Merge e6d3a440cd into 0980e7e82d

.gitignore

@@ -6,7 +6,6 @@ config*.json
 .hyperopt
 logfile.txt
 hyperopt_trials.pickle
-user_data/
 freqtrade-plot.html
 
 # Byte-compiled / optimized / DLL files

config.json.example

@@ -5,6 +5,9 @@
     "fiat_display_currency": "USD",
     "ticker_interval" : "5m",
     "dry_run": false,
+    "trailing_stop": {
+        "positive" : 0.005
+    },
     "unfilledtimeout": 600,
     "bid_strategy": {
         "ask_last_balance": 0.0

config_full.json.example

@@ -5,6 +5,7 @@
     "fiat_display_currency": "USD",
     "dry_run": false,
     "ticker_interval": "5m",
+    "trailing_stop": true,
     "minimal_roi": {
         "40":  0.0,
         "30":  0.01,

freqtrade/analyze.py

@@ -14,7 +14,6 @@ from freqtrade.exchange import get_ticker_history
 from freqtrade.persistence import Trade
 from freqtrade.strategy.resolver import StrategyResolver
 
-
 logger = logging.getLogger(__name__)
 
 
@@ -31,6 +30,7 @@ class Analyze(object):
     Analyze class contains everything the bot need to determine if the situation is good for
     buying or selling.
     """
+
     def __init__(self, config: dict) -> None:
         """
         Init Analyze
@@ -195,10 +195,41 @@ class Analyze(object):
         :return True if bot should sell at current rate
         """
         current_profit = trade.calc_profit_percent(current_rate)
-        if self.strategy.stoploss is not None and current_profit < self.strategy.stoploss:
+
+        if trade.stop_loss is None:
+            # initially adjust the stop loss to the base value
+            trade.adjust_stop_loss(trade.open_rate, self.strategy.stoploss)
+
+        # evaluate if the stoploss was hit
+        if self.strategy.stoploss is not None and trade.stop_loss >= current_rate:
+
+            if 'trailing_stop' in self.config and self.config['trailing_stop']:
+                logger.warning(
+                    "HIT STOP: current price at {:.6f}, stop loss is {:.6f}, "
+                    "initial stop loss was at {:.6f}, trade opened at {:.6f}".format(
+                        current_rate, trade.stop_loss, trade.initial_stop_loss, trade.open_rate))
+                logger.debug("trailing stop saved us: {:.6f}"
+                             .format(trade.stop_loss - trade.initial_stop_loss))
+
             logger.debug('Stop loss hit.')
             return True
 
+        # update the stop loss afterwards, after all by definition it's supposed to be hanging
+        if 'trailing_stop' in self.config and self.config['trailing_stop']:
+
+            # check if we have a special stop loss for positive condition
+            # and if profit is positive
+            stop_loss_value = self.strategy.stoploss
+            if isinstance(self.config['trailing_stop'], dict) and \
+                    'positive' in self.config['trailing_stop'] and \
+                    current_profit > 0:
+
+                logger.debug("using positive stop loss mode: {} since we have profit {}".format(
+                    self.config['trailing_stop']['positive'], current_profit))
+                stop_loss_value = self.config['trailing_stop']['positive']
+
+            trade.adjust_stop_loss(current_rate, stop_loss_value)
+
         # Check if time matches and current rate is above threshold
         time_diff = (current_time.timestamp() - trade.open_date.timestamp()) / 60
         for duration, threshold in self.strategy.minimal_roi.items():

freqtrade/persistence.py

@@ -148,6 +148,12 @@ class Trade(_DECL_BASE):
     open_date = Column(DateTime, nullable=False, default=datetime.utcnow)
     close_date = Column(DateTime)
     open_order_id = Column(String)
+    # absolute value of the stop loss
+    stop_loss = Column(Float, nullable=True, default=0.0)
+    # absolute value of the initial stop loss
+    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)
 
     def __repr__(self):
         return 'Trade(id={}, pair={}, amount={:.8f}, open_rate={:.8f}, open_since={})'.format(
@@ -158,6 +164,50 @@ class Trade(_DECL_BASE):
             arrow.get(self.open_date).humanize() if self.is_open else 'closed'
         )
 
