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Matthias 2024-05-12 16:43:43 +02:00
parent 439b8a0320
commit 5783a44c86
4 changed files with 167 additions and 159 deletions
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130
freqtrade/templates/FreqaiExampleHybridStrategy.py
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@ -61,27 +61,28 @@ class FreqaiExampleHybridStrategy(IStrategy):
""" """
minimal_roi = { minimal_roi = {
# "120": 0.0, # exit after 120 minutes at break even
"60": 0.01, "60": 0.01,
"30": 0.02, "30": 0.02,
"0": 0.04 "0": 0.04
} }
plot_config = { plot_config = {
'main_plot': { "main_plot": {
'tema': {}, "tema": {},
}, },
'subplots': { "subplots": {
"MACD": { "MACD": {
'macd': {'color': 'blue'}, "macd": {"color": "blue"},
'macdsignal': {'color': 'orange'}, "macdsignal": {"color": "orange"},
}, },
"RSI": { "RSI": {
'rsi': {'color': 'red'}, "rsi": {"color": "red"},
}, },
"Up_or_down": { "Up_or_down": {
'&s-up_or_down': {'color': 'green'}, "&s-up_or_down": {"color": "green"},
} },
} },
} }
process_only_new_candles = True process_only_new_candles = True
@ -91,13 +92,14 @@ class FreqaiExampleHybridStrategy(IStrategy):
can_short = True can_short = True
# Hyperoptable parameters # Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, def feature_engineering_expand_all(
metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined This function will automatically expand the defined features on the config defined
@ -136,12 +138,9 @@ class FreqaiExampleHybridStrategy(IStrategy):
dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = ( dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"] dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"] ) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = ( dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
@ -152,7 +151,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
return dataframe return dataframe
def feature_engineering_expand_basic( def feature_engineering_expand_basic(
self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined This function will automatically expand the defined features on the config defined
@ -185,7 +185,8 @@ class FreqaiExampleHybridStrategy(IStrategy):
return dataframe return dataframe
def feature_engineering_standard( def feature_engineering_standard(
self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe. This optional function will be called once with the dataframe of the base timeframe.
@ -226,13 +227,13 @@ class FreqaiExampleHybridStrategy(IStrategy):
usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"] usage example: dataframe["&-target"] = dataframe["close"].shift(-1) / dataframe["close"]
""" """
self.freqai.class_names = ["down", "up"] self.freqai.class_names = ["down", "up"]
dataframe['&s-up_or_down'] = np.where(dataframe["close"].shift(-50) > dataframe["&s-up_or_down"] = np.where(
dataframe["close"], 'up', 'down') dataframe["close"].shift(-50) > dataframe["close"], "up", "down"
)
return dataframe return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901 def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: # noqa: C901
# User creates their own custom strat here. Present example is a supertrend # User creates their own custom strat here. Present example is a supertrend
# based strategy. # based strategy.
@ -240,78 +241,81 @@ class FreqaiExampleHybridStrategy(IStrategy):
# TA indicators to combine with the Freqai targets # TA indicators to combine with the Freqai targets
# RSI # RSI
dataframe['rsi'] = ta.RSI(dataframe) dataframe["rsi"] = ta.RSI(dataframe)
# Bollinger Bands # Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower'] dataframe["bb_lowerband"] = bollinger["lower"]
dataframe['bb_middleband'] = bollinger['mid'] dataframe["bb_middleband"] = bollinger["mid"]
dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_upperband"] = bollinger["upper"]
dataframe["bb_percent"] = ( dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
(dataframe["close"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"] - dataframe["bb_lowerband"]
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
) )
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
"bb_middleband"
]
# TEMA - Triple Exponential Moving Average # TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
return dataframe return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[ df.loc[
( (
# Signal: RSI crosses above 30 # Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.buy_rsi.value)) & (qtpylib.crossed_above(df["rsi"], self.buy_rsi.value))
(df['tema'] <= df['bb_middleband']) & # Guard: tema below BB middle & (df["tema"] <= df["bb_middleband"]) # Guard: tema below BB middle
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising & (df["tema"] > df["tema"].shift(1)) # Guard: tema is raising
