Add market Change

closes #2524 and #3518

freqtrade/data/btanalysis.py

@@ -168,6 +168,26 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
     return trades
 
 
+def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
+    """
+    Calculate market change based on "column".
+    Calculation is done by taking the first non-null and the last non-null element of each column
+    and calculating the pctchange as "(last - first) / first".
+    Then the results per pair are combined as mean.
+
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return:
+    """
+    tmp_means = []
+    for pair, df in data.items():
+        start = df[column].dropna().iloc[0]
+        end = df[column].dropna().iloc[-1]
+        tmp_means.append((end - start) / start)
+
+    return np.mean(tmp_means)
+
+
 def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
                                  column: str = "close") -> pd.DataFrame:
     """

freqtrade/optimize/optimize_reports.py

@@ -8,7 +8,7 @@ from pandas import DataFrame
 from tabulate import tabulate
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT
-from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.data.btanalysis import calculate_max_drawdown, calculate_market_change
 from freqtrade.misc import file_dump_json
 
 logger = logging.getLogger(__name__)
@@ -208,6 +208,8 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
     stake_currency = config['stake_currency']
     max_open_trades = config['max_open_trades']
     result: Dict[str, Any] = {'strategy': {}}
+    market_change = calculate_market_change(btdata, 'close')
+
     for strategy, results in all_results.items():
 
         pair_results = generate_pair_metrics(btdata, stake_currency=stake_currency,
@@ -232,7 +234,8 @@ def generate_backtest_stats(config: Dict, btdata: Dict[str, DataFrame],
             'backtest_end': max_date.datetime,
             'backtest_end_ts': max_date.timestamp,
             'backtest_days': backtest_days,
-            'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else None
+            'trades_per_day': round(len(results) / backtest_days, 2) if backtest_days > 0 else None,
+            'market_change': market_change,
         }
         result['strategy'][strategy] = strat_stats
 
@@ -348,11 +351,12 @@ def text_table_add_metrics(strategy_results: Dict) -> str:
             ('Max Drawdown', f"{round(strategy_results['max_drawdown'] * 100, 2)}%"),
             ('Drawdown Start', strategy_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
             ('Drawdown End', strategy_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
+            ('Market change', f"{round(strategy_results['market_change'] * 100, 2)}%"),
         ]
 
         return tabulate(metrics, headers=["Metric", "Value"], tablefmt="orgtbl")
     else:
-        return
+        return ''
 
 
 def show_backtest_results(config: Dict, backtest_stats: Dict):

tests/data/test_btanalysis.py

@@ -8,6 +8,7 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
 from freqtrade.configuration import TimeRange
 from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
                                        analyze_trade_parallelism,
+                                       calculate_market_change,
                                        calculate_max_drawdown,
                                        combine_dataframes_with_mean,
                                        create_cum_profit,
@@ -135,6 +136,14 @@ def test_load_trades(default_conf, mocker):
     assert bt_mock.call_count == 0
 
 
+def test_calculate_market_change(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
+    result = calculate_market_change(data)
+    assert isinstance(result, float)
+    assert pytest.approx(result) == 0.00955514
+
+
 def test_combine_dataframes_with_mean(testdatadir):
     pairs = ["ETH/BTC", "ADA/BTC"]
     data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')