# source: https://raw.githubusercontent.com/syuraj/freq-test/12243c6e3ebf9a245cca012b5d5e35b9e49aae91/user_data/strategies_backup/TSPredict.py
# pragma pylint: disable=W0105, C0103, C0114, C0115, C0116, C0301, C0302, C0303, C0325, C0411, C0413,  W1203

"""
####################################################################################
TS_Predict - base class for 'simple' time series prediction
             Handles most of the logic for time series prediction. Subclasses should
             override the model-related functions

             Note that I use gain rather than price because it is a normalised value, and works better with prediction
             algorithms. I use the actual (future) gain to train a base model, which is then further refined for each
             individual pair.
             The model is created if it does not exist, and is trained on all available data before being saved.
             Models are saved in user_data/strategies/Github_syuraj_freq_test__TSPredict__20240129_002406/models/<class>/<class>.sav, where <class> is the name
             of the current class (TS_Predict if running this directly, or the name of the subclass).
             If the model already exits, then it is just loaded and used.
             So, it makes sense to do initial training over a long period of time to create the base model.
             If training, then no backtesting or tuning for individual pairs is performed (way faster).
             If you want to retrain (e.g. you changed indicators), then delete the model and run the strategy over a
             long time period

####################################################################################
"""


import copy
import cProfile
import os
import pstats

import sys
import traceback
from datetime import datetime
from functools import reduce
from pathlib import Path
from typing import Optional

import logging
import warnings

import joblib
import numpy as np


import pandas as pd
import pywt
from pandas import DataFrame, Series

import talib.abstract as ta

# from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import RobustScaler

import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.persistence import Trade

# import freqtrade.vendor.qtpylib.indicators as qtpylib
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IStrategy


# from lightgbm import LGBMRegressor
# from sklearn.linear_model import PassiveAggressiveRegressor, SGDRegressor
from xgboost import XGBRegressor

group_dir = str(Path(__file__).parent)
strat_dir = str(Path(__file__).parent.parent)
sys.path.append(strat_dir)
sys.path.append(group_dir)


from utils.DataframeUtils import DataframeUtils, ScalerType  # pylint: disable=E0401

log = logging.getLogger(__name__)
# log.setLevel(logging.DEBUG)
warnings.simplefilter(action="ignore", category=pd.errors.PerformanceWarning)
warnings.simplefilter(action="ignore", category=FutureWarning)
warnings.simplefilter(action="ignore", category=UserWarning)

pd.options.mode.chained_assignment = None  # default='warn'


class Github_syuraj_freq_test__TSPredict__20240129_002406(IStrategy):
    # Do *not* hyperopt for the roi and stoploss spaces

    plot_config = {
        "main_plot": {"close": {"color": "cornflowerblue"}},
        "subplots": {
            "Diff": {
                "predicted_gain": {"color": "purple"},
                "gain": {"color": "lightblue"},
                "target_profit": {"color": "lightgreen"},
                "target_loss": {"color": "lightsalmon"},
            },
        },
    }

    # ROI table:
    minimal_roi = {"0": 0.04, "100": 0.02}

    # Stoploss:
    stoploss = -0.10

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = None
    trailing_stop_positive_offset = 0.0
    trailing_only_offset_is_reached = False

    timeframe = "5m"
    inf_timeframe = "15m"

    use_custom_stoploss = True

    # Recommended
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = True

    # Required
    startup_candle_count: int = 128  # must be power of 2
    win_size = 14

    process_only_new_candles = True

    custom_trade_info = {}  # pair-specific data
    curr_pair = ""

    ###################################

    # Strategy Specific Variable Storage

    ## Hyperopt Variables

    # model_window = startup_candle_count
    model_window = 128

    lookahead = 6

    df_coeffs: DataFrame = None
    coeff_table = None
    coeff_array = None
    gain_data = None

    profit_nstd = 2.6
    loss_nstd = 2.6

    training_data = None
    training_labels = None
    training_mode = False  # do not set manually
    supports_incremental_training = True
    model_per_pair = False
    combine_models = True
    model_trained = False
    new_model = False

    norm_data = True
    # retrain_period = 12 # number of candles before retrining
    retrain_period = 2  # for testing only!

