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StackingDemo.py
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StackingDemo.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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from freqtrade.persistence import Trade
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from datetime import datetime,timezone,timedelta
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"""
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Warning:
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This is still work in progress, so there is no warranty that everything works as intended,
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it is possible that this strategy results in huge losses or doesn't even work at all.
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Make sure to only run this in dry_mode so you don't lose any money.
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"""
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class StackingDemo(IStrategy):
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"""
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This is the default strategy template with added functions for trade stacking / buying the same positions multiple times.
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It should function like this:
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Find good buys using indicators.
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When a new buy occurs the strategy will enable rebuys of the pair like this:
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self.custom_info[metadata["pair"]]["rebuy"] = 1
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Then, if the price should drop after the last buy within the timerange of rebuy_time_limit_hours,
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the same pair will be purchased again. This is intended to help with reducing possible losses.
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If the price only goes up after the first buy, the strategy won't buy this pair again, and after the time limit is over,
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look for other pairs to buy.
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For selling there is this flag:
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self.custom_info[metadata["pair"]]["resell"] = 1
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which should simply sell all trades of this pair until none are left.
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You can set how many pairs you want to trade and how many trades you want to allow for a pair,
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but you must make sure to set max_open_trades to the produce of max_open_pairs and max_open_trades in your configuration file.
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Also allow_position_stacking has to be set to true in the configuration file.
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For backtesting make sure to provide --enable-position-stacking as an argument in the command line.
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Backtesting will be slow.
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Hyperopt was not tested.
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# run the bot:
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freqtrade trade -c StackingConfig.json -s StackingDemo --db-url sqlite:///tradesv3_StackingDemo_dry-run.sqlite --dry-run
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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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# how many pairs to trade / trades per pair if allow_position_stacking is enabled
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max_open_pairs, max_trades_per_pair = 4, 3
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# make sure to have this value in your config file
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max_open_trades = max_open_pairs * max_trades_per_pair
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# debugging
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print_trades = True
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# specify for how long to want to allow rebuys of this pair
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rebuy_time_limit_hours = 2
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# store additional information needed for this strategy:
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custom_info = {}
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custom_num_open_pairs = {}
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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": 0.02,
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"0": 0.001
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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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# Trailing stoploss
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trailing_stop = False
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# trailing_only_offset_is_reached = False
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# trailing_stop_positive = 0.01
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# trailing_stop_positive_offset = 0.0 # Disabled / not configured
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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': 'market',
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'sell': 'market',
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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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# STACKING STUFF
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# confirm config
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self.max_trades_per_pair = self.config['max_open_trades'] / self.max_open_pairs
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if not self.config["allow_position_stacking"]:
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self.max_trades_per_pair = 1
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# store number of open pairs
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self.custom_num_open_pairs = {"num_open_pairs": 0}
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# Store custom information for this pair:
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if not metadata["pair"] in self.custom_info:
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self.custom_info[metadata["pair"]] = {}
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if not "rebuy" in self.custom_info[metadata["pair"]]:
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# number of trades for this pair
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self.custom_info[metadata["pair"]]["num_trades"] = 0
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# use rebuy/resell as buy-/sell- indicators
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self.custom_info[metadata["pair"]]["rebuy"] = 0
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self.custom_info[metadata["pair"]]["resell"] = 0
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# store latest open_date for this pair
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self.custom_info[metadata["pair"]]["last_open_date"] = datetime.now(timezone.utc) - timedelta(days=100)
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# stare the value of the latest open price for this pair
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self.custom_info[metadata["pair"]]["latest_open_rate"] = 0
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# INDICATORS
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# Momentum Indicators
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# ------------------------------------
