Overall Statistics
Total Trades
0
Average Win
0%
Average Loss
0%
Compounding Annual Return
0%
Drawdown
0%
Expectancy
0
Net Profit
0%
Sharpe Ratio
0
Probabilistic Sharpe Ratio
0%
Loss Rate
0%
Win Rate
0%
Profit-Loss Ratio
0
Alpha
0
Beta
0
Annual Standard Deviation
0
Annual Variance
0
Information Ratio
0
Tracking Error
0
Treynor Ratio
0
Total Fees
$0.00
Estimated Strategy Capacity
$0
Lowest Capacity Asset
 
import numpy as np
import math
import scipy.stats as scs 
from AlgorithmImports import *

class VolumeEMArelationship(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 10, 1)
        self.SetEndDate(2019, 10, 1)
        self.SetCash(100000)
        self.SetTimeZone(TimeZones.NewYork)
        
        # calling in BTC
        self.BTC = self.AddCrypto("BTCUSD", Resolution.Minute)
        
        # symbol creation
        self.BTC_symbol = self.AddCrypto("BTCUSD", Resolution.Minute).Symbol
        
        # Consolidators
        self.dt_fifteen_min = timedelta(minutes=15)
        
        # 15 Minute
        BTCFifteenMinuteConsolidator = TradeBarConsolidator(self.dt_fifteen_min)
        BTCFifteenMinuteConsolidator.DataConsolidated += self.BTCFifteenMinuteConsolidated
        self.SubscriptionManager.AddConsolidator(self.BTC_symbol, BTCFifteenMinuteConsolidator)
        
        # BTC MAs
        #self.EMA_30_FifteenMin_BTC = ExponentialMovingAverage(30)
        #self.RegisterIndicator(self.BTC_symbol, self.EMA_30_FifteenMin_BTC, BTCFifteenMinuteConsolidator)
        #self.WarmUpIndicator(self.BTC_symbol, self.EMA_30_FifteenMin_BTC, self.dt_fifteen_min)
    
    def OnData(self, data):
        if self.IsWarmingUp: return
        Minute_volume_current = self.Securities[self.BTC_symbol].Volume
        self.Log("Check")  

    def BTCFifteenMinuteConsolidated(self, sender, bar):
        if self.IsWarmingUp: return
        fifteenminute_volume_current = bar.Volume
        self.Plot("volume", "15 min consolidator no EMA", fifteenminute_volume_current)
        self.Log("Check")