| Overall Statistics |
|
Total Trades 148 Average Win 1.84% Average Loss -1.30% Compounding Annual Return 3.341% Drawdown 27.600% Expectancy 0.021 Net Profit 3.490% Sharpe Ratio 0.268 Probabilistic Sharpe Ratio 22.062% Loss Rate 58% Win Rate 42% Profit-Loss Ratio 1.42 Alpha 0 Beta 0 Annual Standard Deviation 0.318 Annual Variance 0.101 Information Ratio 0.268 Tracking Error 0.318 Treynor Ratio 0 Total Fees $447.38 Estimated Strategy Capacity $86000000.00 |
from QuantConnect import *
from QuantConnect.Parameters import *
from QuantConnect.Benchmarks import *
from QuantConnect.Brokerages import *
from QuantConnect.Util import *
from QuantConnect.Interfaces import *
from QuantConnect.Algorithm import *
from QuantConnect.Algorithm.Framework import *
from QuantConnect.Algorithm.Framework.Selection import *
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import *
from QuantConnect.Algorithm.Framework.Execution import *
from QuantConnect.Algorithm.Framework.Risk import *
from QuantConnect.Indicators import *
from QuantConnect.Data import *
from QuantConnect.Data.Consolidators import *
from QuantConnect.Data.Custom import *
from QuantConnect.Data.Fundamental import *
from QuantConnect.Data.Market import *
from QuantConnect.Data.UniverseSelection import *
from QuantConnect.Notifications import *
from QuantConnect.Orders import *
from QuantConnect.Orders.Fees import *
from QuantConnect.Orders.Fills import *
from QuantConnect.Orders.Slippage import *
from QuantConnect.Scheduling import *
from QuantConnect.Securities import *
from QuantConnect.Securities.Equity import *
from QuantConnect.Securities.Forex import *
from QuantConnect.Securities.Interfaces import *
from datetime import date, datetime, timedelta
from QuantConnect.Python import *
from QuantConnect.Storage import *
QCAlgorithmFramework = QCAlgorithm
QCAlgorithmFrameworkBridge = QCAlgorithm
import math
import numpy as np
import pandas as pd
import scipy as sp
class RBreaker(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2020, 3, 1) # Set Start Date
self.SetCash(100000) # Set Strategy Cash
self.spy = self.AddEquity("SPY", Resolution.Hour)
self.ibm = self.AddEquity("IBM", Resolution.Hour)
self.spy.SetDataNormalizationMode(DataNormalizationMode.Raw)
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
self.SetWarmup(2)
self.R_breaker_dict = {}
self.weight = 1.0
def OnData(self, data):
tradebars = data.Bars
if not self.IsWarmingUp:
for symbol in self.R_breaker_dict:
sym_str = symbol.Value
if data.ContainsKey(sym_str) and data[sym_str] is not None:
resist_support = self.R_breaker_dict[symbol]
r3 = resist_support.r3
r2 = resist_support.r2
r1 = resist_support.r1
s1 = resist_support.s1
s2 = resist_support.s2
s3 = resist_support.s3
curr_high = tradebars[sym_str].High
curr_close = tradebars[sym_str].Close
curr_low = tradebars[sym_str].Low
if curr_close >= r3:
self.SetHoldings(symbol,self.weight)
elif curr_close <= s3:
self.SetHoldings(symbol,-self.weight)
elif self.Portfolio[sym_str].Quantity > 0 and curr_high >= r2 and curr_close <= r1 :
self.SetHoldings(symbol,-self.weight)
elif self.Portfolio[sym_str].Quantity < 0 and curr_low <= s2 and curr_close >= s1 :
self.SetHoldings(symbol,self.weight)
self.R_breaker_dict[symbol] = Resist_Support([curr_high,curr_close,curr_low])
else:
symbol = self.spy.Symbol
sym_str = "SPY"
curr_high = tradebars[sym_str].High
curr_close = tradebars[sym_str].Close
curr_low = tradebars[sym_str].Low
self.R_breaker_dict[symbol] = Resist_Support([curr_high,curr_close,curr_low])
class Resist_Support():
def __init__(self, price_list):
hi = price_list[0]
cl = price_list[1]
lo = price_list[2]
self.pivot = np.mean(price_list)
self.r3 = hi + 2*(self.pivot-lo)
self.r2 = self.pivot + hi-lo
self.r1 = 2*(self.pivot-lo)
self.s1 = 2*self.pivot-hi
self.s2 = self.pivot-(hi-lo)
self.s3 = lo-2*(hi-self.pivot)