Merge branch 'develop' into feat/short

.github/workflows/ci.yml

@@ -196,7 +196,7 @@ jobs:
       uses: rjstone/discord-webhook-notify@v1
       if: failure() && ( github.event_name != 'pull_request' || github.event.pull_request.head.repo.fork == false)
       with:
-          severity: error
+          severity: info
           details: Test Succeeded!
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 

docs/backtesting.md

@@ -312,7 +312,7 @@ A backtesting result will look like that:
 |                       |                     |
 | Min balance           | 0.00945123 BTC      |
 | Max balance           | 0.01846651 BTC      |
-| Drawdown              | 50.63%              |
+| Drawdown (Account)    | 13.33%              |
 | Drawdown              | 0.0015 BTC          |
 | Drawdown high         | 0.0013 BTC          |
 | Drawdown low          | -0.0002 BTC         |
@@ -405,7 +405,7 @@ It contains some useful key metrics about performance of your strategy on backte
 |                        |                     |
 | Min balance            | 0.00945123 BTC      |
 | Max balance            | 0.01846651 BTC      |
-| Drawdown               | 50.63%              |
+| Drawdown (Account)     | 13.33%              |
 | Drawdown               | 0.0015 BTC          |
 | Drawdown high          | 0.0013 BTC          |
 | Drawdown low           | -0.0002 BTC         |
@@ -432,7 +432,8 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
 - `Rejected Buy signals`: Buy signals that could not be acted upon due to max_open_trades being reached.
 - `Min balance` / `Max balance`: Lowest and Highest Wallet balance during the backtest period.
-- `Drawdown`: Maximum drawdown experienced. For example, the value of 50% means that from highest to subsequent lowest point, a 50% drop was experienced).
+- `Drawdown (Account)`: Maximum Account Drawdown experienced. Calculated as $(Absolute Drawdown) / (DrawdownHigh + startingBalance)$.
+- `Drawdown`: Maximum, absolute drawdown experienced. Difference between Drawdown High and Subsequent Low point.
 - `Drawdown high` / `Drawdown low`: Profit at the beginning and end of the largest drawdown period. A negative low value means initial capital lost.
 - `Drawdown Start` / `Drawdown End`: Start and end datetime for this largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
 - `Market change`: Change of the market during the backtest period. Calculated as average of all pairs changes from the first to the last candle using the "close" column.

freqtrade/data/btanalysis.py

@@ -9,21 +9,13 @@
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
+from freqtrade.exceptions import OperationalException
 from freqtrade.misc import json_load
 from freqtrade.persistence import LocalTrade, Trade, init_db
 
 
 logger = logging.getLogger(__name__)
 
-# Old format - maybe remove?
-BT_DATA_COLUMNS_OLD = ["pair", "profit_percent", "open_date", "close_date", "index",
-                       "trade_duration", "open_rate", "close_rate", "open_at_end", "sell_reason"]
-
-# Mid-term format, created by BacktestResult Named Tuple
-BT_DATA_COLUMNS_MID = ['pair', 'profit_percent', 'open_date', 'close_date', 'trade_duration',
-                       'open_rate', 'close_rate', 'open_at_end', 'sell_reason', 'fee_open',
-                       'fee_close', 'amount', 'profit_abs', 'profit_ratio']
-
 # Newest format
 BT_DATA_COLUMNS = ['pair', 'stake_amount', 'amount', 'open_date', 'close_date',
                    'open_rate', 'close_rate',
@@ -170,23 +162,9 @@ def load_backtest_data(filename: Union[Path, str], strategy: Optional[str] = Non
                                               )
     else:
         # old format - only with lists.
-        df = pd.DataFrame(data, columns=BT_DATA_COLUMNS_OLD)
-        if not df.empty:
-            df['open_date'] = pd.to_datetime(df['open_date'],
-                                             unit='s',
-                                             utc=True,
-                                             infer_datetime_format=True
-                                             )
-            df['close_date'] = pd.to_datetime(df['close_date'],
-                                              unit='s',
-                                              utc=True,
-                                              infer_datetime_format=True
-                                              )
-            # Create compatibility with new format
-            df['profit_abs'] = df['close_rate'] - df['open_rate']
+        raise OperationalException(
+            "Backtest-results with only trades data are no longer supported.")
     if not df.empty:
-        if 'profit_ratio' not in df.columns:
-            df['profit_ratio'] = df['profit_percent']
         df = df.sort_values("open_date").reset_index(drop=True)
     return df
 
