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dsyoon
2023-01-15 12:40:10 +09:00
parent 9f9c7e290e
commit 5821f7bfa5
7 changed files with 923 additions and 307 deletions
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16
HTS_252670_daily.py
View File

@@ -62,24 +62,14 @@ class HTS_252670_DAILY (HTS):
print ("START...") print ("START...")
THIS_TIME = datetime.now() THIS_TIME = datetime.now()
final_sell_check = False final_sell_check = False
LAST_DATA = self.dailyStatus.getLastData_realtime(self.stock_code, today) LAST_DATA = self.dailyStatus.getLastData(stock_code, 200)
while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 153100", '%Y%m%d %H%M%S'): while datetime.strptime(today + " 070000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 153100", '%Y%m%d %H%M%S'):
# 1515 까지만 매수를 시도한다.
if datetime.strptime(today + " 090000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'): if datetime.strptime(today + " 090000", '%Y%m%d %H%M%S') < THIS_TIME < datetime.strptime(today + " 151500", '%Y%m%d %H%M%S'):
# 3시 까지만 매수를 시도한다. # 매분 3초마다 실행한다.
if THIS_TIME < datetime.strptime(today + " 145000", '%Y%m%d %H%M%S'):
if THIS_TIME.strftime('%S') in ("06", "16", "26", "36", "46", "56"):
# 데이터를 가지고 온다.
result = self.getClosePrice_realtime(self.stock_code, today)
final_price = result["close"][len(result["close"])-1]
# 10초마다 체크하여 체결된 내역이 있으면 50원 높게 매도를 주문한다.
self.getSellingPrice(THIS_TIME, self.stock_code, final_price, check=True)
if THIS_TIME.strftime('%S') == "03": if THIS_TIME.strftime('%S') == "03":
# 매분 3초마다 실행한다.
# 데이터를 가지고 온다. # 데이터를 가지고 온다.
result = self.getRealTime(self.stock_code, today, LAST_DATA) result = self.getRealTime(self.stock_code, today, LAST_DATA)

298
Simulation_Daily.py
View File

@@ -1,8 +1,11 @@
import numpy as np import numpy as np
from math import nan from math import nan
import shutil
import sqlite3
import pandas as pd import pandas as pd
import plotly.graph_objects as go import plotly.graph_objects as go
from plotly import subplots from plotly import subplots
import plotly.io as po
import os import os
from datetime import datetime from datetime import datetime
@@ -34,25 +37,6 @@ class Simulation (HTS):
buy_weight_line = bsLine['buy_weight'] buy_weight_line = bsLine['buy_weight']
sell_line = bsLine['sell'] sell_line = bsLine['sell']
# 그래프 설정을 위한 변수를 생성한다.
data = data.astype(
{
'open': 'int',
'high': 'int',
'low': 'int',
'close': 'int',
'slow_k': 'float',
'slow_d': 'float',
'macd': 'float',
'macds': 'float',
'envelope_upper': 'float',
'envelope_lower': 'float',
'envelope_middle': 'float',
'rsi': 'float',
'rsis': 'float'
}
)
buy_size = [] buy_size = []
buy_colors = [] buy_colors = []
for i in range(len(buy_line)): for i in range(len(buy_line)):
@@ -62,7 +46,7 @@ class Simulation (HTS):
buy_size.append(0) buy_size.append(0)
else: else:
buy_colors.append("#B2028C") buy_colors.append("#B2028C")
buy_size.append(10 + (5 * buy_weight_line[i])) buy_size.append(10 + (0.1 * buy_weight_line[i]))
sell_colors = [] sell_colors = []
for i in range(len(sell_line)): for i in range(len(sell_line)):
@@ -79,6 +63,9 @@ class Simulation (HTS):
envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786') 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') 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) 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_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
@@ -91,7 +78,12 @@ class Simulation (HTS):
rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi') 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') rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
candle_data = [candle_stick, envelope_upper, envelope_middle, envelope_lower, buy_check, sell_check] 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] macd_data = [macd_line, macd_s_line]
stochastic_data = [slow_k_line, slow_d_line] stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line] rsi_data = [rsi_line, rsis_line]
@@ -104,11 +96,11 @@ class Simulation (HTS):
""" """
fig = subplots.make_subplots( fig = subplots.make_subplots(
rows=4, cols=1, rows=5, cols=1,
subplot_titles=("MACD", "RSI", "스토캐스틱", '캔들'), subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
#specs=[[{}], [{}], [{}], [{}], [{}], [{}]], #specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01, shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
row_heights=[200, 200, 200, 750] row_heights=[200, 200, 200, 200, 750]
) )
for trace in macd_data: for trace in macd_data:
fig.append_trace(trace, 1, 1) fig.append_trace(trace, 1, 1)
@@ -116,8 +108,10 @@ class Simulation (HTS):
fig.append_trace(trace, 2, 1) fig.append_trace(trace, 2, 1)
for trace in stochastic_data: for trace in stochastic_data:
fig.append_trace(trace, 3, 1) fig.append_trace(trace, 3, 1)
for trace in candle_data: for trace in disparity_data:
fig.append_trace(trace, 4, 1) fig.append_trace(trace, 4, 1)
for trace in candle_data:
fig.append_trace(trace, 5, 1)
#fig.update_xaxes(nticks=5) #fig.update_xaxes(nticks=5)
@@ -134,16 +128,241 @@ class Simulation (HTS):
return return
def writeFile(self, dailyDirName, stock_code, stock_name, given_day, data, bsLine):
if bsLine is None:
return
def simulate(self, stock_code, n=100): # 어제 데이터는 지운다.
today = datetime.today().strftime('%Y%m%d') buy_line = bsLine['buy']
data = self.dailyStatus.getLastData(stock_code, today, n) buy_weight_line = bsLine['buy_weight']
sell_line = bsLine['sell']
# 사야 할 시점과 팔아야 할 시점을 체크한다. buy_size = []
bsLine, data = self.buySellChecker.checkEnvelopeTiming(data, stock_code, isRealTime=False) 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. # 그래프를 그린다.
