| Overall Statistics |
|
Total Trades 5 Average Win 0% Average Loss 0% Compounding Annual Return -26.984% Drawdown 26.600% Expectancy 0 Net Profit -19.069% Sharpe Ratio -0.573 Probabilistic Sharpe Ratio 5.438% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.001 Beta 1.239 Annual Standard Deviation 0.284 Annual Variance 0.081 Information Ratio -0.23 Tracking Error 0.14 Treynor Ratio -0.131 Total Fees $5.00 Estimated Strategy Capacity $65000000.00 Lowest Capacity Asset BABA VU1EHIDJYJXH |
#region imports
from AlgorithmImports import *
#endregion
class RetrospectiveBlueSeahorse(QCAlgorithm):
def Initialize(self):
# # # # # User input area # # # # #
# Set start date
self.SetStartDate(2022, 1, 13)
# Strategy cash
self.SetCash(100000)
# User input list of stocks
list_of_stocks = ["AMZN","AAPL","TSLA","BABA","AMD"]
# # # # # End user input area # # # # #
# Rolling RSI dictionary
self.rolling_rsi_dictionary = {}
# MACD dictionary
self.macd_dictionary = {}
# RSI dictionary
self.rsi_dictionary = {}
# SMA 14-period dictionary
self.sma_forteen_dictionary = {}
# SMA 26-period dictionary
self.sma_twentysix_dictionary = {}
# SMA 30-period dictionary
self.sma_thirty_dictionary = {}
# Yesterday close dictionary
self.yesterday_close = {}
# Symbol list
symbol_list = []
# Loop through stocks
for ticker in list_of_stocks:
# Initialize stock
symbol = self.AddEquity(ticker, Resolution.Minute).Symbol
# Add to symbol list
symbol_list.append(symbol)
# Minute history call
history = self.History(symbol_list, 100, Resolution.Minute)
# Daily history call
daily_history = self.History(symbol_list, 1, Resolution.Daily)
# Loop through added symbols
for symbol in symbol_list:
# Initialize security in rolling RSI dictionary
self.rolling_rsi_dictionary[symbol] = []
# MACD dictionary
self.macd_dictionary[symbol] = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Minute)
# RSI dictionary
self.rsi_dictionary[symbol] = self.RSI(symbol, 14, MovingAverageType.Simple, Resolution.Minute)
# SMA 14-period dictionary
self.sma_forteen_dictionary[symbol] = self.SMA(symbol, 14, Resolution.Minute)
# SMA 26-period dictionary
self.sma_twentysix_dictionary[symbol] = self.SMA(symbol, 26, Resolution.Minute)
# SMA 30-period dictionary
self.sma_thirty_dictionary[symbol] = self.SMA(symbol, 30, Resolution.Minute)
# # Yesterday close dictionary
self.yesterday_close[symbol] = daily_history.loc[symbol]["close"][-1]
# Loc history data for asset
asset_history = history.loc[symbol]
# Loop through history of asset
for time, row in asset_history.iterrows():
# Update macd indicator of asset
self.macd_dictionary[symbol].Update(time, row.close)
# Update rsi indicator for asset
self.rsi_dictionary[symbol].Update(time, row.close)
# Update SMA 14-period for asset
self.sma_forteen_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_twentysix_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_thirty_dictionary[symbol].Update(time, row.close)
# Add SPY to schedule events
self.AddEquity("SPY", Resolution.Minute)
# Schedule saving of market closing price
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 0),
self.save_market_closing)
# Save market closing price function
def save_market_closing(self):
# Loop through all assets
for symbol in self.yesterday_close:
# Update close price
self.yesterday_close[symbol] = self.Securities[symbol].Close
def OnData(self, data):
# # # # # Long position logic # # # # #
# Loop through all assets
for symbol in self.rolling_rsi_dictionary:
# Update rolling RSI
self.rolling_rsi_dictionary[symbol].append(self.rsi_dictionary[symbol].Current.Value)
# Check if length of rolling RSI dictionary is greater than 60
if len(self.rolling_rsi_dictionary[symbol]) > 60:
# Cut length
self.rolling_rsi_dictionary[symbol] = self.rolling_rsi_dictionary[symbol][-60:]
