PASTE OUTPUT FROM HYPEROPT HERE Can be overridden for specific sub-strategies (stake currencies) at the bottom. MaxDrawDownRelativeHyperOptLoss 31/900: 801 trades. 800/0/1 Wins/Draws/Losses. Avg profit 4.04%. Median profit 3.70%. Total profit 57.02887841 USDT ( 5.70%). Avg duration 3:05:00 min. Objective: -14799833083.36105
Timeframe
5m
Direction
Long Only
Stoploss
-99.0%
Trailing Stop
No
ROI
0m: 0.6%, 15m: 0.3%, 30m: 0.0%
Interface Version
N/A
Startup Candles
48
Indicators
21
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy as np
from functools import reduce
import talib.abstract as ta
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import (
merge_informative_pair,
DecimalParameter,
stoploss_from_open,
RealParameter,
IntParameter,
informative,
)
from pandas import DataFrame, Series
from datetime import datetime
import math
import logging
from freqtrade.persistence import Trade
import pandas_ta as pta
from technical.indicators import RMI
logger = logging.getLogger(__name__)
def ewo(dataframe, sma1_length=5, sma2_length=35):
sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
smadif = (sma1 - sma2) / dataframe["close"] * 100
return smadif
def top_percent_change_dca(dataframe: DataFrame, length: int) -> float:
"""
Percentage change of the current close from the range maximum Open price
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
if length == 0:
return (dataframe["open"] - dataframe["close"]) / dataframe["close"]
else:
return (
dataframe["open"].rolling(length).max() - dataframe["close"]
) / dataframe["close"]
def EWO(dataframe, ema_length=5, ema2_length=3):
df = dataframe.copy()
ema1 = ta.EMA(df, timeperiod=ema_length)
ema2 = ta.EMA(df, timeperiod=ema2_length)
emadif = (ema1 - ema2) / df["close"] * 100
return emadif
def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
"""Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
of its recent trading range.
The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
"""
highest_high = dataframe["high"].rolling(center=False, window=period).max()
lowest_low = dataframe["low"].rolling(center=False, window=period).min()
WR = Series(
(highest_high - dataframe["close"]) / (highest_high - lowest_low),
name="{0} Williams %R".format(period),
)
return WR * -100
def VWAPB(dataframe, window_size=20, num_of_std=1):
df = dataframe.copy()
df["vwap"] = qtpylib.rolling_vwap(df, window=window_size)
rolling_std = df["vwap"].rolling(window=window_size).std()
df["vwap_low"] = df["vwap"] - (rolling_std * num_of_std)
df["vwap_high"] = df["vwap"] + (rolling_std * num_of_std)
return df["vwap_low"], df["vwap"], df["vwap_high"]
def bollinger_bands(stock_price, window_size, num_of_std):
rolling_mean = stock_price.rolling(window=window_size).mean()
rolling_std = stock_price.rolling(window=window_size).std()
lower_band = rolling_mean - (rolling_std * num_of_std)
return np.nan_to_num(rolling_mean), np.nan_to_num(lower_band)
def chaikin_money_flow(dataframe, n=20, fillna=False) -> Series:
"""Chaikin Money Flow (CMF)
It measures the amount of Money Flow Volume over a specific period.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
Args:
dataframe(pandas.Dataframe): dataframe containing ohlcv
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
mfv = (
(dataframe["close"] - dataframe["low"])
- (dataframe["high"] - dataframe["close"])
) / (dataframe["high"] - dataframe["low"])
mfv = mfv.fillna(0.0) # float division by zero
mfv *= dataframe["volume"]
cmf = (
mfv.rolling(n, min_periods=0).sum()
/ dataframe["volume"].rolling(n, min_periods=0).sum()
)
if fillna:
cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
return Series(cmf, name="cmf")
def ha_typical_price(bars):
res = (bars["ha_high"] + bars["ha_low"] + bars["ha_close"]) / 3.0
return Series(index=bars.index, data=res)
class Lmao(IStrategy):
"""
PASTE OUTPUT FROM HYPEROPT HERE
Can be overridden for specific sub-strategies (stake currencies) at the bottom.
