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BinClucMadDevelop.py
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BinClucMadDevelop.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, CategoricalParameter, DecimalParameter, IntParameter
from functools import reduce
import logging
# -------------------------------------------------------------------------------------------------
# --- logger for parameter merging output, only remove if you remove it further down too! ---------
logger = logging.getLogger(__name__)
# -------------------------------------------------------------------------------------------------
# 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 BinClucMadDevelop(IStrategy):
INTERFACE_VERSION = 2
# minimal_roi = {"0": 0.10, "10": 0.028, "40": 0.015, "180": 0.018 }
# minimal_roi = {"0": 0.038, "20": 0.028, "40": 0.02, "60": 0.015, "180": 0.018 }
minimal_roi = {"0": 0.20, "38": 0.074, "78": 0.025, "194": 0}
stoploss = -0.99
timeframe = "5m"
informative_timeframe = "1h"
# Sell signal
use_sell_signal = True
sell_profit_only = False
sell_profit_offset = 0.001
ignore_roi_if_buy_signal = True
# Trailing stoploss
trailing_stop = True
trailing_only_offset_is_reached = True
trailing_stop_positive = 0.01
trailing_stop_positive_offset = 0.05
# Custom stoploss
use_custom_stoploss = False
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
buy_params = {
"buy_minimum_conditions": 1,
#############
# Enable/Disable conditions
"v6_buy_condition_0_enable": False, # avg 0.47 dd 27%
"v6_buy_condition_1_enable": True, # no trade
"v6_buy_condition_2_enable": True, # avg 2.32
"v6_buy_condition_3_enable": True, # avg 1.12 dd 6%
"v8_buy_condition_0_enable": True, # avg 0.74
"v8_buy_condition_1_enable": False, # avg 0.41 dd 37%
"v8_buy_condition_2_enable": True, # avg 1.37
"v8_buy_condition_3_enable": False, # avg 0.41
"v8_buy_condition_4_enable": True, # avg 1.29
"v9_buy_condition_0_enable": False,
"v9_buy_condition_1_enable": True,
"v9_buy_condition_2_enable": True,
"v9_buy_condition_3_enable": True,
"v9_buy_condition_4_enable": False,
"v9_buy_condition_5_enable": True,
"v9_buy_condition_6_enable": True,
"v9_buy_condition_7_enable": True,
"v9_buy_condition_8_enable": False,
"v9_buy_condition_9_enable": False,
"v9_buy_condition_10_enable": False,
}
sell_params = {
#############
# Enable/Disable conditions
"v9_sell_condition_0_enable": False,
"v8_sell_condition_0_enable": True,
"v8_sell_condition_1_enable": True,
}
# if you want to see which buy conditions were met
# or if there is an trade exit override due to high RSI set to True
# logger will output the buy and trade exit conditions
cust_log_verbose = False
############################################################################
# Buy CombinedBinHClucAndMADV6
v6_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v6_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v6_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v6_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
# Buy CombinedBinHClucV8
v8_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v8_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v8_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v8_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v8_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v8_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True)
v8_sell_condition_1_enable = CategoricalParameter([True, False], default=True, space="sell", optimize=False, load=True)
v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space="sell", decimals=2, optimize=False, load=True)
buy_dip_threshold_0 = DecimalParameter(0.001, 0.1, default=0.015, space="buy", decimals=3, optimize=False, load=True)
buy_dip_threshold_1 = DecimalParameter(0.08, 0.2, default=0.12, space="buy", decimals=2, optimize=False, load=True)
