| Overall Statistics |
|
Total Trades 3954 Average Win 0.20% Average Loss -0.17% Compounding Annual Return 14.762% Drawdown 64.400% Expectancy 0.255 Net Profit 141.571% Sharpe Ratio 0.518 Loss Rate 43% Win Rate 57% Profit-Loss Ratio 1.20 Alpha 0.148 Beta 0.696 Annual Standard Deviation 0.307 Annual Variance 0.094 Information Ratio 0.465 Tracking Error 0.307 Treynor Ratio 0.229 Total Fees $0.00 |
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from clr import AddReference
AddReference("System")
AddReference("QuantConnect.Algorithm")
AddReference("QuantConnect.Indicators")
AddReference("QuantConnect.Common")
from System import *
from QuantConnect import *
from QuantConnect.Data import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *
from datetime import timedelta
import numpy as np
### <summary>
### Algorithm demonstrating FOREX asset types and requesting history on them in bulk. As FOREX uses
### QuoteBars you should request slices or
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="history and warm up" />
### <meta name="tag" content="history" />
### <meta name="tag" content="forex" />
class SalixcRider(QCAlgorithm):
def Initialize(self):
# Set the cash we'd like to use for our backtest
self.SetCash(10000)
# Start and end dates for the backtest.
self.SetStartDate(2013, 1, 1)
self.SetEndDate(2019, 5, 27)
# Add FOREX contract you want to trade
# find available contracts here https://www.quantconnect.com/data#forex/oanda/cfd
self.forex = self.AddForex("EURUSD", Resolution.Daily)
self.entry_price = 0
self.buy_price = 0
# Create a Rolling Window to keep the 2 QuoteBar
self.quoteBarWindow = RollingWindow[QuoteBar](2)
def OnData(self, data):
if data.ContainsKey("EURUSD"):
# Update our rolling windows
self.quoteBarWindow.Add(data["EURUSD"])
# Wait for windows to be ready.
if not (self.quoteBarWindow.IsReady): return
#Bearish trade setup
if self.entry_price > 0:
newTicket = self.MarketOrder(self.forex.Symbol, 2000, asynchronous = False)
if newTicket.Status != OrderStatus.Filled:
self.Log("Sell order cancelled")
self.entry_price = 0
if self.quoteBarWindow[1].Close > self.quoteBarWindow[1].Open:
newTicket = self.MarketOrder(self.forex.Symbol, -2000, asynchronous = False)
if newTicket.Status != OrderStatus.Filled:
self.Log("Market sell order filled!")
self.entry_price = 1
#Bullish trade setup
if self.buy_price > 0:
newTicket = self.MarketOrder(self.forex.Symbol, -2000, asynchronous = False)
if newTicket.Status != OrderStatus.Filled:
self.Log("Buy order cancelled")
self.buy_price = 0
if self.quoteBarWindow[1].Close < self.quoteBarWindow[1].Open:
newTicket = self.MarketOrder(self.forex.Symbol, 2000, asynchronous = False)
if newTicket.Status != OrderStatus.Filled:
self.Log("Market buy order filled!")
self.buy_price = 1
#self.Debug('{} Current Close: {}, Prev Close: {}'.format(self.Time, round(self.quoteBarWindow[0].Close,5), round(self.quoteBarWindow[1].Close,5)))