Overall Statistics
Total Trades
819
Average Win
0.39%
Average Loss
-0.34%
Compounding Annual Return
13.245%
Drawdown
6.500%
Expectancy
0.988
Net Profit
440.809%
Sharpe Ratio
1.641
Probabilistic Sharpe Ratio
98.872%
Loss Rate
8%
Win Rate
92%
Profit-Loss Ratio
1.15
Alpha
0.111
Beta
-0.004
Annual Standard Deviation
0.067
Annual Variance
0.005
Information Ratio
0.025
Tracking Error
0.197
Treynor Ratio
-28.64
Total Fees
$1484.83
Estimated Strategy Capacity
$1000000000.00
Lowest Capacity Asset
QQQ RIWIV7K5Z9LX
from System import *
from QuantConnect import *
from QuantConnect.Indicators import *
from QuantConnect.Data import *
from QuantConnect.Data.Market import *
from QuantConnect.Orders import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Portfolio import *

import numpy as np
import tweepy
import statistics

# import datetime
from datetime import timedelta, datetime

class ScheduledExecutionModel(ExecutionModel):
    '''Execution model that submits orders while the current market price is more favorable that the current volume weighted average price.'''

    def __init__(self, algorithm, *args, **kwargs):
        super().__init__()
        '''Initializes a new instance of the VolumeWeightedAveragePriceExecutionModel class'''
        self.targetsCollection = PortfolioTargetCollection()
        self.symbolData = {}

        # Gets or sets the maximum order quantity as a percentage of the current bar's volume.
        # This defaults to 0.01m = 1%. For example, if the current bar's volume is 100,
        # then the maximum order size would equal 1 share.
        self.MaximumOrderQuantityPercentVolume = 0.01

        # Gets or sets the maximum spread compare to current price in percentage.
        self.acceptingSpreadPercent = 0.005
        
    def Execute(self, algorithm, targets):
        '''Executes market orders if the standard deviation of price is more
       than the configured number of deviations in the favorable direction.
       Args:
           algorithm: The algorithm instance
           targets: The portfolio targets'''

        # update the complete set of portfolio targets with the new targets
        self.targetsCollection.AddRange(targets)

        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
        if self.targetsCollection.Count > 0:
            
            for target in self.targetsCollection.OrderByMarginImpact(algorithm):
                symbol = target.Symbol

                # calculate remaining quantity to be ordered
                unorderedQuantity = OrderSizing.GetUnorderedQuantity(algorithm, target)
                
                # fetch our symbol data containing our VWAP indicator
                data = self.symbolData.get(symbol, None)
                if data is None: return
                
                # check order entry conditions  
                if self.PriceIsFavorable(data, unorderedQuantity):
                    # adjust order size to respect maximum order size based on a percentage of current volume
                    orderSize = OrderSizing.GetOrderSizeForPercentVolume(data.Security, self.MaximumOrderQuantityPercentVolume, unorderedQuantity)
                    
                    max_quantity = algorithm.CalculateOrderQuantity(symbol, 0.45)
          
                    # suggested amount divided by the total possible amount
                    try:
                        order_percent = round(float(orderSize/max_quantity), 4)
                    except:
                        if max_quantity == 0:
                            order_percent = 0.0
                        else:
                            cash = algorithm.Portfolio.Cash
                            max_quantity = int(cash/algorithm.Securities[symbol].Close)
                            order_percent = round(float(orderSize/max_quantity), 4)
                    
                    if (orderSize != 0) and (abs(order_percent) > 0.05):
                        if algorithm.Portfolio[symbol].Invested:
                            coef = abs(order_percent) * 0.5
                            if coef < 0.25:
                                coef = int(abs(float(order_percent))/0.05)*10
                            if self.breakout(symbol, algorithm):
                                coef = coef * 20
                            orderSize = OrderSizing.GetOrderSizeForPercentVolume(data.Security, self.MaximumOrderQuantityPercentVolume*coef, unorderedQuantity)
                        projected_cost = round(algorithm.Securities[symbol].Close * orderSize, 3) * 1.05
                            
                        if algorithm.Portfolio.Cash > projected_cost:
                            algorithm.MarketOrder(symbol, orderSize)
            self.targetsCollection.ClearFulfilled(algorithm)

    def OnSecuritiesChanged(self, algorithm, changes):
        '''Event fired each time the we add/remove securities from the data feed
        Args:
            algorithm: The algorithm instance that experienced the change in securities
            changes: The security additions and removals from the algorithm'''
        for removed in changes.RemovedSecurities:
            # clean up removed security data
            if removed.Symbol in self.symbolData:
                if self.IsSafeToRemove(algorithm, removed.Symbol):
                    data = self.symbolData.pop(removed.Symbol)
                    algorithm.SubscriptionManager.RemoveConsolidator(removed.Symbol, data.Consolidator)

        for added in changes.AddedSecurities:
            if added.Symbol not in self.symbolData:
                self.symbolData[added.Symbol] = SymbolData(algorithm, added)

    def PriceIsFavorable(self, data, unorderedQuantity):
        '''Determines if the current price is more than the configured
       number of standard deviations away from the mean in the favorable direction.'''
        if unorderedQuantity > 0:
            if data.Security.BidPrice < data.VWAP:
                return True
        else:
            if data.Security.AskPrice > data.VWAP:
                return True

        return False

    def SpreadIsFavorable(self, data):
        '''Determines if the spread is in desirable range.'''
        # Price has to be larger than zero to avoid zero division error, or negative price causing the spread percentage < 0 by error
        # Has to be in opening hours of exchange to avoid extreme spread in OTC period
        return data.Security.Exchange.ExchangeOpen \
            and data.Security.Price > 0 and data.Security.AskPrice > 0 and data.Security.BidPrice > 0 \
            and (data.Security.AskPrice - data.Security.BidPrice) / data.Security.Price <= self.acceptingSpreadPercent

    def IsSafeToRemove(self, algorithm, symbol):
        '''Determines if it's safe to remove the associated symbol data'''
        # confirm the security isn't currently a member of any universe
        return not any([kvp.Value.ContainsMember(symbol) for kvp in algorithm.UniverseManager])

    def breakout(self, symbol, algorithm):
        self.lookback = 20
        self.ceiling, self.floor = 30, 10
        
        close = algorithm.History(symbol, 31, Resolution.Daily)["close"]
        todayvol = np.std(close[1:31])
        yesterdayvol = np.std(close[0:30])
        deltavol = (todayvol - yesterdayvol) / todayvol
        self.lookback = round(self.lookback * (1 + deltavol))

        # Account for upper/lower limit of lockback length
        if self.lookback > self.ceiling:
            self.lookback = self.ceiling
        elif self.lookback < self.floor:
            self.lookback = self.floor

        # List of daily highs
        self.high = algorithm.History(symbol, self.lookback, Resolution.Daily)["high"]
        
        # Buy in case of breakout
        #if not self.Securities[symbol].Invested and \
        if algorithm.Securities[symbol].Close >= max(self.high[:-1]):
            return True
            
    def UpdateTickets(self, algorithm, symbol, orderSize):
        close = algorithm.Securities[symbol].Close
        #algorithm.StopMarketOrder(symbol, -orderSize, close * 0.95)
        algorithm.LimitOrder(symbol, -int(orderSize/2), close * 1.025)
        
class SymbolData:
    def __init__(self, algorithm, security):
        self.Security = security
        self.Consolidator = algorithm.ResolveConsolidator(security.Symbol, security.Resolution)
        name = algorithm.CreateIndicatorName(security.Symbol, "VWAP", security.Resolution)
        self.vwap = IntradayVwap(name)
        algorithm.RegisterIndicator(security.Symbol, self.vwap, self.Consolidator)

    @property
    def VWAP(self):
       return self.vwap.Value
       
    def dispose(self, algorithm):
        algorithm.SubscriptionManager.RemoveConsolidator(security.Symbol, self.consolidator)

class IntradayVwap:
    '''Defines the canonical intraday VWAP indicator'''
    def __init__(self, name):
        self.Name = name
        self.Value = 0.0
        self.lastDate = datetime.min
        self.sumOfVolume = 0.0
        self.sumOfPriceTimesVolume = 0.0

    @property
    def IsReady(self):
        return self.sumOfVolume > 0.0

    def Update(self, input):
        '''Computes the new VWAP'''
        success, volume, averagePrice = self.GetVolumeAndAveragePrice(input)
        if not success:
            return self.IsReady

