Merge pull request freqtrade#1018 from freqtrade/feat/sell_reason

Record sell reason

docs/backtesting.md

@@ -83,7 +83,7 @@ with filename.open() as file:
         data = json.load(file)
 
 columns = ["pair", "profit", "opents", "closets", "index", "duration",
-           "open_rate", "close_rate", "open_at_end"]
+           "open_rate", "close_rate", "open_at_end", "sell_reason"]
 df = pd.DataFrame(data, columns=columns)
 
 df['opents'] = pd.to_datetime(df['opents'],
@@ -98,6 +98,8 @@ df['closets'] = pd.to_datetime(df['closets'],
                               )
 ```
 
+If you have some ideas for interesting / helpful backtest data analysis, feel free to submit a PR so the community can benefit from it.
+
 #### Exporting trades to file specifying a custom filename
 
 ```bash

freqtrade/freqtradebot.py

@@ -20,6 +20,7 @@
 from freqtrade.persistence import Trade
 from freqtrade.rpc import RPCManager, RPCMessageType
 from freqtrade.state import State
+from freqtrade.strategy.interface import SellType
 from freqtrade.strategy.resolver import IStrategy, StrategyResolver
 
 logger = logging.getLogger(__name__)
@@ -53,7 +54,6 @@ def __init__(self, config: Dict[str, Any])-> None:
         self.rpc: RPCManager = RPCManager(self)
         self.persistence = None
         self.exchange = Exchange(self.config)
-
         self._init_modules()
 
     def _init_modules(self) -> None:
@@ -392,7 +392,9 @@ def execute_buy(self, pair: str, stake_amount: float) -> bool:
             open_rate_requested=buy_limit,
             open_date=datetime.utcnow(),
             exchange=self.exchange.id,
-            open_order_id=order_id
+            open_order_id=order_id,
+            strategy=self.strategy.get_strategy_name(),
+            ticker_interval=constants.TICKER_INTERVAL_MINUTES[self.config['ticker_interval']]
         )
         Trade.session.add(trade)
         Trade.session.flush()
@@ -505,8 +507,9 @@ def handle_trade(self, trade: Trade) -> bool:
             (buy, sell) = self.strategy.get_signal(self.exchange,
                                                    trade.pair, self.strategy.ticker_interval)
 
-        if self.strategy.should_sell(trade, current_rate, datetime.utcnow(), buy, sell):
-            self.execute_sell(trade, current_rate)
+        should_sell = self.strategy.should_sell(trade, current_rate, datetime.utcnow(), buy, sell)
+        if should_sell.sell_flag:
+            self.execute_sell(trade, current_rate, should_sell.sell_type)
             return True
         logger.info('Found no sell signals for whitelisted currencies. Trying again..')
         return False
@@ -607,17 +610,19 @@ def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
         # TODO: figure out how to handle partially complete sell orders
         return False
 
-    def execute_sell(self, trade: Trade, limit: float) -> None:
+    def execute_sell(self, trade: Trade, limit: float, sell_reason: SellType) -> None:
         """
         Executes a limit sell for the given trade and limit
         :param trade: Trade instance
         :param limit: limit rate for the sell order
+        :param sellreason: Reason the sell was triggered
         :return: None
         """
         # Execute sell and update trade record
         order_id = self.exchange.sell(str(trade.pair), limit, trade.amount)['id']
         trade.open_order_id = order_id
         trade.close_rate_requested = limit
+        trade.sell_reason = sell_reason.value
 
         profit_trade = trade.calc_profit(rate=limit)
         current_rate = self.exchange.get_ticker(trade.pair)['bid']

freqtrade/optimize/backtesting.py

@@ -20,6 +20,7 @@
 from freqtrade.exchange import Exchange
 from freqtrade.misc import file_dump_json
 from freqtrade.persistence import Trade
+from freqtrade.strategy.interface import SellType
 from freqtrade.strategy.resolver import IStrategy, StrategyResolver
 
 logger = logging.getLogger(__name__)
@@ -40,6 +41,7 @@ class BacktestResult(NamedTuple):
     open_at_end: bool
     open_rate: float
     close_rate: float
+    sell_reason: SellType
 
 
 class Backtesting(object):
@@ -120,11 +122,21 @@ def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str
         ])
         return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
 
