Merge pull request #2439 from freqtrade/fix/plotprofit

Plot-profit does not work with db file

freqtrade/data/btanalysis.py

@@ -150,15 +150,21 @@ def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "c
     return df_comb
 
 
-def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
+def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
+                      timeframe: str) -> pd.DataFrame:
     """
     Adds a column `col_name` with the cumulative profit for the given trades array.
     :param df: DataFrame with date index
     :param trades: DataFrame containing trades (requires columns close_time and profitperc)
+    :param col_name: Column name that will be assigned the results
+    :param timeframe: Timeframe used during the operations
     :return: Returns df with one additional column, col_name, containing the cumulative profit.
     """
-    # Use groupby/sum().cumsum() to avoid errors when multiple trades sold at the same candle.
+    from freqtrade.exchange import timeframe_to_minutes
-    df[col_name] = trades.groupby('close_time')['profitperc'].sum().cumsum()
+    ticker_minutes = timeframe_to_minutes(timeframe)
+    # Resample to ticker_interval to make sure trades match candles
+    _trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
+    df.loc[:, col_name] = _trades_sum.cumsum()
     # Set first value to 0
     df.loc[df.iloc[0].name, col_name] = 0
     # FFill to get continuous

freqtrade/plot/plotting.py

@@ -264,12 +264,12 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
 
 
 def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
-                          trades: pd.DataFrame) -> go.Figure:
+                          trades: pd.DataFrame, timeframe: str) -> go.Figure:
     # Combine close-values for all pairs, rename columns to "pair"
     df_comb = combine_tickers_with_mean(tickers, "close")
 
     # Add combined cumulative profit
-    df_comb = create_cum_profit(df_comb, trades, 'cum_profit')
+    df_comb = create_cum_profit(df_comb, trades, 'cum_profit', timeframe)
 
     # Plot the pairs average close prices, and total profit growth
     avgclose = go.Scatter(
@@ -293,7 +293,7 @@ def generate_profit_graph(pairs: str, tickers: Dict[str, pd.DataFrame],
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'
-        df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col)
+        df_comb = create_cum_profit(df_comb, trades[trades['pair'] == pair], profit_col, timeframe)
 
         fig = add_profit(fig, 3, df_comb, profit_col, f"Profit {pair}")
 
@@ -382,9 +382,9 @@ def plot_profit(config: Dict[str, Any]) -> None:
                          )
     # Filter trades to relevant pairs
     trades = trades[trades['pair'].isin(plot_elements["pairs"])]
-
     # Create an average close price of all the pairs that were involved.
     # this could be useful to gauge the overall market trend
-    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"], trades)
+    fig = generate_profit_graph(plot_elements["pairs"], plot_elements["tickers"],
+                                trades, config.get('ticker_interval', '5m'))
     store_plot_file(fig, filename='freqtrade-profit-plot.html',
                     directory=config['user_data_dir'] / "plot", auto_open=True)

tests/data/test_btanalysis.py

@@ -2,7 +2,7 @@ from unittest.mock import MagicMock
 
 import pytest
 from arrow import Arrow
-from pandas import DataFrame, to_datetime
+from pandas import DataFrame, DateOffset, to_datetime
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
@@ -130,7 +130,25 @@ def test_create_cum_profit(testdatadir):
 
     cum_profits = create_cum_profit(df.set_index('date'),
                                     bt_data[bt_data["pair"] == 'POWR/BTC'],
-                                    "cum_profits")
+                                    "cum_profits", timeframe="5m")
+    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_create_cum_profit1(testdatadir):
+    filename = testdatadir / "backtest-result_test.json"
+    bt_data = load_backtest_data(filename)
+    # Move close-time to "off" the candle, to make sure the logic still works
+    bt_data.loc[:, 'close_time'] = bt_data.loc[:, 'close_time'] + DateOffset(seconds=20)
+    timerange = TimeRange.parse_timerange("20180110-20180112")
+
+    df = load_pair_history(pair="POWR/BTC", ticker_interval='5m',
+                           datadir=testdatadir, timerange=timerange)
+
+    cum_profits = create_cum_profit(df.set_index('date'),
+                                    bt_data[bt_data["pair"] == 'POWR/BTC'],
+                                    "cum_profits", timeframe="5m")
     assert "cum_profits" in cum_profits.columns
     assert cum_profits.iloc[0]['cum_profits'] == 0
     assert cum_profits.iloc[-1]['cum_profits'] == 0.0798005

tests/test_plotting.py

@@ -234,7 +234,7 @@ def test_add_profit(testdatadir):
 
     cum_profits = create_cum_profit(df.set_index('date'),
                                     bt_data[bt_data["pair"] == 'POWR/BTC'],
-                                    "cum_profits")
+                                    "cum_profits", timeframe="5m")
 
     fig1 = add_profit(fig, row=2, data=cum_profits, column='cum_profits', name='Profits')
     figure = fig1.layout.figure
@@ -256,7 +256,7 @@ def test_generate_profit_graph(testdatadir):
                                 )
     trades = trades[trades['pair'].isin(pairs)]
 
-    fig = generate_profit_graph(pairs, tickers, trades)
+    fig = generate_profit_graph(pairs, tickers, trades, timeframe="5m")
     assert isinstance(fig, go.Figure)
 
     assert fig.layout.title.text == "Freqtrade Profit plot"