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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
class defaultB(IStrategy):
"""
This is a strategy template to get you started.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
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
"""
# 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".
minimal_roi = {
"4320": 0.0018,
#"300": 0.01,
#"60": 0.04, # 0.01
#"30": 0.06, # 0.02
"0": 0.30 # 0.04
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.0521 # -0.05
# Trailing stoploss
trailing_stop = True
trailing_only_offset_is_reached = True
# trailing_stop_positive = 0.005
trailing_stop_positive = 0.0005
trailing_stop_positive_offset = 0.034
# Optimal timeframe for the strategy.
timeframe = '30m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values canconfig222 be overridden in the "ask_strategy" section in the config.
use_sell_signal = False
sell_profit_only = True
ignore_roi_if_buy_signal = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
# 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'
}
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'},
}
}
}
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"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Momentum Indicators
# ------------------------------------
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['adx50'] = ta.ADX(dataframe, timeperiod=50)
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
dataframe['cci'] = ta.CCI(dataframe)
dataframe['cmo'] = ta.CMO(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['rsi25'] = ta.RSI(dataframe, timeperiod=25)
dataframe['rsi50'] = ta.RSI(dataframe, timeperiod=50)
dataframe['rsi100'] = ta.RSI(dataframe, timeperiod=100)
dataframe['rsi200'] = ta.RSI(dataframe, timeperiod=200)
dataframe['rsi400'] = ta.RSI(dataframe, timeperiod=400)
dataframe['rsi1000'] = ta.RSI(dataframe, timeperiod=1000)
# Stochastic Fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
dataframe['mfi20'] = ta.MFI(dataframe, timeperiod=20)
dataframe['mfi50'] = ta.MFI(dataframe, timeperiod=50)
dataframe['mfi100'] = ta.MFI(dataframe, timeperiod=100)
dataframe['mfi200'] = ta.MFI(dataframe, timeperiod=200)
dataframe['mfi400'] = ta.MFI(dataframe, timeperiod=400)
dataframe['mfi1000'] = ta.MFI(dataframe, timeperiod=1000)
# # ROC
dataframe['roc'] = ta.ROC(dataframe)
# Overlap Studies
# ------------------------------------
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema200'] = ta.EMA(dataframe, timeperiod=100)
dataframe['ema400'] = ta.EMA(dataframe, timeperiod=100)
# Parabolic SAR
dataframe['sar'] = ta.SAR(dataframe)
# TEMA - Triple Exponential Moving Average
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
dataframe['tema3'] = ta.TEMA(dataframe, timeperiod=3)
dataframe['tema5'] = ta.TEMA(dataframe, timeperiod=5)
dataframe['tema14'] = ta.TEMA(dataframe, timeperiod=14)
dataframe['tema21'] = ta.TEMA(dataframe, timeperiod=21)
dataframe['tema50'] = ta.TEMA(dataframe, timeperiod=50)
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# # Chart type
# # ------------------------------------
# helps with pattern recognition
dataframe['average'] = (dataframe['close'] + dataframe['open'] + dataframe['high'] + dataframe['low']) / 4
dataframe['sma_slow'] = ta.SMA(dataframe, timeperiod=200, price='close')
dataframe['sma_medium'] = ta.SMA(dataframe, timeperiod=100, price='close')
dataframe['sma_fast'] = ta.SMA(dataframe, timeperiod=50, price='close')
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode 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]
"""
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[ # Breakout: TEMA crosses over longer TEMA
(
(qtpylib.crossed_above(dataframe['tema5'], dataframe['tema14'])) &
(dataframe['adx'] > 25)
),
'buy'] = 1
dataframe.loc[ # Breakout: RSI plus momentum
(
(qtpylib.crossed_above(dataframe['rsi'], 60)) &
(dataframe['adx'] > 30)
),
'buy'] = 1
dataframe.loc[ # Breakout: crossing middle of Bollinger Bands
(
(qtpylib.crossed_above(dataframe['close'], dataframe['bb_middleband'])) &
(dataframe['adx'] > 30) &
(dataframe['adx'] > dataframe['adx'].shift(1)) &
(dataframe['adx'].shift(1) > dataframe['adx'].shift(2)) &
(dataframe['adx'].shift(2) > dataframe['adx'].shift(3)) &
(dataframe['close'] > dataframe['ema21']) &
(dataframe['close'] > dataframe['ema50']) &
(dataframe['roc'] > 0) &
(dataframe['ema50'] > 0) &
(dataframe['ema21'] > dataframe['ema50'])
),
'buy'] = 1
dataframe.loc[ # Breakout: Increasing momentum and positive price action
(
(qtpylib.crossed_above(dataframe['adx'], 25)) &
(dataframe['ema21'] > dataframe['ema50'])
),
'buy'] = 1
dataframe.loc[ # BTFD: CCI bounce
# Doesn't like BTC Dominance tracking sideways
# Enable by uncommenting the indicators
(
#(qtpylib.crossed_above(dataframe['cci'], -100))
),
'buy'] = 1
dataframe.loc[ # BTFD: Price Crosses into the Bollinger Bands from below
# Doesn't like BTC Dominance tracking sideways
# Enable by uncommenting the indicators
(
#(qtpylib.crossed_above(dataframe['close'], dataframe['bb_lowerband']))
),
'buy'] = 1
dataframe.loc[ # BTFD: V-shaped bottom.
# Doesn't like BTC Dominance tracking sideways
# Enable by uncommenting the indicators
(
#(dataframe['average'].shift(5) > dataframe['average'].shift(4)) &
#(dataframe['average'].shift(4) > dataframe['average'].shift(3)) &
#(dataframe['average'].shift(3) > dataframe['average'].shift(2)) &
#(dataframe['average'].shift(2) > dataframe['average'].shift(1)) &
#(dataframe['average'].shift(1) < dataframe['average'].shift(0)) &
#(dataframe['low'].shift(1) < dataframe['bb_middleband']) &
#(dataframe['cci'].shift(1) < -100)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['mfi'], 95)) # Signal: RSI crosses above 70
),
'sell'] = 1
dataframe.loc[
(
(dataframe['mfi'] > 90) & # Signal: RSI crosses above 70
(dataframe['tema'] < dataframe['tema'].shift(3)) & # Guard: negative trend
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'sell'] = 1
dataframe.loc[
(
(qtpylib.crossed_above(dataframe['close'], dataframe['bb_upperband'])) &
(dataframe['mfi'] > 60) # High MFI
),
'sell'] = 1
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['adx'], 28)) &
(dataframe['rsi'] > 60) # High RSI
),
'sell'] = 1
dataframe.loc[
(
(qtpylib.crossed_below(dataframe['adx'], 28)) &
(dataframe['mfi'] > 60) # High MFI
),
'sell'] = 1
return dataframe