designed a test which works!

dockerfiles/Dockerfile

@@ -0,0 +1,21 @@
+FROM python:3.11-buster
+
+RUN curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.7.1 python3 -
+
+
+ENV POETRY_NO_INTERACTION=1 \
+    POETRY_VIRTUALENVS_IN_PROJECT=1 \
+    POETRY_VIRTUALENVS_CREATE=1 \
+    POETRY_CACHE_DIR=/tmp/poetry_cache
+
+WORKDIR /app
+
+COPY pyproject.toml poetry.lock /app/
+COPY market_trade /app/market_trade/
+COPY tools /app/tools
+
+RUN mkdir --parents --mode 0700 ~/.ssh && ssh-keyscan github.com >> ~/.ssh/known_hosts
+RUN --mount=type=ssh  --mount=type=cache,target=$POETRY_CACHE_DIR $HOME/.local/bin/poetry install --without dev
+
+ENV VIRTUAL_ENV=/app/.venv \
+    PATH="/app/.venv/bin:$PATH"

dockerfiles/save_currencies_data.Dockerfile

@@ -1,23 +1,3 @@
-FROM python:3.11-buster
+FROM registry.karmaxplan.ru/market_trade:0.2.0
 
-RUN curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.7.1 python3 -
-
-
-ENV POETRY_NO_INTERACTION=1 \
-    POETRY_VIRTUALENVS_IN_PROJECT=1 \
-    POETRY_VIRTUALENVS_CREATE=1 \
-    POETRY_CACHE_DIR=/tmp/poetry_cache
-
-WORKDIR /app
-
-COPY pyproject.toml poetry.lock /app/
-COPY market_trade /app/market_trade/
-COPY tools /app/tools
-
-RUN mkdir --parents --mode 0700 ~/.ssh && ssh-keyscan github.com >> ~/.ssh/known_hosts
-RUN --mount=type=ssh  --mount=type=cache,target=$POETRY_CACHE_DIR $HOME/.local/bin/poetry install --without dev
-
-ENV VIRTUAL_ENV=/app/.venv \
-    PATH="/app/.venv/bin:$PATH"
-
-ENTRYPOINT ["python", "tools/save_currencies_data.py"]
+ENTRYPOINT ["python", "tools/save_currencies_data.py"]

market_trade/core/CoreDraw.py

@@ -15,7 +15,7 @@ import datetime
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 
-import mplfinance as mpf
+#import mplfinance as mpf
 
 import plotly
 #import plotly.plotly as py
@@ -79,9 +79,10 @@ class corePlt():
 
 
 class coreDraw():
-    def __init__(self, data=[],needShow=False):
+    def __init__(self, data=[],needShow=False,subplot_titles={}):
         self.data=self.getPlts(data)
         self.needShow=needShow
+        self.subplot_titles=subplot_titles
         self.ans=self.getAns()
         
         
@@ -156,11 +157,11 @@ class coreDraw():
             rows=maxRow,
             cols=maxCol,
             shared_xaxes=True,
-            vertical_spacing=0.02,
+            vertical_spacing=0.1,
             shared_yaxes=True,
-            horizontal_spacing=0.02,
+            #horizontal_spacing=0.02,
             #column_widths=[]
-        
+            subplot_titles=self.subplot_titles
         )
         
         
@@ -188,7 +189,7 @@ class coreDraw():
                         except:
                             colorType='normal'
                         colors=self.getBarColorList(i.df[j],colorType)
-                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors))
+                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors),row=i.row, col=i.col)
                 
         
         
@@ -196,4 +197,4 @@ class coreDraw():
         ans=fig
         if self.needShow:
             plotly.offline.iplot(fig)
-        return ans
+        return ans

market_trade/core/CoreTraidMath.py

@@ -1,93 +1,102 @@
+ 
+import pandas as pd
+import datetime
 import numpy as np
+import plotly as pl
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import math
+import scipy
+import random
+import statistics 
+
+
+import datetime 
+
+
+
 
 
 class CoreMath:
-
-    def __init__(self, base_df, params=None):
-        """
-        Этот класс нужен для того, чтобы проводить операции над датафреймами
-        :param base_df: pandas.DataFrame , датафрейм, над которым будут проведены математические операции
-        :param params: словарь, который определяет какие данные пришли в класс, и что с ними нужно делать, и как
-        """
-        if params is None:
-            params = {
-                'dataType': 'ohcl',
-                'action': None,
-                'actionOptions': {}
-            }
-
-        # нужно переопределить индексы, потому что нам ничего не известно об индексации входного файла
-        self.base_df = base_df.reset_index(drop=True)
-
-        self.params = params
-
-        # Эта часть определяет с какой частью данных нужно проводить вычисления
+    
+    def __init__(self, base_df, params={
+        'dataType':'ohcl',
+        'action': None,
+        'actionOptions':{}
+    }
+                ):
+    
+        self.base_df=base_df.reset_index(drop=True)
+        self.params=params
         if self.params['dataType']=='ohcl':
             self.col=self.base_df[self.params['actionOptions']['valueType']]
         elif self.params['dataType']=='series':
             self.col=self.base_df
-
-        # собственно производим вычисления
         self.ans=self.getAns()
-
+        
+    
     def getAns(self):
-        """
-        Эта функция занимается собственно рутингом вычислений, в зависимости от параметров
-        :return: ans, неопределенный тип данных, в заивисимости от action
-        """
         ans=None
-
-        # в зависимости от параметра action производятся соответсвующие действия
+        
         if self.params['action']=='findExt':
             ans = self.getExtremumValue()
         elif self.params['action']=='findMean':
             ans = self.getMeanValue()
         elif self.params['action']=='findSTD':
-            ans = self.getSTD()
-
+            ans=self.getSTD()
+        
+            
         return ans
-
-
+    
+    
     def getExtremumValue(self):
-        """
-        Эта функция возвращает экстремум произвольного типа внутри одного столбца
-        Тип контролируется разделом внутри словаря параметров `self.params` по ключу `actionOptions`:
-            'extremumtype': -- тип экстремума
+        ans=None
+        '''
+        actionOptions:
+            'extremumtype':
                 'min'
                 'max'
-        :return ans, экстремум произвольного типа
-        """
-        ans=None
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+        '''
         if self.params['actionOptions']['extremumtype']=='max':
             ans=max(self.col)
-
+            
         if self.params['actionOptions']['extremumtype']=='min':
             ans=min(self.col)
 
+            
         return ans
-
+     
+        
     def getMeanValue(self):
-        """
-        Божественный код
-        Эта функция возвращает среднее значение одного из следующих типов.
-        Для определения типа используется словарь `self.params`, по ключу `actionOptions`, релевантные ключи выглядят
-        так:
+        '''
+        actionOptions:
             'MeanType':
-                'MA' -- среднее по всему столбцу
-                'SMA'  -- скользящее среднее
-                'EMA' -- экспоненциальное скользящее среднее
-                'WMA' -- взвешенное скользящее среднее
-            'window' -- размер окна
-            'span' -- >=1 , аналог окна для экспоненциального среднего, чем он больше тем меньше коэффициент сглаживания
-            'weights' -- numpy.ndarray, список размером в параметр `window`, конкретные веса для каждого элемента
-        """
-
+                'MA'
+                'SMA'
+                'EMA'
+                'WMA'
+                --'SMMA'
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+            'window'
+            'span'
+            'weights'
+        '''
         ans=None
-
         if self.params['actionOptions']['MeanType']=='MA':
             ans = self.col.mean()
         if self.params['actionOptions']['MeanType']=='SMA':
             ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
+            #ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
+            
         if self.params['actionOptions']['MeanType']=='EMA':
             ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
         if self.params['actionOptions']['MeanType']=='WMA':
@@ -97,8 +106,10 @@ class CoreMath:
                 weights=np.arange(1,self.params['actionOptions']['window']+1)
             ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
 
-        return ans
-
+        
+        
+        return(ans)
+    
     def getSTD(self):
         '''
         actionOptions:
@@ -109,7 +120,7 @@ class CoreMath:
         
         
         '''
-
+        
         ans=None
 
 
@@ -117,11 +128,11 @@ class CoreMath:
             window=self.params['actionOptions']['window']
             ans=np.asarray([])
             for i in range(len(self.col)-window+1):
-                ans=np.append(ans, np.std(self.col[i:i+window], ddof=1))
-
+                ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
+            
         except:
             #window = len(self.col)
             ans=np.std(self.col, ddof=1)
 
         return ans
-
+        

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/dealManager.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import datetime
+import numpy as np
+import uuid 
+
+class DealManager():
+    
+    def __init__(self):
+        #self.commission=0.04
+        self.columns=['uuid','figi','amount','startPrice']
+        self.deals = pd.DataFrame(columns=self.columns)
+        self.deals = self.deals.set_index('uuid')
+        
+    def findDealByPriceAndFig(self,price,figi):
+        ans=None
+        for i in range(self.deals.shape[0]):
+            if self.deals.iloc[i].startPrice == price and self.deals.iloc[i].figi == figi:
+                ans = self.deals.iloc[i].name
+                break
+        return ans
+
+    def openDeal(self,figi,startPrice,amount=1):
+        desiredDeal=self.findDealByPriceAndFig(startPrice,figi)
+        if desiredDeal == None:
+            newDealDict={
+                'uuid':[str(uuid.uuid4())],
+                'figi':[figi],
+                'startPrice':[startPrice],
+                'amount':[amount]
+            }
+
+            #newDealDict['profit']=[startPrice*pow(1+self.commission,2)]
+
+
+
+            newDeal=pd.DataFrame.from_dict(newDealDict).set_index('uuid')
+            self.deals=pd.concat([self.deals, newDeal])
+        else:
+            self.deals.at[desiredDeal,'amount'] += amount
+
+    def closeDeal(self,uuid,amount):
+        
+        desiredDeal=self.deals.loc[uuid]
+        if desiredDeal.amount - amount == 0:
+            self.deals = self.deals.drop(labels = [uuid],axis = 0)
+        else:
+            self.deals.at[uuid,'amount'] -= amount
+            #self.deals.loc[uuid].amount = desiredDeal.amount - amount
+        