        # reset vwap on daily boundaries
        if self.lastDate != input.EndTime.date():
            self.sumOfVolume = 0.0
            self.sumOfPriceTimesVolume = 0.0
            self.lastDate = input.EndTime.date()

        # running totals for Σ PiVi / Σ Vi
        self.sumOfVolume += volume
        self.sumOfPriceTimesVolume += averagePrice * volume

        if self.sumOfVolume == 0.0:
           # if we have no trade volume then use the current price as VWAP
           self.Value = input.Value
           return self.IsReady

        self.Value = self.sumOfPriceTimesVolume / self.sumOfVolume
        return self.IsReady

    def GetVolumeAndAveragePrice(self, input):
        '''Determines the volume and price to be used for the current input in the VWAP computation'''

        if type(input) is Tick:
            if input.TickType == TickType.Trade:
                return True, float(input.Quantity), float(input.LastPrice)

        if type(input) is TradeBar:
            if not input.IsFillForward:
                averagePrice = float(input.Open + input.High + input.Low + input.Close) / 4
                medianPrice = statistics.median([input.Open, input.High, input.Low, input.Close])
                return True, float(input.Volume), medianPrice

        return False, 0.0, 0.0
        
import pandas as pd
import numpy as np
from scipy.optimize import minimize


class myTrailingStopRiskManagementModel:
    '''
    Credit goes to: Alex Catarino and many of his friends at QuantConnect
    
    https://github.com/QuantConnect/Lean/blob/master/Algorithm.Framework/Risk/TrailingStopRiskManagementModel.py
    
    Description:
        Limits the maximum possible loss measured from the highest unrealized profit
    '''
    
    def __init__(self, maximumDrawdownPercent = 0.08):
        '''initializes the class
        Args: maximumDrawdownPercent: The maximum percentage drawdown allowed for algorithm portfolio compared with the highest unrealized profit, defaults to 5% drawdown
        '''
        self.maximumDrawdownPercent = -abs(maximumDrawdownPercent)
        self.trailingHighs = dict()

    def setDD(self, maximumDrawdownPercent = 0.08):
        '''allows to change the drawdown
        Args: maximumDrawdownPercent: The maximum percentage drawdown allowed for algorithm portfolio compared with the highest unrealized profit, defaults to 5% drawdown
        '''
        self.maximumDrawdownPercent = -abs(maximumDrawdownPercent)

    def setWTtoZeroIfDDtooHigh(self, algorithm, targets=None):
        '''If drawdown is too high, set wt[symbol] to zero
           algo.wt[symbol] = weights which will be set to 0 in case drawdown exceeds the maximum    
        '''
        for kvp in algorithm.Securities:
            symbol = kvp.Key
            security = kvp.Value
            
            # Remove from trailingHighs dict if not invested
            if not security.Invested:
                self.trailingHighs.pop(symbol, None)
                continue
            
            # Add newly invested securities to trailingHighs dict
            if symbol not in self.trailingHighs:
                self.trailingHighs[symbol] = security.Holdings.AveragePrice
                continue
            
            # Check for new highs and update trailingHighs dict
            if self.trailingHighs[symbol] < security.High:
                self.trailingHighs[symbol] = security.High
                continue
            
            # Calc the drawdown
            securityHigh = self.trailingHighs[symbol]
            drawdown = (security.Low / securityHigh) - 1
            
            # If drawdown is too high, set symbol weight to zero
            if drawdown < self.maximumDrawdownPercent:
                algorithm.wt[symbol] = 0
        
        return
    
    
    
class myPortfolioOptimizer:
    '''
    Credit goes to: Emilio Freire / InnoQuantivity
    
    https://innoquantivity.com/blogs/inno-blog/portfolio-optimization-quantconnect-research-algorithm
    https://www.quantconnect.com/forum/discussion/8128/portfolio-optimization-research-amp-algorithm-for-better-workflows/p1/comment-22952
    
    Description:
        Implementation of a custom optimizer that calculates the weights for each asset to optimize a given objective function
    Details:
        Optimization can be:
            - Equal Weighting
            - Maximize Portfolio Return
            - Minimize Portfolio Standard Deviation
            - Mean-Variance (minimize Standard Deviation given a target return)
            - Maximize Portfolio Sharpe Ratio
            - Maximize Portfolio Sortino Ratio
            - Risk Parity Portfolio
        Constraints:
            - Weights must be between some given boundaries
            - Weights must sum to 1
    '''
    def __init__(self, 
                 minWeight = 0,
                 maxWeight = 1):
        '''
        Description:
            Initialize the CustomPortfolioOptimizer
        Args:
            minWeight(float): The lower bound on portfolio weights
            maxWeight(float): The upper bound on portfolio weights
        '''
        self.minWeight = minWeight
        self.maxWeight = maxWeight
        
        
    def CalcWeights(self, algorithm, symbols, objectiveFunction='riskParity', lookback=63, targetReturn=None):
        '''
        Description:
            Calculate weights from daily returns, return a pandas Series
        '''
        history = np.log10(algorithm.History(symbols, lookback, Resolution.Daily)['close'].unstack(level = 0))
        returnsDf = history.pct_change().dropna()
        returnsDf.columns = [algorithm.AddEquity(i).Symbol.Value for i in list(returnsDf.columns)]
        weights = self.Optimize(objectiveFunction, returnsDf, targetReturn)
        return pd.Series(weights, index=returnsDf.columns, name='weights')
        
        
    def Optimize(self, objFunction, dailyReturnsDf, targetReturn = None):
        '''
        Description:
            Perform portfolio optimization given a series of returns
        Args:
            objFunction: The objective function to optimize (equalWeighting, maxReturn, minVariance, meanVariance, maxSharpe, maxSortino, riskParity)
            dailyReturnsDf: DataFrame of historical daily arithmetic returns
        Returns:
            Array of double with the portfolio weights (size: K x 1)
        '''
        # initial weights: equally weighted
        size = dailyReturnsDf.columns.size # K x 1
        self.initWeights = np.array(size * [1. / size])
        
        # get sample covariance matrix
        covariance = dailyReturnsDf.cov()
        # get the sample covariance matrix of only negative returns for sortino ratio
        negativeReturnsDf = dailyReturnsDf[dailyReturnsDf < 0]
        covarianceNegativeReturns = negativeReturnsDf.cov()
        
        if objFunction == 'equalWeighting':
            return self.initWeights
        
        bounds = tuple((self.minWeight, self.maxWeight) for x in range(size))
        constraints = [{'type': 'eq', 'fun': lambda x: np.sum(x) - 1.0}]
        
        if objFunction == 'meanVariance':
            # if no target return is provided, use the resulting from equal weighting
            if targetReturn is None:
                targetReturn = self.CalculateAnnualizedPortfolioReturn(dailyReturnsDf, self.initWeights)
            constraints.append( {'type': 'eq', 'fun': lambda weights:
                                self.CalculateAnnualizedPortfolioReturn(dailyReturnsDf, weights) - targetReturn} )
        
        opt = minimize(lambda weights: self.ObjectiveFunction(objFunction, dailyReturnsDf,
                                                                covariance, covarianceNegativeReturns,
                                                                weights),
                                                            x0 = self.initWeights,
                                                            bounds = bounds,
                                                            constraints = constraints,
                                                            method = 'SLSQP')
        return opt['x']
        
        
    def ObjectiveFunction(self, objFunction, dailyReturnsDf, covariance, covarianceNegativeReturns, weights):
        
