#region imports
from AlgorithmImports import *
#endregion
import os
import re

def extract_stock_symbols(project_dir):
    # Create a set to store unique stock symbols
    symbols_set = set()

    # Traverse through all subdirectories in the project directory
    for root, dirs, files in os.walk(project_dir):
        for file in files:
            if file.endswith(".py"):
                file_path = os.path.join(root, file)
                with open(file_path, "r") as f:
                    content = f.read()
                    # Extract stock symbols from the file content
                    symbols_array = extract_symbols(content)
                    # Add unique symbols to the set
                    symbols_set.update(symbols_array)

    # Print the unique stock symbols to the terminal
    print("Unique Stock Symbols:")
    print("'" + "','".join(sorted(symbols_set)) + "'")

def extract_symbols(text):
    pattern = r"'\b([A-Z]{2,5})\b'"
    matches = re.findall(pattern, text)
    return list(set(matches))

# Provide the path to your project directory
project_directory = "Strategies"

# Run the script
extract_stock_symbols(project_directory)

#region imports
from AlgorithmImports import *
#endregion
import os
import re

def replace_pattern(project_dir):
    pattern = r"CumReturn\(self\.algo,\s*'([A-Z]+)',\s*(\d+)\)\s*([<>]=?)\s*([-+]?\d+(?:\.\d+)?)"
    
    for root, dirs, files in os.walk(project_dir):
        for file in files:
            if file.endswith(".py"):
                file_path = os.path.join(root, file)
                with open(file_path, "r") as f:
                    content = f.read()
                
                matches = list(re.finditer(pattern, content))
                modified_content = content
                
                for match in reversed(matches):
                    found = match.group()
                    number = float(match.group(4))
                    
                    if abs(number) >= 1:
                        fixed = replace_match(match)
                        modified_content = modified_content[:match.start()] + fixed + modified_content[match.end():]
                        print(f"File: {file_path}")
                        print(f"Found: {found}")
                        print(f"Fixed: {fixed}")
                        print("---")
                
                if modified_content != content:
                    with open(file_path, "w") as f:
                        f.write(modified_content)

def replace_match(match):
    ticker = match.group(1)
    period = match.group(2)
    operator = match.group(3)
    number = float(match.group(4))
    
    if abs(number) >= 1:
        number /= 100
    
    return f"CumReturn(self.algo, '{ticker}', {period}) {operator} {number}"

# Provide the path to your project directory
project_directory = "Strategies"

# Run the script
replace_pattern(project_directory)

#region imports
from AlgorithmImports import *
#endregion


# Your New Python File

#CombinedStrategy1/version1.py
from AlgorithmImports import *
from indicators import *
from main import DogeStrat


#https://app.composer.trade/symphony/FdIbE2K1ktRIfMQDMJYo/details
class TMFMomentumStrategy(DogeStrat):
    def __init__(self, algo):
        super().__init__()
        self.algo = algo

    def Execute(self):
        self.group_tmf_momentum()

    def group_tmf_momentum(self):
        if RSI(self.algo, 'QQQ', 10) > 80:
            AH(self.algo, 'VIXY', 1, 1)
        elif RSI(self.algo, 'SPY', 10) > 80:
            AH(self.algo, 'VIXY', 1, 1)
        elif RSI(self.algo, 'TMF', 10) < 32:
            AH(self.algo, 'TMF', 1, 1)
        elif RSI(self.algo, 'BIL', 30) < RSI(self.algo, 'TLT', 20):
            if EMA(self.algo, 'TMF', 8) < SMA(self.algo, 'TMF', 10):
                if RSI(self.algo, 'TMF', 10) > 72:
                    AH(self.algo, 'SHV', 1, 1)
                else:
                    AH(self.algo, 'TMF', 1, 1)
            else:
                if RSI(self.algo, 'TQQQ', 10) < 31:
                    AH(self.algo, 'TECL', 1, 1)
                else:
                    AH(self.algo, 'SHV', 1, 1)
        else:
            if RSI(self.algo, 'TQQQ', 10) < 31:
                AH(self.algo, 'TECL', 1, 1)
            else:
                AH(self.algo, 'SHV', 1, 1)

