# source: https://raw.githubusercontent.com/coffeecloudgit/freqailstm/cd5cfd35d54b589b18a6e85e3445abcf65864ec4/V9/AlexStrategyFinalV90.py import numpy as np import pandas_ta as pta from typing import Optional import math import freqtrade.vendor.qtpylib.indicators as qtpylib import logging from functools import reduce from typing import Dict from datetime import timedelta, datetime, timezone from freqtrade.persistence import Trade from pandas import DataFrame, Series import talib.abstract as ta from pandas import DataFrame from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy,BooleanParameter from technical.pivots_points import pivots_points from scipy.signal import argrelextrema import logging from functools import reduce import numpy as np import pandas as pd import talib.abstract as ta from pandas import DataFrame from technical import qtpylib from freqtrade.exchange.exchange_utils import * from freqtrade.strategy import IStrategy, RealParameter from technical.pivots_points import pivots_points logger = logging.getLogger(__name__) """ ifconfig sudo ifconfig wlan1 down source ./.venv/bin/activate freqtrade download-data -c config-torch.json --timerange 20240101-20240909 --timeframes 5m freqtrade download-data -c config-torch.json --timerange 20220101-20240909 --timeframes 1h 2h 4h freqtrade backtesting --cache none -c config-torch.json --breakdown day week month --timeframe-detail 5m --timerange 20240201-20240301 freqtrade trade --strategy AlexStrategyFinalV89Hyper --config config-torch.json --freqaimodel PyTorchLSTMRegressor freqtrade backtesting --breakdown day week --cache none -c config-torch.json --timerange 20240209-20240220 freqtrade backtesting --strategy Github_coffeecloudgit_freqailstm__AlexStrategyFinalV90__20250612_073813 -c config-torch.json --freqaimodel PyTorchLSTMRegressor --timerange 20240801-20240820 freqtrade backtesting --strategy Github_coffeecloudgit_freqailstm__AlexStrategyFinalV90__20250612_073813 -c config-torch.json --freqaimodel XGBoostRegressor --breakdown day week month --timerange 20240801-20240810 freqtrade lookahead-analysis --strategy Github_coffeecloudgit_freqailstm__AlexStrategyFinalV90__20250612_073813 -c config-torch.json --timerange 20240101-20240820 CHANGELOG: V83 - Corrected Market Remige Filter R, and Dynamic Weights Adjustment V85 - added Trailing Dynamic, Leverage Dynamic and Stop Loss """ class Github_coffeecloudgit_freqailstm__AlexStrategyFinalV90__20250612_073813(IStrategy): """ This is an example strategy that uses the LSTMRegressor model to predict the target score. Use at your own risk. This is a simple example strategy and should be used for educational purposes only. """ # Hyperspace parameters: buy_params = { "threshold_buy": 0.59453, "w0": 0.54347, "w1": 0.82226, "w2": 0.56675, "w3": 0.77918, "w4": 0.98488, "w5": 0.31368, "w6": 0.75916, "w7": 0.09226, "w8": 0.85667, } sell_params = { "threshold_sell": 0.80573, } # ROI table: minimal_roi = { "0": 0.239, "79": 0.058, "231": 0.029, "543": 0 } # Stoploss: stoploss = -0.305 # Were letting the model decide when to sell # Trailing stop: #trailing_stop = False #trailing_only_offset_is_reached = False timeframe = "1h" can_short = True use_exit_signal = True process_only_new_candles = True exit_profit_only = False exit_profit_offset = 0.01 ignore_roi_if_entry_signal = False startup_candle_count = 100 leverage_value = 5.0 threshold_buy = RealParameter(-1, 1, default=0, space='buy') threshold_sell = RealParameter(-1, 1, default=0, space='sell') # Weights for calculating the aggregate score - normalized to sum to 1 w0 = RealParameter(0, 1, default=0.10, space='buy') # moving average (normalized_ma) w1 = RealParameter(0, 1, default=0.15, space='buy') # MACD (normalized_macd) w2 = RealParameter(0, 1, default=0.10, space='buy') # Rate of Change (ROC) w3 = RealParameter(0, 1, default=0.15, space='buy') # RSI (normalized_rsi) w4 = RealParameter(0, 1, default=0.10, space='buy') # Bollinger Band width w5 = RealParameter(0, 1, default=0.10, space='buy') # CCI (normalized_cci) w6 = RealParameter(0, 1, default=0.10, space='buy') # OBV (normalized_obv) w7 = RealParameter(0, 1, default=0.05, space='buy') # ATR (normalized_atr) w8 = RealParameter(0, 1, default=0.10, space='buy') # Stochastic Oscillator (normalized_stoch) def feature_engineering_expand_all(self, dataframe: DataFrame, period: int, metadata: Dict, **kwargs): # #---------------------------------- # dataframe["%-mfi-period"] = ta.MFI(dataframe, timeperiod=period) # dataframe["%-adx-period"] = ta.ADX(dataframe, window=period) # dataframe["%-er-period"] = pta.er(dataframe['close'], length=period) # dataframe["%-rocr-period"] = ta.ROCR(dataframe, timeperiod=period) # dataframe["%-cmf-period"] = chaikin_mf(dataframe, periods=period) # dataframe["%-tcp-period"] = top_percent_change(dataframe, period) # dataframe["%-cti-period"] = pta.cti(dataframe['close'], length=period) # dataframe["%-chop-period"] = qtpylib.chopiness(dataframe, period) # dataframe["%-linear-period"] = ta.LINEARREG_ANGLE(dataframe['close'], timeperiod=period) # dataframe["%-atr-period"] = ta.ATR(dataframe, timeperiod=period) # dataframe["%-atr-periodp"] = dataframe["%-atr-period"] / dataframe['close'] * 1000 # #---------------------------------- dataframe["%-cci-period"] = ta.CCI(dataframe, timeperiod=20) dataframe["%-rsi-period"] = ta.RSI(dataframe, timeperiod=10) dataframe["%-momentum-period"] = ta.MOM(dataframe, timeperiod=4) dataframe['%-ma-period'] = ta.SMA(dataframe, timeperiod=10) dataframe['%-macd-period'], dataframe['%-macdsignal-period'], dataframe['%-macdhist-period'] = ta.MACD( dataframe['close'], slowperiod=12, fastperiod=26) dataframe['%-roc-period'] = ta.ROC(dataframe, timeperiod=2) bollinger = qtpylib.bollinger_bands( qtpylib.typical_price(dataframe), window=period, stds=2.2 ) dataframe["bb_lowerband-period"] = bollinger["lower"] dataframe["bb_middleband-period"] = bollinger["mid"] dataframe["bb_upperband-period"] = bollinger["upper"] dataframe["%-bb_width-period"] = ( dataframe["bb_upperband-period"] - dataframe["bb_lowerband-period"] ) / dataframe["bb_middleband-period"] dataframe["%-close-bb_lower-period"] = ( dataframe["close"] / dataframe["bb_lowerband-period"] ) return dataframe def feature_engineering_expand_basic(self, dataframe: DataFrame, metadata: Dict, **kwargs): dataframe["%-pct-change"] = dataframe["close"].pct_change() dataframe["%-raw_volume"] = dataframe["volume"] dataframe["%-raw_price"] = dataframe["close"] return dataframe def feature_engineering_standard(self, dataframe: DataFrame, metadata: Dict, **kwargs): dataframe['date'] = pd.to_datetime(dataframe['date']) dataframe["%-day_of_week"] = dataframe["date"].dt.dayofweek dataframe["%-hour_of_day"] = dataframe["date"].dt.hour return dataframe def set_freqai_targets(self, dataframe: DataFrame, metadata: Dict, **kwargs) -> DataFrame: dataframe['ma'] = ta.SMA(dataframe, timeperiod=10) dataframe['roc'] = ta.ROC(dataframe, timeperiod=2) dataframe['macd'], dataframe['macdsignal'], dataframe['macdhist'] = ta.MACD(dataframe['close'], slowperiod=12, fastperiod=26) dataframe['momentum'] = ta.MOM(dataframe, timeperiod=4) dataframe['rsi'] = ta.RSI(dataframe, timeperiod=10) bollinger = ta.BBANDS(dataframe, timeperiod=20) dataframe['bb_upperband'] = bollinger['upperband'] dataframe['bb_middleband'] = bollinger['middleband'] dataframe['bb_lowerband'] = bollinger['lowerband'] dataframe['cci'] = ta.CCI(dataframe, timeperiod=20) dataframe['stoch'] = ta.STOCH(dataframe)['slowk'] dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) dataframe['obv'] = ta.OBV(dataframe) dataframe['ma_100'] = ta.SMA(dataframe, timeperiod=100) # Step 1: Normalize Indicators: # Why? Normalizing the indicators will make them comparable and allow us to assign weights to them. # How? We will calculate the z-score of each indicator by subtracting the rolling mean and dividing by the # rolling standard deviation. This will give us a normalized value that is centered around 0 with a standard # deviation of 