| Overall Statistics |
|
Total Trades 30 Average Win 1222.38% Average Loss -6.10% Compounding Annual Return 120.624% Drawdown 83.800% Expectancy 79.547 Net Profit 5897.517% Sharpe Ratio 1.751 Probabilistic Sharpe Ratio 68.294% Loss Rate 60% Win Rate 40% Profit-Loss Ratio 200.37 Alpha 1.147 Beta -0.17 Annual Standard Deviation 0.642 Annual Variance 0.412 Information Ratio 1.492 Tracking Error 0.667 Treynor Ratio -6.632 Total Fees $2677111.33 Estimated Strategy Capacity $10000000.00 Lowest Capacity Asset BTCUSD XJ |
import numpy as np
import pandas as pd
from sklearn.linear_model import RidgeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.decomposition import PCA
from AlgorithmImports import *
class MachineLearningAlgo(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2016, 5, 2)
self.SetEndDate(2021, 7, 2)
self.SetCash(1000000)
self.AddEquity("SPY", Resolution.Daily)
self.SetBenchmark("SPY")
self.SetBrokerageModel(BrokerageName.AlphaStreams)
self.SetExecution(ImmediateExecutionModel())
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.lookback = int(self.GetParameter("lookback"))
self.inputLayer = 10
self.outputLayer = 10
self.ticker = self.AddCrypto("BTCUSD", Resolution.Daily).Symbol
self.NN_scale = int(self.GetParameter("NN-scale"))
self.hiddenLayer = self.lookback * (self.NN_scale * (self.inputLayer + self.outputLayer))
self.AddUniverseSelection(ManualUniverseSelectionModel(self.ticker))
self.SetWarmup(30)
self.AddAlpha(MachineLearningAlphaModel(self, self.ticker,
self.inputLayer,self.hiddenLayer, self.outputLayer,
self.lookback ))
class MachineLearningAlphaModel(AlphaModel):
def __init__(self, algo, symbol,
inputLayer, hiddenLayer, outputLayer,
lookback):
self.inputLayer = inputLayer
self.hiddenLayer = hiddenLayer
self.outputLayer = outputLayer
self.algo = algo
self.ticker = symbol
self.dataBySymbol = {}
self.dataBySymbol[self.ticker] = SymbolData(self.algo,symbol)
self.period = 30
def GetMLModel(self):
self.MLModel = 0
# self.MLModel = RidgeClassifier(random_state=18)
self.MLModel = MLPClassifier(hidden_layer_sizes = (self.inputLayer, self.hiddenLayer, self.outputLayer ), max_iter = 1000)
def Update(self, algorithm, data):
insights = []
if data.Bars.ContainsKey(self.ticker) and not algorithm.IsWarmingUp and self.dataBySymbol[ self.ticker ].IsReady():
self.dataBySymbol[ self.ticker ].Update(data)
if self.dataBySymbol[ self.ticker ].Close_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].Volume_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].RSI_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].Trend_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].AD_rolling.IsReady\
and self.dataBySymbol[ self.ticker ].STOK_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].STOD_rolling.IsReady \
and self.dataBySymbol[ self.ticker ].KAMA_rolling.IsReady:
df1 = pd.DataFrame(self.dataBySymbol[ self.ticker ].Close_rolling, columns=["Close"]).reset_index(drop=True)
df2 = pd.DataFrame(self.dataBySymbol[ self.ticker ].Volume_rolling, columns=["Volume"]).reset_index(drop=True)
df3 = pd.DataFrame(self.dataBySymbol[ self.ticker ].RSI_rolling, columns=["RSI"]).reset_index(drop=True)
df4 = pd.DataFrame(self.dataBySymbol[ self.ticker ].Trend_rolling, columns=["Trend"]).reset_index(drop=True)
df5 = pd.DataFrame(self.dataBySymbol[ self.ticker ].AD_rolling, columns=["AD"]).reset_index(drop=True)
df6 = pd.DataFrame(self.dataBySymbol[ self.ticker ].STOK_rolling, columns=["STOK"]).reset_index(drop=True)
df7 = pd.DataFrame(self.dataBySymbol[ self.ticker ].STOD_rolling, columns=["STOD"]).reset_index(drop=True)
df8 = pd.DataFrame(self.dataBySymbol[ self.ticker ].KAMA_rolling, columns=["KAMA"]).reset_index(drop=True)
self.df = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8], axis=1)
# calculate daily forward returns to be used to set Target / Signal
self.df['Return'] = np.log(self.df["Close"].shift(-1)/self.df["Close"])
self.df = self.df.dropna()
