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8158e199b0
| Author | SHA1 | Date |
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 Claudio Maradonna
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8158e199b0
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Claudio Maradonna
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d70a30011e
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Claudio Maradonna
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07c30d7fd4
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@ -0,0 +1,76 @@
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from freqtrade.strategy.interface import IStrategy
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from typing import Dict, List
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from functools import reduce
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from pandas import DataFrame
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy
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__author__ = "Kevin Ossenbrück"
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__copyright__ = "Free For Use"
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__credits__ = ["Bloom Trading, Mohsen Hassan"]
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__license__ = "MIT"
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__version__ = "1.0"
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__maintainer__ = "Kevin Ossenbrück"
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__email__ = "kevin.ossenbrueck@pm.de"
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__status__ = "Live"
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# CCI timerperiods and values
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cciBuyTP = 47
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cciBuyVal = -39
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cciSellTP = 42
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cciSellVal = 135
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# RSI timeperiods and values
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rsiBuyTP = 24
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rsiBuyVal = 30
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rsiSellTP = 15
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rsiSellVal = 69
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class ADAETHDOT(IStrategy):
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timeframe = '15m'
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stoploss = -0.291
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minimal_roi = {
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"0": 0.238,
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"115": 0.131,
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"272": 0.048,
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"613": 0
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}
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def informative_pairs(self):
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['cci-'+str(cciBuyTP)] = ta.CCI(dataframe, timeperiod=cciBuyTP)
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dataframe['cci-'+str(cciSellTP)] = ta.CCI(dataframe, timeperiod=cciSellTP)
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dataframe['rsi-'+str(rsiBuyTP)] = ta.RSI(dataframe, timeperiod=rsiBuyTP)
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dataframe['rsi-'+str(rsiSellTP)] = ta.RSI(dataframe, timeperiod=rsiSellTP)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['cci-'+str(cciBuyTP)] < cciBuyVal) &
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(dataframe['rsi-'+str(rsiBuyTP)] < rsiBuyVal)
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),
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'buy'] = 1
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe.loc[
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(
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(dataframe['cci-'+str(cciSellTP)] > cciSellVal) &
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(dataframe['rsi-'+str(rsiSellTP)] > rsiSellVal)
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),
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'sell'] = 1
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return dataframe
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@ -10,3 +10,5 @@
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| [CAKE_6](USDT/CAKE_6.py) | 2021-03-27 21:30:00 | 2021-04-30 19:00:00 | 97.07% | 1.10% | -1,301,216.89221 | SortinoHyperOptLoss | 38580 | |
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| [CAKE_7](USDT/CAKE_7.py) | 2021-03-31 21:30:00 | 2021-05-01 17:40:00 | 77.63% | 1.32% | -406,221.09900 | SortinoHyperOptLoss | 7723 | 31/28/0 |
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| [CAKE_8](USDT/CAKE_8.py) | 2021-03-31 21:30:00 | 2021-05-03 07:15:00 | 80.36% | 1.30% | -403,721.30770 | SortinoHyperOptLoss | 33420 | 32/30/0 |
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| [CAKE_9](USDT/CAKE_9.py) | 2021-03-31 21:30:00 | 2021-05-07 16:25:00 | 78.31% | 0.99% | -28,686.06459 | SortinoHyperOptLoss | 5963 | 46/33/0 |
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| [CAKE_10](USDT/CAKE_10.py) | 2021-03-31 21:30:00 | 2021-05-09 07:35:00 | 82.37% | 1.16% | -370,765.94923 | SortinoHyperOptLoss | 19839 | 37/34/0 |
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@ -0,0 +1,386 @@
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# --- Do not remove these libs ---
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import numpy as np # noqa
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import pandas as pd # noqa
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from pandas import DataFrame
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from freqtrade.strategy import IStrategy
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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class CAKE_10(IStrategy):
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"""
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This is a strategy template to get you started.
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More information in https://github.com/freqtrade/freqtrade/blob/develop/docs/bot-optimization.md
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You can:
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:return: a Dataframe with all mandatory indicators for the strategies
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- Rename the class name (Do not forget to update class_name)
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- Add any methods you want to build your strategy
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- Add any lib you need to build your strategy
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You must keep:
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- the lib in the section "Do not remove these libs"
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- the prototype for the methods: minimal_roi, stoploss, populate_indicators, populate_buy_trend,
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populate_sell_trend, hyperopt_space, buy_strategy_generator
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"""
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 2
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# Minimal ROI designed for the strategy.
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# This attribute will be overridden if the config file contains "minimal_roi".