self.draw(stock_code, today, data, bsLine) """
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()
fileName = "%s/%s_%s.html" % (dailyDirName, stock_code, stock_name.replace(" ", ""))
po.write_html(fig, file=fileName, auto_open=False)
return
def getData(self, today, stock):
close = stock['PRICE'][len(stock['PRICE']) - 1]["close"]
open = stock['PRICE'][len(stock['PRICE']) - 1]["open"]
high = stock['PRICE'][len(stock['PRICE']) - 1]["high"]
low = stock['PRICE'][len(stock['PRICE']) - 1]["low"]
volume = stock['PRICE'][len(stock['PRICE']) - 1]["volume"]
stock['PRICE'].append(
{
"ymd": today,
"close": close,
"diff": stock['PRICE'][len(stock['PRICE']) - 1]["close"] - close,
"open": open,
"high": high,
"low": low,
"volume": volume,
"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
def simulate(self, stock_code=None, isRealTime=False):
if not isRealTime:
n = 200
else:
n = 200
if stock_code is not None:
stock = self.dailyStatus.getLastData(stock_code, n)
today = datetime.today().strftime('%Y%m%d')
self.getData(today, stock)
analyzed_day = 60
data = self.dailyStatus.analyze(stock, analyzed_day)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
data.drop(data.index[: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, isRealTime=False)
# 그래프를 그린다.
self.draw(stock_code, today, data, bsLine)
else:
stockTableName = 'stock'
PROJECT_HOME = os.path.join(os.path.dirname(__file__))
stockFileName = PROJECT_HOME + '/resources/stock.db'
conn = sqlite3.connect(stockFileName)
cursor = conn.cursor()
cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code')
items = cursor.fetchall()
cursor.close()
conn.close()
today = datetime.today().strftime('%Y%m%d')
dailyDirName = os.path.join(RESOURCE_PATH, 'analysis', today, 'daily')
if os.path.exists(dailyDirName):
shutil.rmtree(dailyDirName)
dailyDirName = os.path.join(RESOURCE_PATH, 'analysis', today)
if not os.path.isdir(dailyDirName):
os.mkdir(dailyDirName)
dailyDirName = os.path.join(dailyDirName, 'daily')
if not os.path.isdir(dailyDirName):
os.mkdir(dailyDirName)
for idx, item in enumerate(items):
stock_code = item[0]
stock_name = item[1]
print(idx, stock_code, stock_name)
print("Analysis # :", idx, ", CODE: ", stock_code, ", NAME: ", stock_name)
stock = self.dailyStatus.getLastData(stock_code, n)
today = datetime.today().strftime('%Y%m%d')
self.getData(today, stock)
analyzed_day = 60
data = self.dailyStatus.analyze(stock, analyzed_day)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
data.drop(data.index[: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, isRealTime=False)
# 그래프를 그린다.
if len(data.index) > 10 and max(bsLine['buy'][len(bsLine['buy'])-2:]) > 0:
self.writeFile(dailyDirName, stock_code, stock_name, today, data, bsLine)
return return
@@ -152,13 +371,18 @@ if __name__ == "__main__":
PROJECT_HOME = os.getcwd() PROJECT_HOME = os.getcwd()
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources") RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
# to check bying simulation = Simulation(RESOURCE_PATH)
stock_codes = ["252670", "122630"]
#stock_codes = ["252670"]
#stock_codes = ["122630"]
# to check buying
#stock_codes = ["252670", "122630"]
#stock_codes = ["051600", "139130", "066570", "000990"]
stock_codes = ["102950"]
"""
for stock_code in stock_codes: for stock_code in stock_codes:
simulation = Simulation(RESOURCE_PATH) simulation.simulate(stock_code)
simulation.simulate(stock_code, 2000) """
simulation.simulate()
print ("done...") print ("done...")

4
StockCrawler.py
View File

@@ -54,13 +54,13 @@ if week in (0, 1, 2, 3, 4): # 0:월, 1:화, 2:수, 3:목, 4:금, 5:토, 6:일
print("\n[종목 분석]") print("\n[종목 분석]")
# S: 분석까지 진행 # S: 분석까지 진행
inFileName = PROJECT_HOME + '/resources/stock.db' inFileName = PROJECT_HOME + '/resources/stock.db'
analyzerSqlite = AnalyzerSqlite(PROJECT_HOME, stockFileName) analyzerSqlite = AnalyzerSqlite(stockFileName)
analyzerSqlite.analyzeDaily() analyzerSqlite.analyzeDaily()
analyzerSqlite.analyzeGrouping("weekly") analyzerSqlite.analyzeGrouping("weekly")
analyzerSqlite.analyzeGrouping("monthly") analyzerSqlite.analyzeGrouping("monthly")
analyzerSqlite = AnalyzerSqlite(PROJECT_HOME, stockFileName) analyzerSqlite = AnalyzerSqlite(stockFileName)
print("\n[종목 결정]") print("\n[종목 결정]")
# HTML 출력 # HTML 출력
outPath = os.path.join(PROJECT_HOME, "resources", "analysis") outPath = os.path.join(PROJECT_HOME, "resources", "analysis")

584
hts/BuySellChecker.py
View File

@@ -1,4 +1,5 @@
import pandas as pd import pandas as pd
from stock.analysis.Common import Common from stock.analysis.Common import Common
from stock.analysis.Stochastic import Stochastic from stock.analysis.Stochastic import Stochastic
from stock.analysis.RSI import RSI from stock.analysis.RSI import RSI
@@ -6,7 +7,6 @@ from stock.analysis.MACD import MACD
from stock.analysis.IchimokuCloud import IchimokuCloud from stock.analysis.IchimokuCloud import IchimokuCloud
class BuySellChecker: class BuySellChecker:
common = None common = None
stochastic = None stochastic = None
rsi = None rsi = None
@@ -23,8 +23,8 @@ class BuySellChecker:
self.ichimokuCloud = IchimokuCloud() self.ichimokuCloud = IchimokuCloud()
self.BUY_COUNT = 0 self.BUY_COUNT = 0
return
return
def isYangbong(self, data, i): def isYangbong(self, data, i):
if data['close'][i] > data['open'][i]: if data['close'][i] > data['open'][i]:
@@ -67,23 +67,28 @@ class BuySellChecker:
check_under_20_for_10 = False check_under_20_for_10 = False
for c in range(i - within, i - during): for c in range(i - within, i - during):
value = [1 if max(data['avg3'][d], data['avg6'][d], data['avg9'][d], data['avg12'][d]) < data['avg20'][d] else 0 for d in range(c, c + during)] value = [
1 if max(data['avg3'][d], data['avg6'][d], data['avg9'][d], data['avg12'][d]) < data['avg20'][d] else 0
for d in range(c, c + during)]
if len(value) == during and sum(value) == during: if len(value) == during and sum(value) == during:
