import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

import os

class Config:
  def __init__(self, instrument='SPX500', timeframe='D1', force_regenerate_mxfiles=True, mfi_flag=True, balligator_flag=True, talligator_flag=True, regenerate_cds=True, use_fresh=True, quiet=False, jgtdroot_default="/b/Dropbox/jgt", drop_subdir="drop", result_file_basename_default="jgtml_obsds_240515_TIDE_SIGNALS.result"):
    self.instrument = instrument
    self.timeframe = timeframe
    self.force_regenerate_mxfiles = force_regenerate_mxfiles
    self.mfi_flag = mfi_flag
    self.balligator_flag = balligator_flag
    self.talligator_flag = talligator_flag
    self.regenerate_cds = regenerate_cds
    self.use_fresh = use_fresh
    self.quiet = quiet
    self.jgtdroot = os.getenv("jgtdroot", jgtdroot_default)
    self.drop_subdir = drop_subdir
    self.result_file_basename = result_file_basename_default

  def get_config(self):
    return {
      'instrument': self.instrument,
      'timeframe': self.timeframe,
      'force_regenerate_mxfiles': self.force_regenerate_mxfiles,
      'mfi_flag': self.mfi_flag,
      'balligator_flag': self.balligator_flag,
      'talligator_flag': self.talligator_flag,
      'regenerate_cds': self.regenerate_cds,
      'use_fresh': self.use_fresh,
      'quiet': self.quiet,
      'jgtdroot': self.jgtdroot,
      'drop_subdir': self.drop_subdir,
      'result_file_basename': self.result_file_basename
    }

class TideAlligatorAnalysis:
  def __init__(self, instrument, timeframe):
    self.instrument = instrument
    self.timeframe = timeframe

  def read_market_data(self):
    # Implement functionality to read historical market data
    pass

  def calculate_indicators(self):
    # Implement functionality to calculate technical indicators
    pass

  def identify_signals(self):
    # Implement functionality to identify valid SELL and BUY signals
    pass

  def evaluate_profitability(self):
    # Implement functionality to evaluate the profitability of each valid signal's direction
    pass

  def analyze_profit_distribution(self):
    # Implement functionality to analyze the distribution of profit potential across different signal types
    pass

  def compare_performance(self):
    # Implement functionality to compare the performance of different signal types
    pass

  def output_results(self):
    # Implement functionality to output the results of the analysis as a CSV file
    pass

  def generate_summary(self):
    # Implement functionality to generate a markdown file summarizing the key findings
    pass