Hello guys, I´m trying to take access to the candlestick patterns libraries that are in the GitHub LEAN documentation, but there is not too much information related to how to calculate it in a universe selection approach. Second, I want to know if there is any way to get access to TradeBars in order to obtain the OHLC of equities and with this information develop my own custom candlestick pattern?

In the next code, I want to use for the moment a simple condition to filter the stock that has an RSI value lower than 40 and the DojiStar built-in CandlestickPatterns library, but I don´t know how to use it in a universe selection approach:

import typing import QuantConnect.Indicators.CandlestickPatterns import datetime class EMAMomentumUniverse(QCAlgorithm): def Initialize(self): self.SetStartDate(2020, 10, 11) self.SetEndDate(2020, 11, 11) self.SetCash(1000) self.UniverseSettings.Resolution = Resolution.Daily self.AddUniverse(self.CoarseSelectionFunction) self.averages = { } def CoarseSelectionFunction(self, universe): selected = [] universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True) universe = [c for c in universe if (c.Price > 10)][:100] for coarse in universe: symbol = coarse.Symbol if symbol not in self.averages: # 1. Call history to get an array of 200 days of history data history = self.History(symbol, 200, Resolution.Daily) #2. Adjust SelectionData to pass in the history result self.averages[symbol] = SelectionData(history) #here is where im trying to create the bool variable. self.hammer = DojiStar(symbol) self.averages[symbol].update(self.Time, coarse.AdjustedPrice) if self.averages[symbol].is_ready() and (self.averages[symbol].rsi.Current.Value < 40): selected.append(symbol) return selected[:10] def OnSecuritiesChanged(self, changes): for security in changes.RemovedSecurities: self.Liquidate(security.Symbol) for security in changes.AddedSecurities: self.SetHoldings(security.Symbol, 0.10) class SelectionData(): #3. Update the constructor to accept a history array def __init__(self, history ): self.rsi = RelativeStrengthIndex(14) #4. Loop over the history data and update the indicators for data in history.itertuples(): self.rsi.Update(data.Index[1], data.close) def is_ready(self): return self.rsi.IsReady def update(self, time, price): self.rsi.Update(time, price)


Thanks a lot!