check_under_20_for_10 = True check_under_20_for_10 = True
break break
return check_under_20_for_10 return check_under_20_for_10
def max_min_avg(self, data, i): def max_min_avg(self, data, i):
return max(data['avg3'][i], data['avg6'][i], data['avg9'][i], data['avg12'][i], data['avg20'][i]) - min(data['avg3'][i], data['avg6'][i], data['avg9'][i], data['avg12'][i], data['avg20'][i]) return max(data['avg3'][i], data['avg6'][i], data['avg9'][i], data['avg12'][i], data['avg20'][i]) - min(
data['avg3'][i], data['avg6'][i], data['avg9'][i], data['avg12'][i], data['avg20'][i])
def check_inverse_arrangement_before(self, data, i, within=30, during=5): def check_inverse_arrangement_before(self, data, i, within=30, during=5):
if i - within < 381: if i - within < 381:
return False return False
inverse_arrangement = False inverse_arrangement = False
for c in range(i-within, i-during): for c in range(i - within, i - during):
value = [1 if data['avg3'][d] < data['avg6'][d] < data['avg9'][d] < data['avg12'][d] < data['avg20'][d] else 0 for d in range(c, c + during)] value = [
if len(value) == during and sum(value) == during-1: 1 if data['avg3'][d] < data['avg6'][d] < data['avg9'][d] < data['avg12'][d] < data['avg20'][d] else 0
for d in range(c, c + during)]
if len(value) == during and sum(value) == during - 1:
inverse_arrangement = True inverse_arrangement = True
break break
return inverse_arrangement return inverse_arrangement
@@ -91,15 +96,15 @@ class BuySellChecker:
def checkUpDirection(self, data, i): def checkUpDirection(self, data, i):
# 0: 무추세, -1: 하락 추세, 1: 상승 추세 # 0: 무추세, -1: 하락 추세, 1: 상승 추세
close = data['close'][i] close = data['close'][i]
#up_count = sum([1 if data['high'][c] < close else 0 for c in range(i-20, i)]) # up_count = sum([1 if data['high'][c] < close else 0 for c in range(i-20, i)])
#down_count = sum([1 if close < data['low'][c] else 0 for c in range(i - 20, i)]) # down_count = sum([1 if close < data['low'][c] else 0 for c in range(i - 20, i)])
lagging_change = sum([1 if data['laggingSpan'][c] < data['changeLine'][c] else 0 for c in range(i-20, i)]) lagging_change = sum([1 if data['laggingSpan'][c] < data['changeLine'][c] else 0 for c in range(i - 20, i)])
change_lagging = sum([1 if data['laggingSpan'][c] > data['changeLine'][c] else 0 for c in range(i-20, i)]) change_lagging = sum([1 if data['laggingSpan'][c] > data['changeLine'][c] else 0 for c in range(i - 20, i)])
if lagging_change>10: if lagging_change > 10:
return 1 return 1
if change_lagging==20: if change_lagging == 20:
return -1 return -1
return 0 return 0
@@ -159,8 +164,10 @@ class BuySellChecker:
if i > START_TIME_INDEX: if i > START_TIME_INDEX:
# 매도 분석 # 매도 분석
if data['changeLine'][i - 1] >= data['laggingSpan'][i - 1] and data['laggingSpan'][i] < data['changeLine'][i]: if data['changeLine'][i - 1] >= data['laggingSpan'][i - 1] and data['laggingSpan'][i] < data['changeLine'][
changeLine_count = sum([1 if data['changeLine'][c] <= data['laggingSpan'][c] else 0 for c in range(i - 20, i)]) i]:
changeLine_count = sum(
[1 if data['changeLine'][c] <= data['laggingSpan'][c] else 0 for c in range(i - 20, i)])
if changeLine_count >= 17: if changeLine_count >= 17:
sell = min(data["open"][i], data["close"][i]) sell = min(data["open"][i], data["close"][i])
weight = 1 weight = 1
@@ -225,8 +232,10 @@ class BuySellChecker:
if i > START_TIME_INDEX: if i > START_TIME_INDEX:
# 매도 분석 # 매도 분석
if data['changeLine'][i - 1] >= data['laggingSpan'][i - 1] and data['laggingSpan'][i] < data['changeLine'][i]: if data['changeLine'][i - 1] >= data['laggingSpan'][i - 1] and data['laggingSpan'][i] < data['changeLine'][
changeLine_count = sum([1 if data['changeLine'][c] <= data['laggingSpan'][c] else 0 for c in range(i - 20, i)]) i]:
changeLine_count = sum(
[1 if data['changeLine'][c] <= data['laggingSpan'][c] else 0 for c in range(i - 20, i)])
if changeLine_count >= 17: if changeLine_count >= 17:
sell = min(data["open"][i], data["close"][i]) sell = min(data["open"][i], data["close"][i])
weight = 1 weight = 1
@@ -273,8 +282,8 @@ class BuySellChecker:
df = pd.DataFrame(close) df = pd.DataFrame(close)
max20 = df.rolling(window=20).mean() max20 = df.rolling(window=20).mean()
stddev20 = df.rolling(window=20).std() stddev20 = df.rolling(window=20).std()
upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드 upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드 lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
upper, lower = [], [] upper, lower = [], []
for i in range(len(upper_df)): for i in range(len(upper_df)):
@@ -321,13 +330,15 @@ class BuySellChecker:
temp = { temp = {
"date": point_temp, "date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "open": open, "high": high, "low": low, "close": close, "volume": vol,
"avg3": avg3,"avg6": avg6,"avg9": avg9,"avg12": avg12, "avg20": avg20, "avg3": avg3, "avg6": avg6, "avg9": avg9, "avg12": avg12, "avg20": avg20,
"disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20, "disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120, "disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20,
"disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120,
"upper": upper, "lower": lower, "upper": upper, "lower": lower,
"macd": macd, "macds": macds, "macdo": macdo, "macd": macd, "macds": macds, "macdo": macdo,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
"rsi": rsi, "rsis": rsis, "rsi": rsi, "rsis": rsis,
"changeLine": changeLine, "baseLine": baseLine, "laggingSpan": laggingSpan, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2, "changeLine": changeLine, "baseLine": baseLine, "laggingSpan": laggingSpan, "leadingSpan1": leadingSpan1,
"leadingSpan2": leadingSpan2,
"label": label "label": label
} }
@@ -368,9 +379,15 @@ class BuySellChecker:
avg54_list = close_df.rolling(window=54).mean().fillna(close[0]).values.tolist() avg54_list = close_df.rolling(window=54).mean().fillna(close[0]).values.tolist()
