Merge pull request #3018 from freqtrade/max_drawdown

Max drawdown in plot-profit

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

@@ -3,7 +3,7 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
-from typing import Dict, Union
+from typing import Dict, Union, Tuple
 
 import numpy as np
 import pandas as pd
@@ -188,3 +188,28 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     # FFill to get continuous
     df[col_name] = df[col_name].ffill()
     return df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_time',
+                           value_col: str = 'profitperc'
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_time and profitperc)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_time')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profitperc')
+    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col)
+    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']
+
+    high_date = profit_results.loc[max_drawdown_df['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[max_drawdown_df['drawdown'].idxmin(), date_col]
+
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date

freqtrade/plot/plotting.py

@@ -5,7 +5,8 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.btanalysis import (combine_tickers_with_mean,
+from freqtrade.data.btanalysis import (calculate_max_drawdown,
+                                       combine_tickers_with_mean,
                                        create_cum_profit,
                                        extract_trades_of_period, load_trades)
 from freqtrade.data.converter import trim_dataframe
@@ -111,6 +112,36 @@ def add_profit(fig, row, data: pd.DataFrame, column: str, name: str) -> make_sub
     return fig
 
 
+def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame) -> make_subplots:
+    """
+    Add scatter points indicating max drawdown
+    """
+    try:
+        max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
+
+        drawdown = go.Scatter(
+            x=[highdate, lowdate],
+            y=[
+                df_comb.loc[highdate, 'cum_profit'],
+                df_comb.loc[lowdate, 'cum_profit'],
+            ],
+            mode='markers',
+            name=f"Max drawdown {max_drawdown:.2f}%",
+            text=f"Max drawdown {max_drawdown:.2f}%",
+            marker=dict(
+                symbol='square-open',
+                size=9,
+                line=dict(width=2),
+                color='green'
+
+            )
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting max drawdown.")
+    return fig
+
+
 def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     """
     Add trades to "fig"
@@ -364,6 +395,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
 
     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)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'

tests/data/test_btanalysis.py

@@ -2,15 +2,17 @@ from unittest.mock import MagicMock
 
 import pytest
 from arrow import Arrow
-from pandas import DataFrame, DateOffset, to_datetime
+from pandas import DataFrame, DateOffset, to_datetime, Timestamp
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
+                                       analyze_trade_parallelism,
+                                       calculate_max_drawdown,
                                        combine_tickers_with_mean,
                                        create_cum_profit,
                                        extract_trades_of_period,
                                        load_backtest_data, load_trades,
-                                       load_trades_from_db, analyze_trade_parallelism)
+                                       load_trades_from_db)
 from freqtrade.data.history import load_data, load_pair_history
 from tests.test_persistence import create_mock_trades
 
@@ -163,3 +165,17 @@ def test_create_cum_profit1(testdatadir):
     assert "cum_profits" in cum_profits.columns
     assert cum_profits.iloc[0]['cum_profits'] == 0
     assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005
+
+
+def test_calculate_max_drawdown(testdatadir):
+    filename = testdatadir / "backtest-result_test.json"
+    bt_data = load_backtest_data(filename)
+    drawdown, h, low = calculate_max_drawdown(bt_data)
+    assert isinstance(drawdown, float)
+    assert pytest.approx(drawdown) == 0.21142322
+    assert isinstance(h, Timestamp)
+    assert isinstance(low, Timestamp)
+    assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
+    assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
+    with pytest.raises(ValueError, match='Trade dataframe empty.'):
+        drawdown, h, low = calculate_max_drawdown(DataFrame())

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}")