Simple strategy based on Contrarian Donchian Channel Bounce from the bottom band
Timeframe
N/A
Direction
Long Only
Stoploss
N/A
Trailing Stop
No
ROI
N/A
Interface Version
N/A
Startup Candles
N/A
Indicators
11
freqtrade/freqtrade-strategies
Strategy 003 author@: Gerald Lonlas github@: https://github.com/freqtrade/freqtrade-strategies
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
# --------------------------------
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import numpy # noqa
from freqtrade.strategy.hyper import CategoricalParameter, DecimalParameter, IntParameter
from user_data.strategies import Config
class DonchianBounce(IStrategy):
"""
Simple strategy based on Contrarian Donchian Channel Bounce from the bottom band
How to use it?
> python3 ./freqtrade/main.py -s DonchianBounce.py
"""
# Hyperparameters
# Buy hyperspace params:
buy_params = {
"buy_adx": 43.0,
"buy_adx_enabled": False,
"buy_dc_gain": 0.05,
"buy_dc_period": 60,
"buy_ema_enabled": False,
"buy_sar_enabled": False,
"buy_sma_enabled": True,
}
buy_dc_period = IntParameter(10, 120, default=43, space="buy")
buy_dc_gain = DecimalParameter(0.01, 0.10, decimals=2, default=0.05, space="buy")
buy_adx = DecimalParameter(1, 99, decimals=0, default=60, space="buy")
buy_sma_enabled = CategoricalParameter([True, False], default=True, space="buy")
buy_ema_enabled = CategoricalParameter([True, False], default=False, space="buy")
buy_adx_enabled = CategoricalParameter([True, False], default=False, space="buy")
buy_sar_enabled = CategoricalParameter([True, False], default=False, space="buy")
sell_sar_enabled = CategoricalParameter([True, False], default=False, space="sell")
sell_hold = CategoricalParameter([True, False], default=True, space="sell")
# set the startup candles count to the longest average used (EMA, EMA etc)
startup_candle_count = max(buy_dc_period.value, 20)
# set common parameters
minimal_roi = Config.minimal_roi
trailing_stop = Config.trailing_stop
trailing_stop_positive = Config.trailing_stop_positive
trailing_stop_positive_offset = Config.trailing_stop_positive_offset
trailing_only_offset_is_reached = Config.trailing_only_offset_is_reached
stoploss = Config.stoploss
timeframe = Config.timeframe
process_only_new_candles = Config.process_only_new_candles
use_sell_signal = Config.use_sell_signal
sell_profit_only = Config.sell_profit_only
ignore_roi_if_buy_signal = Config.ignore_roi_if_buy_signal
order_types = Config.order_types
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"),
]
"""
return []
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
bollinger = qtpylib.weighted_bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
dataframe['bb_upperband'] = bollinger['upper']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_lowerband'] = bollinger['lower']
# Donchian Channels
dataframe['dc_upper'] = ta.MAX(dataframe['high'], timeperiod=self.buy_dc_period.value)
dataframe['dc_lower'] = ta.MIN(dataframe['low'], timeperiod=self.buy_dc_period.value)
dataframe['dc_mid'] = ((dataframe['dc_upper'] + dataframe['dc_lower']) / 2)
dataframe["dc_gain"] = ((dataframe["dc_upper"] - dataframe["close"]) / dataframe["close"])
# Fibonacci Levels (of Donchian Channel)
dataframe['dc_dist'] = (dataframe['dc_upper'] - dataframe['dc_lower'])
dataframe['dc_hf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.236 # Highest Fib
dataframe['dc_chf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.382 # Centre High Fib
dataframe['dc_clf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.618 # Centre Low Fib
dataframe['dc_lf'] = dataframe['dc_upper'] - dataframe['dc_dist'] * 0.764 # Low Fib
#print("\nupper: ", dataframe['dc_upper'])