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# ADX
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dataframe['adx'] = ta.ADX(dataframe)
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# # Plus Directional Indicator / Movement
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# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
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# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
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# # Minus Directional Indicator / Movement
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# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
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# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
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# # Aroon, Aroon Oscillator
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# aroon = ta.AROON(dataframe)
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# dataframe['aroonup'] = aroon['aroonup']
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# dataframe['aroondown'] = aroon['aroondown']
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# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
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# # Awesome Oscillator
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# dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
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# # Keltner Channel
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# keltner = qtpylib.keltner_channel(dataframe)
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# dataframe["kc_upperband"] = keltner["upper"]
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# dataframe["kc_lowerband"] = keltner["lower"]
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# dataframe["kc_middleband"] = keltner["mid"]
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# dataframe["kc_percent"] = (
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# (dataframe["close"] - dataframe["kc_lowerband"]) /
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
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# )
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# dataframe["kc_width"] = (
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# (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
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# )
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# # Ultimate Oscillator
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# dataframe['uo'] = ta.ULTOSC(dataframe)
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# # Commodity Channel Index: values [Oversold:-100, Overbought:100]
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# dataframe['cci'] = ta.CCI(dataframe)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
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# rsi = 0.1 * (dataframe['rsi'] - 50)
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# dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
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# # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
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# dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
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# # Stochastic Slow
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# stoch = ta.STOCH(dataframe)
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# dataframe['slowd'] = stoch['slowd']
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# dataframe['slowk'] = stoch['slowk']
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# Stochastic Fast
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stoch_fast = ta.STOCHF(dataframe)
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dataframe['fastd'] = stoch_fast['fastd']
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dataframe['fastk'] = stoch_fast['fastk']
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# # Stochastic RSI
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# Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
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# STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
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# stoch_rsi = ta.STOCHRSI(dataframe)
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# dataframe['fastd_rsi'] = stoch_rsi['fastd']
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# dataframe['fastk_rsi'] = stoch_rsi['fastk']
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# MACD
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macd = ta.MACD(dataframe)
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dataframe['macd'] = macd['macd']
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dataframe['macdsignal'] = macd['macdsignal']
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dataframe['macdhist'] = macd['macdhist']
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# MFI
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dataframe['mfi'] = ta.MFI(dataframe)
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# # ROC
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# dataframe['roc'] = ta.ROC(dataframe)
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# Overlap Studies
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# ------------------------------------
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# Bollinger Bands
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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# Bollinger Bands - Weighted (EMA based instead of SMA)
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# weighted_bollinger = qtpylib.weighted_bollinger_bands(
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# qtpylib.typical_price(dataframe), window=20, stds=2
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# )
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# dataframe["wbb_upperband"] = weighted_bollinger["upper"]
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# dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
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# dataframe["wbb_middleband"] = weighted_bollinger["mid"]
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# dataframe["wbb_percent"] = (
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# (dataframe["close"] - dataframe["wbb_lowerband"]) /
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
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# )
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# dataframe["wbb_width"] = (
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# (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"]
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# )
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# # EMA - Exponential Moving Average
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# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
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# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
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# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
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# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
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# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
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# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
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# # SMA - Simple Moving Average
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# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
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# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
