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Replace all "ticker_interval" with timeframe

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This commit is contained in:
Matthias
2020-11-06 07:04:29 +01:00
parent 705d5b9de8
commit 9fd0faddd8
36 changed files with 94 additions and 92 deletions
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user_data/strategies/InformativeSample.py
+5 -4
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@@ -9,6 +9,7 @@ from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class InformativeSample(IStrategy):
"""
Sample strategy implementing Informative Pairs - compares stake_currency with USDT.
@@ -33,8 +34,8 @@ class InformativeSample(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
@@ -68,7 +69,7 @@ class InformativeSample(IStrategy):
("BTC/USDT", "15m"),
]
"""
return [(f"{self.config['stake_currency']}/USDT", self.ticker_interval)]
return [(f"{self.config['stake_currency']}/USDT", self.timeframe)]
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@@ -85,7 +86,7 @@ class InformativeSample(IStrategy):
if self.dp:
# Get ohlcv data for informative pair.
data = self.dp.get_pair_dataframe(pair=f"{self.stake_currency}/USDT",
timeframe=self.ticker_interval)
timeframe=self.timeframe)
# Combine the 2 dataframes using 'close'.
# This will result in a column named 'closeETH' or 'closeBTC' - depending on stake_currency.
dataframe = dataframe.merge(data[["date", "close"]], on="date", how="left", suffixes=("", self.config['stake_currency']))
user_data/strategies/Strategy001.py
+3 -2
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@@ -9,6 +9,7 @@ from pandas import DataFrame
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class Strategy001(IStrategy):
"""
Strategy 001
@@ -32,8 +33,8 @@ class Strategy001(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
user_data/strategies/Strategy002.py
+3 -2
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@@ -10,6 +10,7 @@ import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Strategy002(IStrategy):
"""
Strategy 002
@@ -33,8 +34,8 @@ class Strategy002(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
user_data/strategies/Strategy003.py
+3 -2
View File
@@ -10,6 +10,7 @@ import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Strategy003(IStrategy):
"""
Strategy 003
@@ -33,8 +34,8 @@ class Strategy003(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
user_data/strategies/Strategy004.py
+3 -2
View File
@@ -8,6 +8,7 @@ from pandas import DataFrame
import talib.abstract as ta
class Strategy004(IStrategy):
"""
@@ -32,8 +33,8 @@ class Strategy004(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
user_data/strategies/Strategy005.py
+2 -2
View File
@@ -35,8 +35,8 @@ class Strategy005(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.10
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# trailing stoploss
trailing_stop = False
user_data/strategies/berlinguyinca/ADXMomentum.py
+1 -1
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@@ -28,7 +28,7 @@ class ADXMomentum(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal ticker interval for the strategy
# Optimal timeframe for the strategy
timeframe = '1h'
# Number of candles the strategy requires before producing valid signals
user_data/strategies/berlinguyinca/ASDTSRockwellTrading.py
+2 -2
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@@ -44,8 +44,8 @@ class ASDTSRockwellTrading(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
user_data/strategies/berlinguyinca/AdxSmas.py
+2 -2
View File
@@ -29,8 +29,8 @@ class AdxSmas(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '1h'
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
user_data/strategies/berlinguyinca/AverageStrategy.py
+2 -2
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@@ -26,8 +26,8 @@ class AverageStrategy(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.2
# Optimal ticker interval for the strategy
ticker_interval = '4h'
# Optimal timeframe for the strategy
timeframe = '4h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
user_data/strategies/berlinguyinca/AwesomeMacd.py
+2 -2
View File
@@ -29,8 +29,8 @@ class AwesomeMacd(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '1h'
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
user_data/strategies/berlinguyinca/BbandRsi.py
+2 -2
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@@ -29,8 +29,8 @@ class BbandRsi(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '1h'
# Optimal timeframe for the strategy
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
user_data/strategies/berlinguyinca/BinHV27.py
+3 -1
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@@ -26,8 +26,10 @@ class BinHV27(IStrategy):
minimal_roi = {
"0": 1
}
stoploss = -0.50
ticker_interval = '5m'
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = numpy.nan_to_num(ta.RSI(dataframe, timeperiod=5))
rsiframe = DataFrame(dataframe['rsi']).rename(columns={'rsi': 'close'})
user_data/strategies/berlinguyinca/BinHV45.py
+3 -2