#print("\nlower: ", dataframe['dc_lower'])
#print("\nmid: ", dataframe['dc_mid'])
# ADX
dataframe['adx'] = ta.ADX(dataframe)
dataframe['dm_plus'] = ta.PLUS_DM(dataframe)
dataframe['dm_minus'] = ta.MINUS_DM(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# MACD
macd = ta.MACD(dataframe)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
# Stoch fast
stoch_fast = ta.STOCHF(dataframe)
dataframe['fastd'] = stoch_fast['fastd']
dataframe['fastk'] = stoch_fast['fastk']
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# Inverse Fisher transform on RSI, values [-1.0, 1.0] (https://goo.gl/2JGGoy)
rsi = 0.1 * (dataframe['rsi'] - 50)
dataframe['fisher_rsi'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
# EMA - Exponential Moving Average
dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# SAR Parabolic
dataframe['sar'] = ta.SAR(dataframe)
# SMA - Simple Moving Average
dataframe['sma'] = ta.SMA(dataframe, timeperiod=200)
#print("\nSMA: ", dataframe['sma'])
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
conditions = []
# GUARDS AND TRENDS
# check that volume is not 0 (can happen in testing, or if there are issues with exchange data)
# conditions.append(dataframe['volume'] > 0)
# during back testing, data can be undefined, so check
conditions.append(dataframe['dc_hf'].notnull())
if self.buy_sar_enabled.value:
conditions.append(dataframe['sar'].notnull())
conditions.append(dataframe['close'] < dataframe['sar'])
if self.buy_sma_enabled.value:
conditions.append(dataframe['sma'].notnull())
conditions.append(dataframe['close'] > dataframe['sma'])
if self.buy_ema_enabled.value:
conditions.append(dataframe['ema50'].notnull())
conditions.append(dataframe['close'] > dataframe['ema50'])
# ADX with DM+ > DM- indicates uptrend
if self.buy_adx_enabled.value:
conditions.append(
(dataframe['adx'] > self.buy_adx.value)
# (dataframe['adx'] > self.buy_adx.value) &
# (dataframe['dm_plus'] >= dataframe['dm_minus'])
)
# Potential gain greater than goal
conditions.append(dataframe['dc_gain'] >= self.buy_dc_gain.value)
# TRIGGERS
# closing price above SAR
#conditions.append(dataframe['sar'] < dataframe['close'])
# price crosses or jumps above lower band, green candle
conditions.append(
(dataframe['dc_lower'].notnull()) &
# (dataframe['close'] >= dataframe['open']) &
# (
# (dataframe['close'] >= dataframe['dc_lower']) &
# (dataframe['low'] <= dataframe['dc_lower'])
# )
(dataframe['close'] >= dataframe['open']) &
(
(qtpylib.crossed_above(dataframe['close'], dataframe['dc_lower'])) |
(
(dataframe['close'] >= dataframe['dc_lower']) &
(dataframe['close'].shift(1) < dataframe['dc_lower'].shift(1))
)
)
)
# build the dataframe using the conditions
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'buy'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell signal for the given dataframe
:param dataframe: DataFrame
:return: DataFrame with buy column
"""
# if hold, then don't set a sell signal
if self.sell_hold.value:
dataframe.loc[(dataframe['close'].notnull() ), 'sell'] = 0
else:
conditions = []
# price crosses or jumps below high band, red candle
conditions.append(
(dataframe['dc_upper'].notnull()) &
(dataframe['close'] < dataframe['open']) &
(
(qtpylib.crossed_below(dataframe['close'], dataframe['dc_upper'])) |
(
(dataframe['close'] <= dataframe['dc_upper']) &
(dataframe['close'].shift(1) > dataframe['dc_upper'].shift(1))
)
)
)
if self.sell_sar_enabled.value:
conditions.append(dataframe['sar'].notnull())
conditions.append(dataframe['close'] < dataframe['sar'])
# build the dataframe using the conditions
if conditions:
dataframe.loc[reduce(lambda x, y: x & y, conditions), 'sell'] = 1
return dataframe