forked from Unitoo/freqtrade-strategies
385 lines
15 KiB
Python
385 lines
15 KiB
Python
# 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 import IStrategy
|
|
|
|
# --------------------------------
|
|
# Add your lib to import here
|
|
import talib.abstract as ta
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
|
|
class CAKE_8(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 = {
|
|
"0": 0.247,
|
|
"21": 0.082,
|
|
"40": 0.038,
|
|
"115": 0
|
|
}
|
|
|
|
# Stoploss:
|
|
stoploss = -0.24852
|
|
|
|
# Trailing stop:
|
|
trailing_stop = True
|
|
trailing_stop_positive = 0.25579
|
|
trailing_stop_positive_offset = 0.28124
|
|
trailing_only_offset_is_reached = False
|
|
|
|
unfilledtimeout = {
|
|
'buy': 10,
|
|
'sell': 30
|
|
}
|
|
|
|
# Optimal timeframe for the strategy.
|
|
timeframe = '5m'
|
|
|
|
# Run "populate_indicators()" only for new candle.
|
|
process_only_new_candles = False
|
|
|
|
# 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'
|
|
}
|
|
|
|
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)
|
|
|
|
# # Plus Directional Indicator / Movement
|
|
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
|
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
|
|
|
# # Minus Directional Indicator / Movement
|
|
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
|
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
|
|
|
# # Aroon, Aroon Oscillator
|
|
# aroon = ta.AROON(dataframe)
|
|
# dataframe['aroonup'] = aroon['aroonup']
|
|
# dataframe['aroondown'] = aroon['aroondown']
|
|
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
|
|
|
|
# # Awesome Oscillator
|
|
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
|
|
|
# # Keltner Channel
|
|
# keltner = qtpylib.keltner_channel(dataframe)
|
|
# dataframe["kc_upperband"] = keltner["upper"]
|
|
# dataframe["kc_lowerband"] = keltner["lower"]
|
|
# dataframe["kc_middleband"] = keltner["mid"]
|
|
# dataframe["kc_percent"] = (
|
|
# (dataframe["close"] - dataframe["kc_lowerband"]) /
|
|
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
|
|
# )
|
|
# dataframe["kc_width"] = (
|
|
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
|
|
# )
|
|
|
|
# # Ultimate Oscillator
|
|
# dataframe['uo'] = ta.ULTOSC(dataframe)
|
|
|
|
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
|
|
# dataframe['cci'] = ta.CCI(dataframe)
|
|
|
|
# RSI
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
|
|
# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
|
|
# rsi = 0.1 * (dataframe['rsi'] - 50)
|
|
# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
|
|
|
|
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
|
|
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
|
|
|
|
# # Stochastic Slow
|
|
# stoch = ta.STOCH(dataframe)
|
|
# dataframe['slowd'] = stoch['slowd']
|
|
# dataframe['slowk'] = stoch['slowk']
|
|
|
|
# Stochastic Fast
|
|
stoch_fast = ta.STOCHF(dataframe)
|
|
dataframe['fastd'] = stoch_fast['fastd']
|
|
dataframe['fastk'] = stoch_fast['fastk']
|
|
|
|
# # Stochastic RSI
|
|
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
|
|
# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
|
|
# stoch_rsi = ta.STOCHRSI(dataframe)
|
|
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
|
|
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
|
|
|
|
# MACD
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
dataframe['macdhist'] = macd['macdhist']
|
|
|
|
# MFI
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
|
|
|
# # 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"]
|
|
)
|
|
|
|
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
|
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
|
# qtpylib.typical_price(dataframe), window=20, stds=2
|
|
# )
|
|
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
|
|
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
|
|
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
|
|
# dataframe["wbb_percent"] = (
|
|
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
|
|
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
|
|
# )
|
|
# dataframe["wbb_width"] = (
|
|
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_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['ema120'] = ta.EMA(dataframe, timeperiod=120)
|
|
#dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
|
|
#dataframe['ema180'] = ta.EMA(dataframe, timeperiod=180)
|
|
#dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
|
|
|
|
# # SMA - Simple Moving Average
|
|
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
|
|
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
|
|
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
|
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
|
|
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
|
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
|
|
|
# Parabolic SAR
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
|
|
|
# TEMA - Triple Exponential Moving Average
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
|
|
|
# Cycle Indicator
|
|
# ------------------------------------
|
|
# Hilbert Transform Indicator - SineWave
|
|
hilbert = ta.HT_SINE(dataframe)
|
|
dataframe['htsine'] = hilbert['sine']
|
|
dataframe['htleadsine'] = hilbert['leadsine']
|
|
|
|
# Pattern Recognition - Bullish candlestick patterns
|
|
# ------------------------------------
|
|
# # Hammer: values [0, 100]
|
|
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
|
|
# # Inverted Hammer: values [0, 100]
|
|
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
|
|
# # Dragonfly Doji: values [0, 100]
|
|
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
|
|
# # Piercing Line: values [0, 100]
|
|
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
|
|
# # Morningstar: values [0, 100]
|
|
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
|
|
# # Three White Soldiers: values [0, 100]
|
|
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
|
|
|
|
# Pattern Recognition - Bearish candlestick patterns
|
|
# ------------------------------------
|
|
# # Hanging Man: values [0, 100]
|
|
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
|
|
# # Shooting Star: values [0, 100]
|
|
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
|
|
# # Gravestone Doji: values [0, 100]
|
|
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
|
|
# # Dark Cloud Cover: values [0, 100]
|
|
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
|
|
# # Evening Doji Star: values [0, 100]
|
|
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
|
|
# # Evening Star: values [0, 100]
|
|
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
|
|
|
|
# Pattern Recognition - Bullish/Bearish candlestick patterns
|
|
# ------------------------------------
|
|
# # Three Line Strike: values [0, -100, 100]
|
|
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
|
|
# # Spinning Top: values [0, -100, 100]
|
|
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
|
|
# # Engulfing: values [0, -100, 100]
|
|
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
|
|
# # Harami: values [0, -100, 100]
|
|
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
|
# # Three Outside Up/Down: values [0, -100, 100]
|
|
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
|
|
# # Three Inside Up/Down: values [0, -100, 100]
|
|
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
|
|
|
# # Chart type
|
|
# # ------------------------------------
|
|
# # Heikin Ashi Strategy
|
|
# heikinashi = qtpylib.heikinashi(dataframe)
|
|
# dataframe['ha_open'] = heikinashi['open']
|
|
# dataframe['ha_close'] = heikinashi['close']
|
|
# dataframe['ha_high'] = heikinashi['high']
|
|
# dataframe['ha_low'] = heikinashi['low']
|
|
|
|
# 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[
|
|
(
|
|
#(dataframe['adx'] > 22) &
|
|
(dataframe['mfi'] < 21) &
|
|
(dataframe['fastd'] < 44) &
|
|
(dataframe['close'] < dataframe['bb_lowerband'])
|
|
#qtpylib.crossed_above(dataframe['close'], dataframe['sar'])
|
|
),
|
|
'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[
|
|
(
|
|
#(dataframe['adx'] < 57) &
|
|
(dataframe['fastd'] > 93) &
|
|
(dataframe['mfi'] > 92) &
|
|
#(dataframe['rsi'] > 82) &
|
|
#(dataframe['close'] > dataframe['bb_upperband'])
|
|
(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd']))
|
|
|
|
#(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
|
|
#(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
|
|
#(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
|
|
#(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
|
|
#(dataframe['volume'] > 0) # Make sure Volume is not 0
|
|
),
|
|
'sell'] = 1
|
|
return dataframe
|
|
|