#indicators.py
from AlgorithmImports import *
from AlgorithmImports import *
import math
import pandas as pd
from cmath import sqrt
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Custom import *
from QuantConnect.Python import PythonData
import csv
import io
import time
import json
import random
import re

def RSI(self, equity, period):
    extension = min(period * 5, 250)
    r_w = RollingWindow[float](extension)
    history = self.History(equity, extension - 1, Resolution.Daily)
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    while r_w.Count < extension:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    if r_w.IsReady:
        average_gain = 0
        average_loss = 0
        gain = 0
        loss = 0
        for i in range(extension - 1, extension - period - 1, -1):
            if i - 1 < 0:
                diff = random.randint(0, 99)
            else:
                diff = r_w[i - 1] - r_w[i]
            gain += max(diff, 0)
            loss += abs(min(diff, 0))
        average_gain = gain / period
        average_loss = loss / period
        for i in range(extension - period - 1, 0, -1):
            if i - 1 < 0:
                diff = random.randint(0, 99)
            else:
                diff = r_w[i - 1] - r_w[i]
            average_gain = (average_gain * (period - 1) + max(diff, 0)) / period
            average_loss = (average_loss * (period - 1) + abs(min(diff, 0))) / period
        if average_loss == 0:
            return 100
        else:
            rsi = 100 - (100 / (1 + average_gain / average_loss))
            return rsi
    else:
        return None
def CumReturn(self,equity,period):
    history = self.History(equity,period,Resolution.Daily)
    closing_prices = pd.Series([bar.Close for bar in history])
    current_price = self.Securities[equity].Price
    closing_prices = pd.concat([closing_prices, pd.Series([current_price])])
    first_price = closing_prices.iloc[0]
    if first_price == 0:
        return 0
    else:
        return_val = (current_price/first_price) - 1
        return return_val
def STD(self, equity, period):
    r_w = RollingWindow[float](period + 1)
    r_w_return = RollingWindow[float](period)
    history = self.History(equity, period, Resolution.Daily)
    
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    
    while r_w.Count < period + 1:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    
    for i in range(period, 0, -1):
        if r_w[i] != 0:
            daily_return = (r_w[i-1] / r_w[i] - 1)
        else:
            daily_return = 0  # or any other appropriate value
        r_w_return.Add(daily_return)
    
    dfstd = pd.DataFrame({'r_w_return': r_w_return})
    
    if r_w.IsReady:
        std = dfstd['r_w_return'].std()
        if std == 0:
            return 0
        else:
            return std
    else:
        return 0
def MaxDD(self, equity, period):
        history = self.History(equity,period - 1,Resolution.Daily)
        closing_prices = pd.Series([bar.Close for bar in history])
        current_price = self.Securities[equity].Price
        closing_prices = pd.concat([closing_prices, pd.Series([current_price])])
        rolling_max = closing_prices.cummax()
        drawdowns = (rolling_max - closing_prices)/rolling_max
        max_dd = drawdowns.max()
        return max_dd
def SMA(self,equity,period):

    r_w = RollingWindow[float](period)
    history = self.History(equity,period - 1,Resolution.Daily)
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    while r_w.Count < period:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    if r_w.IsReady:
        sma = sum(r_w)/period
        return sma
    else:
        return 0
def IV(self,equity,period):
    r_w = RollingWindow[float](period + 1)
    r_w_return = RollingWindow[float](period)
    history = self.History(equity,period,Resolution.Daily)
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    while r_w.Count < period + 1:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    for i in range (period,0,-1):
        if r_w[i] == 0:
            return 0
        else:
            daily_return = (r_w[i-1]/r_w[i] - 1)
            r_w_return.Add(daily_return)
    dfinverse = pd.DataFrame({'r_w_return':r_w_return})
    if r_w.IsReady:
        std = dfinverse['r_w_return'].std()
        if std == 0:
            return 0
        else:
            inv_vol = 1/std
            return inv_vol
    else:
        return 0
def SMADayRet(self, equity, period):
    r_w = RollingWindow[float](period + 1)
    r_w_return = RollingWindow[float](period)
    history = self.History(equity, period, Resolution.Daily)
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    while r_w.Count < period + 1:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    for i in range(period, 0, -1):
        if r_w[i] == 0:
            return 0  # Return 0 instead of None
        daily_return = (r_w[i-1] / r_w[i] - 1)
        r_w_return.Add(daily_return)
    if r_w.IsReady:
        smareturn = sum(r_w_return) / period
        return smareturn
    else:
        return 0
def EMA(self,equity,period):
    extension = period + 50
    r_w = RollingWindow[float](extension)
    history = self.History(equity,extension - 1,Resolution.Daily)
    for historical_bar in history:
        r_w.Add(historical_bar.Close)
    while r_w.Count < extension:
        current_price = self.Securities[equity].Price
        r_w.Add(current_price)
    if r_w.IsReady:
        total_price = 0
        for i in range(extension - 1,extension - period - 2,-1):
            total_price += r_w[i]
        average_price = total_price/period
        for i in range(extension - period - 2,-1,-1):
            average_price = r_w[i]*2/(period+1) + average_price*(1-2/(period+1))
        return average_price
    else:
        return None

