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Update strategies to new Strategy interface

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This commit is contained in:
Matthias
2022-07-10 11:34:40 +02:00
parent 393b94a249
commit e705e712e7
55 changed files with 294 additions and 289 deletions
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README.md
+3 -1
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@@ -2,6 +2,8 @@
This Git repo contains free buy/sell strategies for [Freqtrade](https://github.com/freqtrade/freqtrade).
All strategies should work with a freqtrade version of 2022.4 or newer.
## Disclaimer
These strategies are for educational purposes only. Do not risk money
@@ -31,7 +33,7 @@ bot.
## Free trading strategies
Value below are result from backtesting from 2018-01-10 to 2018-01-30 and
`ask_strategy.sell_profit_only` enabled. More detail on each strategy
`exit_profit_only` enabled. More detail on each strategy
page.
| Strategy | Buy count | AVG profit % | Total profit | AVG duration | Backtest period |
user_data/hyperopts/GodStraHo.py
+6 -6
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@@ -77,7 +77,7 @@ class GodStraHo(IHyperOpt):
"""
Define the buy strategy parameters to be used by Hyperopt.
"""
def populate_buy_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
@@ -119,11 +119,11 @@ class GodStraHo(IHyperOpt):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
'enter_long'] = 1
return dataframe
return populate_buy_trend
return populate_entry_trend
@ staticmethod
def sell_indicator_space() -> List[Dimension]:
@@ -149,7 +149,7 @@ class GodStraHo(IHyperOpt):
"""
Define the sell strategy parameters to be used by Hyperopt.
"""
def populate_sell_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
@@ -192,8 +192,8 @@ class GodStraHo(IHyperOpt):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
'exit_long']=1
return dataframe
return populate_sell_trend
return populate_exit_trend
user_data/strategies/BreakEven.py
+4 -4
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@@ -53,16 +53,16 @@ class BreakEven(IStrategy):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'buy'] = 0
'enter_long'] = 0
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'sell'] = 0
'exit_long'] = 0
return dataframe
user_data/strategies/Diamond.py
+4 -4
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@@ -121,7 +121,7 @@ class Diamond(IStrategy):
# dataframe['{...}'] = ta.{...}(dataframe, timeperiod={...})
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
qtpylib.crossed_above
@@ -134,11 +134,11 @@ class Diamond(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy']=1
'enter_long']=1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
conditions.append(
qtpylib.crossed_below
@@ -150,5 +150,5 @@ class Diamond(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
'exit_long']=1
return dataframe
user_data/strategies/GodStra.py
+4 -4
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@@ -93,7 +93,7 @@ class GodStra(IStrategy):
# dataframe.to_csv("df.csv", index=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
# /5: Cuz We have 5 Group of variables inside buy_param
for i in range(self.dna_size(self.buy_params)):
@@ -132,11 +132,11 @@ class GodStra(IStrategy):
print(conditions)
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
for i in range(self.dna_size(self.sell_params)):
OPR = self.sell_params[f'sell-oper-{i}']
@@ -172,6 +172,6 @@ class GodStra(IStrategy):
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Heracles.py
+4 -4
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@@ -87,7 +87,7 @@ class Heracles(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Buy strategy Hyperopt will build and use.
"""
@@ -108,13 +108,13 @@ class Heracles(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy']=1
'enter_long']=1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Sell strategy Hyperopt will build and use.