        '''
        Description:
            Compute the objective function
        Args:
            objFunction: The objective function to optimize (equalWeighting, maxReturn, minVariance, meanVariance,
                                                                maxSharpe, maxSortino, riskParity)
            dailyReturnsDf: DataFrame of historical daily returns
            covariance: Sample covariance
            covarianceNegativeReturns: Sample covariance matrix of only negative returns
            weights: Portfolio weights
        '''
        if objFunction == 'maxReturn':
            f = self.CalculateAnnualizedPortfolioReturn(dailyReturnsDf, weights)
            return -f # convert to negative to be minimized
        elif objFunction == 'minVariance':
            f = self.CalculateAnnualizedPortfolioStd(covariance, weights)
            return f
        elif objFunction == 'meanVariance':
            f = self.CalculateAnnualizedPortfolioStd(covariance, weights)
            return f
        elif objFunction == 'maxSharpe':
            f = self.CalculateAnnualizedPortfolioSharpeRatio(dailyReturnsDf, covariance, weights)
            return -f # convert to negative to be minimized
        elif objFunction == 'maxSortino':
            f = self.CalculateAnnualizedPortfolioSortinoRatio(dailyReturnsDf, covarianceNegativeReturns, weights)
            return -f # convert to negative to be minimized
        elif objFunction == 'riskParity':
            f = self.CalculateRiskParityFunction(covariance, weights)
            return f
        else:
            raise ValueError(f'PortfolioOptimizer.ObjectiveFunction: objFunction input has to be one of equalWeighting,'
             + ' maxReturn, minVariance, meanVariance, maxSharpe, maxSortino or riskParity')
        
        
    def CalculateAnnualizedPortfolioReturn(self, dailyReturnsDf, weights):
        
        annualizedPortfolioReturns = np.sum( ((1 + dailyReturnsDf.mean())**252 - 1) * weights )
        
        return annualizedPortfolioReturns
        
            
    def CalculateAnnualizedPortfolioStd(self, covariance, weights):
        
        annualizedPortfolioStd = np.sqrt( np.dot(weights.T, np.dot(covariance * 252, weights)) )
        
        if annualizedPortfolioStd == 0:
            raise ValueError(f'PortfolioOptimizer.CalculateAnnualizedPortfolioStd: annualizedPortfolioStd cannot be zero. Weights: {weights}')
            
        return annualizedPortfolioStd
        
        
    def CalculateAnnualizedPortfolioNegativeStd(self, covarianceNegativeReturns, weights):
    
        annualizedPortfolioNegativeStd = np.sqrt( np.dot(weights.T, np.dot(covarianceNegativeReturns * 252, weights)) )        
        
        if annualizedPortfolioNegativeStd == 0:
            raise ValueError(f'PortfolioOptimizer.CalculateAnnualizedPortfolioNegativeStd: annualizedPortfolioNegativeStd cannot be zero. Weights: {weights}')
        
        return annualizedPortfolioNegativeStd
        
        
    def CalculateAnnualizedPortfolioSharpeRatio(self, dailyReturnsDf, covariance, weights):
        
        annualizedPortfolioReturn = self.CalculateAnnualizedPortfolioReturn(dailyReturnsDf, weights)
        annualizedPortfolioStd = self.CalculateAnnualizedPortfolioStd(covariance, weights)
        annualizedPortfolioSharpeRatio = annualizedPortfolioReturn / annualizedPortfolioStd
            
        return annualizedPortfolioSharpeRatio
        
        
    def CalculateAnnualizedPortfolioSortinoRatio(self, dailyReturnsDf, covarianceNegativeReturns, weights):
        
        annualizedPortfolioReturn = self.CalculateAnnualizedPortfolioReturn(dailyReturnsDf, weights)
        annualizedPortfolioNegativeStd = self.CalculateAnnualizedPortfolioNegativeStd(covarianceNegativeReturns, weights)
        annualizedPortfolioSortinoRatio = annualizedPortfolioReturn / annualizedPortfolioNegativeStd
            
        return annualizedPortfolioSortinoRatio
        
        
    def CalculateRiskParityFunction(self, covariance, weights):
        
        ''' Spinu formulation for risk parity portfolio '''
        
        assetsRiskBudget = self.initWeights
        portfolioVolatility = self.CalculateAnnualizedPortfolioStd(covariance, weights)
        
        x = weights / portfolioVolatility
        riskParity = (np.dot(x.T, np.dot(covariance, x)) / 2) - np.dot(assetsRiskBudget.T, np.log(x))
            
        return riskParity
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *

import sys
import tweepy
from tweepy import OAuthHandler
from time import sleep
import json

# Important - Don't name file 'tweepy.py'

# Used below to supress problems and continue instead of try/except/continue
from contextlib import suppress

# --- # --------------------------------------------------------------------- # --- #
# --- # --------------------------------------------------------------------- # --- #

# Set True to follow a defined list of twitter users.
# Set False to stream from all accounts based on defined keywords.

FollowerMode = True

# --- # --------------------------------------------------------------------- # --- #
# --- # --------------------------------------------------------------------- # --- #

# --- # ---------------------------------- # --- #
# --- # ---- Follower Mode Dictionary ---- # --- #
# --- # ---------------------------------- # --- #

# Lookup Twitter ID's here http://gettwitterid.com/ by entering the accounts @Usernamehandle (without the @)
# It doesn't care what you name them. The names are only displayed to the console.

idsdict = {'TT3Private'}

# --- # --------------------------------------------------------------------- # --- #
# --- # --------------------------------------------------------------------- # --- #

# --- # ------------------------------ # --- #
# --- # ---- Search Mode Keywords ---- # --- #
# --- # ------------------------------ # --- #

# --- # ----------------- # --- #
# --- # SEARCH BY KEYWORD # --- #
# --- # ----------------- # --- #

# Example
# search = ['breaking news']

# --- # ---------- # --- #
# --- # SEARCH ALL # --- #
# --- # ---------- # --- #

# [' '] and [''] yields no results. The only way to truly stream all of the tweets (unfiltered)
# requires a connection to the firehose(https://developer.twitter.com/en/docs/tweets/sample-realtime/overview/decahose.html), 
# which is granted only in specific use enterprise cases by Twitter.

search = ['.','a','@','\'','this','to',':(','?','!','$',
          'h','+','_','-','#','b','you', 'c',',','the',
          'i','/','lol','at','this','need','and','RT',
          'if','1', 'd','e','f','g'] # Feel free to expand on this. I believe there's a limit on how much you can add.


# --- # -------------------- # --- #
# --- # SEARCH BY USER INPUT # --- #
# --- # -------------------- # --- #

# search = [input('Enter keyword\n\n')]

# --- # -------------------------------------------------------------- # --- #
# --- # -------------------------------------------------------------- # --- #



# --- # ---------------------- # --- #
# --- # --- AUTHENTICATION --- # --- #
# --- # ---------------------- # --- #

consumer_key = self.GetParameter("Consumer_Api")
consumer_secret = self.GetParameter("Consumer_Api_Secret")
access_token = self.GetParameter("Access_Token")
access_token_secret = self.GetParameter("Access_Token_Secret")

auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_token, access_token_secret)
api = tweepy.API(auth)

# --- # -------------------------------------------------------------- # --- #
# --- # -------------------------------------------------------------- # --- #

print('Listening for tweets...\n')

if FollowerMode == True:
    # gets all IDs from 'idsdict' and converts them to strings
    ids = [str(i) for i in list(idsdict.values())]

# Main Stream Listener Class
class MyStreamListener(tweepy.StreamListener):
    global ids
    global FollowerMode
    tweets = 0
    
    # on_status is a built in tweepy method to fetch tweets.
    # on_data is another one and shows more detailed information for analytical reasons,
    # but be aware that you will have to parse the json manually like data['text'], data['user']['location'], etc.
    # print out status or data(if using on_data) and run the script to fetch the full JSON to see everything that it can do.
    # You can find a good example of that here https://github.com/varadhbhatnagar/Emoyto
    def on_status(self, status):
        """
        # Use this if you plan to use the json functionality below. 
        # If you use this, tab the rest of this class below over by one indent(4 spaces)
        # with open ('tweets.json', 'a', encoding='utf-8') as f:        

        # Supress errors so if that specific tweet has an issue for whatever reason, it will skip it. Similar to try/except.
        # Don't use this if you want to debug/look for issues.
        """

        with suppress(Exception):
            
            userid = str(status.user.id)
            
            # "userid in ids" mentioned below removes all of the mentions and retweets and makes sure it only comes from the original account.
            # Tweepy has no built in way to exclude that to my knowledge based on stackoverflow answers.
            
            if FollowerMode == True and userid in ids:
                
                # You can do this for example - " if status.place.country == 'United States': ",
                # but most people don't have their country listed. status.user.location often shows 'state' or 'city, state' and/or country,
                # but their location is user set so it can really be something made up like 'outer space'. If it's that important,
                # you could always try and use an API to see if it's a valid location.
                
                print('-' * 80)
                
                # Prints the name for this ID that's defined in 'idsdict'
                with suppress(Exception):
                    print(list(idsdict.keys())[list(idsdict.values()).index(int(userid))])
                
                print('User: ' + status.user.screen_name)
                # Attempt to display location and/or country if it exists
                with suppress(Exception):
                    if status.user.location != None and status.user.location != 'None':
                        print('Location: ' + status.user.location)
                with suppress(Exception):
                    print('Country: ' + status.place.country)
                
                # Checks to see if tweet is 'extended'/long. If it is, it will display the full tweet.
                try:
                    text = status.extended_tweet['full_text']
                except AttributeError:
                    text = status.text
                print('Tweet: ' + text)
                sleep(0.015)
                
            elif FollowerMode == False:
                
                print('-' * 80)
                print('User: ' + status.user.screen_name)
                with suppress(Exception):
                    if status.user.location != None and status.user.location != 'None':
                        print('Location: ' + status.user.location)
                with suppress(Exception):
                    print('Country: ' + status.place.country)
                        
                try:
                    text = status.extended_tweet['full_text']
                except AttributeError:
                    text = status.text
                print('Tweet: ' + text)
                
                # Prevents the display from hiccups and keeps the scrolling smooth when scanning all
                sleep(0.016)
                            
            # --- # --------------------------------------------------------------------- # --- #
            # --- # --------------------------------------------------------------------- # --- #
            
            # Optional - Write tweet into json file. You can store just tweets for example
            # Make sure to un-comment the 'with f.open' above and tab the rest of the class below it.
          