    dataframeUtils = None
    scaler = RobustScaler()
    model = None
    base_model = None

    curr_dataframe: DataFrame = None

    target_profit = 0.0
    target_loss = 0.0

    # hyperparams


    # Buy hyperspace params:
    buy_params = {
        "cexit_min_profit_th": 0.4,
        "cexit_profit_nstd": 0.0,
        "enable_entry_guards": True,  # value loaded from strategy
        "entry_guard_fwr": -0.2,  # value loaded from strategy
    }

    # Sell hyperspace params:
    sell_params = {
        "cexit_fwr_overbought": 0.99,
        "cexit_fwr_take_profit": 0.99,
        "cexit_loss_nstd": 0.0,
        "cexit_min_loss_th": -0.1,
        "cexit_enable_large_drop": False,  # value loaded from strategy
        "cexit_large_drop": -1.5,  # value loaded from strategy
        "enable_exit_guards": False,  # value loaded from strategy
        "enable_exit_signal": False,  # value loaded from strategy
        "exit_guard_fwr": 0.2,  # value loaded from strategy
    }

    # enable entry/exit guards (safety vs profit)
    enable_entry_guards = CategoricalParameter(
        [True, False], default=True, space="buy", load=True, optimize=False
        )
    entry_guard_fwr = DecimalParameter(
        -1.0, 0.0, default=-0.2, decimals=1, space="buy", load=True, optimize=False
        )

    enable_exit_guards = CategoricalParameter(
        [True, False], default=False, space="sell", load=True, optimize=False
        )
    exit_guard_fwr = DecimalParameter(
        0.0, 1.0, default=0.2, decimals=1, space="sell", load=True, optimize=False
        )

    # use exit signal? If disabled, just rely on the custom exit checks (or stoploss) to get out
    enable_exit_signal = CategoricalParameter(
        [True, False], default=False, space="sell", load=True, optimize=False
        )

    # Custom Exit

    """
    # profit threshold exit
    cexit_profit_threshold = DecimalParameter(
        0.005, 0.065, default=0.047, decimals=3, space='sell', load=True, optimize=True)
    cexit_use_profit_threshold = CategoricalParameter(
        [True, False], default=False, space='sell', load=True, optimize=True)

    # loss threshold exit
    cexit_loss_threshold = DecimalParameter(-0.065, -0.005, default=-
                                            0.046, decimals=3, space='sell', load=True, optimize=True)
    cexit_use_loss_threshold = CategoricalParameter(
        [True, False], default=False, space='sell', load=True, optimize=True)

    """

    # No. Standard Deviations of profit/loss for target, and lower limit
    cexit_min_profit_th = DecimalParameter(0.4, 1.0, default=0.4, decimals=1, space="buy", load=True, optimize=True)
    cexit_profit_nstd = DecimalParameter(0.0, 4.0, default=0.9, decimals=1, space="buy", load=True, optimize=True)

    cexit_min_loss_th = DecimalParameter(-1.0, -0.1, default=-0.2, decimals=1, space="sell", load=True, optimize=True)
    cexit_loss_nstd = DecimalParameter(0.0, 4.0, default=0.7, decimals=1, space="sell", load=True, optimize=True)

    # Fisher/Williams sell limits
    cexit_fwr_overbought = DecimalParameter(
        0.90, 0.99, default=0.99, decimals=2, space="sell", load=True, optimize=True
        )
    cexit_fwr_take_profit = DecimalParameter(
        0.90, 0.99, default=0.99, decimals=2, space="sell", load=True, optimize=True
        )

    # sell if we see a large drop, and how large?
    cexit_enable_large_drop = CategoricalParameter(
        [True, False], default=False, space="sell", load=True, optimize=False
        )
    cexit_large_drop = DecimalParameter(
        -3.0, -1.00, default=-1.9, decimals=1, space="sell", load=True, optimize=False
        )

    ###################################

    def bot_start(self, **kwargs) -> None:
        if self.dataframeUtils is None:
            self.dataframeUtils = DataframeUtils()
            self.dataframeUtils.set_scaler_type(ScalerType.Robust)

        return

    ###################################

    """
    Informative Pair Definitions
    """

    def informative_pairs(self):
        return []

