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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 Fileas 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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