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# dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
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# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
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# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
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# dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
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# Parabolic SAR
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dataframe['sar'] = ta.SAR(dataframe)
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# TEMA - Triple Exponential Moving Average
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dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
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# Cycle Indicator
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# ------------------------------------
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# Hilbert Transform Indicator - SineWave
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hilbert = ta.HT_SINE(dataframe)
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dataframe['htsine'] = hilbert['sine']
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dataframe['htleadsine'] = hilbert['leadsine']
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# Pattern Recognition - Bullish candlestick patterns
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# ------------------------------------
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# # Hammer: values [0, 100]
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# dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
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# # Inverted Hammer: values [0, 100]
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# dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
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# # Dragonfly Doji: values [0, 100]
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# dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
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# # Piercing Line: values [0, 100]
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# dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
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# # Morningstar: values [0, 100]
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# dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
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# # Three White Soldiers: values [0, 100]
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# dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
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# Pattern Recognition - Bearish candlestick patterns
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# ------------------------------------
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# # Hanging Man: values [0, 100]
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# dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
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# # Shooting Star: values [0, 100]
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# dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
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# # Gravestone Doji: values [0, 100]
|
|
||||||
# dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
|
|
||||||
# # Dark Cloud Cover: values [0, 100]
|
|
||||||
# dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
|
|
||||||
# # Evening Doji Star: values [0, 100]
|
|
||||||
# dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
|
|
||||||
# # Evening Star: values [0, 100]
|
|
||||||
# dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
|
|
||||||
|
|
||||||
# Pattern Recognition - Bullish/Bearish candlestick patterns
|
|
||||||
# ------------------------------------
|
|
||||||
# # Three Line Strike: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
|
|
||||||
# # Spinning Top: values [0, -100, 100]
|
|
||||||
# dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
|
|
||||||
# # Engulfing: values [0, -100, 100]
|
|
||||||
# dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
|
|
||||||
# # Harami: values [0, -100, 100]
|
|
||||||
# dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
|
|
||||||
# # Three Outside Up/Down: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
|
|
||||||
# # Three Inside Up/Down: values [0, -100, 100]
|
|
||||||
# dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
|
|
||||||
|
|
||||||
# # Chart type
|
|
||||||
# # ------------------------------------
|
|
||||||
# # Heikin Ashi Strategy
|
|
||||||
# heikinashi = qtpylib.heikinashi(dataframe)
|
|
||||||
# dataframe['ha_open'] = heikinashi['open']
|
|
||||||
# dataframe['ha_close'] = heikinashi['close']
|
|
||||||
# dataframe['ha_high'] = heikinashi['high']
|
|
||||||
# dataframe['ha_low'] = heikinashi['low']
|
|
||||||
|
|
||||||
# Retrieve best bid and best ask from the orderbook
|
|
||||||
# ------------------------------------
|
|
||||||
"""
|
|
||||||
# first check if dataprovider is available
|
|
||||||
if self.dp:
|
|
||||||
if self.dp.runmode.value in ('live', 'dry_run'):
|
|
||||||
ob = self.dp.orderbook(metadata['pair'], 1)
|
|
||||||
dataframe['best_bid'] = ob['bids'][0][0]
|
|
||||||
dataframe['best_ask'] = ob['asks'][0][0]
|
|
||||||
"""
|
|
||||||
|
|
||||||
return dataframe
|
|
||||||
|
|
||||||
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
||||||
"""
|
|
||||||
Based on TA indicators, populates the buy signal for the given dataframe
|
|
||||||
:param dataframe: DataFrame populated with indicators
|
|
||||||
:param metadata: Additional information, like the currently traded pair
|
|
||||||
:return: DataFrame with buy column
|
|
||||||
"""
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
(
|
|
||||||
(qtpylib.crossed_above(dataframe['rsi'], 30)) & # Signal: RSI crosses above 30
|
|
||||||
(dataframe['tema'] <= dataframe['bb_middleband']) & # Guard: tema below BB middle
|
|
||||||
(dataframe['tema'] > dataframe['tema'].shift(1)) | # Guard: tema is raising
|
|
||||||
# use either buy signal or rebuy flag to trigger a buy
|
|
||||||
(self.custom_info[metadata["pair"]]["rebuy"] == 1)
|
|
||||||
) &
|
|
||||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
'buy'] = 1
|
|
||||||
|
|
||||||
return dataframe
|
|
||||||
|
|
||||||
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
||||||
"""
|
|
||||||
Based on TA indicators, populates the sell signal for the given dataframe
|
|
||||||
:param dataframe: DataFrame populated with indicators
|
|
||||||
:param metadata: Additional information, like the currently traded pair
|
|
||||||
:return: DataFrame with buy column
|
|
||||||
"""
|
|
||||||
dataframe.loc[
|
|
||||||
(
|
|
||||||
(
|
|
||||||
(qtpylib.crossed_above(dataframe['rsi'], 70)) & # Signal: RSI crosses above 70
|
|
||||||
(dataframe['tema'] > dataframe['bb_middleband']) & # Guard: tema above BB middle
|
|
||||||
(dataframe['tema'] < dataframe['tema'].shift(1)) | # Guard: tema is falling
|
|
||||||
# use either sell signal or resell flag to trigger a sell
|
|
||||||
(self.custom_info[metadata["pair"]]["resell"] == 1)
|
|
||||||
) &
|
|
||||||
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
|
||||||
),
|
|
||||||
'sell'] = 1
|
|
||||||
return dataframe
|
|
||||||
|
|
||||||
# use_custom_sell = True
|
|
||||||
|
|
||||||
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
|
|
||||||
current_profit: float, **kwargs) -> 'Optional[Union[str, bool]]':
|
|
||||||
"""
|
|
||||||
Custom sell signal logic indicating that specified position should be sold. Returning a
|
|
||||||
string or True from this method is equal to setting sell signal on a candle at specified
|
|
||||||
time. This method is not called when sell signal is set.