+    def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
+        """
+        Generate small table outlining Backtest results
+        """
+        tabular_data = []
+        headers = ['Sell Reason', 'Count']
+        for reason, count in results['sell_reason'].value_counts().iteritems():
+            tabular_data.append([reason.value,  count])
+        return tabulate(tabular_data, headers=headers, tablefmt="pipe")
+
     def _store_backtest_result(self, recordfilename: Optional[str], results: DataFrame) -> None:
 
         records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
                     t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
-                    t.open_rate, t.close_rate, t.open_at_end)
+                    t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
                    for index, t in results.iterrows()]
 
         if records:
@@ -153,8 +165,9 @@ def _get_sell_trade_entry(
                 trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
 
             buy_signal = sell_row.buy
-            if self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
-                                         sell_row.sell):
+            sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
+                                             sell_row.sell)
+            if sell.sell_flag:
 
                 return BacktestResult(pair=pair,
                                       profit_percent=trade.calc_profit_percent(rate=sell_row.open),
@@ -167,7 +180,8 @@ def _get_sell_trade_entry(
                                       close_index=sell_row.Index,
                                       open_at_end=False,
                                       open_rate=buy_row.open,
-                                      close_rate=sell_row.open
+                                      close_rate=sell_row.open,
+                                      sell_reason=sell.sell_type
                                       )
         if partial_ticker:
             # no sell condition found - trade stil open at end of backtest period
@@ -183,7 +197,8 @@ def _get_sell_trade_entry(
                                  close_index=sell_row.Index,
                                  open_at_end=True,
                                  open_rate=buy_row.open,
-                                 close_rate=sell_row.open
+                                 close_rate=sell_row.open,
+                                 sell_reason=SellType.FORCE_SELL
                                  )
             logger.debug('Force_selling still open trade %s with %s perc - %s', btr.pair,
                          btr.profit_percent, btr.profit_abs)
@@ -318,21 +333,31 @@ def start(self) -> None:
             self._store_backtest_result(self.config.get('exportfilename'), results)
 
         logger.info(
-            '\n================================================= '
-            'BACKTESTING REPORT'
-            ' ==================================================\n'
+            '\n' + '=' * 49 +
+            ' BACKTESTING REPORT ' +
+            '=' * 50 + '\n'
             '%s',
             self._generate_text_table(
                 data,
                 results
             )
         )
+        # logger.info(
+        #     results[['sell_reason']].groupby('sell_reason').count()
+        # )
 
         logger.info(
-            '\n=============================================== '
-            'LEFT OPEN TRADES REPORT'
-            ' ===============================================\n'
-            '%s',
+            '\n' +
+            ' SELL READON STATS '.center(119, '=') +
+            '\n%s \n',
+            self._generate_text_table_sell_reason(data, results)
+
+        )
+
+        logger.info(
+            '\n' +
+            ' LEFT OPEN TRADES REPORT '.center(119, '=') +
+            '\n%s',
             self._generate_text_table(
                 data,
                 results.loc[results.open_at_end]

freqtrade/persistence.py

@@ -90,6 +90,9 @@ def check_migrate(engine) -> None:
         stop_loss = get_column_def(cols, 'stop_loss', '0.0')
         initial_stop_loss = get_column_def(cols, 'initial_stop_loss', '0.0')
         max_rate = get_column_def(cols, 'max_rate', '0.0')
+        sell_reason = get_column_def(cols, 'sell_reason', 'null')
+        strategy = get_column_def(cols, 'strategy', 'null')
+        ticker_interval = get_column_def(cols, 'ticker_interval', 'null')
 
         # Schema migration necessary
         engine.execute(f"alter table trades rename to {table_back_name}")
@@ -101,7 +104,8 @@ def check_migrate(engine) -> None:
                 (id, exchange, pair, is_open, fee_open, fee_close, open_rate,
                 open_rate_requested, close_rate, close_rate_requested, close_profit,
                 stake_amount, amount, open_date, close_date, open_order_id,
-                stop_loss, initial_stop_loss, max_rate
+                stop_loss, initial_stop_loss, max_rate, sell_reason, strategy,
+                ticker_interval
                 )
             select id, lower(exchange),
                 case
@@ -116,7 +120,8 @@ def check_migrate(engine) -> None:
                 {close_rate_requested} close_rate_requested, close_profit,
                 stake_amount, amount, open_date, close_date, open_order_id,
                 {stop_loss} stop_loss, {initial_stop_loss} initial_stop_loss,
-                {max_rate} max_rate
+                {max_rate} max_rate, {sell_reason} sell_reason, {strategy} strategy,
+                {ticker_interval} ticker_interval
                 from {table_back_name}
              """)
 
@@ -172,6 +177,9 @@ class Trade(_DECL_BASE):
     initial_stop_loss = Column(Float, nullable=True, default=0.0)
     # absolute value of the highest reached price
     max_rate = Column(Float, nullable=True, default=0.0)
+    sell_reason = Column(String, nullable=True)
+    strategy = Column(String, nullable=True)
+    ticker_interval = Column(Integer, nullable=True)
 
     def __repr__(self):
         open_since = arrow.get(self.open_date).humanize() if self.is_open else 'closed'

freqtrade/rpc/rpc.py

@@ -16,6 +16,7 @@
 from freqtrade.misc import shorten_date
 from freqtrade.persistence import Trade
 from freqtrade.state import State
+from freqtrade.strategy.interface import SellType
 
 logger = logging.getLogger(__name__)
 
@@ -344,7 +345,7 @@ def _exec_forcesell(trade: Trade) -> None:
 