@@ -395,15 +373,17 @@ def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
 
 
 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]:
+                           value_col: str = 'profit_abs', starting_balance: float = 0
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
     """
     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.
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
+    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
+             with absolute max drawdown, high and low time and high and low value,
+             and the relative account drawdown
     :raise: ValueError if trade-dataframe was found empty.
     """
     if len(trades) == 0:
@@ -419,7 +399,18 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
                                    ['high_value'].idxmax(), 'cumulative']
     low_val = max_drawdown_df.loc[idxmin, 'cumulative']
-    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
+    max_drawdown_rel = 0.0
+    if high_val + starting_balance != 0:
+        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
+
+    return (
+        abs(min(max_drawdown_df['drawdown'])),
+        high_date,
+        low_date,
+        high_val,
+        low_val,
+        max_drawdown_rel
+    )
 
 
 def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:

freqtrade/exchange/exchange.py

@@ -65,6 +65,8 @@ class Exchange:
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
+        # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
+        "ohlcv_volume_currency": "base",  # "base" or "quote"
         "trades_pagination": "time",  # Possible are "time" or "id"
         "trades_pagination_arg": "since",
         "l2_limit_range": None,
@@ -797,7 +799,8 @@ def get_dry_market_fill_price(self, pair: str, side: str, amount: float, rate: f
             max_slippage_val = rate * ((1 + slippage) if side == 'buy' else (1 - slippage))
 
             remaining_amount = amount
-            filled_amount = 0
+            filled_amount = 0.0
+            book_entry_price = 0.0
             for book_entry in ob[ob_type]:
                 book_entry_price = book_entry[0]
                 book_entry_coin_volume = book_entry[1]

freqtrade/exchange/ftx.py

@@ -20,6 +20,7 @@ class Ftx(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "ohlcv_candle_limit": 1500,
+        "ohlcv_volume_currency": "quote",
         "mark_ohlcv_price": "index",
         "mark_ohlcv_timeframe": "1h",
     }

freqtrade/exchange/gateio.py

@@ -22,6 +22,7 @@ class Gateio(Exchange):
 
     _ft_has: Dict = {
         "ohlcv_candle_limit": 1000,
+        "ohlcv_volume_currency": "quote",
     }
 
     _headers = {'X-Gate-Channel-Id': 'freqtrade'}

freqtrade/exchange/okex.py

@@ -15,7 +15,7 @@ class Okex(Exchange):
     """
 
     _ft_has: Dict = {
-        "ohlcv_candle_limit": 100,
+        "ohlcv_candle_limit": 300,
         "mark_ohlcv_timeframe": "4h",
         "funding_fee_timeframe": "8h",
     }

freqtrade/optimize/backtesting.py

@@ -448,8 +448,10 @@ def _get_sell_trade_entry(self, trade: LocalTrade, sell_row: Tuple) -> Optional[
             detail_data.loc[:, 'exit_long'] = sell_row[ELONG_IDX]
             detail_data.loc[:, 'enter_long'] = sell_row[LONG_IDX]
             detail_data.loc[:, 'exit_long'] = sell_row[ELONG_IDX]
+            detail_data.loc[:, 'enter_tag'] = sell_row[ENTER_TAG_IDX]
+            detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
             headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
-                       'enter_short', 'exit_short']
+                       'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
             for det_row in detail_data[headers].values.tolist():
                 res = self._get_sell_trade_entry_for_candle(trade, det_row)
                 if res:

freqtrade/optimize/hyperopt.py

@@ -76,6 +76,7 @@ def __init__(self, config: Dict[str, Any]) -> None:
         self.config = config
 
         self.backtesting = Backtesting(self.config)
+        self.pairlist = self.backtesting.pairlists.whitelist
 