avg54 = [item[0] for item in avg54_list] avg54 = [item[0] for item in avg54_list]
abs_avg_1 = [max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i], avg54[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i], avg54[i]) for i in range(0, len(close))] abs_avg_1 = [
abs_avg_2 = [max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) for i in range(0, len(close))] max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i], avg54[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i],
abs_avg_3 = [max(avg3[i], avg6[i], avg9[i], avg12[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i]) for i in range(0, len(close))] avg27[i], avg54[i]) for i in
range(0, len(close))]
abs_avg_2 = [
max(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i], avg27[i]) for
i in range(0, len(close))]
abs_avg_3 = [max(avg3[i], avg6[i], avg9[i], avg12[i]) - min(avg3[i], avg6[i], avg9[i], avg12[i]) for i in
range(0, len(close))]
abs_avg_4 = [max(avg3[i], avg6[i], avg9[i]) - min(avg3[i], avg6[i], avg9[i]) for i in range(0, len(close))] abs_avg_4 = [max(avg3[i], avg6[i], avg9[i]) - min(avg3[i], avg6[i], avg9[i]) for i in range(0, len(close))]
abs_avg_5 = [max(avg3[i], avg6[i]) - min(avg3[i], avg6[i]) for i in range(0, len(close))] abs_avg_5 = [max(avg3[i], avg6[i]) - min(avg3[i], avg6[i]) for i in range(0, len(close))]
@@ -391,7 +408,7 @@ class BuySellChecker:
diff_avg3, diff_avg6, diff_avg9, diff_avg12, diff_avg27, diff_avg54 = [], [], [], [], [], [] diff_avg3, diff_avg6, diff_avg9, diff_avg12, diff_avg27, diff_avg54 = [], [], [], [], [], []
diff_avg3.append(0) diff_avg3.append(0)
for i in range(1, len(avg3)): for i in range(1, len(avg3)):
diff_avg3.append(avg3[i]-avg3[i-1]) diff_avg3.append(avg3[i] - avg3[i - 1])
diff_avg6.append(0) diff_avg6.append(0)
for i in range(1, len(avg6)): for i in range(1, len(avg6)):
diff_avg6.append(avg6[i] - avg6[i - 1]) diff_avg6.append(avg6[i] - avg6[i - 1])
@@ -408,7 +425,6 @@ class BuySellChecker:
for i in range(1, len(avg54)): for i in range(1, len(avg54)):
diff_avg54.append(avg54[i] - avg54[i - 1]) diff_avg54.append(avg54[i] - avg54[i - 1])
diff_avg3_avg6 = [avg3[i] - avg6[i] for i in range(0, len(close))] diff_avg3_avg6 = [avg3[i] - avg6[i] for i in range(0, len(close))]
diff_avg3_avg9 = [avg3[i] - avg9[i] for i in range(0, len(close))] diff_avg3_avg9 = [avg3[i] - avg9[i] for i in range(0, len(close))]
diff_avg3_avg12 = [avg3[i] - avg12[i] for i in range(0, len(close))] diff_avg3_avg12 = [avg3[i] - avg12[i] for i in range(0, len(close))]
@@ -429,8 +445,8 @@ class BuySellChecker:
df = pd.DataFrame(close) df = pd.DataFrame(close)
max20 = df.rolling(window=20).mean() max20 = df.rolling(window=20).mean()
stddev20 = df.rolling(window=20).std() stddev20 = df.rolling(window=20).std()
upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드 upper_df = max20 + (stddev20 * 2) # 상단 볼린저 밴드
lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드 lower_df = max20 - (stddev20 * 2) # 하단 볼린저 밴드
upper, lower = [], [] upper, lower = [], []
for i in range(len(upper_df)): for i in range(len(upper_df)):
@@ -445,7 +461,8 @@ class BuySellChecker:
STOCK = [] STOCK = []
for i in range(len(open)): for i in range(len(open)):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i], STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg3': avg3[i], 'avg6': avg6[i],'avg9': avg9[i],'avg12': avg12[i],'avg27': avg27[i],'avg54': avg54[i]}) 'avg3': avg3[i], 'avg6': avg6[i], 'avg9': avg9[i], 'avg12': avg12[i], 'avg27': avg27[i],
'avg54': avg54[i]})
# stochastic # stochastic
stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5) stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
@@ -484,7 +501,6 @@ class BuySellChecker:
diff_low_lower = [low[i] - lower[i] for i in range(0, len(low))] diff_low_lower = [low[i] - lower[i] for i in range(0, len(low))]
diff_low_upper = [low[i] - upper[i] for i in range(0, len(low))] diff_low_upper = [low[i] - upper[i] for i in range(0, len(low))]
##### 일목균형표 ##### 일목균형표
diff_lead1_lead2 = [leadingSpan1[i] - leadingSpan2[i] for i in range(0, len(leadingSpan1))] diff_lead1_lead2 = [leadingSpan1[i] - leadingSpan2[i] for i in range(0, len(leadingSpan1))]
diff_change_base = [changeLine[i] - baseLine[i] for i in range(0, len(baseLine))] diff_change_base = [changeLine[i] - baseLine[i] for i in range(0, len(baseLine))]
@@ -512,7 +528,6 @@ class BuySellChecker:
diff_slowk_slowd = [slow_k[i] - slow_d[i] for i in range(0, len(slow_k))] diff_slowk_slowd = [slow_k[i] - slow_d[i] for i in range(0, len(slow_k))]
diff_rsi_rsis = [rsi[i] - rsis[i] for i in range(0, len(rsi))] diff_rsi_rsis = [rsi[i] - rsis[i] for i in range(0, len(rsi))]
diff_macd, diff_macdo, diff_macds = [], [], [] diff_macd, diff_macdo, diff_macds = [], [], []
diff_macd.append(0) diff_macd.append(0)
for i in range(1, len(macd)): for i in range(1, len(macd)):
@@ -570,14 +585,16 @@ class BuySellChecker:
"rsi": rsi, "rsis": rsis, "rsi": rsi, "rsis": rsis,
"changeLine": changeLine, "baseLine": baseLine, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2, "changeLine": changeLine, "baseLine": baseLine, "leadingSpan1": leadingSpan1, "leadingSpan2": leadingSpan2,
"height": height, "top_tail_height": top_tail_height, "bottom_tail_height": bottom_tail_height, "height": height, "top_tail_height": top_tail_height, "bottom_tail_height": bottom_tail_height,
"abs_avg_1": abs_avg_1, "abs_avg_2": abs_avg_2, "abs_avg_3": abs_avg_3, "abs_avg_4": abs_avg_4, "abs_avg_5": abs_avg_5, "abs_avg_1": abs_avg_1, "abs_avg_2": abs_avg_2, "abs_avg_3": abs_avg_3, "abs_avg_4": abs_avg_4,
"abs_avg_5": abs_avg_5,
"diff_open": diff_open, "diff_close": diff_close, "diff_low": diff_low, "diff_high": diff_high, "diff_open": diff_open, "diff_close": diff_close, "diff_low": diff_low, "diff_high": diff_high,
"diff_avg3":diff_avg3, "diff_avg6":diff_avg6, "diff_avg9":diff_avg9, "diff_avg12":diff_avg12, "diff_avg27":diff_avg27, "diff_avg54":diff_avg54, "diff_avg3": diff_avg3, "diff_avg6": diff_avg6, "diff_avg9": diff_avg9, "diff_avg12": diff_avg12,