+    def adjust_stop_loss(self, current_price, stoploss):
+        """
+
+        this adjusts the stop loss to it's most recently observed
+        setting
+        :param current_price:
+        :param stoploss:
+        :return:
+        """
+
+        new_loss = Decimal(current_price * (1 - abs(stoploss)))
+
+        # keeping track of the highest observed rate for this trade
+        if self.max_rate is None:
+            self.max_rate = current_price
+        else:
+            if current_price > self.max_rate:
+                self.max_rate = current_price
+
+        # no stop loss assigned yet
+        if self.stop_loss is None or self.stop_loss == 0:
+            logger.debug("assigning new stop loss")
+            self.stop_loss = new_loss
+            self.initial_stop_loss = new_loss
+
+        # evaluate if the stop loss needs to be updated
+        else:
+            if new_loss > self.stop_loss:  # stop losses only walk up, never down!
+                self.stop_loss = new_loss
+                logger.debug("adjusted stop loss")
+            else:
+                logger.debug("keeping current stop loss")
+
+        logger.debug(
+            "{} - current price {:.8f}, bought at {:.8f} and calculated "
+            "stop loss is at: {:.8f} initial stop at {:.8f}. trailing stop loss saved us: {:.8f} "
+            "and max observed rate was {:.8f}".format(
+                self.pair, current_price, self.open_rate,
+                self.initial_stop_loss,
+                self.stop_loss, float(self.stop_loss) - float(self.initial_stop_loss),
+                self.max_rate
+
+            ))
+
     def update(self, order: Dict) -> None:
         """
         Updates this entity with amount and actual open/close rates.

freqtrade/rpc/rpc.py

@@ -98,10 +98,11 @@ class RPC(object):
                     trade.id,
                     trade.pair,
                     shorten_date(arrow.get(trade.open_date).humanize(only_distance=True)),
-                    '{:.2f}%'.format(100 * trade.calc_profit_percent(current_rate))
+                    '{:.2f}%'.format(100 * trade.calc_profit_percent(current_rate)),
+                    '{:.6f}'.format(trade.amount * current_rate)
                 ])
 
-            columns = ['ID', 'Pair', 'Since', 'Profit']
+            columns = ['ID', 'Pair', 'Since', 'Profit', 'Value']
             df_statuses = DataFrame.from_records(trades_list, columns=columns)
             df_statuses = df_statuses.set_index(columns[0])
             # The style used throughout is to return a tuple

freqtrade/tests/optimize/test_backtesting.py

@@ -84,6 +84,7 @@ def load_data_test(what):
 
 def simple_backtest(config, contour, num_results, mocker) -> None:
     mocker.patch('freqtrade.exchange.validate_pairs', MagicMock(return_value=True))
+
     backtesting = Backtesting(config)
 
     data = load_data_test(contour)
@@ -97,6 +98,7 @@ def simple_backtest(config, contour, num_results, mocker) -> None:
             'realistic': True
         }
     )
+
     # results :: <class 'pandas.core.frame.DataFrame'>
     assert len(results) == num_results
 

freqtrade/tests/rpc/test_rpc.py

@@ -106,6 +106,7 @@ def test_rpc_status_table(default_conf, ticker, fee, mocker) -> None:
     assert 'just now' in result['Since'].all()
     assert 'ETH/BTC' in result['Pair'].all()
     assert '-0.59%' in result['Profit'].all()
+    assert 'Value' in result
 
 
 def test_rpc_daily_profit(default_conf, update, ticker, fee,

freqtrade/tests/strategy/test_strategy.py

@@ -29,6 +29,14 @@ def test_load_strategy(result):
 def test_load_strategy_custom_directory(result):
     resolver = StrategyResolver()
     extra_dir = os.path.join('some', 'path')
+
+    if os.name == 'nt':
+        with pytest.raises(
+                FileNotFoundError,
+                match="FileNotFoundError: [WinError 3] The system cannot find the "
+                      "path specified: '{}'".format(extra_dir)):
+            resolver._load_strategy('TestStrategy', extra_dir)
+    else:
         with pytest.raises(
                 FileNotFoundError,
                 match=r".*No such file or directory: '{}'".format(extra_dir)):

freqtrade/tests/test_persistence.py

@@ -444,6 +444,8 @@ def test_migrate_new(default_conf, fee):
                                 close_profit FLOAT,
                                 stake_amount FLOAT NOT NULL,
                                 amount FLOAT,
+                                initial_stop_loss FLOAT,
+                                max_rate FLOAT,
                                 open_date DATETIME NOT NULL,
                                 close_date DATETIME,
                                 open_order_id VARCHAR,

scripts/plot_dataframe.py

@@ -159,6 +159,15 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
         fillcolor="rgba(0,176,246,0.2)",
         line={'color': "transparent"},
     )
+    bb_middle = go.Scatter(
+        x=data.date,
+        y=data.bb_middleband,
+        name='BB middle',
+        fill="tonexty",
+        fillcolor="rgba(0,176,246,0.2)",
+        line={'color': "red"},
+    )
+
     macd = go.Scattergl(x=data['date'], y=data['macd'], name='MACD')
     macdsignal = go.Scattergl(x=data['date'], y=data['macdsignal'], name='MACD signal')
     volume = go.Bar(x=data['date'], y=data['volume'], name='Volume')
@@ -173,7 +182,9 @@ def plot_analyzed_dataframe(args: Namespace) -> None:
 
     fig.append_trace(candles, 1, 1)
     fig.append_trace(bb_lower, 1, 1)
+    fig.append_trace(bb_middle, 1, 1)
     fig.append_trace(bb_upper, 1, 1)
+
     fig.append_trace(buys, 1, 1)
     fig.append_trace(sells, 1, 1)
     fig.append_trace(volume, 2, 1)

