RSIMomentumV1

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,
    BooleanParameter,
    CategoricalParameter,
    DecimalParameter,
    IntParameter,
    RealParameter,
    timeframe_to_minutes,
    timeframe_to_next_date,
    timeframe_to_prev_date,
    merge_informative_pair,
    stoploss_from_absolute,
    stoploss_from_open,
    AnnotationType,
)

import talib.abstract as ta
from technical import qtpylib


class RSIMomentumV1(IStrategy):

    """
    Simple RSI + momentum strategy.
    Uses RSI cross + TEMA relative to Bollinger mid as entry/exit conditions.
    """

    INTERFACE_VERSION = 3

    timeframe = "5m"
    can_short: bool = False

    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04,
    }

    stoploss = -0.10

    trailing_stop = False

    process_only_new_candles = True

    use_exit_signal = True
    exit_profit_only = False
    ignore_roi_if_entry_signal = False

    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")

    order_types = {
        "entry": "limit",
        "exit": "limit",
        "stoploss": "market",
        "stoploss_on_exchange": False,
    }

    order_time_in_force = {
        "entry": "GTC",
        "exit": "GTC",
    }

    @property
    def plot_config(self):
        return {
            "main_plot": {
                "tema": {},
                "sar": {"color": "white"},
            },
            "subplots": {
                "MACD": {
                    "macd": {"color": "blue"},
                    "macdsignal": {"color": "orange"},
                },
                "RSI": {
                    "rsi": {"color": "red"},
                },
            },
        }

    def informative_pairs(self):
        return []

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

        # ADX
        dataframe["adx"] = ta.ADX(dataframe)

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

        # Stochastic Fast
        stoch_fast = ta.STOCHF(dataframe)
        dataframe["fastd"] = stoch_fast["fastd"]
        dataframe["fastk"] = stoch_fast["fastk"]

        # MACD
        macd = ta.MACD(dataframe)
        dataframe["macd"] = macd["macd"]
        dataframe["macdsignal"] = macd["macdsignal"]
        dataframe["macdhist"] = macd["macdhist"]

        # MFI
        dataframe["mfi"] = ta.MFI(dataframe)

        # Bollinger Bands
        bollinger = qtpylib.bollinger_bands(
            qtpylib.typical_price(dataframe), window=20, stds=2
        )
        dataframe["bb_lowerband"] = bollinger["lower"]
        dataframe["bb_middleband"] = bollinger["mid"]
        dataframe["bb_upperband"] = bollinger["upper"]
        dataframe["bb_percent"] = (
            (dataframe["close"] - dataframe["bb_lowerband"])
            / (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
        )
        dataframe["bb_width"] = (
            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
            / dataframe["bb_middleband"]
        )

        # Parabolic SAR
        dataframe["sar"] = ta.SAR(dataframe)

        # TEMA
        dataframe["tema"] = ta.TEMA(dataframe, timeperiod=9)

        # Hilbert Transform SineWave
        hilbert = ta.HT_SINE(dataframe)
        dataframe["htsine"] = hilbert["sine"]
        dataframe["htleadsine"] = hilbert["leadsine"]

        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:

        dataframe.loc[
            (
                qtpylib.crossed_above(
                    dataframe["rsi"], float(self.buy_rsi.value)
                )
            )
            & (dataframe["tema"] <= dataframe["bb_middleband"])
            & (dataframe["tema"] > dataframe["tema"].shift(1))
            & (dataframe["volume"] > 0),
            "enter_long",
        ] = 1

        return dataframe

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

        dataframe.loc[
            (
                qtpylib.crossed_above(
                    dataframe["rsi"], float(self.sell_rsi.value)
                )
            )
            & (dataframe["tema"] > dataframe["bb_middleband"])
            & (dataframe["tema"] < dataframe["tema"].shift(1))
            & (dataframe["volume"] > 0),
            "exit_long",
        ] = 1

        return dataframe