"diff_macd":diff_macd, "diff_macdo":diff_macdo, "diff_macds":diff_macds, "diff_avg27": diff_avg27, "diff_avg54": diff_avg54,
"diff_fast_k":diff_fast_k, "diff_slow_k":diff_slow_k, "diff_slow_d":diff_slow_d, "diff_macd": diff_macd, "diff_macdo": diff_macdo, "diff_macds": diff_macds,
"diff_rsi":diff_rsi, "diff_rsis":diff_rsis, "diff_fast_k": diff_fast_k, "diff_slow_k": diff_slow_k, "diff_slow_d": diff_slow_d,
"diff_changeLine":diff_changeLine, "diff_baseLine":diff_baseLine, "diff_rsi": diff_rsi, "diff_rsis": diff_rsis,
"diff_upper":diff_upper, "diff_lower":diff_lower, "diff_changeLine": diff_changeLine, "diff_baseLine": diff_baseLine,
"diff_upper": diff_upper, "diff_lower": diff_lower,
"diff_avg3_avg6": diff_avg3_avg6, "diff_avg3_avg6": diff_avg3_avg6,
"diff_avg3_avg9": diff_avg3_avg9, "diff_avg3_avg9": diff_avg3_avg9,
"diff_avg3_avg12": diff_avg3_avg12, "diff_avg3_avg12": diff_avg3_avg12,
@@ -790,7 +807,6 @@ class BuySellChecker:
return bsLine, data return bsLine, data
def checkTransactionML(self, data, stock_code, predY, isRealTime=True): def checkTransactionML(self, data, stock_code, predY, isRealTime=True):
# 4일치 중에서 앞에 2일은 제거한다. # 4일치 중에서 앞에 2일은 제거한다.
date = data['date'].dt.date.unique().tolist() date = data['date'].dt.date.unique().tolist()
@@ -841,88 +857,68 @@ class BuySellChecker:
# middle line에 맞다은 적 없이, low line에 붙었거나 아래에 있었던 캔들의 높은 가격을 얻어옴
def getPrice_UnderLowWithoutMiddle(self, last_index, data):
if data['high'][last_index] < data['envelope_middle'][last_index]:
for i in range(last_index - 1, 0, -1):
if data['high'][i] > data['envelope_middle'][i]:
return -1, -1
if data['low'][i] < data['envelope_lower'][i]:
return i, max(data['open'][i], data['close'][i])
return -1, -1
def getBuyPriceAndWeight_Envelope_trend(self, data, i): def getBuyPriceAndWeight_Envelope(self, i, data):
buy, weight, type = -1, -1, -1 buy, weight = -1, -1
if data['close'][i-1] < data['envelope_lower'][i-1] and data['envelope_lower'][i] < data['close'][i]: """
buy = data["close"][i] # middle line에 맞다은 적 없이, low line에 붙었거나 아래에 있었던 캔들의 높은 가격을 얻어옴
weight = 1 index, price = self.getPrice_UnderLowWithoutMiddle(i, data)
type = 1 if price > -1:
# 해당 가격보다 높은 가격이면 매수한다
if price < data['close'][i]:
buy = data['close'][i]
weight = 10
"""
if i > 100:
if -0.004 < data['gradient1'][i] < 0.001:
#if data['high'][i] < data['envelope_middle'][i]:
if data['slow_k'][i] < 20:
buy = data['close'][i]
weight = 10
if data['close'][i] < data['envelope_lower'][i]: """
if data['rsis'][i] < data['rsi'][i]: if i > 100:
buy = data["close"][i] if min(data['gradient1'][i-5:i]) < -0.009 and -0.009 < data['gradient1'][i]:
weight = 1 if data['high'][i] < data['envelope_middle'][i]:
type = 2 buy = data['close'][i]
weight = 10
"""
return buy, weight
if data['close'][i-1] < data['envelope_middle'][i-1] and data['envelope_middle'][i] < data['close'][i]: def getSellPriceAndWeight_Envelope(self, data, i):
if data['slow_k'][i] < 25:
buy = data["close"][i]
weight = 1
type = 3
check1 = False
check2 = False
if data['slow_k'][i] < 40:
if data['close'][i-1] < data['envelope_middle'][i-1] and data['envelope_middle'][i] < data['close'][i]:
idx = -1
for t1 in range(i-1, i-10, -1):
if data['close'][t1] < data['envelope_middle'][t1]:
check1 = True
idx = t1
break
if check1:
for t1 in range(idx-1, i - 10, -1):
if data['envelope_middle'][t1] < data['close'][t1]:
check2 = True
break
if check2:
buy = data["close"][i]
weight = 1
type = 4
if data.index[i].strftime("%Y.%m.%d") == "2021.12.21":
print(1)
check = True
if data['slow_k'][i] < 40:
if data['close'][i - 1] < data['envelope_middle'][i - 1] and data['envelope_middle'][i] < data['close'][i]:
for t1 in range(i-1, i-10, -1):
if data['envelope_middle'][t1] < data['close'][t1]:
check = False
if check:
buy = data["close"][i]
weight = 1
type = 4
return buy, weight, type
def getSellPriceAndWeight_Envelope_trend(self, data, i):
sell, weight, type = -1, -1, -1 sell, weight, type = -1, -1, -1
if data.index[i].strftime("%Y.%m.%d") == "2022.12.01": if data.index[i].strftime("%Y.%m.%d") == "2022.12.01":
print (1) print(1)
# upper lined에서 처리 # upper lined에서 처리
if data['close'][i-1] < data['envelope_upper'][i - 1] and data['envelope_upper'][i] < data['close'][i]: if data['close'][i - 1] < data['envelope_upper'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]: if data['slow_d'][i - 1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]:
sell = data["close"][i] sell = data["close"][i]
weight = 1 weight = 1
type = 1 type = 1
if data['envelope_upper'][i-1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]: if data['envelope_upper'][i - 1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]: if data['slow_d'][i - 1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]:
sell = data["close"][i] sell = data["close"][i]
weight = 1 weight = 1
type = 2 type = 2
if data['envelope_upper'][i-1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]: if data['envelope_upper'][i - 1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] +2 <= data['slow_k'][i - 1] and data['slow_d'][i]+1 == data['slow_k'][i]: if data['slow_d'][i - 1] + 2 <= data['slow_k'][i - 1] and data['slow_d'][i] + 1 == data['slow_k'][i]:
sell = data["close"][i] sell = data["close"][i]
weight = 1 weight = 1
type = 3 type = 3
if data['envelope_upper'][i]<data['high'][i] and data['open'][i] < data['close'][i]: if data['envelope_upper'][i] < data['high'][i] and data['open'][i] < data['close'][i]:
if data['close'][i] - data['open'][i] < data['high'][i] - data['close'][i]: if data['close'][i] - data['open'][i] < data['high'][i] - data['close'][i]:
sell = data["close"][i] sell = data["close"][i]
weight = 1 weight = 1
@@ -930,109 +926,303 @@ class BuySellChecker:
return sell, weight, type return sell, weight, type
def getBuyPriceAndWeight_Envelope(self, data, i): # 팔아야 할 시점을 체크하기 위함
buy, weight, type = -1, -1, -1 # 이전에 산 가격보다 지금 5원이상 떨어졌다면 매도 한다.