|
|
||||||
|
|
||||||
This method should be overridden to create sell signals that depend on trade parameters. For
|
|
||||||
example you could implement a sell relative to the candle when the trade was opened,
|
|
||||||
or a custom 1:2 risk-reward ROI.
|
|
||||||
|
|
||||||
Custom sell reason max length is 64. Exceeding characters will be removed.
|
|
||||||
|
|
||||||
:param pair: Pair that's currently analyzed
|
|
||||||
:param trade: trade object.
|
|
||||||
:param current_time: datetime object, containing the current datetime
|
|
||||||
:param current_rate: Rate, calculated based on pricing settings in ask_strategy.
|
|
||||||
:param current_profit: Current profit (as ratio), calculated based on current_rate.
|
|
||||||
:param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
|
|
||||||
:return: To execute sell, return a string with custom sell reason or True. Otherwise return
|
|
||||||
None or False.
|
|
||||||
"""
|
|
||||||
# if self.custom_info[pair]["resell"] == 1:
|
|
||||||
# return 'resell'
|
|
||||||
return None
|
|
||||||
|
|
||||||
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
|
|
||||||
time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
|
|
||||||
return_statement = True
|
|
||||||
|
|
||||||
if self.config['allow_position_stacking']:
|
|
||||||
return_statement = self.check_open_trades(pair, rate, current_time)
|
|
||||||
|
|
||||||
# debugging
|
|
||||||
if return_statement and self.print_trades:
|
|
||||||
# use str.join() for speed
|
|
||||||
out = (current_time.strftime("%c"), " Bought: ", pair, ", rate: ", str(rate), ", rebuy: ", str(self.custom_info[pair]["rebuy"]), ", trades: ", str(self.custom_info[pair]["num_trades"]))
|
|
||||||
print("".join(out))
|
|
||||||
|
|
||||||
return return_statement
|
|
||||||
|
|
||||||
def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
|
|
||||||
rate: float, time_in_force: str, sell_reason: str,
|
|
||||||
current_time: 'datetime', **kwargs) -> bool:
|
|
||||||
|
|
||||||
if self.config["allow_position_stacking"]:
|
|
||||||
|
|
||||||
# unlock open pairs limit after every sell
|
|
||||||
self.unlock_reason('Open pairs limit')
|
|
||||||
|
|
||||||
# unlock open pairs limit after last item is sold
|
|
||||||
if self.custom_info[pair]["num_trades"] == 1:
|
|
||||||
# decrement open_pairs_count by 1 if last item is sold
|
|
||||||
self.custom_num_open_pairs["num_open_pairs"]-=1
|
|
||||||
self.custom_info[pair]["resell"] = 0
|
|
||||||
# reset rate
|
|
||||||
self.custom_info[pair]["latest_open_rate"] = 0.0
|
|
||||||
self.unlock_reason('Trades per pair limit')
|
|
||||||
|
|
||||||
# change dataframe to produce sell signal after a sell
|
|
||||||
if self.custom_info[pair]["num_trades"] >= self.max_trades_per_pair:
|
|
||||||
self.custom_info[pair]["resell"] = 1
|
|
||||||
|
|
||||||
# decrement number of trades by 1:
|
|
||||||
self.custom_info[pair]["num_trades"]-=1
|
|
||||||
|
|
||||||
# debugging stuff
|
|
||||||
if self.print_trades:
|
|
||||||
# use str.join() for speed
|
|
||||||
out = (current_time.strftime("%c"), " Sold: ", pair, ", rate: ", str(rate),", profit: ", str(trade.calc_profit_ratio(rate)), ", resell: ", str(self.custom_info[pair]["resell"]), ", trades: ", str(self.custom_info[pair]["num_trades"]))
|
|
||||||
print("".join(out))
|
|
||||||
|
|
||||||
return True
|
|
||||||
|
|
||||||
def check_open_trades(self, pair: str, rate: float, current_time: datetime):
|
|
||||||
|
|
||||||
# retrieve information about current open pairs
|