market_trade/core/decisionManager.md

@@ -2,9 +2,9 @@ project:: #bibasCopy
 # Импорт
 Для начала импортим все что нужно
 ```python
-from decisionManager_v2 import *
-from indicators_v2 import *
-from signals_v2 import *
+from market_trade.core.decisionManager_v2 import *
+from market_trade.core.indicators_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 indicators_v2 import *
-from signals_v2 import *
+from market_trade.core.decisionManager_v2 import *
+from market_trade.core.indicators_v2 import *
+from market_trade.core.signals_v2 import *
 import pandas as pd
 
 

market_trade/core/decisionManager.py

@@ -0,0 +1,116 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import pickle
+from signals import *
+from dealManager import *
+from trandeVoter import *
+from riskManager import riskManager
+
+
+class decsionManager():
+
+    
+    def __init__(self,name):
+        self.name = name
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalAgrigator()
+        pass
+    
+    #вытащенный из signalAgrigator метод теста для сигналов
+    def getSignalTest(self,data: pd.DataFrame(),reqSig: dict, batchSize=30, dataType='candel') -> dict:
+        
+        self.SA.mode = 'retroFast'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans
+    
+    #метод для генерации матрицы вероятностей.
+    def generateMatrixProbability(self, 
+                                  data: pd.DataFrame(),
+                                  reqSig: dict, 
+                                  target: str, 
+                                  batchSize=30, 
+                                  #dataType='candel'
+                                 ):
+        data=data.reset_index(drop=True)
+        
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=batchSize
+        )
+        self.TV.createMatrixAmounts(reqSig.keys())
+        for i in range(data.shape[0]-batchSize-1):
+            sigAns=self.SA.getAns(data[i:i+batchSize])
+            rightAns=self.getRetroStepAns(data[target][i],data[target][i+1])
+            self.TV.setDecisionBySignals(self.KostilEbaniy(sigAns),rightAns)
+        self.TV.generateMatrixProbability()
+    
+    #без коментариев блять
+    def KostilEbaniy(self,d):
+        ans={}
+        for i in d.keys():
+            if d[i] == 0:
+                ans[i] = 'none'
+            elif d[i] == 1:
+                ans[i] = 'up'
+            elif d[i] == -1:
+                ans[i] = 'down'
+        return ans
+    
+    #тож понятная хуита
+    def getRetroStepAns(self, value1,value2):
+        
+        if value1 == value2:
+            ans = 'none'
+        elif value1 < value2:
+            ans = 'up'
+        else:
+            ans = 'down'
+        return ans
+        
+    #метод для онлай получения решения по сигналу
+    def getSignal(self,data: pd.DataFrame(),reqSig: dict, dataType='candel') -> dict:
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans        
+    
+    
+    #Создание сигналов. Вызывать перед getOnlineAns
+    def crateSignals(self,data: pd.DataFrame(),reqSig: dict, dataType='candel'):
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        
+    
+    def getOnlineAns(self,data: pd.DataFrame(),price):
+        sigAns = self.SA.getAns(data)
+        prob = self.TV.getDecisionBySignals(sigAns)
+        ans = self.RM.getDecision(sigAns,prob,price)
+        return ans
+        
+        
+        
+        

market_trade/core/decisionManager_v2.py

@@ -0,0 +1,161 @@
+import os
+
+import pandas as pd
+import datetime
+import numpy as np
+
+from tqdm import tqdm
+
+from market_trade.core.indicators_v2 import *
+from market_trade.core.signals_v2 import *
+from market_trade.core.dealManager import *
+from market_trade.core.trandeVoter import *
+from market_trade.core.riskManager import *
+import pickle
+
+
+class decsionManager:
+    '''
+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}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }     
+}    
+    
+    
+    
+    
+    
+    
+    '''
+    
+    
+    
+    
+    def __init__(self,name, sigDict: dict):
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalsAgrigator(sigDict)
+        self.sigDict = sigDict
+        
+        
+    def getOnlineAns(self, signalsAns: dict, price: float) -> dict:
+        probabilityDecsion = self.TV.getDecisionBySignals(self.getSignalsAns(signalsAns))
+        RMD = self.RM.getDecision(probabilityDecision=probabilityDecsion, price=price, deals = self.DM.deals)
+        return RMD
+        
+    def getSignalsAns(self, signalsDataDict: dict) -> dict:
+        return self.SA.getAns(signalsDataDict)
+    
+    def getRightAns(self,value_1, value_2):
+        
+        ans=''
+        
+        if value_1 > value_2:
+            ans = 'down'
+        elif value_1 < value_2:
+            ans = 'up'
+        else:
+            ans = 'none'
+        
+        return ans
+        
+    def getRetroTrendAns(self, retroTemplateDict: dict, data: pd.DataFrame(), window: int) -> list:
+        
+        reqSig={} 
+        ans = {
+            'signalsAns':[],
+            'rightAns':[]
+            
+        }
+        target = ''
+        
+        
+        for k in tqdm(range(data.shape[0]-window-1)):
+            for i in retroTemplateDict.keys():
+                reqSig[i] = {'signalData': data[k:k+window], 'indicatorData':{}}
+                target = self.SA.signals[i].params['target']
+                for j in retroTemplateDict[i]['indicatorData'].keys():
+                    reqSig[i]['indicatorData'][j] = data[k:k+window]
+                    
+            sigAns = self.getSignalsAns(reqSig)
+            rightAns = self.getRightAns(data[target][k], data[target][k+1])
+
+            ans['signalsAns'].append(sigAns)
+            ans['rightAns'].append(rightAns)
+
+        return ans
+ 
+
+    def generateMatrixProbabilityFromDict(self, dictSignals: dict) -> dict:
+        self.TV.createMatrixAmounts(dictSignals['signalsAns'][0].keys())
+        for i in range(len(dictSignals['signalsAns'])):
+            self.TV.setDecisionBySignals(signalDecisions = dictSignals['signalsAns'][i],
+                                        trande = dictSignals['rightAns'][i])
+        self.TV.generateMatrixProbability()
+        
+    def createDump(self,postfix='') -> str:
+        dataDict = {
+            'RM':self.RM,
+            'DM':self.DM,
+            'TV':self.TV,
+            'SA':self.SA,
+            'sigDict':self.sigDict
+        }
+        fileName='data_'+postfix+'.pickle'
+        with open(fileName, 'wb') as f:
+            pickle.dump(dataDict, f)
+            
+        return os.path.abspath(fileName)
+    
+    def loadDump(self,path: str) -> None:
+        
+        with open(path, 'rb') as f:
+            dataDict = pickle.load(f)
+        
+        self.RM = dataDict['RM']
+        self.DM = dataDict['DM']
+        self.TV = dataDict['TV']
+        self.SA = dataDict['SA']
+        self.sigDict = dataDict['sigDict']

market_trade/core/indicators.py

@@ -2,8 +2,8 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import market_trade.core.CoreTraidMath as CoreTraidMath
-import market_trade.core.CoreDraw as CoreDraw
+import market_trade.core.CoreTraidMath
+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

@@ -0,0 +1,89 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTraidMath
+
+class coreIndicator():
+    
+    def __init__(self,options: dict, dataType: str = None, predictType: str = None, name: str = None):
+        self.options = options
+        self.dataType = dataType #ochl
+        self.predictType = predictType #trend
+        
+    
+    def getAns(self, data: pd.DataFrame() ):
+        return "ERROR"
+    
+class indicatorsAgrigator:
+    """
+    indicators = {
+        'ind_BB':{
+            'className':ind_BB,
+            'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+        }
+    }
+    dataDic={
+        'ind_BB':df_candle[:1000]
+    }        
+    
+    
+    """
+    
+    def __init__ (self,indDict={}):
+        self.indDict = indDict
+        self.indInst = {}
+        self.ans={}
+        self.createIndicatorsInstance()
+    
+    def createIndicatorsInstance(self):
+        for i in self.indDict.keys():
+            self.indInst[i]=self.indDict[i]['className'](self.indDict[i]['params'])
+            
+    def getAns(self,dataDict={}):
+        ans={}
+        for i in dataDict.keys():
+            ans[i] = self.indInst[i].getAns(dataDict[i])
+        return ans
+    
+class ind_BB(coreIndicator):
+    """
+    options
+        MeanType -> SMA
+        window -> int
+        valueType -> str: low, high, open, close
+        kDev -> float
+    
+    """
+    
+    def __init__(self,options: dict,name = None):
+        super().__init__(
+            options = options,
+            dataType = 'ochl',
+            predictType = 'trend',
+            name = name
+        )
+    
+    def getAns(self, data: pd.DataFrame()):
+        data=data.reset_index(drop=True)
+        ans={}
+        opMA={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':{
+                'MeanType':self.options['MeanType'],
+                'valueType':self.options['valueType'],
+                'window':self.options['window']
+            }
+        }
+        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']=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())
+        self.ans= ans
+        return ans
+    

market_trade/core/riskManager.py

@@ -0,0 +1,29 @@
+import pandas as pd
+import datetime
+import numpy as np
+import random
+
+class riskManager:
+    
+    def __init__(self,commision=0.04):
+        self.commision = commision
+        pass
+    def getDecision(self,probabilityDecision, price, deals=None) -> dict:
+        ans = {}
+        ans['decision'] = 'none'
+        if probabilityDecision['trande'] == 'up':
+            ans['decision'] = 'buy'
+            ans['amount'] = 1
+        elif probabilityDecision['trande'] == 'none':
+            ans['decision'] = 'none'
+        elif probabilityDecision['trande'] == 'down': 
+            for i in range(deals.shape[0]):
+                ans['decision'] = 'None'
+                ans['deals'] = []
+                row = deals.iloc[i]
+                if row.startPrice < price*pow(1+self.commission,2):
+                    ans['decision'] = 'sell'
+                    ans['deals'].append(row.name)
+        return ans
+        
+    

market_trade/core/signals.py

@@ -2,13 +2,12 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import market_trade.core.CoreTraidMath as CoreTraidMath
-import market_trade.core.CoreDraw as CoreDraw
+import market_trade.core.CoreTraidMath
+import market_trade.core.CoreDraw
 from tqdm import tqdm
 
 from market_trade.core.indicators import *
 
-
 class coreSignalTrande():
     def __init__(self,
                  data=pd.DataFrame(),
@@ -17,140 +16,133 @@ class coreSignalTrande():
                  batchSize=None,
                  indParams=None,
                  signalParams=None,
-                 # needFig=False,
-                 # showOnlyIndex=False,
-                 # drawFig=False,
-                 # equalityGap=0
-                 ):
-
-        self.data = data.reset_index(drop=True)
-        self.onlineData = data.reset_index(drop=True)
-        self.dataType = dataType
-        self.mode = mode
-        self.ans = None
-        self.softAnalizList = np.asarray([])
-        self.hardAnalizList = np.asarray([])
-        self.analizMetrics = {}
-        self.indParams = indParams
-        self.signalParams = signalParams
-        self.batchSize = batchSize
-        # self.needFig=needFig
-        # self.showOnlyIndex=showOnlyIndex
-        # self.drawFig=drawFig
-        # self.equalityGap=equalityGap
-
-    # Роутер получения ответа
-    def getAns(self, data):
-        # ans='Error: unknown Mode!'
-        ans = None
+                 #needFig=False,
+                 #showOnlyIndex=False,
+                 #drawFig=False,
+                 #equalityGap=0
+                ):
+        
+        self.data=data.reset_index(drop=True)
+        self.onlineData=data.reset_index(drop=True)
+        self.dataType=dataType
+        self.mode=mode
+        self.ans=None
+        self.softAnalizList=np.asarray([])
+        self.hardAnalizList=np.asarray([])
+        self.analizMetrics={}
+        self.indParams=indParams
+        self.signalParams=signalParams
+        self.batchSize=batchSize
+        #self.needFig=needFig
+        #self.showOnlyIndex=showOnlyIndex
+        #self.drawFig=drawFig
+        #self.equalityGap=equalityGap
+    #Роутер получения ответа
+    def getAns(self,data):
+        #ans='Error: unknown Mode!'
+        ans=None
         print("Start processing...")
         if self.mode == 'online':
-            ans = self.getOnlineAns(data.reset_index(drop=True))
+            ans=self.getOnlineAns(data.reset_index(drop=True))
         elif self.mode == 'retro':
-            ans = self.getRetroAns(data)
+            ans=self.getRetroAns(data)
         elif self.mode == 'retroFast':
-            ans = self.getRetroFastAns(data)
+            ans=self.getRetroFastAns(data)
         print("Processing DONE!")
         return ans
-
-    # Ретро режим, где расширяется окно добавлением новых элементов
-    def getRetroAns(self, data):
-        ans = np.asarray([])
-        for i in tqdm(range(self.batchSize, len(data) - 1)):
-            # self.onlineData=self.data[0:i]
+    #Ретро режим, где расширяется окно добавлением новых элементов
+    def getRetroAns(self,data):
+        ans=np.asarray([])
+        for i in tqdm(range(self.batchSize,len(data)-1)):
+            #self.onlineData=self.data[0:i]
             window_data = data[0:i]
             window_data.reset_index(drop=True)
-            ans = np.append(ans, (self.getOnlineAns(window_data)))
-        self.ans = ans
+            ans=np.append(ans,(self.getOnlineAns(window_data)))
+        self.ans=ans
         self.getAnaliz()
         self.getMetrix()
         return ans
-
-    # Ретро режим, где двигается окно
-    def getRetroFastAns(self, data):
-        # print('d - ',data)
-        ans = np.asarray([])
