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DeepStock/stock/analysis/StockStatus.py
dsyoon cb3ff550b5 init
2023-02-12 00:31:53 +09:00

617 lines
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Python
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import os.path
import pandas as pd
import sqlite3
import shutil
from math import nan
import plotly.graph_objects as go
from plotly import subplots
import plotly.io as po
from datetime import datetime
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from hts.HTS import HTS
from hts.BuySellChecker import BuySellChecker
from stock.analysis.AnalyzerSqlite import AnalyzerSqlite
class StockStatus (HTS):
tableName = None
dbFileName = None
RESOURCE_PATH = None
analyzerSqlite = None
def __init__(self, RESOURCE_PATH):
super().__init__(RESOURCE_PATH)
self.RESOURCE_PATH = RESOURCE_PATH
self.tableName = 'stock'
self.dbFileName = "stock.db"
self.analyzerSqlite = AnalyzerSqlite(os.path.join(self.RESOURCE_PATH, self.dbFileName))
self.buySellChecker = BuySellChecker()
return
def getDBData(self, cursor, stock_code, day, result):
if cursor is None:
return
cursor.execute('SELECT ymd, close, open, high, low, envelope_upper, envelope_lower, envelope_middle, rsi, rsis, macd, macds, stochastic_slow_k, stochastic_slow_d FROM ' + self.tableName + ' WHERE CODE=? and ymd=? order by ymd', (stock_code, day,))
db_result = cursor.fetchall()
for rows in db_result:
ymd = rows[0]
close = rows[1]
open = rows[2]
high = rows[3]
low = rows[4]
envelope_upper = rows[5]
envelope_lower = rows[6]
envelope_middle = rows[7]
rsi = 0 if rows[8] is None else rows[8]
rsis = 0 if rows[9] is None else rows[9]
macd = rows[10]
macds = rows[11]
stochastic_slow_k = 0 if rows[12] is None else rows[12]
stochastic_slow_d = 0 if rows[13] is None else rows[13]
result["ymd"].append(ymd)
result["open"].append(int(open))
result["close"].append(int(close))
result["high"].append(int(high))
result["low"].append(int(low))
result["envelope_upper"].append(int(envelope_upper))
result["envelope_lower"].append(int(envelope_lower))
result["envelope_middle"].append(int(envelope_middle))
result["rsi"].append(int(rsi))
result["rsis"].append(int(rsis))
result["macd"].append(int(macd))
result["macds"].append(int(macds))
result["slow_k"].append(int(stochastic_slow_k))
result["slow_d"].append(int(stochastic_slow_d))
return
def isValidYMD(self, cursor, stock_code, day):
if cursor is None:
return
cursor.execute('SELECT ymd, count(*) as cnt FROM ' + self.tableName + ' WHERE CODE=? and ymd=?', (stock_code, day,))
db_result = cursor.fetchone()
if db_result[1] > 0:
return True
return False
def fetchLastData(self, cursor, stock_code, limit=350):
stock = {"CODE": stock_code, "NAME": "", "PRICE": []}
if cursor is None:
return stock
sql = 'SELECT ymd, close, diff, open, high, low, volume FROM stock where CODE=? order by ymd desc '
sql += ' limit ' + str(limit)
cursor.execute(sql, (stock['CODE'],))
items = cursor.fetchall()
items_reverse = reversed(items)
for item in items_reverse:
stock['PRICE'].append(
{
"ymd": item[0],
"close": item[1],
"diff": item[2],
"open": item[3],
"high": item[4],
"low": item[5],
"volume": item[6],
"avg3": -1,
"avg4": -1,
"avg5": -1,
"avg6": -1,
"avg10": -1,
"avg12": -1,
"avg20": -1,
"avg36": -1,
"avg40": -1,
"avg48": -1,
"avg60": -1,
"avg120": -1,
"avg200": -1,
"avg240": -1,
"avg300": -1,
"disparity_avg5": -1,
"disparity_avg10": -1,
"disparity_avg20": -1,
"disparity_avg60": -1,
"disparity_avg120": -1,
"bolingerband_upper": -1,
"bolingerband_lower": -1,
"bolingerband_middle": -1,
"envelope_upper": -1,
"envelope_lower": -1,
"envelope_middle": -1,
"ichimokucloud_changeLine": -1,
"ichimokucloud_baseLine": -1,
"ichimokucloud_leadingSpan1": -1,
"ichimokucloud_leadingSpan2": -1,
"stochastic_fast_k": -1,
"stochastic_slow_k": -1,
"stochastic_slow_d": -1,
"rsi": -1,
"rsis": -1,
"macd": -1,
"macds": -1,
"macdo": -1,
}
)
return stock
def analyze (self, stock, days=120):
stock["PRICE"] = sorted(stock["PRICE"], key=lambda x: x['ymd'])
self.analyzerSqlite.get_moving_average(stock["PRICE"])
# 이동 평균을 이용한 이격도 계산
self.analyzerSqlite.get_disparity(stock["PRICE"])
self.analyzerSqlite.ichimokuCloud.analyze(stock)
self.analyzerSqlite.stochastic.analyze(stock)
self.analyzerSqlite.bolingerBand.analyze(stock)
self.analyzerSqlite.envelope.analyze(stock)
self.analyzerSqlite.rsi.analyze(stock)
self.analyzerSqlite.macd.analyze(stock)
result = {
"ymd": [],
"open": [],
"close": [],
"high": [],
"low": [],
"avg3": [],
"avg4": [],
"avg5": [],
"avg6": [],
"avg10": [],
"avg12": [],
"avg20": [],
"avg36": [],
"avg40": [],
"avg48": [],
"avg60": [],
"avg120": [],
"avg200": [],
"avg240": [],
"avg300": [],
"disparity_avg5": [],
"disparity_avg20": [],
"disparity_avg60": [],
"disparity_avg120": [],
"disparity": [],
"disparity_type": [],
"envelope_upper": [],
"envelope_lower": [],
"envelope_middle": [],
"rsi": [],
"rsis": [],
"macd": [],
"macds": [],
"slow_k": [],
"slow_d": [],
"buy": [],
"sell": [],
}
for item in stock['PRICE']:
result["ymd"].append(item['ymd'])
result["open"].append(item['open'])
result["close"].append(item['close'])
result["high"].append(item['high'])
result["low"].append(item['low'])
result["avg3"].append(item['avg3'])
result["avg4"].append(item['avg4'])
result["avg5"].append(item['avg5'])
result["avg6"].append(item['avg6'])
result["avg10"].append(item['avg10'])
result["avg12"].append(item['avg12'])
result["avg20"].append(item['avg20'])
result["avg36"].append(item['avg36'])
result["avg40"].append(item['avg40'])
result["avg48"].append(item['avg48'])
result["avg60"].append(item['avg60'])
result["avg120"].append(item['avg120'])
result["avg200"].append(item['avg200'])
result["avg240"].append(item['avg240'])
result["avg300"].append(item['avg300'])
result["disparity_avg5"].append(item['disparity_avg5'])
result["disparity_avg20"].append(item['disparity_avg20'])
result["disparity_avg60"].append(item['disparity_avg60'])
result["disparity_avg120"].append(item['disparity_avg120'])
result['disparity'].append(max(item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']) - min(item['disparity_avg5'], item['disparity_avg20'], item['disparity_avg60']))
if item['disparity_avg60'] < item['disparity_avg20'] < item['disparity_avg5']:
result['disparity_type'].append(1)
elif item['disparity_avg5'] < item['disparity_avg20'] < item['disparity_avg60']:
result['disparity_type'].append(-1)
else:
result['disparity_type'].append(0)
result["envelope_upper"].append(item['envelope_upper'])
result["envelope_lower"].append(item['envelope_lower'])
result["envelope_middle"].append(item['envelope_middle'])
result["rsi"].append(item['rsi'])
result["rsis"].append(item['rsis'])
result["macd"].append(item['macd'])
result["macds"].append(item['macds'])
result["slow_k"].append(item['stochastic_slow_k'])
result["slow_d"].append(item['stochastic_slow_d'])
result["buy"].append(-1)