    ###################################

    """
    Indicator Definitions
    """

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # NOTE: if you change the indicators, you need to regenerate the model

        # Base pair dataframe timeframe indicators
        curr_pair = metadata["pair"]

        self.curr_dataframe = dataframe
        self.curr_pair = curr_pair

        # The following are needed for base functions, so do not remove.
        # Add custom indicators to add_strategy_indicators()

        # backward looking gain
        dataframe["gain"] = (
            100.0
            * (dataframe["close"] - dataframe["close"].shift(self.lookahead))
            / dataframe["close"].shift(self.lookahead)
        )
        dataframe["gain"].fillna(0.0, inplace=True)

        dataframe["gain"] = self.smooth(dataframe["gain"], 8)

        # need to save the gain data for later scaling
        self.gain_data = dataframe["gain"].to_numpy().copy()

        # target profit/loss thresholds
        dataframe["profit"] = dataframe["gain"].clip(lower=0.0)
        dataframe["loss"] = dataframe["gain"].clip(upper=0.0)

        dataframe = self.update_gain_targets(dataframe)

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe, timeperiod=self.win_size)

        # Williams %R
        dataframe["wr"] = 0.02 * (self.williams_r(dataframe, period=self.win_size) + 50.0)

        # Fisher RSI
        rsi = 0.1 * (dataframe["rsi"] - 50)
        dataframe["fisher_rsi"] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)

        # Combined Fisher RSI and Williams %R
        dataframe["fisher_wr"] = (dataframe["wr"] + dataframe["fisher_rsi"]) / 2.0

        # init prediction column
        dataframe["predicted_gain"] = 0.0

        # Add strategy-specific indicators
        dataframe = self.add_strategy_indicators(dataframe)

        # create and init the model, if first time (dataframe has to be populated first)
        if self.model is None:
            # #print("    Loading model")
            self.load_model(np.shape(dataframe))

        # add the predictions
        # #print("    Making predictions...")
        dataframe = self.add_predictions(dataframe)

        dataframe.fillna(0.0, inplace=True)

        # #DBG (cannot include this in 'real' strat because it's forward looking):
        # dataframe['dwt'] = self.get_dwt(dataframe['gain'])

        return dataframe

    def update_gain_targets(self, dataframe):
        win_size = max(self.lookahead, 6)
        self.profit_nstd = float(self.cexit_profit_nstd.value)
        self.loss_nstd = float(self.cexit_loss_nstd.value)

        dataframe["target_profit"] = (
            dataframe["profit"].rolling(window=win_size).mean()
            + self.profit_nstd * dataframe["profit"].rolling(window=win_size).std()
        )

        dataframe["target_loss"] = dataframe["loss"].rolling(window=win_size).mean() - self.loss_nstd * abs(
            dataframe["loss"].rolling(window=win_size).std()
        )

        dataframe["target_profit"] = dataframe["target_profit"].clip(lower=float(self.cexit_min_profit_th.value))
        dataframe["target_loss"] = dataframe["target_loss"].clip(upper=float(self.cexit_min_loss_th.value))

        dataframe["target_profit"] = np.nan_to_num(dataframe["target_profit"])
        dataframe["target_loss"] = np.nan_to_num(dataframe["target_loss"])

        dataframe["local_mean"] = dataframe["close"].rolling(window=win_size).mean()
        dataframe["local_min"] = dataframe["close"].rolling(window=win_size).min()
        dataframe["local_max"] = dataframe["close"].rolling(window=win_size).max()

        return dataframe

    ###################################

    def add_strategy_indicators(self, dataframe):
        # Override this function in subclasses and add extra indicators here

        return dataframe

    ###################################

    def smooth(self, y, window):
        box = np.ones(window) / window
        y_smooth = np.convolve(y, box, mode="same")
        # Hack: constrain to 3 decimal places (should be elsewhere, but convenient here)
        y_smooth = np.round(y_smooth, decimals=3)
        return np.nan_to_num(y_smooth)

    # look ahead to get future gain. Do *not* put this into the main dataframe!
    def get_future_gain(self, dataframe):
        df = self.convert_dataframe(dataframe)
        future_gain = df["gain"].shift(-self.lookahead).to_numpy()

        # future_gain = dataframe['gain'].shift(-self.lookahead).to_numpy()
        return self.smooth(future_gain, 8)

    ###################################

    # Williams %R
    def williams_r(self, dataframe: DataFrame, period: int = 14) -> Series:
        """
        Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the
        high and low of the past N days (for a given N). It was developed by a publisher and promoter of trading
        materials, Larry Williams.
        Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in
        between,  of its recent trading range.
        The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
        """

        highest_high = dataframe["high"].rolling(center=False, window=period).max()
        lowest_low = dataframe["low"].rolling(center=False, window=period).min()

        WR = Series(
            (highest_high - dataframe["close"]) / (highest_high - lowest_low),
            name=f"{period} Williams %R",
        )

        return WR * -100

    ###################################

    # -------------

    def convert_dataframe(self, dataframe: DataFrame) -> DataFrame:
        df = dataframe.copy()

        # convert date column so that it can be scaled.
        if "date" in df.columns:
            dates = pd.to_datetime(df["date"], utc=True)
            df["date"] = dates.astype("int64")

        df.fillna(0.0, inplace=True)

        df.set_index("date")
        df.reindex()

        if self.norm_data:
            # scale the dataframe
            self.scaler.fit(df)
            df = pd.DataFrame(self.scaler.transform(df), columns=df.columns)

        return df

    ###################################

    # Model-related funcs. Override in subclass to use a different type of model

    def get_model_path(self, pair):
        category = self.__class__.__name__
        root_dir = group_dir + "/models/" + category
        model_name = category
        if self.model_per_pair and (len(pair) > 0):
            model_name = model_name + "_" + pair.split("/")[0]
        path = root_dir + "/" + model_name + ".sav"
        return path

    def load_model(self, df_shape):
        model_path = self.get_model_path("")

        # load from file or create new model
        if os.path.exists(model_path):
            # use joblib to reload model state
            #print("    loading from: ", model_path)
            self.model = joblib.load(model_path)
            self.model_trained = True
            self.new_model = False
            self.training_mode = False
        else:
            self.create_model(df_shape)
            self.model_trained = False
            self.new_model = True
            self.training_mode = True

        # sklearn family of regressors sometimes support starting with an existing model (warm_start), 
        # or incremental training (partial_fit())
        if hasattr(self.model, "warm_start"):
            self.model.warm_start = True
            self.supports_incremental_training = True  # override default

        if hasattr(self.model, "partial_fit"):
            self.supports_incremental_training = True  # override default

        if self.model is None:
            #print("***    ERR: model was not created properly ***")

        return

    # -------------

    def save_model(self):
        # save trained model (but only if didn't already exist)

        model_path = self.get_model_path("")

        # create directory if it doesn't already exist
        save_dir = os.path.dirname(model_path)
        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        # use joblib to save model state
        #print("    saving to: ", model_path)
        joblib.dump(self.model, model_path)

        return

    # -------------

    # override this method if you want a different type of prediction model
    def create_model(self, df_shape):
        # #print("    creating new model using: XGBRegressor")
        params = {"n_estimators": 100, "max_depth": 4, "learning_rate": 0.1}
        self.model = XGBRegressor(**params)

        # # self.model = PassiveAggressiveRegressor(warm_start=True)
        # self.model = SGDRegressor(loss='huber')

        #print(f"    creating new model using: {type(self.model)}")

        if self.model is None:
            #print("***    ERR: create_model() - model was not created ***")
        return

    # -------------

    # train the model. Override if not an sklearn-compatible algorithm
    # set save_model=False if you don't want to save the model (needed for ML algorithms)
    def train_model(self, model, data: np.array, results: np.array, save_model):
        if self.model is None:
            #print("***    ERR: no model ***")
            return

        x = np.nan_to_num(data)