MaxDrawDownRelativeHyperOptLoss
31/900: 801 trades. 800/0/1 Wins/Draws/Losses. Avg profit 4.04%. Median profit 3.70%. Total profit 57.02887841 USDT ( 5.70%). Avg duration 3:05:00 min. Objective: -14799833083.36105
"""
can_short = False
buy_params = {
"bbdelta_close": 0.00082,
"bbdelta_tail": 0.85788,
"close_bblower": 0.00128,
"closedelta_close": 0.00987,
"low_offset": 0.991,
"rocr1_1h": 0.9346,
"rocr_1h": 0.65666,
"base_nb_candles_buy": 5, # value loaded from strategy
"buy_bb_delta": 0.015, # value loaded from strategy
"buy_bb_factor": 0.995, # value loaded from strategy
"buy_bb_width": 0.035, # value loaded from strategy
"buy_bb_width_1h": 1.074, # value loaded from strategy
"buy_cci": -116, # value loaded from strategy
"buy_cci_length": 25, # value loaded from strategy
"buy_closedelta": 4.0, # value loaded from strategy
"buy_clucha_bbdelta_close": 0.049, # value loaded from strategy
"buy_clucha_bbdelta_tail": 1.146, # value loaded from strategy
"buy_clucha_close_bblower": 0.018, # value loaded from strategy
"buy_clucha_closedelta_close": 0.017, # value loaded from strategy
"buy_clucha_rocr_1h": 0.526, # value loaded from strategy
"buy_ema_diff": 0.025, # value loaded from strategy
"buy_rmi": 49, # value loaded from strategy
"buy_rmi_length": 17, # value loaded from strategy
"buy_roc_1h": 10, # value loaded from strategy
"buy_srsi_fk": 32, # value loaded from strategy
}
sell_params = {
"high_offset": 1.012,
"high_offset_2": 1.016,
"sell_deadfish_bb_factor": 1.089,
"sell_deadfish_bb_width": 0.11,
"sell_deadfish_profit": -0.107,
"sell_deadfish_volume_factor": 1.761,
"base_nb_candles_sell": 8, # value loaded from strategy
"pHSL": -0.397, # value loaded from strategy
"pPF_1": 0.012, # value loaded from strategy
"pPF_2": 0.07, # value loaded from strategy
"pSL_1": 0.015, # value loaded from strategy
"pSL_2": 0.068, # value loaded from strategy
"sell_bbmiddle_close": 1.09092, # value loaded from strategy
"sell_fisher": 0.46406, # value loaded from strategy
"sell_trail_down_1": 0.03, # value loaded from strategy
"sell_trail_down_2": 0.015, # value loaded from strategy
"sell_trail_profit_max_1": 0.4, # value loaded from strategy
"sell_trail_profit_max_2": 0.11, # value loaded from strategy
"sell_trail_profit_min_1": 0.1, # value loaded from strategy
"sell_trail_profit_min_2": 0.04, # value loaded from strategy
}
minimal_roi = {"0": 0.006, "15": 0.003, "30": 0}
position_adjustment_enable = True
stoploss = -0.99 # use custom stoploss
trailing_stop = False
trailing_stop_positive = 0.003 # povodne 0.001
trailing_stop_positive_offset = 0.007 # povodne 0.012
trailing_only_offset_is_reached = True
"""
END HYPEROPT
"""
timeframe = "5m"
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
use_custom_stoploss = False
process_only_new_candles = True
startup_candle_count = 48
order_types = {
"entry": "market",
"exit": "market",
"stoploss": "market",
"stoploss_on_exchange": False,
}
def is_support(self, row_data) -> bool:
conditions = []
for row in range(len(row_data) - 1):
if row < len(row_data) / 2:
conditions.append(row_data[row] > row_data[row + 1])
else:
conditions.append(row_data[row] < row_data[row + 1])
return reduce(lambda x, y: x & y, conditions)
fast_ewo = 50
slow_ewo = 200
buy_44_ma_offset = 0.982
buy_44_ewo = -18.143
buy_44_cti = -0.8
buy_44_r_1h = -75.0
buy_37_ma_offset = 0.98
buy_37_ewo = 9.8
buy_37_rsi = 56.0
buy_37_cti = -0.7
buy_ema_open_mult_7 = 0.030
buy_cti_7 = -0.89
is_optimize_dip = False
buy_rmi = IntParameter(30, 50, default=35, optimize=is_optimize_dip)
buy_cci = IntParameter(-135, -90, default=-133, optimize=is_optimize_dip)
buy_srsi_fk = IntParameter(30, 50, default=25, optimize=is_optimize_dip)
buy_cci_length = IntParameter(25, 45, default=25, optimize=is_optimize_dip)
buy_rmi_length = IntParameter(8, 20, default=8, optimize=is_optimize_dip)
is_optimize_break = False
buy_bb_width = DecimalParameter(
0.065, 0.135, default=0.095, optimize=is_optimize_break
)
buy_bb_delta = DecimalParameter(
0.018, 0.035, default=0.025, optimize=is_optimize_break
)
is_optimize_check = False
buy_roc_1h = IntParameter(-25, 200, default=10, optimize=is_optimize_check)
buy_bb_width_1h = DecimalParameter(
0.3, 2.0, default=0.3, optimize=is_optimize_check
)
is_optimize_clucha = False
buy_clucha_bbdelta_close = DecimalParameter(
0.01, 0.05, default=0.02206, optimize=is_optimize_clucha
)
buy_clucha_bbdelta_tail = DecimalParameter(
