Hey,

I already have list of Support levels of the Daily chart and of the 4hr timeframe, as well as resistance levels of daily and 4 hour timeframe. The challenge i have is, how do i identify the most relevant support and resitance level? (The ones closest to my current_price) so that i can use it as a condition to trade. depending on how close i am to it.

Here is my code block for the Daily support and resistance levels:

from datetime import datetime,timedelta
import numpy as np
class ReversalAlpha(QCAlgorithm):
    def Initialize(self):
        
     
        tickers = ["EURUSD","USDCAD"]
        # Rolling Windows to hold bar close data keyed by symbol
        self.closingData = {}
        for ticker in tickers:
            symbol = self.AddForex(ticker, Resolution.Daily, Market.Oanda).Symbol
            self.closingData[symbol] = RollingWindow[float](50)
            # Warm up our rolling windows
        self.SetWarmUp(50)
        
    def OnData(self, data):
        
        for symbol, window in self.closingData.items():
            if data.ContainsKey(symbol) and data[symbol] is not None:
                window.Add(data[symbol].Close)
        
        if self.IsWarmingUp or not all([window.IsReady for window in self.closingData.values()]):
            return
        
        for symbol, window in self.closingData.items(): #references the key-value pairs in the dictionary
            supports_D, resistances_D = self.GetPriceLevels(window) # Daily Supports and Daily Resistances
            self.Log(f"Symbol: {symbol.Value} , Supports: {supports_D} , Resistances: {resistances_D}")
            
            
    def GetPriceLevels(self, series, variation = 0.005, h = 3):
        
        supports_D = [] # List that will hold daily Supports points
        resistances_D = [] # List that will hold daily Resistances points
        
        maxima = []
        minima = []
        
        # finding maxima and minima by looking for hills/troughs locally
        for i in range(h, series.Size-h):
            if series[i] > series[i-h] and series[i] > series[i+h]:
                maxima.append(series[i])
            elif series[i] < series[i-h] and series[i] < series[i+h]:
                minima.append(series[i])
       
        # identifying maximas which are resistances
        for m in maxima:
            r = m * variation
            # maxima which are near each other
            commonLevel = [x for x in maxima if x > m - r and x < m + r]
            # if 2 or more maxima are clustered near an area, it is a resistance
            if len(commonLevel) > 1:
                # we pick the highest maxima if the cluster as our resistance
                level = max(commonLevel)
                if level not in resistances_D:
                    resistances_D.append(level)
        
        # identify minima which are supports
        for l in minima:
            r = l * variation
            # minima which are near each other
            commonLevel = [x for x in minima if x > l - r and x < l + r]
            # if 2 or more minima are clustered near an area, it is a support
            if len(commonLevel) > 1:
                # We pick the lowest minima of the cluster as our support
                level = min(commonLevel)
                if level not in supports_D:
                    supports_D.append(level)
            
        
        return supports_D, resistances_D
# Your New Python File
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as well as the code block for the 4hr support and resistance;

from datetime import datetime,timedelta
import numpy as np
class ReversalBeta(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2020, 1, 30)  # Set Start Date
        self.SetEndDate(2020, 12, 30)
        self.SetCash(100000)  # Set Strategy Cash
     
        tickers = ["EURUSD","USDCAD"]
        # Rolling Windows to hold bar close data keyed by symbol
        self.closingData = {} # Dictionary to hold a rolling window with bar close data for each ticker symbol
        for ticker in tickers:
            symbol = self.AddForex(ticker, Resolution.Hour, Market.Oanda).Symbol
            self.closingData[symbol] = SymbolData(self, symbol) #RollingWindow[float](50)
            
            # Warm up our rolling windows
        self.SetWarmUp(50)
        
    def OnData(self, data):
        
        if self.IsWarmingUp:
            return
        
        for symbol, symbolData in self.closingData.items(): #Returns self.closingData's dictionary key-value pairs
            if not (data.ContainsKey(symbol) and data[symbol] is not None and symbolData.IsReady):
                continue
        
        for symbol, window in self.closingData.items(): #references the key-value pairs in the dictionary
            supports_4H, resistances_4H = self.GetPriceLevels(window) #4 Hour Support and resistances
            self.Log(f"Symbol: {symbol.Value} , Supports: {supports_4H} , Resistances: {resistances_4H}")
            
            
    def GetPriceLevels(self, series, variation = 0.01, h = 3):
        
        supports_4H = [] # List that will hold 4 hour Supports points
        resistances_4H = [] # List that will hold 4 hour resistance points
        
        maxima = []
        minima = []
        
        # finding maxima and minima by looking for hills/troughs locally
        for i in range(h, series.Size-h):
            if series[i] > series[i-h] and series[i] > series[i+h]:
                maxima.append(series[i])
            elif series[i] < series[i-h] and series[i] < series[i+h]:
                minima.append(series[i])
       
        # identifying maximas which are resistances
        for m in maxima:
            r = m * variation
            # maxima which are near each other
            commonLevel = [x for x in maxima if x > m - r and x < m + r]
            # if 2 or more maxima are clustered near an area, it is a resistance
            if len(commonLevel) > 1:
                # we pick the highest maxima if the cluster as our resistance
                level = max(commonLevel)
                if level not in resistances_4H:
                    resistances_4H.append(level)
        
        # identify minima which are supports
        for l in minima:
            r = l * variation
            # minima which are near each other
            commonLevel = [x for x in minima if x > l - r and x < l + r]
            # if 2 or more minima are clustered near an area, it is a support
            if len(commonLevel) > 1:
                # We pick the lowest minima of the cluster as our support
                level = min(commonLevel)
                if level not in supports_4H:
                    supports_4H.append(level)
            
        
        return supports_4H, resistances_4H
#creating a class for consolidating data into 4hour time frames
class SymbolData:
    def __init__(self, algorithm, symbol):
        self.closeWindow = RollingWindow[float](50)
        
        # Add consolidator to track rolling close prices
        self.consolidator = TradeBarConsolidator(4)
        self.consolidator.DataConsolidated += self.CloseUpdated
        algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
        
    def CloseUpdated(self, sender, bar):
        '''Event holder to update the close Rolling Window values'''
        self.closeWindow.Add(bar.Close)
       
    @property 
    def IsReady(self):
        return self.closeWindow.IsReady
        
        
        #the next support is the one with the smallest difference between current price
        #since we have a list of support levels we need to merge the 4hr and the daily support level into one list
        # and then rank them from them in decsending order
        
        # we can further process the support data and resitance data gathered from above, by perhaps creating another function.
        # where the final output will be directly below or above the current price.
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