        # #print('    Updating existing model')
        if isinstance(model, XGBRegressor):
            # #print('    Updating xgb_model')
            if self.new_model and (not self.model_trained):
                model.fit(x, results)
            else:
                model.fit(x, results, xgb_model=self.model)
        elif hasattr(model, "partial_fit"):
            # #print('    partial_fit()')
            model.partial_fit(x, results)
        else:
            # #print('    fit()')
            model.fit(x, results)

        return

    # -------------

    # single prediction (for use in rolling calculation)
    def predict(self, df) -> float:
        data = np.array(self.convert_dataframe(df))

        # y_pred = self.model.predict(data)[-1]
        y_pred = self.predict_data(self.model, data)[-1]

        return y_pred

    # -------------

    # get the data for this straegy. Override if necessary
    def get_data(self, dataframe):
        # default is to just normalise the dataframe and convert to numpy array
        df = np.array(self.convert_dataframe(dataframe))
        return df

    # -------------

    # initial training of the model
    def init_model(self, dataframe: DataFrame):
        # if model is not yet trained, or this is a new model and we want to combine across pairs, then train
        if (not self.model_trained) or (self.new_model and self.combine_models):
            df = dataframe

            future_gain_data = self.get_future_gain(df)
            data = self.get_data(df)

            training_data = data[: -self.lookahead - 1].copy()
            training_labels = future_gain_data[: -self.lookahead - 1].copy()

            if not self.model_trained:
                #print(f"    initial training ({self.curr_pair})")
            else:
                #print(f"    incremental training ({self.curr_pair})")

            self.train_model(self.model, training_data, training_labels, True)

            self.model_trained = True

            if self.new_model:
                self.save_model()

        # #print(f'    model_trained:{self.model_trained} new_model:{self.new_model}  combine_models:{self.combine_models}')

        return

    # -------------

    # generate predictions for an np array (intended to be overriden if needed)
    def predict_data(self, model, data):
        x = np.nan_to_num(data)
        preds = model.predict(x)

        # de-norm
        scaler = RobustScaler()
        scaler.fit(self.gain_data.reshape(-1, 1))
        denorm_preds = scaler.inverse_transform(preds.reshape(-1, 1)).squeeze()

        denorm_preds = np.clip(denorm_preds, -3.0, 3.0)
        return denorm_preds

    # -------------

    # add predictions in a jumping fashion. This is a compromise - the rolling version is very slow
    # Note: you probably need to manually tune the parameters, since there is some limited lookahead here
    def add_jumping_predictions(self, dataframe: DataFrame) -> DataFrame:
        df = dataframe

        # roll through the close data and predict for each step
        nrows = np.shape(df)[0]

        # set up training data
        future_gain_data = self.get_future_gain(df)
        data = self.get_data(dataframe)

        self.training_data = data.copy()
        self.training_labels = np.zeros(np.shape(future_gain_data), dtype=float)
        self.training_labels = future_gain_data.copy()

        # initialise the prediction array, using the close data
        pred_array = np.zeros(np.shape(future_gain_data), dtype=float)

        # win_size = 974
        win_size = 128

        # loop until we get to/past the end of the buffer
        start = win_size
        end = start + win_size
        train_end = start - 1
        train_size = 2 * win_size
        train_start = max(0, train_end - train_size)

        pair_model = copy.deepcopy(self.model)  # make a deep copy so that we don't override the baseline model

        while end < nrows:
            # extract the data and coefficients from the current window

            # (re-)train the model on prior data and get predictions

            if (not self.training_mode) and (self.supports_incremental_training):
                train_data = self.training_data[train_start : start - 1].copy()
                train_results = self.training_labels[train_start : start - 1].copy()
                pair_model = copy.deepcopy(self.model)  # reset to avoid over-training
                self.train_model(pair_model, train_data, train_results, False)

            # rebuild data up to end of current window
            dslice = self.training_data[start:end].copy()
            self.gain_data = np.array(dataframe["gain"].iloc[start:end])  # needed for scaling
            preds = self.predict_data(pair_model, dslice)

            # copy the predictions for this window into the main predictions array
            pred_array[start:end] = preds.copy()

            # move the window to the next segment
            end = end + win_size
            start = start + win_size
            train_end = start - 1
            train_start = max(0, train_end - train_size)