def checkBelow5WonFromPreviousBuyPrice(self, last_index, data, price):
for i in range(last_index - 1, 0, -1):
if data['sell'][i] != -1:
return False
if data['buy'][i] != -1:
if data['buy'][i] - price > 5:
return True
return
if data['close'][i-1] < data['envelope_lower'][i-1] and data['envelope_lower'][i] < data['close'][i]: def checkWithEnvelope_252670(self, data, isRealTime=False):
buy = data["close"][i]
weight = 1
type = 1
if data['close'][i] < data['envelope_lower'][i]:
if data['rsis'][i] < data['rsi'][i]:
buy = data["close"][i]
weight = 1
type = 2
return buy, weight, type
def getSellPriceAndWeight_Envelope(self, data, i):
sell, weight, type = -1, -1, -1
if data.index[i].strftime("%Y.%m.%d") == "2022.12.01":
print (1)
# upper lined에서 처리
if data['close'][i-1] < data['envelope_upper'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]:
sell = data["close"][i]
weight = 1
type = 1
if data['envelope_upper'][i-1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] <= data['slow_k'][i - 1] and data['slow_k'][i] <= data['slow_d'][i]:
sell = data["close"][i]
weight = 1
type = 2
if data['envelope_upper'][i-1] < data['close'][i - 1] and data['envelope_upper'][i] < data['close'][i]:
if data['slow_d'][i-1] +2 <= data['slow_k'][i - 1] and data['slow_d'][i]+1 == data['slow_k'][i]:
sell = data["close"][i]
weight = 1
type = 3
# middle lined에서 처리
middle_up = False
if data['envelope_middle'][i-1] < data['close'][i-1] or data['envelope_middle'][i] < data['open'][i]:
if data['envelope_middle'][i-2] < data['close'][i-2] or data['envelope_middle'][i-1] < data['close'][i-1] or data['envelope_middle'][i] < data['close'][i]:
for t in range(i-1, i-20, -1):
if data['envelope_upper'][t] <= data['close'][t]:
middle_up = False
break
if data['close'][t] < data['envelope_middle'][t]:
middle_up = True
break
if middle_up:
if data['slow_d'][i-1] < data['slow_k'][i-1] and data['slow_k'][i] < data['slow_d'][i]:
sell = data["close"][i]
weight = 1
type = 4
return sell, weight, type
def checkEnvelopeTiming(self, data, stock_code, isRealTime=True):
# 어제 오늘 데이터로 분석
bsLine = {} bsLine = {}
size = len(data["close"]) size = len(data["close"])
if isRealTime: bsLine['buy'] = [-1 for i in range(size)]
# isRealTime=True, 실시간 적용 bsLine['buy_weight'] = [-1 for i in range(size)]
last_index = size - 1 bsLine['sell'] = [-1 for i in range(size)]
bsLine['sell_weight'] = [-1 for i in range(size)]
if stock_code == "252670": for i in range(size):
buy, buy_weight, buy_type = self.getBuyPriceAndWeight_Envelope(data, last_index) if isRealTime:
sell, sell_weight, sell_type = self.getSellPriceAndWeight_Envelope(data, last_index) if i < size - 1:
else: continue
buy, buy_weight, buy_type = self.getBuyPriceAndWeight_Envelope(data, last_index)
sell, sell_weight, sell_type = self.getSellPriceAndWeight_Envelope(data, last_index)
bsLine['buy'] = [buy] """
bsLine['buy_weight'] = [buy_weight] # 이전에 산 가격보다 지금 5원이상 떨어졌다면 매도 한다.