buy_dip_threshold_2 = DecimalParameter(0.02, 0.4, default=0.28, space="buy", decimals=2, optimize=False, load=True)
buy_dip_threshold_3 = DecimalParameter(0.25, 0.44, default=0.36, space="buy", decimals=2, optimize=False, load=True)
buy_bb40_bbdelta_close = DecimalParameter(0.005, 0.04, default=0.031, space="buy", optimize=False, load=True)
buy_bb40_closedelta_close = DecimalParameter(0.01, 0.03, default=0.021, space="buy", optimize=False, load=True)
buy_bb40_tail_bbdelta = DecimalParameter(0.2, 0.4, default=0.264, space="buy", optimize=False, load=True)
buy_bb20_close_bblowerband = DecimalParameter(0.8, 1.1, default=0.992, space="buy", optimize=False, load=True)
buy_bb20_volume = IntParameter(18, 36, default=29, space="buy", optimize=False, load=True)
buy_rsi_diff = DecimalParameter(34.0, 60.0, default=50.48, space="buy", decimals=2, optimize=False, load=True)
buy_min_inc = DecimalParameter(0.005, 0.05, default=0.01, space="buy", decimals=2, optimize=False, load=True)
buy_rsi_1h = DecimalParameter(40.0, 70.0, default=67.0, space="buy", decimals=2, optimize=False, load=True)
buy_rsi = DecimalParameter(30.0, 40.0, default=38.5, space="buy", decimals=2, optimize=False, load=True)
buy_mfi = DecimalParameter(36.0, 65.0, default=36.0, space="buy", decimals=2, optimize=False, load=True)
buy_volume_1 = DecimalParameter(1.0, 10.0, default=2.0, space="buy", decimals=2, optimize=False, load=True)
buy_ema_open_mult_1 = DecimalParameter(0.01, 0.05, default=0.02, space="buy", decimals=3, optimize=False, load=True)
sell_custom_roi_profit_1 = DecimalParameter(
0.01, 0.03, default=0.02, space="sell", decimals=2, optimize=True, load=True
)
sell_custom_roi_rsi_1 = DecimalParameter(40.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True)
sell_custom_roi_profit_2 = DecimalParameter(
0.01, 0.20, default=0.04, space="sell", decimals=2, optimize=True, load=True
)
sell_custom_roi_rsi_2 = DecimalParameter(42.0, 56.0, default=50, space="sell", decimals=2, optimize=True, load=True)
sell_custom_roi_profit_3 = DecimalParameter(
0.15, 0.30, default=0.08, space="sell", decimals=2, optimize=True, load=True
)
sell_custom_roi_rsi_3 = DecimalParameter(44.0, 58.0, default=56, space="sell", decimals=2, optimize=False, load=True)
sell_custom_roi_profit_4 = DecimalParameter(0.3, 0.7, default=0.14, space="sell", decimals=2, optimize=True, load=True)
sell_custom_roi_rsi_4 = DecimalParameter(44.0, 60.0, default=58, space="sell", decimals=2, optimize=False, load=True)
sell_custom_roi_profit_5 = DecimalParameter(0.01, 0.1, default=0.04, space="sell", decimals=2, optimize=True, load=True)
sell_trail_profit_min_1 = DecimalParameter(0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=True, load=True)
sell_trail_profit_max_1 = DecimalParameter(0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=True, load=True)
sell_trail_down_1 = DecimalParameter(0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=True, load=True)
sell_trail_profit_min_2 = DecimalParameter(0.01, 0.1, default=0.02, space="sell", decimals=3, optimize=True, load=True)
sell_trail_profit_max_2 = DecimalParameter(0.08, 0.25, default=0.1, space="sell", decimals=2, optimize=True, load=True)
sell_trail_down_2 = DecimalParameter(0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=True, load=True)
sell_custom_stoploss_1 = DecimalParameter(
-0.15, -0.03, default=-0.05, space="sell", decimals=2, optimize=True, load=True
)
# Buy CombinedBinHClucAndMADV9
v9_buy_condition_0_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_1_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_2_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_3_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_4_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_5_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_6_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_7_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_8_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_9_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
