Overall Statistics
Total Orders
4863
Average Win
0.16%
Average Loss
-0.45%
Compounding Annual Return
3.525%
Drawdown
32.700%
Expectancy
0.056
Start Equity
100000
End Equity
180045.66
Net Profit
80.046%
Sharpe Ratio
0.126
Sortino Ratio
0.123
Probabilistic Sharpe Ratio
0.023%
Loss Rate
22%
Win Rate
78%
Profit-Loss Ratio
0.35
Alpha
-0.005
Beta
0.283
Annual Standard Deviation
0.104
Annual Variance
0.011
Information Ratio
-0.331
Tracking Error
0.15
Treynor Ratio
0.046
Total Fees
$8482.12
Estimated Strategy Capacity
$1800000.00
Lowest Capacity Asset
GSG TKH7EPK7SRC5
Portfolio Turnover
6.81%
 
#region imports
from AlgorithmImports import *

import numpy as np
from datetime import datetime
#endregion
# https://quantpedia.com/Screener/Details/1
# Use 5 ETFs (SPY - US stocks, EFA - foreign stocks, BND - bonds, VNQ - REITs, 
# GSG - commodities), equal weight the portfolio. Hold asset class ETF only when 
# it is over its 10 month Simple Moving Average, otherwise stay in cash.


class AssetClassTrendFollowingAlgorithm(QCAlgorithm):

    def initialize(self):
        self.set_start_date(2007, 5, 1)  
        self.set_cash(100000)           
        self._data = {}
        period = 10*21
        self.set_warm_up(period)
        symbols = ["SPY", "EFA", "BND", "VNQ", "GSG"]
        for symbol in symbols:
            self.add_equity(symbol, Resolution.DAILY)
            self._data[symbol] = self.sma(symbol, period, Resolution.DAILY)
            
    def on_data(self, data):
        if self.is_warming_up: 
            return
        is_uptrend = []
        for symbol, sma in self._data.items():
            if self.securities[symbol].price > sma.current.value:
                is_uptrend.append(symbol)
            else:
                self.liquidate(symbol)
        
        for symbol in is_uptrend:
            self.set_holdings(symbol, 1/len(is_uptrend))