-        for i in tqdm(range(len(data) - 1 - self.batchSize)):
-            # self.onlineData=self.data[i:i+self.batchSize]
-            window_data = data[i:i + self.batchSize]
-            # print('win - ',window_data)
+    #Ретро режим, где двигается окно
+    def getRetroFastAns(self,data):
+        #print('d - ',data)
+        ans=np.asarray([])
+        for i in tqdm(range(len(data)-1-self.batchSize)):
+            #self.onlineData=self.data[i:i+self.batchSize]
+            window_data = data[i:i+self.batchSize]
+            #print('win - ',window_data)
             window_data.reset_index(drop=True)
-            # print('win - ',window_data)
-            ans = np.append(ans, (self.getOnlineAns(window_data)))
-        self.ans = ans
+            #print('win - ',window_data)
+            ans=np.append(ans,(self.getOnlineAns(window_data)))
+        self.ans=ans
         self.getAnaliz()
         self.getMetrix()
         return ans
-
-    # Метод, который будет переопределять каждый дочерний класс
+    #Метод, который будет переопределять каждый дочерний класс
     def getOnlineAns(self):
         return 'Error'
-
     def getAnaliz(self):
         print("Start analiz...")
         for i in (range(len(self.ans))):
-            sourceValue = self.data[self.signalParams['source']][i + self.batchSize]
-            targetValue = self.data[self.signalParams['target']][i + self.batchSize + 1]
-            if (targetValue) > sourceValue:
-                if self.ans[i] == 1:
-                    self.softAnalizList = np.append(self.softAnalizList, 1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, 1)
-                elif self.ans[i] == -1:
-                    self.softAnalizList = np.append(self.softAnalizList, -1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
+            sourceValue=self.data[self.signalParams['source']][i+self.batchSize]
+            targetValue=self.data[self.signalParams['target']][i+self.batchSize + 1]
+            if (targetValue)>sourceValue:
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
                 else:
-                    self.softAnalizList = np.append(self.softAnalizList, 0)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
-
-            elif (targetValue) < sourceValue:
-                if self.ans[i] == 1:
-                    self.softAnalizList = np.append(self.softAnalizList, -1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
-                elif self.ans[i] == -1:
-                    self.softAnalizList = np.append(self.softAnalizList, 1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, 1)
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+            
+            elif (targetValue)<sourceValue:
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)
                 else:
-                    self.softAnalizList = np.append(self.softAnalizList, 0)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
             else:
-                if self.ans[i] == 1:
-                    self.softAnalizList = np.append(self.softAnalizList, -1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
-                elif self.ans[i] == -1:
-                    self.softAnalizList = np.append(self.softAnalizList, -1)
-                    self.hardAnalizList = np.append(self.hardAnalizList, -1)
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
                 else:
-                    self.softAnalizList = np.append(self.softAnalizList, 0)
-                    self.hardAnalizList = np.append(self.hardAnalizList, 1)
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)   
         print("Analiz DONE!")
         return 0
-
-    def getMeteixDict(self, d):
+    def getMeteixDict(self,d):
         '''
         1 - (сбывшиеся + несбывшиеся) \ (сбывшиеся + несбывшиеся +0)
         2 - (сбывшиеся - несбывшиеся) \ (сбывшиеся + несбывшиеся +0)
         '''
         return {
-
-            '1': (d['1'] + d['-1']) / (d['1'] + d['-1'] + d['0']),
-            '2': (d['1'] - d['-1']) / (d['1'] + d['-1'] + d['0']),
-
+ 
+            '1':(d['1'] + d['-1']) / (d['1'] + d['-1'] + d['0']),
+            '2':(d['1'] - d['-1']) / (d['1'] + d['-1'] + d['0']),
+            
         }
-
+        
     def getMetrix(self):
 
-        softAnalizCount = {'-1': 0, '0': 0, '1': 0}
-        hardAnalizCount = {'-1': 0, '0': 0, '1': 0}
+        softAnalizCount = {'-1':0,'0':0,'1':0}
+        hardAnalizCount = {'-1':0,'0':0,'1':0}
         for i in range(len(self.softAnalizList)):
-            softAnalizCount[str(int(self.softAnalizList[i]))] += 1
-            hardAnalizCount[str(int(self.hardAnalizList[i]))] += 1
-        self.analizMetrics = {'softAnaliz': self.getMeteixDict(softAnalizCount),
-                              'hardAnaliz': self.getMeteixDict(hardAnalizCount)
-                              }
-
+            softAnalizCount[str(int(self.softAnalizList[i]))]+=1
+            hardAnalizCount[str(int(self.hardAnalizList[i]))]+=1
+        self.analizMetrics = {'softAnaliz':self.getMeteixDict(softAnalizCount),
+                              'hardAnaliz':self.getMeteixDict(hardAnalizCount)
+                             }
 
 class signal_BB(coreSignalTrande):
-
+    
     def __init__(self,
                  data=pd.DataFrame(),
                  dataType='candel',
@@ -158,37 +150,101 @@ class signal_BB(coreSignalTrande):
                  batchSize=None,
                  indParams=None,
                  signalParams=None,
-                 ):
+                ):
         super().__init__(
-            data=data,
-            dataType=dataType,
-            mode=mode,
-            batchSize=batchSize,
-            indParams=indParams,
-            signalParams=signalParams,
-        )
-
+                 data=data,
+                 dataType=dataType,
+                 mode=mode,
+                 batchSize=batchSize,
+                 indParams=indParams,
+                 signalParams=signalParams,
+                )
+        
         if self.indParams == None:
-            indParams = {'MeanType': 'SMA', 'window': 15, 'valueType': 'low', 'kDev': 2}
+            indParams={'MeanType':'SMA','window':15,'valueType':'low','kDev':2}
         else:
-            indParams = self.indParams
-        self.BB = ind_BB(
+            indParams=self.indParams
+        self.BB=ind_BB(
             data=data,
             options=indParams,
         )
-
-    def getOnlineAns(self, data):
-        ans = 0
-        # print(data)
+        
+    
+    def getOnlineAns(self,data):
+        ans=0
+        #print(data)
 
         self.BB.getAns(data)
-        # print(BB)
-        lastValue = data[self.signalParams['source']].to_list()[-1]
-        if lastValue > self.BB.ans['pSTD'][-1]:
-            ans = -1
-        elif lastValue < self.BB.ans['mSTD'][-1]:
-            ans = +1
+        #print(BB)
+        lastValue=data[self.signalParams['source']].to_list()[-1]
+        if lastValue>self.BB.ans['pSTD'][-1]:
+            ans=-1
+        elif lastValue<self.BB.ans['mSTD'][-1]:
+            ans=+1
         else:
-            ans = 0
-
+            ans=0
+        
         return ans
+    
+class signalAgrigator:
+    """
+    dictAgrigSignal
+        key - name str
+        value - dict
+            className - class
+            indParams - dict
+            signalParams - dict
+            batchSize - int
+        
+    
+    """
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 dictAgrigSignal={},
+                 mode='online',
+                 dataType='candel',
+                 batchSize=None
+                ):
+        self.createSingnalInstances(
+            data,
+            dictAgrigSignal,
+            dataType,
+            batchSize
+        )
+        self.mode=mode
+         
+         
+
+    def createSingnalInstances(
+        self,
+        data,
+        dictAgrigSignal,
+        dataType,
+        batchSize
+        ):
+        
+        ans={}
+        
+        for i in dictAgrigSignal:
+            ans[i]=dictAgrigSignal[i]['className'](
+                data=data,
+                dataType=dataType,
+                batchSize=batchSize,
+                indParams=dictAgrigSignal[i]['indParams'],
+                signalParams=dictAgrigSignal[i]['signalParams'],
+                mode=self.mode
+            )
+        self.signalsInstances = ans
+        return ans
+    
+    def getAns(self, data):
+        ans={}
+        
+        if self.mode == 'online':
+            for i in self.signalsInstances:
+                ans[i]=(self.signalsInstances[i].getAns(data))
+        elif self.mode == 'retroFast' or self.mode == 'retro':
+            for i in self.signalsInstances:
+                self.signalsInstances[i].getAns(data)
+                ans[i]=self.signalsInstances[i].analizMetrics
+        return ans

market_trade/core/signals_v2.py

@@ -0,0 +1,112 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTraidMath
+#import market_trade.core.CoreDraw
+from tqdm import tqdm
+
+from market_trade.core.indicators_v2 import *
+
+
+
+class coreSignalTrande:
+    
+    def __init__(self, name: str, req: dict, dataType: str):
+        self.name = name
+        self.agrigateInds = self.createIndicatorsInstance(req)
+        self.params = req['params']
+        self.dataType = dataType
+        
+    
+    def createIndicatorsInstance(self,req: dict) -> dict:
+        return indicatorsAgrigator(req['indicators'])
+    
+    def getIndAns(self, dataDict: dict) -> dict:
+        return self.agrigateInds.getAns(dataDict)
+    
+    def getAns(self, data: pd.DataFrame(), indDataDict: dict) -> dict:
+        return self.getSigAns(data, self.getIndAns(indDataDict)) 
+        
+    
+    
+class sig_BB(coreSignalTrande):
+    """
+    ind keys:
+        ind_BB
+    """
+    
+    def __init__(self, name: str, req:dict):
+        super().__init__(name, req, 'ochl')
+    
+    def getSigAns(self, data: pd.DataFrame(), indAnsDict: dict) -> dict:
+
+        lastValue = data[self.params['source']].to_list()[-1]
+        if lastValue>indAnsDict['ind_BB']['pSTD'][-1]:
+            ans='down'
+        elif lastValue<indAnsDict['ind_BB']['mSTD'][-1]:
+            ans='up'
+        else:
+            ans='none'
+        
+        return ans
+        
+        
+class signalsAgrigator:
+    
+    """
+    sigAgrReq = {
+        'sig_BB':{
+            'className':sig_BB,
+            'params':{'source':'close','target':'close'},
+            'indicators':{
+                'ind_BB':{
+                    'className':ind_BB,
+                    'params':{'MeanType':'SMA','window':15,'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}
+                }            
+            }
+        }
+    }
+
+    sigAgrData = {
+        'sig_BB':{
+            'signalData': df_candle[990:1000],
+            'indicatorData' :{'ind_BB': df_candle[:1000]}
+        },
+        'sig_BB_2':{
+            'signalData': df_candle[990:1000],
+            'indicatorData' :{'ind_BB': df_candle[:1000]}
+        }    
+    }    
+
+
+
+
+
+    """
+    
+    def __init__ (self,req:dict):
+        self.signals = self.createSignalsInstance(req)
+        
+    def createSignalsInstance(self, siganlsDict: dict) -> dict:
+        ans = {}
+        for i in siganlsDict.keys():
+            ans[i]=siganlsDict[i]['className'](name = i, req = siganlsDict[i])
+        return ans
+    
+    def getAns(self, dataDict: dict) -> dict:
+        ans = {}
+        for i in dataDict.keys():
+            ans[i] = self.signals[i].getAns(data = dataDict[i]['signalData'], 
+                                   indDataDict = dataDict[i]['indicatorData'])
+        return ans

market_trade/core/trandeVoter.py

@@ -0,0 +1,84 @@
+import pandas as pd
+import datetime
+import numpy as np
+#import random
+
+class trandeVoter():
+    
+    def __init__(self,name):
+       
+        self.name = name # просто имя
+        self.trandeValuesList = ['up','none','down'] #словарь трегдов
+        self.matrixAmounts = None # матрица сумм
+        self.keysMatrixAmounts = None #ключи матрицы сумм, техническое поле
+        self.matrixProbability = None # матрица вероятностей
+        
+    
+    #функция которая создает df с заданным набором колонок и индексов. индексы - уникальные соотношения 
+    def createDFbyNames(self, namesIndex, namesColoms,defaultValue=0.0):
+        df = pd.DataFrame(dict.fromkeys(namesColoms, [defaultValue]*pow(3,len(namesIndex))),
+                          index=pd.MultiIndex.from_product([self.trandeValuesList]*len(namesIndex), names=namesIndex)
+                            #,columns=namesColoms
+                        )
+        return(df)
+    
+    #создание матрицы сумм с дефолтным значением
+    def createMatrixAmounts(self,namesIndex: list) -> pd.DataFrame():
+        self.matrixAmounts = self.createDFbyNames(namesIndex,self.trandeValuesList,0)
+        self.keysMatrixAmounts = self.matrixAmounts.to_dict('tight')['index_names']
+        self.createMatrixProbability(namesIndex)
+        return(self.matrixAmounts)
+    
+    #создание матрицы вероятностей с дефолтным значением
+    def createMatrixProbability(self,namesIndex: list) -> pd.DataFrame():
+        self.matrixProbability = self.createDFbyNames(namesIndex,self.trandeValuesList)
+        return(self.matrixProbability)
+    
+    #установка значений в матрицы сумм. signalDecisions - значения индикаторов key:value; trande - реальное значение
+    def setDecisionBySignals(self,signalDecisions: dict,trande: str) -> None:
+        buff=[]
+        for i in self.keysMatrixAmounts:
+            buff.append(signalDecisions[i])
+        self.matrixAmounts.loc[tuple(buff),trande] += 1
+        
+    #заполнение матрицы вероятностей вычисляемыми значениями из матрицы сумм
+    def generateMatrixProbability(self) -> None:
+        for i in range(self.matrixAmounts.shape[0]):
+            print(self.matrixAmounts)
+            rowSum=sum(self.matrixAmounts.iloc[i]) + 1
+            self.matrixProbability.iloc[i]['up'] = self.matrixAmounts.iloc[i]['up'] / rowSum
+            self.matrixProbability.iloc[i]['none'] = self.matrixAmounts.iloc[i]['none'] / rowSum
