def __init__(self, sym='SPY', sd=dt.datetime(2008,1, 1), \
ed=dt.datetime(2009,12,31),sv=100000,verbose=False):
self.sym = sym
self.verbose = verbose
self.shares = 0
self.position = 0
self.sv = sv
self.cash = sv
self.pp = 0
#period
n = 28
chart = False
#take in, normalize data
df = ut.get_data([sym], pd.date_range(sd - dt.timedelta(100), ed))[sym]
normed = df / df.ix[0]
#get features
sma = ind.SMA(normed, n)
smap = ind.standardize(normed / sma)
bbp = ind.standardize(ind.bband(normed, n, chart)[0])
stdev = ind.standardize(normed.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(normed, n, chart))
momvol = ind.standardize(mom / stdev)
#daily returns & combined features
rets = pd.DataFrame(df)
daily_rets = rets[sym].copy()
daily_rets[1:] = (rets[sym].ix[1:] / rets[sym].ix[:-1].values) - 1
daily_rets.ix[0] = 0
#print df
#df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).\
# assign(stdev=stdev).assign(bbp=bbp).assign(mom=mom)[sd:]
df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).\
assign(bbp=bbp).assign(mom=mom)[sd:]
#print df
daily_rets.ix[0] = 0
df = df.assign(dr=daily_rets)
#print df
self.df = df
self.market = df.iterrows()
self.curr = self.market.next()
self.action = self.Action()
#self.features = ['smap', 'stdev', 'bbp', 'mom']
self.features = ['smap', 'bbp', 'mom']
def testPolicy(symbol = "AAPL", sd=dt.datetime(2010,1,1), \
ed = dt.datetime(2011,12,31), sv=100000):
chart = False
n = 14
#symbol = [symbol]
prices = get_data(symbol, pd.date_range(sd, ed))
#print prices
#ffill and drop SPY
prices.fillna(method='ffill', inplace=True)
prices.fillna(method='bfill', inplace=True)
prices = prices[symbol]
#Generate indicators
sma = ind.SMA(prices, n)
smap = ind.standardize(prices / sma)
bbp = ind.standardize(ind.bband(prices, n, chart)[0])
stdev = ind.standardize(prices.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(prices, n, chart))
momvol = ind.standardize(mom / stdev)
orders = prices.copy()
orders.ix[:, :] = np.nan
smap_cross = pd.DataFrame(0, index=smap.index, columns=smap.columns)
smap_cross[smap >= 1] = 1
smap_cross[1:] = smap_cross.diff()
smap_cross.ix[0] = 0
orders[(smap < 0.95) & (bbp < 0) & (stdev < 0.1) & (mom < 0)] = 1000
orders[(smap > 1.05) & (bbp > 1) & (stdev > 0.3) & (mom > 0)] = -1000
orders[(smap_cross != 0)] = 0
orders.ffill(inplace=True)
orders.fillna(0, inplace=True)
orders[1:] = orders.diff()
#orders.ix[0] = 1000
#orders.ix[-1] = -1000
#orders = orders.loc[(orders!=0).any(axis=1)]
#orders.ix[0] = 0
#orders.ix[-1] = orders.ix[-2]*-1
#print orders
return orders
def __init__(self, sym='SPY', sd=dt.datetime(2008,1, 1), \
ed=dt.datetime(2009,12,31),sv=100000,verbose=False, experiment=False, impact=0.0):
self.sym = sym
self.verbose = verbose
self.shares = 0
self.position = 2
self.sv = sv
self.cash = sv
self.pp = 0
self.impact = impact
self.experiment = experiment
#n = 3 #period
chart = False
#take in, normalize data
df = ut.get_data([sym], pd.date_range(sd - dt.timedelta(100), ed))[sym]
#print df
#print sd, ed
normed = df / df.ix[0]
#print normed
#daily returns & combined features
rets = pd.DataFrame(df)
daily_rets = rets[sym].copy()
daily_rets[1:] = (rets[sym].ix[1:] / rets[sym].ix[:-1].values) - 1
daily_rets.ix[0] = 0
#Simple moving average
sma5 = normed.rolling(5).mean()
sma15 = normed.rolling(15).mean()
sma20 = normed.rolling(20).mean()
sma25 = normed.rolling(25).mean()
