| Overall Statistics |
|
Total Trades 68 Average Win 1.34% Average Loss -0.02% Compounding Annual Return 70.936% Drawdown 12.200% Expectancy 56.680 Net Profit 70.936% Sharpe Ratio 2.025 Probabilistic Sharpe Ratio 77.540% Loss Rate 19% Win Rate 81% Profit-Loss Ratio 70.41 Alpha 0.084 Beta 2.067 Annual Standard Deviation 0.243 Annual Variance 0.059 Information Ratio 1.965 Tracking Error 0.15 Treynor Ratio 0.238 Total Fees $70.22 Estimated Strategy Capacity $1000000.00 Lowest Capacity Asset DBC TFVSB03UY0DH |
#region imports
from AlgorithmImports import *
#endregion
#Price Action ML
def create_model_PA(self):
for symbol in self.tickers:
recent_prices = self.History([symbol], self.history_range)['close'].values
price_changes = np.diff(recent_prices).tolist()
for i in range(self.history_range-self.lookback-1):
self.X.append(price_changes[i:i+self.lookback])
self.y.append(price_changes[i+self.lookback])
self.model.fit(self.X, self.y)
# Your New Python File
def trainKelly(self):
""" Train model and calculate kelly position daily """
if self.model is None: self.model = DummyClassifier(strategy="uniform") # Random binary generator
features, returns = self.get_data(252) # Use last year of data for training
target = returns >= 0 # Up/Down binary target
model_temp = sklearn.base.clone(self.model)
x_train, x_test, y_train, y_test, r_train, r_test = \
train_test_split(features, target, returns, train_size=0.5, shuffle=False)
model_temp.fit(x_train, y_train)
y_pred = model_temp.predict(x_test)
self.kelly_size = kelly_size(y_test, y_pred, r_test) # Calculate kelly position on test data
self.kelly_size = np.clip(self.kelly_size, 0, 1) # Applies fractional kelly and clips between 0 and 1
self.model.fit(features, target)
self.Debug(f"{self.Time} Training - Kelly: {self.kelly_size:.1%}\n")
self.Plot("ML", "Score", self.kelly_size)
def tradeKelly(self):
""" Trades based on prediction at market open """
if self.model is None: return # Don't trade until the model is trained
self.Transactions.CancelOpenOrders()
x_pred = self.get_data(self.lookback, include_y=False)
if len(x_pred) == 0: return
y_pred = self.model.predict(x_pred)[0]
position = y_pred * self.kelly_size # Sizing based on Kelly and individual probabilty
self.Plot("ML", "Prediction", y_pred.mean())
self.Debug(f"{self.Time} Trading\tPos: {position:.1%}")
self.SetHoldings(self.ticker, position)
def get_data(self, datapoints, include_y=True):
""" Calculate features and target data """
data = self.History([self.ticker], datapoints, self.resolution)
features = data.eval("close/open - 1").to_frame("returns")
x = pd.concat([features.shift(s) for s in range(self.lookback)],
axis=1).dropna() # Sequence of last "lookback" returns
if include_y:
y = features["returns"].shift(-1).reindex_like(x).dropna()
return x.loc[y.index], y
else:
return x
def kelly_size(y_true, y_pred, returns):
""" Calculate Kelly position based on the prediction accuracy """
trades = y_pred!=0
wins = y_true[trades]==y_pred[trades]
win_rate = wins.mean()
loss_rate = 1-win_rate
avg_win = abs(returns[trades][wins].mean())
avg_loss = abs(returns[trades][~wins].mean())
return win_rate/avg_loss - loss_rate/avg_win
from System import *
from QuantConnect import *
import numpy as np
import datetime as dt
from datetime import timedelta, datetime
from AlgorithmImports import *
from itertools import groupby
from sklearn.ensemble import GradientBoostingRegressor
from risk import BracketRiskModel
from QuantConnect.Python import PythonQuandl
from trades import *
from trend import *
from machineLearning import *
class NadionResistanceShield(QCAlgorithm):
def Initialize(self):
self.SetStartDate(int(self.GetParameter("YearStart")),1,1) # Set Start Date
self.SetEndDate(int(self.GetParameter("YearEnd")), 1, 1)
self.SetCash(10000) # Set Strategy Cash
#self.tickers = self.ActiveSecurities.Keys
#self.manually_selected = [self.AddEquity(symbol).Symbol for symbol in self.tickers]
self.tickers = ["SPY", "BAC"]#,"AIG","BEF","BJH","DBV","DDN","DGP","DZR","FAY","EWM","GDVD","LJJ","LZP","MPD","OCA","PGY","QHG","QOC","ROK","ROL"]#["aapl","AB","AFG","AMP","CNS","FIX","GAL""DHI","SAFM","AMAT","AYI"]
self.quandlCode = "OECD/KEI_LOLITOAA_OECD_ST_M"
#Quandl.SetAuthCode("MLNarxdsMU92vk-ZJDvg")
Quandl.SetAuthCode("RXk7Mxue6oH1TM-U8b7c")
self.SetBenchmark("SPY")
self.init = True
self.stage = 0
self.MarketTrend = 1
self.MarketDir = 1
#1 Up, 0 Down
self.kei = self.AddData(QuandlCustomColumns, self.quandlCode, Resolution.Daily, TimeZones.NewYork).Symbol
self.sma = self.SMA(self.kei, 1)
self.mom = self.MOMP(self.kei, 2)
self.MKT = self.AddEquity('SPY', Resolution.Minute).Symbol
self.bond = self.AddEquity('TLT', Resolution.Minute).Symbol
self.previous_SPY = 0
self.symbolDataBySymbol = {}
self.MarketCaps = ["XLF","XLE","XLB","XLY","XLI","XLP","XLU","XLK","XLV","XLC","VCR","VFH","VGT","VOX","VDC","VDE","VAW"]# "QQQ"]#,"MDY","IWM"]
self.MarketCapsDataBySymbol = {}
self.Market = ['SPY']
self.symbolMarketDataBySymbol = {}
self.QQQ = ["TQQQ"]
self.volatilityDataBySymbol = {}
#Bull Market
self.RecoveryStocks = ["XLB","XLY"]
self.EarlyStocks = ["XLE","XLF","XLI"]
self.ReboundStocks = ["XLK","XLU"]
self.LateStocks = ["XLK","XLV"]
self.DeclineStocks = ["XLP"]
self.RecoveryStocksDataBySymbol = {}
self.EarlyStocksDataBySymbol = {}
self.ReboundStocksDataBySymbol = {}
self.LateStocksDataBySymbol = {}
self.DeclineStocksDataBySymbol = {}
self.ticketRecovery = None
self.ticketEarly = None