             # Get current rate and execute sell
             current_rate = self._freqtrade.exchange.get_ticker(trade.pair, False)['bid']
-            self._freqtrade.execute_sell(trade, current_rate)
+            self._freqtrade.execute_sell(trade, current_rate, SellType.FORCE_SELL)
         # ---- EOF def _exec_forcesell ----
 
         if self._freqtrade.state != State.RUNNING:

freqtrade/strategy/interface.py

@@ -6,7 +6,7 @@
 from abc import ABC, abstractmethod
 from datetime import datetime
 from enum import Enum
-from typing import Dict, List, Tuple
+from typing import Dict, List, NamedTuple, Tuple
 
 import arrow
 from pandas import DataFrame
@@ -27,6 +27,26 @@ class SignalType(Enum):
     SELL = "sell"
 
 
+class SellType(Enum):
+    """
+    Enum to distinguish between sell reasons
+    """
+    ROI = "roi"
+    STOP_LOSS = "stop_loss"
+    TRAILING_STOP_LOSS = "trailing_stop_loss"
+    SELL_SIGNAL = "sell_signal"
+    FORCE_SELL = "force_sell"
+    NONE = ""
+
+
+class SellCheckTuple(NamedTuple):
+    """
+    NamedTuple for Sell type + reason
+    """
+    sell_flag: bool
+    sell_type: SellType
+
+
 class IStrategy(ABC):
     """
     Interface for freqtrade strategies
@@ -69,6 +89,12 @@ def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
         :return: DataFrame with sell column
         """
 
+    def get_strategy_name(self) -> str:
+        """
+        Returns strategy class name
+        """
+        return self.__class__.__name__
+
     def analyze_ticker(self, ticker_history: List[Dict]) -> DataFrame:
         """
         Parses the given ticker history and returns a populated DataFrame
@@ -137,40 +163,42 @@ def get_signal(self, exchange: Exchange, pair: str, interval: str) -> Tuple[bool
         )
         return buy, sell
 
-    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool, sell: bool) -> bool:
+    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
+                    sell: bool) -> SellCheckTuple:
         """
         This function evaluate if on the condition required to trigger a sell has been reached
         if the threshold is reached and updates the trade record.
         :return: True if trade should be sold, False otherwise
         """
         current_profit = trade.calc_profit_percent(rate)
-        if self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
-                                  current_profit=current_profit):
-            return True
+        stoplossflag = self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
+                                              current_profit=current_profit)
+        if stoplossflag.sell_flag:
+            return stoplossflag
 
         experimental = self.config.get('experimental', {})
 
         if buy and experimental.get('ignore_roi_if_buy_signal', False):
             logger.debug('Buy signal still active - not selling.')
-            return False
+            return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
 
         # Check if minimal roi has been reached and no longer in buy conditions (avoiding a fee)
         if self.min_roi_reached(trade=trade, current_profit=current_profit, current_time=date):
             logger.debug('Required profit reached. Selling..')
-            return True
+            return SellCheckTuple(sell_flag=True, sell_type=SellType.ROI)
 
         if experimental.get('sell_profit_only', False):
             logger.debug('Checking if trade is profitable..')
             if trade.calc_profit(rate=rate) <= 0:
-                return False
+                return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
         if sell and not buy and experimental.get('use_sell_signal', False):
             logger.debug('Sell signal received. Selling..')
-            return True
+            return SellCheckTuple(sell_flag=True, sell_type=SellType.SELL_SIGNAL)
 
-        return False
+        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
 
     def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: datetime,
-                          current_profit: float) -> bool:
+                          current_profit: float) -> SellCheckTuple:
         """
         Based on current profit of the trade and configured (trailing) stoploss,
         decides to sell or not
@@ -182,8 +210,9 @@ def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: dat
 
         # evaluate if the stoploss was hit
         if self.stoploss is not None and trade.stop_loss >= current_rate:
-
+            selltype = SellType.STOP_LOSS
             if trailing_stop:
+                selltype = SellType.TRAILING_STOP_LOSS
                 logger.debug(
                     f"HIT STOP: current price at {current_rate:.6f}, "
                     f"stop loss is {trade.stop_loss:.6f}, "
@@ -192,7 +221,7 @@ def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: dat
                 logger.debug(f"trailing stop saved {trade.stop_loss - trade.initial_stop_loss:.6f}")
 
             logger.debug('Stop loss hit.')
-            return True
+            return SellCheckTuple(sell_flag=True, sell_type=selltype)
 
         # update the stop loss afterwards, after all by definition it's supposed to be hanging
         if trailing_stop:
@@ -209,7 +238,7 @@ def stop_loss_reached(self, current_rate: float, trade: Trade, current_time: dat
 
             trade.adjust_stop_loss(current_rate, stop_loss_value)
 
-        return False
+        return SellCheckTuple(sell_flag=False, sell_type=SellType.NONE)
 
     def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
         """