+            self.matrixProbability.iloc[i]['down'] = self.matrixAmounts.iloc[i]['down'] / rowSum
+
+    #получение рещения из матрицы вероятностей по заданным значениям сигналов
+    def getDecisionBySignals(self,signalDecisions: dict) -> dict:
+        ans = {}
+        spliceSearch =self.matrixProbability.xs(tuple(signalDecisions.values()),
+                                                       level=list(signalDecisions.keys())
+                                                      ) 
+        ans['probability'] = spliceSearch.to_dict('records')[0]
+        ans['trande'] = spliceSearch.iloc[0].idxmax()
+        return ans
+    
+    #получение матриц вероятностей и суммы в видей словарей
+    def getMatrixDict(self) -> dict:
+        ans={}
+        ans['amounts'] = self.matrixAmounts.to_dict('tight')
+        ans['probability'] = self.matrixProbability.to_dict('tight')
+        return ans
+    
+    #установка матриц вероятностей и суммы в видей словарей
+    def setMatrixDict(self,matrixDict: dict) -> dict:
+        if matrixDict['amounts'] != None:
+            self.matrixAmounts = pd.DataFrame.from_dict(y['amounts'], orient='tight')
+        if matrixDict['probability'] != None:
+            self.matrixProbability = pd.DataFrame.from_dict(y['probability'], orient='tight') 
+        
+        
+
+    
+    
+    
+    
+
+        

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.py

@@ -4,7 +4,7 @@ import pandas as pd
 
 
 if __name__ == '__main__':
-    df_candle = pd.read_csv(market_trade.src.constants.TEST_CANDLESTICKS_PATH)
+    df_candle = pd.read_csv(market_trade.constants.TEST_CANDLESTICKS_PATH)
     df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
     ind_params = {'MeanType': 'SMA', 'window': 15, 'valueType': 'close', 'kDev': 2.5}
     signalParams = {'source': 'close', 'target': 'close'}

market_trade/tests/test_dataloader.py

@@ -3,11 +3,11 @@ import market_trade.constants
 
 
 def test_dataloader(data_path):
-    duka_interface = (market_trade.src.dataloader.DukaMTInterface(data_path))
+    duka_interface = (market_trade.data.dataloader.DukaMTInterface(data_path))
     print(duka_interface.ask_candlesticks)
 
 
 if __name__ == '__main__':
-    candlesticks_filepaths = [filepath for filepath in market_trade.src.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
+    candlesticks_filepaths = [filepath for filepath in market_trade.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
     candlesticks_filepath = candlesticks_filepaths[0]
     test_dataloader(candlesticks_filepath)

market_trade/tests/test_decision.py

@@ -0,0 +1,63 @@
+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[5000:6000].reset_index(drop=True),40)
+
+test.generateMatrixProbabilityFromDict(retroAns)
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }
+}
+
+test.getOnlineAns(sigAgrData, 0.0)

notebooks/RiskManager.ipynb

@@ -0,0 +1,78 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ad08f522",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "b9b18667",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class riskManager:\n",
+    "    \n",
+    "    def __init__(self,commision=0.04):\n",
+    "        self.commision = commision\n",
+    "        pass\n",
+    "    def getDecision(self,signalDecision,probabilityDecision, price, deals=None) -> dict:\n",
+    "        ans = {}\n",
+    "        if probabilityDecision['trande'] == 'up':\n",
+    "            ans['decision'] = 'buy'\n",
+    "            ans['amount'] = 1\n",
+    "        elif probabilityDecision['trande'] == 'none':\n",
+    "            ans['decision'] = 'none'\n",
+    "        elif probabilityDecision['trande'] == 'down': \n",
+    "            for i in deals.shape[0]:\n",
+    "                ans['decision'] = 'None'\n",
+    "                ans['deals'] = []\n",
+    "                row = deals.iloc[i]\n",
+    "                if row.startPrice < price*pow(1+self.commission,2):\n",
+    "                    ans['decision'] = 'sell'\n",
+    "                    ans['deals'].append(row.name)\n",
+    "        return ans\n",
+    "        \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f5dd64e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

notebooks/autogen/Indicators.py

@@ -0,0 +1,267 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+#import plotly as pl
+
+#import plotly.graph_objs as go
+#from plotly.offline import init_notebook_mode, iplot
+#from plotly.subplots import make_subplots
+#init_notebook_mode()
+
+import CoreTraidMath 
+import CoreDraw
+
+
+# In[3]:
+
+
+class coreIndicator():
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 options={},
+                 showMode='None',
+                ):
+        '''
+        showMode = None/Ind/PartOf        
+        '''
+        self.data=data
+        self.showMode=showMode
+        self.options=options
+        self.overlayInd=None #True/False
+        self.ans=None
+        self.figDict=None
+        
+    def getAns(self,data=None):
+        if type(data)!=type(None):
+            self.data=data
+        self.ans=self.getCalculate()
+        if self.showMode=='Ind' or self.showMode=='PartOf':
+            self.figDict=self.getFigDict()
+            if self.showMode=='Ind':
+                self.getFig()
+        return self.ans
+    def getFig(self,row=1):
+        CoreDraw.coreDraw(self.figDict,True)
+    def getCalculate(self):
+        return "Error"
+    def getFigDict(self):
+        return "Error"
+
+class indicatorAgrigator():
+    '''
+    Тема чисто для отладки
+    jj=indicatorAgrigator().runAll([o1,o2],df_candle[:30])
+    #jj.createIndFromList([o1,o2])
+    #jj.calculateInd(df_candle[:30])
+    
+    '''
+    def __init__(self):
+        self.indList=None
+        self.data=None
+    def createInd(self,classDict):
+        return classDict['name'](
+                options=classDict['params'],
+                showMode=classDict['showMode']
+            )
+        
+        
+    def createIndFromList(self,indList):
+        self.indList=indList
+        ans=[]
+        for i in self.indList:
+            ans.append(self.createInd(i))
+        self.indList=ans
+        return ans
+    
+    def calculateInd(self,data):
+        self.data=data
+        for i in self.indList:
+            #i.getAns(data)
+            i.data=self.data
+            i.ans=i.getCalculate()
+            i.figDict=i.getFigDict()
+            #i.getFig()
+    def agrigateFig(self):
+        req=[[]]
+        
+        for i in self.indList:
+            if i.overlayInd==True:
+                req[0].append(i)
+            else:
+                req.append([i])
+        CoreDraw.agrigateFig(req,True)
+    def runAll(self,indList,df,needDraw=False):
+        self.createIndFromList(indList)
+        self.calculateInd(df)
+        if needDraw:
+            self.agrigateFig()
+
+        
+        
+
+
+# In[4]:
+
+
+class ind_BB(coreIndicator):
+    
+    def getCalculate(self):
+        self.overlayInd=True
+        ans={}
+        opMA={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':{
+                'MeanType':self.options['MeanType'],
+                'valueType':self.options['valueType'],
+                'window':self.options['window']
+            }
+        }
+        ans['BB']=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['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())
+        return ans
+    def getFigDict(self,row=1):
+        req=[]
+        
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['BB'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'BB'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['pSTD'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'pSTD'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['mSTD'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'mSTD'
+            
+        })
+        
+        return req
+
+
+# In[5]:
+
+
+class ind_OCHL(coreIndicator):
+    def getCalculate(self):
+        self.overlayInd=True
+    def getFigDict(self,row=1):
+        req=[]
+        
+        req.append({
+                'vtype':'OCHL',
+                'df':self.data,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+        })
+        return req
+
+
+# In[7]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[8]:
+
+
+o1={
+    'name':ind_OCHL,
+    'params':{},
+    'showMode':'PartOf',
+}
+o2={
+    'name':ind_BB,
+    'params':{'MeanType':'SMA','window':25,'valueType':'low','kDev':2},
+    'showMode':'PartOf',
+}
+jj=indicatorAgrigator().runAll([o1,o2],df_candle[:300],True)
+#jj.createIndFromList([o1,o2])
+#jj.calculateInd(df_candle[:30])
+
+
+# In[9]:
+
+
+op={'MeanType':'SMA','window':5,'valueType':'low','kDev':2}
+a=ind_BB(df_candle[:100],op,'PartOf')
+
+
+# In[10]:
+
+
+a.getAns()
+
+
+# In[11]:
+
+
+b=ind_OCHL(df_candle[:30],{},'Ind')
+b.getAns(df_candle[:100])
+
+
+# In[12]:
+
+
+opc={'MeanType':'SMA','window':20,'valueType':'low','kDev':2}
+c=ind_BB(df_candle[:100],opc,'PartOf')
+c.getAns()
+
+
+# In[13]:
+
+
+hhh = CoreDraw.agrigateFig([[b,a,c]],True)
+
+
+# In[14]:
+
+
+import indicators
+
+
+# In[15]:
+
+
+op_1={'MeanType':'SMA','window':5,'valueType':'low','kDev':2}
+test_1=indicators.ind_BB(df_candle[:100],op)
+test_1.getAns()
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/RiskManager.py

@@ -0,0 +1,45 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+import random
+
+
+# In[2]:
+
+
+class riskManager:
+    
+    def __init__(self,commision=0.04):
+        self.commision = commision
+        pass
+    def getDecision(self,signalDecision,probabilityDecision, price, deals=None) -> dict:
+        ans = {}
+        if probabilityDecision['trande'] == 'up':
+            ans['decision'] = 'buy'
+            ans['amount'] = 1
+        elif probabilityDecision['trande'] == 'none':
+            ans['decision'] = 'none'
+        elif probabilityDecision['trande'] == 'down': 
+            for i in deals.shape[0]:
+                ans['decision'] = 'None'
+                ans['deals'] = []
+                row = deals.iloc[i]
+                if row.startPrice < price*pow(1+self.commission,2):
+                    ans['decision'] = 'sell'
+                    ans['deals'].append(row.name)
+        return ans
+        
+    
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/Signals.py

@@ -0,0 +1,390 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import CoreTraidMath 
+import CoreDraw
+from tqdm import tqdm
+
+from indicators import *
+
+
+# In[2]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[3]:
+
+
+class coreSignalTrande():
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 dataType='candel',
+                 mode='online',
+                 batchSize=None,
+                 indParams=None,
+                 signalParams=None,
+                 #needFig=False,
+                 #showOnlyIndex=False,
+                 #drawFig=False,
+                 #equalityGap=0
+                ):
+        
+        self.data=data.reset_index(drop=True)
+        self.onlineData=data.reset_index(drop=True)
+        self.dataType=dataType
+        self.mode=mode
+        self.ans=None
+        self.softAnalizList=np.asarray([])
+        self.hardAnalizList=np.asarray([])
+        self.analizMetrics={}
+        self.indParams=indParams
+        self.signalParams=signalParams
+        self.batchSize=batchSize
+        #self.needFig=needFig
+        #self.showOnlyIndex=showOnlyIndex
+        #self.drawFig=drawFig
+        #self.equalityGap=equalityGap
+    #Роутер получения ответа
+    def getAns(self,data):
+        #ans='Error: unknown Mode!'
+        ans=None
+        print("Start processing...")
+        if self.mode == 'online':
+            ans=self.getOnlineAns(data.reset_index(drop=True))
+        elif self.mode == 'retro':
+            ans=self.getRetroAns(data)
+        elif self.mode == 'retroFast':
+            ans=self.getRetroFastAns(data)
+        print("Processing DONE!")
+        return ans
+    #Ретро режим, где расширяется окно добавлением новых элементов
+    def getRetroAns(self,data):
+        ans=np.asarray([])
+        for i in tqdm(range(self.batchSize,len(data)-1)):
+            #self.onlineData=self.data[0:i]
+            window_data = data[0:i]
+            window_data.reset_index(drop=True)
+            ans=np.append(ans,(self.getOnlineAns(window_data)))
+        self.ans=ans
+        self.getAnaliz()
+        self.getMetrix()
+        return ans
+    #Ретро режим, где двигается окно
+    def getRetroFastAns(self,data):
+        #print('d - ',data)
+        ans=np.asarray([])
+        for i in tqdm(range(len(data)-1-self.batchSize)):
+            #self.onlineData=self.data[i:i+self.batchSize]
+            window_data = data[i:i+self.batchSize]
+            #print('win - ',window_data)
+            window_data.reset_index(drop=True)
+            #print('win - ',window_data)
+            ans=np.append(ans,(self.getOnlineAns(window_data)))
+        self.ans=ans
+        self.getAnaliz()
+        self.getMetrix()
+        return ans
+    #Метод, который будет переопределять каждый дочерний класс
+    def getOnlineAns(self):
+        return 'Error'
+    def getAnaliz(self):
+        print("Start analiz...")
+        for i in (range(len(self.ans))):
+            sourceValue=self.data[self.signalParams['source']][i+self.batchSize]
+            targetValue=self.data[self.signalParams['target']][i+self.batchSize + 1]
+            if (targetValue)>sourceValue:
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                else:
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+            
+            elif (targetValue)<sourceValue:
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)
+                else:
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+            else:
+                if self.ans[i]==1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                elif self.ans[i]==-1:
+                    self.softAnalizList=np.append(self.softAnalizList,-1)
+                    self.hardAnalizList=np.append(self.hardAnalizList,-1)
+                else:
+                    self.softAnalizList=np.append(self.softAnalizList,0)
+                    self.hardAnalizList=np.append(self.hardAnalizList,1)   
