TrendlineStrategy

import numpy as np
import pandas as pd
from pandas import DataFrame
from datetime import datetime
from typing import Optional, Union

from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IntParameter, IStrategy, merge_informative_pair)

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


def detect_trendline(df, window=2):
    # Define the rolling window
    roll_window = window
    # Create new columns for the linear regression slope and y-intercept
    df['slope'] = np.nan
    df['intercept'] = np.nan

    for i in range(window, len(df)):
        x = np.array(range(i-window, i))
        y = df['Close'][i-window:i]
        A = np.vstack([x, np.ones(len(x))]).T
        m, c = np.linalg.lstsq(A, y, rcond=None)[0]
        df.at[df.index[i], 'slope'] = m
        df.at[df.index[i], 'intercept'] = c

    # Create a boolean mask for trendline support
    mask_support = df['slope'] > 0

    # Create a boolean mask for trendline resistance
    mask_resistance = df['slope'] < 0

    # Create new columns for trendline support and resistance
    df['support'] = np.nan
    df['resistance'] = np.nan

    # Populate the new columns using the boolean masks
    df.loc[mask_support, 'support'] = df['Close'] * df['slope'] + df['intercept']
    df.loc[mask_resistance, 'resistance'] = df['Close'] * df['slope'] + df['intercept']

    return df
class TrendlineStrategy(IStrategy):


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

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['Close'] > dataframe['support']) &
            (qtpylib.crossed_above(dataframe['close'], dataframe['support']))
            'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (dataframe['Close'] < dataframe['resistance']) &
            (qtpylib.crossed_below(dataframe['Close'], dataframe['resistance']))
            'exit_long'] = 1
        return dataframe