| Overall Statistics |
|
Total Orders 2 Average Win 0% Average Loss 0% Compounding Annual Return 3.844% Drawdown 0.500% Expectancy 0 Start Equity 1000000 End Equity 1002138 Net Profit 0.214% Sharpe Ratio 0.199 Sortino Ratio 0.216 Probabilistic Sharpe Ratio 59.672% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.005 Beta 0.129 Annual Standard Deviation 0.013 Annual Variance 0 Information Ratio -0.747 Tracking Error 0.078 Treynor Ratio 0.02 Total Fees $2.00 Estimated Strategy Capacity $87000.00 Lowest Capacity Asset GOOCV XC7Z2Q3F70KM|GOOCV VP83T1ZUHROL Portfolio Turnover 0.68% |
# region imports
from AlgorithmImports import *
# endregion
class Magnificent7(QCAlgorithm):
def initialize(self):
self.set_start_date(2020, 1, 10)
self.set_end_date(2020, 1, 30) # Set End Date
self.set_cash(1000000)
self.underlying_tickers = ['GOOG', 'AAPL', 'NVDA', 'AMZN', 'MSFT','TSLA','META']
self.option_symbols = {} # Dictionary to store symbols
self.sma_dict = {} # Dictionary to store the SMA for each symbol
# self.vwap_dict = {} # Dictionary to store VWAP
self.SetBenchmark("SPY")
self.sma_window = 100
# self.vwap_window = 100
for ticker in self.underlying_tickers:
# Add equity to universe
equity = self.add_equity(ticker)
# Add equity option contracts to universe and create list
option = self.add_option(ticker)
# Filters the option chain so that only
# strikes that are +$100 above current underlying price and
# strikes $0 below current underlying price option and
# within 10 to 45 days to expiration (DTE) of current date
# are available. (Speeds up code!)
option.set_filter(0, 100, timedelta(10), timedelta(45))
self.option_symbols[ticker] = option.symbol
# Add SMA indicator to dictionary based on ticker symbol
self.sma_dict[ticker] = self.sma(equity.Symbol, self.sma_window)
# self.vma_dict[ticker] = self.vwap(equity.Symbol, self.vwap_window)
def on_data(self, slice: Slice) -> None:
for ticker in self.underlying_tickers:
bar = slice.bars.get(ticker)
if bar:
if not self.portfolio.invested and bar.Close < self.sma_dict[ticker].Current.Value:
chain = slice.option_chains.get(self.option_symbols[ticker])
if chain is None: return
# we sort the contracts to identify call options
call = [x for x in chain if x.Right == OptionRight.Call]
# we sort the contracts by closest expiration
contracts = sorted(call, key = lambda x: x.expiry, reverse=False)
# we sort the contracts based on given delta
delta_contracts = sorted(contracts, key = lambda x: abs(x.Greeks.Delta - 0.25))
# if found, trade it
if len(delta_contracts) == 0: return
self.market_order(ticker, 100)
self.market_order(delta_contracts[0].symbol,-1)
# region imports
from AlgorithmImports import *
# endregion
class Magnificent7(QCAlgorithm):
def initialize(self):
self.set_start_date(2020, 11, 10)
# self.set_end_date(2024, 4, 1) # Set End Date
self.set_cash(100000)
self.underlying_tickers = ['GOOG', 'AAPL', 'NVDA', 'AMZN', 'MSFT','TSLA','META']
self.sma_dict = {} # Dictionary to store the SMA for each symbol
self.SetBenchmark("SPY")
self.sma_window = 100
for ticker in self.underlying_tickers:
equity = self.add_equity(ticker)
# Add SMA indicator to dictionary based on ticker symbol
self.sma_dict[ticker] = self.sma(equity.Symbol, self.sma_window)
def on_data(self, slice: Slice):
for ticker in self.underlying_tickers:
bar = slice.bars.get(ticker)
if bar:
if not self.portfolio.invested and bar.Close < self.sma_dict[ticker].Current.Value:
self.set_holdings(ticker, 1/len(self.underlying_tickers))
elif self.portfolio.invested and bar.Close > 1.1*self.sma_dict[ticker].Current.Value:
self.set_holdings(ticker, 0)