"""
dataframe.loc[:, 'sell'] = 0
dataframe.loc[:, 'exit_long'] = 0
return dataframe
user_data/strategies/HourBasedStrategy.py
+4 -4
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@@ -85,19 +85,19 @@ class HourBasedStrategy(IStrategy):
dataframe['hour'] = dataframe['date'].dt.hour
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['hour'].between(self.buy_hour_min.value, self.buy_hour_max.value))
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['hour'].between(self.sell_hour_min.value, self.sell_hour_max.value))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/InformativeSample.py
+9 -9
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@@ -46,14 +46,14 @@ class InformativeSample(IStrategy):
ta_on_candle = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -99,7 +99,7 @@ class InformativeSample(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -111,11 +111,11 @@ class InformativeSample(IStrategy):
# stake/USDT above sma(stake/USDT, 20)
(dataframe['close_15m'] > dataframe['sma20_15m'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -127,5 +127,5 @@ class InformativeSample(IStrategy):
# stake/USDT below sma(stake/USDT, 20)
(dataframe['close_15m'] < dataframe['sma20_15m'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/MultiMa.py
+4 -4
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@@ -69,7 +69,7 @@ class MultiMa(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# I used range(self.buy_ma_count.value) instade of self.buy_ma_count.range
# Cuz it returns range(7,8) but we need range(8) for all modes hyperopt, backtest and etc
@@ -81,10 +81,10 @@ class MultiMa(IStrategy):
conditions.append(dataframe[key] < dataframe[past_key])
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1
dataframe.loc[reduce(lambda x, y: x & y, conditions), "enter_long"] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
for ma_count in range(self.sell_ma_count.value):
@@ -94,5 +94,5 @@ class MultiMa(IStrategy):
conditions.append(dataframe[key] > dataframe[past_key])
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1
dataframe.loc[reduce(lambda x, y: x | y, conditions), "exit_long"] = 1
return dataframe
user_data/strategies/Strategy001.py
+9 -9
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@@ -45,14 +45,14 @@ class Strategy001(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -89,7 +89,7 @@ class Strategy001(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -101,11 +101,11 @@ class Strategy001(IStrategy):
(dataframe['ha_close'] > dataframe['ema20']) &
(dataframe['ha_open'] < dataframe['ha_close']) # green bar
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -117,5 +117,5 @@ class Strategy001(IStrategy):
(dataframe['ha_close'] < dataframe['ema20']) &
(dataframe['ha_open'] > dataframe['ha_close']) # red bar
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Strategy001_custom_sell.py → user_data/strategies/Strategy001_custom_exit.py
+14 -13
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@@ -9,15 +9,16 @@ from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Strategy001_custom_sell(IStrategy):
class Strategy001_custom_exit(IStrategy):
"""
Strategy 001_custom_sell
Strategy 001_custom_exit
author@: Gerald Lonlas, froggleston
github@: https://github.com/freqtrade/freqtrade-strategies
How to use it?
> python3 ./freqtrade/main.py -s Strategy001_custom_sell
> python3 ./freqtrade/main.py -s Strategy001_custom_exit
"""
# Minimal ROI designed for the strategy.
@@ -45,14 +46,14 @@ class Strategy001_custom_sell(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -91,7 +92,7 @@ class Strategy001_custom_sell(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -103,11 +104,11 @@ class Strategy001_custom_sell(IStrategy):
(dataframe['ha_close'] > dataframe['ema20']) &
(dataframe['ha_open'] < dataframe['ha_close']) # green bar
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -119,10 +120,10 @@ class Strategy001_custom_sell(IStrategy):
(dataframe['ha_close'] < dataframe['ema20']) &
(dataframe['ha_open'] > dataframe['ha_close']) # red bar
),
'sell'] = 1
'exit_long'] = 1
return dataframe
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
"""
Sell only when matching some criteria other than those used to generate the sell signal
:return: str sell_reason, if any, otherwise None
user_data/strategies/Strategy002.py
+9 -9
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@@ -46,14 +46,14 @@ class Strategy002(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -103,7 +103,7 @@ class Strategy002(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -116,11 +116,11 @@ class Strategy002(IStrategy):