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# ROI table:
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minimal_roi = {
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"0": 0.195,
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"38": 0.037,
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"96": 0.018,
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"115": 0
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}
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# Stoploss:
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stoploss = -0.24852
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# Trailing stop:
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trailing_stop = True
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trailing_stop_positive = 0.334
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trailing_stop_positive_offset = 0.394
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trailing_only_offset_is_reached = True
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unfilledtimeout = {
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'buy': 10,
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'sell': 30
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}
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# Optimal timeframe for the strategy.
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timeframe = '5m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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# These values can be overridden in the "ask_strategy" section in the config.
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 30
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# Optional order type mapping.
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc'
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}
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plot_config = {
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# Main plot indicators (Moving averages, ...)
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'main_plot': {
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'tema': {},
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'sar': {'color': 'white'},
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},
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'subplots': {
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# Subplots - each dict defines one additional plot
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"MACD": {
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'macd': {'color': 'blue'},
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'macdsignal': {'color': 'orange'},
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},
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"RSI": {
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'rsi': {'color': 'red'},
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}
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}
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}
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def informative_pairs(self):
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"""
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Define additional, informative pair/interval combinations to be cached from the exchange.
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These pair/interval combinations are non-tradeable, unless they are part
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of the whitelist as well.
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For more information, please consult the documentation
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:return: List of tuples in the format (pair, interval)
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Sample: return [("ETH/USDT", "5m"),
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("BTC/USDT", "15m"),
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]
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"""
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return []
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Adds several different TA indicators to the given DataFrame
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Performance Note: For the best performance be frugal on the number of indicators
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you are using. Let uncomment only the indicator you are using in your strategies
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or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
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:param dataframe: Dataframe with data from the exchange
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:param metadata: Additional information, like the currently traded pair
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:return: a Dataframe with all mandatory indicators for the strategies
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"""
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# Momentum Indicators
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# # Plus Directional Indicator / Movement
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# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# # Minus Directional Indicator / Movement
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# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe['aroonup'] = aroon['aroonup']
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# dataframe['aroondown'] = aroon['aroondown']
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# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Keltner Channel
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# keltner = qtpylib.keltner_channel(dataframe)
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# dataframe["kc_upperband"] = keltner["upper"]
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# dataframe["kc_lowerband"] = keltner["lower"]
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# dataframe["kc_middleband"] = keltner["mid"]
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# dataframe["kc_percent"] = (
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# (dataframe["close"] - dataframe["kc_lowerband"]) /
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
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# )
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# dataframe["kc_width"] = (
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
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# )
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# # Ultimate Oscillator
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# dataframe['uo'] = ta.ULTOSC(dataframe)
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# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
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dataframe['cci'] = ta.CCI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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# rsi = 0.1 * (dataframe['rsi'] - 50)
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# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
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# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
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# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# # Stochastic Slow
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# stoch = ta.STOCH(dataframe)
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# dataframe['slowd'] = stoch['slowd']
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# dataframe['slowk'] = stoch['slowk']
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# # Stochastic RSI
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# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