            #json_str = json.dumps(status._json)
            #f.write(status.text + '\n')
            
            # --- # --------------------------------------------------------------------- # --- #
            # --- # --------------------------------------------------------------------- # --- #
            
            #  # Optional - Print something out every certain number of tweets to show how many tweets have came through.
            
            #  MyStreamListener.tweets += 1
            # if MyStreamListener.tweets % 1000 == 0:
            #     print(str(MyStreamListener.tweets) + ' Tweets')
            #     for i in range(15):
            #         print(f'|||||||||||||||||||||||||||||||||||----- {MyStreamListener.tweets} ------||||||||||||||||||||||||||||||||||||||| \n')
            #     sleep(1)
                
# Define the listener
listener = MyStreamListener()
stream = tweepy.Stream(auth, listener)


if FollowerMode == True:
    stream.filter(follow=ids)
else:
    stream.filter(languages=["en"], track = search )
from System import *
from QuantConnect import *

import tweepy, statistics
from datetime import datetime, timedelta, date
import numpy as np
from scipy import stats

from helpers import myTrailingStopRiskManagementModel

class DualMomentumWithOutDaysAlphaModel(AlphaModel):

    def __init__(self, algorithm, VOLA = 126, BASE_RET = 83, resolution = Resolution.Daily, *args, **kwargs):
        super().__init__()
        self.VOLA = VOLA
        self.BASE_RET = BASE_RET
        self.resolution = resolution
        self.MKT = algorithm.AddEquity('SPY', resolution).Symbol
        self.SLV = algorithm.AddEquity('SLV', resolution).Symbol
        self.GLD = algorithm.AddEquity('GLD', resolution).Symbol
        self.XLI = algorithm.AddEquity('XLI', resolution).Symbol
        self.XLU = algorithm.AddEquity('XLU', resolution).Symbol
        self.DBB = algorithm.AddEquity('DBB', resolution).Symbol
        self.UUP = algorithm.AddEquity('UUP', resolution).Symbol

        self.count = self.BASE_RET
        self.outday = 5

        pairs = [self.MKT, self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]
        for symbol in pairs:
            self.consolidator = TradeBarConsolidator(timedelta(hours=1))
            self.consolidator.DataConsolidated += self.consolidation_handler
            algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidator)

        self.history = algorithm.History(pairs, self.VOLA + 1, self.resolution)
        # self.history = self.history['close'].unstack(level=0).dropna()

        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 1)
        resolutionString = Extensions.GetEnumString(resolution, Resolution)
        self.Name = f"{self.__class__.__name__}({resolutionString})"

        # Force alpha to only produce insights Daily at 11.10am
        self.set_flag = False
        algorithm.Schedule.On(algorithm.DateRules.EveryDay(),
                              algorithm.TimeRules.AfterMarketOpen('SPY', 60),
                              self.SetFlag)
                              
    def SetFlag(self):
        self.set_flag = True

    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
        self.history = self.history.iloc[-(self.VOLA + 1):]
        
    def Update(self, algorithm, _data):
        if algorithm.IsWarmingUp or not self.set_flag:
            return []

        self.set_flag = False
        insights = []
        
        # Volatility
        vola = self.history[self.MKT].pct_change().std() * np.sqrt(252)
        wait_days = int(vola * self.BASE_RET)
        period = int((1.0 - vola) * self.BASE_RET)
        r = self.history.pct_change(period).iloc[-1]

        exit_market = r[self.SLV] < r[self.GLD] and r[self.XLI] < r[self.XLU] and r[self.DBB] < r[self.UUP]

        direction = InsightDirection.Down
            
        if (exit_market):
            #algorithm.Plot("In vs Out", "Market", -1)
            direction = InsightDirection.Down
            self.outday = self.count
        else:
            if (self.count >= wait_days + self.outday):
                #algorithm.Plot("In vs Out", "Market", 1)
                direction = InsightDirection.Up
        self.count += 1
        
        # algorithm.Plot("Wait Days", "Actual", self.count)
        # algorithm.Plot("Wait Days", "Expected", wait_days + self.outday)
        # algorithm.Plot("Market Volatility", str(self.MKT), float(vola))
                                                                    
        insights.append(Insight.Price(self.MKT, self.predictionInterval, direction))

        return insights
from itertools import groupby
import tweepy
from datetime import datetime, timedelta, date
import time
import pandas as pd
import numpy as np
import re, math
import scipy
from math import ceil
from collections import deque
from itertools import chain

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data.Market import TradeBar
from QuantConnect.Algorithm.Framework.Execution import StandardDeviationExecutionModel, VolumeWeightedAveragePriceExecutionModel
from QuantConnect.Algorithm.Framework.Risk import MaximumDrawdownPercentPortfolio, MaximumUnrealizedProfitPercentPerSecurity, MaximumDrawdownPercentPerSecurity, TrailingStopRiskManagementModel

from dual_momentum_with_out_days_alpha import DualMomentumWithOutDaysAlphaModel
from trade_execution import ScheduledExecutionModel
from portfolio_management import PortfolioManagementModel

import os
import time
import tweepy

import tweepy

def authenticate(self):
    '''Use credentials to authenticate user'''
    Consumer_Api = self.GetParameter("Consumer_Api")
    Consumer_Api_Secret = self.GetParameter("Consumer_Api_Secret")
    Access_Token = self.GetParameter("Access_Token")
    Access_Token_Secret = self.GetParameter("Access_Token_Secret")
    # Authenticate to Twitter
    auth = tweepy.OAuthHandler(str(Consumer_Api), str(Consumer_Api_Secret))
    auth.set_access_token(str(Access_Token), str(Access_Token_Secret))
    api = tweepy.API(auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
    return auth, api
    
class MyStreamListener(tweepy.StreamListener):
    
    keywords = ['#TT3ALERT', '#TT3LOTTO', '$SPY', '$TSLA', '$AAPL', '$GLD', '@', 'Long', 'Short']
    
    def on_status(self, status):
        if any(substring in str(status.text) for substring in keywords):
            message = str(status.text).replace('@', '[at]')
            tweet = self.api.update_status(f"{message}")
            self.Debug(f"Created Tweet ID {tweet.id}")

VOLA = 126; BASE_RET = 83; RET = 252; EXCL = 21; LEV = 1.00;

class HorizontalQuantumCoil(QCAlgorithm):

    def Initialize(self):
        self.Portfolio.MarginCallModel = MarginCallModel.Null
        self.SetStartDate(2008, 1, 1)
        self.SetCash(100000)
        self.added_cash = 115
        self.upkeep = 28
        self.simulate_live = False
        self.SetWarmUp(timedelta(252))
        self.Settings.FreePortfolioValuePercentage = 0.05
        #self.SetBrokerageModel(BrokerageName.AlphaStreams)
        self.SetAlpha(DualMomentumWithOutDaysAlphaModel(self, VOLA, BASE_RET, Resolution.Daily))

        stonks = ['FDN', 'QQQ', 'IWM', 'SPY', 'VTI', 'DIA', 'IWF', 'TLT', 'TLH', 'IEI', 'IEF']
        symbols = []
        for stonk in stonks:
            val = Symbol.Create(stonk, SecurityType.Equity, Market.USA)
            symbols.append(val)
        self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
        self.UniverseSettings.Resolution = Resolution.Daily
        self.SetPortfolioConstruction(PortfolioManagementModel(self, RET, EXCL, LEV, Resolution.Daily))
        self.SetExecution(ScheduledExecutionModel(self))
        # self.SetRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(maximumUnrealizedProfitPercent = 0.10))
        self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(maximumDrawdownPercent = 0.035)) # 0.075
        # self.SetRiskManagement(MaximumDrawdownPercentPortfolio(maximumDrawdownPercent = 0.05, isTrailing = True))

        self.createPlots("SPY")
        
        if self.LiveMode:
            # Authenticate to Twitter
            Consumer_Api = self.GetParameter("Consumer_Api")
            Consumer_Api_Secret = self.GetParameter("Consumer_Api_Secret")
            Access_Token = self.GetParameter("Access_Token")
            Access_Token_Secret = self.GetParameter("Access_Token_Secret")
            # Authenticate to Twitter
            self.auth = tweepy.OAuthHandler(str(Consumer_Api), str(Consumer_Api_Secret))
            self.auth.set_access_token(str(Access_Token), str(Access_Token_Secret))
            self.api = tweepy.API(self.auth, wait_on_rate_limit = True, wait_on_rate_limit_notify = True)
            self.api.update_status("Spawn More Overlords")
            
            myStreamListener = MyStreamListener()
            self.myStream = tweepy.Stream(auth = self.api.auth, listener=myStreamListener)
            users = [str(self.api.get_user(screen_name = i).id) for i in ['@TT3Private', '@PrivatePRT']]
            self.myStream.filter(follow = users, is_async=True)
        