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@@ -12,18 +12,19 @@ import freqtrade.vendor.qtpylib.indicators as qtpylib
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return rolling_mean, lower_band
class BinHV45(IStrategy):
minimal_roi = {
"0": 0.0125
}
stoploss = -0.05
ticker_interval = '1m'
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
mid, lower = bollinger_bands(dataframe['close'], window_size=40, num_of_std=2)
user_data/strategies/berlinguyinca/CCIStrategy.py
+3 -3
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@@ -20,11 +20,11 @@ class CCIStrategy(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.02
# Optimal ticker interval for the strategy
ticker_interval = '1m'
# Optimal timeframe for the strategy
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.ticker_interval, 5)
dataframe = self.resample(dataframe, self.timeframe, 5)
dataframe['cci_one'] = ta.CCI(dataframe, timeperiod=170)
dataframe['cci_two'] = ta.CCI(dataframe, timeperiod=34)
user_data/strategies/berlinguyinca/CMCWinner.py
+3 -3
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@@ -40,8 +40,8 @@ class CMCWinner(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '15m'
# Optimal timeframe for the strategy
timeframe = '15m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
@@ -57,7 +57,7 @@ class CMCWinner(IStrategy):
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# CMO
dataframe['cmo'] = ta.CMO(dataframe)
user_data/strategies/berlinguyinca/ClucMay72018.py
+2 -2
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@@ -36,8 +36,8 @@ class ClucMay72018(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=5)
user_data/strategies/berlinguyinca/CofiBitStrategy.py
+2 -2
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@@ -26,8 +26,8 @@ class CofiBitStrategy(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
user_data/strategies/berlinguyinca/CombinedBinHAndCluc.py
+1 -1
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@@ -24,7 +24,7 @@ class CombinedBinHAndCluc(IStrategy):
"0": 0.05
}
stoploss = -0.05
ticker_interval = '5m'
timeframe = '5m'
use_sell_signal = True
sell_profit_only = True
user_data/strategies/berlinguyinca/DoesNothingStrategy.py
+2 -2
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@@ -23,8 +23,8 @@ class DoesNothingStrategy(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
user_data/strategies/berlinguyinca/EMASkipPump.py
+2 -2
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@@ -30,8 +30,8 @@ class EMASkipPump(IStrategy):
# should be converted to a trailing stop loss
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
""" Adds several different TA indicators to the given DataFrame
user_data/strategies/berlinguyinca/Freqtrade_backtest_validation_freqtrade1.py
+2 -3
View File
@@ -11,7 +11,6 @@ from pandas import DataFrame
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy
class Freqtrade_backtest_validation_freqtrade1(IStrategy):
@@ -23,8 +22,8 @@ class Freqtrade_backtest_validation_freqtrade1(IStrategy):
"0": 2.04
}
stoploss = -09.90
ticker_interval = '1h'
stoploss = -0.90
timeframe = '1h'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# SMA - Simple Moving Average
user_data/strategies/berlinguyinca/Low_BB.py
+2 -2
View File
@@ -42,8 +42,8 @@ class Low_BB(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.015
# Optimal ticker interval for the strategy
ticker_interval = '1m'
# Optimal timeframe for the strategy
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
##################################################################################
user_data/strategies/berlinguyinca/MACDStrategy.py
+2 -2
View File
@@ -39,8 +39,8 @@ class MACDStrategy(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
user_data/strategies/berlinguyinca/MACDStrategy_crossed.py
+2 -2
View File
@@ -33,8 +33,8 @@ class MACDStrategy_crossed(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.3
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
user_data/strategies/berlinguyinca/MultiRSI.py
+3 -3
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@@ -21,11 +21,11 @@ class MultiRSI(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def get_ticker_indicator(self):
return int(self.ticker_interval[:-1])
return int(self.timeframe[:-1])
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
user_data/strategies/berlinguyinca/Quickie.py
+2 -2
View File
@@ -31,8 +31,8 @@ class Quickie(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
macd = ta.MACD(dataframe)
user_data/strategies/berlinguyinca/ReinforcedAverageStrategy.py
+3 -4
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@@ -30,8 +30,8 @@ class ReinforcedAverageStrategy(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.2
# Optimal ticker interval for the strategy
ticker_interval = '4h'
# Optimal timeframe for the strategy
timeframe = '4h'
# trailing stoploss
trailing_stop = False
@@ -47,7 +47,6 @@ class ReinforcedAverageStrategy(IStrategy):
sell_profit_only = False
ignore_roi_if_buy_signal = False
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['maShort'] = ta.EMA(dataframe, timeperiod=8)
@@ -58,7 +57,7 @@ class ReinforcedAverageStrategy(IStrategy):