+        print("Analiz DONE!")
+        return 0
+    def getMeteixDict(self,d):
+        '''
+        1 - (сбывшиеся + несбывшиеся) \ (сбывшиеся + несбывшиеся +0)
+        2 - (сбывшиеся - несбывшиеся) \ (сбывшиеся + несбывшиеся +0)
+        '''
+        return {
+ 
+            '1':(d['1'] + d['-1']) / (d['1'] + d['-1'] + d['0']),
+            '2':(d['1'] - d['-1']) / (d['1'] + d['-1'] + d['0']),
+            
+        }
+        
+    def getMetrix(self):
+
+        softAnalizCount = {'-1':0,'0':0,'1':0}
+        hardAnalizCount = {'-1':0,'0':0,'1':0}
+        for i in range(len(self.softAnalizList)):
+            softAnalizCount[str(int(self.softAnalizList[i]))]+=1
+            hardAnalizCount[str(int(self.hardAnalizList[i]))]+=1
+        self.analizMetrics = {'softAnaliz':self.getMeteixDict(softAnalizCount),
+                              'hardAnaliz':self.getMeteixDict(hardAnalizCount)
+                             }
+
+
+
+# In[4]:
+
+
+class signal_BB(coreSignalTrande):
+    
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 dataType='candel',
+                 mode='online',
+                 batchSize=None,
+                 indParams=None,
+                 signalParams=None,
+                ):
+        super().__init__(
+                 data=data,
+                 dataType=dataType,
+                 mode=mode,
+                 batchSize=batchSize,
+                 indParams=indParams,
+                 signalParams=signalParams,
+                )
+        
+        if self.indParams == None:
+            indParams={'MeanType':'SMA','window':15,'valueType':'low','kDev':2}
+        else:
+            indParams=self.indParams
+        self.BB=ind_BB(
+            data=data,
+            options=indParams,
+        )
+        
+    
+    def getOnlineAns(self,data):
+        ans=0
+        #print(data)
+
+        self.BB.getAns(data)
+        #print(BB)
+        lastValue=data[signalParams['source']].to_list()[-1]
+        if lastValue>self.BB.ans['pSTD'][-1]:
+            ans=-1
+        elif lastValue<self.BB.ans['mSTD'][-1]:
+            ans=+1
+        else:
+            ans=0
+        
+        return ans
+
+
+# In[5]:
+
+
+ind_params={'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+signalParams={'source':'close','target':'close'}
+b=signal_BB(data=df_candle[:99999],
+            mode='retroFast',
+            indParams=ind_params,
+            signalParams=signalParams,
+            batchSize=15
+            
+        
+        )
+
+
+# In[6]:
+
+
+a=b.getAns(df_candle[:99900])
+
+
+# In[7]:
+
+
+b.analizMetrics
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[8]:
+
+
+from signals import *
+
+
+# In[9]:
+
+
+class signalAgrigator:
+    """
+    dictAgrigSignal
+        key - name str
+        value - dict
+            className - class
+            indParams - dict
+            signalParams - dict
+            batchSize - int
+        
+    
+    """
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 dictAgrigSignal={},
+                 mode='online',
+                 dataType='candel',
+                 batchSize=None
+                ):
+        self.createSingnalInstances(
+            data,
+            dictAgrigSignal,
+            dataType,
+            batchSize
+        )
+        self.mode=mode
+         
+         
+
+    def createSingnalInstances(
+        self,
+        data,
+        dictAgrigSignal,
+        dataType,
+        batchSize
+        ):
+        
+        ans={}
+        
+        for i in dictAgrigSignal:
+            ans[i]=dictAgrigSignal[i]['className'](
+                data=data,
+                dataType=dataType,
+                batchSize=batchSize,
+                indParams=dictAgrigSignal[i]['indParams'],
+                signalParams=dictAgrigSignal[i]['signalParams'],
+                mode=self.mode
+            )
+        self.signalsInstances = ans
+        return ans
+    
+    def getAns(self, data):
+        ans={}
+        
+        if self.mode == 'online':
+            for i in self.signalsInstances:
+                ans[i]=(self.signalsInstances[i].getAns(data))
+        elif self.mode == 'retroFast' or self.mode == 'retro':
+            for i in self.signalsInstances:
+                self.signalsInstances[i].getAns(data)
+                ans[i]=self.signalsInstances[i].analizMetrics
+        return ans
+
+
+# In[10]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[11]:
+
+
+reqSig.values()
+
+
+# In[12]:
+
+
+testh=signalAgrigator(df_candle[:99999],reqSig,'online','ohcl',30)
+
+
+# In[13]:
+
+
+testh.signalsInstances['BB1'].__dict__
+
+
+# In[ ]:
+
+
+testh.getAns(df_candle[:100])
+
+
+# In[ ]:
+
+
+testh.signalsInstances['BB1'].__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/Signals_v2.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import CoreTraidMath 
+import CoreDraw
+from tqdm import tqdm
+
+from indicators_v2 import *
+
+
+# In[3]:
+
+
+df_candle = pd.read_csv(r"../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[4]:
+
+
+class coreSignalTrande:
+    
+    def __init__(self, name: str, req: dict, dataType: str):
+        self.name = name
+        self.agrigateInds = self.createIndicatorsInstance(req)
+        self.params = req['params']
+        self.dataType = dataType
+        
+    
+    def createIndicatorsInstance(self,req: dict) -> dict:
+        return indicatorsAgrigator(req['indicators'])
+    
+    def getIndAns(self, dataDict: dict) -> dict:
+        return self.agrigateInds.getAns(dataDict)
+    
+    def getAns(self, data: pd.DataFrame(), indDataDict: dict) -> dict:
+        return self.getSigAns(data, self.getIndAns(indDataDict)) 
+        
+    
+    
+class sig_BB(coreSignalTrande):
+    """
+    ind keys:
+        ind_BB
+    """
+    
+    def __init__(self, name: str, req:dict):
+        super().__init__(name, req, 'ochl')
+    
+    def getSigAns(self, data: pd.DataFrame(), indAnsDict: dict) -> dict:
+
+        lastValue = data[self.params['source']].to_list()[-1]
+        if lastValue>indAnsDict['ind_BB']['pSTD'][-1]:
+            ans='down'
+        elif lastValue<indAnsDict['ind_BB']['mSTD'][-1]:
+            ans='up'
+        else:
+            ans='none'
+        
+        return ans
+        
+
+
+# In[5]:
+
+
+class signalsAgrigator:
+    
+    def __init__ (self,req:dict):
+        self.signals = self.createSignalsInstance(req)
+        
+    def createSignalsInstance(self, siganlsDict: dict) -> dict:
+        ans = {}
+        for i in siganlsDict.keys():
+            ans[i]=siganlsDict[i]['className'](name = i, req = siganlsDict[i])
+        return ans
+    
+    def getAns(self, dataDict: dict) -> dict:
+        ans = {}
+        for i in dataDict.keys():
+            ans[i] = self.signals[i].getAns(data = dataDict[i]['signalData'], 
+                                   indDataDict = dataDict[i]['indicatorData'])
+        return ans
+
+
+# In[ ]:
+
+
+
+
+
+# In[6]:
+
+
+sigreq= {
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+            }            
+        }
+    }
+    
+indReqDict ={'ind_BB':df_candle[:1000]}
+
+
+# In[7]:
+
+
+sigAgrReq = {
+    'sig_BB':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':15,'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}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+# In[ ]:
+
+
+
+
+
+# In[8]:
+
+
+ttt=signalsAgrigator(sigAgrReq)
+
+
+# In[9]:
+
+
+ttt.__dict__
+
+
+# In[10]:
+
+
+ttt.signals['sig_BB'].__dict__
+
+
+# In[11]:
+
+
+ttt.getAns(sigAgrData)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[12]:
+
+
+list({'ttt':2}.keys())[0]
+
+
+# In[13]:
+
+
+test = sig_BB('sig_BB', sigreq)
+
+
+# In[14]:
+
+
+test.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[15]:
+
+
+test.agrigateInds.__dict__
+
+
+# In[16]:
+
+
+ians = test.getIndAns(indReqDict)
+ians
+
+
+# In[17]:
+
+
+test.getAns(df_candle[:100],indReqDict)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/TrandVoter.py

@@ -0,0 +1,360 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+import random
+from signals import * #потом удалить
+
+
+# In[2]:
+
+
+class trandeVoter():
+
+    def __init__(self,name):
+       
+        self.name = name # просто имя
+        self.trandeValuesList = ['up','none','down'] #словарь трегдов
+        self.matrixAmounts = None # матрица сумм
+        self.keysMatrixAmounts = None #ключи матрицы сумм, техническое поле
+        self.matrixProbability = None # матрица вероятностей
+        
+    
+    #функция которая создает df с заданным набором колонок и индексов. индексы - уникальные соотношения 
+    def createDFbyNames(self, namesIndex, namesColoms,defaultValue=0.0):
+        df = pd.DataFrame(dict.fromkeys(namesColoms, [defaultValue]*pow(3,len(namesIndex))),
+                          index=pd.MultiIndex.from_product([self.trandeValuesList]*len(namesIndex), names=namesIndex)
+                            #,columns=namesColoms
+                        )
+        return(df)
+    
+    #создание матрицы сумм с дефолтным значением
+    def createMatrixAmounts(self,namesIndex: list) -> pd.DataFrame():
+        self.matrixAmounts = self.createDFbyNames(namesIndex,self.trandeValuesList,0)
+        self.keysMatrixAmounts = self.matrixAmounts.to_dict('tight')['index_names']
+        self.createMatrixProbability(namesIndex)
+        return(self.matrixAmounts)
+    
+    #создание матрицы вероятностей с дефолтным значением
+    def createMatrixProbability(self,namesIndex: list) -> pd.DataFrame():
+        self.matrixProbability = self.createDFbyNames(namesIndex,self.trandeValuesList)
+        return(self.matrixProbability)
+    
+    #установка значений в матрицы сумм. signalDecisions - значения индикаторов key:value; trande - реальное значение
+    def setDecisionBySignals(self,signalDecisions: dict,trande: str) -> None:
+        buff=[]
+        for i in self.keysMatrixAmounts:
+            buff.append(signalDecisions[i])
+        self.matrixAmounts.loc[tuple(buff),trande] += 1
+        
+    #заполнение матрицы вероятностей вычисляемыми значениями из матрицы сумм
+    def generateMatrixProbability(self) -> None:
+        for i in range(self.matrixAmounts.shape[0]):
+            rowSum=sum(self.matrixAmounts.iloc[i])
+            self.matrixProbability.iloc[i]['up'] = (self.matrixAmounts.iloc[i]['up'] / rowSum)
+            self.matrixProbability.iloc[i]['none'] = self.matrixAmounts.iloc[i]['none'] / rowSum
+            self.matrixProbability.iloc[i]['down'] = self.matrixAmounts.iloc[i]['down'] / rowSum
+
+    #получение рещения из матрицы вероятностей по заданным значениям сигналов
+    def getDecisionBySignals(self,signalDecisions: dict) -> dict:
+        ans = {}
+        spliceSearch =self.matrixProbability.xs(tuple(signalDecisions.values()),
+                                                       level=list(signalDecisions.keys())
+                                                      ) 
+        ans['probability'] = spliceSearch.to_dict('records')[0]
+        ans['trande'] = spliceSearch.iloc[0].idxmax()
+        return ans
+    
+    #получение матриц вероятностей и суммы в видей словарей
+    def getMatrixDict(self) -> dict:
+        ans={}
+        ans['amounts'] = self.matrixAmounts.to_dict('tight')
+        ans['probability'] = self.matrixProbability.to_dict('tight')
+        return ans
+    
+    #установка матриц вероятностей и суммы в видей словарей
+    def setMatrixDict(self,matrixDict: dict) -> dict:
+        if matrixDict['amounts'] != None:
+            self.matrixAmounts = pd.DataFrame.from_dict(y['amounts'], orient='tight')
+        if matrixDict['probability'] != None:
+            self.matrixProbability = pd.DataFrame.from_dict(y['probability'], orient='tight') 
+
+
+# In[3]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+
+# In[4]:
+
+
+reqDS={'BB1':'up','BB2':'none'}
+
+
+# In[7]:
+
+
+reqCreate=list(reqSig.keys())
+reqCreate
+
+
+# In[8]:
+
+
+t=trandeVoter('piu')
+o=t.createMatrixAmounts(['BB1', 'BB2'])
+o
+
+
+# In[9]:
+
+
+for i in range(100000):
+    t.setDecisionBySignals({'BB1':random.choice(['up','down','none']),
+                            'BB2':random.choice(['up','down','none'])},
+                           random.choice(['up','down','none']))
+    
+
+
+# In[10]:
+
+
+t.matrixAmounts
+
+
+# In[11]:
+
+
+t.generateMatrixProbability()
+
+
+# In[577]:
+
+
+t.matrixProbability
+
+
+# In[14]:
+
+
+t.setMatrixDict(y)
+
+
+# In[15]:
+
+
+t.getDecisionBySignals(reqDS)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[13]:
+
+
+y = t.getMatrixDict()
+y
+
+
+# In[16]:
+
+
+ddf = pd.DataFrame.from_dict(y['amounts'], orient='tight')
+ddf
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[17]:
+
+
+t.matrixProbability.iloc[0]['up'] = (t.matrixProbability.iloc[0]['up'] / (sum(t.matrixProbability.iloc[0])))
+t.matrixProbability
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[18]:
+
+
+t.matrixProbability['trandе']
+
+
+# In[19]:
+
+
+random.choice(['up','down','none'])
+
+
+# In[20]:
+
+
+t.setDecisionBySignals(reqDS,'up')
+
+
+# In[21]:
+
+
+#t.matrixAmounts.at(bbb,'up')
+    
+t.matrixAmounts.iloc[0]
+
+
+# In[22]:
+
+
+for i in t.matrixAmounts.iloc[0]:
+    print (i)
+
+
+# In[23]:
+
+
+(t.matrixAmounts.iloc[0]).idxmax()
+
+
+# In[24]:
+
+
+t.matrixAmounts
+
+
+# In[ ]:
+
+
+
+
+
+# In[25]:
+
+
+o.xs(('up','down'), level=['BB1','BB2'])['up'].iloc[0]
+
+#oldValue = o.xs(('up','down'), level=['BB1','BB2'])['up']
+
+#o=o.replace(oldValue,oldValue.iloc[0]+1) 
+#o.xs(('up','down'), level=['BB1','BB2'])
+
+
+# In[26]:
+
+
+o.xs(('up','down'), level=['BB1','BB2'], drop_level=False)#.iloc[0].loc['up']=2#.at['up']=4
+
+
+# In[27]:
+
+
+o.xs(('up','down'), level=['BB1','BB2']).iloc[0].at['up']
+
+
+# In[28]:
+
+
+o.loc['up'].loc['down']
+
+
+# In[29]:
+
+
+bbb=tuple(['up','down'])
+bbb
+
+
+# In[30]:
+
+
+o.loc[bbb,]
+
+
+# In[31]:
+
+
+o.at[bbb, 'up']+=1
+o
+
+
+# In[32]:
+
+
+o.loc[bbb]
+
+
+# In[33]:
+
+
+dict(zip(['a','b','c'], [1,2,3]))
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/Voter_ne_tot.py

@@ -0,0 +1,427 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+from signals import * #потом удалить
+
+
+# In[2]:
+
+
+class voter_v2():
+    
