Timeframe
1m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
0m: 100.0%
Interface Version
2
Startup Candles
N/A
Indicators
5
freqtrade/freqtrade-strategies
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import merge_informative_pair
from freqtrade.persistence import Trade
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Tuple
from cachetools import TTLCache
import technical.indicators as ftt
import time
class LongShortRangeTradingMachetesV1(IStrategy):
INTERFACE_VERSION = 2
TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE = 0
TF_ENTERED_MARKET_CONDITION_WAITING_FOR_CONFIRMATION_STATE = 1
TF_MARKET_CONDITION_CONFIRMED_WAITING_FOR_ENTRY_SIGNAL_STATE = 2
TF_ENTRY_SIGNAL_FOUND_STATE = 3
TF_ENTRY_SIGNAL_FOUND_WAITING_FOR_EXIT_SIGNAL_STATE = 4
custom_trade_flow_info = {}
custom_trade_info = {}
custom_current_price_cache = TTLCache(maxsize=100, ttl=300)
# ROI table:
minimal_roi = {
"0": 1
}
# Stoploss:
stoploss = -0.1
# Trailing stop:
trailing_stop = False
#trailing_stop_positive = 0.097
#trailing_stop_positive_offset = 0.161
#trailing_only_offset_is_reached = True
timeframe = '1m'
timeframe_medium = '15m'
timeframe_long = '5m'
candels_per_timeframe_medium = 4
candels_per_timeframe_long = 16
process_only_new_candles = False
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
startup_candle_count: int = 500
use_dynamic_roi = True
use_custom_stoploss = True
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
f'base_open_{timeframe}': {},
f'base_high_{timeframe}': {},
f'base_low_{timeframe}': {},
f'base_close_{timeframe}': {},
f'base_overbought_price_{timeframe}': {},
f'base_oversold_price_{timeframe}': {},
f'base_overbought_price_{timeframe_medium}': {},
f'base_oversold_price_{timeframe_medium}': {},
f'base_overbought_price_{timeframe_long}': {},
f'base_oversold_price_{timeframe_long}': {},
},
'subplots': {
"cci": {
f'base_cci_{timeframe}': {'color': 'yellow'},
f'base_cci_{timeframe_medium}': {'color': 'yellow'},
f'base_cci_{timeframe_long}': {'color': 'yellow'},
f'base_cci_overbought_value_{timeframe}': {
'color': 'rgba(35, 138, 29, 0.75)',
'fill_to': f'base_cci_oversold_value_{timeframe}',
'fill_label': 'cci',
'fill_color': 'rgba(51, 255, 117, 0.2)',
}
},
"signals": {
'has_entered_market_condition': {'color': 'red'},
'is_in_market_condition': {'color': 'red'},
'has_confirmation': {'color': 'yellow'},
'has_entry_signal': {'color': 'green'},
'entry_signal': {'color': 'blue'},
'has_exit_signal': {'color': 'green'},
'exit_signal': {'color': 'blue'}
}
}
}
#
# Hyperopt params
#
# Tradeflow
# Dynamic ROI
droi_trend_type = CategoricalParameter(['rmi', 'ssl', 'candle', 'any'], default='any', space='sell', optimize=True)
droi_pullback = CategoricalParameter([True, False], default=True, space='sell', optimize=True)
droi_pullback_amount = DecimalParameter(0.005, 0.02, default=0.005, space='sell', optimize=True)
droi_pullback_respect_table = CategoricalParameter([True, False], default=False, space='sell', optimize=True)
# Custom Stoploss
cstp_threshold = DecimalParameter(-0.05, 0, default=-0.03, space='sell', optimize=True)
cstp_bail_how = CategoricalParameter(['roc', 'time', 'any'], default='roc', space='sell', optimize=True)
cstp_bail_roc = DecimalParameter(-0.05, -0.01, default=-0.03, space='sell', optimize=True)
cstp_bail_time = IntParameter(720, 1440, default=720, space='sell', optimize=True)
cstp_trailing_stop_positive_offset = DecimalParameter(0.005, 0.06,default=0.01,space='sell', optimize=True)
cstp_trailing_stop_profit_devider = IntParameter(2, 4,default=2,space='sell', optimize=True)