# Check if MACD is greater than MACD signal
if self.macd_dictionary[symbol].Current.Value > self.macd_dictionary[symbol].Signal.Current.Value:
# Check if RSI is greater than its value 1 hour ago
if self.rsi_dictionary[symbol].Current.Value > self.rolling_rsi_dictionary[symbol][-60]:
# Check if 14-period SMA is greater than 30-period SMA
if self.sma_forteen_dictionary[symbol].Current.Value > self.sma_thirty_dictionary[symbol].Current.Value:
# Check if current close greater than yesterday close
if self.Securities[symbol].Close > self.yesterday_close[symbol]:
# If not invested
if not self.Portfolio[symbol].Invested:
# Get cash available
cash = self.Portfolio.Cash
# Get 25% of cash available
cash_allocation = cash * 0.25
# Calculate quantity
quantity = int(cash_allocation / self.Securities[symbol].Close)
# Submit market order
self.MarketOrder(symbol, quantity)
# # # # # Liquidation logic # # # # #
# Get holdings
holdings = [x.Key for x in self.Portfolio if x.Value.Invested]
# Loop through holdings
for symbol in holdings:
# If current MACD < MACD signal
if self.macd_dictionary[symbol].Current.Value < self.macd_dictionary[symbol].Signal.Current.Value:
# If RSI is less than its value 1 hour ago
if self.rsi_dictionary[symbol].Current.Value < self.rolling_rsi_dictionary[symbol][-1]:
# If 26-period SMA less than most recent close price
if self.sma_twentysix_dictionary[symbol].Current.Value < self.Securities[symbol].Close:
# Close position with market order
self.MarketOrder(symbol, -self.Portfolio[symbol].Quantity)#region imports
from AlgorithmImports import *
#endregion
class RetrospectiveBlueSeahorse(QCAlgorithm):
def Initialize(self):
# # # # # User input area # # # # #
# Set start date
self.SetStartDate(2022, 1, 13)
# Strategy cash
self.SetCash(25000)
# User input list of stocks
# # # Tickers need to be in capital letters # # #
list_of_stocks = ["AMZN","AAPL","TSLA","BABA","AMD","BP","F","GME","AMC"]
# # # # # End user input area # # # # #
# Rolling RSI dictionary
self.rolling_rsi_dictionary = {}
# MACD dictionary
self.macd_dictionary = {}
# RSI 6-minute dictionary
self.rsi_six_minute_dictionary = {}
# RSI 14-minute dictionary
self.rsi_forteen_minute_dictionary = {}
# SMA 14-period dictionary
self.sma_forteen_dictionary = {}
# SMA 26-period dictionary
self.sma_twentysix_dictionary = {}
# SMA 30-period dictionary
self.sma_thirty_dictionary = {}
# Yesterday close dictionary
self.yesterday_close = {}
# Symbol list
symbol_list = []
# Loop through stocks
for ticker in list_of_stocks:
# Initialize stock
# # # QuantConnect supports tick, second, minute, hour and daily resolution # # #
symbol = self.AddEquity(ticker, Resolution.Minute, "USA", True, 0, False).Symbol
# Add to symbol list
symbol_list.append(symbol)
# Minute history call
# # # The first parameter has to be a list # # #
history = self.History(symbol_list, 100, Resolution.Minute)
# Daily history call
daily_history = self.History(symbol_list, 1, Resolution.Daily)
# Loop through added symbols
for symbol in symbol_list:
# Initialize security in rolling RSI dictionary
self.rolling_rsi_dictionary[symbol] = []
# MACD dictionary
self.macd_dictionary[symbol] = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Minute, Field.Open)
# 6-minute RSI dictionary
self.rsi_six_minute_dictionary[symbol] = self.RSI(symbol, 6, MovingAverageType.Simple, Resolution.Minute)
# 14-minute RSI dictionary
self.rsi_forteen_minute_dictionary[symbol] = self.RSI(symbol, 14, MovingAverageType.Simple, Resolution.Minute)
# SMA 14-period dictionary
self.sma_forteen_dictionary[symbol] = self.SMA(symbol, 14, Resolution.Minute)
# SMA 26-period dictionary
self.sma_twentysix_dictionary[symbol] = self.SMA(symbol, 26, Resolution.Minute)
# SMA 30-period dictionary
self.sma_thirty_dictionary[symbol] = self.SMA(symbol, 30, Resolution.Minute)
# # Yesterday close dictionary
self.yesterday_close[symbol] = daily_history.loc[symbol]["close"][-1]