result["sell"].append(-1)
data = pd.DataFrame(result)
df_final_time = pd.DatetimeIndex(result['ymd'])
data.index = df_final_time
data = data.astype(
{
'open': 'int',
'high': 'int',
'low': 'int',
'close': 'int',
'avg3': 'float',
'avg4': 'float',
'avg5': 'float',
'avg6': 'float',
'avg10': 'float',
'avg12': 'float',
'avg20': 'float',
'avg36': 'float',
'avg40': 'float',
'avg48': 'float',
'avg60': 'float',
'avg120': 'float',
'avg200': 'float',
'avg240': 'float',
'avg300': 'float',
'disparity_avg5': 'float',
'disparity_avg20': 'float',
'disparity_avg60': 'float',
'disparity_avg120': 'float',
'buy': 'int',
'sell': 'int',
'slow_k': 'float',
'slow_d': 'float',
'macd': 'float',
'macds': 'float',
'envelope_upper': 'float',
'envelope_lower': 'float',
'envelope_middle': 'float',
'rsi': 'float',
'rsis': 'float'
}
)
scaler = StandardScaler()
low_df = pd.DataFrame(data['low'])
low_df.index = [c for c in range(len(low_df))]
low_std = scaler.fit_transform(data['low'].values.reshape(-1, 1))
low_std = pd.DataFrame(low_std, columns=['low_std'])
min_df = pd.DataFrame({'open': data['open'].to_list(), 'close': data['close'].to_list()})
min_df['min_std'] = min_df.min(axis=1)
min_df.index = [c for c in range(len(min_df))]
min_std = scaler.fit_transform(min_df['min_std'].values.reshape(-1, 1))
min_std = pd.DataFrame(min_std, columns=['min_std'])
line_fitter = LinearRegression()
size = len(data["close"])
gradients_low = []
gradients_avg5 = []
gradients_avg20 = []
gradients_avg60 = []
for i in range(size):
coef_low = -999
coef_avg5 = -999
coef_avg20 = -999
coef_avg60 = -999
if i > 0:
l = days if i >= days else i
x = pd.DataFrame([c for c in range(i - l, i + 1)])
y = pd.DataFrame(low_std.values.tolist()[i - l:i + 1])
line_fitter.fit(x.values.reshape(-1, 1), y)
coef_low = line_fitter.coef_[0][0]
l = 5 if i >= 5 else i
x = pd.DataFrame([c for c in range(i - l, i + 1)])
y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
line_fitter.fit(x.values.reshape(-1, 1), y)
coef_avg5 = line_fitter.coef_[0][0]
l = 20 if i >= 20 else i
x = pd.DataFrame([c for c in range(i - l, i + 1)])
y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
line_fitter.fit(x.values.reshape(-1, 1), y)
coef_avg20 = line_fitter.coef_[0][0]
l = 60 if i >= 60 else i
x = pd.DataFrame([c for c in range(i - l, i + 1)])
y = pd.DataFrame(min_std.values.tolist()[i - l:i + 1])
line_fitter.fit(x.values.reshape(-1, 1), y)
coef_avg60 = line_fitter.coef_[0][0]
gradients_low.append(coef_low)
gradients_avg5.append(coef_avg5)
gradients_avg20.append(coef_avg20)
gradients_avg60.append(coef_avg60)
gradients_low_df = pd.DataFrame(gradients_low, columns=['gradients_low'])
gradients_avg5_df = pd.DataFrame(gradients_avg5, columns=['gradients_avg5'])
gradients_avg20_df = pd.DataFrame(gradients_avg20, columns=['gradients_avg20'])
gradients_avg60_df = pd.DataFrame(gradients_avg60, columns=['gradients_avg60'])
gradients_low_df.index = df_final_time
gradients_avg5_df.index = df_final_time
gradients_avg20_df.index = df_final_time
gradients_avg60_df.index = df_final_time
data = data.merge(gradients_low_df, left_index=True, right_index=True)
data = data.merge(gradients_avg5_df, left_index=True, right_index=True)
data = data.merge(gradients_avg20_df, left_index=True, right_index=True)
data = data.merge(gradients_avg60_df, left_index=True, right_index=True)
return data
def draw(self, stock_code, given_day, data, bsLine):
if bsLine is None:
