| Overall Statistics |
|
Total Trades 22 Average Win 4.64% Average Loss -0.42% Compounding Annual Return 407.574% Drawdown 4.800% Expectancy 2.304 Net Profit 10.799% Sharpe Ratio 8.825 Probabilistic Sharpe Ratio 88.124% Loss Rate 73% Win Rate 27% Profit-Loss Ratio 11.11 Alpha 2.875 Beta 0.187 Annual Standard Deviation 0.31 Annual Variance 0.096 Information Ratio 6.362 Tracking Error 0.546 Treynor Ratio 14.623 Total Fees $234.98 Estimated Strategy Capacity $13000.00 Lowest Capacity Asset MATICUSD 2S7 |
#region imports
from AlgorithmImports import *
#endregion
class MATICana_USD(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 10, 20) # Set Start Date
self.SetEndDate(2022, 11, 11) #Set End Date
self.SetCash(10000) # Set Strategy Cash
self.SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash)
crypto = self.AddCrypto("MATICUSD", Resolution.Hour, Market.BinanceUS)
self.lot_size = crypto.SymbolProperties.LotSize
self.symbol = crypto.Symbol
self.sto = Stochastic(14, 1, 3)
self.highestPrice = 0
self.next_trade_time = datetime.min
self.entryPrice = 0
self.entryTicket = None
self.month = 0
def IsRebalanceDue(self, time):
if time.month == self.month or time.month not in [1, 4, 7, 10]:
return None
self.month = time.month
return time
def OnData(self, data):
if data.QuoteBars.ContainsKey(self.symbol):
self.sto.Update(data.QuoteBars[self.symbol])
if self.sto.IsReady:
indicator_value = self.sto.Current.Value
MATIC_stoch_k = self.sto.StochK.Current.Value
MATIC_stoch_d = self.sto.StochD.Current.Value
price = self.Securities[self.symbol].Price
holding_quantity = self.Portfolio[self.symbol].Quantity
invested = abs(self.Portfolio[self.symbol].Quantity) > self.lot_size
if not invested and not self.Transactions.GetOpenOrders(self.symbol):
if MATIC_stoch_d <= 15 and MATIC_stoch_d > 3 and self.Time > self.next_trade_time:
quantity = self.CalculateOrderQuantity(self.symbol, 1)
self.entryTicket = self.MarketOrder(self.symbol, quantity)
self.entryPrice = price
invested = abs(self.Portfolio[self.symbol].Quantity) > self.lot_size
if invested:
if MATIC_stoch_k >= 96 and MATIC_stoch_k < 105:
self.MarketOrder(self.symbol, self.get_sell_quantity(0))
self.next_trade_time = self.Time + timedelta(hours = 4)
if price > self.highestPrice:
self.highestPrice = price
if self.Securities[self.symbol].Price < (self.highestPrice * .95):
self.MarketOrder(self.symbol, self.get_sell_quantity(0))
self.next_trade_time = self.Time + timedelta(hours = 4)
def get_sell_quantity(self, weight):
return int(self.CalculateOrderQuantity(self.symbol, weight) / self.lot_size) * self.lot_size