스토캐스틱, RSI 추가

hts/HTS.py

@@ -1,12 +1,15 @@
-import win32com.client
+#import win32com.client
 import time
 import os
 from datetime import datetime, timedelta
 import pandas as pd
 from enum import Enum
-#import plotly.graph_objects as go
+import plotly.graph_objects as go
 from stockpredictor.analysis.Common import Common
 
+from stockpredictor.analysis.Stochastic import Stochastic
+from stockpredictor.analysis.RSI import RSI
+
 # enum 주문 상태 세팅용
 class EorderBS(Enum):
     buy = 1          # 매수
@@ -32,10 +35,14 @@ class HTS:
     objCpCodeMgr = None
     common = None
 
-    stock = []
+    stochastic = None
+    rsi = None
 
     def __init__(self):
         self.common = Common()
+        self.stochastic = Stochastic()
+        self.rsi = RSI()
+
         #self.connect()
         return
 
@@ -558,7 +565,6 @@ class HTS:
         upper_df = max20 + (stddev20 * 2)                  # 상단 볼린저 밴드
         lower_df = max20 - (stddev20 * 2)                  # 하단 볼린저 밴드
 
-        size = len(result["open"])
         window = 5
         open = result["open"]
         close = result["close"]
@@ -597,7 +603,31 @@ class HTS:
 
         point_temp = result["time"]
 
-        temp = {"Date": point_temp, "Open": open, "High": high, "Low": low, "Close": close, "Volume": vol, "avg1": avg1, "avg2": avg2, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60}
+        STOCK = []
+        for i in range(len(result["open"])):
+            STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i],
+                          'high': high[i], 'low': low[i], 'avg5': avg2[i],
+                          'avg20': avg5[i], 'avg60': avg10[i], 'avg120': avg20[i],
+                          'avg240': avg30[i]})
+
+        # stochastic 계산
+        stochastic_df = self.stochastic.apply(pd.DataFrame(STOCK))
+        stochastic_df = stochastic_df.fillna(0)
+        fast_k = stochastic_df['fast_k'].values.tolist()
+        slow_k = stochastic_df['slow_k'].values.tolist()
+        slow_d = stochastic_df['slow_d'].values.tolist()
+
+        # rsi 계산
+        rsi_df = self.rsi.apply(pd.DataFrame(STOCK))
+        rsi_df = rsi_df.fillna(0)
+        rsi = rsi_df['rsi'].values.tolist()
+        rsis = rsi_df['rsis'].values.tolist()
+
+        temp = {"Date": point_temp,
+                "Open": open, "High": high, "Low": low, "Close": close, "Volume": vol,
+                "avg1": avg1, "avg2": avg2, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60,
+                "fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
+                "rsi": rsi, "rsis": rsis}
         data = pd.DataFrame(temp)
         df_final_time = pd.DatetimeIndex(point_temp)
         data.index = df_final_time
@@ -1115,9 +1145,9 @@ if __name__ == "__main__":
         #for stock_code in stock_codes:
             #hts.simulate(stock_code, given_day)
 
-    given_day = datetime.today().strftime('%Y%m%d')
+    #given_day = datetime.today().strftime('%Y%m%d')
     #hts.writeStockData(stock_codes, given_day)
-    #hts.simulate(stock_codes[0], given_day)
-    hts.buyRealTime(stock_codes[0], given_day)
+    hts.simulate(stock_codes[0], given_days[0])
+    #hts.buyRealTime(stock_codes[0], given_day)
 
     print ("done...")