v9_buy_condition_10_enable = CategoricalParameter([True, False], default=False, space="buy", optimize=False, load=True)
# Sell
v9_sell_condition_0_enable = CategoricalParameter([True, False], default=False, space="sell", optimize=False, load=True)
buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space="buy", optimize=False, load=True)
buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space="buy", optimize=False, load=True)
buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=False, load=True)
buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=False, load=True)
buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=False, load=True)
buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=False, load=True)
buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=False, load=True)
# minimum conditions to match in buy
buy_minimum_conditions = IntParameter(1, 2, default=1, space='buy', optimize=False, load=True)
# Sell Hyperopt params
sell_roi_profit_1 = DecimalParameter(0.08, 0.16, default=0.1, space='sell', decimals=2, optimize=False, load=True)
sell_roi_rsi_1 = DecimalParameter(30.0, 38.0, default=34, space='sell', decimals=2, optimize=False, load=True)
sell_roi_profit_2 = DecimalParameter(0.02, 0.05, default=0.05, space='sell', decimals=2, optimize=False, load=True)
sell_roi_rsi_2 = DecimalParameter(34.0, 44.0, default=38, space='sell', decimals=2, optimize=False, load=True)
sell_roi_profit_3 = DecimalParameter(0.0, 0.0, default=0.03, space='sell', decimals=2, optimize=False, load=True)
sell_roi_rsi_3 = DecimalParameter(48.0, 56.0, default=50, space='sell', decimals=2, optimize=False, load=True)
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:
return True
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Prevent sell, if there is more potential, in order to maximize profit
if (last_candle is not None):
current_profit = trade.calc_profit_ratio(rate)
if (sell_reason == 'roi'):
if (current_profit > self.sell_roi_profit_1.value):
if (last_candle['rsi'] > self.sell_roi_rsi_1.value):
return False
elif (current_profit > self.sell_roi_profit_2.value):
if (last_candle['rsi'] > self.sell_roi_rsi_2.value):
return False
elif (current_profit > self.sell_roi_profit_3.value):
if (last_candle['rsi'] > self.sell_roi_rsi_3.value):
return False
return True
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:
return 0.99
else:
trade_time_50 = trade.open_date_utc + timedelta(minutes=240)
# trade_time_240 = trade.open_date_utc + timedelta(minutes=240)
# Trade open more then 60 minutes. For this strategy it's means -> loss
# Let's try to minimize the loss
if current_time > trade_time_50:
try:
number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
candle = dataframe.iloc[-number_of_candle_shift].squeeze()
if (candle["sma_200_dec"]) & (candle["sma_200_dec_1h"]):
return 0.01
# We are at bottom. Wait...
if candle["rsi_1h"] < 30:
return 0.99
# Are we still sinking?
if candle["close"] > candle["ema_200"]:
if current_rate * 1.025 < candle["open"]:
return 0.01
if current_rate * 1.015 < candle["open"]:
return 0.01
except IndexError as error:
# Whoops, set stoploss at 10%
return 0.1
return 0.99
def custom_sell(
self, pair: str, trade: "Trade", current_time: "datetime", current_rate: float, current_profit: float, **kwargs
):
# return False
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if last_candle is not None:
if (current_profit > self.sell_custom_roi_profit_4.value) & (
last_candle["rsi"] < self.sell_custom_roi_rsi_4.value
):
return "roi_target_4"
elif (current_profit > self.sell_custom_roi_profit_3.value) & (
last_candle["rsi"] < self.sell_custom_roi_rsi_3.value
):
return "roi_target_3"