user_data/strategies/Long.py

@@ -0,0 +1,94 @@
+
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+import numpy # noqa
+
+
+class Long(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60":  0.05,
+        "30":  0.06,
+        "20":  0.07,
+        "0":  0.08
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.15
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 60
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+        dataframe['cci'] = ta.CCI(dataframe)
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=50)
+
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        rsi = 0.1 * (dataframe['rsi'] - 50)
+        dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
+
+        # SAR Parabol
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['macd'] > dataframe['macdsignal']) &
+                (dataframe['macd'] > 0) &
+                (dataframe['cci'] <= 0.0)
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+#                (dataframe['tema'] < dataframe['close'])
+
+                (dataframe['sar'] > dataframe['close']) &
+                (dataframe['fisher_rsi'] > 0.3)
+            ),
+            'sell'] = 1
+        return dataframe

user_data/strategies/Quickie.py

@@ -0,0 +1,75 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Quickie(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    idea:
+        momentum based strategie. The main idea is that it closes trades very quickly, while avoiding excessive losses. Hence a rather moderate stop loss in this case
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60": 0.005,
+        "10": 0.01,
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.25
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        dataframe['sma_200'] = ta.SMA(dataframe, timeperiod=200)
+        dataframe['sma_50'] = ta.SMA(dataframe, timeperiod=50)
+
+
+        # required for graphing
+        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        dataframe.loc[
+            (
+                    (
+                            (dataframe['adx'] > 30) &
+                            (dataframe['tema'] < dataframe['bb_middleband']) &
+                            (dataframe['tema'] > dataframe['tema'].shift(1)) &
+                            (dataframe['sma_200'] > dataframe['close'])
+                    )
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                    (dataframe['adx'] > 70) &
+                    (dataframe['tema'] > dataframe['bb_middleband']) &
+                    (dataframe['tema'] < dataframe['tema'].shift(1))
+                )
+            ),
+            'sell'] = 1
+        return dataframe

user_data/strategies/Simple.py

@@ -0,0 +1,76 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class Simple(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+
+    idea:
+        this strategy is based on the book, 'The Simple Strategy' and can be found in detail here:
+
+        https://www.amazon.com/Simple-Strategy-Powerful-Trading-Futures-ebook/dp/B00E66QPCG/ref=sr_1_1?ie=UTF8&qid=1525202675&sr=8-1&keywords=the+simple+strategy
+    """
+
+    # Minimal ROI designed for the strategy.
+    # since this strategy is planned around 5 minutes, we assume any time we have a 5% profit we should call it a day
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "0": 0.01
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.25
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
+
+        # required for graphing
+        bollinger = qtpylib.bollinger_bands(dataframe['close'], window=12, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_upperband'] = bollinger['upper']
+        dataframe['bb_middleband'] = bollinger['mid']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                        (dataframe['macd'] > 0)  # over 0
+                        & (dataframe['macd'] > dataframe['macdsignal'])  # over signal
+                        & (dataframe['bb_upperband'] > dataframe['bb_upperband'].shift(1))  # pointed up
+                        & (dataframe['rsi'] > 70)  # optional filter, need to investigate
+                )
+            ),
+            'buy'] = 1
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        # different strategy used for sell points, due to be able to duplicate it to 100%
+        dataframe.loc[
+            (
+                (dataframe['rsi'] > 80)
+            ),
+            'sell'] = 1
+        return dataframe

user_data/strategies/ZLC.py

@@ -0,0 +1,90 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from typing import Dict, List
+from hyperopt import hp
+from functools import reduce
+from pandas import DataFrame
+# --------------------------------
+
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+class ZLC(IStrategy):
+    """
+
+    author@: Gert Wohlgemuth
+    """
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi"
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.03,
+        "20": 0.04,
+        "0": 0.01
+    }
+
+    # Optimal stoploss designed for the strategy
+    # This attribute will be overridden if the config file contains "stoploss"
+    stoploss = -0.3
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = 5
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        dataframe['cci-slow'] = ta.CCI(dataframe, timeperiod=25)
+        dataframe['cci-fast'] = ta.CCI(dataframe, timeperiod=50)
+        dataframe['expo'] = ta.EMA(dataframe, timeperiod=35)
+
+        # required for graphing
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                #don't buy on peak tops
+                        (dataframe['close'] < dataframe['bb_middleband'])
+                        # this is the main concept of evaluating buys
+                        & (dataframe['cci-fast'] > 0)
+                        & (dataframe['cci-slow'] > 0)
+                        & (dataframe['close'] > dataframe['expo'])
+
+            )
+            ,
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (dataframe['close'] >= dataframe['bb_upperband']) |
+            (
+                    (dataframe['cci-fast'] < 0)
+                    & (dataframe['cci-slow'] < 0)
+                    & (dataframe['close'] < dataframe['expo'])
+
+            )
+            ,
+            'sell'] = 0
+        return dataframe