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config_examples/Strategy002.py
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165
config_examples/Strategy002.py
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# --- Do not remove these libs ---
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from freqtrade.strategy 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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# --------------------------------
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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 # noqa
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PROJECT_DIR = "/home/ubuntu/workspace/execution/"
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import sys
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sys.path.append(PROJECT_DIR)
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from executor import *
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class Strategy002(IStrategy):
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"""
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Strategy 002
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author@: Gerald Lonlas
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github@: https://github.com/freqtrade/freqtrade-strategies
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How to use it?
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> python3 ./freqtrade/main.py -s Strategy002
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"""
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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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"60": 0.01,
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"30": 0.03,
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"20": 0.04,
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"0": 0.05
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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 = -0.10
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# Optimal timeframe for the strategy
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timeframe = '5m'
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# trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.02
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# run "populate_indicators" only for new candle
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process_only_new_candles = False
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# Experimental settings (configuration will overide these if set)
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use_sell_signal = True
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sell_profit_only = True
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ignore_roi_if_buy_signal = False
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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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executor = Executor()
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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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"""
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# Stoch
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stoch = ta.STOCH(dataframe)
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dataframe['slowk'] = stoch['slowk']
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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'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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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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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# Hammer: values [0, 100]
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dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] < 30) &
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(dataframe['slowk'] < 20) &
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(dataframe['bb_lowerband'] > dataframe['close']) &
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(dataframe['CDLHAMMER'] == 100)
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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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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['sar'] > dataframe['close']) &
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(dataframe['fisher_rsi'] > 0.3)
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),
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'sell'] = 1
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return dataframe
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
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time_in_force: str, current_time: datetime, **kwargs) -> bool:
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"""
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Called right before placing a buy order.
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Timing for this function is critical, so avoid doing heavy computations or
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network requests in this method.
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For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
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When not implemented by a strategy, returns True (always confirming).
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:param pair: Pair that's about to be bought.
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:param order_type: Order type (as configured in order_types). usually limit or market.
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:param amount: Amount in target (quote) currency that's going to be traded.
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:param rate: Rate that's going to be used when using limit orders
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:param time_in_force: Time in force. Defaults to GTC (Good-til-cancelled).
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:param current_time: datetime object, containing the current datetime
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:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
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:return bool: When True is returned, then the buy-order is placed on the exchange.
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False aborts the process
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"""
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self.executor.start_signal()
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return True
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152
config_examples/Strategy003.py
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152
config_examples/Strategy003.py
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# --- Do not remove these libs ---
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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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# --------------------------------
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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 # noqa
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class Strategy003(IStrategy):
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"""
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Strategy 003
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author@: Gerald Lonlas
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github@: https://github.com/freqtrade/freqtrade-strategies
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How to use it?
