def test_plot():
stock = testdata()
draw = Draw("N225", "日経平均株価")
ti = TechnicalIndicators(stock)
rsi = ti.calc_rsi(timeperiod=9)
rsi = ti.calc_rsi(timeperiod=14)
roc = ti.calc_roc()
roc = ti.calc_roc(timeperiod=25)
mfi = ti.calc_mfi()
ultosc = ti.calc_ultosc()
stoch = ti.calc_stoch()
willr = ti.calc_willr()
tr = ti.calc_tr()
vr = ti.calc_volume_rate()
ewma = ti.calc_ewma(span=5)
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=50)
ewma = ti.calc_ewma(span=75)
ewma = ti.calc_ewma(span=200)
bbands = ti.calc_bbands()
sar = ti.calc_sar()
draw.plot(stock,
'test',
ewma,
bbands,
sar,
rsi,
roc,
mfi,
ultosc,
willr,
stoch,
tr,
vr,
clf_result=0,
reg_result=5000)
eq_(draw.code, 'N225')
eq_(draw.name, '日経平均株価')
filename = 'test_N225.png'
expected = True
eq_(expected, os.path.exists(filename))
if os.path.exists(filename):
os.remove(filename)
def test_calc_ewma():
stock = testdata()
ti = TechnicalIndicators(stock)
ewma = ti.calc_ewma()
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=75)
expected = [19429., 18821., 17991.]
result = (ewma.ix['2015-03-20',
'ewma5'], ewma.ix['2015-03-20',
'ewma25'], ewma.ix['2015-03-20',
'ewma75'])
result = [round(x, 0) for x in result]
eq_(expected, result)
result = [round(x, 0) for x in result]
return ewma
def test_calc_ewma():
stock = testdata()
ti = TechnicalIndicators(stock)
ewma = ti.calc_ewma()
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=75)
expected = [19429.,
18821.,
17991.]
result = (ewma.ix['2015-03-20', 'ewma5'],
ewma.ix['2015-03-20', 'ewma25'],
ewma.ix['2015-03-20', 'ewma75'])
result = [round(x, 0) for x in result]
eq_(expected, result)
result = [round(x, 0) for x in result]
return ewma
def test_plot():
stock = testdata()
draw = Draw("N225", "日経平均株価")
ti = TechnicalIndicators(stock)
rsi = ti.calc_rsi(timeperiod=9)
rsi = ti.calc_rsi(timeperiod=14)
roc = ti.calc_roc()
roc = ti.calc_roc(timeperiod=25)
mfi = ti.calc_mfi()
ultosc = ti.calc_ultosc()
stoch = ti.calc_stoch()
willr = ti.calc_willr()
tr = ti.calc_tr()
vr = ti.calc_volume_rate()
ewma = ti.calc_ewma(span=5)
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=50)
ewma = ti.calc_ewma(span=75)
bbands = ti.calc_bbands()
sar = ti.calc_sar()
draw.plot(stock, ewma, bbands, sar,
rsi, roc, mfi, ultosc, willr,
stoch, tr, vr,
clf_result=0, reg_result=5000,
ref=[])
eq_(draw.code, 'N225')
eq_(draw.name, '日経平均株価')
filename = 'chart_N225.png'
expected = True
eq_(expected, os.path.exists(filename))
if os.path.exists(filename):
os.remove(filename)
def run(self):
io = FileIO()
will_update = self.update
if self.csvfile:
stock_tse = io.read_from_csv(self.code, self.csvfile)
msg = "".join([
"Read data from csv: ", self.code, " Records: ",
str(len(stock_tse))
])
print(msg)
if self.update and len(stock_tse) > 0:
index = pd.date_range(start=stock_tse.index[-1],
periods=2,
freq='B')
ts = pd.Series(None, index=index)
next_day = ts.index[1]
t = next_day.strftime('%Y-%m-%d')
newdata = io.read_data(self.code, start=t, end=self.end)
msg = "".join([
"Read data from web: ", self.code, " New records: ",
str(len(newdata))
])
print(msg)
if len(newdata) < 1:
will_update = False
