This is a sample strategy to inspire you. More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
Timeframe
5m
Direction
Long Only
Stoploss
-3.0%
Trailing Stop
No
ROI
0m: 4.0%, 30m: 3.0%, 60m: 1.0%
Interface Version
2
Startup Candles
N/A
Indicators
5
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
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
from freqtrade.strategy import IStrategy, merge_informative_pair
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
# This class is a sample. Feel free to customize it.
class ForexSignal(IStrategy):
"""
This is a sample strategy to inspire you.
More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
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 prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
populate_sell_trend, hyperopt_space, buy_strategy_generator
"""
# Strategy interface version - allow new iterations of the strategy interface.
# Check the documentation or the Sample strategy to get the latest version.
INTERFACE_VERSION = 2
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
minimal_roi = {
"60": 0.01,
"30": 0.03,
"0": 0.04
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.03
# Trailing stoploss
trailing_stop = False
# trailing_only_offset_is_reached = False
# trailing_stop_positive = 0.01
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
# Optimal ticker interval for the strategy.
timeframe = '5m'
# Run "populate_indicators()" only for new candle.
process_only_new_candles = False
# These values can be overridden in the "ask_strategy" section in the config.
use_sell_signal = True
sell_profit_only = False
ignore_roi_if_buy_signal = False
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 30
# Optional order type mapping.
order_types = {
'buy': 'limit',
'sell': 'limit',
'stoploss': 'market',
'stoploss_on_exchange': False
}
# Optional order time in force.
order_time_in_force = {
'buy': 'gtc',
'sell': 'gtc'
}
plot_config = {
'main_plot': {
'tema': {},
'sar': {'color': 'white'},
},
'subplots': {
"MACD": {
'macd': {'color': 'blue'},
'macdsignal': {'color': 'orange'},
},
"RSI": {
'rsi': {'color': 'red'},
}
}
}
def informative_pairs(self):
# get access to all pairs available in whitelist.
pairs = self.dp.current_whitelist()
# Assign tf to each pair so they can be downloaded and cached for strategy.
informative_pairs = [(pair, '1h') for pair in pairs]
# Optionally Add additional "static" pairs
return informative_pairs
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if not self.dp:
# Don't do anything if DataProvider is not available.
return dataframe
inf_tf = '1h'
# Get the informative pair
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=inf_tf)
informative['ema8'] = ta.EMA(informative, timeperiod=8)
informative['ema21'] = ta.EMA(informative, timeperiod=21)
# Use the helper function merge_informative_pair to safely merge the pair
# Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
# use ffill to have the 1d value available in every row throughout the day.
# Without this, comparisons between columns of the original and the informative pair would only work once per day.
# Full documentation of this method, see below
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
# # EMA - Exponential Moving Average for 5m timeframe
dataframe['ema8'] = ta.EMA(dataframe, timeperiod=8)
dataframe['ema13'] = ta.EMA(dataframe, timeperiod=13)
dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the buy signal for the given dataframe
:param dataframe: DataFrame populated with indicators
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with buy column
"""
dataframe.loc[
(
(dataframe['ema8_1h'] > dataframe['ema21_1h']) & # Onko nousutrendi
(dataframe['ema8'] > dataframe['ema13']) &
(dataframe['ema13'] > dataframe['ema21']) &
(dataframe['low'] < dataframe['ema8'])
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe.loc[
(
(dataframe['ema8'] < dataframe['ema8'].shift(1))
),
'sell'] = 1
return dataframe