sma30 = normed.rolling(30).mean()
sma40 = normed.rolling(40).mean()
#print sma5
# Volatility
vol5 = normed.rolling(5).std()
vol10 = normed.rolling(10).std()
vol20 = normed.rolling(20).std()
vol30 = normed.rolling(30).std()
# Bollinger bands
sma_bb = normed.rolling(5).mean()
sma_bb_std = normed.rolling(5).std()
bb = (normed -
(sma_bb - 2 * sma_bb_std)) / ((sma_bb + 2 * sma_bb_std) -
(sma_bb - 2 * sma_bb_std))
# Moving average convergence/divergence
#ema12 = pd.ewma(np.asarray(normed), span=12)
#ema26 = pd.ewma(np.asarray(normed), span=26)
#macd = ema12 - ema26
# Momentum
momentum2 = normed / normed.shift(2) - 1
momentum5 = normed / normed.shift(5) - 1
momentum10 = normed / normed.shift(10) - 1
# Combine into new dataframe
df = pd.DataFrame(df).assign(sma5=normed / sma5 - 1).assign(\
bb=bb).assign(momentum2=momentum2).assign(normed=normed).\
assign(vol10=vol10).assign(vol20=vol20).assign(vol30=vol30).assign(vol10=vol10)\
.assign(vol5=vol5).assign(\
momentum5=momentum5).assign(momentum10=momentum10)[sd:]
df = df.assign(dr=daily_rets)
#print df
# Determine optimal features for states
corr_df = df.corr().abs()
corr = corr_df['dr'][:]
corr.ix[0] = 0
corr['normed'] = 0
icorr = np.asarray(corr)
scorr = icorr.argsort(
)[-4:][::-1] # select top 3 features and daily returns
scorr = scorr[1:] # remove daily returns from possible features
optimal_ftrs = []
for i in scorr:
optimal_ftrs.append(corr_df.columns[i])
if experiment:
#print "Experiment 1 ACTIVATED"
n = 14
sma = ind.SMA(normed, n)
smap = ind.standardize(normed / sma)
bbp = ind.standardize(ind.bband(normed, n, chart)[0])
stdev = ind.standardize(
normed.rolling(window=n, min_periods=n).std())
mom = ind.standardize(ind.momentum(normed, n, chart))
momvol = ind.standardize(mom / stdev)
df = pd.DataFrame(df).assign(normed=normed).assign(
smap=smap).assign(stdev=stdev).assign(bbp=bbp).assign(
mom=mom)[sd:]
optimal_ftrs = ['smap', 'stdev', 'bbp', 'mom']
#df = pd.DataFrame(df).assign(normed = normed).assign(smap=smap).assign(bbp=bbp).assign(mom=mom)[sd:]
#df = pd.DataFrame(df).assign(normed = normed)[sd:]
self.df = df
self.market = df.iterrows()
self.curr = self.market.next()
self.action = self.Action()
self.features = optimal_ftrs
# generate a buy signal (1) if price falls significantly below sma
# generate a sell signal (-1) if prices rises significantly above sma
smaSignal = 1 * (smaIndicator < -smaThreshold) + -1 * (smaIndicator >
smaThreshold)
momIndicator, momWindow = getMomIndicator(data)
momThreshold = 0.06 # 0.055 optimized value on manual trading strategy 1
# generate a buy/sell signal if momentum is greatly positive/negative
momSignal = -1 * (momIndicator < -momThreshold) + 1 * (momIndicator >
momThreshold)
bbWindow = 10 # 48 NOT OPTIMIZED
bbIndicator = getBb(data, bbWindow)
bbThreshold = 0 # 0.2 NOT OPTIMIZED
# generate a buy/sell signal if indicator is below/above the BB and the indicator is rising/falling significantly
bbSignal = -1 * ((bbIndicator > 1) & (standardize(data).diff(1) < -bbThreshold)) + \
1 * ((bbIndicator < -1) & (standardize(data).diff(1) > bbThreshold))
crossIndicator, crossWindow = getCrossIndicator(data)
crossThreshold = 0.08 # 0.08 optimized value on manual trading strategy 1
# generate a buy/sell signal if indicator is close to 0.5/-0.5
crossSignal = 1 * ((crossIndicator - 0.5).abs() < crossThreshold) + \
-1 * ((crossIndicator + 0.5).abs() < crossThreshold)
