def createIndicators(self):
##mycode
dates = pd.date_range(self.start_date, self.end_date)
price = ut.get_data(self.symbol, dates, addSPY=True)
price_SPY = price['SPY']
price = price.drop(['SPY'], axis=1)
##get data for all the indicators including T - 30 trading days of data (to account for creation of historical indicators)
sma_ind = ind.get_price_sma_ind(price, 20)
momentum_ind = ind.getMomentumInd(price, 14)
bb_ind = ind.getBBInd(price, 14)
macd_ind = ind.getMACDInd(price)
vol_ind = ind.volatility(price, 14)
#we now remove the last 30 days of data (December 2007) after creating the indicators so that we only have training period data.
price = price.loc[price.index >= self.start_date +
dt.timedelta(days=30)]
sma_ind = sma_ind.loc[sma_ind.index >= self.start_date +
dt.timedelta(days=30)]
vol_ind = vol_ind.loc[vol_ind.index >= self.start_date +
dt.timedelta(days=30)]
momentum_ind = momentum_ind.loc[momentum_ind.index >= self.start_date +
dt.timedelta(days=30)]
bb_ind = bb_ind.loc[bb_ind.index >= self.start_date +
dt.timedelta(days=30)]
macd_ind = macd_ind.loc[macd_ind.index >= self.start_date +
dt.timedelta(days=30)]
##create cross over signals for each day
price_sma_crossover = pd.DataFrame(0,
index=sma_ind.index,
columns=sma_ind.columns)
price_sma_crossover[sma_ind > 0] = 1
price_sma_crossover = price_sma_crossover.diff()
price_sma_crossover[price_sma_crossover != 0] = 1
macd_sigal_diff = ind.getMACDHistogramInd(price)
#macd cross below signal = sell
macd_cross_below_signal = pd.DataFrame(0,
index=macd_ind.index,
columns=macd_ind.columns)
macd_cross_below_signal[macd_sigal_diff < 0] = 1
macd_cross_below_signal[1:] = macd_cross_below_signal.diff()
macd_cross_below_signal.ix[0] = 0
#print(macd_cross_above_signal)
#macd cross above signal = buy
macd_cross_above_signal = pd.DataFrame(0,
index=macd_ind.index,
columns=macd_ind.columns)
macd_cross_above_signal[macd_sigal_diff > 0] = 1
macd_cross_above_signal[1:] = macd_cross_above_signal.diff()
macd_cross_above_signal.ix[0] = 0
#print(macd_cross_above_signal)
#bollinger crossovers
##this is a sell signal
bb_upper_cross_signal = ind.getBBUpperCross(price, 20)
#this is a buy signal
bb_lower_cross_signal = ind.getBBLowerCross(price, 20)
##create and discretize states for Q Learner
#print sma_ind
daily_rets = ((price.shift(-5) / price) - 1)
daily_rets.ix[-1] = 0
price['Price_Sma'] = sma_ind
price['Volatility'] = vol_ind
price['Momentum'] = momentum_ind
price['BB_Ind'] = bb_ind
#price['MACD_Ind'] = macd_ind
#price['BB_Upper_Cross'] = bb_upper_cross_signal
#price['BB_Lower_Cross'] = bb_lower_cross_signal
price['MACD_Cross_Below'] = macd_cross_below_signal
price['MACD_Cross_Above'] = macd_cross_above_signal
#price['Price_SMA_Crossover'] = price_sma_crossover
daily_ret_classes = pd.DataFrame(0,
index=daily_rets.index,
columns=daily_rets.columns,
dtype=int)
Y_buy = 0.005
Y_sell = -0.005
daily_ret_classes[daily_rets > Y_buy] = 1
daily_ret_classes[daily_rets < Y_sell] = -1
price['Action'] = daily_ret_classes
#print price.iloc[:,1:].sum(axis=1)
#print price
return price
def testPolicy(self):
dates = pd.date_range(self.start_date, self.end_date)
price = get_data(self.symbol, dates, addSPY=True)
price = price.drop(['SPY'], axis=1)
##get data for all the indicators including T - 30 trading days of data (to account for creation of historical indicators)
sma_ind = ind.get_price_sma_ind(price, 20)
momentum_ind = ind.getMomentumInd(price, 14)
bb_ind = ind.getBBInd(price, 14)
macd_ind = ind.getMACDInd(price)
vol_ind = ind.volatility(price, 14)
#we now remove the last 30 days of data (December 2007) after creating the indicators so that we only have training period data.
