fixed some hierarchial stuff

market_trade/core/CoreDraw.py

@@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import CoreTraidMath 
+import market_trade.core.CoreTraidMath
 import plotly.express as px
 
 

market_trade/core/Ind_Alligator.py

@@ -46,7 +46,7 @@ class Alligator:
                                  'valueType':self.options['valueType'],
                                  'window':self.options[keyAns]['window']}
             }
-        ans=CoreTraidMath.CoreMath(self.base_df,op).ans
+        ans=market_trade.core.CoreTraidMath.CoreMath(self.base_df,op).ans
         
         return ans
     

market_trade/core/Ind_Envelopes.py

@@ -64,7 +64,7 @@ class Envelopes:
         }
         if dictResp['MeanType']=='SMA':
 
-            y=CoreTraidMath.CoreMath(self.base_df,op).ans
+            y=market_trade.core.CoreTraidMath.CoreMath(self.base_df,op).ans
             ans['MainEnv']=y[:len(y)-self.options['shift']]
             ans['PlusEnv']=ans['MainEnv']*(1+self.options['kProc']/100)
             ans['MinusEnv']=ans['MainEnv']*(1-self.options['kProc']/100)

market_trade/core/Ind_Ishimoku.py

@@ -23,7 +23,7 @@ import plotly.graph_objs as go
 # these two lines allow your code to show up in a notebook
 from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
-import CoreDraw
+import market_trade.core.CoreDraw
 init_notebook_mode()
 import market_trade.core.CoreTraidMath
 import plotly.express as px

market_trade/core/Ind_Stochastic.py

@@ -69,7 +69,7 @@ class Stochastic:
             'action':'findMean',
             'actionOptions':{'MeanType':'SMA','window':self.options['windowSMA']}
         }        
-        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)        
+        ans=np.asarray(market_trade.core.CoreTraidMath.CoreMath(ser,op).ans)
         return ans
         #return np.convolve(col, np.ones(self.options['windowSMA']), 'valid') /self.options['windowSMA']
         

market_trade/core/decisionManager.md

@@ -2,9 +2,9 @@ project:: #bibasCopy
 # Импорт
 Для начала импортим все что нужно
 ```python
-from decisionManager_v2 import *
+from market_trade.core.decisionManager_v2 import *
-from indicators_v2 import *
+from market_trade.core.indicators_v2 import *
-from signals_v2 import *
+from market_trade.core.signals_v2 import *
 ```
 
 Подготавливаем данные по которым собирется модель. Модель представляет из себя словарь в следующем формате:
@@ -174,9 +174,9 @@ test.generateMatrixProbabilityFromDict(retroAns)
 ```
 ## Итоговый код
 ```python
-from decisionManager_v2 import *
+from market_trade.core.decisionManager_v2 import *
-from indicators_v2 import *
+from market_trade.core.indicators_v2 import *
-from signals_v2 import *
+from market_trade.core.signals_v2 import *
 import pandas as pd
 
 

market_trade/core/decisionManager_v2.py

@@ -6,11 +6,11 @@ import numpy as np
 
 from tqdm import tqdm
 
-from indicators_v2 import *
+from market_trade.core.indicators_v2 import *
-from signals_v2 import *
+from market_trade.core.signals_v2 import *
-from dealManager import *
+from market_trade.core.dealManager import *
-from trandeVoter import *
+from market_trade.core.trandeVoter import *
-from riskManager import *
+from market_trade.core.riskManager import *
 import pickle
 
 

market_trade/core/indicators.py

@@ -2,8 +2,8 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import CoreTraidMath 
+import market_trade.core.CoreTraidMath
-import CoreDraw
+import market_trade.core.CoreDraw
 
