init

hts/BuySellChecker.py

@@ -304,6 +304,10 @@ class BuySellChecker:
                 if data["macd"][i-3] > data["macd"][i-2] and data["macd"][i-2] > data["macd"][i-1] and data["macd"][i-1] < data["macd"][i]:
                     buy = data["close"][i]
 
+            if data["slow_d"][i] > 90 and data["rsi"][i] > 65:
+                if data["upper"][i] <= data["high"][i]:
+                    sell = data["close"][i] - 5
+
         return buy, weight, sell
 
 

hts/HTS_252670.py

@@ -164,7 +164,7 @@ if __name__ == "__main__":
 
     # KODEX 인버스 * 2
     stock_code = "252670"
-    buy_count = 120
+    buy_count = 100
 
     hts = HTS_252670(stock_code, buy_count)
     today_str = today.strftime('%Y%m%d')

hts/Simulation.py

@@ -94,7 +94,7 @@ class Simulation:
         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')
         avg3 = go.Scatter(x=data['date'], y=data["avg3"], name="avg3", line_color='#000000')
-        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#0A9127')
+        avg5 = go.Scatter(x=data['date'], y=data["avg5"], name="avg5", line_color='#085F1B')
         avg10 = go.Scatter(x=data['date'], y=data["avg10"], name="avg10", line_color='#ff00ff')
         avg20 = go.Scatter(x=data['date'], y=data["avg20"], name="avg20", line_color='#1469F4')
         avg30 = go.Scatter(x=data['date'], y=data["avg30"], name="avg30", line_color='#FFA500')
@@ -181,10 +181,13 @@ class Simulation:
 if __name__ == "__main__":
 
     stock_codes = {
-        # "252670": ['20220620', '20220621', '20220622', '20220623', '20220624'],
+        #"252670": [('20220620', '20220621'), ('20220621', '20220622'), ('20220622', '20220623'), ('20220623', '20220624')],
-        # "122630": ['20220620', '20220621', '20220622', '20220623', '20220624']
+        "122630": [('20220620', '20220621'),
-        "252670": [('20220620', '20220621')],
+                   ('20220621', '20220622'),
-        "122630": [('20220620', '20220621')]
+                   ('20220622', '20220623'),
+                   ('20220623', '20220624')],
+        # "252670": [('20220620', '20220621')],
+        # "122630": [('20220620', '20220621')]
     }
 
     for stock_code in stock_codes:

hts/timecheck.csv

@@ -1,4 +1,6 @@
 time, check
+090103,False
+090203,False
 090303,False
 090403,False
 090503,False

stockpredictor/analysis/MACD.py

@@ -1,9 +1,5 @@
 import pandas as pd
 from stockpredictor.analysis.Common import Common
-from plotly import tools, subplots
-import plotly.offline as offline
-import plotly.graph_objs as go
-import plotly.io as po
 
 # [청송촌놈] 파생을 알아야 시장이 보인다. 청송이 종목 고르는법! https://www.youtube.com/watch?v=weABtgZDeGg
 # 6. Pandas와 Plotly를 이용한 MACD 차트 그리기 https://excelsior-cjh.tistory.com/110
@@ -20,31 +16,6 @@ class MACD:
         self.common = Common()
         return
 
-    def draw(self, stock):
-        item_name = stock["NAME"]
-        item_code = stock["CODE"]
-
-        df = pd.DataFrame(stock["PRICE"])
-        macd = go.Scatter(x=df.DATE, y=df['macd'], name="MACD")
-        signal = go.Scatter(x=df.DATE, y=df['macds'], name="Signal")
-        oscillator = go.Bar(x=df.DATE, y=df['macdo'], name="oscillator")
-        trade_volume = go.Bar(x=df.DATE, y=df['volume'], name="volume")
-        data = [macd, signal, oscillator]
-
-        layout = go.Layout(title='{} MACD 그래프'.format(item_name))
-        fig = subplots.make_subplots(rows=2, cols=1, shared_xaxes=True)
-
-        for trace in data:
-            fig.append_trace(trace, 1,1)
-
-        fig.append_trace(trade_volume, 2,1)
-        fig = go.Figure(data=data, layout=layout)
-
-        path = "/Users/dsyoon/workspace/StockPredictor/resources/analysis/html"
-        po.write_html(fig, file=path + "/macd_" + item_code+'.html', auto_open=False)
-        return fig
-
-
     # macd 0선 위에서 매수를 한다. 0이하는 절대 처다보지 않는다.
     def apply(self, df, short=12, long=26, t=9):
         # 입력받은 값이 dataframe이라는 것을 정의해줌