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
self.resample_interval = timeframe_to_minutes(self.ticker_interval) * 12
self.resample_interval = timeframe_to_minutes(self.timeframe) * 12
dataframe_long = resample_to_interval(dataframe, self.resample_interval)
dataframe_long['sma'] = ta.SMA(dataframe_long, timeperiod=50, price='close')
dataframe = resampled_merge(dataframe, dataframe_long, fill_na=True)
user_data/strategies/berlinguyinca/ReinforcedQuickie.py
+3 -3
View File
@@ -37,8 +37,8 @@ class ReinforcedQuickie(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 12
@@ -48,7 +48,7 @@ class ReinforcedQuickie(IStrategy):
EMA_LONG_TERM = 21
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.resample(dataframe, self.ticker_interval, self.resample_factor)
dataframe = self.resample(dataframe, self.timeframe, self.resample_factor)
##################################################################################
# buy and sell indicators
user_data/strategies/berlinguyinca/ReinforcedSmoothScalp.py
+2 -2
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@@ -24,9 +24,9 @@ class ReinforcedSmoothScalp(IStrategy):
# should not be below 3% loss
stoploss = -0.1
# Optimal ticker interval for the strategy
# Optimal timeframe for the strategy
# the shorter the better
ticker_interval = '1m'
timeframe = '1m'
# resample factor to establish our general trend. Basically don't buy if a trend is not given
resample_factor = 5
user_data/strategies/berlinguyinca/Scalp.py
+5 -9
View File
@@ -6,13 +6,10 @@ from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, DatetimeIndex, merge
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
class Scalp(IStrategy):
"""
@@ -23,8 +20,6 @@ class Scalp(IStrategy):
Recommended is to only sell based on ROI for this strategy
"""
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi"
minimal_roi = {
@@ -35,9 +30,9 @@ class Scalp(IStrategy):
# should not be below 3% loss
stoploss = -0.04
# Optimal ticker interval for the strategy
# Optimal timeframe for the strategy
# the shorter the better
ticker_interval = '1m'
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
@@ -54,8 +49,8 @@ class Scalp(IStrategy):
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
@@ -69,6 +64,7 @@ class Scalp(IStrategy):
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
user_data/strategies/berlinguyinca/Simple.py
+2 -2
View File
@@ -31,8 +31,8 @@ class Simple(IStrategy):
# This attribute will be overridden if the config file contains "stoploss"
stoploss = -0.25
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# MACD
user_data/strategies/berlinguyinca/SmoothOperator.py
+2 -2
View File
@@ -34,8 +34,8 @@ class SmoothOperator(IStrategy):
# should be converted to a trailing stop loss
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
##################################################################################
user_data/strategies/berlinguyinca/SmoothScalp.py
+2 -2
View File
@@ -32,9 +32,9 @@ class SmoothScalp(IStrategy):
# should not be below 3% loss
stoploss = -0.5
# Optimal ticker interval for the strategy
# Optimal timeframe for the strategy
# the shorter the better
ticker_interval = '1m'
timeframe = '1m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['ema_high'] = ta.EMA(dataframe, timeperiod=5, price='high')
user_data/strategies/berlinguyinca/TDSequentialStrategy.py
+7 -7
View File
@@ -10,15 +10,15 @@ class TDSequentialStrategy(IStrategy):
Strategy based on TD Sequential indicator.
source:
https://hackernoon.com/how-to-buy-sell-cryptocurrency-with-number-indicator-td-sequential-5af46f0ebce1
Buy trigger:
When you see 9 consecutive closes "lower" than the close 4 bars prior.
An ideal buy is when the low of bars 6 and 7 in the count are exceeded by the low of bars 8 or 9.
Sell trigger:
When you see 9 consecutive closes "higher" than the close 4 candles prior.
An ideal sell is when the the high of bars 6 and 7 in the count are exceeded by the high of bars 8 or 9.
Created by @bmoulkaf
"""
INTERFACE_VERSION = 2
@@ -28,16 +28,16 @@ class TDSequentialStrategy(IStrategy):
# Optimal stoploss designed for the strategy
stoploss = -0.05
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy
ticker_interval = '1h'
# Optimal timeframe for the strategy
timeframe = '1h'
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
user_data/strategies/berlinguyinca/TechnicalExampleStrategy.py
+4 -4
View File
@@ -11,8 +11,8 @@ class TechnicalExampleStrategy(IStrategy):
stoploss = -0.05
# Optimal ticker interval for the strategy
ticker_interval = '5m'
# Optimal timeframe for the strategy
timeframe = '5m'
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe['cmf'] = cmf(dataframe, 21)
@@ -23,7 +23,7 @@ class TechnicalExampleStrategy(IStrategy):
dataframe.loc[
(
(
(dataframe['cmf'] < 0)
(dataframe['cmf'] < 0)
)
),
@@ -34,7 +34,7 @@ class TechnicalExampleStrategy(IStrategy):
# different strategy used for sell points, due to be able to duplicate it to 100%
dataframe.loc[
(
(dataframe['cmf'] > 0)
(dataframe['cmf'] > 0)
),
'sell'] = 1
return dataframe