 class coreIndicator():
     def __init__(self,
@@ -31,7 +31,7 @@ class coreIndicator():
                 self.getFig()
         return self.ans
     def getFig(self,row=1):
-        CoreDraw.coreDraw(self.figDict,True)
+        market_trade.core.CoreDraw.market_trade.core.CoreDraw(self.figDict,True)
     def getCalculate(self):
         return "Error"
     def getFigDict(self):
@@ -79,7 +79,7 @@ class indicatorAgrigator():
                 req[0].append(i)
             else:
                 req.append([i])
-        CoreDraw.agrigateFig(req,True)
+        market_trade.core.CoreDraw.agrigateFig(req,True)
     def runAll(self,indList,df,needDraw=False):
         self.createIndFromList(indList)
         self.calculateInd(df)
@@ -99,12 +99,12 @@ class ind_BB(coreIndicator):
                 'window':self.options['window']
             }
         }
-        ans['BB']=CoreTraidMath.CoreMath(self.data,opMA).ans
+        ans['BB']=market_trade.core.CoreTraidMath.CoreMath(self.data,opMA).ans
         opSTD={'dataType':'ohcl',
             'action':'findSTD',
             'actionOptions':{'valueType':self.options['valueType'],'window':self.options['window']}
         }
-        ans['STD']=CoreTraidMath.CoreMath(self.data,opSTD).ans
+        ans['STD']=market_trade.core.CoreTraidMath.CoreMath(self.data,opSTD).ans
         ans['pSTD']=ans['BB']+ans['STD']*self.options['kDev']
         ans['mSTD']=ans['BB']-ans['STD']*self.options['kDev']
         ans['x']=np.array(self.data['date'][self.options['window']-1:].to_list())

market_trade/core/indicators_v2.py

@@ -2,7 +2,7 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import CoreTraidMath 
+import market_trade.core.CoreTraidMath
 
 class coreIndicator():
     
@@ -75,12 +75,12 @@ class ind_BB(coreIndicator):
                 'window':self.options['window']
             }
         }
-        ans['BB']=CoreTraidMath.CoreMath(data,opMA).ans
+        ans['BB']=market_trade.core.CoreTraidMath.CoreMath(data,opMA).ans
         opSTD={'dataType':'ohcl',
             'action':'findSTD',
             'actionOptions':{'valueType':self.options['valueType'],'window':self.options['window']}
         }
-        ans['STD']=CoreTraidMath.CoreMath(data,opSTD).ans
+        ans['STD']=market_trade.core.CoreTraidMath.CoreMath(data,opSTD).ans
         ans['pSTD']=ans['BB']+ans['STD']*self.options['kDev']
         ans['mSTD']=ans['BB']-ans['STD']*self.options['kDev']
         ans['x']=np.array(data['date'][self.options['window']-1:].to_list())

market_trade/core/signals.py

@@ -2,11 +2,11 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import CoreTraidMath 
+import market_trade.core.CoreTraidMath
-import CoreDraw
+import market_trade.core.CoreDraw
 from tqdm import tqdm
 
-from indicators import *
+from market_trade.core.indicators import *
 
 class coreSignalTrande():
     def __init__(self,

market_trade/core/signals_v2.py

@@ -2,11 +2,11 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import CoreTraidMath 
+import market_trade.core.CoreTraidMath
-#import CoreDraw
+#import market_trade.core.CoreDraw
 from tqdm import tqdm
 
-from indicators_v2 import *
+from market_trade.core.indicators_v2 import *
 
 
 

market_trade/data/dataloader.py

@@ -22,7 +22,7 @@ class DukaMTInterface:
         # droppnig old timestamp index
         self.duka_dataset.reset_index(inplace=True, drop=True)
 
-
+        print(self.duka_dataset)
         # adding bids
         self.bid_candlesticks = self.duka_dataset['bid'].copy()
         self.bid_candlesticks['date'] = self.duka_dataset['date']

market_trade/tests/test_decision.py

@@ -1 +1,50 @@
-im
+from market_trade.core.decisionManager_v2 import *
+from market_trade.core.indicators_v2 import *
+from market_trade.core.signals_v2 import *
+import market_trade.data.dataloader
+
+
+sigAgrReq = {
+    'sig_BB':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}
+            }
+        }
+    },
+    'sig_BB_2':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
+            }
+        }
+    }
+}
+
+test = decsionManager('Pipa', sigAgrReq)
+
+
+import pandas as pd
+
+df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv")
+df_candle["date"] = df_candle["timestamp"]
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }
+}
+
+retroAns = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[:5000],40)
+
+print(test.generateMatrixProbabilityFromDict(retroAns))