Timeframe
1m
Direction
Long Only
Stoploss
-16.7%
Trailing Stop
Yes
ROI
0m: 15.6%, 311m: 10.9%, 708m: 7.5%, 1454m: 0.0%
Interface Version
3
Startup Candles
N/A
Indicators
7
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)
from technical.util import resample_to_interval, resampled_merge
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
# 13% APR 1 year backtest
class momentum_tf_divergence(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'
informative_timeframe = '15m'
# 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": 0.156,
"311": 0.109,
"708": 0.075,
"1454": 0
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.167
# Trailing stoploss
trailing_stop= True
trailing_stop_positive=0.01
trailing_stop_positive_offset= 0.012
trailing_only_offset_is_reached= True
# 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': False
}
# 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': {
"MACD": {
'fastd': {'color': 'blue'},
'fastk': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
},
"Pivot": {
'pivot': {'color': 'black'},
},
'SMA': {
'sma15': {'color': 'white'},
'sma50': {'color': 'yellow'},
},
},
'subplots': {
}
}
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 1
}
]
def leverage(self, pair: str, current_time: 'datetime', current_rate: float,
proposed_leverage: float, max_leverage: float, side: str,
**kwargs) -> float:
"""
Customize leverage for each new trade.
:param pair: Pair that's currently analyzed
:param current_time: datetime object, containing the current datetime
:param current_rate: Rate, calculated based on pricing settings in exit_pricing.
:param proposed_leverage: A leverage proposed by the bot.
:param max_leverage: Max leverage allowed on this pair
:param side: 'long' or 'short' - indicating the direction of the proposed trade
:return: A leverage amount, which is between 1.0 and max_leverage.
"""
return 20.0
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 #- (self.wallets.get_total_stake_amount() / 20)
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"),
]
"""
pairs = self.dp.current_whitelist()
informative_pairs = [(pair, self.informative_timeframe) for pair in pairs]
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
period = 14
smoothD = 3
SmoothK = 3
#1h DF
dataframe1h = resample_to_interval(dataframe, 60)
macd, macdsignal, macdhist = ta.MACD(dataframe1h['close'], fastperiod=12, slowperiod=26, signalperiod=9)
dataframe1h['macdf'] = macd
dataframe1h['macdd'] = macdsignal
dataframe1h['macdh'] = macdhist
stoch_fast = ta.STOCHF(dataframe1h)
dataframe1h['fastd'] = stoch_fast['fastd']
dataframe1h['fastk'] = stoch_fast['fastk']
dataframe1h['rsi'] = ta.RSI(dataframe1h, timeperiod=14)
dataframe1h['cci'] = ta.CCI(dataframe1h)
stochrsi = (dataframe1h['rsi'] - dataframe1h['rsi'].rolling(period).min()) / (dataframe1h['rsi'].rolling(period).max() - dataframe1h['rsi'].rolling(period).min())
dataframe1h['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
dataframe1h['srsi_d'] = dataframe1h['srsi_k'].rolling(smoothD).mean()
#1m DF
dataframe15m = resample_to_interval(dataframe, 15)
macd, macdsignal, macdhist = ta.MACD(dataframe15m['close'], fastperiod=12, slowperiod=26, signalperiod=9)
dataframe15m['macdf'] = macd
dataframe15m['macdd'] = macdsignal
dataframe15m['macdh'] = macdhist
stoch_fast = ta.STOCHF(dataframe15m)
dataframe15m['fastd'] = stoch_fast['fastd']
dataframe15m['fastk'] = stoch_fast['fastk']
dataframe15m['rsi'] = ta.RSI(dataframe15m, timeperiod=14)
dataframe15m['cci'] = ta.CCI(dataframe15m)
stochrsi = (dataframe15m['rsi'] - dataframe15m['rsi'].rolling(period).min()) / (dataframe15m['rsi'].rolling(period).max() - dataframe15m['rsi'].rolling(period).min())
dataframe15m['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
dataframe15m['srsi_d'] = dataframe15m['srsi_k'].rolling(smoothD).mean()
#1m DF
macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macdf'] = macd
dataframe['macdd'] = macdsignal
dataframe['macdh'] = macdhist
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
dataframe['cci'] = ta.CCI(dataframe)
stochrsi = (dataframe['rsi'] - dataframe['rsi'].rolling(period).min()) / (dataframe['rsi'].rolling(period).max() - dataframe['rsi'].rolling(period).min())
dataframe['srsi_k'] = stochrsi.rolling(SmoothK).mean() * 100
dataframe['srsi_d'] = dataframe['srsi_k'].rolling(smoothD).mean()
#Resampling
dataframe = resampled_merge(dataframe, dataframe15m, fill_na=True)
dataframe = resampled_merge(dataframe, dataframe1h, fill_na=True)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with entry columns populated
"""
dataframe.loc[
(
# LONG
(dataframe["resample_15_macdh"] < 0) &
(dataframe["resample_15_cci"] > 100) &
(dataframe["cci"] > 100) &
(dataframe["macdh"].rolling(15).sum() > 0) & # Checking 15 last exercices before entering position to verify signal
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'enter_long'] = 0
dataframe.loc[
(
(dataframe["resample_15_macdh"] > -100) &
(dataframe["resample_15_cci"] < -100) &
(dataframe["cci"] > -100) &
(dataframe["macdh"].rolling(15).sum() < 0) & # Checking 15 last exercices before entering position to verify signal
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'enter_short'] = 1
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'] = 0
# Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
dataframe.loc[
(
(dataframe['volume'] == 0) # Make sure Volume is not 0
),
'exit_short'] = 1
return dataframe