plot1.spines['top'].set_color('white')
plot1.spines['right'].set_color('white')
plot1.spines['bottom'].set_linewidth(2)
plot1.spines['left'].set_linewidth(2)
plot1.spines['top'].set_linewidth(2)
plot1.spines['right'].set_linewidth(2)
plt.savefig("./image/BreakOut.png", facecolor='#17202A', edgecolor=None)
plt.close()
return sig
if __name__ == "__main__":
np.set_printoptions(threshold=np.nan)
pd.set_option("display.max_rows", 280)
dt = Data()
#df = dt.getCSVData()
excel = "../Data/hsi_futures.xlsx"
xl = pd.ExcelFile(excel)
sheets = xl.sheet_names
print sheets
#sheet = 'FEB16'
sheet = raw_input()
if sheet in sheets:
df = dt.getExcelSheetData(excel, sheet)
breakouts_swing().analysis(df)
else:
print "No sheet name:", sheet
exit(0)
for sheet in sheets:
df = dt.getExcelSheetData(excel, sheet)
def analysis(self, df, quantity=1):
"""
:param df:
:param quantity:
:return:
"""
self.dailydata(df)
""" Data """
float_close = Data.toFloatArray(df['Close'])
float_high = Data.toFloatArray(df['High'])
float_low = Data.toFloatArray(df['Low'])
CCI_5min = CCI(np.array(float_high),np.array(float_low),np.array(float_close), timeperiod=10)
CCI_day = CCI(np.array(self.dailyhigh),np.array(self.dailylow),np.array(self.dailyclose),timeperiod=10)
#print 'CCI min', CCI_5min, 'CCI len', len(CCI_5min)
#print 'CCI day',CCI_day, 'len',len(CCI_day)
""" Trade Logic """
signals = []
longshort_flag = 0
for i in xrange(len(CCI_day)):
#print '#', i
if np.isnan(CCI_day[i]):
continue
dt = self.daylist[i]
if longshort_flag == 0:
intraCCI = []
num = []
if CCI_day[i] > 100:
for j in xrange(len(CCI_5min)):
if df.loc[j,'Date'].date() == dt:
num.append(j)
intraCCI.append(CCI_5min[j])
for k in xrange(len(intraCCI)):
if intraCCI[k] < -100:
if k != len(intraCCI)-1:
signal = self.Long('HSI', df, num[k], quantity, self.name)
longshort_flag = 1
signals.append(signal)
break
#print 'break'
if CCI_day[i] < -100:
dt = self.daylist[i]
for j in xrange(len(CCI_5min)):
if df.loc[j,'Date'].date() == dt:
num.append(j)
intraCCI.append(CCI_5min[j])
for k in xrange(len(intraCCI)):
if intraCCI[k] > 100:
if k != len(intraCCI)-1:
signal = self.Short('HSI', df, num[k], quantity, self.name)
longshort_flag = -1
signals.append(signal)
break
#print 'break 2'
if longshort_flag == 1:
for j in xrange(len(CCI_5min)):
if df.loc[j,'Date'] > signal[1] and df.loc[j,'Date'].date() == dt:
if df.loc[j,'Open'] >= signal[4]*1.02 or df.loc[j,'Open'] <= signal[4]*0.99:
signal = self.SellToCover('HSI', df, j, quantity, self.name)
longshort_flag = 0
signals.append(signal)
break
#print 'break3'
if longshort_flag == -1:
for j in xrange(len(CCI_5min)):
if df.loc[j,'Date'] > signal[1] and df.loc[j,'Date'].date() == dt:
if df.loc[j,'Open'] <= signal[4]*0.98 or df.loc[j,'Open'] >= signal[4]*1.01:
signal = self.BuyToCover('HSI', df, j, quantity, self.name)
longshort_flag = 0
signals.append(signal)
break
# print signals
""" Signal Table """
sig = pd.DataFrame(signals, columns=['Code', 'Time', 'Action', 'Qnt', 'Price', 'Volume', 'Strategy'])
print sig
""" Simple Profit Calculation """
profits = []
for k in range(0,len(signals)/2):
if sig['Action'][k*2] == "Long":
profit = sig['Price'][k*2+1] - sig['Price'][k*2]
else:
profit = sig['Price'][k*2]- sig['Price'][k*2+1]
profits.append(profit)
print "=" * 100
print self.name, "profit: $", np.sum(profits)
print(profits)
print "=" * 100
""" Plot """
fig = plt.figure(1)
fig.set_figheight(6)
fig.set_figwidth(16)
gs = gridspec.GridSpec(20, 10)
rect = fig.patch
rect.set_facecolor('#1B2631')
plot1 = plt.subplot(gs[0:20, :])
# plot2 = plt.subplot(gs[13:20, :])
# plot3 = plt.subplot(gs[16:20, :])
longSignals = sig[sig['Action'] == 'Long']
sellToCoverSignals = sig[sig['Action'] == 'SellToCover']
shortSignals = sig[sig['Action'] == 'Short']
buyToCoverSignals = sig[sig['Action'] == 'BuyToCover']
## Plot 1
markerSize = 15
priceline = plot1.plot(df['Date'], df['Close'], '#251E73',lw=2)
longline = plot1.plot(longSignals['Time'], longSignals['Price'], '^', markersize=markerSize)
bcline = plot1.plot(buyToCoverSignals['Time'], buyToCoverSignals['Price'], '^', markersize=markerSize)
shortline = plot1.plot(shortSignals['Time'], shortSignals['Price'], 'v', markersize=markerSize)
scline = plot1.plot(sellToCoverSignals['Time'], sellToCoverSignals['Price'], 'v', markersize=markerSize)
# Set every line
plt.title("CCI Correction", color='white', fontsize=20)
plt.xlabel("Time", color='white')
plt.ylabel("Price", color='white')
plt.setp(longline, color='#E74C3C', markeredgecolor='#E74C3C')
plt.setp(bcline, color='#E74C3C', markeredgecolor='#E74C3C')
plt.setp(shortline, color='#27AE60', markeredgecolor='#27AE60')
plt.setp(scline, color='#27AE60', markeredgecolor='#27AE60')
# Legend and grid
red_patch = mpatches.Patch(color='#E74C3C', label='Long')
green_patch = mpatches.Patch(color='#27AE60', label='Short')
plot1.legend(handles=[red_patch, green_patch])
# Axis
plot1.set_axis_bgcolor('white')
plot1.tick_params(axis='x', colors='white')
plot1.tick_params(axis='y', colors='white')
plot1.spines['bottom'].set_color('white')
plot1.spines['left'].set_color('white')
plot1.spines['top'].set_color('white')
plot1.spines['right'].set_color('white')
plot1.spines['bottom'].set_linewidth(2)
plot1.spines['left'].set_linewidth(2)
plot1.spines['top'].set_linewidth(2)
plot1.spines['right'].set_linewidth(2)
plt.savefig("./image/CCI_Correction.png", facecolor='#17202A', edgecolor=None)
plt.close()
return sig
