Timeframe
1m
Direction
Long Only
Stoploss
-15.0%
Trailing Stop
Yes
ROI
0m: 100.0%
Interface Version
3
Startup Candles
N/A
Indicators
6
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/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 datetime import datetime, timedelta, timezone
from freqtrade.exchange import timeframe_to_minutes
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter, merge_informative_pair, informative)
from technical.util import resample_to_interval, resampled_merge
from freqtrade.persistence import Trade
from freqtrade.optimize.space import Categorical, Dimension, Integer, SKDecimal, Real # noqa
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.pivots_points import pivots_points
from typing import Any, Dict, List, Optional
# 13% APR 1 year backtest
class multiple_tf_rsi(IStrategy):
custom_info = {}
"""
This is a strategy template to get you started.
More information in https://www.freqtrade.io/en/latest/strategy-customization/
You can:
:return: a Dataframe with all mandatory indicators for the strategies
- Rename the class name (Do not forget to update class_name)
- Add any methods you want to build your strategy
- Add any lib you need to build your strategy
You must keep:
- the lib in the section "Do not remove these libs"
- the methods: populate_indicators, populate_entry_trend, populate_exit_trend
You should keep:
- timeframe, minimal_roi, stoploss, trailing_*
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 3
# Optimal timeframe for the strategy.
timeframe = '1m'
# Can this strategy go short?
can_short: bool = True
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 1
}
# HO parameters
buy_adx = IntParameter(20, 40, default=30, space="buy")
buy_rsi5 = IntParameter(20, 40, default=30, space="buy")
buy_rsi15 = IntParameter(20, 40, default=30, space="buy")
buy_adx_short = IntParameter(20, 40, default=30, space="buy")
buy_rsi5_short = IntParameter(60, 80, default=70, space="buy")
buy_rsi15_short = IntParameter(60, 80, default=70, space="buy")
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.15
# Trailing stoploss
trailing_stop= True
trailing_stop_positive=0.02
trailing_stop_positive_offset= 0.10
trailing_only_offset_is_reached= True
custom_price_max_distance_ratio = 1
# Run "populate_indicators()" only for new candle.
process_only_new_candles = True
# These values can be overridden in the config.
use_exit_signal = True
exit_profit_only = False
ignore_roi_if_entry_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 50
# Optional order type mapping.
order_types = {
'entry': 'market',
'exit': 'market',
'stoploss': 'market',
'stoploss_on_exchange': True
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
@property
def plot_config(self):
return {
# Main plot indicators (Moving averages, ...)
'main_plot': {
},
'subplots': {
"MACD": {
'macdh': {'color': 'blue'},
'macdd': {'color': 'cyan'},
'macdf': {'color': 'purple'},
},
"CCI": {
'cci': {'color': 'red'},
},
}
}
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 5
}
]
def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
proposed_stake: float, min_stake: float, max_stake: float,
entry_tag: str, **kwargs) -> float:
return (100)
def leverage(self, pair: str, current_time: 'datetime', current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
return 10
def informative_pairs(self):
"""
Define additional, informative pair/interval combinations to be cached from the exchange.
These pair/interval combinations are non-tradeable, unless they are part
of the whitelist as well.
For more information, please consult the documentation
:return: List of tuples in the format (pair, interval)
Sample: return [("ETH/USDT", "5m"),
("BTC/USDT", "15m"),
]
"""
return []
@informative('5m')
@informative('1h')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['adx'] = ta.ADX(dataframe)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# 5m #
dataframe.loc[
(
(dataframe['adx'] > self.buy_adx.value) &
(dataframe['rsi_5m'] < self.buy_rsi5.value) &
(dataframe['rsi_1h'] < self.buy_rsi15.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'bullish_5m')
dataframe.loc[
(
(dataframe['adx'] < self.buy_adx_short.value) &
(dataframe['rsi_5m'] > self.buy_rsi5_short.value) &
(dataframe['rsi_1h'] > self.buy_rsi15_short.value) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_short', 'enter_tag']] = (1, "bearish_5m")
#15m
"""
dataframe.loc[
(
(dataframe['bullish_engulfing_15m'] == 100) &
(dataframe['rsi_15m'] > 30) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'bullish_15m')
dataframe.loc[
(
(dataframe['bearish_engulfing_15m'] == -100) &
(dataframe['rsi_15m'] < 70) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_short', 'enter_tag']] = (1, "bearish_15m")
"""
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (0, "open_ai_told_me_to_exit")
dataframe.loc[
(
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_short', 'exit_tag']] = (0, 'yodo_knows_better_ex')
return dataframe