Merge branch 'develop' into rate_caching
This commit is contained in:
hroff-1902
GitHub
@@ -71,6 +71,8 @@ class JsonDataHandler(IDataHandler):
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return DataFrame(columns=self._columns)
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pairdata = read_json(filename, orient='values')
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pairdata.columns = self._columns
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pairdata = pairdata.astype(dtype={'open': 'float', 'high': 'float',
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'low': 'float', 'close': 'float', 'volume': 'float'})
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pairdata['date'] = to_datetime(pairdata['date'],
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unit='ms',
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utc=True,
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@@ -6,7 +6,6 @@ import logging
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import traceback
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from datetime import datetime
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from math import isclose
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from os import getpid
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from threading import Lock
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from typing import Any, Dict, List, Optional, Tuple
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@@ -53,13 +52,9 @@ class FreqtradeBot:
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# Init objects
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self.config = config
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self._heartbeat_msg = 0
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self._sell_rate_cache = TTLCache(maxsize=100, ttl=5)
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self._buy_rate_cache = TTLCache(maxsize=100, ttl=5)
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self.heartbeat_interval = self.config.get('internals', {}).get('heartbeat_interval', 60)
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self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
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# Check config consistency here since strategies can set certain options
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@@ -163,11 +158,6 @@ class FreqtradeBot:
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self.check_handle_timedout()
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Trade.session.flush()
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if (self.heartbeat_interval
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and (arrow.utcnow().timestamp - self._heartbeat_msg > self.heartbeat_interval)):
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logger.info(f"Bot heartbeat. PID={getpid()}")
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self._heartbeat_msg = arrow.utcnow().timestamp
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def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
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"""
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Refresh whitelist from pairlist or edge and extend it with trades.
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@@ -124,24 +124,70 @@ class SampleStrategy(IStrategy):
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# Momentum Indicators
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# ------------------------------------
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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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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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
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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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# 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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@@ -151,60 +197,58 @@ class SampleStrategy(IStrategy):
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# MFI
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dataframe['mfi'] = ta.MFI(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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# # 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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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(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, value [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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# # Stoch
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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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# 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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# # Stoch RSI
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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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# Overlap Studies
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# ------------------------------------
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# Bollinger bands
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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"]) /
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# 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['sma'] = ta.SMA(dataframe, timeperiod=40)
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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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# SAR Parabol
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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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@@ -264,7 +308,7 @@ class SampleStrategy(IStrategy):
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# # Chart type
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# # ------------------------------------
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# # Heikinashi stategy
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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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@@ -2,24 +2,70 @@
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# Momentum Indicators
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# ------------------------------------
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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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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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Commodity Channel Index: values Oversold:<-100, Overbought:>100
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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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# 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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@@ -29,60 +75,57 @@ dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(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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# # 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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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(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, value [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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# # Stoch
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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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# 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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# # Stoch RSI
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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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# Overlap Studies
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# ------------------------------------
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# Bollinger bands
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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['sma'] = ta.SMA(dataframe, timeperiod=40)
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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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# SAR Parabol
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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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@@ -142,7 +185,7 @@ dataframe['htleadsine'] = hilbert['leadsine']
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# # Chart type
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# # ------------------------------------
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# # Heikinashi stategy
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# # Heikin Ashi Strategy
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# heikinashi = qtpylib.heikinashi(dataframe)
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# dataframe['ha_open'] = heikinashi['open']
|
||||
# dataframe['ha_close'] = heikinashi['close']
|
||||
|
||||
@@ -4,6 +4,7 @@ Main Freqtrade worker class.
|
||||
import logging
|
||||
import time
|
||||
import traceback
|
||||
from os import getpid
|
||||
from typing import Any, Callable, Dict, Optional
|
||||
|
||||
import sdnotify
|
||||
@@ -26,12 +27,15 @@ class Worker:
|
||||
"""
|
||||
Init all variables and objects the bot needs to work
|
||||
"""
|
||||
logger.info('Starting worker %s', __version__)
|
||||
logger.info(f"Starting worker {__version__}")
|
||||
|
||||
self._args = args
|
||||
self._config = config
|
||||
self._init(False)
|
||||
|
||||
self.last_throttle_start_time: float = 0
|
||||
self._heartbeat_msg: float = 0
|
||||
|
||||
# Tell systemd that we completed initialization phase
|
||||
if self._sd_notify:
|
||||
logger.debug("sd_notify: READY=1")
|
||||
@@ -48,10 +52,10 @@ class Worker:
|
||||
# Init the instance of the bot
|
||||
self.freqtrade = FreqtradeBot(self._config)
|
||||
|
||||
self._throttle_secs = self._config.get('internals', {}).get(
|
||||
'process_throttle_secs',
|
||||
constants.PROCESS_THROTTLE_SECS
|
||||
)
|
||||
internals_config = self._config.get('internals', {})
|
||||
self._throttle_secs = internals_config.get('process_throttle_secs',
|
||||
constants.PROCESS_THROTTLE_SECS)
|
||||
self._heartbeat_interval = internals_config.get('heartbeat_interval', 60)
|
||||
|
||||
self._sd_notify = sdnotify.SystemdNotifier() if \
|
||||
self._config.get('internals', {}).get('sd_notify', False) else None
|
||||
@@ -63,31 +67,33 @@ class Worker:
|
||||
if state == State.RELOAD_CONF:
|
||||
self._reconfigure()
|
||||
|
||||
def _worker(self, old_state: Optional[State], throttle_secs: Optional[float] = None) -> State:
|
||||
def _worker(self, old_state: Optional[State]) -> State:
|
||||
"""
|
||||
Trading routine that must be run at each loop
|
||||
The main routine that runs each throttling iteration and handles the states.
