init
This commit is contained in:
dsyoon
@@ -2,7 +2,7 @@ import time
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import os
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from datetime import datetime
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from hts.DailyStatus import DailyStatus
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from stock.analysis.DailyStatus import DailyStatus
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from hts.HTS import HTS
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from hts.OrderType import OrderType
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@@ -1,4 +1,3 @@
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import numpy as np
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from math import nan
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import pandas as pd
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import plotly.graph_objects as go
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@@ -9,6 +8,7 @@ from hts.HTS import HTS
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from stock.util.Stock2Vector import Stock2Vector
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from stock.util.LabelChecker import LabelChecker
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from stock.util.StockPredictor import StockPredictor
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from stock.analysis.DailyStatus import DailyStatus
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from hts.BuySellChecker import BuySellChecker
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class Simulation (HTS):
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@@ -163,36 +163,24 @@ class Simulation (HTS):
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return
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def simulate(self, stock_code, today, method="rule"):
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def simulate(self, stock_codes:dict=None):
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if method == "answer":
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#self.labelMaker.makeCandidate(stock_code, today, view=True)
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self.labelChecker.showLabels(stock_code, today)
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else:
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if method == "ml":
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LAST_DATA = self.stock2Vector.getLastData(stock_code, today, n=3)
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result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
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X, Y = self.stock2Vector.getVectorData(result)
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predY = self.stockPredictor.predict(X, Y)
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predY = np.argmax(predY, axis=1)
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# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
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data = self.buySellChecker.analyze(result)
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# 사야 할 시점과 팔아야 할 시점을 체크한다.
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bsLine, data = self.buySellChecker.checkTransactionML(data, stock_code, predY, isRealTime=False)
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else:
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LAST_DATA = self.stock2Vector.getLastData(stock_code, today)
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result = self.stock2Vector.getRealTime(stock_code, today, LAST_DATA)
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if stock_codes is not None:
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for stock_code in stock_codes:
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for given_day in stock_codes[stock_code]:
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LAST_DATA = self.stock2Vector.getLastData(stock_code, given_day)
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result = self.stock2Vector.getRealTime(stock_code, given_day, LAST_DATA)
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# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
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data = self.buySellChecker.analyze(result)
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# 사야 할 시점과 팔아야 할 시점을 체크한다.
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bsLine, data = self.buySellChecker.checkTransaction(data, stock_code, isRealTime=False)
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if data is not None:
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# 그래프를 그린다.
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self.draw(stock_code, today, data, bsLine)
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self.draw(stock_code, given_day, data, bsLine)
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else:
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dailyStatus = DailyStatus(self.RESOURCE_PATH)
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dailyStatus.checkEnvelope()
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return
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@@ -201,6 +189,8 @@ if __name__ == "__main__":
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PROJECT_HOME = os.getcwd()
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RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
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simulation = Simulation(RESOURCE_PATH)
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# to check bying
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stock_codes = {
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"252670": [
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@@ -208,24 +198,7 @@ if __name__ == "__main__":
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'20220908','20220913','20220914','20220915','20220916'
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]
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}
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"""
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# 122630
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"252670": [
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'20220801', '20220802', '20220803', '20220804', '20220805',
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'20220808', '20220809', '20220810', '20220811', '20220812',
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'20220816', '20220817', '20220818', '20220819', '20220822',
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'20220823', '20220824', '20220825', '20220826', '20220829',
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'20220830', '20220831',
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'20220901', '20220902', '20220905', '20220906','20220907',
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'20220908'
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],
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"""
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method = "rule" # "rule", "ml", "answer"
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for stock_code in stock_codes:
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simulation = Simulation(RESOURCE_PATH)
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for given_day in stock_codes[stock_code]:
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simulation.simulate(stock_code, given_day, method=method)
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#simulation.simulate(stock_codes)
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simulation.simulate()
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print ("done...")
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@@ -1,388 +0,0 @@
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import numpy as np
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from math import nan
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import shutil
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import sqlite3
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import pandas as pd
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import plotly.graph_objects as go
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from plotly import subplots
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import plotly.io as po
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import os
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from datetime import datetime
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from hts.HTS import HTS
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from hts.DailyStatus import DailyStatus
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from hts.BuySellChecker import BuySellChecker
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class Simulation (HTS):
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buySellChecker = None
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stockPredictor = None
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dailyStatus = None
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def __init__(self, RESOURCE_PATH):
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super().__init__(RESOURCE_PATH)
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self.RESOURCE_PATH = RESOURCE_PATH
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self.buySellChecker = BuySellChecker()
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self.dailyStatus = DailyStatus(RESOURCE_PATH)
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return
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def draw(self, stock_code, given_day, data, bsLine):
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if bsLine is None:
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return
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# 어제 데이터는 지운다.
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buy_line = bsLine['buy']
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buy_weight_line = bsLine['buy_weight']
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sell_line = bsLine['sell']
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buy_size = []
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buy_colors = []
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for i in range(len(buy_line)):
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if buy_line[i] < 0:
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buy_colors.append("#ffffff")
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buy_line[i] = nan
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buy_size.append(0)
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else:
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buy_colors.append("#B2028C")
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buy_size.append(10 + (0.1 * buy_weight_line[i]))
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sell_colors = []
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for i in range(len(sell_line)):
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if sell_line[i] < 0:
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sell_colors.append("#ffffff")
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sell_line[i] = nan
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else:
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sell_colors.append("#00ced1")
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# 그래프를 설정한다.
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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))
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sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
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envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000')
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envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786')
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envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000')
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avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507')
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avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43')
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avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543')
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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)
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macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
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macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
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# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
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slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
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slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
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rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
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rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
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disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
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disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
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disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
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candle_data = [candle_stick, avg5, avg20, avg60, envelope_upper, envelope_middle, envelope_lower, buy_check, sell_check]
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disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60]
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macd_data = [macd_line, macd_s_line]
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stochastic_data = [slow_k_line, slow_d_line]
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rsi_data = [rsi_line, rsis_line]
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# 그래프를 그린다.
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"""
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fig = go.Figure(data=candle_data)
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fig.update_layout(title=stock_code + "_" + given_day)
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fig.show()
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"""
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fig = subplots.make_subplots(
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rows=5, cols=1,
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subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
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#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
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shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
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row_heights=[200, 200, 200, 200, 750]
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)
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for trace in macd_data:
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fig.append_trace(trace, 1, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 2, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 3, 1)
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for trace in disparity_data:
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fig.append_trace(trace, 4, 1)
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for trace in candle_data:
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fig.append_trace(trace, 5, 1)
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#fig.update_xaxes(nticks=5)
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#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
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df = pd.DataFrame(bsLine)
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df = df.fillna(-1)
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buy_count = len(df.loc[df["buy"] > 0])
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sell_count = len(df.loc[df["sell"] > 0])
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fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
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#fig.update_layout(title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
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fig.show()
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return
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def writeFile(self, dailyDirName, stock_code, stock_name, given_day, data, bsLine):
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if bsLine is None:
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return
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# 어제 데이터는 지운다.
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buy_line = bsLine['buy']
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buy_weight_line = bsLine['buy_weight']
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sell_line = bsLine['sell']
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buy_size = []
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buy_colors = []
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for i in range(len(buy_line)):
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if buy_line[i] < 0:
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buy_colors.append("#ffffff")
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buy_line[i] = nan
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buy_size.append(0)
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else:
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buy_colors.append("#B2028C")
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buy_size.append(10 + (0.1 * buy_weight_line[i]))
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sell_colors = []
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for i in range(len(sell_line)):
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if sell_line[i] < 0:
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sell_colors.append("#ffffff")
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sell_line[i] = nan
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else:
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sell_colors.append("#00ced1")
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# 그래프를 설정한다.
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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))
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sell_check = go.Scatter(x=data['ymd'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
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envelope_upper = go.Scatter(x=data['ymd'], y=data["envelope_upper"], name="upper", line_color='#000000')
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envelope_middle = go.Scatter(x=data['ymd'], y=data["envelope_middle"], name="upper", line_color='#927786')
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envelope_lower = go.Scatter(x=data['ymd'], y=data["envelope_lower"], name="lower", line_color='#000000')
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avg5 = go.Scatter(x=data['ymd'], y=data["avg5"], name="avg5", line_color='#6C2507')
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avg20 = go.Scatter(x=data['ymd'], y=data["avg20"], name="avg20", line_color='#f84c43')
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avg60 = go.Scatter(x=data['ymd'], y=data["avg60"], name="avg60", line_color='#f89543')
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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)
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macd_line = go.Scatter(x=data['ymd'], y=data["macd"], line=dict(color='red', width=2), name='macd')
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macd_s_line = go.Scatter(x=data['ymd'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
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# fast_k_line = go.Scatter(x=hts['date'], y=hts["fast_k"], mode='lines', name='fast_k')
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slow_k_line = go.Scatter(x=data['ymd'], y=data["slow_k"], line=dict(color='red', width=2), name='slow_k')
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slow_d_line = go.Scatter(x=data['ymd'], y=data["slow_d"], line=dict(dash='dashdot', color='black', width=2), name='slow_d')
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rsi_line = go.Scatter(x=data['ymd'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
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rsis_line = go.Scatter(x=data['ymd'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
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disparity_avg5 = go.Scatter(x=data['ymd'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203')
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disparity_avg20 = go.Scatter(x=data['ymd'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff')
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disparity_avg60 = go.Scatter(x=data['ymd'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4')
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candle_data = [candle_stick, avg5, avg20, avg60, envelope_upper, envelope_middle, envelope_lower, buy_check, sell_check]
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disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60]
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macd_data = [macd_line, macd_s_line]
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stochastic_data = [slow_k_line, slow_d_line]
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rsi_data = [rsi_line, rsis_line]
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# 그래프를 그린다.
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"""
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fig = go.Figure(data=candle_data)
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fig.update_layout(title=stock_code + "_" + given_day)
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fig.show()
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"""
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fig = subplots.make_subplots(
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rows=5, cols=1,
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subplot_titles=("MACD", "RSI", "스토캐스틱", '이격도', '캔들'),
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#specs=[[{}], [{}], [{}], [{}], [{}], [{}]],
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shared_xaxes=True, horizontal_spacing=0.03, vertical_spacing=0.01,
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row_heights=[200, 200, 200, 200, 750]
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)
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for trace in macd_data:
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fig.append_trace(trace, 1, 1)
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for trace in rsi_data:
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fig.append_trace(trace, 2, 1)
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for trace in stochastic_data:
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fig.append_trace(trace, 3, 1)
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for trace in disparity_data:
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fig.append_trace(trace, 4, 1)
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for trace in candle_data:
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fig.append_trace(trace, 5, 1)
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#fig.update_xaxes(nticks=5)
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#fig.update_layout(height=1800, title=stock_code + "_" + given_day, xaxis_rangeslider_visible=False)
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df = pd.DataFrame(bsLine)
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df = df.fillna(-1)
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buy_count = len(df.loc[df["buy"] > 0])
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sell_count = len(df.loc[df["sell"] > 0])
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fig.update_layout(height=1700, title=stock_code + "_" + given_day + "_" + str(buy_count)+","+str(sell_count))
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#fig.update_layout(title=stock_code + "_" + given_day + "_" + str(buy_count) + "," + str(sell_count))
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#fig.show()
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fileName = "%s/%s_%s.html" % (dailyDirName, stock_code, stock_name.replace(" ", ""))
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po.write_html(fig, file=fileName, auto_open=False)
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return
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def getData(self, today, stock):
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close = stock['PRICE'][len(stock['PRICE']) - 1]["close"]
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open = stock['PRICE'][len(stock['PRICE']) - 1]["open"]
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high = stock['PRICE'][len(stock['PRICE']) - 1]["high"]
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low = stock['PRICE'][len(stock['PRICE']) - 1]["low"]
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volume = stock['PRICE'][len(stock['PRICE']) - 1]["volume"]
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stock['PRICE'].append(
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{
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"ymd": today,
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"close": close,
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"diff": stock['PRICE'][len(stock['PRICE']) - 1]["close"] - close,
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"open": open,
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"high": high,
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"low": low,
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"volume": volume,
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"avg3": -1,
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"avg4": -1,
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"avg5": -1,
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"avg6": -1,
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"avg10": -1,
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"avg12": -1,
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"avg20": -1,
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"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
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
PROJECT_HOME = os.getcwd()
|
||||
RESOURCE_PATH = os.path.join(PROJECT_HOME, "resources")
|
||||
|
||||
simulation = Simulation(RESOURCE_PATH)
|
||||
|
||||
# to check buying
|
||||
#stock_codes = ["252670", "122630"]
|
||||
#stock_codes = ["051600", "139130", "066570", "000990"]
|
||||
stock_codes = ["102950"]
|
||||
|
||||
"""
|
||||
for stock_code in stock_codes:
|
||||
simulation.simulate(stock_code)
|
||||
"""
|
||||
|
||||
simulation.simulate()
|
||||
print ("done...")
|
||||
|
||||
@@ -7,6 +7,7 @@ from stock.crawler.FnGuideCrawler import FnGuideCrawler
|
||||
from stock.crawler.MetaCrawler import MetaCrawler
|
||||
from stock.crawler.StockCrawler import StockCrawler
|
||||
from stock.analysis.AnalyzerSqlite import AnalyzerSqlite
|
||||
from stock.analysis.DailyStatus import DailyStatus
|
||||
|
||||
today = datetime.now().strftime("%Y-%m-%d")
|
||||
|
||||
@@ -16,7 +17,8 @@ PROJECT_HOME = os.getcwd()
|
||||
|
||||
START_DATE = "2000.01.01"
|
||||
start = time.time()
|
||||
stockFileName = PROJECT_HOME + '/resources/stock.db'
|
||||
RESOURCE_PATH = os.path.join(PROJECT_HOME, 'resources')
|
||||
stockFileName = os.path.join(RESOURCE_PATH, 'stock.db')
|
||||
|
||||
week = datetime.today().weekday()
|
||||
|
||||
@@ -71,9 +73,13 @@ if week in (0, 1, 2, 3, 4): # 0:월, 1:화, 2:수, 3:목, 4:금, 5:토, 6:일
|
||||
if os.path.isdir(outPath):
|
||||
shutil.rmtree(outPath)
|
||||
os.mkdir(outPath)
|
||||
print("print to Html...")
|
||||
analyzerSqlite.findCandidate(outPath)
|
||||
|
||||
print("print to Html...")
|
||||
# envelopes를 이용한 daily check
|
||||
dailyStatus = DailyStatus(RESOURCE_PATH)
|
||||
dailyStatus.checkEnvelope()
|
||||
|
||||
analyzerSqlite.findCandidate(outPath)
|
||||
|
||||
print("time : %6.2f 초", (time.time() - start))
|
||||
print ("done...")
|
||||
|
||||
@@ -1071,12 +1071,27 @@ class BuySellChecker:
|
||||
bsLine['sell'] = [-1 for i in range(size)]
|
||||
bsLine['sell_weight'] = [-1 for i in range(size)]
|
||||
|
||||
gap_interval = 60
|
||||
gap_state = False
|
||||
for i in range(size):
|
||||
if isRealTime:
|
||||
if i < size - 1:
|
||||
continue
|
||||
|
||||
if i > 10:
|
||||
# 만약 전일 저가와 오늘 종의 차이가 1만원이 넘으면 향후 60일은 분석하지 않는다.
|
||||
if data['low'][i-1] - data['high'][i] > 10000:
|
||||
gap_state = True
|
||||
gap_interval -= 1
|
||||
continue
|
||||
if gap_state:
|
||||
if gap_interval <= 0:
|
||||
gap_state = False
|
||||
gap_interval = 60
|
||||
else:
|
||||
gap_interval -= 1
|
||||
continue
|
||||
|
||||
if data['disparity'][i] < 2:
|
||||
check = True
|
||||
for l in range(i-3, i):
|
||||
|
||||
@@ -1,356 +0,0 @@
|
||||
import os.path
|
||||
import pandas as pd
|
||||
import platform
|
||||
if platform.system().lower().find("window") >= 0 and platform.architecture()[0] != "64bit" :
|
||||
import win32com.client
|
||||
|
||||
import sqlite3
|
||||
from sklearn.linear_model import LinearRegression
|
||||
from sklearn.preprocessing import StandardScaler, MinMaxScaler
|
||||
|
||||
from stock.analysis.AnalyzerSqlite import AnalyzerSqlite
|
||||
class DailyStatus:
|
||||
tableName = None
|
||||
dbFileName = None
|
||||
RESOURCE_PATH = None
|
||||
analyzerSqlite = None
|
||||
|
||||
def __init__(self, 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))
|
||||
return
|
||||
|
||||
def getDBData(self, stock_code, day, result):
|
||||
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
|
||||
cursor = conn.cursor()
|
||||
|
||||
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, stock_code, day):
|
||||
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
|
||||
cursor = conn.cursor()
|
||||
|
||||
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 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 = {
|
||||
"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
|
||||
|
||||
@@ -13,7 +13,7 @@ rc('font', family='AppleGothic')
|
||||
plt.rcParams['axes.unicode_minus'] = False
|
||||
|
||||
import plotly.graph_objs as go
|
||||
from plotly import tools, subplots
|
||||
from plotly import subplots
|
||||
import plotly.io as po
|
||||
|
||||
from stock.analysis.Common import Common
|
||||
@@ -1027,7 +1027,8 @@ if __name__ == "__main__":
|
||||
start = time.time()
|
||||
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'
|
||||
RESOURCE_PATH = os.path.join(PROJECT_HOME, 'resources')
|
||||
stockFileName = os.path.join(RESOURCE_PATH, 'stock.db')
|
||||
analyzer = AnalyzerSqlite(stockFileName)
|
||||
|
||||
#analyzer.analyzeDaily()
|
||||
|
||||
703
stock/analysis/DailyStatus.py
Normal file
703
stock/analysis/DailyStatus.py
Normal file
@@ -0,0 +1,703 @@
|
||||
import os.path
|
||||
import pandas as pd
|
||||
import platform
|
||||
if platform.system().lower().find("window") >= 0 and platform.architecture()[0] != "64bit" :
|
||||
import win32com.client
|
||||
|
||||
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 DailyStatus (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, stock_code, day, result):
|
||||
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
|
||||
cursor = conn.cursor()
|
||||
|
||||
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, stock_code, day):
|
||||
conn = sqlite3.connect(os.path.join(self.RESOURCE_PATH, self.dbFileName))
|
||||
cursor = conn.cursor()
|
||||
|
||||
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 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 = {
|
||||
"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 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 checkEnvelope(self, stock_codes:list=None, isRealTime=False):
|
||||
if not isRealTime:
|
||||
n = 200
|
||||
else:
|
||||
n = 200
|
||||
|
||||
if stock_codes is not None:
|
||||
for stock_code in stock_codes:
|
||||
stock = self.getLastData(stock_code, n)
|
||||
|
||||
today = datetime.today().strftime('%Y%m%d')
|
||||
self.getData(today, stock)
|
||||
analyzed_day = 60
|
||||
data = self.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'
|
||||
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()
|
||||
cursor.close()
|
||||
conn.close()
|
||||
|
||||
today = datetime.today().strftime('%Y%m%d')
|
||||
dailyDirName = os.path.join(self.RESOURCE_PATH, 'analysis', today, 'daily')
|
||||
if os.path.exists(dailyDirName):
|
||||
shutil.rmtree(dailyDirName)
|
||||
|
||||
dailyDirName = os.path.join(self.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.getLastData(stock_code, n)
|
||||
|
||||
today = datetime.today().strftime('%Y%m%d')
|
||||
self.getData(today, stock)
|
||||
analyzed_day = 60
|
||||
data = self.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
|
||||
|
||||
Reference in New Issue
Block a user