self.ticketRebound = None
self.ticketLate = None
self.ticketDecline = None
#Bear Market
#self.BearRecoveryStocks = ["XLY"]
#self.BearEarlyStocks = ["XLF"]
#self.BearReboundStocks = ["XLK","XLC"]
#self.BearLateStocks = ["XLP","XLE"]
#self.BearDeclineStocks = ["XLE","XLB"]
self.BearRecoveryStocks = ["VCR"]
self.BearEarlyStocks = ["VFH"]
self.BearReboundStocks = ["VGT","VOX"]
self.BearLateStocks = ["VDE"]
self.BearDeclineStocks = ["VDE","VAW"]
self.BearRecoveryStocksDataBySymbol = {}
self.BearEarlyStocksDataBySymbol = {}
self.BearReboundStocksDataBySymbol = {}
self.BearLateStocksDataBySymbol = {}
self.BearDeclineStocksDataBySymbol = {}
self.vix = ["VIX"]
self.volatilityDataBySymbol = {}
#InOut
self.Trade = True
#main switch
self.trade = True
self.atr=[]
self.uvxy = "UVXY"
self.spxl = "SPXL"
self.iwm = "IWM"
self.mdy = "MDY"
#self.offensive
self.spyg = self.AddEquity("SPYG", Resolution.Hour).Symbol
self.eem = self.AddEquity("EEM", Resolution.Hour).Symbol
self.efa = self.AddEquity("EFA", Resolution.Hour).Symbol
self.vug = self.AddEquity("VUG", Resolution.Hour).Symbol
#netural
self.bnd = self.AddEquity("EFA", Resolution.Hour).Symbol
#self.defensive
self.ief = self.AddEquity("IEF", Resolution.Hour).Symbol
self.bil = self.AddEquity("BIL", Resolution.Hour).Symbol
self.vglt = self.AddEquity("VGLT", Resolution.Hour).Symbol
self.lqd = self.AddEquity("LQD", Resolution.Hour).Symbol
self.tip = self.AddEquity("TIP", Resolution.Hour).Symbol
self.dbc = self.AddEquity("DBC", Resolution.Hour).Symbol
# Algo Parameters
self.prds = [1,3,6,12]
self.prdwts = np.array([12,6,2,1])
self.LO, self.LD, self.LP, self.B, self.TO, self.TD = [int(self.GetParameter("LO")),int(self.GetParameter("LD")),int(self.GetParameter("LP")),int(self.GetParameter("B")),int(self.GetParameter("TO")),int(self.GetParameter("TD"))]# [12,12,0,1,1,3]6,12,0,1,1,1]
self.hprd = max(self.prds+[self.LO,self.LD])*21+50
# Assets
self.canary = ['SPY','EFA','EEM','BND']
self.offensive = ['SPYG','EFA','EEM','BND']
self.defensive = ['BIL','BND','DBC','IEF','LQD','TIP','VGLT']
self.safe = 'BIL'
# repeat safe asset so it can be selected multiple times
self.alldefensive = self.defensive + [self.safe] * max(0,self.TD - sum([1*(e==self.safe) for e in self.defensive]))
self.eqs = list(dict.fromkeys(self.canary+self.offensive+self.alldefensive))
for eq in self.eqs:
self.AddEquity(eq,Resolution.Minute)
self.spy = self.AddEquity("SPY", Resolution.Minute)
self.spy_sma200 = self.SMA("SPY", 200, Resolution.Daily)
self.symbols_dict = dict([
('SPY',['SPY',0]),('XMMO',['XMMO',0]),('MTUM',['MTUM',0]),('FVAL',['FVAL',0]),
('DXD',['DXD',0]),('GLD',['GLD',0]),('DIA',['DIA',0]),('TLT',['TLT',0]),('QQQ',['QQQ',0])
])
self.all_symbols = ["XLF","XLE","XLB","XLY","XLI","XLP","XLU","XLK","XLV","XLC",'SPY','EFA','EEM','BND','BIL','BND','DBC','IEF','LQD','TIP','TLT','QQQ','EFA','EEM','BND',"XMMO","MTUM","FVAL","DIA","DXD"]
#use this dataframe to hold the stats about the stage
self.columns = ['stage_date', 'stage_num','stage','stage_days','symbol','price']
self.stage_log_df = pd.DataFrame(columns=self.columns)
for key in self.symbols_dict:
self.AddEquity(self.symbols_dict[key][0], Resolution.Minute).Symbol
self.up_value = 0.5
self.down_value = 0.5
self.SetRiskManagement(BracketRiskModel(self.down_value, self.up_value))
self.model = GradientBoostingRegressor()
self.lookback = 30
self.history_range = 200
self.X = []
self.y = []
# Stock Selector
self.AddUniverse(self.Coarse, self.Fine)
self.activelyTrading = []
self.weight = 1
self.numberOfSymbolsCoarse = 5000
self.exposureToSector = 2
self.lastMonth = -3
for symbol in self.Market:
sma7 = self.SMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 100, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
DCHindicator = self.DCH(symbol, 10, 10)
self.DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0SPY = Trend0('Trend0', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0SPY, Resolution.Hour)
self.symbolMarketDataBySymbol[symbol] = symbolMarketData(symbol, sma7, sma20, sma50, sma100, sma200, DCHindicator)
for symbolmark in self.MarketCaps:
symbol = self.AddEquity(symbolmark, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
self.DCHindicator = self.DCH(symbol, 20, 20)
self.DCHindicatorShort = self.DCH(symbol, 10,10)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
# create the 4 hour data consolidator
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
# create the 4 hour data consolidator
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
# register the 4 hour consolidated bar data to automatically update the indicator
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
self.MarketCapsDataBySymbol[symbol] = symbolMarkData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, RSIConsolidate, smaConsolidate)
for symbol in self.RecoveryStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Recovery = Trend0('Trend0 Recovery', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0Recovery, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.RecoveryStocksDataBySymbol[symbol] = symbolData
for symbol in self.EarlyStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Early = Trend0('Trend0 Early', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0Early, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.EarlyStocksDataBySymbol[symbol] = symbolData
