first commit
This commit is contained in:
Jérôme Delacotte
506
StJD01.py
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506
StJD01.py
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# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# flake8: noqa: F401
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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 (BooleanParameter, CategoricalParameter, DecimalParameter,
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IStrategy, IntParameter)
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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 StJD01(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://www.freqtrade.io/en/latest/strategy-customization/
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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 methods: populate_indicators, populate_buy_trend, populate_sell_trend
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You should keep:
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- timeframe, minimal_roi, stoploss, trailing_*
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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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minimal_roi = {
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"0": 100
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}
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# Optimal stoploss designed for the strategy.
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# This attribute will be overridden if the config file contains "stoploss".
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stoploss = -1
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# Trailing stoploss
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trailing_stop = True
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trailing_stop_positive = 0.001
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trailing_stop_positive_offset = 0.015
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trailing_only_offset_is_reached = True
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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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#
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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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#
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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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#
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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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fast_period = 5
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slow_period = 50
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dataframe['fast_MA'] = ta.SMA(dataframe, timeperiod=fast_period)
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dataframe['slow_MA'] = ta.SMA(dataframe, timeperiod=slow_period)
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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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# # 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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#
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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)
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# dataframe['ha_open'] = heikinashi['open']
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# dataframe['ha_close'] = heikinashi['close']
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# dataframe['ha_high'] = heikinashi['high']
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# dataframe['ha_low'] = heikinashi['low']
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# Retrieve best bid and best ask from the orderbook
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# ------------------------------------
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"""
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# first check if dataprovider is available
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if self.dp:
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if self.dp.runmode.value in ('live', 'dry_run'):
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ob = self.dp.orderbook(metadata['pair'], 1)
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dataframe['best_bid'] = ob['bids'][0][0]
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dataframe['best_ask'] = ob['asks'][0][0]
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"""
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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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qtpylib.crossed_above(dataframe['fast_MA'], dataframe['slow_MA'])
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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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qtpylib.crossed_below(dataframe['fast_MA'], dataframe['slow_MA'])
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),
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'sell'
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] = 1
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return dataframe
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class StrategyHelperLocal:
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"""
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simple helper class to predefine a couple of patterns for our
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strategy
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"""
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@staticmethod
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def two_green_candles(dataframe):
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"""
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evaluates if we are having 7 green candles in a row
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:param self:
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:param dataframe:
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:return:
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"""
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return (
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(dataframe['open'] < dataframe['close']) &
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(dataframe['open'].shift(1) < dataframe['close'].shift(1)) # &
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# (dataframe['open'].shift(2) < dataframe['close'].shift(2))
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)
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@staticmethod
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def three_green_candles(dataframe):
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"""
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evaluates if we are having 7 green candles in a row
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:param self:
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:param dataframe:
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:return:
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"""
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return (
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(dataframe['open'] < dataframe['close']) &
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(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
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(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
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(dataframe['open'].shift(3) < dataframe['close'].shift(3))
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)
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@staticmethod
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def seven_green_candles(dataframe):
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"""
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evaluates if we are having 7 green candles in a row
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:param self:
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:param dataframe:
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:return:
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"""
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return (
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(dataframe['open'] < dataframe['close']) &
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(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
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(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
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(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
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(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
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(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
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(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
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(dataframe['open'].shift(7) < dataframe['close'].shift(7))
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)
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@staticmethod
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def eight_green_candles(dataframe):
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"""
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evaluates if we are having 8 green candles in a row
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:param self:
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:param dataframe:
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:return:
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"""
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return (
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(dataframe['open'] < dataframe['close']) &
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(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
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(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
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(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
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(dataframe['open'].shift(4) < dataframe['close'].shift(4)) &
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(dataframe['open'].shift(5) < dataframe['close'].shift(5)) &
|
||||
(dataframe['open'].shift(6) < dataframe['close'].shift(6)) &
|
||||
(dataframe['open'].shift(7) < dataframe['close'].shift(7)) &
|
||||
(dataframe['open'].shift(8) < dataframe['close'].shift(8))
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def three_red_candles(dataframe, shift=0):
|
||||
"""
|
||||
evaluates if we are having 8 red candles in a row
|
||||
:param self:
|
||||
:param dataframe:
|
||||
:param shift: shift the pattern by n
|
||||
:return:
|
||||
"""
|
||||
return (
|
||||
(dataframe['open'].shift(shift) > dataframe['close'].shift(shift)) &
|
||||
(dataframe['open'].shift(1 + shift) > dataframe['close'].shift(1 + shift)) &
|
||||
(dataframe['open'].shift(2 + shift) > dataframe['close'].shift(2 + shift))
|
||||
# (dataframe['open'].shift(5 + shift) > dataframe['close'].shift(5 + shift)) &
|
||||
# (dataframe['open'].shift(6 + shift) > dataframe['close'].shift(6 + shift)) &
|
||||
# (dataframe['open'].shift(7 + shift) > dataframe['close'].shift(7 + shift)) &
|
||||
# (dataframe['open'].shift(8 + shift) > dataframe['close'].shift(8 + shift))
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def four_red_candles(dataframe, shift=0):
|
||||
"""
|
||||
evaluates if we are having 8 red candles in a row
|
||||
:param self:
|
||||
:param dataframe:
|
||||
:param shift: shift the pattern by n
|
||||
:return:
|
||||
"""
|
||||
return (
|
||||
(dataframe['open'].shift(shift) > dataframe['close'].shift(shift)) &
|
||||
(dataframe['open'].shift(1 + shift) > dataframe['close'].shift(1 + shift)) &
|
||||
(dataframe['open'].shift(2 + shift) > dataframe['close'].shift(2 + shift)) &
|
||||
(dataframe['open'].shift(3 + shift) > dataframe['close'].shift(3 + shift)) &
|
||||
(dataframe['open'].shift(4 + shift) > dataframe['close'].shift(4 + shift))
|
||||
# (dataframe['open'].shift(5 + shift) > dataframe['close'].shift(5 + shift)) &
|
||||
# (dataframe['open'].shift(6 + shift) > dataframe['close'].shift(6 + shift)) &
|
||||
# (dataframe['open'].shift(7 + shift) > dataframe['close'].shift(7 + shift)) &
|
||||
# (dataframe['open'].shift(8 + shift) > dataframe['close'].shift(8 + shift))
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def four_green_one_red_candle(dataframe):
|
||||
"""
|
||||
evaluates if we are having a red candle and 4 previous green
|
||||
:param self:
|
||||
:param dataframe:
|
||||
:return:
|
||||
"""
|
||||
return (
|
||||
(dataframe['open'] > dataframe['close']) &
|
||||
(dataframe['open'].shift(1) < dataframe['close'].shift(1)) &
|
||||
(dataframe['open'].shift(2) < dataframe['close'].shift(2)) &
|
||||
(dataframe['open'].shift(3) < dataframe['close'].shift(3)) &
|
||||
(dataframe['open'].shift(4) < dataframe['close'].shift(4))
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def four_red_one_green_candle(dataframe):
|
||||
"""
|
||||
evaluates if we are having a green candle and 4 previous red
|
||||
:param self:
|
||||
:param dataframe:
|
||||
:return:
|
||||
"""
|
||||
return (
|
||||
(dataframe['open'] < dataframe['close']) &
|
||||
(dataframe['open'].shift(1) > dataframe['close'].shift(1)) &
|
||||
(dataframe['open'].shift(2) > dataframe['close'].shift(2)) &
|
||||
(dataframe['open'].shift(3) > dataframe['close'].shift(3)) &
|
||||
(dataframe['open'].shift(4) > dataframe['close'].shift(4)) &
|
||||
(dataframe['open'].shift(5) > dataframe['close'].shift(5)) &
|
||||
(dataframe['open'].shift(6) > dataframe['close'].shift(6)) &
|
||||
(dataframe['open'].shift(7) > dataframe['close'].shift(7))
|
||||
)
|
||||
Reference in New Issue
Block a user