1. dataframe['normalized_stoch'] = (dataframe['stoch'] - dataframe['stoch'].rolling(window=14).mean()) / dataframe['stoch'].rolling(window=14).std() dataframe['normalized_atr'] = (dataframe['atr'] - dataframe['atr'].rolling(window=14).mean()) / dataframe['atr'].rolling(window=14).std() dataframe['normalized_obv'] = (dataframe['obv'] - dataframe['obv'].rolling(window=14).mean()) / dataframe['obv'].rolling(window=14).std() dataframe['normalized_ma'] = (dataframe['close'] - dataframe['close'].rolling(window=10).mean()) / dataframe['close'].rolling(window=10).std() dataframe['normalized_macd'] = (dataframe['macd'] - dataframe['macd'].rolling(window=26).mean()) / dataframe['macd'].rolling(window=26).std() dataframe['normalized_roc'] = (dataframe['roc'] - dataframe['roc'].rolling(window=2).mean()) / dataframe['roc'].rolling(window=2).std() dataframe['normalized_momentum'] = (dataframe['momentum'] - dataframe['momentum'].rolling(window=4).mean()) / \ dataframe['momentum'].rolling(window=4).std() dataframe['normalized_rsi'] = (dataframe['rsi'] - dataframe['rsi'].rolling(window=10).mean()) / dataframe['rsi'].rolling(window=10).std() dataframe['normalized_bb_width'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']).rolling( window=20).mean() / (dataframe['bb_upperband'] - dataframe['bb_lowerband']).rolling(window=20).std() dataframe['normalized_cci'] = (dataframe['cci'] - dataframe['cci'].rolling(window=20).mean()) / dataframe['cci'].rolling(window=20).std() # Dynamic Weights Adjustment # Calculate trend strength as the absolute difference between MA and close price trend_strength = abs(dataframe['ma'] - dataframe['close']) # Calculate rolling mean and stddev once to avoid redundancy rolling_mean = trend_strength.rolling(window=14).mean() rolling_stddev = trend_strength.rolling(window=14).std() # Calculate a more dynamic strong trend threshold strong_trend_threshold = rolling_mean + 1.5 * rolling_stddev # Determine strong trend condition is_strong_trend = trend_strength > strong_trend_threshold # Apply dynamic weight adjustment, could also consider a more gradual adjustment dataframe['w_momentum'] = self.w3.value * (1 + 0.5 * (trend_strength / strong_trend_threshold)) # Optional: Clip the w_momentum values to prevent extreme cases dataframe['w_momentum'] = dataframe['w_momentum'].clip(lower=self.w3.value, upper=self.w3.value * 2) # Step 2: Calculate aggregate score S # Calculate aggregate score S w = [self.w0.value, self.w1.value, self.w2.value, self.w3.value, self.w4.value, self.w5.value, self.w6.value, self.w7.value, self.w8.value] dataframe['S'] = w[0] * dataframe['normalized_ma'] + \ w[1] * dataframe['normalized_macd'] + \ w[2] * dataframe['normalized_roc'] + \ w[3] * dataframe['normalized_rsi'] + \ w[4] * dataframe['normalized_bb_width'] + \ w[5] * dataframe['normalized_cci'] + \ dataframe['w_momentum'] * dataframe['normalized_momentum'] + \ self.w8.value * dataframe['normalized_stoch'] + \ self.w7.value * dataframe['normalized_atr'] + \ self.w6.value * dataframe['normalized_obv'] # Step 3: Market Regime Filter R dataframe['R'] = 0 dataframe.loc[dataframe['close'] > dataframe['bb_upperband'], 'R'] = 1 dataframe.loc[dataframe['close'] < dataframe['bb_lowerband'], 'R'] = -1 buffer_pct = 0.01 # 1% buffer dataframe['R2'] = np.where(dataframe['close'] > dataframe['ma_100'] * (1 + buffer_pct), 1, np.where(dataframe['close'] < dataframe['ma_100'] * (1 - buffer_pct), -1, np.nan)) # Step 4: Volatility Adjustment V # EXPLANATION: Calculate the Bollinger Band width and assign it to V. The Bollinger Band width is the # difference between the upper and lower Bollinger Bands divided by the middle Bollinger Band. The idea is # that when the Bollinger Bands are wide, the market is volatile, and when the Bollinger Bands are narrow, # the market is less volatile. So we are using the Bollinger Band width as a measure of volatility. You can # use other indicators to measure volatility as well. For example, you can use the ATR (Average True Range) bb_width = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_middleband'] dataframe['V_mean'] = 1 / (bb_width + 1e-8) # Avoid division by zero # ATR-based Volatility Measure dataframe['V2_mean'] = 1 / (dataframe['atr'] + 1e-8) # Avoid division by zero # Rolling window size for adaptive normalization rolling_window = 50 # Normalize V_mean using a rolling window mean_v = dataframe['V_mean'].rolling(window=rolling_window).mean() std_v = dataframe['V_mean'].rolling(window=rolling_window).std() dataframe['V_norm'] = (dataframe['V_mean'] - mean_v) / std_v dataframe['V_norm'] = dataframe['V_norm'].fillna(0) # Normalize V2_mean using a rolling window mean_v2 = dataframe['V2_mean'].rolling(window=rolling_window).mean() std_v2 = dataframe['V2_mean'].rolling(window=rolling_window).std() dataframe['V2_norm'] = (dataframe['V2_mean'] - mean_v2) / std_v2 dataframe['V2_norm'] = dataframe['V2_norm'].fillna(0) # Signal assignment using hysteresis upper_threshold = 1.0 lower_threshold = -1.0 dataframe['V'] = np.where(dataframe['V_norm'] > upper_threshold, 1, np.where(dataframe['V_norm'] < lower_threshold, -1, np.nan)) dataframe['V2'] = np.where(dataframe['V2_norm'] > upper_threshold, 1, np.where(dataframe['V2_norm'] < lower_threshold, -1, np.nan)) # Forward-fill to maintain the last state of the signal dataframe['V'] = dataframe['V'].ffill() # Correct ffill usage dataframe['V2'] = dataframe['V2'].ffill() # Correct ffill usage # Get Final Target Score to incorporate new calculations dataframe['T'] = dataframe['S'] * dataframe['R'] * dataframe['R2'] * dataframe['V'] * dataframe['V2'] # Assign the target score T to the AI target column target_horizon = 1 # Define your prediction horizon here dataframe['&-target'] = dataframe['T'].shift(-target_horizon) return dataframe def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame: self.freqai_info = self.config["freqai"] dataframe = self.freqai.start(dataframe, metadata, self) # One can define indicators here if needed and add logic to populate_entry_trend and populate_exit_trend #print(f'>>>>>>>>>>>>>>>>>>>>> {dataframe.columns.values}') # dataframe["target_roi"] = dataframe["&-target_mean"] + dataframe["&-target_std"] * 1.25 # dataframe["sell_roi"] = dataframe["&-target_mean"] - dataframe["&-target_std"] * 1.25 return dataframe def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame: enter_long_conditions = [ df["do_predict"] == 1, df['&-target'] > self.threshold_buy.value, # df['volume'] > 0 ] enter_short_conditions = [ df["do_predict"] == 1, df['&-target'] < self.threshold_sell.value, df["volume"] > 0 ] df.loc[ reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"] ] = (1, "long") df.loc[ reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"] ] = (1, "short") return df def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame: exit_long_conditions = [ #df["do_predict"] == 1, df['&-target'] < self.threshold_sell.value ] exit_short_conditions = [ #df["do_predict"] == 1, df['&-target'] > self.threshold_buy.value ] if exit_long_conditions: df.loc[ reduce(lambda x, y: x & y, exit_long_conditions), ["exit_long", "exit_tag"] ] = (1, "exit_long") if exit_short_conditions: df.loc[ reduce(lambda x, y: x & y, exit_short_conditions), ["exit_short", "exit_tag"] ] = (1, "exit_short") return df def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime, current_rate: float, current_profit: float, **kwargs) -> float: """ Calculate custom stoploss based on dynamic trailing stop logic. """ dataframe, row = self.dp.get_analyzed_dataframe(pair, self.timeframe) # If no valid dataframe is available (initial trades) if dataframe is None or row is None: return self.stoploss # Get trailing stop and offset based on ATR trailing_stop, trailing_offset = self.trailing_stop_logic(dataframe, row) # If the price has increased above the trailing offset, adjust stoploss if current_profit > trailing_offset: return -trailing_stop return self.stoploss def leverage( self, pair: str, current_time: "datetime", current_rate: float, proposed_leverage: float, **kwargs, ) -> float: """ Dynamically adjust leverage based on volatility (ATR). """ # Get the analyzed dataframe for the pair and timeframe result = self.dp.get_analyzed_dataframe(pair, self.timeframe) if isinstance(result, tuple): dataframe, _ = result else: dataframe = result if dataframe is None or dataframe.empty: return self.leverage_value # Fallback to default leverage dataframe['atr'] = ta.ATR(dataframe, timeperiod=14) # Get the latest ATR value from the last candle atr = dataframe['atr'].iat[-1] # Get the ATR of the most recent candle if pd.isna(atr): return self.leverage_value # Fallback if ATR is NaN # Example: Lower leverage when volatility (ATR) is high rolling_mean = dataframe['atr'].rolling(window=14).mean().iat[-1] rolling_stddev = dataframe['atr'].rolling(window=14).std().iat[-1] if atr > rolling_mean + 1.5 * rolling_stddev: return self.leverage_value * 0.5 # Reduce leverage in volatile markets return self.leverage_value # # #-------------------------------------- # overbuy_factor = 1.295 #2 # position_adjustment_enable = True # initial_safety_order_trigger = -0.02 # max_so_multiplier_orig = 3 # safety_order_step_scale = 2 # safety_order_volume_scale = 1.8 # # just for initialization, now we calculate it... # max_so_multiplier = max_so_multiplier_orig # # We will store the size of stake of each trade's first order here # cust_proposed_initial_stakes = {} # # Amount the strategy should compensate previously partially filled orders for successive safety orders (0.0 - 1.0) # partial_fill_compensation_scale = 1 # if (max_so_multiplier_orig > 0): # if (safety_order_volume_scale > 1): # # print(safety_order_volume_scale * (math.pow(safety_order_volume_scale,(max_so_multiplier - 1)) - 1)) # firstLine = (safety_order_volume_scale * # (math.pow(safety_order_volume_scale, (max_so_multiplier_orig - 1)) - 1)) # divisor = (safety_order_volume_scale - 1) # max_so_multiplier = (2 + firstLine / divisor) # # max_so_multiplier = (2 + # # (safety_order_volume_scale * # # (math.pow(safety_order_volume_scale, (max_so_multiplier - 1)) - 1) / # # (safety_order_volume_scale - 1))) # elif (safety_order_volume_scale < 1): # firstLine = safety_order_volume_scale * \ # (1 - math.pow(safety_order_volume_scale, (max_so_multiplier_orig - 1))) # divisor = 1 - safety_order_volume_scale # max_so_multiplier = (2 + firstLine / divisor) # # max_so_multiplier = (2 + (safety_order_volume_scale * ( # # 1 - math.pow(safety_order_volume_scale, (max_so_multiplier - 1))) / ( # # 1 - safety_order_volume_scale))) # # Since stoploss can only go up and can't go down, if you set your stoploss here, your lowest stoploss will always be tied to the first buy rate # # So disable the hard stoploss here, and use custom_sell or custom_stoploss to handle the stoploss trigger # stoploss = -1 # def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, # current_profit: float, **kwargs): # tag = super().custom_sell(pair, trade, current_time, current_rate, current_profit, **kwargs) # if tag: # return tag # entry_tag = 'empty' # if hasattr(trade, 'entry_tag') and trade.entry_tag is not None: # entry_tag = trade.entry_tag # if current_profit <= -0.35: # return f'stop_loss ({entry_tag})' # return None # def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, # rate: float, time_in_force: str, exit_reason: str, # current_time: datetime, **kwargs) -> bool: # # remove pair from custom initial stake dict only if full exit # if trade.amount == amount and pair in self.cust_proposed_initial_stakes: # del self.cust_proposed_initial_stakes[pair] # return True # # Let unlimited stakes leave funds open for DCA orders # def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float, # proposed_stake: float, min_stake: float, max_stake: float, # **kwargs) -> float: # custom_stake = proposed_stake / self.max_so_multiplier * self.overbuy_factor # self.cust_proposed_initial_stakes[pair] = custom_stake # Setting of first stake size just before each first order of a trade # return custom_stake # set to static 10 to