        # make sure the last section gets processed (the loop above may not exactly fit the data)
        # Note that we cannot use the last section for training because we don't have forward looking data

        # predict for last window
        dslice = self.training_data[-win_size:]
        # preds = self.model.predict(dslice)

        self.gain_data = np.array(dataframe["gain"].iloc[-win_size])  # needed for scaling
        preds = self.predict_data(pair_model, dslice)
        pred_array[-win_size:] = preds.copy()

        dataframe["predicted_gain"] = pred_array.copy()

        return dataframe

    # -------------

    # add the latest prediction, and update training periodically
    def add_latest_prediction(self, dataframe: DataFrame) -> DataFrame:
        df = dataframe

        try:
            # set up training data
            # TODO: see if we can do this incrementally instead of rebuilding every time, or just use portion of data
            future_gain_data = self.get_future_gain(df)
            data = self.get_data(dataframe)

            plen = len(self.custom_trade_info[self.curr_pair]["predictions"])
            dlen = len(dataframe["gain"])
            clen = min(plen, dlen)

            self.training_data = data[-clen:].copy()
            self.training_labels = future_gain_data[-clen:].copy()

            pred_array = np.zeros(clen, dtype=float)

            # #print(f"[predictions]:{np.shape(self.custom_trade_info[self.curr_pair]['predictions'])}  pred_array:{np.shape(pred_array)}")

            # copy previous predictions and shift down by 1
            pred_array[-clen:] = self.custom_trade_info[self.curr_pair]["predictions"][-clen:].copy()
            pred_array = np.roll(pred_array, -1)
            pred_array[-1] = 0.0

            # cannot use last portion because we are looking ahead
            dslice = self.training_data[: -self.lookahead]
            tslice = self.training_labels[: -self.lookahead]

            # retrain base model and get predictions
            base_model = copy.deepcopy(self.model)
            self.train_model(base_model, dslice, tslice, False)

            self.gain_data = np.array(dataframe["gain"].iloc[-clen])  # needed for scaling
            preds = self.predict_data(base_model, self.training_data)

            # self.model = copy.deepcopy(base_model) # restore original model

            # only replace last prediction (i.e. don't overwrite the historical predictions)
            pred_array[-1] = preds[-1]

            dataframe["predicted_gain"] = 0.0
            dataframe["predicted_gain"][-clen:] = pred_array[-clen:].copy()
            self.custom_trade_info[self.curr_pair]["predictions"][-clen:] = pred_array[-clen:].copy()

            '''
            pg = preds[-1]
            if pg <= dataframe["target_loss"].iloc[-1]:
                tag = "(*)"
            elif pg >= dataframe["target_profit"].iloc[-1]:
                tag = " * "
            else:
                tag = "   "
            #print(f"    {tag} predict {pg:6.2f}% gain for: {self.curr_pair}")

            '''

        except Exception as e:
            #print("*** Exception in add_latest_prediction()")
            #print(e)  # prints the error message
            #print(traceback.format_exc())  # prints the full traceback

        return dataframe

    # -------------

    # add predictions to dataframe['predicted_gain']
    def add_predictions(self, dataframe: DataFrame) -> DataFrame:
        # #print(f"    {self.curr_pair} adding predictions")

        run_profiler = False

        if run_profiler:
            prof = cProfile.Profile()
            prof.enable()

        self.scaler = RobustScaler()  # reset scaler each time

        self.init_model(dataframe)

        if self.curr_pair not in self.custom_trade_info:
            self.custom_trade_info[self.curr_pair] = {
                # 'model': self.model,
                "initialised": False,
                "predictions": None,
                "curr_prediction": 0.0,
                "curr_target": 0.0,
            }

        if self.training_mode:
            #print(f"    Training mode. Skipping backtest for {self.curr_pair}")
            dataframe["predicted_gain"] = 0.0
        else:
            if not self.custom_trade_info[self.curr_pair]["initialised"]:
                #print(f"    backtesting {self.curr_pair}")
                dataframe = self.add_jumping_predictions(dataframe)
                # dataframe = self.add_rolling_predictions(dataframe)
                self.custom_trade_info[self.curr_pair]["initialised"] = True
                self.custom_trade_info[self.curr_pair]["predictions"] = dataframe["predicted_gain"].copy()
            else:
                # #print(f'    updating latest prediction for: {self.curr_pair}')
                dataframe = self.add_latest_prediction(dataframe)