cstp_trailing_max_stoploss = DecimalParameter(0.02, 0.08,default=0.02,space='sell', optimize=True)
cstp_trailing_enabled = CategoricalParameter([True, False], default=True, space='sell', optimize=True)
#
# Events
#
def on_populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
base_pair_name = self.get_base_pair_name(metadata['pair'])
dataframe = self.get_base_pair_dataframe(dataframe, base_pair_name, self.timeframe_long)
#dataframe = self.get_base_pair_dataframe(dataframe, base_pair_name, self.timeframe_medium)
dataframe = self.get_base_pair_dataframe(dataframe, base_pair_name, self.timeframe)
dataframe = self.get_indicators_custom_stoploss(dataframe)
self.setup_custom_trade_info(dataframe, metadata)
return dataframe
def on_populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if "UP" in metadata['pair'] or "DOWN" in metadata['pair']:
if "UP" in metadata['pair']:
dataframe = self.calc_indicator_signals_long(dataframe, metadata)
elif "DOWN" in metadata['pair']:
dataframe = self.calc_indicator_signals_short(dataframe, metadata)
dataframe = self.calc_trade_flow(dataframe, metadata)
else:
#print('This pair (' + metadata['pair'] + '} is not a LT.')
dataframe['entry_signal'] = 0
return dataframe
def on_populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if "UP" in metadata['pair'] or "DOWN" in metadata['pair']:
if "UP" in metadata['pair']:
dataframe = self.calc_indicator_signals_long(dataframe, metadata)
elif "DOWN" in metadata['pair']:
dataframe = self.calc_indicator_signals_short(dataframe, metadata)
dataframe = self.calc_trade_flow(dataframe, metadata)
else:
#print('This pair (' + metadata['pair'] + '} is not a LT.')
dataframe['exit_signal'] = 0
return dataframe
#
# IStrategy
#
def informative_pairs(self):
pairs = self.dp.current_whitelist()
informative_pairs = []
informative_pairs += [(pair, self.timeframe) for pair in pairs]
informative_pairs += [(pair, self.timeframe_medium) for pair in pairs]
informative_pairs += [(pair, self.timeframe_long) for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp:
return dataframe
dataframe = self.on_populate_indicators(dataframe, metadata)
#dataframe.info(verbose=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.on_populate_buy_trend(dataframe, metadata)
dataframe.loc[
(
(dataframe['entry_signal'] == 1)
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = self.on_populate_sell_trend(dataframe, metadata)
dataframe.loc[
(
(dataframe['exit_signal'] == 1)
),
'sell'] = 1
return dataframe
def custom_sell(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()
#print(pair,trade.open_date,trade.is_open,current_profit)
return False
#
# Indicators - Data
#
def get_indicators_custom_stoploss(self, dataframe):
def RMI(dataframe, *, length=20, mom=5):
"""
Source: https://github.com/freqtrade/technical/blob/master/technical/indicators/indicators.py#L912
"""
df = dataframe.copy()
df['maxup'] = (df['close'] - df['close'].shift(mom)).clip(lower=0)
df['maxdown'] = (df['close'].shift(mom) - df['close']).clip(lower=0)
df.fillna(0, inplace=True)
df["emaInc"] = ta.EMA(df, price='maxup', timeperiod=length)
df["emaDec"] = ta.EMA(df, price='maxdown', timeperiod=length)
df['RMI'] = np.where(df['emaDec'] == 0, 0, 100 - 100 / (1 + df["emaInc"] / df["emaDec"]))
return df["RMI"]
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']
def SROC(dataframe, roclen=21, emalen=13, smooth=21):
df = dataframe.copy()
roc = ta.ROC(df, timeperiod=roclen)
ema = ta.EMA(df, timeperiod=emalen)
sroc = ta.ROC(ema, timeperiod=smooth)
return sroc
dataframe['atr'] = ta.ATR(dataframe, timeperiod=14)
