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dsyoon
2023-01-29 20:42:23 +09:00
parent 6988b7461d
commit 64ea82570b
3 changed files with 84 additions and 126 deletions
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2
HTS_252670.py
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@@ -125,7 +125,7 @@ class HTS_252670 (HTS):
# 미체결 기록을 가져온다. # 미체결 기록을 가져온다.
ORDER_LIST = self.requestOrderList() ORDER_LIST = self.requestOrderList()
# 매도 주문을 기록을 가져온다. # 매도 주문을 기록을 가져온다.
orderListToCancel = self.orderChecker.remove(self.stock_code, OrderType.sell, ORDER_LIST) orderListToCancel = self.orderChecker.delete(self.stock_code, OrderType.sell, ORDER_LIST)
# 매도 미체결을 모두 취소한다. # 매도 미체결을 모두 취소한다.
self.cancelOrderList(orderListToCancel) self.cancelOrderList(orderListToCancel)

55
Simulation.py
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@@ -38,9 +38,9 @@ class Simulation (HTS):
# 어제 데이터는 지운다. # 어제 데이터는 지운다.
data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])] data = data.loc[pd.DatetimeIndex(data.index).day == int(given_day[6:])]
buy_line = bsLine['buy'][381:] buy_line = bsLine['buy'][len(bsLine['buy'])-len(data):]
buy_weight_line = bsLine['buy_weight'][381:] buy_weight_line = bsLine['buy_weight'][len(bsLine['buy'])-len(data):]
sell_line = bsLine['sell'][381:] sell_line = bsLine['sell'][len(bsLine['buy'])-len(data):]
# 그래프 설정을 위한 변수를 생성한다. # 그래프 설정을 위한 변수를 생성한다.
data = data.astype({'open': 'int', data = data.astype({'open': 'int',
@@ -48,11 +48,11 @@ class Simulation (HTS):
'low': 'int', 'low': 'int',
'close': 'int', 'close': 'int',
'volume': 'int', 'volume': 'int',
'avg3': 'float', 'avg5': 'float',
'avg6': 'float',
'avg9': 'float',
'avg12': 'float',
'avg20': 'float', 'avg20': 'float',
'avg30': 'float',
'avg60': 'float',
'avg120': 'float',
'fast_k': 'float', 'fast_k': 'float',
'slow_k': 'float', 'slow_k': 'float',
'slow_d': 'float', 'slow_d': 'float',
@@ -84,11 +84,11 @@ 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')
avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#8F8203') avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#8F8203')
avg6 = go.Scatter(x=data['date'], y=data["avg6"], name="avg6", line_color='#089B5B') avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#089B5B')
avg9 = go.Scatter(x=data['date'], y=data["avg9"], name="avg9", line_color='#ff00ff') avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#ff00ff')
avg12 = go.Scatter(x=data['date'], y=data["avg12"], name="avg12", line_color='#1469F4') avg60 = go.Scatter(x=data['date'], y=data["avg60"], name="avg60", line_color='#1469F4')
avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#000000') avg120 = go.Scatter(x=data['date'], y=data["avg120"], name="avg120", 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')
@@ -114,7 +114,7 @@ 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, avg3, avg6, avg9, avg12, avg20, buy_check, sell_check, laggingSpan, changeLine, baseLine] candle_data = [candle_stick, upper, lower, avg5, avg20, avg30, avg60, avg120, 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_avg10, disparity_avg20, disparity_avg60, disparity_avg120]
macd_data = [macd_line, macd_s_line, macd_o_line] macd_data = [macd_line, macd_s_line, macd_o_line]
@@ -174,30 +174,41 @@ class Simulation (HTS):
"label": []} "label": []}
for i in range(mins, len(data['time'])+1): for i in range(mins, len(data['time'])+1):
result["check"].add(data['time'][i]) result["check"].add(data['time'][i-1])
result["time"].append(data['time'][i]) result["time"].append(data['time'][i-1])
result["open"].append(data['open'][i-mins]) result["open"].append(data['open'][i-mins])
result["close"].append(data['close'][i-1]) result["close"].append(data['close'][i-1])
result["high"].append(max(data['high'][i - 30: i])) result["high"].append(max(data['high'][i - mins: i]))
result["low"].append(min(data['low'][i - 30: i])) result["low"].append(min(data['low'][i - mins: i]))
result["vol"].append(sum(data[i - 30: i])) result["vol"].append(sum(data['vol'][i - mins: i]))
return result return result
def simulate(self, stock_codes:dict=None): def simulate(self, stock_codes:dict=None, analyzed_day=1000):
if stock_codes is not None: if stock_codes is not None:
for stock_code in stock_codes: for stock_code in stock_codes:
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_30 = self.makeTickData(result, min=30) result_5 = self.makeTickData(result, mins=5)
result_30 = self.makeTickData(result, mins=30)
data = self.buySellChecker.analyze(result)
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.
