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CombinedBinHClucAndMADV6.py
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CombinedBinHClucAndMADV6.py
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import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
import talib.abstract as ta
from freqtrade.persistence import Trade
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame
from datetime import datetime, timedelta
from freqtrade.strategy import merge_informative_pair
###########################################################################################################
## CombinedBinHClucAndMADV6 by ilya ##
## ##
## https://github.com/i1ya/freqtrade-strategies ##
## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ##
## ## ##
###########################################################################################################
## The main point of this strat is: ##
## - make drawdown as low as possible ##
## - buy at dip ##
## - sell quick as fast as you can (release money for the next buy) ##
## - soft check if market if rising ##
## - hard check is market if fallen ##
## ##
## ##
###########################################################################################################
## What's new ##
## ##
## + change ROI from 0-10 min to 2.9% (I feel lucky) ##
## + try to exclude pumping ##
## ##
###########################################################################################################
## GENERAL RECOMMENDATIONS ##
## ##
## For optimal performance, suggested to use between 2 and 4 open trades, with unlimited stake. ##
## With my pairlist which can be found in this repo. ##
## ##
## Ensure that you don't override any variables in your config.json. Especially ##
## the timeframe (must be 5m). ##
## ##
## sell_profit_only: ##
## True - risk more (gives you higher profit and higher Drawdown) ##
## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ##
## ##
###########################################################################################################
## DONATIONS 2 @iterativ (author of the original strategy) ##
## ##
## 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 CombinedBinHClucAndMADV6(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.029, # I feel lucky!
"10": 0.021,
"40": 0.005,
}
stoploss = -0.99 # effectively disabled.
timeframe = "5m"
inf_1h = "1h"
# 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 = False
# Trailing stoploss
trailing_stop = False
trailing_only_offset_is_reached = False
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.025
# Custom stoploss
use_custom_stoploss = True
# 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 custom_stoploss(
self,
pair: str,
trade: "Trade",
current_time: datetime,
current_rate: float,
current_profit: float,
**kwargs
) -> float:
# Manage losing trades and open room for better ones.
if (current_profit < 0) & (
current_time - timedelta(minutes=240) > trade.open_date_utc
):
return 0.01
return 0.99
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_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:
# strategy ClucMay72018
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_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)
# SMA
dataframe["sma_5"] = ta.EMA(dataframe, timeperiod=5)
# 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[
# When market at bull mode (guard)
( # strategy ClucMay72018
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["close"] < dataframe["ema_slow"])
& (dataframe["close"] < 0.99 * dataframe["bb_lowerband"])
& ( # Guard is on, candle should dig not so hard (0,99)
dataframe["volume_mean_slow"]
> dataframe["volume_mean_slow"].shift(30) * 0.4
)
& # Try to exclude pumping
# (dataframe['volume'] < (dataframe['volume'].shift() * 4)) & # Don't buy if someone drop the market.
(dataframe["volume"] > 0)
)
|
# When market at bear mode (without guard)
( # strategy ClucMay72018
(dataframe["close"] < dataframe["ema_slow"])
& (dataframe["close"] < 0.975 * dataframe["bb_lowerband"])
& ( # Guard is off, candle should dig hard (0,975)
dataframe["volume"] < (dataframe["volume"].shift() * 4)
)
& (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market.
& ( # Buy only at dip
dataframe["volume_mean_slow"]
> dataframe["volume_mean_slow"].shift(30) * 0.4
)
& ( # Try to exclude pumping
dataframe["volume"] > 0
) # Make sure Volume is not 0
)
|
# When market at bull mode (guard)
( # strategy MACD Low buy
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["ema_26"] > dataframe["ema_12"])
& (
(dataframe["ema_26"] - dataframe["ema_12"])
> (dataframe["open"] * 0.02)
)
& (
(dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
> (dataframe["open"] / 100)
)
& (dataframe["volume"] < (dataframe["volume"].shift() * 4))
& ( # Don't buy if someone drop the market.
dataframe["close"] < (dataframe["bb_lowerband"])
)
& (
dataframe["volume_mean_slow"]
> dataframe["volume_mean_slow"].shift(30) * 0.4
)
& ( # Try to exclude pumping
dataframe["volume"] > 0
) # Make sure Volume is not 0
)
|
# When market at bear mode (without guard)
( # strategy MACD Low buy
(dataframe["ema_26"] > dataframe["ema_12"])
& (
(dataframe["ema_26"] - dataframe["ema_12"])
> (dataframe["open"] * 0.03)
)
& (
(dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
> (dataframe["open"] / 100)
)
& (dataframe["volume"] < (dataframe["volume"].shift() * 4))
& ( # Don't buy if someone drop the market.
dataframe["close"] < (dataframe["bb_lowerband"])
)
& (dataframe["volume"] > 0) # Make sure Volume is not 0
)
| (
(dataframe["close"] < dataframe["sma_5"])
& (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"])
& (dataframe["ema_slow"] > dataframe["ema_200"])
& (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
& (dataframe["rsi"] < dataframe["rsi_1h"] - 43.276)
& (dataframe["volume"] > 0)
),
"buy",
] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe["close"] > dataframe["bb_middleband"] * 1.01)
& ( # Don't be gready, sell fast
dataframe["volume"] > 0
) # Make sure Volume is not 0
),
"sell",
] = 1
return dataframe