+    def __init__(self,name):
+        self.name=name
+        pass
+    
+    def createPredictMatrixBySignals(self,signalsName):
+        pass
+
+
+# In[ ]:
+
+
+
+
+
+# In[3]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[4]:
+
+
+reqCreate=reqSig.keys()
+reqCreate
+
+
+# In[5]:
+
+
+class Voter():
+    
+    def __init__ (self, name=''):
+        
+        self.name=name
+        self.mop={
+            'up': pd.DataFrame(),
+            'down':pd.DataFrame(),
+            'none':pd.DataFrame()
+        
+        }
+        self.value={}
+        self.decision=''
+        self.real_decision=''
+        self.keys=[]
+        self.slice_dict={}
+        
+    def addValue(self, dic_value):
+        self.value=dic_value
+        self.checkForNew()
+        self.setSlice()
+        self.getDecision()
+        
+
+        
+    def checkForNew(self):
+        
+        if not (list(self.value.keys()) == self.keys):
+            self.createNewMop(list(self.value.keys()))
+
+    
+    def createNewMop(self,missing_indicators):
+        print('reassembly mop')
+        new_columns= (missing_indicators)
+        
+        
+        #new_columns=new_columns.append(['value','p'])
+        
+        
+        
+        n=len(new_columns)
+        start_value=-1
+        variator=3
+        new_lst=[]
+        buf_lst=[]
+        for i in range(n):
+            buf_lst.append(start_value)
+
+
+        for i in range(pow(variator,n)):
+            new_lst.append(buf_lst.copy())
+
+        for j in range(n):
+            for i in range(len(new_lst)):
+                dob_iterator=(i // pow(variator,j)) % variator
+                new_lst[i][j]=new_lst[i][j] + dob_iterator
+                
+        
+        #print (new_columns)
+        self.keys=new_columns
+        new_columns = new_columns+['amount']+['percentage']
+                
+        for i in new_lst:
+            i = i.extend([0,0])
+            #i = i.append(0)
+        
+        #print(new_lst)
+        #print(new_columns)
+        new_df=pd.DataFrame(new_lst,columns=new_columns)
+        
+        self.mop['up']=pd.DataFrame.from_dict(new_df.to_dict())
+        self.mop['down']=pd.DataFrame.from_dict(new_df.to_dict())
+        self.mop['none']=pd.DataFrame.from_dict(new_df.to_dict())
+        
+        
+    def setSlice(self):
+        
+        row_flg=True
+        self.slice_dict={}
+        for j in self.mop.keys():
+            for index, row in self.mop[j].iterrows():
+                for key, value in self.value.items():
+                    if value != row[key]:
+                        #print('fasle ',key,value,row[key])
+                        row_flg=False
+                        break
+                if row_flg:
+                    self.slice_dict[j]=dict(row)
+                    #print(j,dict(row))
+                row_flg=True
+
+    def getDecision (self):
+        
+        max_value=0
+        for key, value in self.slice_dict.items():
+            if value['amount'] >= max_value:
+                max_value = value['amount']
+                self.decision = key
+        return self.decision
+    
+    def setDecision (self,real_decision):
+        self.real_decision=real_decision
+        self.updMop()
+        self.slice_dict[real_decision]['amount']+=1
+
+
+    def updMop(self):
+        
+        row_flg=True
+        for index, row in self.mop[self.real_decision].iterrows():
+            for key, value in self.value.items():
+                if value != row[key]:
+                    row_flg=False
+                    break
+            if row_flg:
+                #self.slice_dict[j]=dict(row)
+                row['amount']=row['amount']+1
+            row_flg=True 
+
+
+# In[6]:
+
+
+test_dic_value_1={'lupa':1 }
+test_dic_value_2={'lupa':1 , 'pupa':1}
+test_dic_value_3={'lupa':1 , 'pupa':1 , 'zalupa':1 , 'zapupa':1 }
+test_dic_value_4={'lupa':1 , 'pupa':1 , 'zalupa':1 , 'zapupa':-1 }
+
+
+# In[7]:
+
+
+test=Voter('huita')
+test.addValue(test_dic_value_2)
+test.decision
+test.getDecision()
+
+
+# In[8]:
+
+
+test.setDecision('down')
+test.getDecision()
+
+
+# In[9]:
+
+
+test.slice_dict
+
+
+# In[ ]:
+
+
+
+
+
+# In[10]:
+
+
+import pickle
+
+
+# In[11]:
+
+
+dictionary_data = {"a": 1, "b": 2}
+
+
+a_file = open("data.pkl", "wb")
+
+pickle.dump(dictionary_data, a_file)
+
+a_file.close()
+
+
+a_file = open("data.pkl", "rb")
+
+output = pickle.load(a_file)
+
+print(output)
+
+
+
+a_file.close()
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[12]:
+
+
+arrays = [
+
+    ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+
+    ["one", "two", "one", "two", "one", "two", "one", "two"],
+
+]
+tuples = list(zip(*arrays))
+tuples
+
+
+# In[13]:
+
+
+index = pd.MultiIndex.from_tuples(tuples, names=["first", "second"])
+
+
+# In[14]:
+
+
+s = pd.DataFrame(np.random.randn(8), index=index)
+s
+
+
+# In[15]:
+
+
+s.to_dict()
+
+
+# In[16]:
+
+
+s.loc(('bar', 'one'))
+
+
+# In[18]:
+
+
+iterables = [["up", "down", "none"], ["up", "down", "none"]]
+df = pd.DataFrame({'col1': np.random.randn(9),'col2': np.random.randn(9)}, index=pd.MultiIndex.from_product(iterables, names=["first", "second"]))
+df
+
+
+# In[19]:
+
+
+df.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[20]:
+
+
+def createDF(namesIndex, namesColoms):
+    trandeValuesList = ['up','none','down']
+    colomsName_lvl = ['trande','amaunt','probability'] 
+    #micolumns = pd.MultiIndex.from_tuples(
+    #[('amaunt', 'up'), ('amaunt', 'none'), ('amaunt', 'down'), ('trande',),('probability',)], names=["lvl0", "lvl1"]
+    #)    
+    df = pd.DataFrame({
+                            'trande': [None]*pow(3,len(namesIndex)),
+                            'amaunt': [None]*pow(3,len(namesIndex)),
+                            'probability': [None]*pow(3,len(namesIndex))
+                        },
+                      index=pd.MultiIndex.from_product([trandeValuesList]*len(namesIndex), names=namesIndex)
+                        ,columns=namesColoms
+                    )
+    return(df)
+
+
+# In[21]:
+
+
+dd=createDF( ['1','2','3'],['trande','amaunt','probability'] )
+dd
+
+
+# In[22]:
+
+
+df.xs(('up','down'), level=['first','second'])
+
+
+# In[23]:
+
+
+dd['trande']
+
+
+# In[24]:
+
+
+tvl = ['up','none','down']
+colomsName_lvl = ['trande','amaunt','probability'] 
+
+
+# In[25]:
+
+
+tuplesCol = list(zip(['amaunt']*3,tvl))
+tuplesCol
+
+
+# In[26]:
+
+
+df.loc['up','down']
+
+
+# In[27]:
+
+
+df.xs(('up','down'), level=['first','second']).iloc[0]
+
+
+# In[28]:
+
+
+df_d=df.to_dict('tight')
+df_d
+
+
+# In[29]:
+
+
+df_d['index_names']
+
+
+# In[30]:
+
+
+ddf = pd.DataFrame.from_dict(df_d, orient='tight')
+ddf
+
+
+# In[31]:
+
+
+tuple([1,2,3])
+
+
+# In[32]:
+
+
+ddf.xs(('up','down'), level=['first','second']).iloc[0]
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/coreDraw.py

@@ -0,0 +1,221 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+import plotly as pl
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import random
+
+
+import datetime 
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+import plotly
+import plotly.graph_objs as go
+from plotly.offline import init_notebook_mode, iplot
+from plotly.subplots import make_subplots
+init_notebook_mode()
+
+#import CoreTraidMath 
+import plotly.express as px
+
+
+
+
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[2]:
+
+
+class agrigateFig():
+    
+    def __init__(self,data=[],needDraw=False ,subplot_titles=None):
+        self.data=data
+        self.ans=self.getAgrPlt()
+        if needDraw:
+            self.subplot_titles=subplot_titles
+            self.fig=coreDraw(self.ans,True,self.subplot_titles)
+        
+        
+    def getAgrPlt(self):
+        count=0
+        ans=[]
+        for i in self.data:
+            count=count+1
+            if type(i)==list:
+                for  g in i:
+                    for j in g.figDict:
+                        ans.append(j)
+                        ans[-1]['row']=count
+            else:
+                for j in i.figDict:
+                    ans.append(j)
+                    ans[-1]['row']=count
+        return ans
+
+
+# In[3]:
+
+
+class corePlt():
+    def __init__(self, params={
+        'vtype':'',
+        'df':pd.DataFrame(),
+        'row':1,
+        'col':1,
+        'name':''
+    }):
+        self.vtype=params['vtype']
+        self.df=params['df']
+        self.row=params['row']
+        self.col=params['col']
+        self.name=params['name']
+        if 'colorType' in params.keys():
+            self.colorType=params['colorType']
+        
+
+
+
+
+class coreDraw():
+    def __init__(self, data=[],needShow=False,subplot_titles={}):
+        self.data=self.getPlts(data)
+        self.needShow=needShow
+        self.subplot_titles=subplot_titles
+        self.ans=self.getAns()
+        
+        
+    
+
+    
+    
+    
+    def getBarColorList(self,l,colorType):
+        if colorType=='diffAbs':
+            ans=['green']
+            for i in range(1,len(l)):
+                if abs(l[i])>abs(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='diff':
+            ans=['green']
+            for i in range(1,len(l)):
+                if (l[i])>(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='normal':
+            ans=[]
+            for i in range(len(l)):
+                ans.append('gray')
+        return ans
+    
+    def getPlts(self, data):
+        ans=None
+        
+        if type(data)==list:
+            ans=[]
+            for i in data:
+                ans.append(corePlt(i))
+        else:
+            ans=[corePlt(data)]
+        
+        
+        
+        
+        return ans
+    
+    def getAns(self):
+        '''
+        data list
+            vtype
+            df
+            row=1
+            col=1
+            name
+        
+        
+        
+        '''
+        
+        ans=None
+        
+        
+        
+       
+        maxRow=1
+        maxCol=1
+        for i in self.data:
+            if i.row > maxRow:
+                maxRow =i.row
+            if i.col > maxCol:
+                maxCol =i.col
+                
+        fig = make_subplots(
+            rows=maxRow,
+            cols=maxCol,
+            shared_xaxes=True,
+            vertical_spacing=0.1,
+            shared_yaxes=True,
+            #horizontal_spacing=0.02,
+            #column_widths=[]
+            subplot_titles=self.subplot_titles
+        )
+        
+        
+        fig.update_layout(xaxis_rangeslider_visible=False)
+        fig.update_layout(barmode='relative')
+        
+        for i in self.data:
+            if i.vtype=='Scatter':
+                fig.add_trace(go.Scatter(x=i.df['date'],y=i.df['value'],name=i.name), row=i.row, col=i.col)
+            elif i.vtype=='OCHL':
+                fig.add_trace(go.Candlestick(
+                        x=i.df['date'],
+                        open=i.df['open'],
+                        high=i.df['high'],
+                        low=i.df['low'],
+                        close=i.df['close'],
+                        name=i.name),
+                              row=i.row, col=i.col
+                        )
+            elif i.vtype=='Bars':
+                for j in i.df.keys():
+                    if j!='date':
+                        try:
+                            colorType=i.colorType
+                        except:
+                            colorType='normal'
+                        colors=self.getBarColorList(i.df[j],colorType)
+                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors),row=i.row, col=i.col)
+                
+        
+        
+        
+        ans=fig
+        if self.needShow:
+            plotly.offline.iplot(fig)
+        return ans
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/dealManager.py

@@ -0,0 +1,145 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[1]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+import uuid 
+
+
+# In[2]:
+
+
+class DealManager():
+    
+    def __init__(self):
+        self.commission=0.04
+        self.columns=['uuid','figi','amount','startPrice','profit']
+        self.deals = pd.DataFrame(columns=self.columns)
+        self.deals = self.deals.set_index('uuid')
+        
+    def findDealByPriceAndFig(self,price,figi):
+        ans=None
+        for i in range(self.deals.shape[0]):
+            if self.deals.iloc[i].startPrice == price and self.deals.iloc[i].figi == figi:
+                ans = self.deals.iloc[i].name
+                break
+        return ans
+
+    def openDeal(self,figi,startPrice,amount=1):
+        desiredDeal=self.findDealByPriceAndFig(startPrice,figi)
+        if desiredDeal == None:
+            newDealDict={
+                'uuid':[str(uuid.uuid4())],
+                'figi':[figi],
+                'startPrice':[startPrice],
+                'amount':[amount]
+            }
+
+            #newDealDict['profit']=[startPrice*pow(1+self.commission,2)]
+
+
+
+            newDeal=pd.DataFrame.from_dict(newDealDict).set_index('uuid')
+            self.deals=pd.concat([self.deals, newDeal])
+        else:
+            self.deals.at[desiredDeal,'amount'] += amount
+
+    def closeDeal(self,uuid,amount):
+        
+        desiredDeal=self.deals.loc[uuid]
+        if desiredDeal.amount - amount == 0:
+            self.deals = self.deals.drop(labels = [uuid],axis = 0)
+        else:
+            self.deals.at[uuid,'amount'] -= amount
+            #self.deals.loc[uuid].amount = desiredDeal.amount - amount
+        
+
+
+# In[3]:
+
+
+t=DealManager()
+t.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[4]:
+
+
+t.deals.shape[0]
+
+
+# In[5]:
+
+
+t.openDeal('huigi',100,1)
+t.openDeal('huigi',100,3)
+t.openDeal('huigi1',100,3)
+t.openDeal('huigi1',200,3)
+
+
+# In[6]:
+
+
+t.deals
+
+
+# In[7]:
+
+
+t.deals[t.deals.figi == 'huigi1']
+
+
+# In[ ]:
+
+
+
+
+
+# In[8]:
+
+
+for i in range(t.deals.shape[0]):
+    print(t.deals.iloc[i])
+
+
+# In[9]:
+
+
+t.findDealByPriceAndFig
+
+
+# In[10]:
+
+
+t.closeDeal('78228979-3daf-470a-9c2a-8db180c8c3b0',1)
+t.deals
+
+
+# In[11]:
+
+
+t.deals.iloc[0].name
+
+
+# In[12]:
+
+
+a=2
+a==None
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/decisionManager.py

@@ -0,0 +1,358 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[4]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import pickle
+from signals import *
+from dealManager import *
+from trandeVoter import *
+from riskManager import riskManager
+
+
+# In[5]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[6]:
+
+
+class decsionManager():
+
+    
+    def __init__(self,name):
+        self.name = name
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalAgrigator()
+        pass
+    
+    #вытащенный из signalAgrigator метод теста для сигналов
+    def getSignalTest(self,data: pd.DataFrame(),reqSig: dict, batchSize=30, dataType='candel') -> dict:
+        
+        self.SA.mode = 'retroFast'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans
+    
+    #метод для генерации матрицы вероятностей.
+    def generateMatrixProbability(self, 
+                                  data: pd.DataFrame(),
+                                  reqSig: dict, 
+                                  target: str, 
+                                  batchSize=30, 
+                                  #dataType='candel'
+                                 ):
+        data=data.reset_index(drop=True)
+        
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=batchSize
+        )
+        self.TV.createMatrixAmounts(reqSig.keys())
+        for i in range(data.shape[0]-batchSize-1):
+            sigAns=self.SA.getAns(data[i:i+batchSize])
+            rightAns=self.getRetroStepAns(data[target][i],data[target][i+1])
+            self.TV.setDecisionBySignals(self.KostilEbaniy(sigAns),rightAns)
+        self.TV.generateMatrixProbability()
+    
+    #без коментариев блять
+    def KostilEbaniy(self,d):
+        ans={}
+        for i in d.keys():
+            if d[i] == 0:
+                ans[i] = 'none'
+            elif d[i] == 1:
+                ans[i] = 'up'
+            elif d[i] == -1:
+                ans[i] = 'down'
+        return ans
+    
+    #тож понятная хуита
+    def getRetroStepAns(self, value1,value2):
+        
+        if value1 == value2:
+            ans = 'none'
+        elif value1 < value2:
+            ans = 'up'
+        else:
+            ans = 'down'
+        return ans
+        
+    #метод для онлай получения решения по сигналу
+    def getSignal(self,data: pd.DataFrame(),reqSig: dict, dataType='candel') -> dict:
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans        
+    
+    
+    #Создание сигналов. Вызывать перед getOnlineAns
+    def crateSignals(self,data: pd.DataFrame(),reqSig: dict, dataType='candel'):
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        
+    
+    def getOnlineAns(self,data: pd.DataFrame(),price):
+        sigAns = self.SA.getAns(data)
+        prob = self.TV.getDecisionBySignals(sigAns)
+        ans = self.RM.getDecision(sigAns,prob,price)
+        return ans
+        
+        
+        
+        
+
+
+# In[ ]:
+
+
+
+
+
+# In[7]:
+
+
+t= decsionManager('TEST')
+
+
+# In[8]:
+
+
+t.__dict__
+
+
+# In[9]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[10]:
+
+
+reqSig.keys()
+
+
+# In[11]:
+
+
+t.SA.__dict__
+
+
+# In[12]:
+
+
+t.generateMatrixProbability(df_candle[:10000],reqSig,'close',40)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[13]:
+
+
+mop = t.TV.matrixProbability
+mop
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[14]:
+
+
+t.getSignal(df_candle[:10000],reqSig)
+
+
+# In[15]:
+
+
+t.getSignalTest(df_candle[:10000],reqSig,40)
+
+
+# In[ ]:
+
+
+
+
+
+# In[16]:
+
+
+t.SA.createSingnalInstances(
+    data = df_candle[:10000],
+    dictAgrigSignal = reqSig,
+    dataType='candel',
+    batchSize=30
+)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[17]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[18]:
+
+
+t=decsionManager(reqSig)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+import pickle
+
+
+# In[ ]:
+
+
+dictionary_data = {"a": 1, "b": 2}
+
+
+a_file = open("data.pkl", "wb")
+
+pickle.dump(reqSig, a_file)
+
+a_file.close()
+
+
+a_file = open("data.pkl", "rb")
+
+output = pickle.load(a_file)
+
+print(output)
+
+
+
+a_file.close()
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/decisionManager_v2.py

@@ -0,0 +1,382 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[5]:
+
+
+import os
+
+import pandas as pd
+import datetime
+import numpy as np
+
+from tqdm import tqdm
+
+from indicators_v2 import *
+from signals_v2 import *
+from dealManager import *
+from trandeVoter import *
+from riskManager import *
+import pickle
+
+
+# In[6]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[7]:
+
+
+df_candle['close']
+
+
+# In[8]:
+
+
+class decsionManager:
+    '''
+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}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }     
+}    
+    
+    
+    
+    
+    
+    
+    '''
+    
+    
+    
+    
+    def __init__(self,name, sigDict: dict):
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalsAgrigator(sigDict)
+        self.sigDict = sigDict
+        
+        
+    def getOnlineAns(self, signalsAns: dict, price: float) -> dict:
+        probabilityDecsion = self.TV.getDecisionBySignals(self.getSignalsAns(signalsAns))
+        RMD = self.RM.getDecision(probabilityDecision=probabilityDecsion, price=price, deals = self.DM.deals)
+        return RMD
+        
+    def getSignalsAns(self, signalsDataDict: dict) -> dict:
+        return self.SA.getAns(signalsDataDict)
+    
+    def getRightAns(self,value_1, value_2):
+        
+        ans=''
+        
+        if value_1 > value_2:
+            ans = 'down'
+        elif value_1 < value_2:
+            ans = 'up'
+        else:
+            ans = 'none'
+        
+        return ans
+        
+    def getRetroTrendAns(self, retroTemplateDict: dict, data: pd.DataFrame(), window: int) -> list:
+        
+        reqSig={} 
+        ans = {
+            'signalsAns':[],
+            'rightAns':[]
+            
+        }
+        target = ''
+        
+        
+        for k in tqdm(range(data.shape[0]-window-1)):
+            for i in retroTemplateDict.keys():
+                reqSig[i] = {'signalData': data[k:k+window], 'indicatorData':{}}
+                target = self.SA.signals[i].params['target']
+                for j in retroTemplateDict[i]['indicatorData'].keys():
+                    reqSig[i]['indicatorData'][j] = data[k:k+window]
+                    
+            sigAns = self.getSignalsAns(reqSig)
+            rightAns = self.getRightAns(data[target][k], data[target][k+1])
+
+            ans['signalsAns'].append(sigAns)
+            ans['rightAns'].append(rightAns)
+
+        return ans
+ 
+
+    def generateMatrixProbabilityFromDict(self, dictSignals: dict) -> dict:
+        self.TV.createMatrixAmounts(dictSignals['signalsAns'][0].keys())
+        for i in range(len(dictSignals['signalsAns'])):
+            self.TV.setDecisionBySignals(signalDecisions = dictSignals['signalsAns'][i],
+                                        trande = dictSignals['rightAns'][i])
+        self.TV.generateMatrixProbability()
+        
+    def createDump(self,postfix='') -> str:
+        dataDict = {
+            'RM':self.RM,
+            'DM':self.DM,
+            'TV':self.TV,
+            'SA':self.SA,
+            'sigDict':self.sigDict
+        }
+        fileName='data_'+postfix+'.pickle'
+        with open(fileName, 'wb') as f:
+            pickle.dump(dataDict, f)
+            
+        return os.path.abspath(fileName)
+    
+    def loadDump(self,path: str) -> None:
+        
+        with open(path, 'rb') as f:
+            dataDict = pickle.load(f)
+        
+        self.RM = dataDict['RM']
+        self.DM = dataDict['DM']
+        self.TV = dataDict['TV']
+        self.SA = dataDict['SA']
+        self.sigDict = dataDict['sigDict']
+
+
+# In[9]:
+
+
+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}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }     
+}
+
+
+# In[10]:
+
+
+test = decsionManager('Pipa', sigAgrReq)
+
+
+# In[11]:
+
+
+test.__dict__
+
+
+# In[12]:
+
+
+test.TV.__dict__
+
+
+# In[13]:
+
+
+test.SA.signals['sig_BB'].params['target']
+
+
+# In[14]:
+
+
+test.getSignalsAns(sigAgrData)
+
+
+# In[15]:
+
+
+#test.loadDump('C:\\Users\\Redsandy\\PyProj\\Trade\\MVP\\data_pupa.pickle')
+
+
+# In[16]:
+
+
+uuu = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[:5000],40)
+uuu
+
+
+# In[17]:
+
+
+test.generateMatrixProbabilityFromDict(uuu)
+
+
+# In[18]:
+
+
+test.TV.__dict__
+
+
+# In[19]:
+
+
+test.getOnlineAns(sigAgrData, 0.0)
+
+
+# In[20]:
+
+
+(test.DM.deals).shape
+
+
+# In[21]:
+
+
+test.createDump('pupa')
+
+
+# In[ ]:
+
+
+
+
+
+# In[22]:
+
+
+with open('C:\\Users\\Redsandy\\PyProj\\Trade\\MVP\\data_pupa.pickle', 'rb') as f:
+    data_new = pickle.load(f)
+data_new
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/indicators_v2.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[8]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import CoreTraidMath 
+
+
+# In[9]:
+
+
+df_candle = pd.read_csv(r"../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[10]:
+
+
+class coreIndicator():
+    
+    def __init__(self,options: dict, dataType: str = None, predictType: str = None, name: str = None):
+        self.options = options
+        self.dataType = dataType #ochl
+        self.predictType = predictType #trend
+        
+    
+    def getAns(self, data: pd.DataFrame() ):
+        return "ERROR"
+
+
+# In[11]:
+
+
+class ind_BB(coreIndicator):
+    """
+    options
+        MeanType -> SMA
+        window -> int
+        valueType -> str: low, high, open, close
+        kDev -> float
+    
+    """
+    
+    def __init__(self,options: dict,name = None):
+        super().__init__(
+            options = options,
+            dataType = 'ochl',
+            predictType = 'trend',
+            name = name
+        )
+    
+    def getAns(self, data: pd.DataFrame()):
+        data=data.reset_index(drop=True)
+        ans={}
+        opMA={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':{
+                'MeanType':self.options['MeanType'],
+                'valueType':self.options['valueType'],
+                'window':self.options['window']
+            }
+        }
+        ans['BB']=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['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())
+        self.ans= ans
+        return ans
+
+
+# In[12]:
+
+
+class indicatorsAgrigator:
+    
+    def __init__ (self,indDict={}):
+        self.indDict = indDict
+        self.indInst = {}
+        self.ans={}
+        self.createIndicatorsInstance()
+    
+    def createIndicatorsInstance(self):
+        for i in self.indDict.keys():
+            self.indInst[i]=self.indDict[i]['className'](self.indDict[i]['params'])
+            
+    def getAns(self,dataDict={}):
+        ans={}
+        for i in dataDict.keys():
+            ans[i] = self.indInst[i].getAns(dataDict[i])
+        return ans
+
+
+# In[13]:
+
+
+indicators = {
+    'ind_BB':{
+        'className':ind_BB,
+        'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+    }
+}
+dataDic={
+    'ind_BB':df_candle[:1000]
+}
+
+
+# In[ ]:
+
+
+
+
+
+# In[14]:
+
+
+ia= indicatorsAgrigator(indicators)
+
+
+# In[15]:
+
+
+ia.__dict__
+
+
+# In[16]:
+
+
+ia.indInst['ind_BB'].__dict__
+
+
+# In[17]:
+
+
+ia.getAns(dataDict=dataDic)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[18]:
+
+
+op = {'MeanType':'SMA','window':5,'valueType':'low','kDev':2}
+
+
+# In[19]:
+
+
+t =  ind_BB(op)
+
+
+# In[20]:
+
+
+t.getAns(df_candle[:100])
+
+
+# In[21]:
+
+
+t.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/dealManager.ipynb

@@ -0,0 +1,467 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0eb4ab92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import datetime\n",
+    "import numpy as np\n",
+    "import uuid "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ed42153",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class DealManager():\n",
+    "    \n",
+    "    def __init__(self):\n",
+    "        self.commission=0.04\n",
+    "        self.columns=['uuid','figi','amount','startPrice','profit']\n",
+    "        self.deals = pd.DataFrame(columns=self.columns)\n",
+    "        self.deals = self.deals.set_index('uuid')\n",
+    "        \n",
+    "    def findDealByPriceAndFig(self,price,figi):\n",
+    "        ans=None\n",
+    "        for i in range(self.deals.shape[0]):\n",
+    "            if self.deals.iloc[i].startPrice == price and self.deals.iloc[i].figi == figi:\n",
+    "                ans = self.deals.iloc[i].name\n",
+    "                break\n",
+    "        return ans\n",
+    "\n",
+    "    def openDeal(self,figi,startPrice,amount=1):\n",
+    "        desiredDeal=self.findDealByPriceAndFig(startPrice,figi)\n",
+    "        if desiredDeal == None:\n",
+    "            newDealDict={\n",
+    "                'uuid':[str(uuid.uuid4())],\n",
+    "                'figi':[figi],\n",
+    "                'startPrice':[startPrice],\n",
+    "                'amount':[amount]\n",
+    "            }\n",
+    "\n",
+    "            #newDealDict['profit']=[startPrice*pow(1+self.commission,2)]\n",
+    "\n",
+    "\n",
+    "\n",
+    "            newDeal=pd.DataFrame.from_dict(newDealDict).set_index('uuid')\n",
+    "            self.deals=pd.concat([self.deals, newDeal])\n",
+    "        else:\n",
+    "            self.deals.at[desiredDeal,'amount'] += amount\n",
+    "\n",
+    "    def closeDeal(self,uuid,amount):\n",
+    "        \n",
+    "        desiredDeal=self.deals.loc[uuid]\n",
+    "        if desiredDeal.amount - amount == 0:\n",
+    "            self.deals = self.deals.drop(labels = [uuid],axis = 0)\n",
+    "        else:\n",
+    "            self.deals.at[uuid,'amount'] -= amount\n",
+    "            #self.deals.loc[uuid].amount = desiredDeal.amount - amount\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "622b8115",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'commission': 0.04,\n",
+       " 'columns': ['uuid', 'figi', 'amount', 'startPrice', 'profit'],\n",
+       " 'deals': Empty DataFrame\n",