        
        # self.equity = self.AddEquity("SPY", Resolution.Daily)
        # self.equity.SetDataNormalizationMode(DataNormalizationMode.Raw)
        # self.symbol = self.equity.Symbol
        # # initialize the option contract with empty string
        # self.contract = str()
        # self.contractsAdded = set()

        # # parameters ------------------------------------------------------------
        # self.DaysBeforeExp = 7 # number of days before expiry to exit
        # self.DTE = 35 # target days till expiration
        # self.OTM = 1.05 # target percentage OTM of call
        # self.options_alloc = 90 # 1 option for X num of shares (balanced would be 100)
        # # ------------------------------------------------------------------------
    
    def OnData(self, data):
        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
            Arguments:
                data: Slice object keyed by symbol containing the stock data
        '''
        pass
        # if (self.IsWarmingUp): return
        
        # if self.Portfolio[self.symbol].Invested:
        #     self.BuyCall(data)
        
        # # if self.Portfolio.Invested:
        # #     invested = [ x.Symbol.Value for x in self.Portfolio.Values if x.Invested]
        # #     self.Debug(str(invested))
        # #     for stonk in invested:
        # #         if self.breakout(stonk):
        # #             # add the underlying asset
        # #             self.equity = self.AddEquity(stonk, Resolution.Minute)
        # #             self.equity.SetDataNormalizationMode(DataNormalizationMode.Raw)
        # #             self.symbol = self.equity.Symbol
        # #             self.BuyCall(data)
        
        # # close call before it expires
        # if self.contract:
        #     if (self.contract.ID.Date - self.Time) <= timedelta(self.DaysBeforeExp):
        #         self.Liquidate(self.contract)
        #         self.Log("Closed: too close to expiration")
        #         self.contract = str()
            
    def AddCash(self):
        self.Portfolio.SetCash(self.Portfolio.Cash + self.added_cash)

    def UpKeep(self):
        self.Portfolio.SetCash(self.Portfolio.Cash - self.upkeep)    

    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
        self.history = self.history.iloc[-(VOLA + 1):]

    def createPlots(self, benchmark):
        self.__benchmark = benchmark

        self.__plot_every_n_days = 5
        self.__plot_every_n_days_i = 0
        plot = Chart('Performance')
        plot.AddSeries(Series(self.__benchmark, SeriesType.Line, 0, '%'))
        plot.AddSeries(Series("Algorithm", SeriesType.Line, 0, '%'))
        self.AddChart(plot)

        self.ResetPlot()

    def ResetPlot(self):
        self.year = self.Time.year
        self.__cost_portfolio = None
        self.__cost_benchmark = None

    def CalculateBenchmarkPerformance(self):
        price = self.Securities[self.__benchmark].Price
        if self.__cost_benchmark == None:
            self.__cost_benchmark = price
        return 100.0 * ((price / self.__cost_benchmark) - 1.0)

    def CalculatePortfolioPerformance(self):
        if self.__cost_portfolio == None:
            self.__cost_portfolio = self.Portfolio.TotalPortfolioValue
        return 100.0 * ((self.Portfolio.TotalPortfolioValue / self.__cost_portfolio) - 1.0)

    def OnEndOfDay(self):
        if self.IsWarmingUp or not self.Securities[self.__benchmark].HasData:
            return

        if self.Time.year != self.year:
            self.ResetPlot()
            self.__plot_every_n_days_i == -1

        self.__plot_every_n_days_i += 1
        if self.__plot_every_n_days_i % self.__plot_every_n_days != 0:
            return
        
        #self.Plot('Performance', self.__benchmark, self.CalculateBenchmarkPerformance())
        #self.Plot('Performance', "Algorithm", self.CalculatePortfolioPerformance())
    
    def UpdateTickets(self, symbol):
        close = self.Securities[symbol].Close
        quantity = self.Portfolio[symbol].Quantity
        self.StopMarketOrder(symbol, -quantity, close * 0.98)
        self.LimitOrder(symbol, -int(quantity/2), close * 1.05)
      
    def breakout(self, symbol):
        self.lookback = 20
        self.ceiling, self.floor = 30, 10
        
        close = self.History(symbol, 31, Resolution.Daily)["close"]
        todayvol = np.std(close[1:31])
        yesterdayvol = np.std(close[0:30])
        deltavol = (todayvol - yesterdayvol) / todayvol
        self.lookback = round(self.lookback * (1 + deltavol))

        # Account for upper/lower limit of lockback length
        if self.lookback > self.ceiling:
            self.lookback = self.ceiling
        elif self.lookback < self.floor:
            self.lookback = self.floor

        # List of daily highs
        self.high = self.History(symbol, self.lookback, Resolution.Daily)["high"]
        
        # Buy in case of breakout
        if self.Securities[symbol].Invested and (self.Securities[symbol].Close >= max(self.high[:-1])):
            return True

    def BuyCall(self, data):
        # get option data
        if self.contract == str():
            try:
                self.contract = self.OptionsFilter(data)
            except:
                pass
            return
        
        # if not invested and option data added successfully, buy option
        elif not self.Portfolio[self.contract].Invested and data.ContainsKey(self.contract):
            self.Buy(self.contract, 1)

            # self.Buy(self.contract, round(self.Portfolio[symbol].Quantity / self.options_alloc))

    def OptionsFilter(self, data):
        ''' OptionChainProvider gets a list of option contracts for an underlying symbol at requested date.
            Then you can manually filter the contract list returned by GetOptionContractList.
            The manual filtering will be limited to the information included in the Symbol
            (strike, expiration, type, style) and/or prices from a History call '''

        contracts = self.OptionChainProvider.GetOptionContractList(self.symbol, data.Time)
        self.underlyingPrice = self.Securities[self.symbol].Price
        # filter the out-of-money put options from the contract list which expire close to self.DTE num of days from now
        otm_calls = [i for i in contracts if i.ID.OptionLeft == OptionRight.Call]# and
                                            #self.underlyingPrice - i.ID.StrikePrice < self.OTM * self.underlyingPrice and
                                            #self.DTE - 14 < (i.ID.Date - data.Time).days < self.DTE + 14]
        if len(otm_calls) > 0:
            # sort options by closest to self.DTE days from now and desired strike, and pick first
            contract = sorted(otm_calls, key = self.OpenInterest)[0]
            # contract = sorted(sorted(sorted(otm_calls, key = lambda x: abs((x.ID.Date - self.Time).days - self.DTE)),
            #                                          key = lambda x: self.underlyingPrice - x.ID.StrikePrice),
            #                                          key = self.OpenInterest)[0]
            if contract not in self.contractsAdded:
                self.contractsAdded.add(contract)
                # use AddOptionContract() to subscribe the data for specified contract
                self.AddOptionContract(contract, Resolution.Minute)
            return contract
        else:
            return str()
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data.Market import TradeBar

