Timeframe
5m
Direction
Long Only
Stoploss
-6.3%
Trailing Stop
No
ROI
0m: 6.3%, 28m: 5.0%, 76m: 4.0%, 125m: 3.0%
Interface Version
2
Startup Candles
N/A
Indicators
9
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from sqlalchemy.orm.base import RELATED_OBJECT_OK
from sqlalchemy.sql.elements import or_
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.exchange import timeframe_to_minutes
from technical import indicators
import logging
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, RealParameter,
IStrategy, IntParameter, merge_informative_pair)
class flawless_lambo(IStrategy):
# Add some logging
logger = logging.getLogger(__name__)
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
# ROI table:
minimal_roi = {
"0": 0.0625,
"28": 0.05,
"76": 0.04,
"125": 0.03,
"240": 0.02,
"360": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
# use_custom_stoploss = True
stoploss = -0.0625 #-0.10
# Trailing stop:
trailing_stop = False
trailing_stop_positive = 0.0069
trailing_stop_positive_offset = 0.038
trailing_only_offset_is_reached = False
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 20 #30
# Optimal timeframe for the strategy.
timeframe = '5m'
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = True
# sell_profit_offset = 0.019
ignore_roi_if_buy_signal = False
# hyperopt params
buy_williams_low = DecimalParameter(-100, -50, default=-58.201)
buy_williams_high = DecimalParameter(-100, -50, default=-92.098)
buy_rsi_low = DecimalParameter(0, 30, default=2.342)
buy_rsi_high = DecimalParameter(30, 60, default=51.22)
buy_volume = DecimalParameter(1000, 100000, default=5000)
sell_rsi = DecimalParameter(60, 100, default=60.631)
sell_williams = DecimalParameter(-40, -10, default=-19.308)
sell_volume = DecimalParameter(1000, 100000, default=1000)
# 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'
}
@property
def plot_config(self):
return {
"main_plot": {
"bb.lower": {
"color": "#9c6edc",
"type": "line"
},
"bb.upper": {
"color": "#9c6edc",
"type": "line"
},
"vwma": {
"color": "#4f9f02",
"type": "line"
}
},
"subplots": {
"obv": {
"OBV": {
"color": "#1b61ab",
"type": "line"
},
"OBVSlope": {
"color": "#f18b7a",
"type": "line"
}
},
"vpci": {
"vpci": {
"color": "#d59a7a",
"type": "line"
}
},
"macd": {
"macd": {
"color": "#1c3d6a",
"type": "line"
},
"macdsignal": {
"color": "#873480",
"type": "line"
},
"macdhist": {
"color": "#478a87",
"type": "bar"
}
},
"wiliams": {
"williamspercent": {
"color": "#10f551",
"type": "line"
}
},
"stoch + rsi": {
"rsi": {
"color": "#d7affd",
"type": "line"
},
"slowd": {
"color": "#d7cc5c",
"type": "line"
},
"fastk": {
"color": "#186f86",
"type": "line"
}
},
"adx": {
"adx": {
"color": "#c392cd",
"type": "line"
},
"plus.di": {
"color": "#bcd6c5",
"type": "line"
},
"minus.di": {
"color": "#eb044c",
"type": "line"
}
}
}
}
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.timeframe) for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
# first check if dataprovider is available
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
# Bollinger!
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb.lower'] = bollinger['lower']
dataframe['bb.middle'] = bollinger['mid']
dataframe['bb.upper'] = bollinger['upper']
# Added PCB Style OBV
dataframe['OBV'] = ta.OBV(dataframe)
dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV'])
# VWMA
# vwma_period = 13
# dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() /
# dataframe['volume'].rolling(vwma_period).sum())
# VWAP
# vwap_period = 20
# dataframe['vwap'] = qtpylib.rolling_vwap(dataframe, window=vwap_period)
# VPCI
dataframe['vpci'] = indicators.vpci(dataframe, period_long=14)
#williamsR
dataframe['williamspercent'] = indicators.williams_percent(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['plus.di'] = ta.PLUS_DI(dataframe)
dataframe['minus.di'] = ta.MINUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stochastic Slow
stoch_slow = ta.STOCH(dataframe)
dataframe['slowd'] = stoch_slow['slowd']
dataframe['slowk'] = stoch_slow['slowd']
return dataframe
def do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Bollinger!
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb.lower'] = bollinger['lower']
dataframe['bb.middle'] = bollinger['mid']
dataframe['bb.upper'] = bollinger['upper']
# Added PCB Style OBV
dataframe['OBV'] = ta.OBV(dataframe)
dataframe['OBVSlope'] = pta.momentum.slope(dataframe['OBV'])
# VWMA
# vwma_period = 13
# dataframe['vwma'] = ((dataframe["close"] * dataframe["volume"]).rolling(vwma_period).sum() /
# dataframe['volume'].rolling(vwma_period).sum())
# VPCI
dataframe['vpci'] = indicators.vpci(dataframe, period_long=14)
#williamsR
dataframe['williamspercent'] = indicators.williams_percent(dataframe)
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['plus.di'] = ta.PLUS_DI(dataframe)
dataframe['minus.di'] = ta.MINUS_DI(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# Stochastic Slow
stoch_slow = ta.STOCH(dataframe)
dataframe['slowd'] = stoch_slow['slowd']
dataframe['slowk'] = stoch_slow['slowd']
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['volume'] > self.buy_volume.value) &
(dataframe['OBVSlope'] > 0) &
(dataframe['williamspercent'] <= self.buy_williams_low.value) &
(dataframe['williamspercent'] > self.buy_williams_high.value) &
(dataframe['rsi'] > self.buy_rsi_low.value) &
(dataframe['rsi'] <= self.buy_rsi_high.value) &
(qtpylib.crossed_above(dataframe['fastk'], dataframe['slowd'])) &
(dataframe['close'] < dataframe['bb.middle'])
),'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['volume'] > self.sell_volume.value) &
(dataframe['close'] >= dataframe['bb.upper']) &
(dataframe['williamspercent'] > self.sell_williams.value) |
(dataframe['rsi'] > self.sell_rsi.value) &
(qtpylib.crossed_below(dataframe['plus.di'], dataframe['minus.di'])) |
(qtpylib.crossed_below(dataframe['macd'], dataframe['macdsignal'])) |
(qtpylib.crossed_below(dataframe['fastk'], dataframe['slowd'])) |
(dataframe['vpci'] >= dataframe['bb.upper'])
),
'sell'] = 1
return dataframe
# "All watched over by machines with loving grace..."