elif (current_profit > self.sell_custom_roi_profit_2.value) & (
last_candle["rsi"] < self.sell_custom_roi_rsi_2.value
):
return "roi_target_2"
elif (current_profit > self.sell_custom_roi_profit_1.value) & (
last_candle["rsi"] < self.sell_custom_roi_rsi_1.value
):
return "roi_target_1"
elif (
(current_profit > 0) & (current_profit < self.sell_custom_roi_profit_5.value) & (last_candle["sma_200_dec"])
):
return "roi_target_5"
elif (
(current_profit > self.sell_trail_profit_min_1.value)
& (current_profit < self.sell_trail_profit_max_1.value)
& (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_1.value))
):
return "trail_target_1"
elif (
(current_profit > self.sell_trail_profit_min_2.value)
& (current_profit < self.sell_trail_profit_max_2.value)
& (((trade.max_rate - trade.open_rate) / 100) > (current_profit + self.sell_trail_down_2.value))
):
return "trail_target_2"
return None
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) 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.informative_timeframe)
# EMA
informative_1h["ema_50"] = ta.EMA(informative_1h, timeperiod=50)
informative_1h["ema_100"] = ta.EMA(informative_1h, timeperiod=100)
informative_1h["ema_200"] = ta.EMA(informative_1h, timeperiod=200)
# SMA
informative_1h["sma_200"] = ta.SMA(informative_1h, timeperiod=200)
informative_1h["sma_200_dec"] = informative_1h["sma_200"] < informative_1h["sma_200"].shift(20)
# 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
informative_1h["ssl-dir"] = np.where(ssl_up_1h > ssl_down_1h, "up", "down")
return informative_1h
def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# strategy BinHV45
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()
# 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["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean()
# EMA
dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)
dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
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_200"] = ta.SMA(dataframe, timeperiod=200)
dataframe["sma_200_dec"] = dataframe["sma_200"] < dataframe["sma_200"].shift(20)
# RSI
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
# MFI
dataframe["mfi"] = ta.MFI(dataframe, timeperiod=14)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# The indicators for the 1h informative timeframe
informative = self.informative_1h_indicators(dataframe, metadata)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, 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:
conditions = []
# reset additional dataframe rows
dataframe.loc[:, "v9_buy_condition_1_enable"] = False
dataframe.loc[:, "v9_buy_condition_2_enable"] = False
dataframe.loc[:, "v9_buy_condition_3_enable"] = False
dataframe.loc[:, "v9_buy_condition_4_enable"] = False
dataframe.loc[:, "v9_buy_condition_5_enable"] = False
dataframe.loc[:, "v9_buy_condition_6_enable"] = False
dataframe.loc[:, "v9_buy_condition_7_enable"] = False
dataframe.loc[:, "v9_buy_condition_8_enable"] = False
dataframe.loc[:, "v9_buy_condition_9_enable"] = False
dataframe.loc[:, "v9_buy_condition_10_enable"] = False
dataframe.loc[:, "v6_buy_condition_0_enable"] = False
dataframe.loc[:, "v6_buy_condition_1_enable"] = False
dataframe.loc[:, "v6_buy_condition_2_enable"] = False
dataframe.loc[:, "v6_buy_condition_3_enable"] = False
dataframe.loc[:, "v8_buy_condition_0_enable"] = False
dataframe.loc[:, "v8_buy_condition_1_enable"] = False
dataframe.loc[:, "v8_buy_condition_2_enable"] = False
dataframe.loc[:, "v8_buy_condition_3_enable"] = False
dataframe.loc[:, "v8_buy_condition_4_enable"] = False
dataframe.loc[:, "conditions_count"] = 0
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["ema_50"] > dataframe["ema_200"])
& (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
& (
((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_1.value
)
& (
((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_2.value
