github-featured/quantconnect--lean
2
0
Fork 0
Code Issues Pull Requests Deployments Models Agent Notes Packages Projects Releases Wiki Activity

Add More Comments and Do Modifications

Browse Source 
Add More Comments and Do Modifications for the 5 ML Algorithms.
This commit is contained in:
Daniel Chen
2019-07-09 08:58:52 -07:00
parent 6e76f5742d
commit 9d20cf0e7a
5 changed files with 84 additions and 65 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Algorithm.Python/ScikitLearnLinearRegressionAlgorithm.py
+22 -14
View File
@@ -29,66 +29,74 @@ class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm):
self.SetStartDate(2013, 10, 7) # Set Start Date
self.SetEndDate(2013, 10, 8) # Set End Date
self.lookback = 30
self.lookback = 30 # number of previous days for training
self.SetCash(100000) # Set Strategy Cash
spy = self.AddEquity("SPY", Resolution.Minute)
self.symbols = [ spy.Symbol ]
self.symbols = [ spy.Symbol ] # In the future, we can include more symbols to the list in this way
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 28), Action(self.Regression))
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), Action(self.Trade))
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 28), self.Regression)
self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade)
def OnData(self, data):
pass
def Regression(self):
history = self.History(self.symbols, self.lookback, Resolution.Daily)
# price dictionary: key: symbol; value: historical price
self.prices = {}
# slope dictionary: key: symbol; value: slope
self.slopes = {}
for symbol in self.symbols:
if not history.empty:
# get historical open price
self.prices[symbol.Value] = list(history.loc[symbol.Value]['open'])
# A is the design matrix
A = range(self.lookback + 1)
for symbol in self.symbols:
if symbol.Value in self.prices:
# response
Y = self.prices[symbol.Value]
# features
X = np.column_stack([np.ones(len(A)), A])
# data preparation
length = min(len(X), len(Y))
X = X[-length:]
Y = Y[-length:]
A = A[-length:]
# fit the linear regression
reg = LinearRegression().fit(X, Y)
# run linear regression y = ax + b
b = reg.intercept_
a = reg.coef_[1]
# store slopes for symbols
self.slopes[symbol] = a/b
def Trade(self):
# if there is no open price
if not self.prices:
return
thod_buy = 0.001
thod_liquidate = -0.001
thod_buy = 0.001 # threshold of slope to buy
thod_liquidate = -0.001 # threshold of slope to liquidate
# liquidate
for i in self.Portfolio.Values:
slope = self.slopes[i.Symbol]
if i.Invested and slope < thod_liquidate:
self.Liquidate(i.Symbol)
for holding in self.Portfolio.Values:
slope = self.slopes[holding.Symbol]
# liquidate when slope smaller than thod_liquidate
if holding.Invested and slope < thod_liquidate:
self.Liquidate(holding.Symbol)
# buy
for symbol in self.symbols:
# buy when slope larger than thod_buy
if self.slopes[symbol] > thod_buy:
self.SetHoldings(symbol, 1 / len(self.symbols))