0.7, 1.2, default=1.02515, optimize=is_optimize_clucha
)
buy_clucha_close_bblower = DecimalParameter(
0.001, 0.05, default=0.03669, optimize=is_optimize_clucha
)
buy_clucha_closedelta_close = DecimalParameter(
0.001, 0.05, default=0.04401, optimize=is_optimize_clucha
)
buy_clucha_rocr_1h = DecimalParameter(
0.1, 1.0, default=0.47782, optimize=is_optimize_clucha
)
is_optimize_local_uptrend = False
buy_ema_diff = DecimalParameter(
0.022, 0.027, default=0.025, optimize=is_optimize_local_uptrend
)
buy_bb_factor = DecimalParameter(0.990, 0.999, default=0.995, optimize=False)
buy_closedelta = DecimalParameter(
12.0, 18.0, default=15.0, optimize=is_optimize_local_uptrend
)
rocr_1h = RealParameter(0.5, 1.0, default=0.54904, space="buy", optimize=True)
rocr1_1h = RealParameter(0.5, 1.0, default=0.72, space="buy", optimize=True)
bbdelta_close = RealParameter(
0.0005, 0.02, default=0.01965, space="buy", optimize=True
)
closedelta_close = RealParameter(
0.0005, 0.02, default=0.00556, space="buy", optimize=True
)
bbdelta_tail = RealParameter(0.7, 1.0, default=0.95089, space="buy", optimize=True)
close_bblower = RealParameter(
0.0005, 0.02, default=0.00799, space="buy", optimize=True
)
sell_fisher = RealParameter(0.1, 0.5, default=0.38414, space="sell", optimize=False)
sell_bbmiddle_close = RealParameter(
0.97, 1.1, default=1.07634, space="sell", optimize=False
)
is_optimize_deadfish = True
sell_deadfish_bb_width = DecimalParameter(
0.03, 0.75, default=0.05, space="sell", optimize=is_optimize_deadfish
)
sell_deadfish_profit = DecimalParameter(
-0.15, -0.05, default=-0.08, space="sell", optimize=is_optimize_deadfish
)
sell_deadfish_bb_factor = DecimalParameter(
0.90, 1.20, default=1.0, space="sell", optimize=is_optimize_deadfish
)
sell_deadfish_volume_factor = DecimalParameter(
1, 2.5, default=1.5, space="sell", optimize=is_optimize_deadfish
)
base_nb_candles_buy = IntParameter(
8, 20, default=buy_params["base_nb_candles_buy"], space="buy", optimize=False
)
base_nb_candles_sell = IntParameter(
8, 20, default=sell_params["base_nb_candles_sell"], space="sell", optimize=False
)
low_offset = DecimalParameter(
0.985, 0.995, default=buy_params["low_offset"], space="buy", optimize=True
)
high_offset = DecimalParameter(
1.005, 1.015, default=sell_params["high_offset"], space="sell", optimize=True
)
high_offset_2 = DecimalParameter(
1.010, 1.020, default=sell_params["high_offset_2"], space="sell", optimize=True
)
sell_trail_profit_min_1 = DecimalParameter(
0.1, 0.25, default=0.1, space="sell", decimals=3, optimize=False, load=True
)
sell_trail_profit_max_1 = DecimalParameter(
0.3, 0.5, default=0.4, space="sell", decimals=2, optimize=False, load=True
)
sell_trail_down_1 = DecimalParameter(
0.04, 0.1, default=0.03, space="sell", decimals=3, optimize=False, load=True
)
sell_trail_profit_min_2 = DecimalParameter(
0.04, 0.1, default=0.04, space="sell", decimals=3, optimize=False, load=True
)
sell_trail_profit_max_2 = DecimalParameter(
0.08, 0.25, default=0.11, space="sell", decimals=2, optimize=False, load=True
)
sell_trail_down_2 = DecimalParameter(
0.04, 0.2, default=0.015, space="sell", decimals=3, optimize=False, load=True
)
pHSL = DecimalParameter(
-0.500,
-0.040,
default=-0.08,
decimals=3,
space="sell",
optimize=False,
load=True,
)
pPF_1 = DecimalParameter(
0.008, 0.020, default=0.016, decimals=3, space="sell", optimize=False, load=True
)
pSL_1 = DecimalParameter(
0.008, 0.020, default=0.011, decimals=3, space="sell", optimize=False, load=True
)
pPF_2 = DecimalParameter(
0.040, 0.100, default=0.080, decimals=3, space="sell", optimize=False, load=True
)
pSL_2 = DecimalParameter(
0.020, 0.070, default=0.040, decimals=3, space="sell", optimize=False, load=True
)
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, "15m") for pair in pairs]
informative_pairs += [(pair, "1h") for pair in pairs]
return informative_pairs
def custom_exit(
self,
pair: str,
trade: "Trade",
current_time: "datetime",
current_rate: float,
current_profit: float,
**kwargs,
):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
filled_buys = trade.select_filled_orders("buy")
count_of_buys = len(filled_buys)
if last_candle is not None:
if (
(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"
elif (current_profit > 3) & (last_candle["rsi"] > 85):
return "RSI-85 target"
if (
(current_profit > 0)
& (count_of_buys < 4)
& (last_candle["close"] > last_candle["hma_50"])