import pandas as pd
import numpy as np
from scipy import stats
import statistics

from helpers import myPortfolioOptimizer

class PortfolioManagementModel(PortfolioConstructionModel):
    def __init__(self,
                 algorithm,
                 RET=252,
                 EXCL=21,
                 LEV=1.00,
                 resolution = Resolution.Daily,
                 *args, **kwargs):
        super().__init__()
        self.resolution = resolution
        self.RET = RET
        self.EXCL = EXCL
        self.LEV = LEV
        self.VOLA = 126
        self.STK1 = algorithm.AddEquity('SPY', resolution).Symbol # SPXL/SPY
        self.STK2 = algorithm.AddEquity('QQQ', resolution).Symbol # TQQQ/QQQ
        self.STK3 = algorithm.AddEquity('IWM', resolution).Symbol # URTY/IWM
        self.STK4 = algorithm.AddEquity('FDN', resolution).Symbol # FDN/FDN
        self.STK5 = algorithm.AddEquity('VTI', resolution).Symbol # AGQ/VTI
        self.STK6 = algorithm.AddEquity('DIA', resolution).Symbol # AGQ/VTI
        self.STK7 = algorithm.AddEquity('IWF', resolution).Symbol # AGQ/VTI
        self.BND1 = algorithm.AddEquity('TLH', resolution).Symbol # TMF/TLH
        self.BND2 = algorithm.AddEquity('TLT', resolution).Symbol # UGL/TLT
        self.BND3 = algorithm.AddEquity('IEI', resolution).Symbol # TMF/TLH
        self.BND4 = algorithm.AddEquity('IEF', resolution).Symbol # UGL/TLT
        self.STOCKS = [self.STK1, self.STK2, self.STK3, self.STK4, self.STK5, self.STK6, self.STK7]
        self.BONDS = [self.BND1, self.BND2, self.BND3, self.BND4]
        self.ASSETS = self.STOCKS + self.BONDS
        self.data = dict()
            
        for symbol in self.ASSETS:
            self.consolidator = TradeBarConsolidator(timedelta(days=1))
            self.consolidator.DataConsolidated += self.consolidation_handler
            algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidator)
            
        self.history = algorithm.History(self.ASSETS, self.VOLA + 1, self.resolution)
        self.pfo = myPortfolioOptimizer(minWeight=0, maxWeight=1)

        for symbol in self.ASSETS:
            algorithm.Securities[symbol].SetLeverage(1)
 
    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = statistics.median([consolidated.High, consolidated.Low, consolidated.Close]) * consolidated.Volume
        self.history = self.history.iloc[-(self.VOLA + 1):]
        
    def OnSecuritiesChanged(self, algorithm, changes):
        addedSymbols = []
        for security in changes.AddedSecurities:
            addedSymbols.append(security.Symbol)
            if security.Symbol not in self.data:
                self.data[security.Symbol] = SymbolData(security.Symbol, algorithm)
   
        if len(addedSymbols) > 0:
            history = algorithm.History(addedSymbols, self.VOLA + 1, self.resolution).loc[addedSymbols]
            for symbol in addedSymbols:
                try:
                    self.data[symbol].Warmup(history.loc[symbol])
                except:
                    algorithm.Debug(str(symbol))
                    continue
        
    def returns_custom(self, symbol, timeframe):
        prices = np.log10(algorithm.History(symbol, TimeSpan.FromDays(self.VOLA), self.resolution).close)
        return round((prices[-timeframe] - prices[-5]) / prices[-5], 4)        

    def rel_vol(self, symbol):
        volume = self.data[symbol].sma.Current.Value
        volumew = self.data[symbol].smaw.Current.Value
        volume_ratio = round(float(volumew - volume)/volumew, 4)
        return volume_ratio

    def custom_filter(self, symbol, filter_type = 'both'):
        if filter_type == 'both':
            if ((self.data[symbol].roc_slope >= 0) and (self.data[symbol].vol_slope >= 0)) and (self.data[symbol].breakout):
                return True
            else:
                return False
        if filter_type == 'either':
            if ((self.data[symbol].roc_slope >= 0) and (self.data[symbol].vol_slope >= 0)) or (self.data[symbol].breakout):
                return True
            else:
                return False
        if filter_type == 'slope':
            if ((self.data[symbol].roc_slope >= 0) and (self.data[symbol].vol_slope >= 0)):
                return True
            else:
                return False
        if filter_type == 'breakout':
            if self.data[symbol].breakout:
                return True
            else:
                return False
                
    def CreateTargets(self, algorithm, insights):
        if algorithm.IsWarmingUp:
            return []

        targets = []
        
        # We expect at most only one active insight since we only
        # generate insights for one equity.
        assert len(insights) <= 1
        if len(insights) == 1:
            insight = insights[0]
            
            # self.bull = insight.Direction == InsightDirection.Up
            if insight.Direction == InsightDirection.Down:
                self.bull = False
            else:
                self.bull = True

            selected = list()
            if self.bull:
                stocks = [(symbol, self.data[symbol].median_roc) for symbol in self.STOCKS if self.custom_filter(symbol, filter_type = 'both') is True]
                stocks.sort(key=lambda x: x[1], reverse=True)
                for sec, roc in stocks[:1]:
                    selected.append(sec)
                if len(selected) < 2:
                    stocks = [(symbol, self.data[symbol].median_roc) for symbol in self.STOCKS if self.custom_filter(symbol, filter_type = 'either') is True]
                    stocks.sort(key=lambda x: x[1], reverse=True)
                    for sec, roc in stocks[:1]:
                        selected.append(sec)
            elif not self.bull:
                bonds = [(symbol, self.data[symbol].rocSignal.Current.Value) for symbol in self.BONDS if self.custom_filter(symbol, filter_type = 'both') is True]
                bonds.sort(key=lambda x: x[1], reverse=True)
                for sec, roc in bonds[:1]:
                    selected.append(sec)
                if len(selected) < 2:
                    selected.append(self.BND3)
                    selected.append(self.BND4)
            else:
                return []
            
            for asset in self.ASSETS:
                if asset in selected:
                    targets.append(PortfolioTarget.Percent(algorithm, asset, 0.5))
                else:
                    targets.append(PortfolioTarget.Percent(algorithm, asset, 0.0))
        return targets

class SymbolData(object):
    def __init__(self, symbol, algorithm):
        self.Symbol = symbol
        self.lookback = 20
        self.ceiling = 30
        self.floor = 10
        self.breakout = False
        self.fir = 0.00
        self.EXCL = 21
        self.scale = 0.00
        self.is_uptrend = False
        self.tolerance = 0.95
        self.vol_slope = 0.00
        self.roc_slope = 0.00
        self.median_roc = 0.00
        self.fast = VolumeWeightedAveragePriceIndicator(8) #ExponentialMovingAverage(8)
        self.fast_ema_window = RollingWindow[float](5)
        self.slow = VolumeWeightedAveragePriceIndicator(14) #ExponentialMovingAverage(14)
        self.slow_ema_window = RollingWindow[float](5)
        self.sma = SimpleMovingAverage(21)
        self.smaw = SimpleMovingAverage(5)
        self.roc = RateOfChange(5)
        self.roc_med = RateOfChange(8)
        self.vol_med = RateOfChange(8)
        self.vol = RateOfChange(5)
        self.roc_window = RollingWindow[float](5)
        self.vol_window = RollingWindow[float](5)
        self.prices_window = RollingWindow[float](41)
        self.high_window = RollingWindow[float](41)
        self.obvm = OnBalanceVolume(7, MovingAverageType.Exponential)
        self.obvmEMA = IndicatorExtensions.EMA(self.obvm, 10)
        self.obvmSignal = IndicatorExtensions.Over(IndicatorExtensions.Minus(self.obvm, self.obvmEMA), self.obvmEMA)
        self.rocSignal = IndicatorExtensions.Over(self.roc, self.roc_med)
        self.volSignal = IndicatorExtensions.Over(self.vol, self.vol_med)
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.roc, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.roc_med, self.consolidator)

        # self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        # algorithm.RegisterIndicator(symbol, self.rocSignal, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.vol, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.vol_med, self.consolidator)

        # self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        # algorithm.RegisterIndicator(symbol, self.volSignal, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.fast, self.consolidator)
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.slow, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.smaw, self.consolidator)
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.sma, self.consolidator)
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.obvm, self.consolidator)

        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        algorithm.RegisterIndicator(symbol, self.obvmEMA, self.consolidator)
        
