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dsyoon
2023-10-08 15:52:20 +09:00
parent d993ebc3f0
commit 9da290b220
2 changed files with 82 additions and 63 deletions
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50
Simulation.py
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@@ -50,9 +50,14 @@ class Simulation (HTS):
'volume': 'int', 'volume': 'int',
'avg5': 'float', 'avg5': 'float',
'avg20': 'float', 'avg20': 'float',
'avg30': 'float',
'avg60': 'float', 'avg60': 'float',
'avg120': 'float', 'avg120': 'float',
'avg200': 'float',
'disparity_avg5': 'float',
'disparity_avg20': 'float',
'disparity_avg60': 'float',
'disparity_avg120': 'float',
'disparity_avg200': 'float',
'fast_k': 'float', 'fast_k': 'float',
'slow_k': 'float', 'slow_k': 'float',
'slow_d': 'float', 'slow_d': 'float',
@@ -84,11 +89,11 @@ class Simulation (HTS):
sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0)) sell_check = go.Scatter(x=data['date'], y=sell_line, mode='markers', name="sell", marker=dict(size=14, color=sell_colors, line_width=0))
upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000') upper = go.Scatter(x=data['date'], y=data["upper"], name="upper", line_color='#000000')
lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000') lower = go.Scatter(x=data['date'], y=data["lower"], name="lower", line_color='#000000')
avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#8F8203') avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#F81191')
avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#089B5B') avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#097F19')
avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#ff00ff') avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#671BEA')
avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#1469F4') avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#DFB809')
avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", line_color='#000000') avg200 = go.Scatter(x=data['date'], y=data["avg200"], name="avg200", line_color='#000000')
laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB') laggingSpan = go.Scatter(x=data['date'], y=data["laggingSpan"], name='laggingSpan', line_color='#B50ABB')
changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200') changeLine = go.Scatter(x=data['date'], y=data["changeLine"], name='changeLine', line_color='#14A200')
baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D') baseLine = go.Scatter(x=data['date'], y=data["baseLine"], name='baseLine', line_color='#CF6E0D')
@@ -97,11 +102,11 @@ class Simulation (HTS):
#volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume') #volume_line = go.Scatter(x=data['date'], y=data["volume"], mode='lines', name='volume')
volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color='red', name='volume') volume_line = go.Bar(x=data['date'], y=data["volume"], marker_color='red', name='volume')
disparity_avg5 = go.Scatter(x=data['date'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#8F8203') disparity_avg5 = go.Scatter(x=data['date'], y=data["disparity_avg5"], name="disparity_avg5", line_color='#F81191')
disparity_avg10 = go.Scatter(x=data['date'], y=data["disparity_avg10"], name="disparity_avg10", line_color='#089B5B') disparity_avg20 = go.Scatter(x=data['date'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#097F19')
disparity_avg20 = go.Scatter(x=data['date'], y=data["disparity_avg20"], name="disparity_avg20", line_color='#ff00ff') disparity_avg60 = go.Scatter(x=data['date'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#671BEA')
disparity_avg60 = go.Scatter(x=data['date'], y=data["disparity_avg60"], name="disparity_avg60", line_color='#1469F4') disparity_avg120 = go.Scatter(x=data['date'], y=data["disparity_avg120"], name="disparity_avg120", line_color='#DFB809')
disparity_avg120 = go.Scatter(x=data['date'], y=data["disparity_avg120"], name="disparity_avg120", line_color='#000000') disparity_avg200 = go.Scatter(x=data['date'], y=data["disparity_avg200"], name="disparity_avg200", line_color='#000000')
macd_line = go.Scatter(x=data['date'], y=data["macd"], line=dict(color='red', width=2), name='macd') macd_line = go.Scatter(x=data['date'], y=data["macd"], line=dict(color='red', width=2), name='macd')
macd_s_line = go.Scatter(x=data['date'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds') macd_s_line = go.Scatter(x=data['date'], y=data["macds"], line=dict(dash='dashdot', color='black', width=2), name='macds')
@@ -114,9 +119,9 @@ class Simulation (HTS):
rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi') rsi_line = go.Scatter(x=data['date'], y=data["rsi"], line=dict(color='red', width=2), name='rsi')
rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis') rsis_line = go.Scatter(x=data['date'], y=data["rsis"], line=dict(dash='dashdot', color='black', width=2), name='rsis')
candle_data = [candle_stick, upper, lower, avg5, avg20, avg30, avg60, avg120, buy_check, sell_check, laggingSpan, changeLine, baseLine] candle_data = [candle_stick, upper, lower, avg5, avg20, avg60, avg120, avg200, buy_check, sell_check, laggingSpan, changeLine, baseLine]
volume_data = [volume_line] volume_data = [volume_line]
disparity_data = [disparity_avg5, disparity_avg10, disparity_avg20, disparity_avg60, disparity_avg120] disparity_data = [disparity_avg5, disparity_avg20, disparity_avg60, disparity_avg120, disparity_avg200]
macd_data = [macd_line, macd_s_line, macd_o_line] macd_data = [macd_line, macd_s_line, macd_o_line]
stochastic_data = [slow_k_line, slow_d_line] stochastic_data = [slow_k_line, slow_d_line]
rsi_data = [rsi_line, rsis_line] rsi_data = [rsi_line, rsis_line]
@@ -191,23 +196,24 @@ class Simulation (HTS):
for given_day in stock_codes[stock_code]: for given_day in stock_codes[stock_code]:
LAST_DATA = self.stock2Vector.getLastData(stock_code, given_day) LAST_DATA = self.stock2Vector.getLastData(stock_code, given_day)
result = self.stock2Vector.getRealTime(stock_code, given_day, LAST_DATA) result = self.stock2Vector.getRealTime(stock_code, given_day, LAST_DATA)
result_5 = self.makeTickData(result, mins=5) #result_5 = self.makeTickData(result, mins=5)
result_30 = self.makeTickData(result, mins=30) #result_30 = self.makeTickData(result, mins=30)
data = self.buySellChecker.analyze(result) data = self.buySellChecker.analyze(result)
data.drop(data.index[:len(data) - analyzed_day], inplace=True) data.drop(data.index[:len(data) - analyzed_day], inplace=True)
# 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다. # 이동평균, RSI, MACD, 일목균형, 볼린저밴드 상/하단을 계산한다.
data_5 = self.buySellChecker.analyze(result_5) #data_5 = self.buySellChecker.analyze(result_5)
# 분석일 데이터만 활용한다 (이전 데이터는 제거) # 분석일 데이터만 활용한다 (이전 데이터는 제거)
data_5.drop(data_5.index[:len(data_5) - analyzed_day], inplace=True) #data_5.drop(data_5.index[:len(data_5) - analyzed_day], inplace=True)
data_30 = self.buySellChecker.analyze(result_30) #data_30 = self.buySellChecker.analyze(result_30)
# 분석일 데이터만 활용한다 (이전 데이터는 제거) # 분석일 데이터만 활용한다 (이전 데이터는 제거)
data_30.drop(data_30.index[:len(data_30) - analyzed_day], inplace=True) #data_30.drop(data_30.index[:len(data_30) - analyzed_day], inplace=True)
# 사야 할 시점과 팔아야 할 시점을 체크한다. # 사야 할 시점과 팔아야 할 시점을 체크한다.
bsLine = self.buySellChecker.checkTransaction(stock_code, data, data_5, data_30, isRealTime=False) #bsLine = self.buySellChecker.checkTransaction(stock_code, data, data_5, data_30, isRealTime=False)
bsLine = self.buySellChecker.checkTransaction(stock_code, data, None, None, isRealTime=False)
# 그래프를 그린다. # 그래프를 그린다.
self.draw(stock_code, given_day, data, bsLine) self.draw(stock_code, given_day, data, bsLine)
@@ -227,10 +233,10 @@ if __name__ == "__main__":
# to check bying # to check bying
stock_codes = { stock_codes = {
"252670": [ "252670": [
'20220901', '20220902', '20220905', '20220906', '20220907', '20220908','20220913','20220914','20220915','20220916' '20220901', '20220902', '20220905', '20220906'
], ],
"122630": [ "122630": [
'20220901', '20220902', '20220905', '20220906', '20220907', '20220908', '20220913', '20220914', '20220915', '20220916' '20220901', '20220902', '20220905', '20220906'
] ]
} }
#simulation.simulate(stock_codes) #simulation.simulate(stock_codes)

39
hts/BuySellChecker.py
View File

@@ -109,9 +109,10 @@ class BuySellChecker:
return -1 return -1
return 0 return 0
def getBuyPriceAndWeight(self, data, data_5, data_30, i): def getBuyPriceAndWeight(self, i, data, data_5=None, data_30=None):
buy, weight = -1, -1 buy, weight = -1, -1
if data_5 is not None and data_30 is not None:
if len(data_5['slow_k']) <= i or len(data_30['slow_k']) <= i: if len(data_5['slow_k']) <= i or len(data_30['slow_k']) <= i:
return buy, weight return buy, weight
@@ -134,12 +135,18 @@ class BuySellChecker:
if data_5['slow_k'][i - 1] < data_5['slow_d'][i - 1] and data_5['slow_d'][i] < data_30['slow_k'][i]: if data_5['slow_k'][i - 1] < data_5['slow_d'][i - 1] and data_5['slow_d'][i] < data_30['slow_k'][i]:
buy = data['close'][i] buy = data['close'][i]
weight = 0.3 weight = 0.3
else:
if data['avg5'][i-1] < data['avg200'][i-1] and data['avg200'][i] < data['avg5'][i]:
if data['avg60'][i]<data['avg20'][i]<data['avg5'][i]:
buy = data['close'][i]
weight = 0.3
return buy, weight return buy, weight
def getSellPriceAndWeight(self, data, data_5, data_30, i): def getSellPriceAndWeight(self, i, data, data_5=None, data_30=None):
sell, weight = -1, -1 sell, weight = -1, -1
if data_5 is not None and data_30 is not None:
if len(data_5['slow_k']) <= i or len(data_30['slow_k']) <= i: if len(data_5['slow_k']) <= i or len(data_30['slow_k']) <= i:
return sell, weight return sell, weight
@@ -155,6 +162,10 @@ class BuySellChecker:
if data_30['slow_k'][i] > 98 and data_5['slow_k'][i] > 98: if data_30['slow_k'][i] > 98 and data_5['slow_k'][i] > 98:
sell = data['close'][i] sell = data['close'][i]
weight = 100 weight = 100
else:
if data['avg200'][i-1] < data['avg5'][i-1] and data['avg5'][i] < data['avg200'][i]:
sell = data['close'][i]
weight = 100
return sell, weight return sell, weight
@@ -247,18 +258,20 @@ class BuySellChecker:
avg60 = [item[0] for item in avg60_list] avg60 = [item[0] for item in avg60_list]
avg120_list = close_df.rolling(window=120).mean().fillna(close[0]).values.tolist() avg120_list = close_df.rolling(window=120).mean().fillna(close[0]).values.tolist()
avg120 = [item[0] for item in avg120_list] avg120 = [item[0] for item in avg120_list]
avg200_list = close_df.rolling(window=200).mean().fillna(close[0]).values.tolist()
avg200 = [item[0] for item in avg120_list]
open_df = pd.DataFrame(close) open_df = pd.DataFrame(close)
disparity_avg5_list = (open_df / close_df.rolling(window=5).mean()).values.tolist() disparity_avg5_list = (open_df / close_df.rolling(window=5).mean()).values.tolist()
disparity_avg5 = [item[0] for item in disparity_avg5_list] disparity_avg5 = [item[0] for item in disparity_avg5_list]
disparity_avg10_list = (open_df / close_df.rolling(window=10).mean()).values.tolist()
disparity_avg10 = [item[0] for item in disparity_avg10_list]
disparity_avg20_list = (open_df / close_df.rolling(window=20).mean()).values.tolist() disparity_avg20_list = (open_df / close_df.rolling(window=20).mean()).values.tolist()
disparity_avg20 = [item[0] for item in disparity_avg20_list] disparity_avg20 = [item[0] for item in disparity_avg20_list]
