diff --git a/StackingDemo.py b/StackingDemo.py deleted file mode 100644 index b88248fac..000000000 --- a/StackingDemo.py +++ /dev/null @@ -1,591 +0,0 @@ -# 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 - # 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