# set Signal / Target
self.df["Signal"] = 0
self.df.loc[self.df["Return"] > 0, "Signal"] = 1
self.df.loc[self.df["Return"] < 0, "Signal"] = -1
# set training data
self.X = self.df.drop(["Close", "Return", "Signal"], axis=1)
self.Y = self.df['Signal']
# align feature set & signal
self.Y, self.X = self.Y.align(self.X, axis=0, join='inner')
self.X_train = self.X[:-1]
self.Y_train = self.Y[:-1]
self.X_train.replace([np.inf, -np.inf], np.nan, inplace=True)
self.Y_train.replace([np.inf, -np.inf], np.nan, inplace=True)
drops = []
[drops.append(i) for i in range(self.X_train.shape[0]) if self.X_train.iloc[i].isnull().any()]
[drops.append(i) for i in range(self.Y_train.shape[0]) if self.Y_train.iloc[i] == np.nan and i not in drops]
self.X_train.drop(index=self.X_train.index[drops], inplace=True)
self.Y_train.drop(index=self.Y_train.index[drops], inplace=True)
if self.X_train.empty or self.Y_train.empty: return []
# fit / train ML model
self.GetMLModel()
self.MLModel.fit(self.X_train, self.Y_train)
# predict next day signal using today's values of feature set
self.X_today = self.X.iloc[-1]
# self.X_today is Series, so convert to numpy array
self.X_today = self.X_today.to_numpy()
# reshape self.X_today because it only has 1 day's sample
self.X_today = self.X_today.reshape(1,-1)
# Y_predict will take predicted signal
self.Y_predict = self.Y.iloc[-1]
try:
self.Y_predict = self.MLModel.predict(self.X_today)
except: return []
# set insight based on predicted signal
if self.Y_predict == 1:
insights.append(Insight(self.ticker, timedelta(days=30), InsightType.Price, InsightDirection.Up))
elif self.Y_predict == -1:
insights.append(Insight(self.ticker, timedelta(days=30), InsightType.Price, InsightDirection.Down))
else:
insights.append(Insight(self.ticker, timedelta(days=30), InsightType.Price, InsightDirection.Flat))
return insights
def OnSecuritiesChanged(self, algorithm, changes):
self.changes = changes
class SymbolData:
def __init__(self, algo, symbol):
self.lookback = 30
self.algo = algo
self.ticker = symbol
self.Close_rolling = RollingWindow[float](self.lookback)
self.Volume_rolling = RollingWindow[float](self.lookback)
self.fast_volume_LWMA_indicator = self.algo.LWMA(self.ticker, 5, Resolution.Daily, Field.Volume)
self.slow_volume_LWMA_indicator = self.algo.LWMA(self.ticker, 20, Resolution.Daily, Field.Volume)
self.RSI_rolling = RollingWindow[float](self.lookback)
self.RSI_indicator = self.algo.RSI(self.ticker, 25, Resolution.Daily)
self.Trend_rolling = RollingWindow[float](self.lookback)
self.trLWMA_indicator = self.algo.LWMA(self.ticker, 15, Resolution.Daily)
self.ROC_indicator = IndicatorExtensions.Of(RateOfChange(1), self.trLWMA_indicator)
self.AD_rolling = RollingWindow[float](self.lookback)
self.AD_indicator = self.algo.AD(self.ticker, Resolution.Daily)
self.STOK_rolling = RollingWindow[float](self.lookback)
self.STOD_rolling = RollingWindow[float](self.lookback)
self.STO_indicator = self.algo.STO(self.ticker, 14, 14, 3, Resolution.Daily)
self.KAMA_rolling = RollingWindow[float](self.lookback)
self.KAMA_indicator = self.algo.KAMA(self.ticker, 25, Resolution.Daily)
def Update(self,data):
self.Close_rolling.Add(data[self.ticker].Close)
self.Volume_rolling.Add(self.fast_volume_LWMA_indicator.Current.Value / self.slow_volume_LWMA_indicator.Current.Value)
self.RSI_rolling.Add(self.RSI_indicator.Current.Value)
self.Trend_rolling.Add(self.ROC_indicator.Current.Value)
self.AD_rolling.Add(self.AD_indicator.Current.Value)
self.STOK_rolling.Add(self.STO_indicator.StochK.Current.Value)
self.STOD_rolling.Add(self.STO_indicator.StochD.Current.Value)
self.KAMA_rolling.Add(self.KAMA_indicator.Current.Value)
def IsReady(self):
return self.RSI_indicator.IsReady \
and self.fast_volume_LWMA_indicator.IsReady and self.slow_volume_LWMA_indicator.IsReady \
and self.trLWMA_indicator.IsReady and self.AD_indicator.IsReady \
and self.STO_indicator.IsReady and self.KAMA_indicator.IsReady