# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from datetime import datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import IStrategy, stoploss_from_open
pd.options.mode.chained_assignment = None  # default='warn'
# --------------------------------
# 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 PrawnstarOBV(IStrategy):
    # Strategy interface version - allow new iterations of the strategy interface.
    # Check the documentation or the Sample strategy to get the latest version.
    INTERFACE_VERSION = 3
    # Optimal timeframe for the strategy
    timeframe = '1h'
    # ROI table:
    #minimal_roi = {
    #    "0": 0.8
    #}
    minimal_roi = {'0': 0.296, '179': 0.137, '810': 0.025, '1024': 0}
    # Stoploss:
    stoploss = -0.15
    # Trailing stop:
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.04
    trailing_only_offset_is_reached = True
    # 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_exit_signal = False
    use_entry_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = True
    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30
    # Optional order type mapping.
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market', 'stoploss_on_exchange': False}

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # Momentum Indicators
        # ------------------------------------
        # Momentum
        dataframe['rsi'] = ta.RSI(dataframe)
        dataframe['obv'] = ta.OBV(dataframe)
        dataframe['obvSma'] = ta.SMA(dataframe['obv'], timeperiod=7)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[qtpylib.crossed_above(dataframe['obv'], dataframe['obvSma']) & (dataframe['rsi'] < 50) | ((dataframe['obvSma'] - dataframe['close']) / dataframe['obvSma'] > 0.1) | (dataframe['obv'] > dataframe['obv'].shift(1)) & (dataframe['obvSma'] > dataframe['obvSma'].shift(5)) & (dataframe['rsi'] < 50), 'entry'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the exit signal for the given dataframe
        :param dataframe: DataFrame
        :return: DataFrame with entry column
        """
        dataframe.loc[(), 'exit'] = 1
        return dataframe