# Loc history data for asset
asset_history = history.loc[symbol]
# Loop through history of asset
for time, row in asset_history.iterrows():
# Update macd indicator of asset
self.macd_dictionary[symbol].Update(time, row.open)
# Update 6-minute rsi indicator for asset
self.rsi_six_minute_dictionary[symbol][symbol].Update(time, row.close)
# Update 14-minute rsi indicator for asset
self.rsi_forteen_minute_dictionary[symbol][symbol].Update(time, row.close)
# Update SMA 14-period for asset
self.sma_forteen_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_twentysix_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_thirty_dictionary[symbol].Update(time, row.close)
# Add SPY to schedule events
self.AddEquity("SPY", Resolution.Minute)
# Schedule saving of market closing price
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 0),
self.save_market_closing)
# Save market closing price function
def save_market_closing(self):
# Loop through all assets
for symbol in self.yesterday_close:
# Update close price
self.yesterday_close[symbol] = self.Securities[symbol].Close
def OnData(self, data):
# # # # # Long position logic # # # # #
# Loop through all assets
for symbol in self.macd_dictionary:
# Check if MACD is greater than MACD signal
if self.macd_dictionary[symbol].Current.Value > self.macd_dictionary[symbol].Signal.Current.Value:
# Check if 14-period SMA is greater than 30-period SMA
if self.sma_forteen_dictionary[symbol].Current.Value > self.sma_thirty_dictionary[symbol].Current.Value:
# Check if current close greater than yesterday close
if self.Securities[symbol].Close > self.yesterday_close[symbol]:
# If not invested
if not self.Portfolio[symbol].Invested:
# Get cash available
cash = self.Portfolio.Cash
# Get 25% of cash available
cash_allocation = cash * 0.25
# Calculate quantity
quantity = int(cash_allocation / self.Securities[symbol].Close)
# If quantity is greater than 1
if quantity > 1:
# Send SMS to Yomi's phone number
self.Notify.Sms("+1234567890", "Buy signal for " + symbol.Value + " triggered")
# Submit market order
self.MarketOrder(symbol, quantity)
# # # # # Liquidation logic # # # # #
# Get holdings
holdings = [x.Key for x in self.Portfolio if x.Value.Invested]
# Loop through holdings
for symbol in holdings:
# If current MACD < MACD signal
if self.macd_dictionary[symbol].Current.Value < self.macd_dictionary[symbol].Signal.Current.Value:
# If 26-period SMA less than most recent close price
if self.sma_twentysix_dictionary[symbol].Current.Value < self.Securities[symbol].Close:
# Send SMS to Yomi's phone number
self.Notify.Sms("+1234567890", "Sell signal for " + symbol.Value + " triggered")
# Close position with market order
self.MarketOrder(symbol, -self.Portfolio[symbol].Quantity)#region imports
from AlgorithmImports import *
#endregion
class RetrospectiveBlueSeahorse(QCAlgorithm):
def Initialize(self):
# # # # # User input area # # # # #
# Set start date
self.SetStartDate(2022, 1, 13)
# Strategy cash
self.SetCash(100000)
# User input list of stocks
list_of_stocks = ["AMZN","AAPL","TSLA","BABA","AMD"]
# # # # # End user input area # # # # #
# Rolling RSI dictionary
self.rolling_rsi_dictionary = {}
# MACD dictionary
self.macd_dictionary = {}
# RSI dictionary
self.rsi_dictionary = {}
# SMA 14-period dictionary
self.sma_forteen_dictionary = {}
# SMA 26-period dictionary
self.sma_twentysix_dictionary = {}
# SMA 30-period dictionary
self.sma_thirty_dictionary = {}
# Yesterday close dictionary
self.yesterday_close = {}
# Symbol list
symbol_list = []
# Loop through stocks
for ticker in list_of_stocks:
# Initialize stock
symbol = self.AddEquity(ticker, Resolution.Minute).Symbol
# Add to symbol list
symbol_list.append(symbol)
# Minute history call
history = self.History(symbol_list, 100, Resolution.Minute)