& (
last_candle["close"]
> (
last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset_2.value
)
)
& (last_candle["rsi"] > 50)
& (last_candle["volume"] > 0)
& (last_candle["rsi_fast"] > last_candle["rsi_slow"])
):
return "sell signal1"
if (
(current_profit > 0)
& (count_of_buys >= 4)
& (last_candle["close"] > last_candle["hma_50"] * 1.01)
& (
last_candle["close"]
> (
last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset_2.value
)
)
& (last_candle["rsi"] > 50)
& (last_candle["volume"] > 0)
& (last_candle["rsi_fast"] > last_candle["rsi_slow"])
):
return "sell signal1 * 1.01"
if (
(current_profit > 0)
& (last_candle["close"] > last_candle["hma_50"])
& (
last_candle["close"]
> (
last_candle[f"ma_sell_{self.base_nb_candles_sell.value}"]
* self.high_offset.value
)
)
& (last_candle["volume"] > 0)
& (last_candle["rsi_fast"] > last_candle["rsi_slow"])
):
return "sell signal2"
return "sell stoploss1"
if (
(current_profit < self.sell_deadfish_profit.value)
and (last_candle["close"] < last_candle["ema_200"])
and (last_candle["bb_width"] < self.sell_deadfish_bb_width.value)
and (
last_candle["close"]
> last_candle["bb_middleband2"] * self.sell_deadfish_bb_factor.value
)
and (
last_candle["volume_mean_12"]
< last_candle["volume_mean_24"]
* self.sell_deadfish_volume_factor.value
)
and (last_candle["cmf"] < 0.0)
):
return f"sell_stoploss_deadfish"
def custom_stoploss(
self,
pair: str,
trade: "Trade",
current_time: datetime,
current_rate: float,
current_profit: float,
**kwargs,
) -> float:
HSL = self.pHSL.value
PF_1 = self.pPF_1.value
SL_1 = self.pSL_1.value
PF_2 = self.pPF_2.value
SL_2 = self.pSL_2.value
if current_profit > PF_2:
sl_profit = SL_2 + (current_profit - PF_2)
elif current_profit > PF_1:
sl_profit = SL_1 + ((current_profit - PF_1) * (SL_2 - SL_1) / (PF_2 - PF_1))
else:
sl_profit = HSL
if sl_profit >= current_profit:
return -0.99
return stoploss_from_open(sl_profit, current_profit)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Main timeframe indicators
inf_tf = "5m"
informative = self.dp.get_pair_dataframe("BTC/USDT", timeframe=inf_tf)
informative_btc = informative.copy().shift(1)
dataframe["btc_close"] = informative_btc["close"]
dataframe["btc_ema_fast"] = ta.EMA(informative_btc, timeperiod=20)
dataframe["btc_ema_slow"] = ta.EMA(informative_btc, timeperiod=25)
dataframe["down"] = (
dataframe["btc_ema_fast"] < dataframe["btc_ema_slow"]
).astype("int")
for val in self.base_nb_candles_sell.range:
dataframe[f"ma_sell_{val}"] = ta.EMA(dataframe, timeperiod=val)
dataframe["volume_mean_12"] = dataframe["volume"].rolling(12).mean().shift(1)
dataframe["volume_mean_24"] = dataframe["volume"].rolling(24).mean().shift(1)
dataframe["cmf"] = chaikin_money_flow(dataframe, 20)
bollinger2 = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=2
)
dataframe["bb_lowerband2"] = bollinger2["lower"]
dataframe["bb_middleband2"] = bollinger2["mid"]
dataframe["bb_upperband2"] = bollinger2["upper"]
dataframe["bb_width"] = (
dataframe["bb_upperband2"] - dataframe["bb_lowerband2"]
) / dataframe["bb_middleband2"]
bollinger2_40 = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=40, stds=2
)
dataframe["bb_lowerband2_40"] = bollinger2_40["lower"]
dataframe["bb_middleband2_40"] = bollinger2_40["mid"]
dataframe["bb_upperband2_40"] = bollinger2_40["upper"]
dataframe["ema_200"] = ta.EMA(dataframe, timeperiod=200)
dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
dataframe["rsi"] = ta.RSI(dataframe, timeperiod=14)
dataframe["rsi_fast"] = ta.RSI(dataframe, timeperiod=4)
dataframe["rsi_slow"] = ta.RSI(dataframe, timeperiod=20)
dataframe["rsi_84"] = ta.RSI(dataframe, timeperiod=84)
dataframe["rsi_112"] = ta.RSI(dataframe, timeperiod=112)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe["ha_open"] = heikinashi["open"]
dataframe["ha_close"] = heikinashi["close"]
dataframe["ha_high"] = heikinashi["high"]
dataframe["ha_low"] = heikinashi["low"]
dataframe["bb_delta_cluc"] = (
dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]
).abs()
dataframe["ha_closedelta"] = (
dataframe["ha_close"] - dataframe["ha_close"].shift()
).abs()
stoch = ta.STOCHRSI(dataframe, 15, 20, 2, 2)
dataframe["srsi_fk"] = stoch["fastk"]
dataframe["srsi_fd"] = stoch["fastd"]
mid, lower = bollinger_bands(
ha_typical_price(dataframe), window_size=40, num_of_std=2
)
dataframe["lower"] = lower
dataframe["mid"] = mid
