Prediction_Strategy

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
import xgboost
import catboost
import sklearn
import pickle
from numba import jit
from scipy import signal

from freqtrade.strategy import IStrategy
import technical.indicators as ftt
# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

# This class is a sample. Feel free to customize it.
class Prediction_Strategy(IStrategy):

    INTERFACE_VERSION = 2

    minimal_roi = {
        "360": 0.0,
        "240": 0.05,
        "0": 0.1
    }

    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.9

    # 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 = '1h'

    # 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 = True

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 240

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    def informative_pairs(self):

        return []

    

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        verbose = False
        col_use = [
                    'volume','smadiff_3','smadiff_5','smadiff_8','smadiff_13',
                    'smadiff_21','smadiff_34','smadiff_55','smadiff_89',
                    'smadiff_120','smadiff_240','maxdiff_3','maxdiff_5','maxdiff_8',
                    'maxdiff_13','maxdiff_21','maxdiff_34','maxdiff_55','maxdiff_89',
                    'maxdiff_120','maxdiff_240','std_3','std_5','std_8','std_13',
                    'std_21','std_34','std_55','std_89','std_120','std_240',
                    'ma_3','ma_5','ma_8','ma_13','ma_21','ma_34','ma_55','ma_89',
                    'ma_120','ma_240','z_score_120','time_hourmin','time_dayofweek','time_hour' ]


        with open('user_data/notebooks/model_portfolio.pkl', 'rb') as f:
            model = pickle.load(f)
        model = model[0]

        # Starting create features
        #sma diff
        for i in [3,5,8,13,21,34,55,89,120,240]:
            dataframe[f"smadiff_{i}"] = (dataframe['close'].rolling(i).mean() - dataframe['close'])
        #max diff
        for i in [3,5,8,13,21,34,55,89,120,240]:
            dataframe[f"maxdiff_{i}"] = (dataframe['close'].rolling(i).max() - dataframe['close'])
        #min diff
        for i in [3,5,8,13,21,34,55,89,120,240]:
            dataframe[f"maxdiff_{i}"] = (dataframe['close'].rolling(i).min() - dataframe['close'])
        #volatiliy
        for i in [3,5,8,13,21,34,55,89,120,240]:
            dataframe[f"std_{i}"] = dataframe['close'].rolling(i).std()
        
        #Return
        for i in [3,5,8,13,21,34,55,89,120,240]:
            dataframe[f"ma_{i}"] = dataframe['close'].pct_change(i).rolling(i).mean()
        
        dataframe['z_score_120'] = ((dataframe.ma_13 - dataframe.ma_13.rolling(21).mean() + 1e-9) 
                            / (dataframe.ma_13.rolling(21).std() + 1e-9))
        
        dataframe["date"] = pd.to_datetime(dataframe["date"], unit='ms')
        dataframe['time_hourmin'] = dataframe.date.dt.hour * 60 + dataframe.date.dt.minute
        dataframe['time_dayofweek'] = dataframe.date.dt.dayofweek
        dataframe['time_hour'] = dataframe.date.dt.hour

        #Model predictions
        preds = pd.DataFrame(model.predict_proba(dataframe[col_use]))
        preds.columns = [f"pred{i}" for i in range(5)]
        dataframe = dataframe.reset_index(drop=True)
        dataframe = pd.concat([dataframe, preds], axis=1)

        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                (dataframe['pred4'] > .45) & 
                #(dataframe["time_hour"].isin([23,2,5,8,11,14,17,20])) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                (dataframe['pred4'] < .28) 
                #& (dataframe["time_hour"].isin([23,2,5,8,11,14,17,20]))
                & (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe