389 lines
17 KiB
Python
389 lines
17 KiB
Python
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,
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"avg40": -1,
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"avg48": -1,
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"avg60": -1,
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"avg120": -1,
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"avg200": -1,
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"avg240": -1,
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"avg300": -1,
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"disparity_avg5": -1,
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"disparity_avg10": -1,
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"disparity_avg20": -1,
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"disparity_avg60": -1,
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"disparity_avg120": -1,
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"bolingerband_upper": -1,
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"bolingerband_lower": -1,
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"bolingerband_middle": -1,
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"envelope_upper": -1,
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"envelope_lower": -1,
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"envelope_middle": -1,
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"ichimokucloud_changeLine": -1,
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"ichimokucloud_baseLine": -1,
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"ichimokucloud_leadingSpan1": -1,
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"ichimokucloud_leadingSpan2": -1,
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"stochastic_fast_k": -1,
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"stochastic_slow_k": -1,
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"stochastic_slow_d": -1,
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"rsi": -1,
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"rsis": -1,
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"macd": -1,
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"macds": -1,
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"macdo": -1,
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}
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)
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return
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def simulate(self, stock_code=None, isRealTime=False):
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if not isRealTime:
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n = 200
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else:
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n = 200
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if stock_code is not None:
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stock = self.dailyStatus.getLastData(stock_code, n)
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today = datetime.today().strftime('%Y%m%d')
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self.getData(today, stock)
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analyzed_day = 60
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data = self.dailyStatus.analyze(stock, analyzed_day)
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# 분석일 데이터만 활용한다 (이전 데이터는 제거)
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data.drop(data.index[:analyzed_day], inplace=True)
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# print logs
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for i in range(len(data.index)):
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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])
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bsLine, data = self.buySellChecker.checkTransactionWithEnvelope(data, stock_code, isRealTime=False)
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# 그래프를 그린다.
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self.draw(stock_code, today, data, bsLine)
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else:
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stockTableName = 'stock'
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PROJECT_HOME = os.path.join(os.path.dirname(__file__))
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stockFileName = PROJECT_HOME + '/resources/stock.db'
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conn = sqlite3.connect(stockFileName)
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cursor = conn.cursor()
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cursor.execute('SELECT distinct code, name FROM ' + stockTableName + ' order by code')
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items = cursor.fetchall()
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cursor.close()
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conn.close()
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today = datetime.today().strftime('%Y%m%d')
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dailyDirName = os.path.join(RESOURCE_PATH, 'analysis', today, 'daily')
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if os.path.exists(dailyDirName):
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shutil.rmtree(dailyDirName)
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dailyDirName = os.path.join(RESOURCE_PATH, 'analysis', today)
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if not os.path.isdir(dailyDirName):
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os.mkdir(dailyDirName)
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dailyDirName = os.path.join(dailyDirName, 'daily')
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if not os.path.isdir(dailyDirName):
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os.mkdir(dailyDirName)
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for idx, item in enumerate(items):
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stock_code = item[0]
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stock_name = item[1]
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print(idx, stock_code, stock_name)
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print("Analysis # :", idx, ", CODE: ", stock_code, ", NAME: ", stock_name)
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stock = self.dailyStatus.getLastData(stock_code, n)
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today = datetime.today().strftime('%Y%m%d')
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self.getData(today, stock)
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analyzed_day = 60
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data = self.dailyStatus.analyze(stock, analyzed_day)
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# 분석일 데이터만 활용한다 (이전 데이터는 제거)
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data.drop(data.index[:analyzed_day], inplace=True)
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# print logs
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# for i in range(len(data.index)):
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# 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])
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bsLine, data = self.buySellChecker.checkTransactionWithEnvelope(data, stock_code, isRealTime=False)
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# 그래프를 그린다.
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if len(data.index) > 10 and max(bsLine['buy'][len(bsLine['buy'])-2:]) > 0:
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self.writeFile(dailyDirName, stock_code, stock_name, today, data, bsLine)
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return
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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 buying
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#stock_codes = ["252670", "122630"]
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#stock_codes = ["051600", "139130", "066570", "000990"]
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stock_codes = ["102950"]
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"""
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for stock_code in stock_codes:
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simulation.simulate(stock_code)
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"""
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simulation.simulate()
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print ("done...")
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