def Sort(self, sort_type, equities, period, reverse, num_assets, number, multiplier):
    # Update the PT value for the given strategy number
    PT = getattr(self, f"PT{number}") * multiplier

    # Define a dictionary to map sort types to functions
    indicator_functions = {
        'EMA': EMA,
        'RSI': RSI,
        'CumReturn': CumReturn,
        'STD': STD,
        'MaxDD': MaxDD,
        'SMA': SMA,
        'IV': IV,
        'SMADayRet': SMADayRet,
        # Add other indicators here as needed
    }

    # Compute the indicator value for each equity
    returns = {}
    for equity in equities:
        indicator_function = indicator_functions.get(sort_type, None)
        if callable(indicator_function):
            indicator_value = indicator_function(self, equity, period)
            if indicator_value is not None:
                returns[equity] = indicator_value

    # Sort the equities based on the indicator values
    sorted_equities = sorted(returns.items(), key=lambda x: x[1], reverse=reverse)

    # Select the top 'num_assets' from the sorted list
    top_equities = sorted_equities[:num_assets]

    # Get the current HT and HTS attributes for the strategy
    HT = getattr(self, f"HT{number}")
    HTS = getattr(self, f"HTS{number}")

    # Assign each of the top equities to the next available slot in HT and HTS
    for equity, value in top_equities:
        for i in HT.keys():
            if HT[i] == 0:
                HT[i] = PT / num_assets  # Distribute PT evenly among the assets
                HTS[i].append(equity)  # Append the asset symbol
                break  # Move to the next asset after appending

    # Update the HT and HTS attributes for the strategy
    setattr(self, f"HT{number}", HT)
    setattr(self, f"HTS{number}", HTS)


def AH(self,equities,PTnumber,multiplier): #AppendHolding
    if not isinstance(equities,list):
        equities = [equities]
    HT = getattr(self,f"HT{PTnumber}")
    HTS = getattr(self,f"HTS{PTnumber}")
    PT = getattr(self,f"PT{PTnumber}") * multiplier
    
    for equity in equities:
        for i in HT.keys():
            if HT[i] == 0:
                HT[i] = PT
                HTS[i].append(equity)
                break

def GetCurrentPrice(self, symbol):
    """
    Gets the current price of a security.

    :param self: The self instance containing the securities.
    :param symbol: The symbol of the security.
    :return: The current price of the security or None if not available.
    """
    if symbol in self.Securities:
        return self.Securities[symbol].Price
    else:
        self.Debug(f"Symbol {symbol} not found in securities.")
        return None

def AHIV(self, assets, period, PTnumber, multiplier):
    if isinstance(assets, str):
        assets = [assets]

    # Calculate sum_IV_assets
    tickers_IV = []
    for ticker in assets:
        try:
            IV_value = eval(f"IV(self, '{ticker}', {period})")
            tickers_IV.append(IV_value)
        except:
            self.Log(f"Error calculating IV for ticker: {ticker}")
            continue

    sum_IV_assets_value = sum(tickers_IV)

    HT = getattr(self, f"HT{PTnumber}")
    HTS = getattr(self, f"HTS{PTnumber}")
    PT = getattr(self, f"PT{PTnumber}") * multiplier