dataframe["bbdelta"] = (mid - dataframe["lower"]).abs()
dataframe["closedelta"] = (
dataframe["ha_close"] - dataframe["ha_close"].shift()
).abs()
dataframe["tail"] = (dataframe["ha_close"] - dataframe["ha_low"]).abs()
dataframe["bb_lowerband"] = dataframe["lower"]
dataframe["bb_middleband"] = dataframe["mid"]
bollinger3 = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe), window=20, stds=3
)
dataframe["bb_lowerband3"] = bollinger3["lower"]
dataframe["bb_middleband3"] = bollinger3["mid"]
dataframe["bb_upperband3"] = bollinger3["upper"]
dataframe["bb_delta"] = (
dataframe["bb_lowerband2"] - dataframe["bb_lowerband3"]
) / dataframe["bb_lowerband2"]
dataframe["ema_fast"] = ta.EMA(dataframe["ha_close"], timeperiod=3)
dataframe["ema_slow"] = ta.EMA(dataframe["ha_close"], timeperiod=50)
dataframe["volume_mean_slow"] = dataframe["volume"].rolling(window=30).mean()
dataframe["rocr"] = ta.ROCR(dataframe["ha_close"], timeperiod=28)
vwap_low, vwap, vwap_high = VWAPB(dataframe, 20, 1)
dataframe["vwap_low"] = vwap_low
dataframe["vwap_upperband"] = vwap_high
dataframe["vwap_middleband"] = vwap
dataframe["vwap_lowerband"] = vwap_low
dataframe["vwap_width"] = (
(dataframe["vwap_upperband"] - dataframe["vwap_lowerband"])
/ dataframe["vwap_middleband"]
) * 100
dataframe["ema_vwap_diff_50"] = (
dataframe["ema_50"] - dataframe["vwap_lowerband"]
) / dataframe["ema_50"]
dataframe["tpct_change_0"] = top_percent_change_dca(dataframe, 0)
dataframe["tpct_change_1"] = top_percent_change_dca(dataframe, 1)
dataframe["tcp_percent_4"] = top_percent_change_dca(dataframe, 4)
dataframe["ewo"] = ewo(dataframe, 50, 200)
for val in self.buy_rmi_length.range:
dataframe[f"rmi_length_{val}"] = RMI(dataframe, length=val, mom=4)
for val in self.buy_cci_length.range:
dataframe[f"cci_length_{val}"] = ta.CCI(dataframe, val)
dataframe["cti"] = pta.cti(dataframe["close"], length=20)
dataframe["bb_delta_cluc"] = (
dataframe["bb_middleband2_40"] - dataframe["bb_lowerband2_40"]
).abs()
dataframe["ema_16"] = ta.EMA(dataframe, timeperiod=16)
dataframe["EWO"] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)
dataframe["r_14"] = williams_r(dataframe, period=14)
dataframe["ema_5"] = ta.EMA(dataframe, timeperiod=5)
dataframe["ema_10"] = ta.EMA(dataframe, timeperiod=10)
dataframe["pm"], dataframe["pmx"] = pmax(
heikinashi, MAtype=1, length=9, multiplier=27, period=10, src=3
)
dataframe["source"] = (
dataframe["high"]
+ dataframe["low"]
+ dataframe["open"]
+ dataframe["close"]
) / 4
dataframe["pmax_thresh"] = ta.EMA(dataframe["source"], timeperiod=9)
dataframe["sma_75"] = ta.SMA(dataframe, timeperiod=75)
rsi = ta.RSI(dataframe)
dataframe["rsi"] = rsi
rsi = 0.1 * (rsi - 50)
dataframe["fisher"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
# --- Informative 1h timeframe
inf_tf = "1h"
informative = self.dp.get_pair_dataframe(
pair=metadata["pair"], timeframe=inf_tf
)
inf_heikinashi = qtpylib.heikinashi(informative)
informative["ha_close"] = inf_heikinashi["close"]
informative["rocr"] = ta.ROCR(informative["ha_close"], timeperiod=168)
informative["rsi_14"] = ta.RSI(dataframe, timeperiod=14)
informative["cmf"] = chaikin_money_flow(dataframe, 20)
sup_series = (
informative["low"]
.rolling(window=5)
.apply(lambda row: self.is_support(row), raw=True)
)
informative["sup_level"] = Series(
np.where(
sup_series,
np.where(
informative["close"] < informative["open"],
informative["close"],
informative["open"],
),
float("NaN"),
)
).ffill()
informative["roc"] = ta.ROC(informative, timeperiod=9)
informative["r_480"] = williams_r(informative, period=480)
bollinger2 = qtpylib.bollinger_bands(
qtpylib.typical_price(informative), window=20, stds=2
)
informative["bb_lowerband2"] = bollinger2["lower"]
informative["bb_middleband2"] = bollinger2["mid"]
informative["bb_upperband2"] = bollinger2["upper"]
informative["bb_width"] = (
informative["bb_upperband2"] - informative["bb_lowerband2"]
) / informative["bb_middleband2"]
informative["r_84"] = williams_r(informative, period=84)
informative["cti_40"] = pta.cti(informative["close"], length=40)
# --- Informative 15m timeframe
inf_tf_15m = "15m"
informative_15m = self.dp.get_pair_dataframe(
pair=metadata["pair"], timeframe=inf_tf_15m
)
informative_15m["rsi_14_15m"] = ta.RSI(informative_15m, timeperiod=14)
informative_15m["cmf_15m"] = chaikin_money_flow(informative_15m, 20)
informative_15m["ema_15m"] = ta.EMA(informative_15m, timeperiod=50)