)
& dataframe["lower"].shift().gt(0)
& dataframe["bbdelta"].gt(dataframe["close"] * self.buy_bb40_bbdelta_close.value)
& dataframe["closedelta"].gt(dataframe["close"] * self.buy_bb40_closedelta_close.value)
& dataframe["tail"].lt(dataframe["bbdelta"] * self.buy_bb40_tail_bbdelta.value)
& dataframe["close"].lt(dataframe["lower"].shift())
& dataframe["close"].le(dataframe["close"].shift())
& (self.v8_buy_condition_0_enable.value == True)
),
"v8_buy_condition_0_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
& (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
& (
((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_1.value
)
& (
((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_2.value
)
& (dataframe["close"] < dataframe["ema_50"])
& (dataframe["close"] < self.buy_bb20_close_bblowerband.value * dataframe["bb_lowerband"])
& (dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * self.buy_bb20_volume.value))
& (self.v8_buy_condition_1_enable.value == True)
),
"v8_buy_condition_1_enable",
] = 1
dataframe.loc[
(
(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["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_1.value
)
& (
((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_2.value
)
& (
((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_3.value
)
& (dataframe["rsi"] < dataframe["rsi_1h"] - self.buy_rsi_diff.value)
& (self.v8_buy_condition_2_enable.value == True)
),
"v8_buy_condition_2_enable",
] = 1
dataframe.loc[
(
(dataframe["sma_200"] > dataframe["sma_200"].shift(20))
& (dataframe["sma_200_1h"] > dataframe["sma_200_1h"].shift(16))
& (
((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_1.value
)
& (
((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_2.value
)
& (
((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_3.value
)
& (
((dataframe["open"].rolling(24).min() - dataframe["close"]) / dataframe["close"])
> self.buy_min_inc.value
)
& (dataframe["rsi_1h"] > self.buy_rsi_1h.value)
& (dataframe["rsi"] < self.buy_rsi.value)
& (dataframe["mfi"] < self.buy_mfi.value)
& (self.v8_buy_condition_3_enable.value == True)
),
"v8_buy_condition_3_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_100_1h"])
& (dataframe["ema_50_1h"] > dataframe["ema_100_1h"])
& (
((dataframe["open"].rolling(2).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_1.value
)
& (
((dataframe["open"].rolling(12).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_2.value
)
& (
((dataframe["open"].rolling(144).max() - dataframe["close"]) / dataframe["close"])
< self.buy_dip_threshold_3.value
)
& (dataframe["volume"].rolling(4).mean() * self.buy_volume_1.value > dataframe["volume"])
& (dataframe["ema_26"] > dataframe["ema_12"])
& ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_ema_open_mult_1.value))
& ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100))
& (dataframe["close"] < (dataframe["bb_lowerband"]))
& (self.v8_buy_condition_4_enable.value == True)
),
"v8_buy_condition_4_enable",
] = 1
# start from here
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_1.value)
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (
dataframe["open"] - dataframe["close"]
< dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2)
)
& (self.v9_buy_condition_1_enable.value == True)
),
"v9_buy_condition_1_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] < dataframe["bb_lowerband"] * self.buy_bb20_close_bblowerband_safe_2.value)
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (
dataframe["open"] - dataframe["close"]
< dataframe["bb_upperband"].shift(2) - dataframe["bb_lowerband"].shift(2)
)
& (self.v9_buy_condition_2_enable.value == True)
),
"v9_buy_condition_2_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["close"] < dataframe["bb_lowerband"])
& (dataframe["rsi"] < self.buy_rsi_3.value)
