304 lines
12 KiB
Python
304 lines
12 KiB
Python
|
|
# --- Do not remove these libs ---
|
|
from freqtrade.strategy.interface import IStrategy
|
|
from pandas import DataFrame
|
|
# --------------------------------
|
|
|
|
# Add your lib to import here
|
|
import talib.abstract as ta
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
import numpy # noqa
|
|
|
|
|
|
# This class is a sample. Feel free to customize it.
|
|
class SampleStrategy(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": 0.02,
|
|
"0": 0.04
|
|
}
|
|
|
|
# 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_stop_positive = 0.01
|
|
# trailing_stop_positive_offset = 0.0 # Disabled / not configured
|
|
|
|
# Optimal ticker interval for the strategy.
|
|
ticker_interval = '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
|
|
|
|
# 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'
|
|
}
|
|
|
|
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: Raw data from the exchange and parsed by parse_ticker_dataframe()
|
|
:param metadata: Additional information, like the currently traded pair
|
|
:return: a Dataframe with all mandatory indicators for the strategies
|
|
"""
|
|
|
|
# Momentum Indicators
|
|
# ------------------------------------
|
|
|
|
# RSI
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
|
|
"""
|
|
# ADX
|
|
dataframe['adx'] = ta.ADX(dataframe)
|
|
|
|
# Awesome oscillator
|
|
dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
|
|
|
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
|
|
dataframe['cci'] = ta.CCI(dataframe)
|
|
|
|
# MACD
|
|
macd = ta.MACD(dataframe)
|
|
dataframe['macd'] = macd['macd']
|
|
dataframe['macdsignal'] = macd['macdsignal']
|
|
dataframe['macdhist'] = macd['macdhist']
|
|
|
|
# MFI
|
|
dataframe['mfi'] = ta.MFI(dataframe)
|
|
|
|
# Minus Directional Indicator / Movement
|
|
dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
|
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
|
|
|
# Plus Directional Indicator / Movement
|
|
dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
|
dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
|
dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
|
|
|
# ROC
|
|
dataframe['roc'] = ta.ROC(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)
|
|
|
|
# Inverse Fisher transform on RSI normalized, value [0.0, 100.0] (https://goo.gl/2JGGoy)
|
|
dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
|
|
|
|
# Stoch
|
|
stoch = ta.STOCH(dataframe)
|
|
dataframe['slowd'] = stoch['slowd']
|
|
dataframe['slowk'] = stoch['slowk']
|
|
|
|
# Stoch fast
|
|
stoch_fast = ta.STOCHF(dataframe)
|
|
dataframe['fastd'] = stoch_fast['fastd']
|
|
dataframe['fastk'] = stoch_fast['fastk']
|
|
|
|
# Stoch RSI
|
|
stoch_rsi = ta.STOCHRSI(dataframe)
|
|
dataframe['fastd_rsi'] = stoch_rsi['fastd']
|
|
dataframe['fastk_rsi'] = stoch_rsi['fastk']
|
|
"""
|
|
|
|
# Overlap Studies
|
|
# ------------------------------------
|
|
|
|
# Bollinger bands
|
|
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
|
dataframe['bb_lowerband'] = bollinger['lower']
|
|
dataframe['bb_middleband'] = bollinger['mid']
|
|
dataframe['bb_upperband'] = bollinger['upper']
|
|
|
|
"""
|
|
# EMA - Exponential Moving Average
|
|
dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
|
|
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 Parabol
|
|
dataframe['sar'] = ta.SAR(dataframe)
|
|
|
|
# SMA - Simple Moving Average
|
|
dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
|
|
"""
|
|
|
|
# TEMA - Triple Exponential Moving Average
|
|
dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
|
|
|
|
# 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
|
|
# ------------------------------------
|
|
"""
|
|
# Heikinashi stategy
|
|
heikinashi = qtpylib.heikinashi(dataframe)
|
|
dataframe['ha_open'] = heikinashi['open']
|
|
dataframe['ha_close'] = heikinashi['close']
|
|
dataframe['ha_high'] = heikinashi['high']
|
|
dataframe['ha_low'] = heikinashi['low']
|
|
"""
|
|
|
|
# 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 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[
|
|
(
|
|
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
|
|
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
|
|
(dataframe['tema'] > dataframe['tema'].shift(1)) & # Guard: tema is raising
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
|
),
|
|
'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[
|
|
(
|
|
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
|
|
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
|
|
(dataframe['tema'] < dataframe['tema'].shift(1)) & # Guard: tema is falling
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
|
),
|
|
'sell'] = 1
|
|
return dataframe
|