dataframe["hma_50"] = qtpylib.hull_moving_average(dataframe["close"], window=50)
dataframe = merge_informative_pair(
dataframe, informative, self.timeframe, inf_tf, ffill=True
)
dataframe = merge_informative_pair(
dataframe, informative_15m, self.timeframe, inf_tf_15m, ffill=True
)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(
dataframe[f"rmi_length_{self.buy_rmi_length.value}"]
< self.buy_rmi.value
)
& (
dataframe[f"cci_length_{self.buy_cci_length.value}"]
<= self.buy_cci.value
)
& (dataframe["srsi_fk"] < self.buy_srsi_fk.value)
& (dataframe["bb_delta"] > self.buy_bb_delta.value)
& (dataframe["bb_width"] > self.buy_bb_width.value)
& (dataframe["closedelta"] > 0) # from BinH
& (
dataframe["close"]
< dataframe["bb_lowerband3"] * self.buy_bb_factor.value
)
& (dataframe["roc_1h"] < self.buy_roc_1h.value)
& (dataframe["bb_width_1h"] < self.buy_bb_width_1h.value)
),
["enter_long", "enter_tag"],
] = (1, "DIP signal")
dataframe.loc[
(
(dataframe["bb_delta"] > self.buy_bb_delta.value)
& (dataframe["bb_width"] > self.buy_bb_width.value)
& (
dataframe["closedelta"]
> dataframe["close"] * self.buy_closedelta.value / 1000
) # from BinH
& (
dataframe["close"]
< dataframe["bb_lowerband3"] * self.buy_bb_factor.value
)
& (dataframe["roc_1h"] < self.buy_roc_1h.value)
& (dataframe["bb_width_1h"] < self.buy_bb_width_1h.value)
),
["enter_long", "enter_tag"],
] = (1, "Break signal")
dataframe.loc[
(
(dataframe["rocr_1h"] > self.buy_clucha_rocr_1h.value)
& (dataframe["bb_lowerband2_40"].shift() > 0)
& (
dataframe["bb_delta_cluc"]
> dataframe["ha_close"] * self.buy_clucha_bbdelta_close.value
)
& (
dataframe["ha_closedelta"]
> dataframe["ha_close"] * self.buy_clucha_closedelta_close.value
)
& (
dataframe["tail"]
< dataframe["bb_delta_cluc"] * self.buy_clucha_bbdelta_tail.value
)
& (dataframe["ha_close"] < dataframe["bb_lowerband2_40"].shift())
& (dataframe["close"] > (dataframe["sup_level_1h"] * 0.88))
& (dataframe["ha_close"] < dataframe["ha_close"].shift())
),
["enter_long", "enter_tag"],
] = (1, "cluc_HA")
dataframe.loc[
(
(dataframe["ema_200"] > (dataframe["ema_200"].shift(12) * 1.01))
& (dataframe["ema_200"] > (dataframe["ema_200"].shift(48) * 1.07))
& (dataframe["bb_lowerband2_40"].shift().gt(0))
& (dataframe["bb_delta_cluc"].gt(dataframe["close"] * 0.056))
& (dataframe["closedelta"].gt(dataframe["close"] * 0.01))
& (dataframe["tail"].lt(dataframe["bb_delta_cluc"] * 0.5))
& (dataframe["close"].lt(dataframe["bb_lowerband2_40"].shift()))
& (dataframe["close"].le(dataframe["close"].shift()))
& (dataframe["close"] > dataframe["ema_50"] * 0.912)
),
["enter_long", "enter_tag"],
] = (1, "NFIX39")
dataframe.loc[
(
(dataframe["close"] > (dataframe["sup_level_1h"] * 0.72))
& (dataframe["close"] < (dataframe["ema_16"] * 0.982))
& (dataframe["EWO"] < -10.0)
& (dataframe["cti"] < -0.9)
),
["enter_long", "enter_tag"],
] = (1, "NFIX29")
dataframe.loc[
(
(dataframe["ema_26"] > dataframe["ema_12"])
& (
dataframe["ema_26"] - dataframe["ema_12"]
> dataframe["open"] * self.buy_ema_diff.value
)
& (
dataframe["ema_26"].shift() - dataframe["ema_12"].shift()
> dataframe["open"] / 100
)
& (
dataframe["close"]
< dataframe["bb_lowerband2"] * self.buy_bb_factor.value
)
& (
dataframe["closedelta"]
> dataframe["close"] * self.buy_closedelta.value / 1000
)
),
["enter_long", "enter_tag"],
] = (1, "local_uptrend")
# --- VWAP signal (restored from prior strategy) ---
dataframe.loc[
(
(dataframe["close"] < dataframe["vwap_low"])
& (dataframe["tcp_percent_4"] > 0.053)
& (dataframe["cti"] < -0.8)
& (dataframe["rsi"] < 35)
& (dataframe["rsi_84"] < 60)
& (dataframe["rsi_112"] < 60)
& (dataframe["volume"] > 0)
),
["enter_long", "enter_tag"],
] = (1, "vwap")
dataframe.loc[
(
(dataframe["bb_width_1h"] > 0.131)
& (dataframe["r_14"] < -51)
& (dataframe["r_84_1h"] < -70)
& (dataframe["cti"] < -0.845)
& (dataframe["cti_40_1h"] < -0.735)
& ((dataframe["close"].rolling(48).max() >= (dataframe["close"] * 1.1)))
& (
dataframe["btc_close"].rolling(24).max()
>= (dataframe["btc_close"] * 1.03)
)
),
["enter_long", "enter_tag"],
] = (1, "insta_signal")
dataframe.loc[
(
(dataframe["close"] < (dataframe["ema_16"] * self.buy_44_ma_offset))
& (dataframe["ewo"] < self.buy_44_ewo)
& (dataframe["cti"] < self.buy_44_cti)
& (dataframe["r_480_1h"] < self.buy_44_r_1h)
& (dataframe["volume"] > 0)
),
["enter_long", "enter_tag"],
] = (1, "NFINext44")
dataframe.loc[
(
