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BinClucMad.py
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BinClucMad.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
# 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 BinClucMad(IStrategy):
INTERFACE_VERSION = 2
minimal_roi = {
"0": 0.038, # I feel lucky!
"10": 0.028,
"40": 0.015,
"180": 0.018, # We're going up?
}
stoploss = -0.99 # effectively disabled.
timeframe = "5m"
informative_timeframe = "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": "market",
"sell": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
}
buy_params = {
#############
# Enable/Disable conditions
"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": True,
"v9_buy_condition_5_enable": True,
"v9_buy_condition_6_enable": True,
"v9_buy_condition_7_enable": True,
"v9_buy_condition_8_enable": True,
"v9_buy_condition_9_enable": True,
"v9_buy_condition_10_enable": True,
"v6_buy_condition_0_enable": True,
"v6_buy_condition_1_enable": True,
"v6_buy_condition_2_enable": True,
"v6_buy_condition_3_enable": True,
"v6_buy_condition_4_enable": True,
}
sell_params = {
#############
# Enable/Disable conditions
"v9_sell_condition_0_enable": False,
"v8_sell_condition_1_enable": True,
"v8_sell_condition_2_enable": False,
}
############################################################################
# Buy CombinedBinHClucAndMADV9
v9_buy_condition_0_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_1_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_2_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_3_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_5_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_6_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_7_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_8_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_9_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
v9_buy_condition_10_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
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)
v6_buy_condition_4_enable = CategoricalParameter([True, False], default=True, space="buy", optimize=False, load=True)
# Sell
v9_sell_condition_0_enable = CategoricalParameter([True, False], default=True, space="sell", 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)
buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.99, space="buy", optimize=True, load=True)
buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space="buy", optimize=True, load=True)
buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space="buy", decimals=1, optimize=True, load=True)
buy_volume_drop_1 = DecimalParameter(1, 10, default=4, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space="buy", decimals=1, optimize=True, load=True)
buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space="buy", decimals=1, optimize=True, load=True)
buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space="buy", decimals=2, optimize=True, load=True)
buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space="buy", decimals=2, optimize=True, load=True)
v8_sell_rsi_main = DecimalParameter(72.0, 90.0, default=80, space="sell", decimals=2, optimize=True, load=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=50)
# 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()
# 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 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_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:
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_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
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 = 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 = []
# START VERSION9
if self.v9_buy_condition_1_enable.value:
conditions.append(
(
(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)
)
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_2_enable.value:
conditions.append(
(
(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)
)
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_3_enable.value:
conditions.append(
(
(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))
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_4_enable.value:
conditions.append(
(
(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))
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_5_enable.value:
conditions.append(
(
(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
)
& (dataframe["volume"] > 0) # Make sure Volume is not 0
)
)
if self.v9_buy_condition_6_enable.value:
conditions.append(
(
(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))
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_7_enable.value:
conditions.append(
(
(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
)
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_8_enable.value:
conditions.append(
(
(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
)
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_9_enable.value:
conditions.append(
(
(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
)
& (dataframe["volume"] > 0)
)
)
if self.v9_buy_condition_10_enable.value:
conditions.append(
(
(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)
& (dataframe["volume"] > 0)
)
)
# END V9
# START V6
if self.v6_buy_condition_0_enable.value:
conditions.append(
( # strategy ClucMay72018
(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))
)
& (dataframe["volume"] > 0)
)
)
if self.v6_buy_condition_1_enable.value:
conditions.append(
( # strategy ClucMay72018
(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))
& (dataframe["volume"] > 0) # Try to exclude pumping # Make sure Volume is not 0
)
)
if self.v6_buy_condition_2_enable.value:
conditions.append(
( # 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))
| (dataframe["volume_mean_slow"] > dataframe["volume_mean_slow"].shift(30) * 0.4)
)
& (dataframe["close"] < (dataframe["bb_lowerband"]))
&
#
(dataframe["volume"] > 0)
)
)
if self.v6_buy_condition_3_enable.value:
conditions.append(
( # 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))
& (dataframe["close"] < (dataframe["bb_lowerband"])) # Don't buy if someone drop the market.
& (dataframe["volume"] > 0) # Make sure Volume is not 0
)
)
# END V6
if conditions:
dataframe.loc[reduce(lambda x, y: x | y, conditions), "buy"] = 1
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