for symbol in self.ReboundStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Rebound = Trend0('Trend0 Rebound', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0Rebound, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.ReboundStocksDataBySymbol[symbol] = symbolData
for symbol in self.LateStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Late = Trend0('Trend0 Late', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0Late, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.LateStocksDataBySymbol[symbol] = symbolData
for symbol in self.DeclineStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Decline = Trend0('Trend0 Decline', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0Decline, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.DeclineStocksDataBySymbol[symbol] = symbolData
for symbol in self.BearLateStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0BearLate = Trend0('Trend0 BearLate', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0BearLate, Resolution.Hour)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.BearLateStocksDataBySymbol[symbol] = symbolData
for symbol in self.BearRecoveryStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.BearRecoveryStocksDataBySymbol[symbol] = symbolData
for symbol in self.BearEarlyStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.BearEarlyStocksDataBySymbol[symbol] = symbolData
for symbol in self.BearReboundStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.BearReboundStocksDataBySymbol[symbol] = symbolData
for symbol in self.BearDeclineStocks:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.BearDeclineStocksDataBySymbol[symbol] = symbolData
for symbol in self.QQQ:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
self.Trend0QQQ = Trend0('Trend0 QQQ', period=10, exponent=1.5)
self.RegisterIndicator(symbol, self.Trend0QQQ, Resolution.Hour)
#symbolvolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.volatilityDataBySymbol[symbol] = symbolvolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
for symbol in self.tickers:
self.AddEquity(symbol, Resolution.Hour).Symbol
ema7 = self.EMA(symbol, 7, Resolution.Daily, Field.Close)
sma20 = self.SMA(symbol, 20, Resolution.Daily, Field.Close)
ema20 = self.EMA(symbol, 20, Resolution.Daily, Field.Close)
sma50 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
ema50 = self.EMA(symbol, 50, Resolution.Daily, Field.Close)
sma100 = self.SMA(symbol, 50, Resolution.Daily, Field.Close)
sma200 = self.SMA(symbol, 200, Resolution.Daily, Field.Close)
rsi = self.RSI(symbol, 14, Resolution.Daily)
macd = self.MACD(symbol, 12, 26, 9, MovingAverageType.Exponential, Resolution.Daily)
macdSlow = self.MACD(symbol, 26, 48, 12, MovingAverageType.Exponential, Resolution.Daily)
self.high = self.MAX(symbol, 5, Resolution.Daily, Field.High)
self.midhigh = self.MAX(symbol, 3, Resolution.Daily, Field.High)
self.buylow = self.MIN(symbol, 4, Resolution.Daily, Field.Low)
self.low = self.MIN(symbol, 5, Resolution.Daily, Field.Low)
self.stoplow = self.MIN(symbol, 20, Resolution.Daily, Field.Low)
DCHindicator = self.DCH(symbol, 20, 20)
self.Trend0Tickers = Trend0('Trend0 Single Equity', period=10, exponent=1.5)
RSIConsolidate = self.RSI(symbol, 14, MovingAverageType.Wilders)
dayConsolidator = TradeBarConsolidator(timedelta(days=5))
self.SubscriptionManager.AddConsolidator(symbol, dayConsolidator)
self.RegisterIndicator(symbol, RSIConsolidate, dayConsolidator)
smaConsolidate = ExponentialMovingAverage(200, MovingAverageType.Simple)
WeekConsolidator = TradeBarConsolidator(timedelta(days=10))
self.SubscriptionManager.AddConsolidator(symbol, WeekConsolidator)
self.RegisterIndicator(symbol, smaConsolidate, WeekConsolidator)
symbolData = SymbolData(symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate)
self.symbolDataBySymbol[symbol] = symbolData
#Schedules:
#self.Schedule.On(self.DateRules.WeekStart(self.MKT), self.TimeRules.AfterMarketOpen(self.MKT, 31),
# self.Rebalance)
# Before the open
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", -5), Action(self.beforeTheOpen))
self.Schedule.On(self.DateRules.MonthStart(self.canary[0]),self.TimeRules.AfterMarketOpen(self.canary[0],30),self.rebal)
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), lambda: stageIndicator(self))
#self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), lambda: stageTradeManager(self))
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), lambda: stageIndicatorBear(self))
#self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 60), lambda: stageBearTradeManager(self))
self.Schedule.On(self.DateRules.WeekStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), lambda: Rebalance(self))
self.Schedule.On(self.DateRules.WeekStart("SPY"), self.TimeRules.AfterMarketOpen("SPY", 25), lambda: BullBearCalc(self))
#self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday, DayOfWeek.Wednesday, DayOfWeek.Friday), self.TimeRules.AfterMarketOpen("SPY", 60), lambda: MarketDirectionSMA200(self))
self.Schedule.On(self.DateRules.WeekEnd("SPY"), self.TimeRules.At(23, 59), lambda: WeekEnd(self))
#self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), lambda: buySectorTrendSignal(self))
#self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 40), lambda: sellSignals(self))
#self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), lambda: sellReversalSignals(self))
#ML
self.Schedule.On(self.DateRules.WeekStart("SPY"), self.TimeRules.BeforeMarketClose('SPY', 10), lambda: create_model_PA(self))
#self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen('SPY', minutes=1), Action(self.trade))