        # Warm up
        history = algorithm.History(symbol, 126, Resolution.Daily)
        if history.empty or 'close' not in history.columns:
            return
        for index, row in history.loc[symbol].iterrows():
            tradeBar = TradeBar(index, row['open'], row['high'], row['low'], row['close'], row['volume'])
            self.roc.Update(index, row['close'])
            self.roc_med.Update(index, row['close'])
            self.rocSignal.Update(index, row['close'])
            self.vol.Update(index, row['volume'])
            self.vol_med.Update(index, row['volume'])
            self.volSignal.Update(index, row['volume'])
            self.fast.Update(tradeBar)
            self.slow.Update(tradeBar)
            self.smaw.Update(index, row['volume'])
            self.sma.Update(index, row['volume'])
            self.obvm.Update(tradeBar)
            
            self.roc_window.Add(self.roc.Current.Value) # self.roc.Current.Value
            if self.roc_window.IsReady:
                roc_sum = sum(list(self.roc_window))
                roc_len = len(list(self.roc_window))
                self.roc_slope = round(float(roc_sum)/roc_len, 4)
            
            self.vol_window.Add(self.vol.Current.Value) # self.vol.Current.Value
            if self.vol_window.IsReady:
                vol_sum = sum(list(self.vol_window))
                vol_len = len(list(self.vol_window))
                self.vol_slope = round(float(vol_sum)/vol_len, 4)
            
            self.prices_window.Add(row['close'])
            if self.prices_window.IsReady:
                prices = np.log10(list(self.prices_window))
                frames = [i+self.EXCL for i in range(-20, 20, 2)]

                self.median_roc = round(statistics.median([round(float(prices[-i] - prices[-5]/ prices[-5]), 4) for i in frames]), 4)
                    
            # # power_weighted_moving_average
                self.power = 1
                    
                weights = np.array([(i+1)**self.power for i in range(len(prices))])
                    
                self.fir = sum(prices * weights)/sum(weights)
                
            # OnBalanceVolumeModified
            # if self.obvm.Current.Value > self.obvmEMA.Current.Value:
            #     self.obvmSignal = True
            # else:
            #     self.obvmSignal = False
            
            self.high_window.Add(row['high'])
            if self.high_window.IsReady and self.prices_window.IsReady:
                close = list(self.prices_window)
                todayvol = np.std(close[1:31])
                yesterdayvol = np.std(close[0:30])
                deltavol = (todayvol - yesterdayvol) / todayvol
                self.lookback = round(self.lookback * (1 + deltavol))
                
                # Account for upper/lower limit of lockback length
                if self.lookback > self.ceiling:
                    self.lookback = self.ceiling
                elif self.lookback < self.floor:
                    self.lookback = self.floor
                    
                high = list(self.high_window)

                # Buy in case of breakout
                #if not algorithm.Securities[symbol].Invested and \
                if algorithm.Securities[symbol].Close >= max(high[:-1]):
                    self.breakout = True
            
            fast = self.fast.Current.Value
            slow = self.slow.Current.Value
            self.is_uptrend = (fast > (slow * self.tolerance)) and (row[tradeBar] > (fast * self.tolerance))
            
        if self.is_uptrend:
            self.scale = round(float(fast - slow) / ((fast + slow) / 2.0), 4)
        else:
            self.scale = 0
from itertools import groupby
import tweepy
from datetime import datetime, timedelta, date
import time
import pandas as pd
import numpy as np
import re, math
import scipy
from math import ceil
from collections import deque
from itertools import chain

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from QuantConnect.Data.Market import TradeBar
from QuantConnect.Algorithm.Framework.Execution import StandardDeviationExecutionModel, VolumeWeightedAveragePriceExecutionModel
from QuantConnect.Algorithm.Framework.Risk import MaximumDrawdownPercentPortfolio, MaximumUnrealizedProfitPercentPerSecurity, MaximumDrawdownPercentPerSecurity, TrailingStopRiskManagementModel

from dual_momentum_with_out_days_alpha import DualMomentumWithOutDaysAlphaModel
from trade_execution import ScheduledExecutionModel
from portfolio_management import PortfolioManagementModel

VOLA = 126; BASE_RET = 83; RET = 252; EXCL = 21; LEV = 1.00;

class HorizontalQuantumCoil(QCAlgorithm):

    def Initialize(self):
        self.Portfolio.MarginCallModel = MarginCallModel.Null
        self.SetStartDate(2008, 1, 1)
        self.SetCash(100000)
        self.added_cash = 115
        self.upkeep = 28
        self.simulate_live = False
        self.SetWarmUp(timedelta(252))
        self.Settings.FreePortfolioValuePercentage = 0.05
        #self.SetBrokerageModel(BrokerageName.AlphaStreams)
        self.SetAlpha(DualMomentumWithOutDaysAlphaModel(self, VOLA, BASE_RET, Resolution.Daily))

        stonks = ['FDN', 'QQQ', 'IWM', 'SPY', 'VTI', 'DIA', 'IWF', 'TLT', 'TLH', 'IEI', 'IEF']
        # lev_stonks = ['UDOW', 'TQQQ', 'URTY', 'SPXL', 'TMF', 'AGQ', 'UGL']
        # stonks = ['ITOT', 'IVV', 'IJH', 'IJR', 'XT', 'IHAK', 'IWFH', 'IDNA', 'IRBO', 'TECB', 'BFTR', 'BTEK', 'BMED']
        symbols = []
        for stonk in stonks:
            val = Symbol.Create(stonk, SecurityType.Equity, Market.USA)
            symbols.append(val)
        self.SetUniverseSelection(ManualUniverseSelectionModel(symbols))
        self.UniverseSettings.Resolution = Resolution.Daily
        self.SetPortfolioConstruction(PortfolioManagementModel(self, RET, EXCL, LEV, Resolution.Daily))
        self.SetExecution(ScheduledExecutionModel(self))
        # self.SetRiskManagement(MaximumUnrealizedProfitPercentPerSecurity(maximumUnrealizedProfitPercent = 0.10))
        # self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(maximumDrawdownPercent = 0.075))
        # self.SetRiskManagement(MaximumDrawdownPercentPortfolio(maximumDrawdownPercent = 0.05, isTrailing = True))

        self.createPlots("SPY")
    
        # if self.simulate_live:
        #     self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), \
        #                      self.TimeRules.BeforeMarketClose("SPY", 0), \
        #                      self.AddCash)
    
        #     self.Schedule.On(self.DateRules.MonthStart("SPY"), \
        #                      self.TimeRules.BeforeMarketClose("SPY", 0), \
        #                      self.UpKeep)
    
    def AddCash(self):
        self.Portfolio.SetCash(self.Portfolio.Cash + self.added_cash)

    def UpKeep(self):
        self.Portfolio.SetCash(self.Portfolio.Cash - self.upkeep)        

    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
        self.history = self.history.iloc[-(VOLA + 1):]

    def createPlots(self, benchmark):
        self.__benchmark = benchmark

        self.__plot_every_n_days = 5
        self.__plot_every_n_days_i = 0
        plot = Chart('Performance')
        plot.AddSeries(Series(self.__benchmark, SeriesType.Line, 0, '%'))
        plot.AddSeries(Series("Algorithm", SeriesType.Line, 0, '%'))
        self.AddChart(plot)

        self.ResetPlot()

    def ResetPlot(self):
        self.year = self.Time.year
        self.__cost_portfolio = None
        self.__cost_benchmark = None

    def CalculateBenchmarkPerformance(self):
        price = self.Securities[self.__benchmark].Price
        if self.__cost_benchmark == None:
            self.__cost_benchmark = price
        return 100.0 * ((price / self.__cost_benchmark) - 1.0)

    def CalculatePortfolioPerformance(self):
        if self.__cost_portfolio == None:
            self.__cost_portfolio = self.Portfolio.TotalPortfolioValue
        return 100.0 * ((self.Portfolio.TotalPortfolioValue / self.__cost_portfolio) - 1.0)

    def OnEndOfDay(self):
        if self.IsWarmingUp or not self.Securities[self.__benchmark].HasData:
            return

        if self.Time.year != self.year:
            self.ResetPlot()
            self.__plot_every_n_days_i == -1

        self.__plot_every_n_days_i += 1
        if self.__plot_every_n_days_i % self.__plot_every_n_days != 0:
            return
        
        #self.Plot('Performance', self.__benchmark, self.CalculateBenchmarkPerformance())
        #self.Plot('Performance', "Algorithm", self.CalculatePortfolioPerformance())
    
    def UpdateTickets(self):
        invested = [x.Key for x in self.Portfolio if x.Value.Invested]
        for symbol in invested:
            if self.breakout(symbol):
                close = self.Securities[symbol].Close
                quantity = self.Portfolio[symbol].Quantity
                self.StopMarketOrder(symbol, quantity, close * 0.95)
                self.LimitOrder(symbol, int(quantity/2), close * 1.05)
      
    def breakout(self, symbol):
        self.lookback = 20
        self.ceiling, self.floor = 30, 10
        
        close = self.History(symbol, 31, Resolution.Daily)["close"]
        todayvol = np.std(close[1:31])
        yesterdayvol = np.std(close[0:30])
        deltavol = (todayvol - yesterdayvol) / todayvol
        self.lookback = round(self.lookback * (1 + deltavol))