return
# 어제 데이터는 지운다.
buy_line = bsLine['buy']
buy_weight_line = bsLine['buy_weight']
sell_line = bsLine['sell']
buy_size = []
buy_colors = []
for i in range(len(buy_line)):
if buy_line[i] < 0:
buy_colors.append("#ffffff")
buy_line[i] = nan
buy_size.append(0)
else:
buy_colors.append("#B2028C")
buy_size.append(10 + (0.1 * buy_weight_line[i]))
sell_colors = []
for i in range(len(sell_line)):
if sell_line[i] < 0:
sell_colors.append("#ffffff")
sell_line[i] = nan
else:
sell_colors.append("#00ced1")
# 그래프를 설정한다.
buy_check = go.Scatter(x=data['ymd'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000')
envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786')
envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000')
avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507')
avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43')
avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543')
candle_stick = go.Candlestick(x=data['ymd'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue', showlegend=False)
macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
candle_data = [candle_stick, avg5, avg20, avg60, envelope_upper, envelope_middle, envelope_lower, buy_check, sell_check]
disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60]
macd_data = [macd_line, macd_s_line]
stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line]
# 그래프를 그린다.
"""
fig = go.Figure(data=candle_data)
fig.update_layout(title=stock_code + "_" + given_day)
fig.show()
"""
fig = subplots.make_subplots(
rows=5, cols=1,
subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
row_heights=[200, 200, 200, 200, 750]
)
for trace in macd_data:
fig.append_trace(trace, 1, 1)
for trace in rsi_data:
fig.append_trace(trace, 2, 1)
for trace in stochastic_data:
fig.append_trace(trace, 3, 1)
for trace in disparity_data:
fig.append_trace(trace, 4, 1)
for trace in candle_data:
fig.append_trace(trace, 5, 1)
#fig.update_xaxes(nticks=5)
#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
df = pd.DataFrame(bsLine)
df = df.fillna(-1)
buy_count = len(df.loc[df["buy"] > 0])
sell_count = len(df.loc[df["sell"] > 0])
fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
#fig.update_layout(title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
fig.show()
return
def writeFile(self, dailyDirName, stock_code, stock_name, given_day, data, bsLine):
if bsLine is None:
return
# 어제 데이터는 지운다.
buy_line = bsLine['buy']
buy_weight_line = bsLine['buy_weight']
sell_line = bsLine['sell']
buy_size = []
buy_colors = []
for i in range(len(buy_line)):
if buy_line[i] < 0:
buy_colors.append("#ffffff")
buy_line[i] = nan
buy_size.append(0)
else:
buy_colors.append("#B2028C")
buy_size.append(10 + (0.1 * buy_weight_line[i]))
sell_colors = []
for i in range(len(sell_line)):
if sell_line[i] < 0:
sell_colors.append("#ffffff")
sell_line[i] = nan
else:
sell_colors.append("#00ced1")
# 그래프를 설정한다.
buy_check = go.Scatter(x=data['ymd'], y=buy_line, mode='markers', name="buy", marker=dict(size=buy_size, color=buy_colors, line_width=0))
sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000')
envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786')
envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000')
avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507')
avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43')
avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543')
candle_stick = go.Candlestick(x=data['ymd'], open=data['open'], high=data['high'], low=data['low'], close=data['close'], increasing_line_color='red', decreasing_line_color='blue', showlegend=False)
macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
candle_data = [candle_stick, avg5, avg20, avg60, envelope_upper, envelope_middle, envelope_lower, buy_check, sell_check]
disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60]
macd_data = [macd_line, macd_s_line]
stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line]