simulate partial fills of 10$, etc # def adjust_trade_position(self, trade: Trade, current_time: datetime, # current_rate: float, current_profit: float, min_stake: float, # max_stake: float, **kwargs) -> Optional[float]: # if current_profit > self.initial_safety_order_trigger: # return None # filled_buys = trade.select_filled_orders(trade.entry_side) # count_of_buys = len(filled_buys) # if 1 <= count_of_buys <= self.max_so_multiplier_orig: # # if (1 <= count_of_buys) and (open_trade_value < self.stake_amount * self.overbuy_factor): # safety_order_trigger = (abs(self.initial_safety_order_trigger) * count_of_buys) # if self.safety_order_step_scale > 1: # safety_order_trigger = abs(self.initial_safety_order_trigger) + ( # abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * ( # math.pow(self.safety_order_step_scale, (count_of_buys - 1)) - 1) / (self.safety_order_step_scale - 1)) # elif self.safety_order_step_scale < 1: # safety_order_trigger = abs(self.initial_safety_order_trigger) + ( # abs(self.initial_safety_order_trigger) * self.safety_order_step_scale * ( # 1 - math.pow(self.safety_order_step_scale, (count_of_buys - 1))) / (1 - self.safety_order_step_scale)) # if current_profit <= (-1 * abs(safety_order_trigger)): # try: # # This returns first order actual stake size # actual_initial_stake = filled_buys[0].cost # # Fallback for when the initial stake was not set for whatever reason # stake_amount = actual_initial_stake # already_bought = sum(filled_buy.cost for filled_buy in filled_buys) # if trade.pair in self.cust_proposed_initial_stakes: # if self.cust_proposed_initial_stakes[trade.pair] > 0: # # This calculates the amount of stake that will get used for the current safety order, # # including compensation for any partial buys # proposed_initial_stake = self.cust_proposed_initial_stakes[trade.pair] # current_actual_stake = already_bought * math.pow(self.safety_order_volume_scale, # (count_of_buys - 1)) # current_stake_preposition = proposed_initial_stake * math.pow(self.safety_order_volume_scale, # (count_of_buys - 1)) # current_stake_preposition_compensation = current_stake_preposition + abs( # current_stake_preposition - current_actual_stake) # total_so_stake = lerp(current_actual_stake, current_stake_preposition_compensation, # self.partial_fill_compensation_scale) # # Set the calculated stake amount # stake_amount = total_so_stake # else: # # Fallback stake amount calculation # stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1)) # else: # # Fallback stake amount calculation # stake_amount = stake_amount * math.pow(self.safety_order_volume_scale, (count_of_buys - 1)) # # amount = stake_amount / current_rate # # logger.info( # # f"Initiating safety order buy #{count_of_buys} " # # f"for {trade.pair} with stake amount of {stake_amount}. " # # f"which equals {amount}. " # # f"Previously bought: {already_bought}. " # # f"Now overall:{already_bought + stake_amount}. ") # return stake_amount # except Exception as exception: # # logger.info(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') # # print(f'Error occured while trying to get stake amount for {trade.pair}: {str(exception)}') # return None # return None # def lerp(a: float, b: float, t: float) -> float: # """Linear interpolate on the scale given by a to b, using t as the point on that scale. # Examples # -------- # 50 == lerp(0, 100, 0.5) # 4.2 == lerp(1, 5, 0.8) # """ # return (1 - t) * a + t * b # #-------------------------------------- def chaikin_mf(df, periods=20): close = df["close"] low = df["low"] high = df["high"] volume = df["volume"] mfv = ((close - low) - (high - close)) / (high - low) mfv = mfv.fillna(0.0) mfv *= volume cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum() return Series(cmf, name="cmf") def top_percent_change(dataframe: DataFrame, length: int) -> float: if length == 0: return (dataframe["open"] - dataframe["close"]) / dataframe["close"] else: return (dataframe["open"].rolling(length).max() - dataframe["close"]) / dataframe["close"]