

@@ -196,6 +196,7 @@ The first graph is good to get a grip of how the overall market progresses.
 
 The second graph will show if your algorithm works or doesn't.
 Perhaps you want an algorithm that steadily makes small profits, or one that acts less often, but makes big swings.
+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.
 

freqtrade/data/btanalysis.py

@@ -190,7 +190,7 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     return df
 
 
-def calculate_max_drawdown(trades: pd.DataFrame, date_col: str = 'close_time',
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
                            value_col: str = 'profitperc'
                            ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
     """

freqtrade/plot/plotting.py

@@ -126,8 +126,8 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> m
                 df_comb.loc[lowdate, 'cum_profit'],
             ],
             mode='markers',
-            name='Max Drawdown',
-            text=f"Max drawdown {max_drawdown}",
+            name=f"Max drawdown {max_drawdown:.2f}%",
+            text=f"Max drawdown {max_drawdown:.2f}%",
             marker=dict(
                 symbol='square-open',
                 size=9,

tests/test_plotting.py

@@ -3,15 +3,16 @@ from copy import deepcopy
 from pathlib import Path
 from unittest.mock import MagicMock
 
+import pandas as pd
 import plotly.graph_objects as go
 import pytest
 from plotly.subplots import make_subplots
 
+from freqtrade.commands import start_plot_dataframe, start_plot_profit
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
 from freqtrade.data.btanalysis import create_cum_profit, load_backtest_data
 from freqtrade.exceptions import OperationalException
-from freqtrade.commands import start_plot_dataframe, start_plot_profit
 from freqtrade.plot.plotting import (add_indicators, add_profit,
                                      create_plotconfig,
                                      generate_candlestick_graph,
@@ -266,6 +267,7 @@ def test_generate_profit_graph(testdatadir):
     trades = load_backtest_data(filename)
     timerange = TimeRange.parse_timerange("20180110-20180112")
     pairs = ["TRX/BTC", "ADA/BTC"]
+    trades = trades[trades['close_time'] < pd.Timestamp('2018-01-12', tz='UTC')]
 
     tickers = history.load_data(datadir=testdatadir,
                                 pairs=pairs,
@@ -283,13 +285,15 @@ def test_generate_profit_graph(testdatadir):
     assert fig.layout.yaxis3.title.text == "Profit"
 
     figure = fig.layout.figure
-    assert len(figure.data) == 4
+    assert len(figure.data) == 5
 
     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 0.00%")
+    assert isinstance(profit, go.Scatter)
 
     for pair in pairs:
         profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")