DCAScalping

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these imports ---
import numpy as np
import pandas as pd
from datetime import datetime, timedelta, timezone
from pandas import DataFrame
from typing import Dict, Optional, Union, Tuple

from freqtrade.strategy import (
    IStrategy,
    Trade,
    Order,
    PairLocks,
    informative,  # @informative decorator
    # Hyperopt Parameters
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    # timeframe helpers
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    # Strategy helper functions
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
)

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import pandas_ta as pta
from technical import qtpylib


class DCAScalping(IStrategy):
    """
    This is a strategy template to get you started.
    More information in https://www.freqtrade.io/en/latest/strategy-customization/

    You can:
        :return: a Dataframe with all mandatory indicators for the strategies
    - Rename the class name (Do not forget to update class_name)
    - Add any methods you want to build your strategy
    - Add any lib you need to build your strategy

    You must keep:
    - the lib in the section "Do not remove these libs"
    - the methods: populate_indicators, populate_entry_trend, populate_exit_trend
    You should keep:
    - timeframe, minimal_roi, stoploss, trailing_*
    """
    # 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 = "5m"

    # Can this strategy go short?
    can_short: bool = True

    # Enable adjustment position
    position_adjustment_enable = True

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {}

    # 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

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = True

    # These values can be overridden in the config.
    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Strategy parameters
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")

    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
        """
        # Momentum Indicators
        # ------------------------------------

        # RSI
        dataframe["rsi"] = ta.RSI(dataframe)

        # 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_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Based on TA indicators, populates the entry signal for the given dataframe
        :param dataframe: DataFrame
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with entry columns populated
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
                (dataframe["volume"] > 0)  # Make sure Volume is not 0
            ),
            "enter_long"] = 1
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'enter_short'] = 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
        :param metadata: Additional information, like the currently traded pair
        :return: DataFrame with exit columns populated
        """
        # dataframe.loc[
        #     (
        #         (qtpylib.crossed_above(dataframe["rsi"], self.sell_rsi.value)) &  # Signal: RSI crosses above sell_rsi
        #         (dataframe["volume"] > 0)  # Make sure Volume is not 0
        #     ),
        #     "exit_long"] = 1
        
        dataframe.loc[:, "exit_long"] = 0
        dataframe.loc[:, "exit_short"] =  0
        # Uncomment to use shorts (Only used in futures/margin mode. Check the documentation for more info)
        """
        dataframe.loc[
            (
                (qtpylib.crossed_above(dataframe["rsi"], self.buy_rsi.value)) &  # Signal: RSI crosses above buy_rsi
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'exit_short'] = 1
        """
        return dataframe
    
    # def custom_entry_price(self, pair: str, trade: Trade | None, current_time: datetime, proposed_rate: float,
    #                        entry_tag: str | None, side: str, **kwargs) -> float:
    #     dataframe, _ = self.dp.get_analyzed_dataframe(pair = pair, timeframe = self.timeframe)
    #     new_entryprice = dataframe["close"].iat[-1]
    #     print(f"new_entryprice::{new_entryprice}")
    #     return new_entryprice
    
    # Example specific variables
    max_entry_position_adjustment = 3
    # This number is explained a bit further down
    max_dca_multiplier = 5.5
    
    # This is called when placing the initial order (opening trade)
    def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                            proposed_stake: float, min_stake: float | None, max_stake: float,
                            leverage: float, entry_tag: str | None, side: str,
                            **kwargs) -> float:

        # We need to leave most of the funds for possible further DCA orders
        # This also applies to fixed stakes
        return proposed_stake / self.max_dca_multiplier # 100 / 5 = 20$/ order
    
    def adjust_trade_position(self, trade: Trade, current_time: datetime,
                              current_rate: float, current_profit: float,
                              min_stake: float | None, max_stake: float,
                              current_entry_rate: float, current_exit_rate: float,
                              current_entry_profit: float, current_exit_profit: float,
                              **kwargs
                              ) -> float | None | tuple[float | None, str | None]:
        """
        Custom trade adjustment logic, returning the stake amount that a trade should be
        increased or decreased.
        This means extra entry or exit orders with additional fees.
        Only called when `position_adjustment_enable` is set to True.

        For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/

        When not implemented by a strategy, returns None

        :param trade: trade object.
        :param current_time: datetime object, containing the current datetime
        :param current_rate: Current entry rate (same as current_entry_profit)
        :param current_profit: Current profit (as ratio), calculated based on current_rate 
                               (same as current_entry_profit).
        :param min_stake: Minimal stake size allowed by exchange (for both entries and exits)
        :param max_stake: Maximum stake allowed (either through balance, or by exchange limits).
        :param current_entry_rate: Current rate using entry pricing.
        :param current_exit_rate: Current rate using exit pricing.
        :param current_entry_profit: Current profit using entry pricing.
        :param current_exit_profit: Current profit using exit pricing.
        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
        :return float: Stake amount to adjust your trade,
                       Positive values to increase position, Negative values to decrease position.
                       Return None for no action.
                       Optionally, return a tuple with a 2nd element with an order reason
        """
        filled_entries = trade.select_filled_orders(trade.entry_side)
        count_of_entries = trade.nr_of_successful_entries
        price_steps = 0.02  # 1%
        target = 0.05   # 5%

        try:
            # This returns first order stake size
            stake_amount = filled_entries[0].stake_amount_filled

            # dca when profit is negative
            if (current_profit < -price_steps * count_of_entries):
                stake_amount *= (1 + (count_of_entries * 0.25))
                return stake_amount, "increase_stake"
            
            elif current_profit >= target:
                # Take all of the profit when get target
                return -(trade.stake_amount), "get_dca_profit"
            
            return None
        except Exception as exception:
            return None