#self.Schedule.On(self.DateRules.EveryDay("SPY"),
# self.TimeRules.Every(timedelta(hours=7.5)),
# self.exitTrade)
# self.Schedule.On(self.DateRules.EveryDay("SPY"),
# self.TimeRules.Every(timedelta(hours=1)),
# self.buySignals)
# self.Schedule.On(self.DateRules.EveryDay("SPY"),
# self.TimeRules.Every(timedelta(hours=1)),
# self.sellSignals)
#self.AddRiskManagement(TrailingStopRiskManagementModel(0.04))
self.SetWarmUp(timedelta(days=200))
def Coarse(self, coarse):
if self.Time.month == self.lastMonth:
return Universe.Unchanged
self.lastMonth = self.Time.month
allCoarse = [x for x in coarse if x.HasFundamentalData and x.Price > 1 and x.Volume > 1]
finalCoarse = sorted(allCoarse, key = lambda x: x.DollarVolume, reverse = True)
return [x.Symbol for x in finalCoarse][:self.numberOfSymbolsCoarse]
return self.tickers
def Fine(self, fine):
filteredSymbols = []
sortedBySector = [x for x in fine]
for code, g in groupby(sortedBySector, lambda x: x.AssetClassification.MorningstarSectorCode):
for x in sorted(g, key = lambda x: x.ValuationRatios.PERatio, reverse = False)[:self.exposureToSector]:
filteredSymbols.append(x.Symbol)
return filteredSymbols[:5]
self.tickers = filteredSymbols + self.manually_selected
self.Log("tickers: {0}".format(self.tickers))
def beforeTheOpen(self):
return
def rebal(self):
self.Trade = True
def OnData(self, data):
if self.Trade:
# Get price data and trading weights
h = self.History(self.eqs,self.hprd,Resolution.Daily)['close'].unstack(level=0)
wts = self.trade_wts(h)
# trade
port_tgt = [PortfolioTarget(x,y) for x,y in zip(wts.index,wts.values/2)]
self.SetHoldings(port_tgt)
self.Trade = False
def trade_wts(self,hist):
# initialize wts Series
wts = pd.Series(0,index=hist.columns)
# end of month values
h_eom = (hist.loc[hist.groupby(hist.index.to_period('M')).apply(lambda x: x.index.max())]
.iloc[:-1,:])
# =====================================
# check if canary universe is triggered
# =====================================
# build dataframe of momentum values
mom = h_eom.iloc[-1,:].div(h_eom.iloc[[-p-1 for p in self.prds],:],axis=0)-1
mom = mom.loc[:,self.canary].T
# Determine number of canary securities with negative weighted momentum
n_canary = np.sum(np.sum(mom.values*self.prdwts,axis=1)<0)
# % equity offensive
pct_in = 1-min(1,n_canary/self.B)
# =====================================
# get weights for offensive and defensive universes
# =====================================
# determine weights of offensive universe
if pct_in > 0:
# price / SMA
mom_in = h_eom.iloc[-1,:].div(h_eom.iloc[[-t for t in range(1,self.LO+1)]].mean(axis=0),axis=0)
mom_in = mom_in.loc[self.offensive].sort_values(ascending=False)
# equal weightings to top relative momentum securities
in_wts = pd.Series(pct_in/self.TO,index=mom_in.index[:self.TO])
wts = pd.concat([wts,in_wts])
# determine weights of defensive universe
if pct_in < 1:
# price / SMA
mom_out = h_eom.iloc[-1,:].div(h_eom.iloc[[-t for t in range(1,self.LD+1)]].mean(axis=0),axis=0)
mom_out = mom_out.loc[self.alldefensive].sort_values(ascending=False)
# equal weightings to top relative momentum securities
out_wts = pd.Series((1-pct_in)/self.TD,index=mom_out.index[:self.TD])
wts = pd.concat([wts,out_wts])
wts = wts.groupby(wts.index).sum()
return wts
#def OnEndOfAlgorithm(self):
# self.portfolio_holdings()
# self.write_stage_log()
def tradeStart(self):
self.trade = True
def tradeEnd(self):
self.trade = False
def portfolio_holdings(self):
self.Log("-- Portfolio --")
for kvp in self.Portfolio:
if kvp.Value.Invested:
symbol = kvp.Key #full security key
holding = kvp.Value
ticker = holding.Symbol.Value
quantity = holding.Quantity
avgprice = holding.AveragePrice
cost = quantity * avgprice
price =self.Portfolio[symbol].Price
unrealized_profit = self.Portfolio[symbol].UnrealizedProfit
net = (unrealized_profit / cost) * 100
s1 = '{:3.2f}'.format(avgprice) + ',' + '{:3.2f}'.format(price) + ',' + '{:3.2f}'.format(net) + ',' + '{:3.2f}'.format(unrealized_profit)
self.Log(ticker + "," + str(quantity) + "," + s1)
return()
def add_stage_log(self, stage, stage_num):
d1 = str(self.Time)
entry_date = d1[0:10]
for key in self.symbols_dict:
self.stage_log_df = self.stage_log_df.append({
'stage_date': entry_date,
'stage_num': stage_num,
'stage': stage,
'stage_days': 0,
'symbol': self.symbols_dict[key][0],
'price' : self.Securities[self.symbols_dict[key][0]].Price
}, ignore_index = True)
self.Log(d1 + "," + stage + "," + str(stage_num) )
def write_stage_log(self):
#Convert each stage_date and stage_num group to a single row
df_out = self.stage_log_df.set_index(['stage_date','stage_num','stage',self.stage_log_df.groupby(['stage_date','stage_num','stage']).cumcount()+1]).unstack().sort_index(level=1, axis=1)
df_out.columns = df_out.columns.map('{0[0]}_{0[1]}'.format)
df_out.reset_index()
#calculate the gain and net change for each symbol in the row
for x in range(1, (len(self.symbols_dict)+1)):
gain_col = 'gain_' + str(x)
price_col = 'price_' + str(x)
net_col = "net_" + str(x)
df_out[gain_col] = (df_out[price_col].shift(-1)- df_out[price_col]) #calculate the gain during the stage
df_out[net_col] = (df_out[gain_col] / df_out[price_col]) * 100 #calculate the net change during the stage
df_out.fillna(0,inplace=True)
"""
#self.stage_log_df['start_date'] = pd.to_datetime(self.stage_log_df['entry_date'], format="%Y-%m-%d")
"""
#for each row build output log string
print_header = True