(dataframe['bb_lowerband'] > dataframe['close']) &
(dataframe['CDLHAMMER'] == 100)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -131,5 +131,5 @@ class Strategy002(IStrategy):
(dataframe['sar'] > dataframe['close']) &
(dataframe['fisher_rsi'] > 0.3)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Strategy003.py
+9 -9
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@@ -46,14 +46,14 @@ class Strategy003(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -113,7 +113,7 @@ class Strategy003(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -133,11 +133,11 @@ class Strategy003(IStrategy):
(dataframe['fastd'] > dataframe['fastk']) &
(dataframe['fastd'] > 0)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -148,5 +148,5 @@ class Strategy003(IStrategy):
(dataframe['sar'] > dataframe['close']) &
(dataframe['fisher_rsi'] > 0.3)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Strategy004.py
+9 -9
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@@ -45,14 +45,14 @@ class Strategy004(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -107,7 +107,7 @@ class Strategy004(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -133,11 +133,11 @@ class Strategy004(IStrategy):
(dataframe['mean-volume'] > 0.75) &
(dataframe['close'] > 0.00000100)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -150,5 +150,5 @@ class Strategy004(IStrategy):
(dataframe['fastk-previous'] < dataframe['fastd-previous']) &
(dataframe['close'] > dataframe['ema5'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Strategy005.py
+9 -9
View File
@@ -48,14 +48,14 @@ class Strategy005(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
@@ -142,7 +142,7 @@ class Strategy005(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -159,11 +159,11 @@ class Strategy005(IStrategy):
(dataframe['fastd'] > self.buy_fastd.value) &
(dataframe['fisher_rsi_norma'] < self.buy_fishRsiNorma.value)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -180,6 +180,6 @@ class Strategy005(IStrategy):
conditions.append(dataframe['fisher_rsi'] > self.sell_fishRsiNorma.value)
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'exit_long'] = 1
return dataframe
user_data/strategies/Supertrend.py
+6 -6
View File
@@ -6,7 +6,7 @@ Supertrend strategy:
* Author: @juankysoriano (Juan Carlos Soriano)
* github: https://github.com/juankysoriano/
*** NOTE: This Supertrend strategy is just one of many possible strategies using `Supertrend` as indicator. It should on any case used at your own risk.
*** NOTE: This Supertrend strategy is just one of many possible strategies using `Supertrend` as indicator. It should on any case used at your own risk.
It comes with at least a couple of caveats:
1. The implementation for the `supertrend` indicator is based on the following discussion: https://github.com/freqtrade/freqtrade-strategies/issues/30 . Concretelly https://github.com/freqtrade/freqtrade-strategies/issues/30#issuecomment-853042401
2. The implementation for `supertrend` on this strategy is not validated; meaning this that is not proven to match the results by the paper where it was originally introduced or any other trusted academic resources
@@ -22,7 +22,7 @@ import numpy as np
class Supertrend(IStrategy):
# Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
# It's encourage you find the values that better suites your needs and risk management strategies
# Buy hyperspace params:
buy_params = {
"buy_m1": 4,
@@ -105,7 +105,7 @@ class Supertrend(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe[f'supertrend_1_buy_{self.buy_m1.value}_{self.buy_p1.value}'] == 'up') &
@@ -113,11 +113,11 @@ class Supertrend(IStrategy):
(dataframe[f'supertrend_3_buy_{self.buy_m3.value}_{self.buy_p3.value}'] == 'up') & # The three indicators are 'up' for the current candle
(dataframe['volume'] > 0) # There is at least some trading volume
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe[f'supertrend_1_sell_{self.sell_m1.value}_{self.sell_p1.value}'] == 'down') &
@@ -125,7 +125,7 @@ class Supertrend(IStrategy):
(dataframe[f'supertrend_3_sell_{self.sell_m3.value}_{self.sell_p3.value}'] == 'down') & # The three indicators are 'down' for the current candle
(dataframe['volume'] > 0) # There is at least some trading volume
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/Swing-High-To-Sky.py
+4 -4
View File
@@ -87,24 +87,24 @@ class SwingHighToSky(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe[f'cci-{self.buy_cciTime.value}'] < self.buy_cci.value) &
(dataframe[f'rsi-{self.buy_rsiTime.value}'] < self.buy_rsi.value)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe[f'cci-sell-{self.sell_cciTime.value}'] > self.sell_cci.value) &
(dataframe[f'rsi-sell-{self.sell_rsiTime.value}'] > self.sell_rsi.value)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/ADXMomentum.py
+4 -4
View File
@@ -43,7 +43,7 @@ class ADXMomentum(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
@@ -52,10 +52,10 @@ class ADXMomentum(IStrategy):
(dataframe['plus_di'] > dataframe['minus_di'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
@@ -64,5 +64,5 @@ class ADXMomentum(IStrategy):
(dataframe['plus_di'] < dataframe['minus_di'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py
+4 -4
View File
@@ -56,7 +56,7 @@ class ASDTSRockwellTrading(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -67,11 +67,11 @@ class ASDTSRockwellTrading(IStrategy):
(dataframe['macd'] > 0) &
(dataframe['macd'] > dataframe['macdsignal'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -81,5 +81,5 @@ class ASDTSRockwellTrading(IStrategy):
(
(dataframe['macd'] < dataframe['macdsignal'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/AdxSmas.py
+4 -4
View File
@@ -39,22 +39,22 @@ class AdxSmas(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 25) &
(qtpylib.crossed_above(dataframe['short'], dataframe['long']))
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] < 25) &
(qtpylib.crossed_above(dataframe['long'], dataframe['short']))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/AverageStrategy.py
+4 -4
View File
@@ -42,7 +42,7 @@ class AverageStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -56,11 +56,11 @@ class AverageStrategy(IStrategy):
) &
(dataframe['volume'] > 0)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -74,5 +74,5 @@ class AverageStrategy(IStrategy):
) &
(dataframe['volume'] > 0)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/AwesomeMacd.py
+4 -4
View File
@@ -43,7 +43,7 @@ class AwesomeMacd(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['macd'] > 0) &
@@ -51,10 +51,10 @@ class AwesomeMacd(IStrategy):
(dataframe['ao'].shift() < 0)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['macd'] < 0) &
@@ -62,5 +62,5 @@ class AwesomeMacd(IStrategy):
(dataframe['ao'].shift() > 0)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/BbandRsi.py
+4 -4
View File
@@ -43,21 +43,21 @@ class BbandRsi(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] < 30) &
(dataframe['close'] < dataframe['bb_lowerband'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > 70)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/BinHV27.py
+6 -4
View File
@@ -54,7 +54,8 @@ class BinHV27(IStrategy):
dataframe['delta'] = dataframe['fastsma'] - dataframe['fastsma'].shift()
dataframe['slowingdown'] = dataframe['delta'].lt(dataframe['delta'].shift())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
dataframe['slowsma'].gt(0) &
dataframe['close'].lt(dataframe['highsma']) &
@@ -89,9 +90,10 @@ class BinHV27(IStrategy):
dataframe['emarsi'].le(25)
)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
@@ -131,5 +133,5 @@ class BinHV27(IStrategy):
dataframe['slowsma'].gt(0)
)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/BinHV45.py
+4 -4
View File
@@ -50,7 +50,7 @@ class BinHV45(IStrategy):
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
dataframe['lower'].shift().gt(0) &
@@ -60,12 +60,12 @@ class BinHV45(IStrategy):
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift())
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
no sell signal
"""
dataframe.loc[:, 'sell'] = 0
dataframe.loc[:, 'exit_long'] = 0
return dataframe
user_data/strategies/berlinguyinca/CCIStrategy.py
+4 -4
View File
@@ -41,7 +41,7 @@ class CCIStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -59,11 +59,11 @@ class CCIStrategy(IStrategy):
& (dataframe['resample_long'] < dataframe['close'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -78,7 +78,7 @@ class CCIStrategy(IStrategy):
& (dataframe['resample_medium'] < dataframe['resample_short'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
def chaikin_mf(self, df, periods=20):
user_data/strategies/berlinguyinca/CMCWinner.py
+6 -6
View File
@@ -23,8 +23,8 @@ class CMCWinner(IStrategy):
You must keep:
- the lib in the section "Do not remove these libs"
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_entry_trend,
populate_exit_trend, hyperopt_space, buy_strategy_generator
"""