        # Account for upper/lower limit of lockback length
        if self.lookback > self.ceiling:
            self.lookback = self.ceiling
        elif self.lookback < self.floor:
            self.lookback = self.floor

        # List of daily highs
        self.high = self.History(symbol, self.lookback, Resolution.Daily)["high"]
        
        # Buy in case of breakout
        #if not self.Securities[symbol].Invested and \
        if self.Securities[symbol].Close >= max(self.high[:-1]):
            return True
from System import *
from QuantConnect import *

from sklearn.ensemble import ExtraTreesClassifier, ExtraTreesRegressor
from sklearn.model_selection import train_test_split, cross_validate, cross_val_score, cross_val_predict
import tweepy, statistics
from datetime import datetime, timedelta, date
import numpy as np
import pickle
from scipy import stats

from helpers import myTrailingStopRiskManagementModel

class DualMomentumWithOutDaysAlphaModel(AlphaModel):

    def __init__(self, algorithm, VOLA = 126, BASE_RET = 83, resolution = Resolution.Daily, *args, **kwargs):
        super().__init__()
        self.algorithm = algorithm
        self.VOLA = VOLA
        self.BASE_RET = BASE_RET
        self.resolution = resolution
        self.MKT = algorithm.AddEquity('SPY', resolution).Symbol
        self.SLV = algorithm.AddEquity('SLV', resolution).Symbol
        self.GLD = algorithm.AddEquity('GLD', resolution).Symbol
        self.XLI = algorithm.AddEquity('XLI', resolution).Symbol
        self.XLU = algorithm.AddEquity('XLU', resolution).Symbol
        self.DBB = algorithm.AddEquity('DBB', resolution).Symbol
        self.UUP = algorithm.AddEquity('UUP', resolution).Symbol

        self.count = self.BASE_RET
        self.outday = 5
        self.data = dict()

        pairs = [self.MKT, self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]
        for symbol in pairs:
            self.consolidator = TradeBarConsolidator(timedelta(days=1))
            self.consolidator.DataConsolidated += self.consolidation_handler
            algorithm.SubscriptionManager.AddConsolidator(symbol, self.consolidator)

        self.history = algorithm.History(pairs, self.VOLA + 1, self.resolution)
        
        # Set TrainingMethod to be executed immediately
        #algorithm.Train(self.TrainingMethod)

        self.predictionInterval = Time.Multiply(Extensions.ToTimeSpan(self.resolution), 1)
        resolutionString = Extensions.GetEnumString(resolution, Resolution)
        self.Name = f"{self.__class__.__name__}({resolutionString})"

        # Force alpha to only produce insights Daily at 11.10am
        self.set_flag = False
        algorithm.Schedule.On(algorithm.DateRules.EveryDay(),
                              algorithm.TimeRules.AfterMarketOpen('SPY', 60),
                              self.SetFlag)
        
        #algorithm.Train(algorithm.DateRules.Every(DayOfWeek.Monday), algorithm.TimeRules.At(1, 0), self.TrainingMethod)
                                
                              
    def SetFlag(self):
        self.set_flag = True

    def consolidation_handler(self, sender, consolidated):
        self.history.loc[consolidated.EndTime, consolidated.Symbol] = consolidated.Close
        self.history = self.history.iloc[-(self.VOLA + 1):]
        
    def OnSecuritiesChanged(self, algorithm, changes):
        addedSymbols = []
        for security in changes.AddedSecurities:
            addedSymbols.append(security.Symbol)
            if security.Symbol not in self.data:
                self.data[security.Symbol] = SymbolData(security.Symbol, algorithm)
   
        if len(addedSymbols) > 0:
            history = self.algorithm.History(addedSymbols, self.VOLA + 1, Resolution.Daily).loc[addedSymbols]
            for symbol in addedSymbols:
                try:
                    self.data[symbol].Warmup(history.loc[symbol])
                except:
                    self.algorithm.Debug(str(symbol))
                    continue
        
    def TrainingMethod(self):

        self.classifier = ExtraTreesClassifier(n_estimators=500,
                                          criterion = "gini",
                                          min_samples_split = 5,
                                          random_state = 1990)
        
        pairs = [self.MKT, self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]

        if self.history is None: return
        df = self.algorithm.History(pairs, 252, Resolution.Daily)

        # Prepare data
        returns = df.unstack(level=1).close.transpose().dropna()
        returns['Volatility'] = returns[self.MKT].pct_change().rolling(self.VOLA).std() * np.sqrt(252)
        returns["Wait_Days"] = returns['Volatility'].apply(lambda x: self.wait_days(x))
        returns["Period"] = returns['Volatility'].apply(lambda x: self.period(x))
        returns["Exit_Market"] = returns["Period"].apply(lambda x: self.exit_market(x))
        
        returns = returns.dropna()
        
        y_clf = returns["Exit_Market"].apply(lambda x: 1 if True else 0).values
        
        del returns['Exit_Market']
        
        X = returns
        
        #y_clf = X["Exit_Market"].apply(lambda x: 1 if True else 0).values
        X_train, X_test, y_train_clf, y_test_clf = train_test_split(X, y_clf, test_size = 0.2, random_state = 1990)
        
        self.classifier.fit(X_train, y_train_clf)

        
    def Update(self, algorithm, _data):
        if algorithm.IsWarmingUp or not self.set_flag:
            return []

        self.set_flag = False
        insights = []
        
        #if self.classifier is None: return
        # Volatility
        vola = self.history[self.MKT].pct_change().std() * np.sqrt(252)
        wait_days = int(vola * self.BASE_RET)
        period = int((1.0 - vola) * self.BASE_RET)
        r = self.history.pct_change(period).iloc[-1]

        exit_market = r[self.SLV] < r[self.GLD] and r[self.XLI] < r[self.XLU] and r[self.DBB] < r[self.UUP]
        
        # pairs = [self.MKT, self.SLV, self.GLD, self.XLI, self.XLU, self.DBB, self.UUP]
        # data = self.history[pairs]
        # data['Volatility'] = vola
        # data['Wait_Days'] = int(vola * self.BASE_RET)
        # data['Period'] = int((1.0 - vola) * self.BASE_RET)
        
        # exit_market = self.classifier.predict(data.tail(1))
        self.algorithm.Debug(self.data[self.MKT].prices)

        direction = InsightDirection.Down
            
        if (exit_market):
            #algorithm.Plot("In vs Out", "Market", -1)
            direction = InsightDirection.Down
            self.outday = self.count
        else:
            if (self.count >= wait_days + self.outday):
                #algorithm.Plot("In vs Out", "Market", 1)
                direction = InsightDirection.Up
        self.count += 1
        
        algorithm.Plot("Exit", str(self.MKT), int(exit_market))
        
        # algorithm.Plot("Wait Days", "Actual", self.count)
        # algorithm.Plot("Wait Days", "Expected", wait_days + self.outday)
                                                                    
        insights.append(Insight.Price(self.MKT, self.predictionInterval, direction))

        return insights

class SymbolData(object):
    def __init__(self, symbol, algorithm):
        self.Symbol = symbol
        self.VOLA = 126
        self.prices = RollingWindow[float](126)
        self.prices_window = RollingWindow[float](126)
        self.algorithm = algorithm
        
        self.consolidator = algorithm.ResolveConsolidator(symbol, Resolution.Daily)
        
        # Warm up
        history = self.algorithm.History(symbol, 126, Resolution.Daily)
        if history.empty or 'close' not in history.columns:
            return
        for index, row in history.loc[symbol].iterrows():
            self.prices_window.Add(row['close'])
            if self.prices_window.IsReady:
                self.prices = list(self.prices_window)
                
        
    def wait_days(self, x):
        try:
            val = int(x * self.BASE_RET)
        except:
            val = np.nan
        return val

    def period(self, x):
        try:
            val = int((1.0 - x) * self.BASE_RET)
        except:
            val = np.nan
        return val
 
    def exit_market(self, x):
        try:
            x = int(x)
            r = self.history.pct_change(x).iloc[-1]
            cond1 = r[self.SLV] < r[self.GLD]
            cond2 = r[self.XLI] < r[self.XLU]
            cond3 = r[self.DBB] < r[self.UUP]
            exit_market = cond1 and cond2 and cond3
        except:
            exit_market = np.nan
        return exit_market