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# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
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# stoch_rsi = ta.STOCHRSI(dataframe)
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# dataframe['fastd_rsi'] = stoch_rsi['fastd']
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# dataframe['fastk_rsi'] = stoch_rsi['fastk']
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(dataframe)
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# Overlap Studies
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# ------------------------------------
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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# Bollinger Bands - Weighted (EMA based instead of SMA)
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# weighted_bollinger = qtpylib.weighted_bollinger_bands(
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# qtpylib.typical_price(dataframe), window=20, stds=2
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# )
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# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
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# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
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# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
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# dataframe["wbb_percent"] = (
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# (dataframe["close"] - dataframe["wbb_lowerband"]) /
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
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# )
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# dataframe["wbb_width"] = (
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
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# )
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# # EMA - Exponential Moving Average
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#dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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#dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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#dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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#dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
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#dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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#dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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#dataframe['ema120'] = ta.EMA(dataframe, timeperiod=120)
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#dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
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#dataframe['ema180'] = ta.EMA(dataframe, timeperiod=180)
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#dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
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# # SMA - Simple Moving Average
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# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
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# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
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# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
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# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
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# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
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# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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# Parabolic SAR
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dataframe['sar'] = ta.SAR(dataframe)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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# # Hammer: values [0, 100]
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# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# # Inverted Hammer: values [0, 100]
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# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# # Dragonfly Doji: values [0, 100]
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# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# # Piercing Line: values [0, 100]
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# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# # Morningstar: values [0, 100]
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# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# # Three White Soldiers: values [0, 100]
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# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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# # Hanging Man: values [0, 100]
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# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# # Shooting Star: values [0, 100]
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# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# # Gravestone Doji: values [0, 100]
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# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
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# # Dark Cloud Cover: values [0, 100]
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# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
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# # Evening Doji Star: values [0, 100]
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# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
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# # Evening Star: values [0, 100]
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# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
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# Pattern Recognition - Bullish/Bearish candlestick patterns
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# ------------------------------------
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# # Three Line Strike: values [0, -100, 100]
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# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
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# # Spinning Top: values [0, -100, 100]
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# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
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# # Engulfing: values [0, -100, 100]
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# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
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# # Harami: values [0, -100, 100]
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# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
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# # Three Outside Up/Down: values [0, -100, 100]
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# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
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# # Three Inside Up/Down: values [0, -100, 100]
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# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
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# # Chart type
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# # ------------------------------------
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# # Heikin Ashi Strategy
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# 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[
|
||||
(
|
||||
#(dataframe['adx'] > 22) &
|
||||
(dataframe['mfi'] < 21) &
|
||||
(dataframe['fastd'] < 44) &
|
||||
(dataframe['close'] < dataframe['bb_lowerband'])
|
||||
#qtpylib.crossed_above(dataframe['close'], dataframe['sar'])