# Minimal ROI designed for the strategy.
@@ -63,7 +63,7 @@ class CMCWinner(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -75,11 +75,11 @@ class CMCWinner(IStrategy):
(dataframe['mfi'].shift(1) < 20) &
(dataframe['cmo'].shift(1) < -50)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -91,5 +91,5 @@ class CMCWinner(IStrategy):
(dataframe['mfi'].shift(1) > 80) &
(dataframe['cmo'].shift(1) > 50)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/ClucMay72018.py
+4 -4
View File
@@ -53,7 +53,7 @@ class ClucMay72018(IStrategy):
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=50)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -65,11 +65,11 @@ class ClucMay72018(IStrategy):
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20))
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -79,5 +79,5 @@ class ClucMay72018(IStrategy):
(
(dataframe['close'] > dataframe['bb_middleband'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/CofiBitStrategy.py
+5 -5
View File
@@ -13,7 +13,7 @@ class CofiBitStrategy(IStrategy):
"""
taken from slack by user CofiBit
"""
# Buy hyperspace params:
buy_params = {
"buy_fastx": 25,
@@ -56,7 +56,7 @@ class CofiBitStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -70,11 +70,11 @@ class CofiBitStrategy(IStrategy):
(dataframe['fastd'] < self.buy_fastx.value) &
(dataframe['adx'] > self.buy_adx.value)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -88,6 +88,6 @@ class CofiBitStrategy(IStrategy):
(qtpylib.crossed_above(dataframe['fastk'], self.sell_fastx.value)) |
(qtpylib.crossed_above(dataframe['fastd'], self.sell_fastx.value))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py
+7 -7
View File
@@ -26,9 +26,9 @@ class CombinedBinHAndCluc(IStrategy):
stoploss = -0.05
timeframe = '5m'
use_sell_signal = True
sell_profit_only = True
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = True
ignore_roi_if_entry_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
@@ -46,7 +46,7 @@ class CombinedBinHAndCluc(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
( # strategy BinHV45
dataframe['lower'].shift().gt(0) &
@@ -61,15 +61,15 @@ class CombinedBinHAndCluc(IStrategy):
(dataframe['close'] < 0.985 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 20))
),
'buy'
'enter_long'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
"""
dataframe.loc[
(dataframe['close'] > dataframe['bb_middleband']),
'sell'
'exit_long'
] = 1
return dataframe
user_data/strategies/berlinguyinca/DoesNothingStrategy.py
+4 -4
View File
@@ -29,16 +29,16 @@ class DoesNothingStrategy(IStrategy):
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/EMASkipPump.py
+4 -4
View File
@@ -59,7 +59,7 @@ class EMASkipPump(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe['volume'] < (dataframe['volume'].rolling(window=30).mean().shift(1) * 20)) &
@@ -67,19 +67,19 @@ class EMASkipPump(IStrategy):
(dataframe['close'] < dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] == dataframe['min']) &
(dataframe['close'] <= dataframe['bb_lowerband']),
'buy'
'enter_long'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_SHORT_TERM)]) &
(dataframe['close'] > dataframe['ema_{}'.format(self.EMA_MEDIUM_TERM)]) &
(dataframe['close'] >= dataframe['max']) &
(dataframe['close'] >= dataframe['bb_upperband']),
'sell'
'exit_long'
] = 1
return dataframe
user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py
+4 -4
View File
@@ -31,19 +31,19 @@ class Freqtrade_backtest_validation_freqtrade1(IStrategy):
dataframe['slowMA'] = ta.SMA(dataframe, timeperiod=28)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['fastMA'] > dataframe['slowMA'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['fastMA'] < dataframe['slowMA'])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/Low_BB.py
+4 -4
View File
@@ -79,7 +79,7 @@ class Low_BB(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -92,11 +92,11 @@ class Low_BB(IStrategy):
)
,
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -104,5 +104,5 @@ class Low_BB(IStrategy):
"""
dataframe.loc[
(),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/MACDStrategy.py
+4 -4
View File
@@ -70,7 +70,7 @@ class MACDStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -82,11 +82,11 @@ class MACDStrategy(IStrategy):