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (self.v9_buy_condition_3_enable.value == True)
),
"v9_buy_condition_3_enable",
] = 1
dataframe.loc[
(
(dataframe["rsi_1h"] < self.buy_rsi_1h_1.value)
& (dataframe["close"] < dataframe["bb_lowerband"])
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (self.v9_buy_condition_4_enable.value == True)
),
"v9_buy_condition_4_enable",
] = 1
dataframe.loc[
(
(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"] * self.buy_macd_1.value))
& ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100))
& (dataframe["close"] < (dataframe["bb_lowerband"]))
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (self.v9_buy_condition_5_enable.value == True)
),
"v9_buy_condition_5_enable",
] = 1
dataframe.loc[
(
(dataframe["ema_26"] > dataframe["ema_12"])
& ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_2.value))
& ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100))
& (dataframe["close"] < (dataframe["bb_lowerband"]))
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (self.v9_buy_condition_6_enable.value == True)
),
"v9_buy_condition_6_enable",
] = 1
dataframe.loc[
(
(dataframe["rsi_1h"] < self.buy_rsi_1h_2.value)
& (dataframe["ema_26"] > dataframe["ema_12"])
& ((dataframe["ema_26"] - dataframe["ema_12"]) > (dataframe["open"] * self.buy_macd_1.value))
& ((dataframe["ema_26"].shift() - dataframe["ema_12"].shift()) > (dataframe["open"] / 100))
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (self.v9_buy_condition_7_enable.value == True)
),
"v9_buy_condition_7_enable",
] = 1
dataframe.loc[
(
(dataframe["rsi_1h"] < self.buy_rsi_1h_3.value)
& (dataframe["rsi"] < self.buy_rsi_1.value)
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (self.v9_buy_condition_8_enable.value == True)
),
"v9_buy_condition_8_enable",
] = 1
dataframe.loc[
(
(dataframe["rsi_1h"] < self.buy_rsi_1h_4.value)
& (dataframe["rsi"] < self.buy_rsi_2.value)
& (dataframe["volume"] < (dataframe["volume"].shift() * self.buy_volume_drop_1.value))
& (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * self.buy_volume_pump_1.value)
& (self.v9_buy_condition_9_enable.value == True)
),
"v9_buy_condition_9_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] < dataframe["sma_5"])
& (dataframe["ssl_up_1h"] > dataframe["ssl_down_1h"])
& (dataframe["ema_50_1h"] > dataframe["ema_200_1h"])
& (dataframe["rsi"] < dataframe["rsi_1h"] - 43.276)
& (self.v9_buy_condition_10_enable.value == True)
),
"v9_buy_condition_10_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] > dataframe["ema_200"])
& (dataframe["close"] > dataframe["ema_200_1h"])
& (dataframe["close"] < dataframe["ema_50"])
& (dataframe["close"] < 0.99 * dataframe["bb_lowerband"])
& (
(dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 21))
| (dataframe["volume_mean_slow"] > (dataframe["volume_mean_slow"].shift(30) * 0.4))
)
& (self.v6_buy_condition_0_enable.value == True)
),
"v6_buy_condition_0_enable",
] = 1
dataframe.loc[
(
(dataframe["close"] < dataframe["ema_50"])
& (dataframe["close"] < 0.975 * dataframe["bb_lowerband"])
& (
(dataframe["volume"] < (dataframe["volume_mean_slow"].shift(1) * 20))
| (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4)
)
& (dataframe["rsi_1h"] < 15) # Don't buy if someone drop the market.
& (dataframe["volume"] < (dataframe["volume"].shift() * 4))
& (self.v6_buy_condition_1_enable.value == True)
),
"v6_buy_condition_1_enable",
] = 1
dataframe.loc[
(
(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))
| (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4)
)
& (dataframe["close"] < (dataframe["bb_lowerband"]))
& (self.v6_buy_condition_2_enable.value == True)
),
"v6_buy_condition_2_enable",
] = 1
dataframe.loc[
(
(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))
& (dataframe["close"] < (dataframe["bb_lowerband"]))
& (self.v6_buy_condition_3_enable.value == True)
),
"v6_buy_condition_3_enable",