                # save latest prediction and threshold for later use (where dataframe is not available)
                self.custom_trade_info[self.curr_pair]["curr_prediction"] = dataframe["predicted_gain"].iloc[-1]
                self.custom_trade_info[self.curr_pair]["curr_target"] = dataframe["target_profit"].iloc[-1]

        # predictions can spike, so constrain range
        dataframe["predicted_gain"] = dataframe["predicted_gain"].clip(lower=-3.0, upper=3.0)

        if run_profiler:
            prof.disable()
            # print profiling output
            stats = pstats.Stats(prof).strip_dirs().sort_stats("cumtime")
            stats.print_stats(20)  # top 20 rows

        return dataframe

    ###################################

    """
    entry Signal
    """

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, "enter_tag"] = ""

        if self.training_mode:
            dataframe["enter_long"] = 0
            return dataframe

        dataframe = self.update_gain_targets(dataframe)

        # some trading volume (otherwise expect spread problems)
        conditions.append(dataframe["volume"] > 1.0)

        if self.enable_entry_guards.value:
            # Fisher/Williams in oversold region
            conditions.append(dataframe["fisher_wr"] < self.entry_guard_fwr.value)


        fwr_cond = dataframe["fisher_wr"] < -0.98

        # model triggers
        model_cond = (
            # model predicts a rise above the entry threshold
            # qtpylib.crossed_above(dataframe["predicted_gain"], dataframe["target_profit"])
            (dataframe["predicted_gain"] > dataframe["target_profit"])
            &
            # in lower portion of previous window
            (dataframe["close"] < dataframe["local_mean"])
        )

        # conditions.append(fwr_cond)
        conditions.append(model_cond)

        # set entry tags
        dataframe.loc[fwr_cond, "enter_tag"] += "fwr_entry "
        dataframe.loc[model_cond, "enter_tag"] += "model_entry "

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "enter_long"] = 1

        return dataframe

    ###################################

    """
    exit Signal
    """

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, "exit_tag"] = ""

        if self.training_mode or (not self.enable_exit_signal.value):
            dataframe["exit_long"] = 0
            return dataframe

        # if self.enable_entry_guards.value:

        # some trading volume (otherwise expect spread problems)
        conditions.append(dataframe["volume"] > 0)

        if self.enable_exit_guards.value:
            # Fisher/Williams in overbought region
            conditions.append(dataframe["fisher_wr"] > self.exit_guard_fwr.value)


        # model triggers
        model_cond = (
            # prediction crossed target
            # qtpylib.crossed_below(dataframe["predicted_gain"], dataframe["target_loss"])

            # use this version if volume checks are enabled, because we might miss the crossing otherwise
            (dataframe["predicted_gain"] < dataframe["target_loss"])
            &
            # in upper portion of previous window
            (dataframe["close"] > dataframe["local_mean"])
        )

        conditions.append(model_cond)

        # set exit tags
        dataframe.loc[model_cond, "exit_tag"] += "model_exit "

        if conditions:
            dataframe.loc[reduce(lambda x, y: x & y, conditions), "exit_long"] = 1

        return dataframe

    ###################################

    def confirm_trade_entry(
        self,
        pair: str,
        order_type: str,
        amount: float,
        rate: float,
        time_in_force: str,
        current_time: datetime,
        entry_tag: Optional[str],
        side: str,
        **kwargs,
    ) -> bool:
        # this only makes sense in 'live' modes
        if self.dp.runmode.value in ("backtest", "plot", "hyperopt"):
            return True

        # in 'real' systems, there is often a delay between the signal and the trade
        # double-check that predicted gain is still above threshold

        if pair in self.custom_trade_info:
            curr_pred = self.custom_trade_info[pair]["curr_prediction"]

