Timeframe
15m
Direction
Long Only
Stoploss
-25.0%
Trailing Stop
No
ROI
0m: 25.0%, 30m: 1.0%, 60m: 1.0%
Interface Version
3
Startup Candles
N/A
Indicators
2
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
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
import os
from datetime import datetime
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IStrategy, IntParameter, RealParameter)
from freqtrade.strategy import merge_informative_pair
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import ta as taichi
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', None)
pd.set_option('display.expand_frame_repr', True)
def delete_log_results():
if os.path.exists("mylogs.txt"):
os.remove("mylogs.txt")
def log_to_results(str_to_log):
fr = open("mylogs.txt", "a")
#fr.write(str(datetime.now()) + " : " + str_to_log + "\n")
fr.write(str_to_log + "\n")
fr.close()
# This class is a sample. Feel free to customize it.
class StratEma7Ema25(IStrategy):
delete_log_results()
# 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
# Can this strategy go short?
can_short: bool = True
#roi0 = RealParameter(0.01, 0.09, decimals=1, default=0.04, space="buy")
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
#"60": 0.01,
#"30": 0.01,
"0": 0.25
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.25
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
trailing_stop_positive = 0.005
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal timeframe for the strategy.
timeframe = '15m'
# 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 = 26
# Optional order type mapping.
order_types = {
'entry': 'limit',
'exit': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'entry': 'gtc',
'exit': 'gtc'
}
def informative_pairs(self):
# get access to all pairs available in whitelist.
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
dataframe['ema20'] = ta.EMA(dataframe, timeperiod = 20)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod = 50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod = 100)
#log_to_results(dataframe.to_string())
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
qtpylib.crossed_above(dataframe['ema20'], dataframe['ema100'])
),
'enter_long'] = 1
"""
dataframe.loc[
(
qtpylib.crossed_below(dataframe['ema20'], dataframe['ema100'])
),
'enter_short'] = 1
"""
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
dataframe.loc[
(
qtpylib.crossed_below(dataframe['ema20'], dataframe['ema50'])
),
'exit_long'] = 1
dataframe.loc[
(
qtpylib.crossed_above(dataframe['ema20'], dataframe['ema50'])
),
'exit_short'] = 1
"""
return dataframe