# Combine individual signals. bbSignal is neglected here since including it
# with the other signals did not result in label-free trading using strategy 1
signal = 1 * ((smaSignal == 1) & (momSignal ==1) & (crossSignal == 1)) \
+ -1 * ((smaSignal == -1) & (momSignal == -1) & (crossSignal == -1))
order = tradingStrategy(signal, holdTime)
createOrder(order, 'rule_based')
def testcode():
data = defineData() # get AAPL between the in-sample dates set as default
holdTime = 21 # in days
smaIndicator, smaWindow = getSmaIndicator(data)
smaThreshold = 0.012 #0.012 # optimized value on manual trading strategy 1
# generate a buy signal (1) if price falls significantly below sma
# generate a sell signal (-1) if prices rises significantly above sma
smaSignal = 1 * (smaIndicator < -smaThreshold) + \
-1 * (smaIndicator > smaThreshold)
momIndicator, momWindow = getMomIndicator(data)
momThreshold = 0.06 #0.055 # optimized value on manual trading strategy 1
# generate a buy/sell signal if momentum is greatly positive/negative
momSignal = -1 * (momIndicator < -momThreshold) + \
1 * (momIndicator > momThreshold)
bbWindow = 10 #48 NOT OPTIMIZED
bbIndicator = getBb(data, bbWindow)
bbThreshold = 0 #0.2 NOT OPTIMIZED
# generate a buy/sell signal if indicator is below/above the lower/upper BB
# and the indicator is rising/falling significantly
bbSignal = -1 * ((bbIndicator > 1) & \
(standardize(data).diff(1) < -bbThreshold)) + \
1 * ((bbIndicator < -1) & \
(standardize(data).diff(1) > bbThreshold))
crossIndicator, crossWindow = getCrossIndicator(data)
crossThreshold = 0.08 #0.08 # optimized value on manual trading strategy 1
# generate a buy/sell signal if indicator is close to 0.5/-0.5
crossSignal = 1 * ( (crossIndicator - 0.5).abs() < crossThreshold) + \
-1 * ( (crossIndicator + 0.5).abs() < crossThreshold )
# Combine individual signals. bbSignal is neglected here since including it
# with the other signals did not result in label-free trading using strategy 1
signal = 1 * ( (smaSignal == 1) & (momSignal ==1 ) & (crossSignal == 1) ) \
+ -1 * ( (smaSignal == -1) & (momSignal == -1) & (crossSignal == -1) )
order = tradingStrategy(signal, holdTime)
createOrder(order, 'rule_based')
cumReturn, portVals = testcode_marketsim('rule_based', verbose=False)
print('Cumulative return [%]: ', round(cumReturn * 100, 4))
plt.figure(figsize=(10, 10))
plt.subplot(311)
plt.plot(data / data[0], label='benchmark', color='k')
plt.plot(portVals / portVals[0], label='rule-based')
plt.xticks(rotation=30)
plotVline(order)
plt.title('rule-based with sma + mom + crossover indicators')
lg = plt.legend(loc='best')
lg.draw_frame(False)
plt.ylabel('normalized')
plt.subplot(312)
plt.plot(smaSignal / 2, label='sma')
plt.plot(momSignal / 1.3, '.', label='mom')
plt.plot(crossSignal / 1.1, '.', label='crossover')
plt.plot(signal, label='overall signal')
plt.xticks(rotation=30)
plt.ylabel('indicator signals [a.u.]')
lg = plt.legend(loc='center right')
lg.draw_frame(False)
plt.subplot(313)
plt.scatter(momIndicator[signal==0], crossIndicator[signal==0], \
color = 'k', label = 'hold')
plt.scatter(momIndicator[signal==1], crossIndicator[signal==1], \
color = 'g', label = 'buy')
plt.scatter(momIndicator[signal==-1], crossIndicator[signal==-1], \
color = 'r', label = 'sell')
lg = plt.legend(loc='best')
lg.draw_frame(True)
plt.xlabel('Momentum Indicator')
plt.ylabel('Crossover Indicator')