(dataframe["pm"] > dataframe["pmax_thresh"])
& (dataframe["close"] < dataframe["sma_75"] * self.buy_37_ma_offset)
& (dataframe["ewo"] > self.buy_37_ewo)
& (dataframe["rsi"] < self.buy_37_rsi)
& (dataframe["cti"] < self.buy_37_cti)
),
["enter_long", "enter_tag"],
] = (1, "NFINext37")
dataframe.loc[
(
(dataframe["ema_26"] > dataframe["ema_12"])
& (
(dataframe["ema_26"] - dataframe["ema_12"])
> (dataframe["open"] * self.buy_ema_open_mult_7)
)
& (
(dataframe["ema_26"].shift() - dataframe["ema_12"].shift())
> (dataframe["open"] / 100)
)
& (dataframe["cti"] < self.buy_cti_7)
),
["enter_long", "enter_tag"],
] = (1, "NFINext7")
# --- Oversold RSI Rebound signal ---
dataframe.loc[
(
(dataframe["rsi"] < 30)
& (dataframe["rsi_fast"] > dataframe["rsi_slow"])
& (dataframe["volume"] > 0)
),
["enter_long", "enter_tag"],
] = (1, "oversold_rebound")
# --- Bounce Reversal at Support signal ---
dataframe.loc[
(
(dataframe["close"] > dataframe["sup_level_1h"] * 1.01)
& (dataframe["close"] < dataframe["sup_level_1h"] * 1.05)
& (dataframe["volume"] > dataframe["volume_mean_24"])
),
["enter_long", "enter_tag"],
] = (1, "bounce_support")
# --- Range-bound Mean Reversion signal ---
dataframe.loc[
(
(dataframe["bb_width"] < 0.04)
& (dataframe["close"] < dataframe["bb_middleband"])
& (dataframe["rsi"] > 40)
& (dataframe["rsi"] < 60)
),
["enter_long", "enter_tag"],
] = (1, "mean_reversion_range")
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(dataframe["fisher"] > self.sell_fisher.value)
& (dataframe["ha_high"].le(dataframe["ha_high"].shift(1)))
& (dataframe["ha_high"].shift(1).le(dataframe["ha_high"].shift(2)))
& (dataframe["ha_close"].le(dataframe["ha_close"].shift(1)))
& (dataframe["ema_fast"] > dataframe["ha_close"])
& (
(dataframe["ha_close"] * self.sell_bbmiddle_close.value)
> dataframe["bb_middleband"]
)
& (dataframe["volume"] > 0),
"exit_long",
] = 0
return dataframe
initial_safety_order_trigger = -0.018
max_safety_orders = 8
safety_order_step_scale = 1.2
safety_order_volume_scale = 1.4
def top_percent_change_dca(self, dataframe: DataFrame, length: int) -> float:
"""
Percentage change of the current close from the range maximum Open price
:param dataframe: DataFrame The original OHLC dataframe
:param length: int The length to look back
"""
if length == 0:
return (dataframe["open"] - dataframe["close"]) / dataframe["close"]
else:
return (
dataframe["open"].rolling(length).max() - dataframe["close"]
) / dataframe["close"]
def adjust_trade_position(
self,
trade: Trade,
current_time: datetime,
current_rate: float,
current_profit: float,
min_stake: float,
max_stake: float,
**kwargs,
):
if current_profit > self.initial_safety_order_trigger:
return None
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
filled_buys = trade.select_filled_orders("buy")
count_of_buys = len(filled_buys)
if (
count_of_buys == 1
and (last_candle["tpct_change_0"] > 0.001)
and (last_candle["close"] < last_candle["open"])
):
return None
elif (
count_of_buys == 2
and (last_candle["tpct_change_0"] > 0.001)
and (last_candle["close"] < last_candle["open"])
and (last_candle["ema_vwap_diff_50"] < 0.5)
):
return None
elif (
count_of_buys == 3
and (last_candle["tpct_change_0"] > 0.001)
and (last_candle["close"] < last_candle["open"])
and (last_candle["ema_vwap_diff_50"] < 0.5)
):
return None
elif (
count_of_buys == 4
and (last_candle["tpct_change_0"] > 0.001)
and (last_candle["close"] < last_candle["open"])
and (last_candle["ema_vwap_diff_50"] < 0.5)
and (last_candle["ema_5"]) >= (last_candle["ema_10"])
):
return None
elif (
count_of_buys == 5
and (last_candle["cmf_1h"] < 0.00)
and (last_candle["close"] < last_candle["open"])
and (last_candle["rsi_14_1h"] < 30)
and (last_candle["tpct_change_0"] > 0.018)
and (last_candle["close"] < last_candle["open"])
and (last_candle["ema_vwap_diff_50"] < 0.215)
and (last_candle["ema_5"]) >= (last_candle["ema_10"])
):
logger.info(
f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
)
return None
elif (
count_of_buys == 6
and (last_candle["cmf_1h"] < 0.00)
and (last_candle["close"] < last_candle["open"])
and (last_candle["rsi_14_1h"] < 30)
and (last_candle["tpct_change_0"] > 0.018)
and (
last_candle["close"] < last_candle["open"]
and (last_candle["ema_vwap_diff_50"] < 0.215)
)
and (last_candle["ema_5"]) >= (last_candle["ema_10"])
):
logger.info(
f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
)
return None
elif (
