def setUp(self):
# setup market data#
# First setup data as list
data = np.array([1, 0.05, 100, 0.1])
data.resize(1, 4)
# Then converst to types needed for pricing
columns = ["zero", "rate", "underlying", "vol"]
data = pd.DataFrame(data, columns=columns)
self.md_for_option = md.market_data(data)
self.testdir = "/home/phcostello/Documents/workspace/FinancePython/strategy_tester/test/"
self.SP500 = pickle.load(open(self.testdir + "SP500.pkl"))
self.md_SP500 = md.market_data(self.SP500)
def setUp(self):
#setup market data#
#First setup data as list
data = np.array([1,0.05,100,0.1])
data.resize(1,4)
#Then converst to types needed for pricing
columns = ["zero",
"rate",
"underlying",
"vol"]
data = pd.DataFrame(data, columns = columns)
md_cash = md.market_data(data)
#need to add to self to use in test functions
self.md_slice = md.market_data_slice(md_cash,time_index=0)
def test_addTechnical(self):
'''Test that adding simple technical indicators'''
md = market_data(self.SP500)
#Test simple moving average
sma = pd.rolling_mean(self.SP500['Adj_Close'], 10)
md.addSMA('Adj_Close','sma',10)
self.assertTrue((sma[10:] == md['sma'][10:]).all(), 'Add simple ma wrong')
#Test adding exponetially weighted moving average works
ema = pd.ewma(self.SP500['Adj_Close'], 10)
md.addEMA('Adj_Close','ema', win_length=10)
self.assertTrue((ema[10:] == md['ema'][10:]).all())
#Test adding rolling stdev average works
stdev = pd.rolling_std(self.SP500['Adj_Close'], 10)
md.addSDev('Adj_Close','stdev', win_length=10)
self.assertTrue((stdev[11:] == md['stdev'][11:]).all())
def setUp(self):
r = 0.05
vol = 0.1
S = 100.0
#setup market data#
#First setup data as list
data = np.array([r,S,vol])
data.resize(1,3)
#Then convert to types needed for pricing
columns = ["rate",
"underlying",
"vol"]
data = pd.DataFrame(data, columns = columns)
self.md1 = md.market_data(data)
#need to add to self to use in test functions
self.md_slice = md.market_data_slice(self.md1,time_index=0)
#Setup vanilla option trade
self.tradeCall = td.TradeVanillaEuroCall(name = "Call",
notional = 0,
strike = 100,
expiry = 0.5)
#Setup portfolio
self.tradeEquity = td.TradeEquity(name = "Equity",
notional = 0,
price_series_label = "underlying")
self.port1 = pf.Portfolio("port1")
#class MyClass(object): # ''' # classdocs # ''' # # # def __init__(selfparams): # ''' # Constructor # ''' import strategy_tester.market_data as md import GenerateData as gd import matplotlib.pyplot as plt import pandas as pd paths = [ gd.GenerateLogNormalTS(100, 0.05, 0.1, 100).get_data() for i in range(0,100)] path1 = gd.GenerateLogNormalTS(100, 0.05, 0.1, 100).get_data() path1 = pd.DataFrame(path1) md1 = md.market_data(path1) #[plt.plot(path) for path in paths ] #plt.show()
K = 100.0
#setup market data#
#First setup data as list
data = np.array([r,S,vol])
data.resize(1,3)
#Then convert to types needed for pricing
columns = ["rate",
"underlying",
"vol"]
data = pd.DataFrame(data, columns = columns)
md1 = md.market_data(data)
#need to add to self to use in test functions
md_slice = md.market_data_slice(md1,time_index=0)
#Setup vanilla option trade
tradeCall = td.TradeVanillaEuroCall(name = "Call",
notional = 1,
strike = 100,
expiry = 0.5)
price = tradeCall.price(md_slice)
print "price = ", price
delta = tradeCall.delta(md_slice)
print "delta = ", delta
def test_MD_readcsv_constructor(self):
#Make sure that not constructing from allowable type throws error
with self.assertRaises(NameError):
market_data(self.AORD)
def DeltaHedgeVanillaCallEg():
steps = 3000