for index, row in df_out.iterrows():
s1 = str(index[0]) + "," + str(index[1]) + "," + str(index[2]) + ","
x1 = str(index[0]) + "," + str(index[1]) + "," + str(index[2]) + ","
for x in range(1, (len(self.symbols_dict)+1)):
symbol_col = 'symbol_' + str(x)
gain_col = 'gain_' + str(x)
price_col = 'price_' + str(x)
net_col = "net_" + str(x)
s2 = symbol_col + "," + price_col + "," + gain_col + "," + net_col + ","
s1 = s1 + s2
symbol_val = row[symbol_col]
gain_val = '{:3.2f}'.format(row[gain_col])
price_val = '{:3.2f}'.format(row[price_col])
net_val = '{:3.2f}'.format(row[net_col])
x2 = symbol_val + "," + price_val + "," + gain_val + "," + net_val + ","
x1 = x1 + x2
if print_header :
self.Log (s1)
print_header = False
self.Log (x1)
class symbolMarkData:
def __init__(self, symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, macd, macdSlow, rsi, RSIConsolidate, smaConsolidate):
self.Symbol = symbol
self.ema7 = ema7
self.sma20 = sma20
self.ema20 = ema20
self.sma50 = sma50
self.sma200 = sma200
self.macd = macd
self.macdSlow = macdSlow
self.rsi = rsi
self.RSIConsolidate = RSIConsolidate
self.smaConsolidate = smaConsolidate
class symbolMarketData:
def __init__(self, symbol, sma7, sma20, sma50, sma100, sma200, DCHindicator):
self.Symbol = symbol
self.sma7 = sma7
self.sma20 = sma20
self.sma50 = sma50
self.sma100 = sma100
self.sma200 = sma200
self.DCHindicator = DCHindicator
class symbolvolData:
def __init__(self, symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate):
self.Symbol = symbol
self.ema7 = ema7
self.sma20 = sma20
self.sma50 = sma50
self.ema50 = ema50
self.sma100 = sma100
self.sma200 = sma200
self.rsi = rsi
self.macd = macd
self.macdSlow = macdSlow
self.DCHindicator = DCHindicator
self.RSIConsolidate = RSIConsolidate
self.smaConsolidate = smaConsolidate
class SymbolData:
def __init__(self, symbol, ema7, sma20, ema20, sma50, ema50, sma100, sma200, rsi, macd, macdSlow, DCHindicator, RSIConsolidate, smaConsolidate):
self.Symbol = symbol
self.ema7 = ema7
self.sma20 = sma20
self.sma50 = sma50
self.ema50 = ema50
self.sma100 = sma100
self.sma200 = sma200
self.rsi = rsi
self.macd = macd
self.macdSlow = macdSlow
self.DCHindicator = DCHindicator
self.RSIConsolidate = RSIConsolidate
self.smaConsolidate = smaConsolidate
class QuandlCustomColumns(PythonQuandl):
def __init__(self):
# Define ValueColumnName: cannot be None, Empty or non-existant column name
self.ValueColumnName = "Value"
class SecurityData:
def __init__(self, symbol, history):
self.symbol = symbol
self.fast = ExponentialMovingAverage(4)
self.slow = ExponentialMovingAverage(30)
self.vol = ExponentialMovingAverage(30)
self.isBubble = False
self.ratio = 0
for bar in history.itertuples():
self.fast.Update(bar.Index[1], bar.close)
self.slow.Update(bar.Index[1], bar.close)
self.vol.Update(bar.Index[1], ((bar.open + bar.close)/2.0) * bar.volume) # approx. dollar volume
def update(self, time, price, volume):
if self.fast.Update(time, price) and self.slow.Update(time, price) and self.vol.Update(time, volume):
self.isBubble = (self.fast.Current.Value > (2.0 * self.slow.Current.Value)) and (price > self.slow.Current.Value)
self.ratio = self.fast.Current.Value/self.slow.Current.Value#region imports
from AlgorithmImports import *
#endregion
from QuantConnect.Algorithm.Framework.Risk import RiskManagementModel
# bracket risk model class
class BracketRiskModel(RiskManagementModel):
'''Creates a trailing stop loss for the maximumDrawdownPercent value and a profit taker for the maximumUnrealizedProfitPercent value'''
def __init__(self, maximumDrawdownPercent = 0.05, maximumUnrealizedProfitPercent = 0.05):
self.maximumDrawdownPercent = -abs(maximumDrawdownPercent)
self.trailingHighs = dict()
self.maximumUnrealizedProfitPercent = abs(maximumUnrealizedProfitPercent)
def ManageRisk(self, algorithm, targets):
riskAdjustedTargets = list()
for kvp in algorithm.Securities:
symbol = kvp.Key
security = kvp.Value
# Remove if not invested
if not security.Invested:
self.trailingHighs.pop(symbol, None)
continue
pnl = security.Holdings.UnrealizedProfitPercent
if pnl > self.maximumUnrealizedProfitPercent:
# liquidate
algorithm.Debug(f"Profit Taken: {security.Symbol}")
algorithm.Log(f"Profit Taken: {security.Symbol}")
riskAdjustedTargets.append(PortfolioTarget(security.Symbol, 0))
return riskAdjustedTargets
# Add newly invested securities
if symbol not in self.trailingHighs:
self.trailingHighs[symbol] = security.Holdings.AveragePrice # Set to average holding cost
continue
# Check for new highs and update - set to tradebar high
if self.trailingHighs[symbol] < security.High:
self.trailingHighs[symbol] = security.High
continue
# Check for securities past the drawdown limit
securityHigh = self.trailingHighs[symbol]
drawdown = (security.Low / securityHigh) - 1
if drawdown < self.maximumDrawdownPercent:
# liquidate
algorithm.Debug(f"Losses Taken: {security.Symbol}")
algorithm.Log(f"Losses Taken: {security.Symbol}")
riskAdjustedTargets.append(PortfolioTarget(symbol, 0))
return riskAdjustedTargets #region imports
from AlgorithmImports import *
from main import *
def Rebalance(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady: return
initial_asset = self.symbols_dict['QQQ'][0] if self.mom.Current.Value > 0 else self.symbols_dict['TLT'][0]
if self.init:
self.SetHoldings(initial_asset, .5)
self.init = False
keihist = self.History([self.kei], 1400)
#returns the historical data for custom 90 day period.