         if not self.config.get('hyperopt'):
             self.custom_hyperopt = HyperOptAuto(self.config)
@@ -332,7 +333,7 @@ def _get_results_dict(self, backtesting_results, min_date, max_date,
         params_details = self._get_params_details(params_dict)
 
         strat_stats = generate_strategy_stats(
-            processed, self.backtesting.strategy.get_strategy_name(),
+            self.pairlist, self.backtesting.strategy.get_strategy_name(),
             backtesting_results, min_date, max_date, market_change=0
         )
         results_explanation = HyperoptTools.format_results_explanation_string(

freqtrade/optimize/hyperopt_loss_calmar.py

@@ -47,10 +47,9 @@ def hyperopt_loss_function(
 
         # calculate max drawdown
         try:
-            _, _, _, high_val, low_val = calculate_max_drawdown(
+            _, _, _, _, _, max_drawdown = calculate_max_drawdown(
                 results, value_col="profit_abs"
             )
-            max_drawdown = (high_val - low_val) / high_val
         except ValueError:
             max_drawdown = 0
 

freqtrade/optimize/hyperopt_tools.py

@@ -308,8 +308,7 @@ def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataF
 
         if not has_drawdown:
             # Ensure compatibility with older versions of hyperopt results
-            trials['results_metrics.max_drawdown_abs'] = None
-            trials['results_metrics.max_drawdown'] = None
+            trials['results_metrics.max_drawdown_account'] = None
 
         # New mode, using backtest result for metrics
         trials['results_metrics.winsdrawslosses'] = trials.apply(
@@ -320,12 +319,15 @@ def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataF
                          'results_metrics.winsdrawslosses',
                          'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
                          'results_metrics.profit_total', 'results_metrics.holding_avg',
-                         'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
+                         'results_metrics.max_drawdown',
+                         'results_metrics.max_drawdown_account', 'results_metrics.max_drawdown_abs',
                          'loss', 'is_initial_point', 'is_best']]
 
-        trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
-                          'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
-                          'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best']
+        trials.columns = [
+            'Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
+            'Total profit', 'Profit', 'Avg duration', 'max_drawdown', 'max_drawdown_account',
+            'max_drawdown_abs', 'Objective', 'is_initial_point', 'is_best'
+            ]
 
         return trials
 
@@ -341,9 +343,9 @@ def get_result_table(config: dict, results: list, total_epochs: int, highlight_b
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
+        has_account_drawdown = 'results_metrics.max_drawdown_account' in trials.columns
 
-        trials = HyperoptTools.prepare_trials_columns(trials, has_drawdown)
+        trials = HyperoptTools.prepare_trials_columns(trials, has_account_drawdown)
 
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
@@ -368,19 +370,20 @@ def get_result_table(config: dict, results: list, total_epochs: int, highlight_b
 
         stake_currency = config['stake_currency']
 
-        if has_drawdown:
-            trials['Max Drawdown'] = trials.apply(
-                lambda x: '{} {}'.format(
-                    round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    f"({x['Max Drawdown']:,.2%})".rjust(10, ' ')
-                ).rjust(25 + len(stake_currency))
-                if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
-                axis=1
-            )
-        else:
-            trials = trials.drop(columns=['Max Drawdown'])
+        trials[f"Max Drawdown{' (Acct)' if has_account_drawdown else ''}"] = trials.apply(
+            lambda x: "{} {}".format(
+                round_coin_value(x['max_drawdown_abs'], stake_currency),
+                (f"({x['max_drawdown_account']:,.2%})"
+                    if has_account_drawdown
+                    else f"({x['max_drawdown']:,.2%})"
+                 ).rjust(10, ' ')
+            ).rjust(25 + len(stake_currency))
+            if x['max_drawdown'] != 0.0 or x['max_drawdown_account'] != 0.0
+            else '--'.rjust(25 + len(stake_currency)),
+            axis=1
+        )
 
-        trials = trials.drop(columns=['max_drawdown_abs'])
+        trials = trials.drop(columns=['max_drawdown_abs', 'max_drawdown', 'max_drawdown_account'])
 
         trials['Profit'] = trials.apply(
             lambda x: '{} {}'.format(