else:
print(newdata.ix[-1, :])
stock_tse = stock_tse.combine_first(newdata)
io.save_data(stock_tse, self.code, 'stock_')
else:
stock_tse = io.read_data(self.code, start=self.start, end=self.end)
msg = "".join([
"Read data from web: ", self.code, " Records: ",
str(len(stock_tse))
])
print(msg)
if stock_tse.empty:
msg = "".join(["Data empty: ", self.code])
print(msg)
return None
if not self.csvfile:
io.save_data(stock_tse, self.code, 'stock_')
try:
stock_d = stock_tse.asfreq('B').dropna()[self.days:]
ti = TechnicalIndicators(stock_d)
ti.calc_sma()
ti.calc_sma(timeperiod=5)
ti.calc_sma(timeperiod=25)
ti.calc_sma(timeperiod=50)
ti.calc_sma(timeperiod=75)
ewma = ti.calc_ewma(span=5)
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=50)
ewma = ti.calc_ewma(span=75)
bbands = ti.calc_bbands()
sar = ti.calc_sar()
draw = Draw(self.code, self.fullname)
ret = ti.calc_ret_index()
ti.calc_vol(ret['ret_index'])
rsi = ti.calc_rsi(timeperiod=9)
rsi = ti.calc_rsi(timeperiod=14)
mfi = ti.calc_mfi()
roc = ti.calc_roc(timeperiod=10)
roc = ti.calc_roc(timeperiod=25)
roc = ti.calc_roc(timeperiod=50)
roc = ti.calc_roc(timeperiod=75)
roc = ti.calc_roc(timeperiod=150)
ti.calc_cci()
ultosc = ti.calc_ultosc()
stoch = ti.calc_stoch()
ti.calc_stochf()
ti.calc_macd()
willr = ti.calc_willr()
ti.calc_momentum(timeperiod=10)
ti.calc_momentum(timeperiod=25)
tr = ti.calc_tr()
ti.calc_atr()
ti.calc_natr()
vr = ti.calc_volume_rate()
ret_index = ti.stock['ret_index']
clf = Classifier(self.clffile)
train_X, train_y = clf.train(ret_index, will_update)
msg = "".join(["Train Records: ", str(len(train_y))])
print(msg)
clf_result = clf.classify(ret_index)[0]
msg = "".join(["Classified: ", str(clf_result)])
print(msg)
ti.stock.ix[-1, 'classified'] = clf_result
reg = Regression(self.regfile, alpha=1, regression_type="Ridge")
train_X, train_y = reg.train(ret_index, will_update)
msg = "".join(["Train Records: ", str(len(train_y))])
base = ti.stock_raw['Adj Close'][0]
reg_result = int(reg.predict(ret_index, base)[0])
msg = "".join(["Predicted: ", str(reg_result)])
print(msg)
ti.stock.ix[-1, 'predicted'] = reg_result
if len(self.reference) > 0:
ti.calc_rolling_corr(self.reference)
ref = ti.stock['rolling_corr']
else:
ref = []
io.save_data(io.merge_df(stock_d, ti.stock), self.code, 'ti_')
draw.plot(stock_d,
ewma,
bbands,
sar,
rsi,
roc,
mfi,
ultosc,
willr,
stoch,
tr,
vr,
clf_result,
reg_result,
ref,
axis=self.axis,
complexity=self.complexity)
return ti
except (ValueError, KeyError):
msg = "".join(["Error occured in ", self.code])
print(msg)
return None
def run(self):
io = FileIO()
will_update = self.update
self.logger.info("".join(["Start Analysis: ", self.code]))
if self.csvfile:
stock_tse = io.read_from_csv(self.code, self.csvfile)
self.logger.info("".join([
"Read data from csv: ", self.code, " Records: ",
str(len(stock_tse))
]))
if self.update and len(stock_tse) > 0:
index = pd.date_range(start=stock_tse.index[-1],
periods=2,
freq='B')
ts = pd.Series(None, index=index)
next_day = ts.index[1]
t = next_day.strftime('%Y-%m-%d')
newdata = io.read_data(self.code, start=t, end=self.end)