    # Call the AH function for each asset with the calculated portion
    for ticker in assets:
        try:
            IV_value = eval(f"IV(self, '{ticker}', {period})")
            portion = IV_value / sum_IV_assets_value
        except:
            self.Log(f"Error calculating portion for ticker: {ticker}")
            continue

        for i in HT.keys():
            if HT[i] == 0:
                HT[i] = portion * PT
                HTS[i].append(ticker)
                break

def GroupSort(self, filter_type, group_methods, window, select_type, num_assets, number, multiplier):
    try:
        # Define a dictionary to map filter types to their corresponding functions
        indicator_functions = {
            'EMA': EMA,
            'RSI': RSI,
            'CumReturn': CumReturn,
            'STD': STD,
            'MaxDD': MaxDD,
            'SMA': SMA,
            'IV': IV,
            'SMADayRet': SMADayRet,
        }

        # Ensure the filter type is supported
        if filter_type not in indicator_functions:
            raise ValueError(f"Unsupported filter type: {filter_type}")

        # Calculate the weighted indicator values for each group
        group_indicator_values = {}
        for group_method in group_methods:
            if group_method is None:
                continue  # Skip None values in group_methods

            # Execute the group method to get the equities and weights
            group_method_func = getattr(self, group_method)
            result = group_method_func()
            if result is not None and isinstance(result, tuple) and len(result) == 2:
                equities, weights = result

                # Calculate the weighted indicator value for the group
                weighted_indicator_value = 0
                for equity, weight in zip(equities, weights):
                    indicator_value = indicator_functions[filter_type](self, equity, window)
                    if indicator_value is not None:
                        weighted_indicator_value += weight * indicator_value
                group_indicator_values[group_method] = weighted_indicator_value

        # Determine sorting direction
        reverse = True if select_type == 'Top' else False

        # Sort groups based on the weighted indicator values
        sorted_groups = sorted(group_indicator_values.items(), key=lambda x: x[1], reverse=reverse)[:num_assets]

        # Get the current HT and HTS attributes for the strategy
        HT = getattr(self, f"HT{number}")
        HTS = getattr(self, f"HTS{number}")

        # Adjust the multiplier based on the number of assets
        adjusted_multiplier = multiplier / num_assets

        # Update HT and HTS based on the sorted groups
        total_weight = sum(weight for _, weight in sorted_groups)
        allocated_tickers = set()  # Track allocated tickers
        total_allocation = 0  # Track total allocation

        for group_method, _ in sorted_groups:
            group_method_func = getattr(self, group_method)
            equities, weights = group_method_func()
            for equity, weight in zip(equities, weights):
                if equity not in allocated_tickers:  # Check if the ticker is already allocated
                    for i in HT.keys():
                        if HT[i] == 0:
                            allocation = (weight / total_weight) * adjusted_multiplier
                            total_allocation += allocation
                            if total_allocation <= 1.0:  # Check if total allocation exceeds 100%
                                HT[i] = allocation
                                HTS[i].append(equity)
                                allocated_tickers.add(equity)  # Mark the ticker as allocated
                            break

        # Scale down the allocations if the total allocation exceeds 100%
        if total_allocation > 1.0:
            scale_factor = 1.0 / total_allocation
            for i in HT.keys():
                HT[i] *= scale_factor

        # Update the HT and HTS attributes for the strategy
        setattr(self, f"HT{number}", HT)
        setattr(self, f"HTS{number}", HTS)

    except:
        # If an error occurs, capture the group methods using regex and execute them with equal allocation
        group_method_pattern = re.compile(r'self\.(\w+)\(\)')
        group_methods_str = str(group_methods)
        captured_group_methods = group_method_pattern.findall(group_methods_str)
        num_groups = len(captured_group_methods)
        equal_allocation = 1.0 / num_groups

        for group_method in captured_group_methods:
            group_method_func = getattr(self, group_method)
            if callable(group_method_func):
                equities, weights = group_method_func()
                total_weight = sum(weights)
                for equity, weight in zip(equities, weights):
                    for i in HT.keys():
                        if HT[i] == 0:
                            allocation = (weight / total_weight) * equal_allocation
                            HT[i] = allocation
                            HTS[i].append(equity)
                            break

        # Update the HT and HTS attributes for the strategy
        setattr(self, f"HT{number}", HT)
        setattr(self, f"HTS{number}", HTS)

