This is a strategy template to get you started. More information in https://www.freqtrade.io/en/latest/strategy-customization/
Timeframe
5m
Direction
Long Only
Stoploss
-1.0%
Trailing Stop
Yes
ROI
0m: 0.1%, 10m: 0.2%, 15m: 0.1%, 45m: 0.2%
Interface Version
2
Startup Candles
N/A
Indicators
8
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
# --- Do not remove these libs ---
import numpy as np # noqa
import pandas as pd # noqa
from pandas import DataFrame, Series
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.exchange import timeframe_to_prev_date
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.indicators import indicators
class Hma(IStrategy):
"""
This is a strategy template to get you started.
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_buy_trend, populate_sell_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 = 2
# Minimal ROI designed for the strategy.
# This attribute will be overridden if the config file contains "minimal_roi".
# ROI table:
minimal_roi = {
"0": 0.0015,
"10": 0.0018,
"15": 0.0015,
"45": 0.002,
"65": 0
}
# Stoploss:
stoploss = -0.01
# Trailing stop:
trailing_stop = True
trailing_stop_positive = 0.021
trailing_stop_positive_offset = 0.03
trailing_only_offset_is_reached = True
# Optimal timeframe 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 = 500
# 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": {
},
"subplots": {
"RSI": {
"rsi": {
"color": "red"
}
},
"hlow": {
"h-low": {
"color": "#2de7cc",
"type": "line"
}
},
"htsine": {
"htsine": {
"color": "#688669",
"type": "line"
}
}
}
}
# Hyperoptable parameters
buy_rsi = IntParameter(low=1, high=30, default=30, space='buy', optimize=True, load=True)
sell_rsi = IntParameter(low=35, high=100, default=40, space='sell', optimize=True, load=True)
use_custom_sell = True
# use_custom_stoploss = True
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:
# Cycle Indicator
# ------------------------------------
# Hilbert Transform Indicator - SineWave
hilbert = ta.HT_SINE(dataframe)
dataframe['htsine'] = hilbert['sine']
dataframe['htleadsine'] = hilbert['leadsine']
# Pattern Recognition - Bullish candlestick patterns
# ------------------------------------
# # Hammer: values [0, 100]
# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
# # Inverted Hammer: values [0, 100]
# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
# # Dragonfly Doji: values [0, 100]
# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
# # Piercing Line: values [0, 100]
# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
# # Morningstar: values [0, 100]
# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
# # Three White Soldiers: values [0, 100]
# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
# Pattern Recognition - Bearish candlestick patterns
# ------------------------------------
# # Hanging Man: values [0, 100]
# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
# # Shooting Star: values [0, 100]
# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
# # Gravestone Doji: values [0, 100]
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
# # Dark Cloud Cover: values [0, 100]
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
# # Evening Doji Star: values [0, 100]
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
# # Evening Star: values [0, 100]
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
# Pattern Recognition - Bullish/Bearish candlestick patterns
# ------------------------------------
# # Three Line Strike: values [0, -100, 100]
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
# # Spinning Top: values [0, -100, 100]
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
# # Engulfing: values [0, -100, 100]
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
# # Harami: values [0, -100, 100]
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
# # Three Outside Up/Down: values [0, -100, 100]
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
# # Three Inside Up/Down: values [0, -100, 100]
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
# # Chart type
# # ------------------------------------
# # Heikin Ashi Strategy
# heikinashi = qtpylib.heikinashi(dataframe)
# dataframe['ha_open'] = heikinashi['open']
# dataframe['ha_close'] = heikinashi['close']
# dataframe['ha_high'] = heikinashi['high']
# dataframe['ha_low'] = heikinashi['low']
dataframe['ohlc4']=(dataframe['open'] + dataframe['high'] + dataframe['low'] + dataframe['close']) / 4
dataframe['hlc3']=(dataframe['high'] + dataframe['low'] + dataframe['close']) / 3
dataframe['hl2']=(dataframe['high'] + dataframe['low'] ) / 2
dataframe['hma16'] = qtpylib.hma(dataframe['ohlc4'], 20)
dataframe['hma8'] = qtpylib.hma(dataframe['hl2'], 8)
dataframe['rsi'] = ta.RSI(dataframe, timeperiod=30)
# MACD
macd = ta.MACD(dataframe, timeperod=9)
dataframe['macd'] = macd['macd']
dataframe['macdsignal'] = macd['macdsignal']
dataframe['macdhist'] = macd['macdhist']
dataframe['cci'] = ta.CCI(dataframe, timeperiod=10)
# dataframe['h-low']= dataframe['hma20'] / dataframe['low']
dataframe['sar'] = ta.SAR(dataframe)
dataframe['vwap'] = Series.vwap(dataframe)
devUD = [1.28, 2.01, 2.51, 3.09, 4.01]
# my std dev calculation = an incrementing std dev of df.VWAP
dataframe['DEV'] = dataframe['vwap'].expanding().std()
# dataframe['vwup1']= dataframe['vwap'] + devUD[0] * dataframe['DEV']
# dataframe['vwdow1']= dataframe['vwap'] - devUD[0] * dataframe['DEV']
# dataframe['vwup2']= dataframe['vwap'] + devUD[1] * dataframe['DEV']
# dataframe['vwdow2']= dataframe['vwap'] - devUD[1] * dataframe['DEV']
for dev in devUD:
up = 'vwup{}'.format(dev)
dow = 'vwdow{}'.format(dev)
dataframe[up]=dataframe['vwap'] + dev * dataframe['DEV']
dataframe[dow]= dataframe['vwap'] - dev * dataframe['DEV']
# Retrieve best bid and best ask from the orderbook
# ------------------------------------
"""
# first check if dataprovider is available
if self.dp:
if self.dp.runmode in ('live', 'dry_run'):
ob = self.dp.orderbook(metadata['pair'], 1)
dataframe['best_bid'] = ob['bids'][0][0]
dataframe['best_ask'] = ob['asks'][0][0]
"""
return dataframe
def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
current_rate: float, current_profit: float, **kwargs) -> float:
# evaluate highest to lowest, so that highest possible stop is used
if current_profit > 0.021:
return stoploss_from_open(0.02, current_profit)
elif current_profit > 0.011:
return stoploss_from_open(0.01, current_profit)
elif current_profit > 0.003:
return stoploss_from_open(0.002, current_profit)
# return maximum stoploss value, keeping current stoploss price unchanged
return 1
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
current_profit: float, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
candlem = dataframe.iloc[0].squeeze()
# Above 20% profit, sell when rsi < 80
# if current_profit > 0.2:
# if last_candle['rsi'] < 60:
# return 'rsi_below_60'
# Between 2% and 10%, sell if EMA-long above EMA-short
# if candlem['close'] < candlem['open']:
if current_profit > 0.031:
return 1
elif current_profit > 0.021:
return 1
elif current_profit > 0.015:
return 1
elif current_profit > 0.011:
return 1
elif current_profit > 0.002:
return 1
# if candlem['hma8'] < candlem['hma16']:
# if current_profit < 0.001:
# return 1
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['close'].shift(2) > dataframe['close'])&
# (dataframe['rsi'] < 45)&
(qtpylib.crossed_above(dataframe['close'], dataframe['hma16']))
),
'buy'] = 1
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Based on TA indicators, populates the sell 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['open'] < dataframe['close'] ) &
(dataframe['open'] < dataframe['close'])
# (dataframe['rsi'] > 37 )
# (qtpylib.crossed_below(dataframe['hma16'], dataframe['close']))
),
'sell'] =0
return dataframe