From c3f3bdaa2ae03ebffba27ba71cf1b7276fa77e76 Mon Sep 17 00:00:00 2001 From: incrementby1 Date: Tue, 26 Oct 2021 00:04:40 +0200 Subject: [PATCH] Add "allow_position_stacking" value to config, which allows rebuys of a pair Add function unlock_reason(str: pair) which removes all PairLocks with reason Provide demo strategy that allows buying the same pair multiple times --- StackingConfig.json | 89 +++ StackingDemo.py | 593 +++++++++++++++++++ freqtrade/configuration/configuration.py | 6 + freqtrade/freqtradebot.py | 17 +- freqtrade/persistence/pairlock_middleware.py | 18 + freqtrade/strategy/interface.py | 9 + 6 files changed, 727 insertions(+), 5 deletions(-) create mode 100644 StackingConfig.json create mode 100644 StackingDemo.py diff --git a/StackingConfig.json b/StackingConfig.json new file mode 100644 index 000000000..750ef92c6 --- /dev/null +++ b/StackingConfig.json @@ -0,0 +1,89 @@ + +{ + "max_open_trades": 12, + "stake_currency": "USDT", + "stake_amount": 100, + "tradable_balance_ratio": 0.99, + "fiat_display_currency": "USD", + "timeframe": "5m", + "dry_run": true, + "cancel_open_orders_on_exit": false, + "allow_position_stacking": true, + "unfilledtimeout": { + "buy": 10, + "sell": 30, + "unit": "minutes" + }, + "bid_strategy": { + "price_side": "ask", + "ask_last_balance": 0.0, + "use_order_book": true, + "order_book_top": 1, + "check_depth_of_market": { + "enabled": false, + "bids_to_ask_delta": 1 + } + }, + "ask_strategy": { + "price_side": "bid", + "use_order_book": true, + "order_book_top": 1 + }, + "exchange": { + "name": "binance", + "key": "", + "secret": "", + "ccxt_config": {}, + "ccxt_async_config": {}, + "pair_whitelist": [ + ], + "pair_blacklist": [ + "BNB/.*" + ] + }, + "pairlists": [ + { + "method": "VolumePairList", + "number_assets": 80, + "sort_key": "quoteVolume", + "min_value": 0, + "refresh_period": 1800 + } + ], + "edge": { + "enabled": false, + "process_throttle_secs": 3600, + "calculate_since_number_of_days": 7, + "allowed_risk": 0.01, + "stoploss_range_min": -0.01, + "stoploss_range_max": -0.1, + "stoploss_range_step": -0.01, + "minimum_winrate": 0.60, + "minimum_expectancy": 0.20, + "min_trade_number": 10, + "max_trade_duration_minute": 1440, + "remove_pumps": false + }, + "telegram": { + "enabled": false, + "token": "", + "chat_id": "" + }, + "api_server": { + "enabled": true, + "listen_ip_address": "127.0.0.1", + "listen_port": 8080, + "verbosity": "error", + "enable_openapi": false, + "jwt_secret_key": "908cd4469c824f3838bfe56e4120d3a3dbda5294ef583ffc62c82f54d2c1bf58", + "CORS_origins": [], + "username": "user", + "password": "pass" + }, + "bot_name": "freqtrade", + "initial_state": "running", + "forcebuy_enable": false, + "internals": { + "process_throttle_secs": 5 + } +} diff --git a/StackingDemo.py b/StackingDemo.py new file mode 100644 index 000000000..739e847b7 --- /dev/null +++ b/StackingDemo.py @@ -0,0 +1,593 @@ +# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement +# flake8: noqa: F401 + +# --- Do not remove these libs --- +import numpy as np # noqa +import pandas as pd # noqa +from pandas import DataFrame + +from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, + IStrategy, IntParameter) + +# -------------------------------- +# Add your lib to import here +import talib.abstract as ta +import freqtrade.vendor.qtpylib.indicators as qtpylib + +from freqtrade.persistence import Trade +from datetime import datetime,timezone,timedelta + +""" + Warning: +This is still work in progress, so there is no warranty that everything works as intended, +it is possible that this strategy results in huge losses or doesn't even work at all. +Make sure to only run this in dry_mode so you don't lose any money. + +""" + +class StackingDemo(IStrategy): + """ + This is the default strategy template with added functions for trade stacking / buying the same positions multiple times. + It should function like this: + Find good buys using indicators. + When a new buy occurs the strategy will enable rebuys of the pair like this: + self.custom_info[metadata["pair"]]["rebuy"] = 1 + Then, if the price should drop after the last buy within the timerange of rebuy_time_limit_hours, + the same pair will be purchased again. This is