# main.py
from AlgorithmImports import *
import math
import pandas as pd
from cmath import sqrt
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Data.Custom import *
from QuantConnect.Python import PythonData
import csv
import io
import time
import json
import os
import importlib
import inspect
import re
import numpy as np
import types
from indicators import *

class DogeStrat(QCAlgorithm):

    def Initialize(self):
        self.cash = 100000
        
        self.SetStartDate(2023,8,11)
        self.SetEndDate(2024,8,10)
        self.SetCash(self.cash)
        self.equities = ['AAPL','ADBE','AGG','AMZN','BA','BIL','BSV','BTAL','CSX','DBC','DIA','DOG','DPST','DRN','DRV','EEM','ERX','ERY','FAS','FAZ','GE','GLD','GOOG','IBM','IEF','INTC','IWM','JPM','LLY','META','MSFT','NAIL','NFLX','NVDA','PSQ','QID','QQQ','RSI','RWM','SH','SHV','SHY','SMH','SOXL','SOXS','SOXX','SPHB','SPLV','SPTL','SPXL','SPXS','SPXU','SPY','SQQQ','SRTY','STX','SVXY','TBF','TECL','TECS','TLH','TLT','TMF','TMV','TNA','TQQQ','TSLA','UCO','UDOW','UPRO','UPS','URTY','USRT','UUP','UVXY','VIXM','VIXY','WDC','WFC','XLE','XLK','XLP','XLRE','YANG','YINN']

        for equity in self.equities:
            self.AddEquity(equity,Resolution.Minute)
            self.Securities[equity].SetDataNormalizationMode(DataNormalizationMode.Adjusted)

        self.exclude_symbols = ['BIL','BOXX','BSV','BUCK','CSHI','ICSH','IEI','MINT','SHV','SHY','SOF','TFLO','USFR','UTWO','STIP','VCIT','LQD','VTIP','TLT','BND','IEF','TIP','VGIT','IYK']
        # Base directory for strategies
        strategy_base_dir = "Strategies"
        
        # List of strategy names (you would populate this list dynamically)
        self.strategy_names = [
            "CombinedStrategy1",

        ]

        # Get the absolute path of the current file (main.py)
        current_file_path = os.path.abspath(__file__)

        # Get the directory of the current file
        current_directory = os.path.dirname(current_file_path)

        # Construct the path to the Strategies folder
        strategies_directory = os.path.join(current_directory, "Strategies")

        # Dynamically import strategies and create instances
        self.strategies = {}

        # Generate setattr(self, f'HT...') from 1 to 1000
        for i in range(1, 500):
            setattr(self, f'HT{i}', {str(j).zfill(2): 0 for j in range(1, 10)})
            setattr(self, f'HTS{i}', {str(j).zfill(2): [] for j in range(1, 10)})

        for strategy_name in self.strategy_names:
            strategy_module_name = f"Strategies.{strategy_name}.version1"
            strategy_module = importlib.import_module(strategy_module_name)

            # Get the source code of the strategy module
            strategy_module_source = inspect.getsource(strategy_module)

            # Find all occurrences of "class ...Strategy" in the source code
            strategy_class_names = re.findall(r'class\s+(\w+Strategy)', strategy_module_source)

            for strategy_class_name in strategy_class_names:
                # Get the strategy class from the module
                strategy_class = getattr(strategy_module, strategy_class_name)

                # Extract the assigned number from the strategy class name if available
                assigned_numbers = re.findall(r'\d+', strategy_class_name)
                if assigned_numbers:
                    assigned_number = int(assigned_numbers[0])
                else:
                    assigned_number = None

                # Create an instance of the strategy class, passing 'self' as the 'context' argument
                strategy_instance = strategy_class(self)

                # Store the strategy instance in the dictionary
                self.strategies[strategy_class_name] = strategy_instance

        self.PTMaster = 1

        self.filter_value = 0.02
        self.buffer_pct = 0.05 #Rebalance
        self.number_of_PT = len(self.strategies) + 10

        # Fixed strategies and their percentages
        self.fixed_strategies = {
            1: 0.94,  # Strategy 1 with 94%
            

        }

        # Initialize PT variables for each strategy
        for i in range(1, self.number_of_PT + 1):
            if i in self.fixed_strategies:
                setattr(self, f'PT{i}', self.fixed_strategies[i] * self.PTMaster)

        # Assign self.algo to self
        self.algo = self

        # Dictionary to store group definitions
        self.group_definitions = {}

        # Iterate over the strategies
        for strategy_name, strategy in self.strategies.items():
            # Get the source code of the strategy module
            strategy_module_source = inspect.getsource(strategy.__class__)