data1 = self.buySellChecker.analyze(result) data_5 = self.buySellChecker.analyze(result_5)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
data_5.drop(data_5.index[:len(data_5) - analyzed_day], inplace=True)
data_30 = self.buySellChecker.analyze(result_30)
# 분석일 데이터만 활용한다 (이전 데이터는 제거)
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, data = self.buySellChecker.checkTransaction(data, stock_code, isRealTime=False) bsLine = self.buySellChecker.checkTransaction(stock_code, data, data_5, data_30, isRealTime=False)
# 그래프를 그린다. # 그래프를 그린다.
self.draw(stock_code, given_day, data, bsLine) self.draw(stock_code, given_day, data, bsLine)

153
hts/BuySellChecker.py
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@@ -109,73 +109,49 @@ class BuySellChecker:
return -1 return -1
return 0 return 0
def getBuyPriceAndWeight(self, data, i): def getBuyPriceAndWeight(self, data, data_5, data_30, i):
buy, weight, type = -1, -1, -1 buy, weight = -1, -1
START_TIME_INDEX = 0 if data_5['slow_k'][i] < 20:
for c in range(370, len(data.index)): if data_5['slow_k'][i - 1] < data_5['slow_d'][i - 1] and data_5['slow_d'][i] < data_5['slow_k'][i]:
if data.index[c].strftime("%H:%M:%S") == "09:01:00": buy = data['low'][i]
START_TIME_INDEX = c weight = 0.3
break
if START_TIME_INDEX + 10 < i < START_TIME_INDEX + 350: if data_30['slow_k'][i] < 30 and data_5['slow_k'][i] < 30:
# 매수 분석 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]
weight = 0.3
if data['macd'][i] < -7: if data_30['slow_k'][i] < 30:
buy = max(data["open"][i], data["close"][i]) if data_5['slow_k'][i] < 30:
weight = 1 if data_5['avg5'][i] < data_5['close'][i]:
type = 1 buy = data['close'][i]
return buy, weight, type weight = 0.2
""" 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['changeLine'][i - 1] <= data['baseLine'][i - 1] and data['baseLine'][i] < data['changeLine'][i]: buy = data['close'][i]
changeLine_count = sum([1 if data['changeLine'][c] <= data['baseLine'][c] else 0 for c in range(i-30, i-10)]) weight = 0.3
if changeLine_count >= 15:
changeLine_count = sum([1 if data['changeLine'][c] <= data['baseLine'][c] else 0 for c in range(i-10, i)])
if changeLine_count >= 7:
buy = min(data["open"][i], data["close"][i])
weight = 1
type = 1
return buy, weight, type
"""
""" return buy, weight
if i > 381 + 26:
if data['laggingSpan'][i-1] < data['avg3'][i-1] and data['avg3'][i] < data['laggingSpan'][i]:
if self.checkUpDirection(data, i) == 1:
avg20_line = sum([1 if data['avg20'][c] < data['avg20'][c-1] else 0 for c in range(i - 10, i)])
if avg20_line < 10:
if data["macd"][i] < 0:
buy = min(data["open"][i], data["close"][i])
weight = 1
type = 1
return buy, weight, type
"""
return buy, weight, type
def getSellPriceAndWeight(self, data, i): def getSellPriceAndWeight(self, data, data_5, data_30, i):
sell, weight, type = -1, -1, -1 sell, weight = -1, -1
START_TIME_INDEX = 0 if data_30['slow_k'][i] > 90:
for c in range(370, len(data.index)): if (data_5['slow_d'][i-1] < data_5['slow_k'][i-1] and data_5['slow_k'][i] < data_5['slow_d'][i]):
if data.index[c].strftime("%H:%M:%S") == "09:01:00": sell = data['close'][i]
START_TIME_INDEX = c weight = 100
break
if i > START_TIME_INDEX: if data_5['slow_k'][i] > 95 and data_5['slow_k'][i] < data_5['slow_d'][i]:
# 매도 분석 sell = data['close'][i]
weight = 100
if data['changeLine'][i - 1] >= data['laggingSpan'][i - 1] and data['laggingSpan'][i] < data['changeLine'][ if data_30['slow_k'][i] > 98 and data_5['slow_k'][i] > 98:
i]: sell = data['close'][i]
changeLine_count = sum( weight = 100
[1 if data['changeLine'][c] <= data['laggingSpan'][c] else 0 for c in range(i - 20, i)])
if changeLine_count >= 17: return sell, weight