#keihist = self.History([self.kei],self.StartDate-timedelta(100),self.StartDate-timedelta(10))
keihistlowt = np.nanpercentile(keihist, 15)
keihistmidt = np.nanpercentile(keihist, 50)
keihisthight = np.nanpercentile(keihist, 90)
kei = self.sma.Current.Value
keimom = self.mom.Current.Value
#if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.Securities[self.bond].Invested):
if (keimom < 0 and kei < keihistmidt and kei > keihistlowt) and not (self.stage == 5):
# DECLINE
self.Liquidate('MTUM')
self.Liquidate('TLT')
self.Liquidate('XMMO')
self.Liquidate('QQQ')
self.stage = 5
self.SetHoldings(self.symbols_dict['GLD'][0], .2)
self.add_stage_log("DECLINE", self.stage)
elif (keimom > 0 and kei < keihistlowt) and not (self.stage == 1):
# RECOVERY
self.Liquidate('GLD')
self.Liquidate('TLT')
self.Liquidate('QQQ')
self.Liquidate('XMMO')
self.stage = 1
self.SetHoldings(self.symbols_dict['MTUM'][0], .2)
self.add_stage_log("RECOVERY", self.stage)
elif (keimom > 0 and kei > keihistlowt and kei < keihistmidt) and not (self.stage == 2):
# EARLY
self.Liquidate('GLD')
self.Liquidate('TLT')
self.Liquidate('MTUM')
self.Liquidate('QQQ')
self.stage = 2
self.SetHoldings(self.symbols_dict['XMMO'][0], .2)
self.add_stage_log("EARLY", self.stage)
elif (keimom > 0 and kei > keihistmidt and kei < keihisthight) and not (self.stage == 3):
# REBOUND
self.Liquidate('GLD')
self.Liquidate('XMMO')
self.Liquidate('QQQ')
self.Liquidate('TLT')
self.stage = 3
self.SetHoldings(self.symbols_dict['MTUM'][0], .2)
self.add_stage_log("REBOUND", self.stage)
elif (keimom < 0 and kei < keihisthight and kei > keihistmidt) and not (self.stage == 4):
# LATE
self.Liquidate('GLD')
self.Liquidate('XMMO')
self.Liquidate('MTUM')
self.Liquidate('TLT')
self.stage = 4
self.SetHoldings(self.symbols_dict['QQQ'][0], .2)
self.add_stage_log("LATE", self.stage)
elif (keimom < 0 and kei < 100 and not self.Securities[self.bond].Invested) and not (self.stage == 6):
# self.Liquidate('GLD')
self.Liquidate('XMMO')
self.Liquidate('MTUM')
self.Liquidate('QQQ')
self.stage = 6
self.SetHoldings(self.symbols_dict['TLT'][0], .2)
self.add_stage_log("BONDS", self.stage)
self.Plot("LeadInd", "SMA(LeadInd)", self.sma.Current.Value)
self.Plot("LeadInd", "THRESHOLD", 100)
self.Plot("MOMP", "MOMP(LeadInd)", self.mom.Current.Value)
self.Plot("MOMP", "THRESHOLD", 0)
self.Plot("Stage","recovery = 1 early = 2 rebound = 3 late = 4 decline = 5",self.stage)
def BullBearCalc(self):
if self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
self.MarketTrend = 1
elif self.Securities[self.bil].Invested or self.Securities[self.dbc].Invested or self.Securities[self.ief].Invested or self.Securities[self.tip].Invested or self.Securities[self.vglt].Invested:
self.MarketTrend = 0
def stageIndicator(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady:
return
if self.stage == 0:
return
if self.stage == 1 and self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
#for symbol in self.RecoveryStocks:
for symbol, symbolData in self.RecoveryStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .15, False, "KEI Long Recovery Stage")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Long Recovery Stage")
# for symbol, symbolvolData in self.volatilityDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Trend0Recovery.Value > 0) and (self.Securities[symbol].Open > symbolvolData.sma50.Current.Value):
# self.SetHoldings(symbol, .15, False, "KEI Long Recovery Stage 2")
if self.stage == 2 and self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
#for symbol in self.EarlyStocks:
for symbol, symbolData in self.EarlyStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .1, False, "KEI Long Early Stage")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Long Early Stage")
# for symbol, symbolvolData in self.volatilityDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Trend0Early.Value > 0) and (self.Securities[symbol].Open > symbolvolData.sma50.Current.Value):
# self.SetHoldings(symbol, .1, False, "KEI Long Early Stage 2")
if self.stage == 3 and self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
#for symbol in self.ReboundStocks:
for symbol, symbolData in self.ReboundStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) or (self.Trend0Rebound.Value > 0):# and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .15, False, "KEI Long Rebound Stage")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Long Rebound Stage")
for symbol, symbolvolData in self.volatilityDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Trend0Rebound.Value > 0) and (self.Securities[symbol].Open > symbolvolData.sma200.Current.Value):
self.SetHoldings(symbol, .15, False, "KEI Long Rebound Stage 2")
if self.stage == 4 and self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
#for symbol in self.LateStocks:
for symbol, symbolData in self.LateStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) or (self.Trend0Late.Value > 0):# and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .15, False, "KEI Long Late Stage")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Long Late Stage")
for symbol, symbolvolData in self.volatilityDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Trend0Late.Value > 0) and (self.Securities[symbol].Open > symbolvolData.sma200.Current.Value):
self.SetHoldings(symbol, .15, False, "KEI Long Late Stage 2")
if self.stage == 5 and self.Securities[self.spyg].Invested or self.Securities[self.eem].Invested or self.Securities[self.efa].Invested:
#for symbol in self.DeclineStocks:
for symbol, symbolData in self.DeclineStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) or (self.Trend0Decline.Value > 0):# and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .25, False, "KEI Long Decline Stage")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Long Decline Stage")
# for symbol, symbolvolData in self.volatilityDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Trend0Decline.Value > 0) and (self.Securities[symbol].Open > symbolvolData.sma50.Current.Value):
# self.SetHoldings(symbol, .15, False, "KEI Long Decline Stage 2")
if self.stage == 6:
return
def stageIndicatorBear(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady:
return
#if self.stage == 1 and self.Securities[self.ief].Invested or self.Securities[self.bil].Invested or self.Securities[self.vglt].Invested or self.Securities[self.lqd].Invested or self.Securities[self.tip].Invested or self.Securities[self.dbc].Invested:
# for symbol, symbolData in self.BearRecoveryStocksDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.SetHoldings(symbol, .15, False, "KEI Bear")
#if self.stage == 2 and self.Securities[self.ief].Invested or self.Securities[self.bil].Invested or self.Securities[self.vglt].Invested or self.Securities[self.lqd].Invested or self.Securities[self.tip].Invested or self.Securities[self.dbc].Invested:
# for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.SetHoldings(symbol, .1, False, "KEI Bear")
#if self.stage == 3 and self.Securities[self.ief].Invested or self.Securities[self.bil].Invested or self.Securities[self.vglt].Invested or self.Securities[self.lqd].Invested or self.Securities[self.tip].Invested or self.Securities[self.dbc].Invested:
# for symbol, symbolData in self.BearReboundStocksDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.SetHoldings(symbol, .15, False, "KEI Bear")
if self.stage == 4 and self.Securities[self.ief].Invested or self.Securities[self.bil].Invested or self.Securities[self.vglt].Invested or self.Securities[self.lqd].Invested or self.Securities[self.tip].Invested or self.Securities[self.dbc].Invested:
for symbol, symbolData in self.BearLateStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.SetHoldings(symbol, .2, False, "KEI Bear")
self.Notify.Web("https://discord.com/api/webhooks/1040435197092569099/86mE02R0GzQrJDuQ9QdsY4CMQInj14H9I_Ncu5q_28Ic5scrdcPaGMOO3A83JobSh-2X", "KEI Bear Energy Stage")
#if self.stage == 5 and self.Securities[self.ief].Invested or self.Securities[self.bil].Invested or self.Securities[self.vglt].Invested or self.Securities[self.lqd].Invested or self.Securities[self.tip].Invested or self.Securities[self.dbc].Invested:
# for symbol, symbolData in self.BearDeclineStocksDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):# and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.SetHoldings(symbol, .15, False, "KEI Bear")
def stageTradeManager(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady:
return
if self.stage == 0:
return
if self.stage == 1:
for symbol, symbolData in self.EarlyStocksDataBySymbol.items() and self.ReboundStocksDataBySymbol.items() and self.LateStocksDataBySymbol.items() and self.DeclineStocksDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and self.UtcTime >= self.ticketRecovery.Time + timedelta(days = 30): # (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate('XLE')
self.Liquidate('XLF')
self.Liquidate('XLI')
self.Liquidate('XLU')
self.Liquidate('XLK')
self.Liquidate('XLV')
self.Liquidate('XLP')
#XLB, XLY
if self.stage == 2:
for symbol, symbolData in self.RecoveryStocksDataBySymbol.items() and self.ReboundStocksDataBySymbol.items() and self.LateStocksDataBySymbol.items() and self.DeclineStocksDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate("XLB")
self.Liquidate("XLY")
self.Liquidate("XLK")
self.Liquidate("XLV")
self.Liquidate("XLK")
self.Liquidate("XLP")
#XLE, XLF,XLI
if self.stage == 3:
for symbol, symbolData in self.EarlyStocksDataBySymbol.items() and self.RecoveryStocksDataBySymbol.items() and self.LateStocksDataBySymbol.items() and self.DeclineStocksDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate("XLB")
self.Liquidate("XLY")
self.Liquidate("XLE")
self.Liquidate("XLF")
self.Liquidate("XLI")
self.Liquidate("XLV")
self.Liquidate("XLP")
#XLK, XLU
if self.stage == 4:
for symbol, symbolData in self.EarlyStocksDataBySymbol.items() and self.ReboundStocksDataBySymbol.items() and self.RecoveryStocksDataBySymbol.items() and self.DeclineStocksDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate("XLB")
self.Liquidate("XLY")
self.Liquidate("XLE")
self.Liquidate("XLF")
self.Liquidate("XLI")
self.Liquidate("XLU")
self.Liquidate("XLP")
#XLK, XLV
#for symbol in self.LateStocks:
#for symbol, symbolData in self.LateStocksDataBySymbol.items():
# if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value):
# self.SetHoldings(symbol, .15, False, "Profit Taken KEI Long")
if self.stage == 5:
for symbol, symbolData in self.EarlyStocksDataBySymbol.items() and self.ReboundStocksDataBySymbol.items() and self.RecoveryStocksDataBySymbol.items() and self.LateStocksDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate("XLB")
self.Liquidate("XLY")
self.Liquidate("XLE")
self.Liquidate("XLF")
self.Liquidate("XLI")
self.Liquidate("XLU")
self.Liquidate("XLV")
#XLP
if self.stage == 6:
return
def stageBearTradeManager(self):
if self.IsWarmingUp or not self.mom.IsReady or not self.sma.IsReady:
return
if self.stage == 0:
return
if self.stage == 1:
#for symbol in self.BearRecoveryStocks:
# for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items() and self.BearReboundStocksDataBySymbol.items() and self.BearLateStocksDataBySymbol.items():
# if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.Liquidate()
for symbol, symbolData in self.BearRecoveryStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):# and (self.Securities[symbol].Close < self.low.Current.Value):
self.Liquidate('VCR')
if self.stage == 2:
#for symbol in self.BearEarlyStocks:
# for symbol, symbolData in self.RecoveryStocksDataBySymbol.items() and self.BearReboundStocksDataBySymbol.items() and self.BearLateStocksDataBySymbol.items():
# if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.Liquidate()
for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate('VFH')
if self.stage == 3:
#for symbol in self.BearReboundStocks:
# for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items() and self.RecoveryStocksDataBySymbol.items() and self.BearLateStocksDataBySymbol.items():
# if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.Liquidate()
for symbol, symbolData in self.BearReboundStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate('VGT')