(df['volume'] > 0) & # Make sure Volume is not 0 & (df["volume"] > 0) # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction & (df["do_predict"] == 1) # Make sure Freqai is confident in the prediction
&
# Only enter trade if Freqai thinks the trend is in this direction # Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'up') (df["&s-up_or_down"] == "up")
), ),
'enter_long'] = 1 "enter_long",
] = 1
df.loc[ df.loc[
( (
# Signal: RSI crosses above 70 # Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.short_rsi.value)) & (qtpylib.crossed_above(df["rsi"], self.short_rsi.value))
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle & (df["tema"] > df["bb_middleband"]) # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling & (df["tema"] < df["tema"].shift(1)) # Guard: tema is falling
(df['volume'] > 0) & # Make sure Volume is not 0 & (df["volume"] > 0) # Make sure Volume is not 0
(df['do_predict'] == 1) & # Make sure Freqai is confident in the prediction & (df["do_predict"] == 1) # Make sure Freqai is confident in the prediction
&
# Only enter trade if Freqai thinks the trend is in this direction # Only enter trade if Freqai thinks the trend is in this direction
(df['&s-up_or_down'] == 'down') (df["&s-up_or_down"] == "down")
), ),
'enter_short'] = 1 "enter_short",
] = 1
return df return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
df.loc[ df.loc[
( (
# Signal: RSI crosses above 70 # Signal: RSI crosses above 70
(qtpylib.crossed_above(df['rsi'], self.sell_rsi.value)) & (qtpylib.crossed_above(df["rsi"], self.sell_rsi.value))
(df['tema'] > df['bb_middleband']) & # Guard: tema above BB middle & (df["tema"] > df["bb_middleband"]) # Guard: tema above BB middle
(df['tema'] < df['tema'].shift(1)) & # Guard: tema is falling & (df["tema"] < df["tema"].shift(1)) # Guard: tema is falling
(df['volume'] > 0) # Make sure Volume is not 0 & (df["volume"] > 0) # Make sure Volume is not 0
), ),
"exit_long",
'exit_long'] = 1 ] = 1
df.loc[ df.loc[
( (
# Signal: RSI crosses above 30 # Signal: RSI crosses above 30
(qtpylib.crossed_above(df['rsi'], self.exit_short_rsi.value)) & (qtpylib.crossed_above(df["rsi"], self.exit_short_rsi.value))
&
# Guard: tema below BB middle # Guard: tema below BB middle
(df['tema'] <= df['bb_middleband']) & (df["tema"] <= df["bb_middleband"])
(df['tema'] > df['tema'].shift(1)) & # Guard: tema is raising & (df["tema"] > df["tema"].shift(1)) # Guard: tema is raising
(df['volume'] > 0) # Make sure Volume is not 0 & (df["volume"] > 0) # Make sure Volume is not 0
), ),
'exit_short'] = 1 "exit_short",
] = 1
return df return df

37
freqtrade/templates/FreqaiExampleStrategy.py
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@ -45,8 +45,9 @@ class FreqaiExampleStrategy(IStrategy):
startup_candle_count: int = 40 startup_candle_count: int = 40
can_short = True can_short = True
def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, def feature_engineering_expand_all(
metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined This function will automatically expand the defined features on the config defined
@ -89,12 +90,9 @@ class FreqaiExampleStrategy(IStrategy):
dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["bb_upperband-period"] = bollinger["upper"]
dataframe["%-bb_width-period"] = ( dataframe["%-bb_width-period"] = (
dataframe["bb_upperband-period"] dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"]
- dataframe["bb_lowerband-period"]
) / dataframe["bb_middleband-period"] ) / dataframe["bb_middleband-period"]
dataframe["%-close-bb_lower-period"] = ( dataframe["%-close-bb_lower-period"] = dataframe["close"] / dataframe["bb_lowerband-period"]
dataframe["close"] / dataframe["bb_lowerband-period"]
)
dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period) dataframe["%-roc-period"] = ta.ROC(dataframe, timeperiod=period)
@ -105,7 +103,8 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe return dataframe
def feature_engineering_expand_basic( def feature_engineering_expand_basic(
self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This function will automatically expand the defined features on the config defined This function will automatically expand the defined features on the config defined
@ -142,7 +141,8 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe return dataframe
def feature_engineering_standard( def feature_engineering_standard(
self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: self, dataframe: DataFrame, metadata: Dict, **kwargs
) -> DataFrame:
""" """
*Only functional with FreqAI enabled strategies* *Only functional with FreqAI enabled strategies*
This optional function will be called once with the dataframe of the base timeframe. This optional function will be called once with the dataframe of the base timeframe.