+       " Columns: [figi, amount, startPrice, profit]\n",
+       " Index: []}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t=DealManager()\n",
+    "t.__dict__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3509306",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "4fae838f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.deals.shape[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "68ec95c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t.openDeal('huigi',100,1)\n",
+    "t.openDeal('huigi',100,3)\n",
+    "t.openDeal('huigi1',100,3)\n",
+    "t.openDeal('huigi1',200,3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "cc5f9cb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>figi</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>startPrice</th>\n",
+       "      <th>profit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uuid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>a38e6fa8-c160-481c-b666-78ec9fade50b</th>\n",
+       "      <td>huigi</td>\n",
+       "      <td>4</td>\n",
+       "      <td>100</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>482abf6b-7a4f-4e49-aea6-1b5940277c24</th>\n",
+       "      <td>huigi1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6e940b6a-30e1-435c-b214-fc952aba02dc</th>\n",
+       "      <td>huigi1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>200</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        figi amount startPrice profit\n",
+       "uuid                                                                 \n",
+       "a38e6fa8-c160-481c-b666-78ec9fade50b   huigi      4        100    NaN\n",
+       "482abf6b-7a4f-4e49-aea6-1b5940277c24  huigi1      3        100    NaN\n",
+       "6e940b6a-30e1-435c-b214-fc952aba02dc  huigi1      3        200    NaN"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.deals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "eed2748a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>figi</th>\n",
+       "      <th>amount</th>\n",
+       "      <th>startPrice</th>\n",
+       "      <th>profit</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>uuid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>482abf6b-7a4f-4e49-aea6-1b5940277c24</th>\n",
+       "      <td>huigi1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>100</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6e940b6a-30e1-435c-b214-fc952aba02dc</th>\n",
+       "      <td>huigi1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>200</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        figi amount startPrice profit\n",
+       "uuid                                                                 \n",
+       "482abf6b-7a4f-4e49-aea6-1b5940277c24  huigi1      3        100    NaN\n",
+       "6e940b6a-30e1-435c-b214-fc952aba02dc  huigi1      3        200    NaN"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.deals[t.deals.figi == 'huigi1']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7a4a820",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0cac1f5c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "figi          huigi\n",
+      "amount            4\n",
+      "startPrice      100\n",
+      "profit          NaN\n",
+      "Name: a38e6fa8-c160-481c-b666-78ec9fade50b, dtype: object\n",
+      "figi          huigi1\n",
+      "amount             3\n",
+      "startPrice       100\n",
+      "profit           NaN\n",
+      "Name: 482abf6b-7a4f-4e49-aea6-1b5940277c24, dtype: object\n",
+      "figi          huigi1\n",
+      "amount             3\n",
+      "startPrice       200\n",
+      "profit           NaN\n",
+      "Name: 6e940b6a-30e1-435c-b214-fc952aba02dc, dtype: object\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i in range(t.deals.shape[0]):\n",
+    "    print(t.deals.iloc[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8906d273",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method DealManager.findDealByPriceAndFig of <__main__.DealManager object at 0x7f05b4351590>>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.findDealByPriceAndFig"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "896d4dac",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'78228979-3daf-470a-9c2a-8db180c8c3b0'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:3791\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3790\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3791\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3792\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[0;32mindex.pyx:152\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mindex.pyx:181\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7080\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7088\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: '78228979-3daf-470a-9c2a-8db180c8c3b0'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcloseDeal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m78228979-3daf-470a-9c2a-8db180c8c3b0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m t\u001b[38;5;241m.\u001b[39mdeals\n",
+      "Cell \u001b[0;32mIn[2], line 38\u001b[0m, in \u001b[0;36mDealManager.closeDeal\u001b[0;34m(self, uuid, amount)\u001b[0m\n\u001b[1;32m     36\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcloseDeal\u001b[39m(\u001b[38;5;28mself\u001b[39m,uuid,amount):\n\u001b[0;32m---> 38\u001b[0m     desiredDeal\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeals\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\u001b[43muuid\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     39\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m desiredDeal\u001b[38;5;241m.\u001b[39mamount \u001b[38;5;241m-\u001b[39m amount \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     40\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeals \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdeals\u001b[38;5;241m.\u001b[39mdrop(labels \u001b[38;5;241m=\u001b[39m [uuid],axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m)\n",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1153\u001b[0m, in \u001b[0;36m_LocationIndexer.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1150\u001b[0m axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxis \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m   1152\u001b[0m maybe_callable \u001b[38;5;241m=\u001b[39m com\u001b[38;5;241m.\u001b[39mapply_if_callable(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj)\n\u001b[0;32m-> 1153\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_getitem_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmaybe_callable\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1393\u001b[0m, in \u001b[0;36m_LocIndexer._getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1391\u001b[0m \u001b[38;5;66;03m# fall thru to straight lookup\u001b[39;00m\n\u001b[1;32m   1392\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_key(key, axis)\n\u001b[0;32m-> 1393\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_label\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/indexing.py:1343\u001b[0m, in \u001b[0;36m_LocIndexer._get_label\u001b[0;34m(self, label, axis)\u001b[0m\n\u001b[1;32m   1341\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get_label\u001b[39m(\u001b[38;5;28mself\u001b[39m, label, axis: AxisInt):\n\u001b[1;32m   1342\u001b[0m     \u001b[38;5;66;03m# GH#5567 this will fail if the label is not present in the axis.\u001b[39;00m\n\u001b[0;32m-> 1343\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mxs\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/generic.py:4236\u001b[0m, in \u001b[0;36mNDFrame.xs\u001b[0;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[1;32m   4234\u001b[0m             new_index \u001b[38;5;241m=\u001b[39m index[loc]\n\u001b[1;32m   4235\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4236\u001b[0m     loc \u001b[38;5;241m=\u001b[39m \u001b[43mindex\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4238\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(loc, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m   4239\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m loc\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m==\u001b[39m np\u001b[38;5;241m.\u001b[39mbool_:\n",
+      "File \u001b[0;32m~/projects/marketTrade/.venv/lib/python3.11/site-packages/pandas/core/indexes/base.py:3798\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3793\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3794\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3795\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3796\u001b[0m     ):\n\u001b[1;32m   3797\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3798\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3799\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3800\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3801\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3802\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3803\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[0;31mKeyError\u001b[0m: '78228979-3daf-470a-9c2a-8db180c8c3b0'"
+     ]
+    }
+   ],
+   "source": [
+    "t.closeDeal('78228979-3daf-470a-9c2a-8db180c8c3b0',1)\n",
+    "t.deals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8fbd7460",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a38e6fa8-c160-481c-b666-78ec9fade50b'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t.deals.iloc[0].name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "83549b44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a=2\n",
+    "a==None\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d794cff",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

poetry.lock

@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
 
 [[package]]
 name = "anyio"
@@ -1099,6 +1099,7 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
 files = [
     {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
 ]
 
 [[package]]
@@ -1598,6 +1599,16 @@ files = [
     {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
     {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:f698de3fd0c4e6972b92290a45bd9b1536bffe8c6759c62471efaa8acb4c37bc"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:aa57bd9cf8ae831a362185ee444e15a93ecb2e344c8e52e4d721ea3ab6ef1823"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ffcc3f7c66b5f5b7931a5aa68fc9cecc51e685ef90282f4a82f0f5e9b704ad11"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:47d4f1c5f80fc62fdd7777d0d40a2e9dda0a05883ab11374334f6c4de38adffd"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1f67c7038d560d92149c060157d623c542173016c4babc0c1913cca0564b9939"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9aad3c1755095ce347e26488214ef77e0485a3c34a50c5a5e2471dff60b9dd9c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:14ff806850827afd6b07a5f32bd917fb7f45b046ba40c57abdb636674a8b559c"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8f9293864fe09b8149f0cc42ce56e3f0e54de883a9de90cd427f191c346eb2e1"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win32.whl", hash = "sha256:715d3562f79d540f251b99ebd6d8baa547118974341db04f5ad06d5ea3eb8007"},
+    {file = "MarkupSafe-2.1.3-cp312-cp312-win_amd64.whl", hash = "sha256:1b8dd8c3fd14349433c79fa8abeb573a55fc0fdd769133baac1f5e07abf54aeb"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
     {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
@@ -1754,13 +1765,13 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>=
 
 [[package]]
 name = "nbconvert"
-version = "7.12.0"
-description = "Converting Jupyter Notebooks"
+version = "7.16.2"
+description = "Converting Jupyter Notebooks (.ipynb files) to other formats.  Output formats include asciidoc, html, latex, markdown, pdf, py, rst, script.  nbconvert can be used both as a Python library (`import nbconvert`) or as a command line tool (invoked as `jupyter nbconvert ...`)."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "nbconvert-7.12.0-py3-none-any.whl", hash = "sha256:5b6c848194d270cc55fb691169202620d7b52a12fec259508d142ecbe4219310"},
-    {file = "nbconvert-7.12.0.tar.gz", hash = "sha256:b1564bd89f69a74cd6398b0362da94db07aafb991b7857216a766204a71612c0"},
+    {file = "nbconvert-7.16.2-py3-none-any.whl", hash = "sha256:0c01c23981a8de0220255706822c40b751438e32467d6a686e26be08ba784382"},
+    {file = "nbconvert-7.16.2.tar.gz", hash = "sha256:8310edd41e1c43947e4ecf16614c61469ebc024898eb808cce0999860fc9fb16"},
 ]
 
 [package.dependencies]
@@ -1787,7 +1798,7 @@ docs = ["ipykernel", "ipython", "myst-parser", "nbsphinx (>=0.2.12)", "pydata-sp
 qtpdf = ["nbconvert[qtpng]"]
 qtpng = ["pyqtwebengine (>=5.15)"]
 serve = ["tornado (>=6.1)"]
-test = ["flaky", "ipykernel", "ipywidgets (>=7)", "pytest"]
+test = ["flaky", "ipykernel", "ipywidgets (>=7.5)", "pytest"]
 webpdf = ["playwright"]
 
 [[package]]
@@ -2483,6 +2494,7 @@ files = [
     {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:855fb52b0dc35af121542a76b9a84f8d1cd886ea97c84703eaa6d88e37a2ad28"},
     {file = "PyYAML-6.0.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:40df9b996c2b73138957fe23a16a4f0ba614f4c0efce1e9406a184b6d07fa3a9"},
+    {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a08c6f0fe150303c1c6b71ebcd7213c2858041a7e01975da3a99aed1e7a378ef"},
     {file = "PyYAML-6.0.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6c22bec3fbe2524cde73d7ada88f6566758a8f7227bfbf93a408a9d86bcc12a0"},
     {file = "PyYAML-6.0.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:8d4e9c88387b0f5c7d5f281e55304de64cf7f9c0021a3525bd3b1c542da3b0e4"},
     {file = "PyYAML-6.0.1-cp312-cp312-win32.whl", hash = "sha256:d483d2cdf104e7c9fa60c544d92981f12ad66a457afae824d146093b8c294c54"},
@@ -3386,4 +3398,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13"
-content-hash = "29bb47a642b8c954617e72b3b407196c5cc1e13c7eb483c712538b08c3d4b8e7"
+content-hash = "8e185f19d0891fa375ca3e5d878742e677c332f20e5969c6e058af9ba5c41e2e"

pyproject.toml

@@ -21,6 +21,7 @@ black = "^23.12.0"
 tinkoff-grpc = {git = "git@github.com:strategy155/tinkoff_grpc.git", branch="master"}
 python-dotenv = "^1.0.0"
 jupyterlab = "^4.0.9"
+nbconvert = "^7.16.2"
 
 [tool.poetry.dev-dependencies]