self.Liquidate('VOX')
if self.stage == 4:
#for symbol in self.BearLateStocks:
# for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items() and self.BearReboundStocksDataBySymbol.items() and self.RecoveryStocksDataBySymbol.items():
# if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.Liquidate()
for symbol, symbolData in self.BearLateStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate('VDE')
if self.stage == 5:
#for symbol in self.BearDeclineStocks:
# for symbol, symbolData in self.BearEarlyStocksDataBySymbol.items() and self.BearReboundStocksDataBySymbol.items() and self.BearRecoveryStocksDataBySymbol.items() and self.BearLateStocksDataBySymbol.items():
# if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
# self.Liquidate()
for symbol, symbolData in self.BearDeclineStocksDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.Liquidate('VDE')
self.Liquidate('VAW')
if self.stage == 6:
return
def buyLongTrendSingal(self):
if self.trade == False:
return
if len(self.y) > self.lookback:
recent_prices = self.History(['SPY'], self.lookback+1)['close'].values
price_changes = np.diff(recent_prices)
prediction = self.model.predict(price_changes.reshape(1, -1))
for symbol, symbolData in self.symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (symbolData.sma20.Current.Value > symbolData.sma50.Current.Value) and (symbolData.ema10.Current.Value > symbolData.sma50.Current.Value) and prediction > 0:
#self.limitPrice = self.Securities[self.symbol].Price * 0.995
#self.quantity = (self.Portfolio.MarginRemaining * 0.99) / self.limitPrice
self.SetHoldings(symbol, .1, False, "Buy Trend Signal")
def buySectorTrendSignal(self):
if self.trade == False:
return
for symbol in self.MarketCaps:
for symbol, symbolData in self.marketDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close < symbolData.sma200.Current.Value) and (self.Securities[symbol].Close < symbolData.DCHindicator.LowerBand.Current.Value):
self.SetHoldings(symbol, .2, False, "Buy Sector Trend Signal")
def shortSectorTrendSignal(self):
if self.trade == False:
return
for symbol in self.MarketCaps:
for symbol, symbolData in self.marketDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (self.Securities[symbol].Close < symbolData.sma200.Current.Value) and (self.Securities[symbol].Close > symbolData.DCHindicator.UpperBand.Current.Value):
self.SetHoldings(symbol, .2, False, "Buy Sector Trend Signal")
def sellSignals(self):
if self.trade == False:
return
for symbol, symbolData in self.marketDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Securities[symbol].Close > self.DCHindicatorShort.UpperBand.Current.Value):
self.SetHoldings(symbol, -.2, False, "Sell Sector Trend Signal")
def sellReversalSignals(self):
if self.trade == False:
return
for symbol, symbolData in self.marketDataBySymbol.items():
if self.Portfolio[symbol].Invested and (symbolData.rsi.Current.Value > 70):
self.SetHoldings(symbol, -.2, False, "Sell Reversal Signal")
#("SPXS", .5, False, "TakeProfit")
def shortSignals(self):
if self.trade == False:
return
for symbol, symbolData in self.symbolDataBySymbol.items():
if not self.Portfolio[symbol].Invested and (symbolData.sma20.Current.Value < symbolData.sma50.Current.Value) and (symbolData.ema10.Current.Value < symbolData.sma50.Current.Value):# and (self.midhigh.Current.Value < self.high.Current.Value):
self.SetHoldings(symbol, -.1, False, "Short Signal")
def coverSignals(self):
if self.trade == False:
return
for symbol, symbolData in self.symbolDataBySymbol.items():
if self.Portfolio[symbol].Invested and (symbolData.RSIConsolidate.Current.Value < 10):
self.SetHoldings(symbol, .1, False, "Cover Signal")
#("SPXS", .5, False, "TakeProfit")
def exitTrade(self):
if self.trade == False:
return
for symbol, symbolData in self.symbolDataBySymbol.items():
if self.Portfolio[symbol].Invested and (self.Portfolio[symbol].UnrealizedProfit<= -250) or (self.Portfolio[symbol].UnrealizedProfit>= 500):
# self.Debug("Sell at "+str(self.Securities[symbol].Price))
self.Liquidate(symbol, "Profit or Loss take")
def exitShort(self):
if self.trade == False:
return
for symbolmark, symbolMarkData in self.marketDataBySymbol.items():
if self.Portfolio[symbolmark].Invested and (self.Portfolio[symbolmark].UnrealizedProfit<= -100) or (self.Portfolio[symbolmark].UnrealizedProfit>= 200):
self.Liquidate("SPXS", "Short Market Cover")
# Times
def WeekEnd(self):
self.state = False
#def MarketDirectionSMA200(self):
# if self.IsWarmingUp:
# return
# for symbol, symbolMarketData in self.marketData.items():
# if (self.Securities['SPY'].Close > symbolMarketData.sma200.Current.Value):
# self.MarketDir = 1
# for symbol, symbolMarketData in self.marketData.items():
# if (self.Securities['SPY'].Close < symbolMarketData.sma200.Current.Value):
# self.MarketDir = 0
def SuperTrendCalc(self):
if self.IsWarmingUp:
return
#If Trend up set.trendstage = 2
#if Trend down set.trendstage = 3
#if Trend down liqudiate
#from AlgorithmImports import *
from main import *
class Trend0(PythonIndicator):
def __init__(self, name, period, exponent):
self.Name = name
self.period = period
self.exponent = exponent
self.Time = datetime.min
self.Value = 0
self.prices = np.array([])
def Update(self, input):
self.prices = np.append(self.prices, input.Close)[-self.period:]
# IsReady?
if len(self.prices) != self.period:
self.Value = 0
return False
self.Value = self.calc_trend()
return True
def calc_trend(self):
changes = np.array([])
for i in range(len(self.prices) - 1):
_return = (self.prices[i + 1] - self.prices[i]) / self.prices[i]
changes = np.append(changes, _return)
return self.power_weighted_moving_average(changes)
def power_weighted_moving_average(self, changes):
return self.weighted_average(changes, self.exponential_weights(len(changes)))
def exponential_weights(self, length):
weights = np.array([])
for i in range(length):
w = i + 1
weights = np.append(weights, w**self.exponent)
return weights
def weighted_average(self, changes, weights):
products = []
for i in range(len(changes)):
products.append(changes[i] * weights[i])
return sum(products) / sum(weights)