# Daily history call
daily_history = self.History(symbol_list, 1, Resolution.Daily)
# Loop through added symbols
for symbol in symbol_list:
# Initialize security in rolling RSI dictionary
self.rolling_rsi_dictionary[symbol] = []
# MACD dictionary
self.macd_dictionary[symbol] = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Minute)
# RSI dictionary
self.rsi_dictionary[symbol] = self.RSI(symbol, 14, MovingAverageType.Simple, Resolution.Minute)
# SMA 14-period dictionary
self.sma_forteen_dictionary[symbol] = self.SMA(symbol, 14, Resolution.Minute)
# SMA 26-period dictionary
self.sma_twentysix_dictionary[symbol] = self.SMA(symbol, 26, Resolution.Minute)
# SMA 30-period dictionary
self.sma_thirty_dictionary[symbol] = self.SMA(symbol, 30, Resolution.Minute)
# # Yesterday close dictionary
self.yesterday_close[symbol] = daily_history.loc[symbol]["close"][-1]
# Loc history data for asset
asset_history = history.loc[symbol]
# Loop through history of asset
for time, row in asset_history.iterrows():
# Update macd indicator of asset
self.macd_dictionary[symbol].Update(time, row.close)
# Update rsi indicator for asset
self.rsi_dictionary[symbol].Update(time, row.close)
# Update SMA 14-period for asset
self.sma_forteen_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_twentysix_dictionary[symbol].Update(time, row.close)
# Update SMA 26-period for asset
self.sma_thirty_dictionary[symbol].Update(time, row.close)
# Add SPY to schedule events
self.AddEquity("SPY", Resolution.Minute)
# Schedule saving of market closing price
self.Schedule.On(self.DateRules.EveryDay("SPY"),
self.TimeRules.BeforeMarketClose("SPY", 0),
self.save_market_closing)
# Save market closing price function
def save_market_closing(self):
# Loop through all assets
for symbol in self.yesterday_close:
# Update close price
self.yesterday_close[symbol] = self.Securities[symbol].Close
def OnData(self, data):
# # # # # Long position logic # # # # #
# Loop through all assets
for symbol in self.rolling_rsi_dictionary:
# Update rolling RSI
self.rolling_rsi_dictionary[symbol].append(self.rsi_dictionary[symbol].Current.Value)
# Check if length of rolling RSI dictionary is greater than 60
if len(self.rolling_rsi_dictionary[symbol]) > 600:
# Cut length
self.rolling_rsi_dictionary[symbol] = self.rolling_rsi_dictionary[symbol][-600:]
# Check if MACD is greater than MACD signal
if self.macd_dictionary[symbol].Current.Value > self.macd_dictionary[symbol].Signal.Current.Value:
# Check if RSI is greater than its value 1 hour ago
if self.rsi_dictionary[symbol].Current.Value > self.rolling_rsi_dictionary[symbol][-600]:
# Check if 14-period SMA is greater than 30-period SMA
if self.sma_forteen_dictionary[symbol].Current.Value > self.sma_thirty_dictionary[symbol].Current.Value:
# Check if current close greater than yesterday close
if self.Securities[symbol].Close > self.yesterday_close[symbol]:
# If not invested
if not self.Portfolio[symbol].Invested:
# Get cash available
cash = self.Portfolio.Cash
# Get 25% of cash available
cash_allocation = cash * 0.25
# Calculate quantity
quantity = int(cash_allocation / self.Securities[symbol].Close)
# Submit market order
self.MarketOrder(symbol, quantity)
# # # # # Liquidation logic # # # # #
# Get holdings
holdings = [x.Key for x in self.Portfolio if x.Value.Invested]
# Loop through holdings
for symbol in holdings:
# If current MACD < MACD signal
if self.macd_dictionary[symbol].Current.Value < self.macd_dictionary[symbol].Signal.Current.Value:
# If RSI is less than its value 1 hour ago
if self.rsi_dictionary[symbol].Current.Value < self.rolling_rsi_dictionary[symbol][-1]:
# If 26-period SMA less than most recent close price
if self.sma_twentysix_dictionary[symbol].Current.Value < self.Securities[symbol].Close:
# Close position with market order
self.MarketOrder(symbol, -self.Portfolio[symbol].Quantity)