dataframe['roc'] = ta.ROC(dataframe, timeperiod=9)
dataframe['rmi'] = RMI(dataframe, length=24, mom=5)
ssldown, sslup = SSLChannels_ATR(dataframe, length=21)
dataframe['sroc'] = SROC(dataframe, roclen=21, emalen=13, smooth=21)
dataframe['ssl-dir'] = np.where(sslup > ssldown,'up','down')
dataframe['rmi-up'] = np.where(dataframe['rmi'] >= dataframe['rmi'].shift(),1,0)
dataframe['rmi-up-trend'] = np.where(dataframe['rmi-up'].rolling(5).sum() >= 3,1,0)
dataframe['candle-up'] = np.where(dataframe['close'] >= dataframe['close'].shift(),1,0)
dataframe['candle-up-trend'] = np.where(dataframe['candle-up'].rolling(5).sum() >= 3,1,0)
return dataframe
def get_indicators(self, dataframe):
dataframe['cci'] = ta.CCI(dataframe)
dataframe['cci_overbought_value'] = 100
dataframe['cci_oversold_value'] = -100
dataframe['overbought_price'] = (
(qtpylib.crossed_below(dataframe['cci'], 100))
).fillna(0).astype('int') * dataframe['high']
dataframe['overbought_price'] = dataframe['overbought_price'].replace(to_replace=0, method='ffill')
dataframe['oversold_price'] = (
(qtpylib.crossed_above(dataframe['cci'], -100))
).fillna(0).astype('int') * dataframe['low']
dataframe['oversold_price'] = dataframe['oversold_price'].replace(to_replace=0, method='ffill')
return dataframe
def get_base_pair_name(self, pair_name):
#extract target pair
pair_name_parts = pair_name.split('/')
target_pair_name = pair_name_parts[0]
#remove up or down
target_pair_suffix = 'DOWN' if ("DOWN" in pair_name) else 'UP'
target_base_name = target_pair_name.replace(target_pair_suffix, "")
#add usd stake
base_pair_name = target_base_name + '/USDT'
return base_pair_name
def get_base_pair_dataframe(self,dataframe, base_pair_name, timeframe_str, smooth_list = None, candels_per_timeframe = 2):
base_pair_dataframe = self.dp.get_pair_dataframe(base_pair_name, timeframe_str)
base_pair_dataframe = self.get_indicators(base_pair_dataframe)
ignore_columns = ['date']
base_pair_dataframe.rename(columns=lambda s: "base_" + s if (not s in ignore_columns) else s, inplace=True)
dataframe = merge_informative_pair(dataframe, base_pair_dataframe, self.timeframe, timeframe_str, ffill=True)
if smooth_list != None:
for indicator_key in smooth_list:
dataframe[f'base_{indicator_key}_{timeframe_str}'] = ta.SMA(dataframe[f'base_{indicator_key}_{timeframe_str}'], timeperiod=candels_per_timeframe)
return dataframe
#
# Indicators - Logic
#
def calc_indicator_signals_long(self, dataframe, metadata):
dataframe['has_entered_market_condition'] = (
(qtpylib.crossed_above(dataframe[f'base_close_{self.timeframe}'], dataframe[f'base_oversold_price_{self.timeframe_long}']))
).fillna(0).astype('int')
dataframe['is_in_market_condition'] = (
(dataframe[f'base_close_{self.timeframe}'] > dataframe[f'base_oversold_price_{self.timeframe_long}'])
).fillna(0).astype('int')
dataframe['has_confirmation'] = (
(qtpylib.crossed_above(dataframe[f'base_cci_{self.timeframe_long}'], 0))
).fillna(0).astype('int')
dataframe['has_entry_signal'] = (
(dataframe[f'base_cci_{self.timeframe_long}'] >= 0)
).fillna(0).astype('int')
dataframe['has_exit_signal'] = (
(qtpylib.crossed_above(dataframe[f'base_close_{self.timeframe}'], dataframe[f'base_overbought_price_{self.timeframe_long}']))
).fillna(0).astype('int')
return dataframe
def calc_indicator_signals_short(self, dataframe, metadata):
dataframe['has_entered_market_condition'] = (
(qtpylib.crossed_below(dataframe[f'base_close_{self.timeframe}'], dataframe[f'base_overbought_price_{self.timeframe_long}']))
).fillna(0).astype('int')
dataframe['is_in_market_condition'] = (