intended to help with reducing possible losses. + 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, + look for other pairs to buy. + For selling there is this flag: + self.custom_info[metadata["pair"]]["resell"] = 1 + which should simply sell all trades of this pair until none are left. + + You can set how many pairs you want to trade and how many trades you want to allow for a pair, + 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. + Also allow_position_stacking has to be set to true in the configuration file. + + For backtesting make sure to provide --enable-position-stacking as an argument in the command line. + Backtesting will be slow. + Hyperopt was not tested. + + # run the bot: + freqtrade trade -c StackingConfig.json -s StackingDemo --db-url sqlite:///tradesv3_StackingDemo_dry-run.sqlite --dry-run + """ + # Strategy interface version - allow new iterations of the strategy interface. + # Check the documentation or the Sample strategy to get the latest version. + INTERFACE_VERSION = 2 + + # how many pairs to trade / trades per pair if allow_position_stacking is enabled + max_open_pairs, max_trades_per_pair = 4, 3 + # make sure to have this value in your config file + max_open_trades = max_open_pairs * max_trades_per_pair + + # debugging + print_trades = True + + # specify for how long to want to allow rebuys of this pair + rebuy_time_limit_hours = 2 + + # store additional information needed for this strategy: + custom_info = {} + custom_num_open_pairs = {} + + # Minimal ROI designed for the strategy. + # This attribute will be overridden if the config file contains "minimal_roi". + minimal_roi = { +# "60": 0.01, +# "30": 0.02, + "0": 0.001 + } + + # Optimal stoploss designed for the strategy. + # This attribute will be overridden if the config file contains "stoploss". + stoploss = -0.10 + + # Trailing stoploss + trailing_stop = False + # trailing_only_offset_is_reached = False + # trailing_stop_positive = 0.01 + # trailing_stop_positive_offset = 0.0 # Disabled / not configured + + # Optimal timeframe for the strategy. + timeframe = '5m' + + # Run "populate_indicators()" only for new candle. + process_only_new_candles = False + + # These values can be overridden in the "ask_strategy" section in the config. + use_sell_signal = True + sell_profit_only = False + ignore_roi_if_buy_signal = False + + # Number of candles the strategy requires before producing valid signals + startup_candle_count: int = 30 + + # Optional order type mapping. + order_types = { + 'buy': 'market', + 'sell': 'market', + 'stoploss': 'market', + 'stoploss_on_exchange': False + } + + # Optional order time in force. + order_time_in_force = { + 'buy': 'gtc', + 'sell': 'gtc' + } + + plot_config = { + # Main plot indicators (Moving averages, ...) + 'main_plot': { + 'tema': {}, + 'sar': {'color': 'white'}, + }, + 'subplots': { + # Subplots - each dict defines one additional plot + "MACD": { + 'macd': {'color': 'blue'}, + 'macdsignal': {'color': 'orange'}, + }, + "RSI": { + 'rsi': {'color': 'red'}, + } + } + } + def informative_pairs(self): + """ + Define additional, informative pair/interval combinations to be cached from the exchange. + These pair/interval combinations are non-tradeable, unless they are part + of the whitelist as well. + For more information, please consult the documentation + :return: List of tuples in the format (pair, interval) + Sample: return [("ETH/USDT", "5m"), + ("BTC/USDT", "15m"), + ] + """ + return [] + + def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: + """ + Adds several different TA indicators to the given DataFrame + + Performance Note: For the best performance be frugal on the number of indicators + you are using. Let uncomment only the indicator you are using in your strategies + or your hyperopt configuration, otherwise you will waste your memory and CPU usage. + :param dataframe: Dataframe with data from the exchange + :param metadata: Additional information, like the currently traded pair + :return: a Dataframe with all mandatory indicators for the strategies + """ + + # STACKING STUFF + + # confirm config + self.max_trades_per_pair = self.config['max_open_trades'] / self.max_open_pairs + if not self.config["allow_position_stacking"]: + self.max_trades_per_pair = 1 + + # store number of open pairs + self.custom_num_open_pairs = {"num_open_pairs": 0} + + # Store custom information for this pair: + if not metadata["pair"] in self.custom_info: + self.custom_info[metadata["pair"]] = {} + + if not "rebuy" in self.custom_info[metadata["pair"]]: + # number of trades for this pair + self.custom_info[metadata["pair"]]["num_trades"] = 0 + # use rebuy/resell as buy-/sell- indicators + self.custom_info[metadata["pair"]]["rebuy"] = 0 + self.custom_info[metadata["pair"]]["resell"] = 0 + # store latest open_date for this pair + self.custom_info[metadata["pair"]]["last_open_date"] = datetime.now(timezone.utc) - timedelta(days=100) + # stare the value of the latest open price for this pair + self.custom_info[metadata["pair"]]["latest_open_rate"] = 0 + + # INDICATORS + + # Momentum Indicators + # ------------------------------------ + + # ADX + dataframe['adx'] = ta.ADX(dataframe) + + # # Plus Directional Indicator / Movement + # dataframe['plus_dm'] = ta.PLUS_DM(dataframe) + # dataframe['plus_di'] = ta.PLUS_DI(dataframe) + + # # Minus Directional Indicator / Movement + # dataframe['minus_dm'] = ta.MINUS_DM(dataframe) + # dataframe['minus_di'] = ta.MINUS_DI(dataframe) + + # # Aroon, Aroon Oscillator + # aroon = ta.AROON(dataframe) + # dataframe['aroonup'] = aroon['aroonup'] + # dataframe['aroondown'] = aroon['aroondown'] + # dataframe['aroonosc'] = ta.AROONOSC(dataframe) + + # # Awesome Oscillator + # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe) + + # # Keltner Channel + # keltner = qtpylib.keltner_channel(dataframe) + # dataframe["kc_upperband"] = keltner["upper"] + # dataframe["kc_lowerband"] = keltner["lower"] + # dataframe["kc_middleband"] = keltner["mid"] + # dataframe["kc_percent"] = ( + # (dataframe["close"] - dataframe["kc_lowerband"]) / + # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) + # ) + # dataframe["kc_width"] = ( + # (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"] + # ) + + # # Ultimate Oscillator + # dataframe['uo'] = ta.ULTOSC(dataframe) + + # # Commodity Channel Index: values [Oversold:-100, Overbought:100] + # dataframe['cci'] = ta.CCI(dataframe) + + # RSI + dataframe['rsi'] = ta.RSI(dataframe) + + # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy) + # rsi = 0.1 * (dataframe['rsi'] - 50) + # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1) + + # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy) + # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1) + + # # Stochastic Slow + # stoch = ta.STOCH(dataframe) + # dataframe['slowd'] = stoch['slowd'] + # dataframe['slowk'] = stoch['slowk'] + + # Stochastic Fast + stoch_fast = ta.STOCHF(dataframe) + dataframe['fastd'] = stoch_fast['fastd'] + dataframe['fastk'] = stoch_fast['fastk'] + + # # Stochastic RSI + # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this. + # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results. + # stoch_rsi = ta.STOCHRSI(dataframe) + # dataframe['fastd_rsi'] = stoch_rsi['fastd'] + # dataframe['fastk_rsi'] = stoch_rsi['fastk'] + + # MACD + macd = ta.MACD(dataframe) + dataframe['macd'] = macd['macd'] + dataframe['macdsignal'] = macd['macdsignal'] + dataframe['macdhist'] = macd['macdhist'] + + # MFI + dataframe['mfi'] = ta.MFI(dataframe) + + # # ROC + # dataframe['roc'] = ta.ROC(dataframe) + + # Overlap Studies + # ------------------------------------ + + # Bollinger Bands + bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2) + dataframe['bb_lowerband'] = bollinger['lower'] + dataframe['bb_middleband'] = bollinger['mid'] + dataframe['bb_upperband'] = bollinger['upper'] + dataframe["bb_percent"] = ( + (dataframe["close"] - dataframe["bb_lowerband"]) / + (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) + ) + dataframe["bb_width"] = ( + (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"] + ) + + # Bollinger Bands - Weighted (EMA based instead of SMA) + # weighted_bollinger = qtpylib.weighted_bollinger_bands( + # qtpylib.typical_price(dataframe), window=20, stds=2 + # ) + # dataframe["wbb_upperband"] = weighted_bollinger["upper"] + # dataframe["wbb_lowerband"] = weighted_bollinger["lower"] + # dataframe["wbb_middleband"] = weighted_bollinger["mid"] + # dataframe["wbb_percent"] = ( + # (dataframe["close"] - dataframe["wbb_lowerband"]) / + # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) + # ) + # dataframe["wbb_width"] = ( + # (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) / dataframe["wbb_middleband"] + # ) + + # # EMA - Exponential Moving Average + # dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3) + # dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5) + # dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10) + # dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21) + # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50) + # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100) + + # # SMA - Simple Moving Average + # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3) + # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5) + # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10) + # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21) + # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50) + # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100) + + # Parabolic SAR + dataframe['sar'] = ta.SAR(dataframe) + + # TEMA - Triple Exponential Moving Average + dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9) + + # Cycle Indicator + # ------------------------------------ + # Hilbert Transform Indicator - SineWave + hilbert = ta.HT_SINE(dataframe) + dataframe['htsine'] = hilbert['sine'] + dataframe['htleadsine'] = hilbert['leadsine'] + + # Pattern Recognition - Bullish candlestick patterns + # ------------------------------------ + # # Hammer: values [0, 100] + # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe) + # # Inverted Hammer: values [0, 100] + # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe) + # # Dragonfly Doji: values [0, 100] + # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe) + # # Piercing Line: values [0, 100] + # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100] + # # Morningstar: values [0, 100] + # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100] + # # Three White Soldiers: values [0, 100] + # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100] + + # Pattern Recognition - Bearish candlestick patterns + # ------------------------------------ + # # Hanging Man: values [0, 100] + # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe) + # # Shooting Star: values [0, 100] + # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe) + # # Gravestone Doji: values [0, 100] + # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe) + # # Dark Cloud Cover: values [0, 100] + # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe) + # # Evening Doji Star: values [0, 100] + # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe) + # # Evening Star: values [0, 100] + # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe) + + # Pattern Recognition - Bullish/Bearish candlestick patterns + # ------------------------------------ + # # Three Line Strike: values [0, -100, 100] + # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe) + # # Spinning Top: values [0, -100, 100] + # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100] + # # Engulfing: values [0, -100, 100] + # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100] + # # Harami: values [0, -100, 100] + # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100] + # # Three Outside Up/Down: values [0, -100, 100] + # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100] + # # Three Inside Up/Down: values [0, -100, 100] + # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100] + + # # Chart type + # # ------------------------------------ + # # Heikin Ashi Strategy + # heikinashi = qtpylib.heikinashi(dataframe) + # dataframe['ha_open'] = heikinashi['open'] + # dataframe['ha_close'] = heikinashi['close'] + # dataframe['ha_high'] = heikinashi['high'] + # dataframe['ha_low'] = heikinashi['low'] + + # Retrieve best bid and best ask from the orderbook + # ------------------------------------ + """ + # first check if dataprovider is available + if self.dp: + if self.dp.runmode.