|
||||||
tr_info = self.get_trade_information(pair)
|
|
||||||
|
|
||||||
# update number of open trades for the pair
|
|
||||||
self.custom_info[pair]["num_trades"] = tr_info[1]
|
|
||||||
self.custom_num_open_pairs["num_open_pairs"] = len(tr_info[0])
|
|
||||||
# update value of the last open price
|
|
||||||
self.custom_info[pair]["latest_open_rate"] = tr_info[2]
|
|
||||||
|
|
||||||
# don't buy if we have enough trades for this pair
|
|
||||||
if self.custom_info[pair]["num_trades"] >= self.max_trades_per_pair:
|
|
||||||
# lock if we already have enough pairs open, will be unlocked after last item of a pair is sold
|
|
||||||
self.lock_pair(pair, until=datetime.now(timezone.utc) + timedelta(days=100), reason='Trades per pair limit')
|
|
||||||
self.custom_info[pair]["rebuy"] = 0
|
|
||||||
return False
|
|
||||||
|
|
||||||
# don't buy if we have enough pairs
|
|
||||||
if self.custom_num_open_pairs["num_open_pairs"] >= self.max_open_pairs:
|
|
||||||
if not pair in tr_info[0]:
|
|
||||||
# lock if this pair is not in our list, will be unlocked after the next sell
|
|
||||||
self.lock_pair(pair, until=datetime.now(timezone.utc) + timedelta(days=100), reason='Open pairs limit')
|
|
||||||
self.custom_info[pair]["rebuy"] = 0
|
|
||||||
return False
|
|
||||||
|
|
||||||
# don't buy at a higher price, try until time limit is exceeded; skips if it's the first trade'
|
|
||||||
if rate > self.custom_info[pair]["latest_open_rate"] and self.custom_info[pair]["latest_open_rate"] != 0.0:
|
|
||||||
# how long do we want to try buying cheaper before we look for other pairs?
|
|
||||||
if (current_time - self.custom_info[pair]['last_open_date']).seconds/3600 > self.rebuy_time_limit_hours:
|
|
||||||
self.custom_info[pair]["rebuy"] = 0
|
|
||||||
self.unlock_reason('Open pairs limit')
|
|
||||||
return False
|
|
||||||
|
|
||||||
# set rebuy flag if num_trades < limit-1
|
|
||||||
if self.custom_info[pair]["num_trades"] < self.max_trades_per_pair-1:
|
|
||||||
self.custom_info[pair]["rebuy"] = 1
|
|
||||||
else:
|
|
||||||
self.custom_info[pair]["rebuy"] = 0
|
|
||||||
|
|
||||||
# update rate
|
|
||||||
self.custom_info[pair]["latest_open_rate"] = rate
|
|
||||||
|
|
||||||
#update date open
|
|
||||||
self.custom_info[pair]["last_open_date"] = current_time
|
|
||||||
|
|
||||||
# increment trade count by 1
|
|
||||||
self.custom_info[pair]["num_trades"]+=1
|
|
||||||
|
|
||||||
return True
|
|
||||||
|
|
||||||
# custom function to help with the strategy
|
|
||||||
def get_trade_information(self, pair:str):
|
|
||||||
|
|
||||||
latest_open_rate, trade_count = 0, 0.0
|
|
||||||
# store all open pairs
|
|
||||||
open_pairs = []
|
|
||||||
|
|
||||||
### start nested function
|
|
||||||
def compare_trade(trade: Trade):
|
|
||||||
nonlocal trade_count, latest_open_rate, pair
|
|
||||||
if trade.pair == pair:
|
|
||||||
# update latest_rate
|
|
||||||
latest_open_rate = trade.open_rate
|
|
||||||
trade_count+=1
|
|
||||||
return trade.pair
|
|
||||||
### end nested function
|
|
||||||
|
|
||||||
# replaced for loop with map for speed
|
|
||||||
open_pairs = map(compare_trade, Trade.get_open_trades())
|
|
||||||
# remove duplicates
|
|
||||||
open_pairs = (list(dict.fromkeys(open_pairs)))
|
|
||||||
|
|
||||||
#print(*open_pairs, sep="\n")
|
|
||||||
|
|
||||||
# put this all together to reduce the amount of loops
|
|
||||||
return open_pairs, trade_count, latest_open_rate
|
|
||||||
Loading…
Reference in New Issue
Block a user