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NostalgiaForInfinityV1.py
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NostalgiaForInfinityV1.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from freqtrade.strategy import merge_informative_pair
from freqtrade.persistence import Trade
###########################################################################################################
## NostalgiaForInfinityV1 by iterativ ##
## ##
## Strategy for Freqtrade https://github.com/freqtrade/freqtrade ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 4 and 6 open trades, with unlimited stake. ##
## A pairlist with 20 to 60 pairs. Volume pairlist works well. ##
## Prefer stable coin (USDT, BUSDT etc) pairs, instead of BTC or ETH pairs. ##
## Highly recommended to blacklist leveraged tokens (*BULL, *BEAR, *UP, *DOWN etc). ##
## Ensure that you don't override any variables in you config.json. Especially ##
## the timeframe (must be 5m). ##
## ##
###########################################################################################################
## DONATIONS ##
## ##
## Absolutely not required. However, will be accepted as a token of appreciation. ##
## ##
## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
## ##
###########################################################################################################
# SSL Channels
def SSLChannels(dataframe, length = 7):
df = dataframe.copy()
df['ATR'] = ta.ATR(df, timeperiod=14)
df['smaHigh'] = df['high'].rolling(length).mean() + df['ATR']
df['smaLow'] = df['low'].rolling(length).mean() - df['ATR']
df['hlv'] = np.where(df['close'] > df['smaHigh'], 1, np.where(df['close'] < df['smaLow'], -1, np.NAN))
df['hlv'] = df['hlv'].ffill()
df['sslDown'] = np.where(df['hlv'] < 0, df['smaHigh'], df['smaLow'])
df['sslUp'] = np.where(df['hlv'] < 0, df['smaLow'], df['smaHigh'])
return df['sslDown'], df['sslUp']
class NostalgiaForInfinityV1(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.25
}
stoploss = -0.36
timeframe = '5m'
inf_1h = '1h'
custom_info = {}
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.3
# Custom stoploss
use_custom_stoploss = False
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# 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
}
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Prevent ROI trigger, if there is more potential, in order to maximize profit
if (sell_reason == 'roi') & (last_candle['rsi'] > 50):
return False
return True
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, '1h') for pair in pairs]
return informative_pairs
def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
assert self.dp, "DataProvider is required for multiple timeframes."
# Get the informative pair
informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
# EMA
informative_1h['ema_20'] = ta.EMA(informative_1h, timeperiod=20)
informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
# RSI
informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
# SSL Channels
ssl_down_1h, ssl_up_1h = SSLChannels(informative_1h, 20)
informative_1h['ssl_down'] = ssl_down_1h
informative_1h['ssl_up'] = ssl_up_1h
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
bb_40 = qtpylib.bollinger_bands(dataframe['close'], window=40, stds=2)
dataframe['lower'] = bb_40['lower']
dataframe['mid'] = bb_40['mid']
dataframe['bbdelta'] = (bb_40['mid'] - dataframe['lower']).abs()
dataframe['closedelta'] = (dataframe['close'] - dataframe['close'].shift()).abs()
dataframe['tail'] = (dataframe['close'] - dataframe['low']).abs()
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['ema_slow'] = ta.EMA(dataframe, timeperiod=50)
dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=30).mean()
# EMA
dataframe['ema_50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
# SMA
dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['sma_9'] = ta.EMA(dataframe, timeperiod=9)
# RSI
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative_1h = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
# The indicators for the normal (5m) timeframe
dataframe = self.normal_tf_indicators(dataframe, metadata)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['close'] < dataframe['sma_9']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['ema_50'] > dataframe['ema_200']) &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
dataframe['lower'].shift().gt(0) &
dataframe['bbdelta'].gt(dataframe['close'] * 0.045) &
dataframe['closedelta'].gt(dataframe['close'] * 0.023) &
dataframe['tail'].lt(dataframe['bbdelta'] * 0.266) &
dataframe['close'].lt(dataframe['lower'].shift()) &
dataframe['close'].le(dataframe['close'].shift()) &
(dataframe['volume'] > 0)
)
|
(
(dataframe['close'] < dataframe['sma_9']) &
(dataframe['close'] > dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_200_1h']) &
(dataframe['close'] < dataframe['ema_slow']) &
(dataframe['close'] < 0.992 * dataframe['bb_lowerband']) &
(dataframe['volume'] < (dataframe['volume_mean_slow'].shift(1) * 34))
)
|
(
(dataframe['close'] < dataframe['sma_5']) &
(dataframe['ssl_up_1h'] > dataframe['ssl_down_1h']) &
(dataframe['ema_50'] > dataframe['ema_200']) &
(dataframe['ema_50_1h'] > dataframe['ema_200_1h']) &
(dataframe['rsi'] < dataframe['rsi_1h'] - 36.815) &
(dataframe['volume'] > 0)
)
,
'buy'
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['close'] > dataframe['bb_upperband']) &
(dataframe['close'].shift(1) > dataframe['bb_upperband'].shift(1)) &
(dataframe['close'].shift(2) > dataframe['bb_upperband'].shift(2)) &
(dataframe['volume'] > 0)
)
|
(
(dataframe['rsi'] > 78) &
(dataframe['volume'] > 0)
)
|
(
(dataframe['close'] < dataframe['ema_200']) &
(dataframe['close'] > dataframe['ema_50']) &
(dataframe['rsi'] > 50) &
(dataframe['volume'] > 0)
)
,
'sell'
] = 1
return dataframe