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> python3 ./freqtrade/main.py -s Strategy003
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"""
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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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"60": 0.01,
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"30": 0.03,
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"20": 0.04,
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"0": 0.05
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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 = -0.10
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# Optimal timeframe for the strategy
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timeframe = '5m'
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# trailing stoploss
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trailing_stop = False
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.02
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# run "populate_indicators" only for new candle
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process_only_new_candles = False
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# Experimental settings (configuration will overide these if set)
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use_sell_signal = True
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sell_profit_only = True
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ignore_roi_if_buy_signal = False
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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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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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"""
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# Stoch 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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# 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'] = (numpy.exp(2 * rsi) - 1) / (numpy.exp(2 * rsi) + 1)
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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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# EMA - Exponential Moving Average
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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['ema50'] = ta.EMA(dataframe, timeperiod=50)
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dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# SAR Parabol
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dataframe['sar'] = ta.SAR(dataframe)
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# SMA - Simple Moving Average
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dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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"""
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Based on TA indicators, populates the buy signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['rsi'] < 28) &
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(dataframe['rsi'] > 0) &
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(dataframe['close'] < dataframe['sma']) &
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(dataframe['fisher_rsi'] < -0.94) &
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(dataframe['mfi'] < 16.0) &
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(
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(dataframe['ema50'] > dataframe['ema100']) |
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(qtpylib.crossed_above(dataframe['ema5'], dataframe['ema10']))
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) &
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(dataframe['fastd'] > dataframe['fastk']) &
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(dataframe['fastd'] > 0)
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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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"""
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Based on TA indicators, populates the sell signal for the given dataframe
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:param dataframe: DataFrame
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:return: DataFrame with buy column
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"""
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dataframe.loc[
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(
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(dataframe['sar'] > dataframe['close']) &
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(dataframe['fisher_rsi'] > 0.3)
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),
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'sell'] = 1
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return dataframe
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83
config_examples/config_json
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83
config_examples/config_json
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{
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"max_open_trades": 1,
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"stake_currency": "USDT",
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"stake_amount": 50,
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"tradable_balance_ratio": 0.99,
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"fiat_display_currency": "USD",
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"dry_run": true,
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"cancel_open_orders_on_exit": false,
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"unfilledtimeout": {
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"buy": 10,
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"sell": 30,
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"unit": "minutes"
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},
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"bid_strategy": {
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"price_side": "bid",
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"ask_last_balance": 0.0,
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"use_order_book": true,
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"order_book_top": 1,
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"check_depth_of_market": {
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"enabled": false,
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"bids_to_ask_delta": 1
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}
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},
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"ask_strategy": {
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"price_side": "ask",
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"use_order_book": true,
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"order_book_top": 1
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},
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"exchange": {
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"name": "binance",
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"key": "r7jNtIb7EWp8VR0Y9wyONEboIDYJ1P6LtY7ywpIH3knxDXQDukeAPTLMztlP2OGB",
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"secret": "qdtxxwCA5USIb1F3086C3a0RlTqoRSX8pzxWqjmTDF8ap5v6CKLNn2GlbukiADHJ",
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"ccxt_config": {},
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"ccxt_async_config": {},
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"pair_whitelist": [
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"SOL/USDT"
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],
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"pair_blacklist": [
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"BNB/.*"
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]
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},
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"pairlists": [
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{
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"method": "StaticPairList"
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}
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],
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"edge": {
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"enabled": false,
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"process_throttle_secs": 3600,
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"calculate_since_number_of_days": 7,
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"allowed_risk": 0.01,
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"stoploss_range_min": -0.01,
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"stoploss_range_max": -0.1,
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"stoploss_range_step": -0.01,
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"minimum_winrate": 0.60,
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"minimum_expectancy": 0.20,
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"min_trade_number": 10,
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"max_trade_duration_minute": 1440,
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"remove_pumps": false
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},
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"telegram": {
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"enabled": true,
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"token": "2123575847:AAHGr3YpgIm3K-atU1qxhEj3ZNjTsiIj2GQ",
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"chat_id": "2091515934"
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},
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"api_server": {
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||||||
|
"enabled": false,
|
||||||
|
"listen_ip_address": "127.0.0.1",
|
||||||
|
"listen_port": 8080,
|
||||||
|
"verbosity": "error",
|
||||||
|
"enable_openapi": false,
|
||||||
|
"jwt_secret_key": "85c2f16af43e3f710301a69b497ea7bfdbc2639cc3af3b3e22257f7e7e56c161",
|
||||||
|
"CORS_origins": [],
|
||||||
|
"username": "",
|
||||||
|
"password": ""
|
||||||
|
},
|
||||||
|
"bot_name": "freqtrade",
|
||||||
|
"initial_state": "running",
|
||||||
|
"forcebuy_enable": false,
|
||||||
|
"internals": {
|
||||||
|
"process_throttle_secs": 5
|
||||||
|
}
|
||||||
|
}
|
||||||
Loading…
Reference in New Issue
Block a user