(dataframe[f'base_close_{self.timeframe}'] < dataframe[f'base_overbought_price_{self.timeframe_long}'])
).fillna(0).astype('int')
dataframe['has_confirmation'] = (
(qtpylib.crossed_below(dataframe[f'base_cci_{self.timeframe_long}'], 0))
).fillna(0).astype('int')
dataframe['has_entry_signal'] = (
(dataframe[f'base_cci_{self.timeframe_long}'] <= 0)
).fillna(0).astype('int')
dataframe['has_exit_signal'] = (
(qtpylib.crossed_below(dataframe[f'base_close_{self.timeframe}'], dataframe[f'base_oversold_price_{self.timeframe_long}']))
).fillna(0).astype('int')
return dataframe
def init_trade_flow_info(self, pair):
if not pair in self.custom_trade_flow_info:
self.custom_trade_flow_info[pair] = {}
self.custom_trade_flow_info[pair]['trade_flow'] = None
self.set_trade_flow_state(self.TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE, pair)
def is_trade_flow_state(self, trade_flow_state, pair):
return self.custom_trade_flow_info[pair]['trade_flow'] == trade_flow_state
def set_trade_flow_state(self, trade_flow_state, pair):
self.custom_trade_flow_info[pair]['trade_flow'] = trade_flow_state
def calc_trade_flow(self, dataframe, metadata):
dataframe['entry_signal'] = 0
dataframe['exit_signal'] = 0
pair = metadata['pair']
start_time = time.time()
self.init_trade_flow_info(pair)
for row_df in zip(dataframe['has_entered_market_condition'],dataframe['is_in_market_condition'],dataframe['has_confirmation'],dataframe['has_entry_signal'],dataframe['has_exit_signal'],dataframe['date']):
if self.is_trade_flow_state(self.TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE, pair):
if row_df[0] == 1:
self.set_trade_flow_state(self.TF_ENTERED_MARKET_CONDITION_WAITING_FOR_CONFIRMATION_STATE, pair)
else:
continue
if self.is_trade_flow_state(self.TF_ENTERED_MARKET_CONDITION_WAITING_FOR_CONFIRMATION_STATE, pair):
if row_df[1] == 1:
if row_df[2] == 1:
self.set_trade_flow_state(self.TF_MARKET_CONDITION_CONFIRMED_WAITING_FOR_ENTRY_SIGNAL_STATE, pair)
else:
continue
else:
self.set_trade_flow_state(self.TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE, pair)
continue
if self.is_trade_flow_state(self.TF_MARKET_CONDITION_CONFIRMED_WAITING_FOR_ENTRY_SIGNAL_STATE, pair):
if row_df[1] == 1:
if row_df[2] == 1:
if row_df[3] == 1:
self.set_trade_flow_state(self.TF_ENTRY_SIGNAL_FOUND_STATE, pair)
else:
continue
else:
self.set_trade_flow_state(self.TF_ENTERED_MARKET_CONDITION_WAITING_FOR_CONFIRMATION_STATE, pair)
continue
else:
self.set_trade_flow_state(self.TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE, pair)
continue
if self.is_trade_flow_state(self.TF_ENTRY_SIGNAL_FOUND_STATE, pair):
dataframe.at[dataframe['date'] == row_df[5], 'entry_signal'] = 1
self.set_trade_flow_state(self.TF_ENTRY_SIGNAL_FOUND_WAITING_FOR_EXIT_SIGNAL_STATE, pair)
if self.is_trade_flow_state(self.TF_ENTRY_SIGNAL_FOUND_WAITING_FOR_EXIT_SIGNAL_STATE, pair):
if row_df[4] == 1:
self.set_trade_flow_state(self.TF_STAND_BY_WAITING_FOR_MARKET_CONDITION_STATE, pair)
dataframe.at[dataframe['date'] == row_df[5], 'exit_signal'] = 1
else:
continue
print(pair, 'calc_trade_flow', time.time() - start_time)
return dataframe
#
# Custom stoploss
#
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float:
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
sroc = dataframe['sroc'].iat[-1]
# If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
else:
sroc = self.custom_trade_info[trade.pair]['sroc'].loc[current_time]['sroc']
new_stoploss = 1
if current_profit < self.cstp_threshold.value:
if self.cstp_bail_how.value == 'roc' or self.cstp_bail_how.value == 'any':
