Strategy motivation: https://www.youtube.com/watch?v=RbQaARxEW9o
Timeframe
5m
Direction
Long Only
Stoploss
-20.0%
Trailing Stop
No
ROI
0m: 4.0%, 30m: 2.0%, 60m: 1.0%
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 libs ---
import numpy as np
import pandas as pd
from freqtrade.persistence import Trade
from pandas import DataFrame
from datetime import datetime, timedelta
from typing import Optional, Union
from functools import reduce
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
IntParameter, IStrategy, merge_informative_pair,
stoploss_from_absolute, timeframe_to_prev_date)
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib
import re
class VWAP_BB_RSI(IStrategy):
"""
Strategy motivation: https://www.youtube.com/watch?v=RbQaARxEW9o
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 = '5m'
# 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 = {
# "60": 0.01,
# "30": 0.02,
# "0": 0.04
# }
# Optimal stoploss designed for the strategy.
# This attribute will be overridden if the config file contains "stoploss".
stoploss = -0.2
updated_stoploss = None
use_custom_stoploss = True
# 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
# 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
# ==========================================================================
# BUYING PARAMETERS
# Bollinger Bands: window and std for buying
buy_bb_window = IntParameter(7, 21, default=14, space="buy", optimize=True)
buy_bb_std = DecimalParameter(2.0, 3.0, default=2.0, decimals=1, space="buy", optimize=True)
# RSI: window
buy_rsi_window = IntParameter(7, 21, default=16, space="buy", optimize=True)
buy_rsi_threshold = DecimalParameter(20.0, 50.0, default=45.0, decimals=1, space="buy", optimize=True)
# VWAP: lookback window, including current candle
buy_vwap_window = IntParameter(10, 20, default=15, space="buy", optimize=True)
# ==========================================================================
# SELLING PARAMETERS
# ATR
sell_atr_window = IntParameter(7, 14, default=7, space="sell", optimize=True)
sell_atr_constant = DecimalParameter(1, 2, default=1.2, decimals=1, space="sell", optimize=True)
# RSI threshold
sell_rsi_threshold = IntParameter(80, 90, default=90, space="sell", optimize=True)
# Reward-risk ratio
sell_rr_ratio = DecimalParameter(1.0, 5.0, default=1.5, decimals=1, space="sell", optimize=True)
# ==========================================================================
# Number of candles the strategy requires before producing valid signals
startup_candle_count: int = int(
max(
buy_bb_window.value,
buy_vwap_window.value,
buy_rsi_window.value,
sell_atr_window.value
)
)
# Optional order type mapping.
order_types = {
'entry': 'limit',
'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': {
'vwap': {},
'bb_lowerband': {},
'bb_middleband': {},
'bb_upperband': {}
},
'subplots': {
# Subplots - each dict defines one additional plot
"RSI": {
'rsi': {},
},
"ATR": {
'atr': {}
}
}
}
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.
:param dataframe: Dataframe with data from the exchange
:param metadata: Additional information, like the currently traded pair
:return: a Dataframe with all mandatory indicators for the strategies
"""
# Set datetime index for the vwap function calculation below
dataframe.set_index('date', drop=False, inplace=True)
# RSI
dataframe['rsi'] = ta.RSI(
dataframe,
timeperiod=self.buy_rsi_window.value
)
# Bollinger Bands
bollinger = qtpylib.bollinger_bands(
qtpylib.typical_price(dataframe),
window=self.buy_bb_window.value,
stds=self.buy_bb_std.value
)
dataframe['bb_lowerband'] = bollinger['lower']
dataframe['bb_middleband'] = bollinger['mid']
dataframe['bb_upperband'] = bollinger['upper']
dataframe["bb_percent"] = (
(dataframe["close"] - dataframe["bb_lowerband"]) /
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
)
dataframe["bb_width"] = (
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
)
# VWAP
dataframe['vwap'] = pta.vwap(
dataframe['high'],
dataframe['low'],
dataframe['close'],
dataframe['volume']
)
# ATR
dataframe['atr'] = pta.atr(
dataframe['high'],
dataframe['low'],
dataframe['close'],
length=self.sell_atr_window.value
)
return dataframe
def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the entry signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with entry columns populated
"""
conditions = []
# Condition 1: all candles need to open/close above the vwap line
for i in range(self.buy_vwap_window.value-1, -1, -1):
conditions.append(
dataframe[['open', 'close']].shift(i).max(axis=1) >=
dataframe['vwap'].shift(i)
)
# Conditions: price closes below the lower Bollinger Band, RSI below
# threshold, positive volume
conditions.append(
(qtpylib.crossed_below(dataframe['close'], dataframe['bb_lowerband'])) &
(dataframe['rsi'] < self.buy_rsi_threshold.value) &
(dataframe['volume'] > 0)
)
if conditions:
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
'enter_long'] = 1
return dataframe
def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the exit signal for the given dataframe
:param dataframe: DataFrame
:param metadata: Additional information, like the currently traded pair
:return: DataFrame with exit columns populated
"""
dataframe.loc[
(
(dataframe['volume'] > 0) # Make sure Volume is not 0
),
'exit_long'] = 0
return dataframe
def custom_stoploss(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, after_fill: bool, **kwargs) -> Optional[float]:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
previous_trade_date = timeframe_to_prev_date(
self.timeframe,
trade.open_date_utc - timedelta(minutes=extract_minutes(self.timeframe))
)
# Look up the candle before the trade date
previous_trade_candle = dataframe.loc[
dataframe['date'] == previous_trade_date]
if not previous_trade_candle.empty:
previous_trade_candle = previous_trade_candle.squeeze()
# Calculate stoploss: (previous close) - alpha * ATR
stoploss = stoploss_from_absolute(
stop_rate=previous_trade_candle['close'] - self.sell_atr_constant.value * previous_trade_candle['atr'],
current_rate=current_rate,
is_short=trade.is_short
)
self.updated_stoploss = stoploss
return stoploss
return 100
def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float,
current_profit: float, **kwargs) -> Optional[Union[str, bool]]:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
# Exit immediately if RSI is over upper threshold, regardless of profit
if last_candle['rsi'] >= self.sell_rsi_threshold.value:
return f'rsi_above_{int(self.sell_rsi_threshold.value)}'
if self.updated_stoploss is not None:
if current_profit >= self.sell_rr_ratio.value * abs(self.updated_stoploss):
return f'{self.sell_rr_ratio.value}:1_rr_ratio'
previous_trade_date = timeframe_to_prev_date(
self.timeframe,
trade.open_date_utc - timedelta(
minutes=extract_minutes(self.timeframe))
)
# Look up the candle before the trade date
previous_trade_candle = dataframe.loc[
dataframe['date'] == previous_trade_date]
if not previous_trade_candle.empty:
previous_trade_candle = previous_trade_candle.squeeze()
stop_rate = previous_trade_candle['close'] - self.sell_atr_constant.value * previous_trade_candle['atr']
previous_trade_candle_close = previous_trade_candle['close']
stoploss = (previous_trade_candle_close/stop_rate) - 1
if current_profit >= self.sell_rr_ratio.value * abs(stoploss):
return f'{self.sell_rr_ratio.value}:1_rr_ratio'
def extract_minutes(timeframe):
timescale = timeframe[-1]
num = int(re.findall('\d+', timeframe)[0])
if timescale == 'm':
return num
elif timescale == 'h':
return num * 60
elif timescale == 'd':
return num * 1440
elif timescale == 'w':
return num * 1440 * 7