disparity_avg60_list = (open_df / close_df.rolling(window=60).mean()).values.tolist() disparity_avg60_list = (open_df / close_df.rolling(window=60).mean()).values.tolist()
disparity_avg60 = [item[0] for item in disparity_avg60_list] disparity_avg60 = [item[0] for item in disparity_avg60_list]
disparity_avg120_list = (open_df / close_df.rolling(window=120).mean()).values.tolist() disparity_avg120_list = (open_df / close_df.rolling(window=120).mean()).values.tolist()
disparity_avg120 = [item[0] for item in disparity_avg120_list] disparity_avg120 = [item[0] for item in disparity_avg120_list]
disparity_avg200_list = (open_df / close_df.rolling(window=200).mean()).values.tolist()
disparity_avg200 = [item[0] for item in disparity_avg200_list]
# 볼린져 밴드 # 볼린져 밴드
df = pd.DataFrame(close) df = pd.DataFrame(close)
@@ -280,7 +293,7 @@ class BuySellChecker:
STOCK = [] STOCK = []
for i in range(len(open)): for i in range(len(open)):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i], STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg5': avg5[i], 'avg20': avg20[i], 'avg30': avg30[i], 'avg60': avg60[i], 'avg120': avg120[i]}) 'avg5': avg5[i], 'avg20': avg20[i], 'avg60': avg60[i], 'avg120': avg120[i], 'avg200': avg200[i]})
# stochastic # stochastic
stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5) stochastic_df = self.stochastic.apply(STOCK, n=30, m=5, t=5)
@@ -312,9 +325,9 @@ class BuySellChecker:
temp = { temp = {
"date": point_temp, "date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "open": open, "high": high, "low": low, "close": close, "volume": vol,
"avg5": avg5, "avg20": avg20, "avg30": avg30, "avg60": avg60, "avg120": avg120, "avg5": avg5, "avg20": avg20, "avg60": avg60, "avg120": avg120, "avg200": avg200,
"disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20, "disparity_avg5": disparity_avg5, "disparity_avg20": disparity_avg20,
"disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120, "disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120, "disparity_avg200": disparity_avg200,
"upper": upper, "lower": lower, "upper": upper, "lower": lower,
"macd": macd, "macds": macds, "macdo": macdo, "macd": macd, "macds": macds, "macdo": macdo,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
@@ -726,7 +739,7 @@ class BuySellChecker:
outFp.write(str(df["label"][i]) + "\n") outFp.write(str(df["label"][i]) + "\n")
return return
def checkTransaction(self, stock_code, data, data_5, data_30, isRealTime=True): def checkTransaction(self, stock_code, data, data_5=None, data_30=None, isRealTime=True):
# 어제 오늘 데이터로 분석 # 어제 오늘 데이터로 분석
bsLine = {} bsLine = {}
size = len(data["close"]) size = len(data["close"])
@@ -735,8 +748,8 @@ class BuySellChecker:
# isRealTime=True, 실시간 적용 # isRealTime=True, 실시간 적용
last_index = size - 1 last_index = size - 1
buy, buy_weight = self.getBuyPriceAndWeight(data, data_5, data_30, last_index) buy, buy_weight = self.getBuyPriceAndWeight(last_index, data, data_5, data_30)
sell, sell_weight = self.getSellPriceAndWeight(data, data_5, data_30, last_index) sell, sell_weight = self.getSellPriceAndWeight(last_index, data, data_5, data_30)
bsLine['buy'] = [buy] bsLine['buy'] = [buy]
bsLine['buy_weight'] = [buy_weight] bsLine['buy_weight'] = [buy_weight]
@@ -751,8 +764,8 @@ class BuySellChecker:
bsLine['sell_weight'] = [-1 for i in range(size)] bsLine['sell_weight'] = [-1 for i in range(size)]
for i in range(size): for i in range(size):
buy, buy_weight = self.getBuyPriceAndWeight(data, data_5, data_30, i) buy, buy_weight = self.getBuyPriceAndWeight(i, data, data_5, data_30)
sell, sell_weight = self.getSellPriceAndWeight(data, data_5, data_30, i) sell, sell_weight = self.getSellPriceAndWeight(i, data, data_5, data_30)
bsLine['buy'][i] = buy bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = buy_weight bsLine['buy_weight'][i] = buy_weight