@ -197,7 +197,7 @@ class FreqaiExampleStrategy(IStrategy):
.mean() .mean()
/ dataframe["close"] / dataframe["close"]
- 1 - 1
) )
# Classifiers are typically set up with strings as targets: # Classifiers are typically set up with strings as targets:
# df['&s-up_or_down'] = np.where( df["close"].shift(-100) > # df['&s-up_or_down'] = np.where( df["close"].shift(-100) >
@ -224,7 +224,6 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# All indicators must be populated by feature_engineering_*() functions # All indicators must be populated by feature_engineering_*() functions
# the model will return all labels created by user in `set_freqai_targets()` # the model will return all labels created by user in `set_freqai_targets()`
@ -237,11 +236,10 @@ class FreqaiExampleStrategy(IStrategy):
return dataframe return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [ enter_long_conditions = [
df["do_predict"] == 1, df["do_predict"] == 1,
df["&-s_close"] > 0.01, df["&-s_close"] > 0.01,
] ]
if enter_long_conditions: if enter_long_conditions:
df.loc[ df.loc[
@ -251,7 +249,7 @@ class FreqaiExampleStrategy(IStrategy):
enter_short_conditions = [ enter_short_conditions = [
df["do_predict"] == 1, df["do_predict"] == 1,
df["&-s_close"] < -0.01, df["&-s_close"] < -0.01,
] ]
if enter_short_conditions: if enter_short_conditions:
df.loc[ df.loc[
@ -261,17 +259,11 @@ class FreqaiExampleStrategy(IStrategy):
return df return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [ exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < 0]
df["do_predict"] == 1,
df["&-s_close"] < 0
]
if exit_long_conditions: if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1 df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [ exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > 0]
df["do_predict"] == 1,
df["&-s_close"] > 0
]
if exit_short_conditions: if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1 df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
@ -289,7 +281,6 @@ class FreqaiExampleStrategy(IStrategy):
side: str, side: str,
**kwargs, **kwargs,
) -> bool: ) -> bool:
df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe) df, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = df.iloc[-1].squeeze() last_candle = df.iloc[-1].squeeze()

20
freqtrade/templates/sample_hyperopt_loss.py
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@ -35,17 +35,23 @@ class SampleHyperOptLoss(IHyperOptLoss):
""" """
@staticmethod @staticmethod
def hyperopt_loss_function(results: DataFrame, trade_count: int, def hyperopt_loss_function(
min_date: datetime, max_date: datetime, results: DataFrame,
config: Config, processed: Dict[str, DataFrame], trade_count: int,
*args, **kwargs) -> float: min_date: datetime,
max_date: datetime,
config: Config,
processed: Dict[str, DataFrame],
*args,
**kwargs,
) -> float:
""" """
Objective function, returns smaller number for better results Objective function, returns smaller number for better results
""" """
total_profit = results['profit_ratio'].sum() total_profit = results["profit_ratio"].sum()
trade_duration = results['trade_duration'].mean() trade_duration = results["trade_duration"].mean()
trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8) trade_loss = 1 - 0.25 * exp(-((trade_count - TARGET_TRADES) ** 2) / 10**5.8)
profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT) profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1) duration_loss = 0.4 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
result = trade_loss + profit_loss + duration_loss result = trade_loss + profit_loss + duration_loss

139
freqtrade/templates/sample_strategy.py
View File

@ -7,8 +7,13 @@ import pandas as pd # noqa
from pandas import DataFrame from pandas import DataFrame
from typing import Optional, Union from typing import Optional, Union
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, from freqtrade.strategy import (
IStrategy, IntParameter) BooleanParameter,
CategoricalParameter,
DecimalParameter,
IStrategy,
IntParameter,
)
# -------------------------------- # --------------------------------
# Add your lib to import here # Add your lib to import here
@ -34,6 +39,7 @@ class SampleStrategy(IStrategy):
You should keep: You should keep:
- timeframe, minimal_roi, stoploss, trailing_* - timeframe, minimal_roi, stoploss, trailing_*
""" """
# Strategy interface version - allow new iterations of the strategy interface. # Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version. # Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3 INTERFACE_VERSION = 3
@ -44,6 +50,7 @@ class SampleStrategy(IStrategy):
# Minimal ROI designed for the strategy. # Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi". # This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = { minimal_roi = {
# "120": 0.0, # exit after 120 minutes at break even
"60": 0.01, "60": 0.01,
"30": 0.02, "30": 0.02,
"0": 0.04 "0": 0.04
@ -60,7 +67,7 @@ class SampleStrategy(IStrategy):
# trailing_stop_positive_offset = 0.0 # Disabled / not configured # trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal timeframe for the strategy. # Optimal timeframe for the strategy.
