Timeframe
1h
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
Yes
ROI
0m: 15.0%, 720m: 10.0%, 1080m: 5.0%, 1440m: 3.0%
Interface Version
2
Startup Candles
N/A
Indicators
8
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from functools import reduce
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class CDCV3HO3(IStrategy):
# from https://www.tradingview.com/script/rGpAOoLi-CDC-ActionZone-V3-2020/
INTERFACE_VERSION = 2
buy_params = {
'buy_0': True,
'buy_1': True,
'buy_2': True,
'ob_lvl': 30,
}
sell_params = {
'sell_0': True,
'sell_1': True,
'sell_2': False,
'os_lvl': 70,
}
# custom params
buy_0 = BooleanParameter(
default=buy_params['buy_0'], space='buy', optimize=True)
buy_1 = BooleanParameter(
default=buy_params['buy_1'], space='buy', optimize=True)
buy_2 = BooleanParameter(
default=buy_params['buy_2'], space='buy', optimize=True)
ob_lvl = IntParameter(
0, 50, default=buy_params['ob_lvl'], space="buy", optimize=True)
sell_0 = BooleanParameter(
default=sell_params['sell_0'], space='sell', optimize=True)
sell_1 = BooleanParameter(
default=sell_params['sell_1'], space='sell', optimize=True)
sell_2 = BooleanParameter(
default=sell_params['sell_2'], space='sell', optimize=True)
os_lvl = IntParameter(
30, 90, default=sell_params['os_lvl'], space="sell", optimize=True)
minimal_roi = {
"0": 0.15,
"720": 0.10,
"1080": 0.05,
"1440": 0.03
}
stoploss = -0.99
# Trailing stoploss
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.05 # Disabled / not configured
# Optimal timeframe for the strategy.
timeframe = '1h'
inf_tf = '1h'
# Run "populate_indicators()" only for new candle.
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 = False
# 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',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
def informative_pairs(self):
pairs = self.dp.current_whitelist()
inf_pair = [(pair, self.inf_tf) for pair in pairs]
return inf_pair
def get_informative_indicators(self, metadata: dict):
dataframe = self.dp.get_pair_dataframe(
pair=metadata['pair'], timeframe=self.inf_tf)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# ema
dataframe["fast_ema"] = ta.EMA(dataframe, 12)
dataframe["slow_ema"] = ta.EMA(dataframe, 26)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, 14)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['s_adx'] = (dataframe['adx'] >= 50)
# STOCHRSI
period = 14
smooth_d = 3
smooth_k = 3
stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (
dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min())
dataframe['srsi_k'] = stochrsi.rolling(smooth_k).mean() * 100
dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smooth_d).mean()
# condition buy
dataframe['bull'] = dataframe['fast_ema'] > dataframe['slow_ema']
dataframe['buy_0'] = ((dataframe['fast_ema'] > dataframe["slow_ema"]) & (dataframe['fast_ema'].shift(
1) <= dataframe['slow_ema'].shift(1)) & dataframe['s_adx'])
dataframe['buy_1'] = (
(dataframe['srsi_d'] < self.ob_lvl.value) & dataframe['s_adx'])
dataframe['buy_2'] = dataframe['srsi_d'] > self.ob_lvl.value
# condition sell
dataframe['bear'] = dataframe['fast_ema'] < dataframe['slow_ema']
dataframe['sell_0'] = ((dataframe['fast_ema'] < dataframe["slow_ema"]) & (dataframe['fast_ema'].shift(
1) >= dataframe['slow_ema'].shift(1)))
dataframe['sell_1'] = dataframe['srsi_d'] > self.os_lvl.value
dataframe['sell_2'] = dataframe['srsi_d'] < self.os_lvl.value
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
if self.buy_0.value:
conditions.append((
dataframe['buy_0']
& (dataframe['volume'] > 0)
))
if self.buy_1.value:
conditions.append(
(
dataframe['buy_1']
& qtpylib.crossed_above(dataframe['srsi_k'], dataframe['srsi_d'])
& (dataframe['volume'] > 0)
)
)
if self.buy_2.value:
conditions.append(
(
dataframe['buy_2']
& qtpylib.crossed_above(dataframe['srsi_k'], dataframe['srsi_d'])
& (dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'buy'
]=1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
if self.sell_0.value:
conditions.append((
(dataframe['sell_0'])
& (dataframe['volume'] > 0)
))
if self.sell_1.value:
conditions.append(
(
dataframe['sell_1']
& dataframe['bear']
& qtpylib.crossed_above(dataframe['srsi_k'], dataframe['srsi_d'])
& (dataframe['volume'] > 0)
)
)
if self.sell_2.value:
conditions.append(
(
dataframe['sell_2']
& dataframe['bear']
& qtpylib.crossed_above(dataframe['srsi_k'], dataframe['srsi_d'])
& (dataframe['volume'] > 0)
)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x | y, conditions),
'sell'
]=1
return dataframe
plot_config = {
# Main plot indicators (Moving averages, ...)
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
# Subplots - each dict defines one additional plot
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}