            # Find all occurrences of "GroupSort(" and "Sort(" in the source code
            group_sort_calls = re.findall(r"GroupSort\(.*?\)", strategy_module_source)

            # Extract the group names from the GroupSort and Sort calls and add them to the dictionary
            for call in group_sort_calls:
                group_names = re.findall(r"'(.*?)'", call)
                for group_name in group_names:
                    if group_name not in self.group_definitions:
                        # Add the group name to the dictionary with a default definition
                        self.group_definitions[group_name] = f"""
            def {group_name}(self):
                # Define the logic for the {group_name} method
                # Return a tuple of equities and weights
                equities = []
                weights = []
                return equities, weights
            """.lstrip()  # Remove leading whitespace

        # Dynamically generate the group methods in the DogeStrat instance
        for group_name, group_def in self.group_definitions.items():
            exec(group_def, globals(), locals())
            setattr(self, group_name, types.MethodType(locals()[group_name], self))

        self.Schedule.On(self.DateRules.EveryDay("SPY"),
                self.TimeRules.BeforeMarketClose("SPY",5),
                self.FunctionBeforeMarketClose)

            
    def OnData(self, data):
        # This function is called every time new data is received
        pass

    def FunctionBeforeMarketClose(self):
        # Execute all strategies stored in the dictionary
        for strategy_name, strategy in self.strategies.items():
            strategy.Execute()
        self.LogStrategies()
        self.ExecuteTrade()
        self.SetVarToZero()

    def ExecuteTrade(self):
        df_list = []

        # Process each top-performing strategy
        for strategy_number in self.FinalTickers:
            HTS_attr = getattr(self, f'HTS{strategy_number}')
            HT_attr = getattr(self, f'HT{strategy_number}')

            group = {
                'HTS': [HTS_attr[i][0] if len(HTS_attr[i]) == 1 else HTS_attr[i] for i in HTS_attr],
                'HT': [HT_attr[i]/1 for i in HT_attr]
            }
            df = pd.DataFrame(group)
            df_list.append(df)

        # Combine all dataframes
        df_combined = pd.concat(df_list)
        df_combined['HTS'] = df_combined['HTS'].astype(str)
        result = df_combined.groupby(['HTS']).sum().reset_index()
        # Dictionary with pairs
        pairs_dict = {'SOXL':'SOXS','TQQQ':'SQQQ','SPXL':'SPXS','WEBL':'WEBS','TECL':'TECS','UPRO':'SPXU','QQQ':'PSQ','SPY':'SH','TMV':'TMF','HIBL':'HIBS','BITO':'BITI','TSLA':'TSLS','AAPL':'AAPD','ERX':'ERY','BOIL':'KOLD','LABU':'LABD','JNUG':'JDST','ARKK':'SARK','IBIT':'BITI','TNA':'TZA','UCO':'SCO','FAS':'FAZ','NRGU':'NRGD','DDM':'SDOW','GUSH':'DRIP','TYD':'TYO','UGL':'GLL','DRN':'DRV'}
        pairs_dict.update({v: k for k,v in pairs_dict.items()}) #ensure both directions are covered
        # Track selling and buying
        processed_pairs_selling = set()
        processed_pairs_buying = set()
        liquidated_equities = set()
        # Exclude symbols
        
        # dictionary
        symbol_dict = dict(zip(result.iloc[:,0],result.iloc[:,1]))
        # Log output
        output = "*****"

        for symbol, percentage in symbol_dict.items():
            output += "{}: {}% - ".format(symbol, round(percentage*100, 2))