self.logger.info("".join([
"Read data from web: ", self.code, " New records: ",
str(len(newdata))
]))
if len(newdata) < 1:
will_update = False
else:
print(newdata.ix[-1, :])
stock_tse = stock_tse.combine_first(newdata)
io.save_data(stock_tse, self.code, 'stock_')
else:
stock_tse = io.read_data(self.code, start=self.start, end=self.end)
self.logger.info("".join([
"Read data from web: ", self.code, " Records: ",
str(len(stock_tse))
]))
if stock_tse.empty:
self.logger.warn("".join(["Data empty: ", self.code]))
return None
if not self.csvfile:
io.save_data(stock_tse, self.code, 'stock_')
try:
stock_d = stock_tse.asfreq('B').dropna()[self.minus_days:]
ti = TechnicalIndicators(stock_d)
ti.calc_sma()
ti.calc_sma(timeperiod=5)
ti.calc_sma(timeperiod=25)
ti.calc_sma(timeperiod=50)
ti.calc_sma(timeperiod=75)
ti.calc_sma(timeperiod=200)
ewma = ti.calc_ewma(span=5)
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=50)
ewma = ti.calc_ewma(span=75)
ewma = ti.calc_ewma(span=200)
bbands = ti.calc_bbands()
sar = ti.calc_sar()
draw = Draw(self.code, self.fullname)
ret = ti.calc_ret_index()
ti.calc_vol(ret['ret_index'])
rsi = ti.calc_rsi(timeperiod=9)
rsi = ti.calc_rsi(timeperiod=14)
mfi = ti.calc_mfi()
roc = ti.calc_roc(timeperiod=10)
roc = ti.calc_roc(timeperiod=25)
roc = ti.calc_roc(timeperiod=50)
roc = ti.calc_roc(timeperiod=75)
roc = ti.calc_roc(timeperiod=150)
ti.calc_cci()
ultosc = ti.calc_ultosc()
stoch = ti.calc_stoch()
ti.calc_stochf()
ti.calc_macd()
willr = ti.calc_willr()
ti.calc_momentum(timeperiod=10)
ti.calc_momentum(timeperiod=25)
tr = ti.calc_tr()
ti.calc_atr()
ti.calc_natr()
vr = ti.calc_volume_rate()
ret_index = ti.stock['ret_index']
clf = Classifier(self.clffile)
train_X, train_y = clf.train(ret_index, will_update)
self.logger.info("".join(
["Classifier Train Records: ",
str(len(train_y))]))
clf_result = clf.classify(ret_index)[0]
self.logger.info("".join(["Classified: ", str(clf_result)]))
ti.stock.ix[-1, 'classified'] = clf_result
reg = Regression(self.regfile, alpha=1, regression_type="Ridge")
train_X, train_y = reg.train(ret_index, will_update)
self.logger.info("".join(
["Regression Train Records: ",
str(len(train_y))]))
base = ti.stock_raw['Adj Close'][0]
reg_result = int(reg.predict(ret_index, base)[0])
self.logger.info("".join(["Predicted: ", str(reg_result)]))
ti.stock.ix[-1, 'predicted'] = reg_result
if will_update is True:
io.save_data(io.merge_df(stock_d, ti.stock), self.code, 'ti_')
if self.minus_days < -300:
_prefix = 'long'
elif self.minus_days >= -60:
_prefix = 'short'
else:
_prefix = 'chart'
draw.plot(stock_d,
_prefix,
ewma,
bbands,
sar,
rsi,
roc,
mfi,
ultosc,
willr,
stoch,
tr,
vr,
clf_result,
reg_result,
axis=self.axis,
complexity=self.complexity)
self.logger.info("".join(["Finish Analysis: ", self.code]))
return ti
except (ValueError, KeyError) as e:
self.logger.error("".join(
["Error occured in ", self.code, " at analysis.py"]))
self.logger.error("".join(['ErrorType: ', str(type(e))]))
self.logger.error("".join(['ErrorMessage: ', str(e)]))
return None
def run(self):
io = FileIO()
will_update = self.update
if self.csvfile:
stock_tse = io.read_from_csv(self.code, self.csvfile)
msg = "".join(["Read data from csv: ", self.code, " Records: ", str(len(stock_tse))])
print(msg)
if self.update and len(stock_tse) > 0:
index = pd.date_range(start=stock_tse.index[-1], periods=2, freq="B")
ts = pd.Series(None, index=index)
next_day = ts.index[1]
t = next_day.strftime("%Y-%m-%d")
newdata = io.read_data(self.code, start=t, end=self.end)
msg = "".join(["Read data from web: ", self.code, " New records: ", str(len(newdata))])
print(msg)
if len(newdata) < 1:
will_update = False
else:
print(newdata.ix[-1, :])
stock_tse = stock_tse.combine_first(newdata)
io.save_data(stock_tse, self.code, "stock_")
else:
stock_tse = io.read_data(self.code, start=self.start, end=self.end)
msg = "".join(["Read data from web: ", self.code, " Records: ", str(len(stock_tse))])
print(msg)
if stock_tse.empty:
msg = "".join(["Data empty: ", self.code])
print(msg)
return None
if not self.csvfile:
io.save_data(stock_tse, self.code, "stock_")
try:
stock_d = stock_tse.asfreq("B").dropna()[self.days :]
ti = TechnicalIndicators(stock_d)
ti.calc_sma()
ti.calc_sma(timeperiod=5)
ti.calc_sma(timeperiod=25)
ti.calc_sma(timeperiod=50)
ti.calc_sma(timeperiod=75)
ewma = ti.calc_ewma(span=5)
ewma = ti.calc_ewma(span=25)
ewma = ti.calc_ewma(span=50)
ewma = ti.calc_ewma(span=75)
bbands = ti.calc_bbands()
sar = ti.calc_sar()
draw = Draw(self.code, self.name)
ret = ti.calc_ret_index()
ti.calc_vol(ret["ret_index"])
rsi = ti.calc_rsi(timeperiod=9)
rsi = ti.calc_rsi(timeperiod=14)
mfi = ti.calc_mfi()
roc = ti.calc_roc(timeperiod=10)
roc = ti.calc_roc(timeperiod=25)
roc = ti.calc_roc(timeperiod=50)
roc = ti.calc_roc(timeperiod=75)
roc = ti.calc_roc(timeperiod=150)
ti.calc_cci()
ultosc = ti.calc_ultosc()
stoch = ti.calc_stoch()
ti.calc_stochf()
ti.calc_macd()
willr = ti.calc_willr()
ti.calc_momentum(timeperiod=10)
ti.calc_momentum(timeperiod=25)
tr = ti.calc_tr()
ti.calc_atr()
ti.calc_natr()
vr = ti.calc_volume_rate()
ret_index = ti.stock["ret_index"]
clf = Classifier(self.clffile)
train_X, train_y = clf.train(ret_index, will_update)
msg = "".join(["Train Records: ", str(len(train_y))])
print(msg)
clf_result = clf.classify(ret_index)[0]
msg = "".join(["Classified: ", str(clf_result)])
print(msg)
ti.stock.ix[-1, "classified"] = clf_result
reg = Regression(self.regfile, alpha=1, regression_type="Ridge")
train_X, train_y = reg.train(ret_index, will_update)
msg = "".join(["Train Records: ", str(len(train_y))])
base = ti.stock_raw["Adj Close"][0]
reg_result = int(reg.predict(ret_index, base)[0])
msg = "".join(["Predicted: ", str(reg_result)])
print(msg)
ti.stock.ix[-1, "predicted"] = reg_result
if len(self.reference) > 0:
ti.calc_rolling_corr(self.reference)
ref = ti.stock["rolling_corr"]
else:
ref = []
io.save_data(io.merge_df(stock_d, ti.stock), self.code, "ti_")
draw.plot(
stock_d,
ewma,
bbands,
sar,
rsi,
roc,
mfi,
ultosc,
willr,
stoch,
tr,
vr,
clf_result,
reg_result,
ref,
axis=self.axis,
complexity=self.complexity,
)
return ti
except (ValueError, KeyError):
msg = "".join(["Error occured in ", self.code])
print(msg)
return None