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 + (dataframe['close'] < dataframe['close'].shift(1)) | + # 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 + (dataframe['close'] > dataframe['close'].shift(1)) | + (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 diff --git a/freqtrade/configuration/configuration.py b/freqtrade/configuration/configuration.py index 822577916..d4cf09821 100644 --- a/freqtrade/configuration/configuration.py +++ b/freqtrade/configuration/configuration.py @@ -137,6 +137,12 @@ class Configuration: setup_logging(config) def _process_trading_options(self, config: Dict[str, Any]) -> None: + + # Allow_position_stacking defaults to False + if not config.get('allow_position_stacking'): + config['allow_position_stacking'] = False + logger.info('Allow_position_stacking is set to ' + str(config['allow_position_stacking'])) + if config['runmode'] not in TRADING_MODES: return diff --git a/freqtrade/freqtradebot.py b/freqtrade/freqtradebot.py index bf4742fdc..850cd1700 100644 --- a/freqtrade/freqtradebot.py +++ b/freqtrade/freqtradebot.py @@ -4,7 +4,7 @@ Freqtrade is the main module of this bot. It contains the class Freqtrade() import copy import logging import traceback -from datetime import datetime, timezone +from datetime import datetime, timedelta, timezone from math import isclose from threading import Lock from typing import Any, Dict, List, Optional @@ -359,10 +359,12 @@ class FreqtradeBot(LoggingMixin): logger.info("Active pair whitelist is empty.") return trades_created # Remove pairs for currently opened trades from the whitelist - for trade in Trade.get_open_trades(): - if trade.pair in whitelist: - whitelist.remove(trade.pair) - logger.debug('Ignoring %s in pair whitelist', trade.pair) + # Allow rebuying of the same pair if allow_position_stacking is set to True + if not self.config['allow_position_stacking']: + for trade in Trade.get_open_trades(): + if trade.pair in whitelist: + whitelist.remove(trade.pair) + logger.debug('Ignoring %s in pair whitelist', trade.pair) if not whitelist: logger.info("No currency pair in active pair whitelist, " @@ -592,6 +594,11 @@ class FreqtradeBot(LoggingMixin): self._notify_enter(trade, order_type) + # Lock pair for 1 timeframe duration to prevent immediate rebuys + if self.config['allow_position_stacking']: + self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc) + timedelta(minutes=timeframe_to_minutes(self.config['timeframe'])), + reason='Prevent immediate rebuys') + return True def _notify_enter(self, trade: Trade, order_type: str) -> None: diff --git a/freqtrade/persistence/pairlock_middleware.py b/freqtrade/persistence/pairlock_middleware.py index 8662fc36d..6e0164182 100644 --- a/freqtrade/persistence/pairlock_middleware.py +++ b/freqtrade/persistence/pairlock_middleware.py @@ -103,6 +103,24 @@ class PairLocks(): if PairLocks.use_db: PairLock.query.session.commit() + @staticmethod + def unlock_reason(reason: str, now: Optional[datetime] = None) -> None: + """ + Release all locks for this reason. + :param reason: Which reason to unlock + :param now: Datetime object (generated via datetime.now(timezone.utc)). + defaults to datetime.now(timezone.utc) + """ + if not now: + now = datetime.now(timezone.utc) + logger.info(f"Releasing all locks with reason \'{reason}\'.") + locks = PairLocks.get_all_locks() + for lock in locks: + if lock.reason == reason: + lock.active = False + if PairLocks.use_db: + PairLock.query.session.commit() + @staticmethod def is_global_lock(now: Optional[datetime] = None) -> bool: """ diff --git a/freqtrade/strategy/interface.py b/freqtrade/strategy/interface.py index 7420bd9fd..547d9313f 100644 --- a/freqtrade/strategy/interface.py +++ b/freqtrade/strategy/interface.py @@ -443,6 +443,15 @@ class IStrategy(ABC, HyperStrategyMixin): """ PairLocks.unlock_pair(pair, datetime.now(timezone.utc)) + def unlock_reason(self, reason: str) -> None: + """ + Unlocks all pairs previously locked using lock_pair with specified reason. + Not used by freqtrade itself, but intended to be used if users lock pairs + manually from within the strategy, to allow an easy way to unlock pairs. + :param reason: Unlock pairs to allow trading again + """ + PairLocks.unlock_reason(reason, datetime.now(timezone.utc)) + def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool: """ Checks if a pair is currently locked