# Dynamic bailout based on rate of change
if (sroc/100) <= self.cstp_bail_roc.value:
new_stoploss = 0.001
if self.cstp_bail_how.value == 'time' or self.cstp_bail_how.value == 'any':
# Dynamic bailout based on time
if trade_dur > self.cstp_bail_time.value:
new_stoploss = 0.001
else:
if self.cstp_trailing_enabled.value == True and current_profit >= self.cstp_trailing_stop_positive_offset.value:
desired_stoploss = current_profit / self.cstp_trailing_stop_profit_devider.value
new_stoploss = max(min(desired_stoploss, self.cstp_trailing_max_stoploss.value), 0.025)
return new_stoploss
#
# Dynamic roi
#
def min_roi_reached(self, trade: Trade, current_profit: float, current_time: datetime) -> bool:
trade_dur = int((current_time.timestamp() - trade.open_date_utc.timestamp()) // 60)
if self.use_dynamic_roi:
_, roi = self.min_roi_reached_dynamic(trade, current_profit, current_time, trade_dur)
else:
_, roi = self.min_roi_reached_entry(trade_dur)
if roi is None:
return False
else:
return current_profit > roi
def min_roi_reached_dynamic(self, trade: Trade, current_profit: float, current_time: datetime, trade_dur: int) -> Tuple[Optional[int], Optional[float]]:
minimal_roi = self.minimal_roi
_, table_roi = self.min_roi_reached_entry(trade_dur)
# see if we have the data we need to do this, otherwise fall back to the standard table
if self.custom_trade_info and trade and trade.pair in self.custom_trade_info:
if self.config['runmode'].value in ('live', 'dry_run'):
dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=trade.pair, timeframe=self.timeframe)
rmi_trend = dataframe['rmi-up-trend'].iat[-1]
candle_trend = dataframe['candle-up-trend'].iat[-1]
ssl_dir = dataframe['ssl-dir'].iat[-1]
# If in backtest or hyperopt, get the indicator values out of the trades dict (Thanks @JoeSchr!)
else:
rmi_trend = self.custom_trade_info[trade.pair]['rmi-up-trend'].loc[current_time]['rmi-up-trend']
candle_trend = self.custom_trade_info[trade.pair]['candle-up-trend'].loc[current_time]['candle-up-trend']
ssl_dir = self.custom_trade_info[trade.pair]['ssl-dir'].loc[current_time]['ssl-dir']
min_roi = table_roi
max_profit = trade.calc_profit_ratio(trade.max_rate)
pullback_value = (max_profit - self.droi_pullback_amount.value)
in_trend = False
if self.droi_trend_type.value == 'rmi' or self.droi_trend_type.value == 'any':
if rmi_trend == 1:
in_trend = True
if self.droi_trend_type.value == 'ssl' or self.droi_trend_type.value == 'any':
if ssl_dir == 'up':
in_trend = True
if self.droi_trend_type.value == 'candle' or self.droi_trend_type.value == 'any':
if candle_trend == 1:
in_trend = True
# Force the ROI value high if in trend
if (in_trend == True):
min_roi = 100
# If pullback is enabled, allow to sell if a pullback from peak has happened regardless of trend
if self.droi_pullback.value == True and (current_profit < pullback_value):
if self.droi_pullback_respect_table.value == True:
min_roi = table_roi
else:
min_roi = current_profit / 2
else:
min_roi = table_roi
return trade_dur, min_roi
def get_current_price(self, pair: str, refresh: bool) -> float:
if not refresh:
rate = self.custom_current_price_cache.get(pair)
# Check if cache has been invalidated
if rate:
return rate
ask_strategy = self.config.get('ask_strategy', {})
if ask_strategy.get('use_order_book', False):
ob = self.dp.orderbook(pair, 1)
rate = ob[f"{ask_strategy['price_side']}s"][0][0]
else:
ticker = self.dp.ticker(pair)
rate = ticker['last']
self.custom_current_price_cache[pair] = rate
return rate
def populate_trades(self, pair: str) -> dict:
# Initialize the trades dict if it doesn't exist, persist it otherwise
if not pair in self.custom_trade_info:
self.custom_trade_info[pair] = {}
# init the temp dicts and set the trade stuff to false
trade_data = {}
trade_data['active_trade'] = False
# active trade stuff only works in live and dry, not backtest
if self.config['runmode'].value in ('live', 'dry_run'):
# find out if we have an open trade for this pair
active_trade = Trade.get_trades([Trade.pair == pair, Trade.is_open.is_(True),]).all()
# if so, get some information
if active_trade:
# get current price and update the min/max rate
current_rate = self.get_current_price(pair, True)
"""
freqtrade | Traceback (most recent call last):
freqtrade | File "/freqtrade/freqtrade/strategy/strategy_wrapper.py", line 17, in wrapper
freqtrade | return f(*args, **kwargs)
freqtrade | File "/freqtrade/freqtrade/strategy/interface.py", line 417, in _analyze_ticker_internal
freqtrade | dataframe = self.analyze_ticker(dataframe, metadata)
freqtrade | File "/freqtrade/freqtrade/strategy/interface.py", line 396, in analyze_ticker
freqtrade | dataframe = self.advise_indicators(dataframe, metadata)
freqtrade | File "/freqtrade/freqtrade/strategy/interface.py", line 763, in advise_indicators
freqtrade | return self.populate_indicators(dataframe, metadata)
freqtrade | File "/freqtrade/user_data/strategies/LongAndShortMachetes.py", line 258, in populate_indicators
freqtrade | dataframe = self.on_populate_indicators(dataframe, metadata)
freqtrade | File "/freqtrade/user_data/strategies/LongAndShortMachetes.py", line 142, in on_populate_indicators
freqtrade | self.setup_custom_trade_info(dataframe, metadata)
freqtrade | File "/freqtrade/user_data/strategies/LongAndShortMachetes.py", line 636, in setup_custom_trade_info
freqtrade | self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
freqtrade | File "/freqtrade/user_data/strategies/LongAndShortMachetes.py", line 629, in populate_trades
freqtrade | active_trade[0].adjust_min_max_rates(current_rate)
freqtrade | TypeError: adjust_min_max_rates() missing 1 required positional argument: 'current_price_low'
freqtrade | 2021-09-18 11:34:11,851 - freqtrade.strategy.interface - WARNING - Unable to analyze candle (OHLCV) data for pair DOTDOWN/USDT: adjust_min_max_rates() missing 1 required positional argument: 'current_price_low'
from interface:should_sell
This function evaluates if one of the conditions required to trigger a sell
has been reached, which can either be a stop-loss, ROI or sell-signal.
:param low: Only used during backtesting to simulate stoploss
:param high: Only used during backtesting, to simulate ROI
:param force_stoploss: Externally provided stoploss
:return: True if trade should be sold, False otherwise
"""
active_trade[0].adjust_min_max_rates(current_rate, current_rate)
return trade_data
def setup_custom_trade_info(self, dataframe, metadata):
self.custom_trade_info[metadata['pair']] = self.populate_trades(metadata['pair'])
if self.dp.runmode.value in ('backtest', 'hyperopt'):
self.custom_trade_info[metadata['pair']]['roc'] = dataframe[['date', 'roc']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['atr'] = dataframe[['date', 'atr']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['sroc'] = dataframe[['date', 'sroc']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['ssl-dir'] = dataframe[['date', 'ssl-dir']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['rmi-up-trend'] = dataframe[['date', 'rmi-up-trend']].copy().set_index('date')
self.custom_trade_info[metadata['pair']]['candle-up-trend'] = dataframe[['date', 'candle-up-trend']].copy().set_index('date')