|
||||
:param old_state: the previous service state from the previous call
|
||||
:return: current service state
|
||||
"""
|
||||
state = self.freqtrade.state
|
||||
if throttle_secs is None:
|
||||
throttle_secs = self._throttle_secs
|
||||
|
||||
# Log state transition
|
||||
if state != old_state:
|
||||
self.freqtrade.notify_status(f'{state.name.lower()}')
|
||||
|
||||
logger.info('Changing state to: %s', state.name)
|
||||
logger.info(f"Changing state to: {state.name}")
|
||||
if state == State.RUNNING:
|
||||
self.freqtrade.startup()
|
||||
|
||||
# Reset heartbeat timestamp to log the heartbeat message at
|
||||
# first throttling iteration when the state changes
|
||||
self._heartbeat_msg = 0
|
||||
|
||||
if state == State.STOPPED:
|
||||
# Ping systemd watchdog before sleeping in the stopped state
|
||||
if self._sd_notify:
|
||||
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: STOPPED.")
|
||||
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: STOPPED.")
|
||||
|
||||
time.sleep(throttle_secs)
|
||||
self._throttle(func=self._process_stopped, throttle_secs=self._throttle_secs)
|
||||
|
||||
elif state == State.RUNNING:
|
||||
# Ping systemd watchdog before throttling
|
||||
@@ -95,28 +101,40 @@ class Worker:
|
||||
logger.debug("sd_notify: WATCHDOG=1\\nSTATUS=State: RUNNING.")
|
||||
self._sd_notify.notify("WATCHDOG=1\nSTATUS=State: RUNNING.")
|
||||
|
||||
self._throttle(func=self._process, min_secs=throttle_secs)
|
||||
self._throttle(func=self._process_running, throttle_secs=self._throttle_secs)
|
||||
|
||||
if self._heartbeat_interval:
|
||||
now = time.time()
|
||||
if (now - self._heartbeat_msg) > self._heartbeat_interval:
|
||||
logger.info(f"Bot heartbeat. PID={getpid()}, "
|
||||
f"version='{__version__}', state='{state.name}'")
|
||||
self._heartbeat_msg = now
|
||||
|
||||
return state
|
||||
|
||||
def _throttle(self, func: Callable[..., Any], min_secs: float, *args, **kwargs) -> Any:
|
||||
def _throttle(self, func: Callable[..., Any], throttle_secs: float, *args, **kwargs) -> Any:
|
||||
"""
|
||||
Throttles the given callable that it
|
||||
takes at least `min_secs` to finish execution.
|
||||
:param func: Any callable
|
||||
:param min_secs: minimum execution time in seconds
|
||||
:return: Any
|
||||
:param throttle_secs: throttling interation execution time limit in seconds
|
||||
:return: Any (result of execution of func)
|
||||
"""
|
||||
start = time.time()
|
||||
self.last_throttle_start_time = time.time()
|
||||
logger.debug("========================================")
|
||||
result = func(*args, **kwargs)
|
||||
end = time.time()
|
||||
duration = max(min_secs - (end - start), 0.0)
|
||||
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
|
||||
time.sleep(duration)
|
||||
time_passed = time.time() - self.last_throttle_start_time
|
||||
sleep_duration = max(throttle_secs - time_passed, 0.0)
|
||||
logger.debug(f"Throttling with '{func.__name__}()': sleep for {sleep_duration:.2f} s, "
|
||||
f"last iteration took {time_passed:.2f} s.")
|
||||
time.sleep(sleep_duration)
|
||||
return result
|
||||
|
||||
def _process(self) -> None:
|
||||
logger.debug("========================================")
|
||||
def _process_stopped(self) -> None:
|
||||
# Maybe do here something in the future...
|
||||
pass
|
||||
|
||||
def _process_running(self) -> None:
|
||||
try:
|
||||
self.freqtrade.process()
|
||||
except TemporaryError as error:
|
||||
|
||||
Reference in New Issue
Block a user