timeframe = '5m' timeframe = "5m"
# Run "populate_indicators()" only for new candle. # Run "populate_indicators()" only for new candle.
process_only_new_candles = True process_only_new_candles = True
@ -71,42 +78,39 @@ class SampleStrategy(IStrategy):
ignore_roi_if_entry_signal = False ignore_roi_if_entry_signal = False
# Hyperoptable parameters # Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) buy_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True) sell_rsi = IntParameter(low=50, high=100, default=70, space="sell", optimize=True, load=True)
short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True) short_rsi = IntParameter(low=51, high=100, default=70, space="sell", optimize=True, load=True)
exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True) exit_short_rsi = IntParameter(low=1, high=50, default=30, space="buy", optimize=True, load=True)
# Number of candles the strategy requires before producing valid signals # Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200 startup_candle_count: int = 200
# Optional order type mapping. # Optional order type mapping.
order_types = { order_types = {
'entry': 'limit', "entry": "limit",
'exit': 'limit', "exit": "limit",
'stoploss': 'market', "stoploss": "market",
'stoploss_on_exchange': False "stoploss_on_exchange": False,
} }
# Optional order time in force. # Optional order time in force.
order_time_in_force = { order_time_in_force = {"entry": "GTC", "exit": "GTC"}
'entry': 'GTC',
'exit': 'GTC'
}
plot_config = { plot_config = {
'main_plot': { "main_plot": {
'tema': {}, "tema": {},
'sar': {'color': 'white'}, "sar": {"color": "white"},
}, },
'subplots': { "subplots": {
"MACD": { "MACD": {
'macd': {'color': 'blue'}, "macd": {"color": "blue"},
'macdsignal': {'color': 'orange'}, "macdsignal": {"color": "orange"},
}, },
"RSI": { "RSI": {
'rsi': {'color': 'red'}, "rsi": {"color": "red"},
} },
} },
} }
def informative_pairs(self): def informative_pairs(self):
@ -138,7 +142,7 @@ class SampleStrategy(IStrategy):
# ------------------------------------ # ------------------------------------
# ADX # ADX
dataframe['adx'] = ta.ADX(dataframe) dataframe["adx"] = ta.ADX(dataframe)
# # Plus Directional Indicator / Movement # # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe) # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
@ -177,7 +181,7 @@ class SampleStrategy(IStrategy):
# dataframe['cci'] = ta.CCI(dataframe) # dataframe['cci'] = ta.CCI(dataframe)
# RSI # RSI
dataframe['rsi'] = ta.RSI(dataframe) dataframe["rsi"] = ta.RSI(dataframe)
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
# rsi = 0.1 * (dataframe['rsi'] - 50) # rsi = 0.1 * (dataframe['rsi'] - 50)
@ -193,8 +197,8 @@ class SampleStrategy(IStrategy):
# Stochastic Fast # Stochastic Fast
stoch_fast = ta.STOCHF(dataframe) stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd'] dataframe["fastd"] = stoch_fast["fastd"]
dataframe['fastk'] = stoch_fast['fastk'] dataframe["fastk"] = stoch_fast["fastk"]
# # Stochastic RSI # # Stochastic RSI
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
@ -205,12 +209,12 @@ class SampleStrategy(IStrategy):
# MACD # MACD
macd = ta.MACD(dataframe) macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd'] dataframe["macd"] = macd["macd"]
dataframe['macdsignal'] = macd['macdsignal'] dataframe["macdsignal"] = macd["macdsignal"]
dataframe['macdhist'] = macd['macdhist'] dataframe["macdhist"] = macd["macdhist"]
# MFI # MFI
dataframe['mfi'] = ta.MFI(dataframe) dataframe["mfi"] = ta.MFI(dataframe)
# # ROC # # ROC
# dataframe['roc'] = ta.ROC(dataframe) # dataframe['roc'] = ta.ROC(dataframe)
@ -220,16 +224,15 @@ class SampleStrategy(IStrategy):
# Bollinger Bands # Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower'] dataframe["bb_lowerband"] = bollinger["lower"]
dataframe['bb_middleband'] = bollinger['mid'] dataframe["bb_middleband"] = bollinger["mid"]
dataframe['bb_upperband'] = bollinger['upper'] dataframe["bb_upperband"] = bollinger["upper"]