(dataframe['cci'] <= self.buy_cci.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -98,6 +98,6 @@ class MACDStrategy(IStrategy):
(dataframe['cci'] >= self.sell_cci.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/MACDStrategy_crossed.py
+4 -4
View File
@@ -46,7 +46,7 @@ class MACDStrategy_crossed(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -57,11 +57,11 @@ class MACDStrategy_crossed(IStrategy):
qtpylib.crossed_above(dataframe['macd'], dataframe['macdsignal']) &
(dataframe['cci'] <= -50.0)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -72,6 +72,6 @@ class MACDStrategy_crossed(IStrategy):
qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal']) &
(dataframe['cci'] >= 100.0)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/MultiRSI.py
+4 -4
View File
@@ -50,21 +50,21 @@ class MultiRSI(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
# must be bearish
(dataframe['sma5'] >= dataframe['sma200']) &
(dataframe['rsi'] < (dataframe['resample_{}_rsi'.format(self.get_ticker_indicator() * 8)] - 20))
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*2)]) &
(dataframe['rsi'] > dataframe['resample_{}_rsi'.format(self.get_ticker_indicator()*8)])
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/Quickie.py
+4 -4
View File
@@ -54,7 +54,7 @@ class Quickie(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 30) &
@@ -63,15 +63,15 @@ class Quickie(IStrategy):
(dataframe['sma_200'] > dataframe['close'])
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['adx'] > 70) &
(dataframe['tema'] > dataframe['bb_middleband']) &
(dataframe['tema'] < dataframe['tema'].shift(1))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py
+7 -7
View File
@@ -43,9 +43,9 @@ class ReinforcedAverageStrategy(IStrategy):
process_only_new_candles = False
# Experimental settings (configuration will overide these if set)
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -64,7 +64,7 @@ class ReinforcedAverageStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -77,11 +77,11 @@ class ReinforcedAverageStrategy(IStrategy):
(dataframe['close'] > dataframe[f'resample_{self.resample_interval}_sma']) &
(dataframe['volume'] > 0)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -92,5 +92,5 @@ class ReinforcedAverageStrategy(IStrategy):
qtpylib.crossed_above(dataframe['maMedium'], dataframe['maShort']) &
(dataframe['volume'] > 0)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/ReinforcedQuickie.py
+4 -4
View File
@@ -93,7 +93,7 @@ class ReinforcedQuickie(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
@@ -134,11 +134,11 @@ class ReinforcedQuickie(IStrategy):
)
)
,
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
@@ -167,7 +167,7 @@ class ReinforcedQuickie(IStrategy):
(dataframe['rsi'] > 70)
)
,
'sell'
'exit_long'
] = 1
return dataframe
user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
+4 -4
View File
@@ -82,7 +82,7 @@ class ReinforcedSmoothScalp(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
if self.buy_mfi_enabled.value:
@@ -104,11 +104,11 @@ class ReinforcedSmoothScalp(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
@@ -132,6 +132,6 @@ class ReinforcedSmoothScalp(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/Scalp.py
+4 -4
View File
@@ -47,7 +47,7 @@ class Scalp(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['open'] < dataframe['ema_low']) &
@@ -58,10 +58,10 @@ class Scalp(IStrategy):
(qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']))
)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['open'] >= dataframe['ema_high'])
@@ -70,5 +70,5 @@ class Scalp(IStrategy):
(qtpylib.crossed_above(dataframe['fastk'], 70)) |
(qtpylib.crossed_above(dataframe['fastd'], 70))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/Simple.py
+4 -4
View File
@@ -52,7 +52,7 @@ class Simple(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
@@ -62,14 +62,14 @@ class Simple(IStrategy):
& (dataframe['rsi'] > 70) # optional filter, need to investigate
)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['rsi'] > 80)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/SmoothOperator.py
+4 -4
View File
@@ -101,7 +101,7 @@ class SmoothOperator(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
@@ -167,11 +167,11 @@ class SmoothOperator(IStrategy):