sell = min(data["open"][i], data["close"][i])
weight = 1
type = 1
return sell, weight, type
return sell, weight, type
def getBuyPriceAndWeight_122630(self, data, i): def getBuyPriceAndWeight_122630(self, data, i):
buy, weight, type = -1, -1, -1 buy, weight, type = -1, -1, -1
@@ -252,20 +228,19 @@ class BuySellChecker:
high = result["high"] high = result["high"]
low = result["low"] low = result["low"]
vol = result["vol"] vol = result["vol"]
label = result["label"]
# 이동 평균 # 이동 평균
close_df = pd.DataFrame(close) close_df = pd.DataFrame(close)
avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist() avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
avg3 = [item[0] for item in avg3_list] avg5 = [item[0] for item in avg5_list]
avg6_list = close_df.rolling(window=6).mean().fillna(close[0]).values.tolist()
avg6 = [item[0] for item in avg6_list]
avg9_list = close_df.rolling(window=9).mean().fillna(close[0]).values.tolist()
avg9 = [item[0] for item in avg9_list]
avg12_list = close_df.rolling(window=12).mean().fillna(close[0]).values.tolist()
avg12 = [item[0] for item in avg12_list]
avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist() avg20_list = close_df.rolling(window=20).mean().fillna(close[0]).values.tolist()
avg20 = [item[0] for item in avg20_list] avg20 = [item[0] for item in avg20_list]
avg30_list = close_df.rolling(window=30).mean().fillna(close[0]).values.tolist()
avg30 = [item[0] for item in avg30_list]
avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
avg60 = [item[0] for item in avg60_list]
avg120_list = close_df.rolling(window=120).mean().fillna(close[0]).values.tolist()
avg120 = [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()
@@ -299,7 +274,7 @@ 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],
'avg20': avg20[i]}) 'avg5': avg5[i], 'avg20': avg20[i], 'avg30': avg30[i], 'avg60': avg60[i], 'avg120': avg120[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)
@@ -331,7 +306,7 @@ class BuySellChecker:
temp = { temp = {
"date": point_temp, "date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol, "open": open, "high": high, "low": low, "close": close, "volume": vol,
"avg3": avg3, "avg6": avg6, "avg9": avg9, "avg12": avg12, "avg20": avg20, "avg5": avg5, "avg20": avg20, "avg30": avg30, "avg60": avg60, "avg120": avg120,
"disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20, "disparity_avg5": disparity_avg5, "disparity_avg10": disparity_avg10, "disparity_avg20": disparity_avg20,
"disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120, "disparity_avg60": disparity_avg60, "disparity_avg120": disparity_avg120,
"upper": upper, "lower": lower, "upper": upper, "lower": lower,
@@ -340,15 +315,13 @@ class BuySellChecker:
"rsi": rsi, "rsis": rsis, "rsi": rsi, "rsis": rsis,
"changeLine": changeLine, "baseLine": baseLine, "laggingSpan": laggingSpan, "leadingSpan1": leadingSpan1, "changeLine": changeLine, "baseLine": baseLine, "laggingSpan": laggingSpan, "leadingSpan1": leadingSpan1,
"leadingSpan2": leadingSpan2, "leadingSpan2": leadingSpan2,
"label": label
} }
data = pd.DataFrame(temp) data = pd.DataFrame(temp)
df_final_time = pd.DatetimeIndex(point_temp) df_final_time = pd.DatetimeIndex(point_temp)
data.index = df_final_time data.index = df_final_time
data = data.fillna(close[0]) data = data.fillna(-1)
return data return data
def analyze1(self, result): def analyze1(self, result):
@@ -747,12 +720,7 @@ class BuySellChecker:
outFp.write(str(df["label"][i]) + "\n") outFp.write(str(df["label"][i]) + "\n")
return return
def checkTransaction(self, data, stock_code, isRealTime=True): def checkTransaction(self, stock_code, data, data_5, data_30, isRealTime=True):
