This is a sample strategy to inspire you. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
1m
Direction
Long Only
Stoploss
-10.0%
Trailing Stop
No
ROI
N/A
Interface Version
3
Startup Candles
N/A
Indicators
4
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 imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Optional, Union
from freqtrade.strategy import (
IStrategy,
Trade,
Order,
PairLocks,
informative, # @informative decorator
# Hyperopt Parameters
BooleanParameter,
CategoricalParameter,
DecimalParameter,
IntParameter,
RealParameter,
# timeframe helpers
timeframe_to_minutes,
timeframe_to_next_date,
timeframe_to_prev_date,
# Strategy helper functions
merge_informative_pair,
stoploss_from_absolute,
stoploss_from_open,
)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
from technical import qtpylib
from functools import reduce
timeperiods = [1,3,6,12,24,48,72,96]
# This class is a sample. Feel free to customize it.
class PlotStrategy(IStrategy):
"""
This is a sample strategy to inspire you.
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
# 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 = {
}
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.10
# 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 timeframe for the strategy.
timeframe = "1m" # price movement timeframe
informative_timeframe = '5m' # Signal timeframe
# 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
# Hyperoptable parameters
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = 200
# 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"}
plot_config = {
"main_plot": {
"tema": {"color":"white"},
},
"subplots": {
"RSI": {
"rsi": {"color": "white"},
},
"CCI": {
"cci": {"color": "white"},
},
},
}
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 do_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe["tema"] = ta.TEMA(dataframe,timeperiod=9)
dataframe["rsi"] = ta.RSI(dataframe)
dataframe["cci"] = ta.CCI(dataframe)
for idx, timeperiod in enumerate(timeperiods):
if timeperiod > 1:
dataframe[f'min_{timeperiod}'] = dataframe['close'].rolling(window=timeperiod).min()
dataframe[f'max_{timeperiod}'] = dataframe['close'].rolling(window=timeperiod).max()
dataframe[f'ema_{timeperiod}'] = ta.EMA(dataframe, timeperiod=timeperiod)
return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
if self.config['runmode'].value in ('backtest', 'hyperopt'):
assert (timeframe_to_minutes(self.timeframe) <= 5), "Backtest this strategy in 5m or 1m timeframe."
if self.timeframe == self.informative_timeframe:
dataframe = self.do_indicators(dataframe, metadata)
else:
if not self.dp:
return dataframe
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.informative_timeframe)
informative = self.do_indicators(informative.copy(), metadata)
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, self.informative_timeframe, ffill=True)
skip_columns = [(s + "_" + self.informative_timeframe) for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
dataframe.rename(columns=lambda s: s.replace("_{}".format(self.informative_timeframe), "") if (not s in skip_columns) else s, inplace=True)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
return dataframe