# 그래프를 그린다.
"""
fig = go.Figure(data=candle_data)
fig.update_layout(title=stock_code + "_" + given_day)
fig.show()
"""
fig = subplots.make_subplots(
rows=5, cols=1,
subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
row_heights=[200, 200, 200, 200, 750]
)
for trace in macd_data:
fig.append_trace(trace, 1, 1)
for trace in rsi_data:
fig.append_trace(trace, 2, 1)
for trace in stochastic_data:
fig.append_trace(trace, 3, 1)
for trace in disparity_data:
fig.append_trace(trace, 4, 1)
for trace in candle_data:
fig.append_trace(trace, 5, 1)
df = pd.DataFrame(bsLine)
df = df.fillna(-1)
buy_count = len(df.loc[df["buy"] > 0])
sell_count = len(df.loc[df["sell"] > 0])
fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
title = "%s (%s) 차트 (<a href=\"https://alphasquare.co.kr/home/stock/financial-information?code=%s\">URL1</a>, <a href=\"https://www.tradingview.com/chart/jJ8zOXz0/?symbol=KRX:%s\">URL2</a>)" % (stock_name, stock_code, stock_code, stock_code)
fig['layout'].update(title=title)
fileName = "%s/%s_%s.html" % (dailyDirName, stock_code, stock_name.replace(" ", ""))
po.write_html(fig, file=fileName, auto_open=False)
return
def findCandidates(self, outPath):
dir_name = os.path.join(outPath, "99_daily_auto_trading")
if os.path.isdir(dir_name):
shutil.rmtree(dir_name)
os.mkdir(dir_name)
today = datetime.today().strftime('%Y%m%d')
stockTableName = 'stock'
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
cursor = conn.cursor()
cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code')
items = cursor.fetchall()
analyzed_day = 120
for idx, item in enumerate(items):
stock_code = item[0]
stock_name = item[1]
if stock_name.find('스팩') >= 0:
continue
print(idx, stock_code, stock_name, ", CODE: ", stock_code, ", NAME: ", stock_name)
stock = self.fetchLastData(cursor, stock_code, limit=350)
data = self.analyze(stock, analyzed_day)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
data.drop(data.index[:len(data) - analyzed_day], inplace=True)
# print logs
# for i in range(len(data.index)):
# print (i, data.index[i], data['macd'][i], data['slow_k'][i], data['gradients_low'][i], data['gradients_avg5'][i], data['gradients_avg20'][i], data['gradients_avg60'][i], data['disparity_avg5'][i], data['disparity_avg20'][i], data['disparity_avg60'][i], data['disparity'][i], data['disparity_type'][i])
bsLine, data = self.buySellChecker.checkTransactionWithEnvelope(data, stock_code, 120, isRealTime=False)
# 그래프를 그린다.
if len(data.index) > 10 and bsLine['buy'][len(bsLine['buy'])-1] > 0:
self.writeFile(dir_name, stock_code, stock_name, today, data, bsLine)
cursor.close()
conn.close()
return
if __name__ == "__main__":
PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
outPath = os.path.join(PROJECT_HOME, "resources", "analysis")
if not os.path.isdir(outPath):
os.mkdir(outPath)
day = datetime.today().strftime("%Y%m%d")
outPath = os.path.join(outPath, day)
if not os.path.isdir(outPath):
os.mkdir(outPath)
stockStatus = StockStatus(RESOURCE_PATH)
stockStatus.findCandidates(outPath)
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