        output = output.rstrip(" - ")
        self.Log(output)
        # Symbols to be transformed
        transform_symbols = ['PSQ','SH','USDU','SPXU','UPRO','QLD','QID','TSLS','ARKK','FNGU','IBIT','SDS','NUGT','DUST','SPYU','NVDU','SSO']
        transform_mapping = {'PSQ':'SQQQ','SH':'SPXS','USDU':'UUP','SPXU':'SPXS','UPRO':'SPXL','QLD':'TQQQ','QID':'SQQQ','TSLS':'TSLQ','ARKK':'TARK','FNGU':'SOXL','GBTC':'BITO','IBIT':'BITO','SDS':'SPXS','NUGT':'JNUG','DUST':'JDST','SPYU':'SPXL','NVDU':'NVDL','SSO':'SPXL'}
        transform_ratios = {'PSQ':3,'SH':3,'USDU':1,'SPXU':1,'UPRO':1,'QLD':1.5,'QID':1.5,'TSLS':1,'ARKK':2,'FNGU':1,'GBTC':1,'IBIT':1,'SDS':1.5,'NUGT':1,'DUST':1,'SPYU':0.75,'NVDU':1,'SSO':1.5}
        # Transform symbols
        for symbol in transform_symbols:
            if symbol in symbol_dict:
                new_symbol = transform_mapping[symbol]
                ratio = transform_ratios[symbol]
                new_percentage = symbol_dict[symbol]/ratio 
                # Adjust percentage allocation
                if new_symbol in symbol_dict:
                    new_percentage += symbol_dict[new_symbol]
                symbol_dict[new_symbol] = new_percentage
                # Remove transformed
                symbol_dict.pop(symbol, None)
        # Ensure updated equities list
        updated_equities = set(symbol_dict.keys())
        # Liquidate equities
        for equity in self.equities:
            if equity not in updated_equities and self.Portfolio[equity].HoldStock and equity not in liquidated_equities:
                self.Liquidate(equity)
                liquidated_equities.add(equity)
        # Iterate pairs selling
        for symbol1,symbol2 in pairs_dict.items():
            if symbol1 in symbol_dict and symbol2 in symbol_dict:
                offset_value = abs(symbol_dict[symbol1] - symbol_dict[symbol2])
                if symbol_dict[symbol1] >= symbol_dict[symbol2] and self.Portfolio[symbol2].HoldStock:
                    self.Liquidate(symbol2)
                elif symbol_dict[symbol1] <= symbol_dict[symbol2] and self.Portfolio[symbol1].HoldStock:
                    self.Liquidate(symbol1)
                # Mark processed selling
                processed_pairs_selling.add(symbol1)
                processed_pairs_selling.add(symbol2)
        # Iterate remaining selling
        for symbol,value in symbol_dict.items():
            if symbol not in processed_pairs_selling and not value == 0 and symbol not in self.exclude_symbols:
                if isinstance(symbol, str) and symbol.startswith("['") and symbol.endswith("']"):
                    symbol_list = eval(symbol)
                    percentage_equity = sum(self.Portfolio[s].HoldingsValue for s in symbol_list) / self.Portfolio.TotalPortfolioValue
                else:
                    percentage_equity = self.Portfolio[symbol].HoldingsValue/self.Portfolio.TotalPortfolioValue
                if value < percentage_equity and abs(value/percentage_equity - 1) > self.buffer_pct:
                    if isinstance(symbol, str) and symbol.startswith("['") and symbol.endswith("']"):
                        symbol_list = eval(symbol)
                        for s in symbol_list:
                            self.SetHoldings(s, value / len(symbol_list))
                    else:
                        self.SetHoldings(symbol,value)
        # Iterate pairs buying
        for symbol1,symbol2 in pairs_dict.items():
            if symbol1 in symbol_dict and symbol2 in symbol_dict and symbol1 not in processed_pairs_buying and symbol2 not in processed_pairs_buying:
                offset_value = abs(symbol_dict[symbol1] - symbol_dict[symbol2])
                if offset_value > self.filter_value:
                    if symbol_dict[symbol1] > symbol_dict[symbol2]:
                        if isinstance(symbol1, list):
                            for s in symbol1:
                                self.SetHoldings(s, offset_value / len(symbol1))
                        else:
                            self.SetHoldings(symbol1,offset_value)
                    else:
                        if isinstance(symbol2, list):
                            for s in symbol2:
                                self.SetHoldings(s, offset_value / len(symbol2))
                        else:
                            self.SetHoldings(symbol2,offset_value)
                else:
                    if isinstance(symbol1, list):
                        for s in symbol1:
                            if self.Portfolio[s].HoldStock:
                                self.Liquidate(s)
                    else:
                        if self.Portfolio[symbol1].HoldStock:
                            self.Liquidate(symbol1)
                    if isinstance(symbol2, list):
                        for s in symbol2:
                            if self.Portfolio[s].HoldStock:
                                self.Liquidate(s)
                    else:
                        if self.Portfolio[symbol2].HoldStock:
                            self.Liquidate(symbol2)
                # Mark as processed buying
                processed_pairs_buying.add(symbol1)
                processed_pairs_buying.add(symbol2)
        # Filter less than 1%
        updated_equities = {symbol for symbol, value in symbol_dict.items() if value >= self.filter_value}
        # Iterate remaining symbol_dict for buying
        for symbol,value in symbol_dict.items():
            if (symbol in updated_equities and 
                symbol not in processed_pairs_buying and 
                symbol not in self.exclude_symbols):
                if isinstance(symbol, str) and symbol.startswith("['") and symbol.endswith("']"):
                    symbol_list = eval(symbol)
                    percentage_equity = sum(self.Portfolio[s].HoldingsValue for s in symbol_list) / self.Portfolio.TotalPortfolioValue
                else:
                    percentage_equity = (self.Portfolio[symbol].HoldingsValue /
                                        self.Portfolio.TotalPortfolioValue)
                if value > percentage_equity and abs(percentage_equity/value - 1) > self.buffer_pct:
                    if isinstance(symbol, str) and symbol.startswith("['") and symbol.endswith("']"):
                        symbol_list = eval(symbol)
                        for s in symbol_list:
                            self.SetHoldings(s, value / len(symbol_list))
                    else:
                        self.SetHoldings(symbol,value)