bsLine['sell'] = [sell] price = data['close'][i] if data['close'][i] >= data['open'][i] else data['open'][i]
bsLine['sell_weight'] = [sell_weight] if self.checkBelow5WonFromPreviousBuyPrice(i, data, price):
data['sell'][i] = price
bsLine['sell'][i] = price
bsLine['sell_weight'][i] = 50
return bsLine, data
"""
else: """
# Type=False, 시뮬레이션 적용 # middle line에 맞다은 적 없이, low line에 붙었거나 아래에 있었던 캔들의 높은 가격을 얻어옴
bsLine['buy'] = [-1 for i in range(size)] buy, buy_weight = self.getBuyPriceAndWeight_Envelope(i, data)
bsLine['buy_weight'] = [-1 for i in range(size)] data['buy'][i] = buy
bsLine['sell'] = [-1 for i in range(size)] bsLine['buy'][i] = buy
bsLine['sell_weight'] = [-1 for i in range(size)] bsLine['buy_weight'][i] = buy_weight
return bsLine, data
"""
for i in range(size):
if stock_code == "252670":
buy, buy_weight, buy_type = self.getBuyPriceAndWeight_Envelope_trend(data, i)
sell, sell_weight, sell_type = self.getSellPriceAndWeight_Envelope_trend(data, i)
else:
buy, buy_weight, buy_type = self.getBuyPriceAndWeight_Envelope(data, i)
sell, sell_weight, sell_type = self.getSellPriceAndWeight_Envelope(data, i)
bsLine['buy'][i] = buy if 0 < data['gradients_avg60'][i] < 0.001:
bsLine['buy_weight'][i] = buy_weight if data['high'][i] < data['envelope_middle'][i]:
bsLine['sell'][i] = sell if -0.015 < data['gradients_avg5'][i] and -0.007 < data['gradients_avg20'][i]:
bsLine['sell_weight'][i] = sell_weight buy = data['low'][i]
if i > 0: data['buy'][i] = buy
if bsLine['sell'][i-1] > -1: bsLine['buy'][i] = buy
bsLine['sell'][i] = -1 bsLine['buy_weight'][i] = 10
bsLine['sell_weight'][i] = -1
if i > 10:
if (
data['gradients_avg60'][i - 10] > 0 and data['gradients_avg60'][i - 9] > 0 and data['gradients_avg60'][i - 8] > 0 and
data['gradients_avg60'][i - 7] > 0 and data['gradients_avg60'][i - 6] > 0 and data['gradients_avg60'][i - 5] > 0 and
data['gradients_avg60'][i - 4] > 0 and data['gradients_avg60'][i - 3] > 0 and data['gradients_avg60'][i - 2] > 0 and
data['gradients_avg60'][i - 1] > 0 and data['gradients_avg60'][i] < 0
):
if data['disparity'][i] < 3:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 10
if i > 10:
if data['disparity_avg60'][i] < 65:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if data['slow_k'][i] < 3:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 10
return bsLine, data return bsLine, data
def checkWithEnvelope_122630(self, data, isRealTime=False):
bsLine = {}
size = len(data["close"])
bsLine['buy'] = [-1 for i in range(size)]
bsLine['buy_weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
bsLine['sell_weight'] = [-1 for i in range(size)]
for i in range(size):
if isRealTime:
if i < size - 1:
continue
if i > 10:
if data['disparity_avg60'][i] < 60:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if data['macd'][i] < -1000:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if data['slow_k'][i] < 7:
if data['slow_d'][i] < data['slow_k'][i]:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 5
return bsLine, data
def notBuy(self, data, i):
if i > 5:
check = True
for l in range(i-4, i+1):
if (
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] > data['gradients_avg20'][l] or
data['gradients_low'][l - 1] > data['gradients_low'][l]
):
check = False
break
if not check:
return False
return True
def checkWithEnvelope(self, data, isRealTime=False):
bsLine = {}
size = len(data["close"])
bsLine['buy'] = [-1 for i in range(size)]
bsLine['buy_weight'] = [-1 for i in range(size)]
bsLine['sell'] = [-1 for i in range(size)]
bsLine['sell_weight'] = [-1 for i in range(size)]
for i in range(size):
if isRealTime:
if i < size - 1:
continue
if i > 10:
if data['disparity'][i] < 2:
check = True
for l in range(i-3, i):
if (
data['gradients_avg60'][l-1] > data['gradients_avg60'][l] or
data['gradients_avg20'][l-1] > data['gradients_avg20'][l] or
data['gradients_low'][l-1] > data['gradients_low'][l] or
data['disparity_avg5'][l-1] > data['disparity_avg5'][l] or
data['disparity'][l-1] < data['disparity'][l]
):
check = False
break
if check and 99 < sum(data['disparity_avg5'][i-4:i+1])/5 < 100 and 99 < sum(data['disparity_avg60'][i-4:i+1])/5 < 100:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
check = True
for l in range(i - 2, i):
if (
data['gradients_avg60'][l - 1] > data['gradients_avg60'][l] or
data['gradients_low'][l - 1] > data['gradients_low'][l]
):
check = False
break
if (
check and
-0.0011 < data['gradients_low'][i] < 0 and -0.007 < data['gradients_avg5'][i] < 0.001 and
-0.0012 < data['gradients_avg60'][i] < 0 and
98.90 < data['disparity_avg5'][i] < 101
):
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
check = True
for l in range(i - 6, i):
if (
data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or
data['gradients_low'][l - 1] < data['gradients_low'][l] or
-0.039 < data['gradients_low'][l - 1] < -0.35 or
-0.05 < data['gradients_avg20'][l - 1] < -0.30 or
-0.40 < data['gradients_avg60'][l - 1] < -0.30
):
check = False
break
if check and 99 < min (data['disparity_avg5'][i - 6:i]) < max (data['disparity_avg5'][i - 6:i]) < 101:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
check = True
for l in range(i - 3, i):
if (
data['gradients_low'][l - 1] < data['gradients_low'][l] or
data['gradients_avg60'][l - 1] < data['gradients_avg60'][l] or
data['gradients_avg20'][l - 1] < data['gradients_avg20'][l] or
0.01 < data['gradients_low'][l - 1] < 0.21 or
-0.09 < data['gradients_avg20'][l - 1] < -0.002 or
0.01 < data['gradients_avg60'][l - 1] < 0.021
):
check = False
break
if check:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if (data['disparity'][i] < 5 and 99.0 < data['disparity_avg60'][i] < 99.1 and
-0.009 < data['gradients_avg60'][i] < -0.008 and 0.015 < data['gradients_avg20'][i] < 0.016 and
-0.006 < data['gradients_avg5'][i] < -0.005 and -0.009 < data['gradients_low'][i] < -0.008):
check = True
for l in range(i-5, i):
if (
data['gradients_avg60'][l-1] > data['gradients_avg60'][l] or
data['gradients_low'][l-1] > data['gradients_low'][l] or
data['disparity'][l - 1] < data['disparity'][l]
):
check = False
break
if check:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if data['macd'][i] < -4000:
if data['macd'][i-1] < data['macd'][i]:
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
# macd 이전에 없던 바닥인 경우 상승할 찰나 매수
if data['macds'][i-1] < min(data['macds'][:i-1]):
if data['macds'][i-1] < data['macds'][i]:
if not self.notBuy(data, i) and data['slow_k'][i] < 30:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
if (
98 < data['disparity_avg5'][i] < 100 and data['disparity_avg20'][i] < 93.5 and data['disparity_avg60'][i] < 89 and
-0.014 < data['gradients_avg60'][i] < -0.013 and -0.03 < data['gradients_avg20'][i] < -0.02 and -0.014 < data['gradients_low'][i] < -0.013 and
data['slow_k'][i] < 11
):
if not self.notBuy(data, i):
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
"""
if data['disparity_avg60'][i] < 60:
buy = data['low'][i]
data['buy'][i] = buy
bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = 20
"""
return bsLine, data
def checkTransactionWithEnvelope(self, data, stock_code, isRealTime=False):
if isRealTime:
if stock_code == "252670":
bsLine, data = self.checkWithEnvelope_252670(data, isRealTime)
elif stock_code == "122630":
bsLine, data = self.checkWithEnvelope_122630(data, isRealTime)
else:
bsLine, data = self.checkWithEnvelope(data, isRealTime)
else:
# 사야 할 시점과 팔아야 할 시점을 체크한다.
if stock_code == "252670":
bsLine, data = self.checkWithEnvelope_252670(data, isRealTime)
elif stock_code == "122630":
bsLine, data = self.checkWithEnvelope_122630(data, isRealTime)
else:
bsLine, data = self.checkWithEnvelope(data, isRealTime)
return bsLine, data

317
hts/DailyStatus.py
View File

@@ -5,17 +5,21 @@ if platform.system().lower().find("window") >= 0 and platform.architecture()[0]
import win32com.client import win32com.client
import sqlite3 import sqlite3
from datetime import datetime, timedelta from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from stock.analysis.AnalyzerSqlite import AnalyzerSqlite
class DailyStatus: class DailyStatus:
tableName = None tableName = None
dbFileName = None dbFileName = None
RESOURCE_PATH = None RESOURCE_PATH = None
analyzerSqlite = None
def __init__(self, RESOURCE_PATH): def __init__(self, RESOURCE_PATH):
self.RESOURCE_PATH = RESOURCE_PATH self.RESOURCE_PATH = RESOURCE_PATH
self.tableName = 'stock_analysis' self.tableName = 'stock'
self.dbFileName = "stock.db" self.dbFileName = "stock.db"
self.analyzerSqlite = AnalyzerSqlite(os.path.join(self.RESOURCE_PATH, self.dbFileName))
return return
def getDBData(self, stock_code, day, result): def getDBData(self, stock_code, day, result):
@@ -67,13 +71,118 @@ class DailyStatus:
return True return True
return False return False
def getLastData(self, stock_code, today, n=100): def getLastData(self, stock_code, limit=350):
stockTableName = 'stock'
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
cursor = conn.cursor()
stock = {"CODE": stock_code, "NAME": "", "PRICE": []}
sql = 'SELECT ymd, close, diff, open, high, low, volume FROM ' + stockTableName + ' 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,
}
)
conn.commit()
cursor.close()
conn.close()
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 = { result = {
"ymd": [], "ymd": [],
"open": [], "open": [],
"close": [], "close": [],
"high": [], "high": [],
"low": [], "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_upper": [],
"envelope_lower": [], "envelope_lower": [],
"envelope_middle": [], "envelope_middle": [],
@@ -82,58 +191,166 @@ class DailyStatus:
"macd": [], "macd": [],
"macds": [], "macds": [],
"slow_k": [], "slow_k": [],
"slow_d": [] "slow_d": [],
"buy": [],
"sell": [],
} }
for item in stock['PRICE']:
days = [] result["ymd"].append(item['ymd'])
for i in range(1, n): result["open"].append(item['open'])
last_day = (datetime.strptime(today, '%Y%m%d') - timedelta(i)).strftime('%Y.%m.%d') result["close"].append(item['close'])
isValid = self.isValidYMD(stock_code, last_day) result["high"].append(item['high'])
if isValid: result["low"].append(item['low'])
days.append(last_day) result["avg3"].append(item['avg3'])
result["avg4"].append(item['avg4'])
days = sorted(days) result["avg5"].append(item['avg5'])
for day in days: result["avg6"].append(item['avg6'])
self.getDBData(stock_code, day, result) 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) data = pd.DataFrame(result)
df_final_time = pd.DatetimeIndex(result['ymd']) df_final_time = pd.DatetimeIndex(result['ymd'])
data.index = df_final_time 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 return data
def getLastData_realtime(self, stock_code, today, n=100):
result = {
"ymd": [],
"open": [],
"close": [],
"high": [],
"low": [],
"envelope_upper": [],
"envelope_lower": [],
"envelope_middle": [],
"rsi": [],
"rsis": [],
"macd": [],
"macds": [],
"slow_k": [],
"slow_d": []
}
days = []
for i in range(1, n):
last_day = (datetime.strptime(today, '%Y%m%d') - timedelta(i)).strftime('%Y.%m.%d')
isValid = self.isValidYMD(stock_code, last_day)
if isValid:
days.append(last_day)
days = sorted(days)
for day in days:
self.getDBData(stock_code, day, result)
data = pd.DataFrame(result)
df_final_time = pd.DatetimeIndex(result['ymd'])
data.index = df_final_time
return data

3
requirements.txt
View File

@@ -14,5 +14,4 @@ ipywidgets==7.0.0
torchvision torchvision
transformers transformers
sklearn scikit-learn
finterstellar

8
stock/analysis/AnalyzerSqlite.py
View File

@@ -8,7 +8,6 @@ from datetime import datetime
from dateutil.relativedelta import relativedelta from dateutil.relativedelta import relativedelta
from matplotlib import rc from matplotlib import rc
import pandas as pd import pandas as pd
import copy
rc('font', family='AppleGothic') rc('font', family='AppleGothic')
plt.rcParams['axes.unicode_minus'] = False plt.rcParams['axes.unicode_minus'] = False
@@ -27,8 +26,6 @@ from stock.analysis.Envelope import Envelope
from stock.crawler.MovingAverage import MovingAverage from stock.crawler.MovingAverage import MovingAverage
class AnalyzerSqlite: class AnalyzerSqlite:
PROJECT_HOME = None
stochastic = None stochastic = None
bolingerBand = None bolingerBand = None
ichimokuCloud = None ichimokuCloud = None
@@ -45,8 +42,7 @@ class AnalyzerSqlite:
moving_avg = None moving_avg = None
def __init__(self, PROJECT_HOME, stockFileName): def __init__(self, stockFileName):
self.PROJECT_HOME = PROJECT_HOME
self.stockFileName = stockFileName self.stockFileName = stockFileName
self.common = Common() self.common = Common()
@@ -1032,7 +1028,7 @@ if __name__ == "__main__":
PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__)))))) PROJECT_HOME = os.path.join(os.path.dirname(os.path.join(os.path.dirname(os.path.join(os.path.dirname(__file__))))))
stockFileName = PROJECT_HOME + '/resources/stock.db' stockFileName = PROJECT_HOME + '/resources/stock.db'
analyzer = AnalyzerSqlite(PROJECT_HOME, stockFileName) analyzer = AnalyzerSqlite(stockFileName)
#analyzer.analyzeDaily() #analyzer.analyzeDaily()
#analyzer.analyzeGrouping("weekly") #analyzer.analyzeGrouping("weekly")