            # check latest prediction against latest target

            curr_target = self.custom_trade_info[pair]["curr_target"]
            if curr_pred < curr_target:
                if self.dp.runmode.value not in ("backtest", "plot", "hyperopt"):
                    #print(
                        f"    *** {pair} Trade cancelled. Prediction ({curr_pred:.2f}%) below target ({curr_target:.2f}%) "
                    )
                return False

            """
            # check that prediction is still a profit, rather than a loss
            if curr_pred < 0.0:
                # if self.dp.runmode.value not in ('backtest', 'plot', 'hyperopt'):
                if self.dp.runmode.value not in ('hyperopt'):
                    #print(
                        f'    *** {pair} Trade cancelled. Prediction ({curr_pred:.2f}%) is now a loss '
                        )
                return False

            """

        # just debug
        if self.dp.runmode.value not in ("backtest", "plot", "hyperopt"):
            #print(f"    Trade Entry: {pair}, rate: {rate:.4f} Predicted gain: {curr_pred:.2f}% Target: {curr_target:.2f}%")

        return True

    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:
        
        if self.dp.runmode.value not in ("backtest", "plot", "hyperopt"):
            #print(f"    Trade Exit: {pair}, rate: {rate:.4f)}")

        return True

    ###################################

    """
    Custom Stoploss
    """

    # simplified version of custom trailing stoploss
    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        after_fill: bool,
        **kwargs,
    ) -> float:
        # this is just here so that we can use custom_exit
        """
        # if enable, use custom trailing ratio, else use default system
        if self.cstop_enable.value:
            # if current profit is above start value, then set stoploss at fraction of current profit
            if current_profit > self.cstop_start.value:
                return current_profit * self.cstop_ratio.value

        """

        # return min(-0.001, max(stoploss_from_open(0.05, current_profit), -0.99))
        return self.stoploss

    ###################################

    """
    Custom Exit
    (Note that this runs even if use_custom_stoploss is False)
    """

    # simplified version of custom exit

    def custom_exit(
        self, pair: str, trade: Trade, current_time: "datetime", current_rate: float, current_profit: float, **kwargs
    ):
        dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
        last_candle = dataframe.iloc[-1].squeeze()

        if not self.use_custom_stoploss:
            return None

        # check volume?!
        if last_candle['volume'] <= 1.0:
            return None

        # strong sell signal, in profit
        if (current_profit > 0.0) and (last_candle["fisher_wr"] >= self.cexit_fwr_overbought.value):
            return "fwr_overbought"

        # Above 0.5%, sell if Fisher/Williams in sell range
        if current_profit > 0.005:
            if last_candle["fisher_wr"] >= self.cexit_fwr_take_profit.value:
                return "take_profit"

        """
        # check profit against ROI target. This sort of emulates the freqtrade roi approach, but is much simpler
        if self.cexit_use_profit_threshold.value:
            if (current_profit >= self.cexit_profit_threshold.value):
                return 'cexit_profit_threshold'

        # check loss against threshold. This sort of emulates the freqtrade stoploss approach, but is much simpler
        if self.cexit_use_loss_threshold.value:
            if (current_profit <= self.cexit_loss_threshold.value):
                return 'cexit_loss_threshold'

        """

        # Sell any positions if open for >= 1 day with any level of profit
        if ((current_time - trade.open_date_utc).days >= 1) & (current_profit > 0):
            return "unclog_1"

        # Sell any positions at a loss if they are held for more than 7 days.
        if (current_time - trade.open_date_utc).days >= 7:
            return "unclog_7"

        # big drop predicted. Should also trigger an exit signal, but this might be quicker (and will likely be 'market' sell)
        if (current_profit > 0) and (last_candle["predicted_gain"] <= last_candle["target_loss"]):
            return "predict_drop"

        # large drop preduicted, just bail no matter profit
        if self.cexit_enable_large_drop.value:
            if last_candle["predicted_gain"] < self.cexit_large_drop.value:
                return "large_drop"

        # if in profit and exit signal is set, sell (even if exit signals are disabled)
        if (current_profit > 0) and (last_candle["exit_long"] > 0):
            return "exit_signal"

        return None