|
||||
),
|
||||
'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['adx'] < 100) &
|
||||
#(dataframe['cci'] > 446) &
|
||||
(dataframe['fastd'] > 93) &
|
||||
#(dataframe['mfi'] > 86) &
|
||||
(dataframe['rsi'] > 97) &
|
||||
#qtpylib.crossed_above(dataframe['sar'], dataframe['close'])
|
||||
(dataframe['close'] > dataframe['bb_upperband'])
|
||||
#(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd']))
|
||||
|
||||
#(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
|
||||
#(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
|
||||
|
||||
|
|
@ -0,0 +1,385 @@
|
|||
# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
||||
|
||||
# --- Do not remove these libs ---
|
||||
import numpy as np # noqa
|
||||
import pandas as pd # noqa
|
||||
from pandas import DataFrame
|
||||
|
||||
from freqtrade.strategy import IStrategy
|
||||
|
||||
# --------------------------------
|
||||
# Add your lib to import here
|
||||
import talib.abstract as ta
|
||||
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
||||
|
||||
class CAKE_9(IStrategy):
|
||||
"""
|
||||
This is a strategy template to get you started.
|
||||
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 = {
|
||||
"0": 0.111,
|
||||
"18": 0.069,
|
||||
"35": 0.011,
|
||||
"51": 0
|
||||
}
|
||||
|
||||
|
||||
# Stoploss:
|
||||
stoploss = -0.24852
|
||||
|
||||
# Trailing stop:
|
||||
trailing_stop = True
|
||||
trailing_stop_positive = 0.238
|
||||
trailing_stop_positive_offset = 0.287
|
||||
trailing_only_offset_is_reached = True
|
||||
|
||||
unfilledtimeout = {
|
||||
'buy': 10,
|
||||
'sell': 30
|
||||
}
|
||||
|
||||
# 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 = 30
|
||||
|
||||
# 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 indicators (Moving averages, ...)
|
||||
'main_plot': {
|
||||
'tema': {},
|
||||
'sar': {'color': 'white'},
|
||||
},
|
||||
'subplots': {
|
||||
# Subplots - each dict defines one additional plot
|
||||
"MACD": {
|
||||
'macd': {'color': 'blue'},
|
||||
'macdsignal': {'color': 'orange'},
|
||||
},
|
||||
"RSI": {
|
||||
'rsi': {'color': 'red'},
|
||||
}
|
||||
}
|
||||
}
|
||||
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
|
||||
"""
|
||||
|
||||
# Momentum Indicators
|
||||
# ------------------------------------
|
||||
|
||||
# ADX
|
||||
dataframe['adx'] = ta.ADX(dataframe)
|
||||
|
||||
# # Plus Directional Indicator / Movement
|
||||
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
|
||||
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
|
||||
|
||||
# # Minus Directional Indicator / Movement
|
||||
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
|
||||
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
|
||||
|
||||
# # Aroon, Aroon Oscillator
|
||||
# aroon = ta.AROON(dataframe)
|
||||
# dataframe['aroonup'] = aroon['aroonup']
|
||||
# dataframe['aroondown'] = aroon['aroondown']
|
||||
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
|
||||
|
||||
# # Awesome Oscillator
|
||||
# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
|
||||
|
||||
# # Keltner Channel
|
||||
# keltner = qtpylib.keltner_channel(dataframe)
|
||||
# dataframe["kc_upperband"] = keltner["upper"]
|
||||
# dataframe["kc_lowerband"] = keltner["lower"]
|
||||
# dataframe["kc_middleband"] = keltner["mid"]
|
||||
# dataframe["kc_percent"] = (
|
||||
# (dataframe["close"] - dataframe["kc_lowerband"]) /
|
||||
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
|
||||
# )
|
||||
# dataframe["kc_width"] = (
|
||||
# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
|
||||
# )
|
||||
|
||||
# # Ultimate Oscillator
|
||||
# dataframe['uo'] = ta.ULTOSC(dataframe)
|
||||
|
||||
# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
|
||||
dataframe['cci'] = ta.CCI(dataframe)
|
||||
|
||||
# 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'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
|
||||
|
||||
# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
|
||||
# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
|
||||
|
||||
# # Stochastic Slow
|
||||
# stoch = ta.STOCH(dataframe)
|
||||
# dataframe['slowd'] = stoch['slowd']
|
||||
# dataframe['slowk'] = stoch['slowk']
|
||||
|
||||
# Stochastic Fast
|
||||
stoch_fast = ta.STOCHF(dataframe)
|
||||
dataframe['fastd'] = stoch_fast['fastd']
|
||||
dataframe['fastk'] = stoch_fast['fastk']
|
||||
|
||||
# # Stochastic RSI
|
||||
# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
|
||||
# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
|
||||
# stoch_rsi = ta.STOCHRSI(dataframe)
|
||||
# dataframe['fastd_rsi'] = stoch_rsi['fastd']
|
||||
# dataframe['fastk_rsi'] = stoch_rsi['fastk']
|
||||
|
||||
# MACD
|
||||
macd = ta.MACD(dataframe)
|
||||
dataframe['macd'] = macd['macd']
|
||||
dataframe['macdsignal'] = macd['macdsignal']
|
||||
dataframe['macdhist'] = macd['macdhist']
|
||||
|
||||
# MFI
|
||||
dataframe['mfi'] = ta.MFI(dataframe)
|
||||
|
||||
# # ROC
|
||||
# dataframe['roc'] = ta.ROC(dataframe)
|
||||
|
||||
# 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']
|
||||
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"]
|
||||
)
|
||||
|
||||
# Bollinger Bands - Weighted (EMA based instead of SMA)
|
||||
# weighted_bollinger = qtpylib.weighted_bollinger_bands(
|
||||
# qtpylib.typical_price(dataframe), window=20, stds=2
|
||||
# )
|
||||
# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
|
||||
# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
|
||||
# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
|
||||
# dataframe["wbb_percent"] = (
|
||||
# (dataframe["close"] - dataframe["wbb_lowerband"]) /
|
||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
|
||||
# )
|
||||
# dataframe["wbb_width"] = (
|
||||
# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
|
||||
# )
|
||||
|
||||
# # 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['ema21'] = ta.EMA(dataframe, timeperiod=21)
|
||||
#dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
|
||||
#dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
|
||||
#dataframe['ema120'] = ta.EMA(dataframe, timeperiod=120)
|
||||
#dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
|
||||
#dataframe['ema180'] = ta.EMA(dataframe, timeperiod=180)
|
||||
#dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
|
||||
|
||||
# # SMA - Simple Moving Average
|
||||
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
|
||||
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
|
||||
# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
||||
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
|
||||
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
|
||||
# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
|
||||
|
||||
# Parabolic SAR
|
||||
dataframe['sar'] = ta.SAR(dataframe)
|
||||
|
||||
# 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
|
||||
# # ------------------------------------
|
||||
# # 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']
|
||||
|
||||
# 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[
|
||||
(
|
||||
#(dataframe['adx'] > 22) &
|
||||
(dataframe['mfi'] < 21) &
|
||||
(dataframe['fastd'] < 44) &
|
||||
(dataframe['close'] < dataframe['bb_lowerband'])
|
||||
#qtpylib.crossed_above(dataframe['close'], dataframe['sar'])
|
||||
),
|
||||
'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['adx'] < 57) &
|
||||
(dataframe['cci'] > 446) &
|
||||
(dataframe['fastd'] > 74) &
|
||||
(dataframe['mfi'] > 86) &
|
||||
qtpylib.crossed_above(dataframe['sar'], dataframe['close'])
|
||||
#(dataframe['rsi'] > 82) &
|
||||
#(dataframe['close'] > dataframe['bb_upperband'])
|
||||
#(qtpylib.crossed_above(dataframe['macdsignal'], dataframe['macd']))
|
||||
|
||||
#(qtpylib.crossed_above(dataframe['sar'], dataframe['close']))
|
||||
#(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
|
||||
Loading…
Reference in New Issue