stepsize = 1.0/steps
r = 0.05
dividend = 0.0
vol = 0.2
S0 = 50.0
t0 = 0.0
expiry = 1.0
K = 50.0
#setup market data#
#Generate Series
rseries = steps*[r]
dividendseries = steps*[dividend]
volseries = steps*[vol]
underlyingSeries = gd.GenerateLogNormalTS(S0, mu=0.03, covariance=vol, stepsize=stepsize,steps=steps-1).get_data()
data2 = [rseries,dividendseries,volseries, underlyingSeries]
data2 = np.array(data2)
data2.shape
data2 = data2.transpose()
data2[1,:]
columns = ['rate','dividend','vol','underlying']
data = pd.DataFrame(data2, columns = columns)
data.index = list(np.arange(0,steps,dtype='float64')/steps)
md1 = md.market_data(data)
#need to add to self to use in test functions
md_slice = md.market_data_slice(md1,time_index=0)
md_slice.data
tradeUnderlying = td.TradeEquity('underlying',
notional= 0,
price_series_label = 'underlying')
tradeCall = td.TradeVanillaEuroCall(name = "Call",
notional = 0,
strike = K,
expiry = expiry)
price = tradeCall.price(md_slice)
print "price = ", price
delta = tradeCall.delta(md_slice)
print "delta = ", delta
#Setup portfolio
#First initialise trade type but empty portfolio
port1 = pf.Portfolio("port1")
port1.add_trade(tradeUnderlying)
port1.add_trade(tradeCall)
#Second initialise starting value
initPort = {'Call':1}
port1.adjustNotional(initPort)
delta = tradeCall.delta(md_slice)
print "delta", delta
trade = {'underlying':-delta}
port1.adjustNotional(trade)
port1Slice = pf.PortfolioSlice(portfolio = port1,
market_data= md1,
time_index = 0)
initHedgPort = {'Call':1, "underlying":-delta}
port1Slice.adjustCash(initHedgPort)
#addsome cash
MoreCash = {'Cash':1}
port1.adjustNotional(MoreCash)
prt1Val = port1Slice.value()
print "Portfolio Value" , prt1Val
prt1Del = port1Slice.delta()
print "Portfolio Del" , prt1Del
ts_deltaHedge = tsc.Delta_Hedging(market_data = md1,
portfolio = port1,
initial_time_index = 0,
stepsize = stepsize)
ts_deltaHedge.run_strategy()
outfile = open('VanillaCallDelta_strat.pkl','wb')
pickle.dump(ts_deltaHedge,outfile)
outfile.close()
print ts_deltaHedge.result.head(20)
print ts_deltaHedge.result.tail(20)
print ts_deltaHedge.portfolio.get_notional()
K = 100.0
#setup market data#
#First setup data as list
data = np.array([r,S,vol])
data.resize(1,3)
#Then convert to types needed for pricing
columns = ["rate",
"underlying",
"vol"]
data = pd.DataFrame(data, columns = columns)
md_cash = md.market_data(data)
#need to add to self to use in test functions
md_slice = md.market_data_slice(md_cash,time_index=0)
#Setup vanilla option trade
tradeCall = TradeVanillaEuroCall(name = "Call",
notional = 1,
strike = 100,
expiry = 0.5)
price = tradeCall.price(md_slice)
print "price = ", price
delta = tradeCall.delta(md_slice)
print "delta = ", delta
#Need to add params to importr as xts and quantmod have conflicts
xts = importr("xts", robject_translations = {".subset.xts": "_subset_xts2", "to.period": "to_period2"})
quantmod = importr('quantmod', robject_translations = {"skeleton.TA": "skeleton_TA2"})
#if __name__ == '__main__':
# pass
#Read Data
AORD = ro.r('read.table("~/Documents/R/StrategyTester/Data/AORD.csv",header=T, sep=",")')
AORD = zoo.as_zoo(AORD)#, order_by = ro.r.rownames(AORD))
bool(zoo.is_zoo(AORD))
type(AORD)
#Read into ST MarketData type
MD1 = md.market_data(AORD)
MD1.names[0]
type(MD1.core_data)
#Plotting with matlibplot
df1 = MD1.core_data['AORD.Close']
type(df1)
df1.plot()
plt.show()
MD1.core_data['AORD.Close'][0]
MD1.core_data.ix[0]
MD1.core_data.index
#Take slice of of MD
MDSlice1 = md.market_data_slice(MD1,1)