price = price.loc[price.index >= self.start_date +
dt.timedelta(days=30)]
sma_ind = sma_ind.loc[sma_ind.index >= self.start_date +
dt.timedelta(days=30)]
vol_ind = vol_ind.loc[vol_ind.index >= self.start_date +
dt.timedelta(days=30)]
momentum_ind = momentum_ind.loc[momentum_ind.index >= self.start_date +
dt.timedelta(days=30)]
bb_ind = bb_ind.loc[bb_ind.index >= self.start_date +
dt.timedelta(days=30)]
macd_ind = macd_ind.loc[macd_ind.index >= self.start_date +
dt.timedelta(days=30)]
##create cross over signals for each day
price_sma_crossover = pd.DataFrame(0,
index=sma_ind.index,
columns=sma_ind.columns)
price_sma_crossover[sma_ind > 0] = 1
price_sma_crossover = price_sma_crossover.diff()
price_sma_crossover[price_sma_crossover != 0] = 1
macd_sigal_diff = ind.getMACDHistogramInd(price)
#macd cross below signal = sell
macd_cross_below_signal = pd.DataFrame(0,
index=macd_ind.index,
columns=macd_ind.columns)
macd_cross_below_signal[macd_sigal_diff < 0] = 1
macd_cross_below_signal[1:] = macd_cross_below_signal.diff()
macd_cross_below_signal.ix[0] = 0
#print(macd_cross_above_signal)
#macd cross above signal = buy
macd_cross_above_signal = pd.DataFrame(0,
index=macd_ind.index,
columns=macd_ind.columns)
macd_cross_above_signal[macd_sigal_diff > 0] = 1
macd_cross_above_signal[1:] = macd_cross_above_signal.diff()
macd_cross_above_signal.ix[0] = 0
#print(macd_cross_above_signal)
#bollinger crossovers
##this is a sell signal
bb_upper_cross_signal = ind.getBBUpperCross(price, 20)
#this is a buy signal
bb_lower_cross_signal = ind.getBBLowerCross(price, 20)
#creaete tradings strategy
df_trades = pd.DataFrame(0, index=price.index, columns=['Shares'])
overbought_or_oversold = pd.DataFrame(index=price.index,
columns=['Action'])
##entry conditions -different combinations
#macd + price/sma + bb % + volatility = final trading strategy
short_ind = (macd_cross_below_signal == 1) | ((sma_ind > 0.05) &
(bb_ind > 1))
buy_ind = (macd_cross_above_signal == 1) | ((sma_ind < -0.05) &
(bb_ind < 0))
exit_ind = (price_sma_crossover == 1) & ((vol_ind > 0.04))
#only overbought/oversold (price/sma + bb%)
#short_ind = ((sma_ind>0.05) & (bb_ind > 1))
#buy_ind = ((sma_ind< -0.05) & (bb_ind < 0))
#either crossovers (MACD or BB)
#buy_ind = (bb_lower_cross_signal==1) | (macd_cross_above_signal==1)
#short_ind = (bb_upper_cross_signal==1) | (macd_cross_below_signal==1)
#both crossovers (MACD and BB)
#buy_ind = (bb_lower_cross_signal==1) & (macd_cross_above_signal==1)
#short_ind = (bb_upper_cross_signal==1) & (macd_cross_below_signal==1)
#only bollinger crossover
#buy_ind = (bb_lower_cross_signal==1)
#short_ind = (bb_upper_cross_signal==1)
#only MACD signal line crossover
#short_ind = (macd_cross_below_signal==1)
#buy_ind = (macd_cross_above_signal==1)
##exit conditions
overbought_or_oversold.iloc[exit_ind == True] = 0 #exit
#overbought
overbought_or_oversold.iloc[short_ind == True] = -1 #short
#oversold
overbought_or_oversold.iloc[buy_ind == True] = 1 #buy
#buy or sell on above if holding constraint is met
holdings = 0
for index_order, row_order in overbought_or_oversold.iterrows():
if row_order['Action'] == 1 and holdings == 0:
df_trades.ix[index_order, 'Shares'] = 1000
holdings = 1000
elif row_order['Action'] == 1 and holdings == -1000:
df_trades.ix[index_order, 'Shares'] = 2000
holdings = 1000
elif row_order['Action'] == -1 and holdings == 0:
df_trades.ix[index_order, 'Shares'] = -1000
holdings = -1000
elif row_order['Action'] == -1 and holdings == 1000:
df_trades.ix[index_order, 'Shares'] = -2000
holdings = -1000
elif row_order['Action'] == 0 and holdings == 1000:
df_trades.ix[index_order, 'Shares'] = -1000
holdings = 0
elif row_order['Action'] == 0 and holdings == -1000:
df_trades.ix[index_order, 'Shares'] = 1000
holdings = 0
else:
df_trades.ix[index_order, 'Shares'] = 0
return df_trades