] = 1
# count the amount of conditions met
dataframe.loc[:, "conditions_count"] = (
dataframe["v9_buy_condition_1_enable"].astype(int)
+ dataframe["v9_buy_condition_2_enable"].astype(int)
+ dataframe["v9_buy_condition_3_enable"].astype(int)
+ dataframe["v9_buy_condition_4_enable"].astype(int)
+ dataframe["v9_buy_condition_5_enable"].astype(int)
+ dataframe["v9_buy_condition_6_enable"].astype(int)
+ dataframe["v9_buy_condition_7_enable"].astype(int)
+ dataframe["v9_buy_condition_8_enable"].astype(int)
+ dataframe["v9_buy_condition_9_enable"].astype(int)
+ dataframe["v9_buy_condition_10_enable"].astype(int)
+ dataframe["v6_buy_condition_0_enable"].astype(int)
+ dataframe["v6_buy_condition_1_enable"].astype(int)
+ dataframe["v6_buy_condition_2_enable"].astype(int)
+ dataframe["v6_buy_condition_3_enable"].astype(int)
+ dataframe["v8_buy_condition_0_enable"].astype(int)
+ dataframe["v8_buy_condition_1_enable"].astype(int)
+ dataframe["v8_buy_condition_2_enable"].astype(int)
+ dataframe["v8_buy_condition_3_enable"].astype(int)
+ dataframe["v8_buy_condition_4_enable"].astype(int)
)
# append the minimum amount of conditions to be met
conditions.append(dataframe["conditions_count"] >= self.buy_minimum_conditions.value)
conditions.append(dataframe["volume"].gt(0))
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), "buy"] = 1
# verbose logging enable only for verbose information or troubleshooting
if self.cust_log_verbose == True:
for index, row in dataframe.iterrows():
if row["buy"] == 1:
# buy_cond_details = f"count={int(row['conditions_count'])}/bin={int(row['buy_cond_bin'])}/cluc={int(row['buy_cond_cluc'])}/v9_={int(row['buy_cond_long'])}"
buy_cond_details = f"count={int(row['conditions_count'])}/v9_1={int(row['v9_buy_condition_1_enable'])}/v9_2={int(row['v9_buy_condition_2_enable'])}/v9_3={int(row['v9_buy_condition_3_enable'])}/v9_4={int(row['v9_buy_condition_4_enable'])}/v9_5={int(row['v9_buy_condition_5_enable'])}/v9_6={int(row['v9_buy_condition_6_enable'])}/v9_7={int(row['v9_buy_condition_7_enable'])}/v9_8={int(row['v9_buy_condition_8_enable'])}/v9_9={int(row['v9_buy_condition_9_enable'])}/v9_10={int(row['v9_buy_condition_10_enable'])}/v6_0={int(row['v6_buy_condition_0_enable'])}/v6_1={int(row['v6_buy_condition_1_enable'])}/v6_2={int(row['v6_buy_condition_2_enable'])}/v6_3={int(row['v6_buy_condition_3_enable'])}/v8_0={int(row['v8_buy_condition_0_enable'])}/v8_1={int(row['v8_buy_condition_1_enable'])}/v8_2={int(row['v8_buy_condition_2_enable'])}/v8_3={int(row['v8_buy_condition_3_enable'])}/v8_4={int(row['v8_buy_condition_4_enable'])}"
logger.info(f"{metadata['pair']} - candle: {row['date']} - buy condition - details: {buy_cond_details}")
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
if self.v9_sell_condition_0_enable.value:
conditions.append(
(
(dataframe["close"] > dataframe["bb_middleband"] * 1.01)
& (dataframe["volume"] > 0) # Don't be gready, sell fast # Make sure Volume is not 0
)
)
if self.v8_sell_condition_0_enable.value:
conditions.append(
(
(dataframe["close"] > dataframe["bb_upperband"])
& (dataframe["close"].shift(1) > dataframe["bb_upperband"].shift(1))
& (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2))
& (dataframe["close"].shift(2) > dataframe["bb_upperband"].shift(2))
& (dataframe["volume"] > 0)
)
)
if self.v8_sell_condition_1_enable.value:
conditions.append(((dataframe["rsi"] > self.v8_sell_rsi_main.value) & (dataframe["volume"] > 0)))
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), "sell"] = 1
return dataframe
# --- custom indicators ---------------------------------------------------------------------------
def SSLChannels_ATR(dataframe, length=7):
"""
SSL Channels with ATR: https://www.tradingview.com/script/SKHqWzql-SSL-ATR-channel/
Credit to @JimmyNixx for python
"""
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"]