count_of_buys == 7
and (last_candle["cmf_1h"] < 0.00)
and (last_candle["close"] < last_candle["open"])
and (last_candle["rsi_14_1h"] < 30)
and (last_candle["tpct_change_0"] > 0.018)
and (
last_candle["close"] < last_candle["open"]
and (last_candle["ema_vwap_diff_50"] < 0.215)
)
and (last_candle["ema_5"]) >= (last_candle["ema_10"])
):
logger.info(
f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
)
return None
elif (
count_of_buys == 8
and (last_candle["cmf_1h"] < 0.00)
and (last_candle["close"] < last_candle["open"])
and (last_candle["rsi_14_1h"] < 30)
and (last_candle["tpct_change_0"] > 0.018)
and (
last_candle["close"] < last_candle["open"]
and (last_candle["ema_vwap_diff_50"] < 0.215)
)
and (last_candle["ema_5"]) >= (last_candle["ema_10"])
):
logger.info(
f"DCA for {trade.pair} waiting for cmf_1h ({last_candle['cmf_1h']}) to rise above 0. Waiting for rsi_1h ({last_candle['rsi_14_1h']})to rise above 30"
)
return None
if 1 <= count_of_buys <= self.max_safety_orders:
safety_order_trigger = (
abs(self.initial_safety_order_trigger) * count_of_buys
)
if self.safety_order_step_scale > 1:
safety_order_trigger = abs(self.initial_safety_order_trigger) + (
abs(self.initial_safety_order_trigger)
* self.safety_order_step_scale
* (math.pow(self.safety_order_step_scale, (count_of_buys - 1)) - 1)
/ (self.safety_order_step_scale - 1)
)
elif self.safety_order_step_scale < 1:
safety_order_trigger = abs(self.initial_safety_order_trigger) + (
abs(self.initial_safety_order_trigger)
* self.safety_order_step_scale
* (1 - math.pow(self.safety_order_step_scale, (count_of_buys - 1)))
/ (1 - self.safety_order_step_scale)
)
if current_profit <= (-1 * abs(safety_order_trigger)):
try:
stake_amount = filled_buys[0].cost
stake_amount = stake_amount * math.pow(
self.safety_order_volume_scale, (count_of_buys - 1)
)
amount = stake_amount / current_rate
logger.info(
f"Initiating safety order buy #{count_of_buys} for {trade.pair} with stake amount of {stake_amount} which equals {amount}"
)
return stake_amount
except Exception as exception:
logger.info(
f"Error occured while trying to get stake amount for {trade.pair}: {str(exception)}"
)
return None
return None
# --- pmax function moved outside the class ---
def pmax(df, period, multiplier, length, MAtype, src):
period = int(period)
multiplier = int(multiplier)
length = int(length)
MAtype = int(MAtype)
src = int(src)
mavalue = "MA_" + str(MAtype) + "_" + str(length)
atr = "ATR_" + str(period)
pm = (
"pm_"
+ str(period)
+ "_"
+ str(multiplier)
+ "_"
+ str(length)
+ "_"
+ str(MAtype)
)
pmx = (
"pmX_"
+ str(period)
+ "_"
+ str(multiplier)
+ "_"
+ str(length)
+ "_"
+ str(MAtype)
)
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
mavalue = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
mavalue = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
mavalue = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
mavalue = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
mavalue = VIDYA(df, length=length)
elif MAtype == 6:
mavalue = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
mavalue = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
mavalue = vwma(df, length)
elif MAtype == 9:
mavalue = zema(df, period=length)
df[atr] = ta.ATR(df, timeperiod=period)
df["basic_ub"] = mavalue + ((multiplier / 10) * df[atr])
df["basic_lb"] = mavalue - ((multiplier / 10) * df[atr])
basic_ub = df["basic_ub"].values
final_ub = np.full(len(df), 0.00)
basic_lb = df["basic_lb"].values
final_lb = np.full(len(df), 0.00)
for i in range(period, len(df)):
final_ub[i] = (
basic_ub[i]
if (basic_ub[i] < final_ub[i - 1] or mavalue[i - 1] > final_ub[i - 1])
else final_ub[i - 1]
)
final_lb[i] = (
basic_lb[i]
if (basic_lb[i] > final_lb[i - 1] or mavalue[i - 1] < final_lb[i - 1])
else final_lb[i - 1]
)
df["final_ub"] = final_ub
df["final_lb"] = final_lb
pm_arr = np.full(len(df), 0.00)
for i in range(period, len(df)):
pm_arr[i] = (
final_ub[i]
if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] <= final_ub[i])
else (
final_lb[i]
if (pm_arr[i - 1] == final_ub[i - 1] and mavalue[i] > final_ub[i])
else (
final_lb[i]
if (pm_arr[i - 1] == final_lb[i - 1] and mavalue[i] >= final_lb[i])
else (
final_ub[i]
if (
pm_arr[i - 1] == final_lb[i - 1]
and mavalue[i] < final_lb[i]
)
else 0.00
)
)
)
)
pm = Series(pm_arr)
pmx = np.where(
(pm_arr > 0.00), np.where((mavalue < pm_arr), "down", "up"), "neutral"
)
return pm, pmx