Timeframe
1m
Direction
Long Only
Stoploss
-1.0%
Trailing Stop
No
ROI
0m: 1.0%
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, 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 spot_coumpound(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 = False
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"0": 0.01
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.01
# Trailing stoploss
trailing_stop= False
trailing_stop_positive=0.005
trailing_stop_positive_offset= 0.02
trailing_only_offset_is_reached= False
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': 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': {
},
'subplots': {
"MACD": {
'macdh': {'color': 'blue'},
'macdd': {'color': 'cyan'},
'macdf': {'color': 'purple'},
},
"CCI": {
'cci': {'color': 'red'},
},
}
}
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 (self.wallets.get_total_stake_amount() / 2) #- (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
@informative('3m')
@informative('5m')
@informative('15m')
@informative('30m')
@informative('1h')
@informative('4h')
@informative('12h')
@informative('1d')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
period = 14
smoothD = 3
SmoothK = 3
dataframe['adx'] = ta.ADX(dataframe, timeperiod=14)
# values [0, 100]
dataframe['doji_short'] = ta.CDLEVENINGDOJISTAR(dataframe)
dataframe['doji_long'] = ta.CDLMORNINGSTAR(dataframe)
dataframe['gravestone'] = ta.CDLGRAVESTONEDOJI(dataframe)
dataframe['dragonfly'] = ta.CDLDRAGONFLYDOJI(dataframe)
macd, macdsignal, macdhist = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
dataframe['macd'] = macd
dataframe['macds'] = 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()
self.custom_info['lclose'] = dataframe['close']
self.custom_info['lopen'] = dataframe['open']
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
dataframe.loc[
(
# LONG
(qtpylib.crossed_above(dataframe['macd_1h'], dataframe['macds_1h'])) &
#(dataframe['adx_1d'] > 25) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'macd_1d')
dataframe.loc[
(
# LONG
(dataframe['macds_1h'] < dataframe['macd_1h']) &
(dataframe['macds_30m'] > dataframe['macd_30m']) &
(dataframe['macds_15m'] > dataframe['macd_15m']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (0, 'macd_1h')
"""
dataframe.loc[
(
# LONG
(qtpylib.crossed_below(dataframe['macd_1h'], dataframe['macds_1h'])) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['enter_long', 'enter_tag']] = (1, 'macd_15m')
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['macd_1h'].iat[-1] > dataframe['macd_1h']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (0, "macd_15m_down")
"""
dataframe.loc[
(
(dataframe['macds_1h'] > dataframe['macd_1h']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (0, "macd_1h")
dataframe.loc[
(
(dataframe['macds_30m'] < dataframe['macd_30m']) &
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
['exit_long', 'exit_tag']] = (0, "macd_30m")
"""
return dataframe