    def SetVarToZero(self):
        for strategy_number in range(1, self.number_of_PT + 1):
            setattr(self, f'HT{strategy_number}', {str(j).zfill(2): 0 for j in range(1, 10)})
            setattr(self, f'HTS{strategy_number}', {str(j).zfill(2): [] for j in range(1, 10)})

    def LogStrategies(self):

        self.FinalTickers = list(self.fixed_strategies.keys())
        # Create a set of current strategies
        current_strategies = set(self.FinalTickers)

        self.Log("Strategies, Percentages and Holdings")
        for index, strategy_number in enumerate(self.FinalTickers, start=1):
            if strategy_number in self.fixed_strategies:
                percentage = self.fixed_strategies[strategy_number] * 100

            strategy_name, holdings_info = self.get_strategy_name_and_holdings(strategy_number)

            self.Log(f"Strategy {index}: {strategy_name}, Percentage: {round(percentage, 2)}%, Holdings: {holdings_info}")

        self.Log(self.FinalTickers)
        self.Log("-" * 50)


    def get_strategy_name_and_holdings(self, strategy_number):
        for strategy_name, strategy_instance in self.strategies.items():
            strategy_module_source = inspect.getsource(strategy_instance.__class__)
            strategy_numbers = re.findall(r'AH\(self(?:\.algo)?,\s*[\'\w\(\)\s,]+,\s*(\d+)\s*,', strategy_module_source)
            strategy_numbers.extend(re.findall(r'Sort\(self(?:\.algo)?,\s*[\'\w]+,\s*[\'\w\(\)\s,]+,\s*\d+\s*,\s*\w+\s*,\s*\d+\s*,\s*(\d+)', strategy_module_source))
            strategy_numbers.extend(re.findall(r'AHIV\(self(?:\.algo)?,\s*[\'\w\(\)\s,]+,\s*\d+\s*,\s*(\d+)\s*,\s*[\d\.]+\)', strategy_module_source))
            strategy_numbers.extend(re.findall(r'GroupSort\(self(?:\.algo)?,\s*[\'\w]+,\s*[\'\w\(\)\s,]+,\s*\d+\s*,\s*\w+\s*,\s*\d+\s*,\s*(\d+)', strategy_module_source))

            #self.Log(f"Strategy: {strategy_name}, Numbers: {strategy_numbers}")  # Debugging log

            if str(strategy_number) in strategy_numbers:
                HT_attr = getattr(self, f'HT{strategy_number}')
                HTS_attr = getattr(self, f'HTS{strategy_number}')

                holdings_info = ', '.join([f"{ticker}: {round(HT_attr[key] * 100, 2)}%"
                                        for key, tickers in HTS_attr.items()
                                        if tickers
                                        for ticker in (tickers if isinstance(tickers, list) else [tickers])])

                return type(strategy_instance).__name__, holdings_info

        return "", ""