# ensure we have an overall uptrend
(dataframe['close'] > dataframe['close'].shift())
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
@@ -200,7 +200,7 @@ class SmoothOperator(IStrategy):
)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/SmoothScalp.py
+4 -4
View File
@@ -62,7 +62,7 @@ class SmoothScalp(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
@@ -78,10 +78,10 @@ class SmoothScalp(IStrategy):
)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
@@ -97,5 +97,5 @@ class SmoothScalp(IStrategy):
) & (dataframe['cci'] > 150)
)
,
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/TDSequentialStrategy.py
+13 -13
View File
@@ -39,14 +39,14 @@ class TDSequentialStrategy(IStrategy):
timeframe = '1h'
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Optional order type mapping
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'limit',
'stoploss_on_exchange': False
}
@@ -56,8 +56,8 @@ class TDSequentialStrategy(IStrategy):
# Optional time in force for orders
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc',
'entry': 'gtc',
'exit': 'gtc',
}
def informative_pairs(self):
@@ -123,29 +123,29 @@ class TDSequentialStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe["buy"] = 0
dataframe["enter_long"] = 0
dataframe.loc[((dataframe['exceed_low']) &
(dataframe['seq_buy'] > 8))
, 'buy'] = 1
, 'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy columnNA / NaN values
"""
dataframe["sell"] = 0
dataframe["exit_long"] = 0
dataframe.loc[((dataframe['exceed_high']) |
(dataframe['seq_sell'] > 8))
, 'sell'] = 1
, 'exit_long'] = 1
return dataframe
user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py
+4 -4
View File
@@ -19,7 +19,7 @@ class TechnicalExampleStrategy(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
@@ -27,14 +27,14 @@ class TechnicalExampleStrategy(IStrategy):
)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['cmf'] > 0)
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/custom_stoploss_with_psar.py
+5 -5
View File
@@ -21,7 +21,7 @@ class CustomStoplossWithPSAR(IStrategy):
you are supposed to take the `custom_stoploss()` and `populate_indicators()`
parts and adapt it to your own strategy
the populate_buy_trend() function is pretty nonsencial
the populate_entry_trend() function is pretty nonsencial
"""
timeframe = '1h'
stoploss = -0.2
@@ -64,7 +64,7 @@ class CustomStoplossWithPSAR(IStrategy):
# dataframe['rsi'] = ta.RSI(dataframe)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Placeholder Strategy: buys when SAR is smaller then candle before
Based on TA indicators, populates the buy signal for the given dataframe
@@ -75,11 +75,11 @@ class CustomStoplossWithPSAR(IStrategy):
(
(dataframe['sar'] < dataframe['sar'].shift())
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Placeholder Strategy: does nothing
Based on TA indicators, populates the sell signal for the given dataframe
@@ -87,5 +87,5 @@ class CustomStoplossWithPSAR(IStrategy):
:return: DataFrame with buy column
"""
# Deactivated sell signal to allow the strategy to work correctly
dataframe.loc[:, 'sell'] = 0
dataframe.loc[:, 'exit_long'] = 0
return dataframe
user_data/strategies/fixed_riskreward_loss.py
+4 -4
View File
@@ -96,7 +96,7 @@ class FixedRiskRewardLoss(IStrategy):
# dataframe['rsi'] = ta.RSI(dataframe)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Placeholder Strategy: buys when SAR is smaller then candle before
Based on TA indicators, populates the buy signal for the given dataframe
@@ -104,10 +104,10 @@ class FixedRiskRewardLoss(IStrategy):
:return: DataFrame with buy column
"""
# Allways buys
dataframe.loc[:, 'buy'] = 1
dataframe.loc[:, 'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Placeholder Strategy: does nothing
Based on TA indicators, populates the sell signal for the given dataframe
@@ -116,5 +116,5 @@ class FixedRiskRewardLoss(IStrategy):
"""
# Never sells
dataframe.loc[:, 'sell'] = 0
dataframe.loc[:, 'exit_long'] = 0
return dataframe
user_data/strategies/hlhb.py
+14 -14
View File
@@ -44,27 +44,27 @@ class hlhb(IStrategy):
process_only_new_candles = True
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = True
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
'entry': 'gtc',
'exit': 'gtc'
}
plot_config = {
# Main plot indicators (Moving averages, ...)
'main_plot': {
@@ -81,7 +81,7 @@ class hlhb(IStrategy):
}
}
}
def informative_pairs(self):
return []
@@ -103,7 +103,7 @@ class hlhb(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['rsi'], 50)) &
@@ -111,11 +111,11 @@ class hlhb(IStrategy):
(dataframe['adx'] > 25) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['rsi'], 50)) &
@@ -123,6 +123,6 @@ class hlhb(IStrategy):
(dataframe['adx'] > 25) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/lookahead_bias/DevilStra.py
+4 -4
View File
@@ -582,7 +582,7 @@ class DevilStra(IStrategy):
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pairs = self.dp.current_whitelist()
pairs_len = len(pairs)
@@ -646,13 +646,13 @@ class DevilStra(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy']=1
'enter_long']=1
# print(len(dataframe.keys()))
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pairs = self.dp.current_whitelist()
pairs_len = len(pairs)
@@ -716,5 +716,5 @@ class DevilStra(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
'exit_long']=1
return dataframe
user_data/strategies/lookahead_bias/GodStraNew.py
+4 -4
View File
@@ -539,7 +539,7 @@ class GodStraNew(IStrategy):
'''
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
@@ -587,13 +587,13 @@ class GodStraNew(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy']=1
'enter_long']=1
# print(len(dataframe.keys()))
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = list()
# TODO: Its not dry code!
@@ -639,5 +639,5 @@ class GodStraNew(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell']=1
'exit_long']=1
return dataframe
user_data/strategies/lookahead_bias/Zeus.py
+4 -4
View File
@@ -103,7 +103,7 @@ class Zeus(IStrategy):
dataframe['trend_kst_diff'] = (tkd-tkd.min())/(tkd.max()-tkd.min())
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
IND = 'trend_ichimoku_base'
REAL = self.buy_real.value
@@ -120,11 +120,11 @@ class Zeus(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
IND = 'trend_kst_diff'
REAL = self.sell_real.value
@@ -142,6 +142,6 @@ class Zeus(IStrategy):
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/lookahead_bias/wtc.py
+4 -4
View File
@@ -128,7 +128,7 @@ class wtc(IStrategy):
dataframe['wt1'], dataframe['wt2'], dataframe['def'], dataframe['slowk'] = 0, 10, 100, 1000
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['wt1'], dataframe['wt2']))
@@ -138,11 +138,11 @@ class wtc(IStrategy):
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# print(dataframe['slowk']/dataframe['wt1'])
dataframe.loc[
(
@@ -152,5 +152,5 @@ class wtc(IStrategy):
& (dataframe['def'].between(self.sell_min.value, self.sell_max.value))
),
'sell'] = 1
'exit_long'] = 1
return dataframe
user_data/strategies/mabStra.py
+4 -4
View File
@@ -63,7 +63,7 @@ class mabStra(IStrategy):
timeperiod=self.sell_slow_ma_timeframe.value)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
@@ -76,11 +76,11 @@ class mabStra(IStrategy):
(dataframe['buy-fastMA'].div(dataframe['buy-slowMA'])
< self.buy_div_max.value)
),
'buy'] = 1
'enter_long'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['sell-fastMA'].div(dataframe['sell-mojoMA'])
@@ -92,5 +92,5 @@ class mabStra(IStrategy):
(dataframe['sell-slowMA'].div(dataframe['sell-fastMA'])
< self.sell_div_max.value)
),
'sell'] = 1
'exit_long'] = 1
return dataframe