# 4일치 중에서 앞에 2일은 제거한다.
date = data['date'].dt.date.unique().tolist()
data = data[data['date'].dt.date != date[0]]
data = data[data['date'].dt.date != date[1]]
# 어제 오늘 데이터로 분석 # 어제 오늘 데이터로 분석
bsLine = {} bsLine = {}
size = len(data["close"]) size = len(data["close"])
@@ -761,22 +729,14 @@ class BuySellChecker:
# isRealTime=True, 실시간 적용 # isRealTime=True, 실시간 적용
last_index = size - 1 last_index = size - 1
buy, buy_weight, buy_type = self.getBuyPriceAndWeight(data, last_index) buy, buy_weight = self.getBuyPriceAndWeight(data, data_5, data_30, last_index)
sell, sell_weight, sell_type = self.getSellPriceAndWeight(data, last_index) sell, sell_weight = self.getSellPriceAndWeight(data, data_5, data_30, last_index)
if buy > -1 or self.BUY_COUNT == 1:
if buy == -1 or self.BUY_COUNT == 1:
buy = min(data['open'][last_index], data['close'][last_index])
buy_weight = 1
self.BUY_COUNT += 1
bsLine['buy'] = [buy] bsLine['buy'] = [buy]
bsLine['buy_weight'] = [buy_weight] bsLine['buy_weight'] = [buy_weight]
bsLine['sell'] = [sell] bsLine['sell'] = [sell]
bsLine['sell_weight'] = [sell_weight] bsLine['sell_weight'] = [sell_weight]
if self.BUY_COUNT > 1:
self.BUY_COUNT = 0
else: else:
# Type=False, 시뮬레이션 적용 # Type=False, 시뮬레이션 적용
bsLine['buy'] = [-1 for i in range(size)] bsLine['buy'] = [-1 for i in range(size)]
@@ -785,28 +745,15 @@ 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):
if stock_code == "252670": buy, buy_weight = self.getBuyPriceAndWeight(data, data_5, data_30, i)
buy, buy_weight, buy_type = self.getBuyPriceAndWeight(data, i) sell, sell_weight = self.getSellPriceAndWeight(data, data_5, data_30, i)
sell, sell_weight, sell_type = self.getSellPriceAndWeight(data, i)
else:
buy, buy_weight, buy_type = self.getBuyPriceAndWeight_122630(data, i)
sell, sell_weight, sell_type = self.getSellPriceAndWeight_122630(data, i)
if buy > -1 or self.BUY_COUNT == 1:
if buy == -1 or self.BUY_COUNT == 1:
buy = data['low'][i]
buy_weight = 1
self.BUY_COUNT += 1
bsLine['buy'][i] = buy bsLine['buy'][i] = buy
bsLine['buy_weight'][i] = buy_weight bsLine['buy_weight'][i] = buy_weight
bsLine['sell'][i] = sell bsLine['sell'][i] = sell
bsLine['sell_weight'][i] = sell_weight bsLine['sell_weight'][i] = sell_weight
if self.BUY_COUNT > 1: return bsLine
self.BUY_COUNT = 0
return bsLine, data
def checkTransactionML(self, data, stock_code, predY, isRealTime=True): def checkTransactionML(self, data, stock_code, predY, isRealTime=True):
# 4일치 중에서 앞에 2일은 제거한다. # 4일치 중에서 앞에 2일은 제거한다.