dataframe["bb_percent"] = ( dataframe["bb_percent"] = (dataframe["close"] - dataframe["bb_lowerband"]) / (
(dataframe["close"] - dataframe["bb_lowerband"]) / dataframe["bb_upperband"] - dataframe["bb_lowerband"]
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
) )
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe[
"bb_middleband"
]
# Bollinger Bands - Weighted (EMA based instead of SMA) # Bollinger Bands - Weighted (EMA based instead of SMA)
# weighted_bollinger = qtpylib.weighted_bollinger_bands( # weighted_bollinger = qtpylib.weighted_bollinger_bands(
@ -264,17 +267,17 @@ class SampleStrategy(IStrategy):
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# Parabolic SAR # Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe) dataframe["sar"] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average # TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)
# Cycle Indicator # Cycle Indicator
# ------------------------------------ # ------------------------------------
# Hilbert Transform Indicator - SineWave # Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe) hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine'] dataframe["htsine"] = hilbert["sine"]
dataframe['htleadsine'] = hilbert['leadsine'] dataframe["htleadsine"] = hilbert["leadsine"]
# Pattern Recognition - Bullish candlestick patterns # Pattern Recognition - Bullish candlestick patterns
# ------------------------------------ # ------------------------------------
@ -353,22 +356,24 @@ class SampleStrategy(IStrategy):
dataframe.loc[ dataframe.loc[
( (
# Signal: RSI crosses above 30 # Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)) & (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value))
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle & (dataframe["tema"] <= dataframe["bb_middleband"]) # Guard: tema below BB middle
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
(dataframe['volume'] > 0) # Make sure Volume is not 0 & (dataframe["volume"] > 0) # Make sure Volume is not 0
), ),
'enter_long'] = 1 "enter_long",
] = 1
dataframe.loc[ dataframe.loc[
( (
# Signal: RSI crosses above 70 # Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) & (qtpylib.crossed_above(dataframe["rsi"], self.short_rsi.value))
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
(dataframe['volume'] > 0) # Make sure Volume is not 0 & (dataframe["volume"] > 0) # Make sure Volume is not 0
), ),
'enter_short'] = 1 "enter_short",
] = 1
return dataframe return dataframe
@ -382,23 +387,25 @@ class SampleStrategy(IStrategy):
dataframe.loc[ dataframe.loc[
( (
# Signal: RSI crosses above 70 # Signal: RSI crosses above 70
(qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) & (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value))
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle & (dataframe["tema"] > dataframe["bb_middleband"]) # Guard: tema above BB middle
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling & (dataframe["tema"] < dataframe["tema"].shift(1)) # Guard: tema is falling
(dataframe['volume'] > 0) # Make sure Volume is not 0 & (dataframe["volume"] > 0) # Make sure Volume is not 0
), ),
"exit_long",
'exit_long'] = 1 ] = 1
dataframe.loc[ dataframe.loc[
( (
# Signal: RSI crosses above 30 # Signal: RSI crosses above 30
(qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) & (qtpylib.crossed_above(dataframe["rsi"], self.exit_short_rsi.value))
&
# Guard: tema below BB middle # Guard: tema below BB middle
(dataframe['tema'] <= dataframe['bb_middleband']) & (dataframe["tema"] <= dataframe["bb_middleband"])
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising & (dataframe["tema"] > dataframe["tema"].shift(1)) # Guard: tema is raising
(dataframe['volume'] > 0) # Make sure Volume is not 0 & (dataframe["volume"] > 0) # Make sure Volume is not 0
), ),
'exit_short'] = 1 "exit_short",
] = 1
return dataframe return dataframe