Merge branch 'add_anaconda_support'

environment.yml

@@ -0,0 +1,19 @@
+name: marketTrade
+channels:
+  - defaults
+dependencies:
+  - python=3.10
+  - scipy
+  - pandas
+  - numpy
+  - matplotlib
+  - plotly
+  - mplfinance
+  - requests-html
+  - nest-asyncio
+  - selenium
+  - requests
+  - beautifulsoup4
+  - black
+  - jupyter
+prefix: /home/parf/anaconda3/envs/marketTrade

market_trade/core/CoreTraidMath.py

@@ -1,102 +1,93 @@
- 
-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={
+    def __init__(self, base_df, params=None):
-        'dataType':'ohcl',
+        """
-        'action': None,
+        Этот класс нужен для того, чтобы проводить операции над датафреймами
-        'actionOptions':{}
+        :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': {}
+            }
 
-        self.base_df=base_df.reset_index(drop=True)
+        # нужно переопределить индексы, потому что нам ничего не известно об индексации входного файла
-        self.params=params
+        self.base_df = base_df.reset_index(drop=True)
+
+        self.params = params
+
+        # Эта часть определяет с какой частью данных нужно проводить вычисления
         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):
-        ans=None
+        """
-        '''
+        Эта функция возвращает экстремум произвольного типа внутри одного столбца
-        actionOptions:
+        Тип контролируется разделом внутри словаря параметров `self.params` по ключу `actionOptions`:
-            'extremumtype':
+            'extremumtype': -- тип экстремума
                 'min'
                 'max'
-            'valueType':
+        :return ans, экстремум произвольного типа
-                'open'
+        """
-                'close'
+        ans=None
-                '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):
-        '''
+        """
-        actionOptions:
+        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:
             'MeanType':
-                'MA'
+                'MA' -- среднее по всему столбцу
-                'SMA'
+                'SMA'  -- скользящее среднее
-                'EMA'
+                'EMA' -- экспоненциальное скользящее среднее
-                'WMA'
+                'WMA' -- взвешенное скользящее среднее
-                --'SMMA'
+            'window' -- размер окна
-            'valueType':
+            'span' -- >=1 , аналог окна для экспоненциального среднего, чем он больше тем меньше коэффициент сглаживания
-                'open'
+            'weights' -- numpy.ndarray, список размером в параметр `window`, конкретные веса для каждого элемента
-                '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':
@@ -106,9 +97,7 @@ 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):
         '''
@@ -128,7 +117,7 @@ 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)

market_trade/core/indicators.py

@@ -0,0 +1,159 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTraidMath as CoreTraidMath
+import market_trade.core.CoreDraw as CoreDraw
+
+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()
+
+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
+    
+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

market_trade/core/signals.py

@@ -0,0 +1,194 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTraidMath as CoreTraidMath
+import market_trade.core.CoreDraw as CoreDraw
+from tqdm import tqdm
+
+from market_trade.core.indicators import *
+
+
+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)
+                              }
+
+
+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[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
+
+        return ans

market_trade/notebooks/autogen/Make unified candlesticks data import.py

@@ -6,9 +6,9 @@
 # In[1]:
 
 
-import market_trade.constants
+import market_trade.core.constants
 
-candlesticks_filepaths = [filepath for filepath in market_trade.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
+candlesticks_filepaths = [filepath for filepath in market_trade.core.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
 candlesticks_filepath = candlesticks_filepaths[0]
 candlesticks_filepath
 
@@ -69,7 +69,7 @@ a.analiz
 # In[5]:
 
 
-test_candlesticks = pd.read_csv(market_trade.constants.TEST_CANDLESTICKS_PATH)
+test_candlesticks = pd.read_csv(market_trade.core.constants.TEST_CANDLESTICKS_PATH)
 test_candlesticks['date'] = test_candlesticks['timestamp']
 test_candlesticks.drop(['timestamp'], axis=1, inplace=True)
 test_candlesticks.head()

market_trade/notebooks/autogen/Signal hypers.py

@@ -1,90 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-
-# This notebook is designed for ray integrations for signals
-# 
-# lets load askbid candlesticks file.
-
-# In[20]:
-
-
-import market_trade.constants
-import market_trade.dataloader
-
-candlesticks_filepaths = [filepath for filepath in market_trade.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
-candlesticks_filepath = candlesticks_filepaths[0]
-duka_interface = market_trade.dataloader.DukaMTInterface(candlesticks_filepath)
-duka_interface.bid_candlesticks
-
-
-# Let's test signal.
-
-# In[21]:
-
-
-import market_trade.signals.Signal1
-
-bid_candlesticks_df = duka_interface.bid_candlesticks[:10000]
-
-ind_params = {'MeanType': 'SMA', 'window': 5, 'valueType': 'low', 'kDev': 2}
-indEl1 = {
-    'df': bid_candlesticks_df,
-    'params': ind_params,
-    'needFig': False,
-    'showOnlyIndex': False,
-    'drawFig': True
-}
-signal_result = market_trade.signals.Signal1.SignalBollingerBands1({'BB': indEl1})
-signal_result.analiz
-
-
-# Now let's design ray trainable.
-
-# In[22]:
-
-
-def trainable(config):
-    ind_params = {'MeanType': 'SMA',
-                  'window': config['window'],
-                  'valueType': config['value_type'],
-                  'kDev': config['k_dev']}
-    indEl1 = {
-        'df': bid_candlesticks_df,
-        'params': ind_params,
-        'needFig': False,
-        'showOnlyIndex': False,
-        'drawFig': True
-    }
-    signal_result = market_trade.signals.Signal1.SignalBollingerBands1({'BB': indEl1})
-    tune.report(accuracy=signal_result.analiz["toch"])
-
-
-# Let's create config space.
-
-# In[23]:
-
-
-from ray import tune
-
-config = {
-    'window': tune.qrandint(5,100,5),
-    'value_type': tune.choice(['open', 'low', 'high', 'close']),
-    'k_dev': tune.uniform(1,4)
-}
-
-
-# Let's run ray tune
-
-# In[24]:
-
-
-analysis = tune.run(trainable, config=config, num_samples=100, mode='max', metric='accuracy')
-
-
-# Let's see who was the best
-
-# In[31]:
-
-
-analysis.best_config
-

market_trade/signals/Signal1.py

@@ -1,129 +0,0 @@
-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 
-import matplotlib.dates as mdates
-import matplotlib.pyplot as plt
-
-import mplfinance as mpf
-
-import plotly
-#import plotly.plotly as py
-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
-init_notebook_mode()
-
-import market_trade.core.CoreTraidMath
-import market_trade.core.CoreDraw
-import market_trade.core.Ind_bollingerBands
-import market_trade.signals.coreSignal
-
-    
-class SignalBollingerBands1(market_trade.signals.coreSignal.coreSignal):
-    def __init__(self,
-                  IndDict={},
-                  mode='retro',
-                 ):
-        super().__init__(name='1.1',mode=mode,target='nextBar',valeType='price')
-        self.IndDict=IndDict
-        self.BB=market_trade.core.Ind_bollingerBands.BB(
-            IndDict['BB']['df'],
-            IndDict['BB']['params'],
-            IndDict['BB']['needFig'],
-            IndDict['BB']['showOnlyIndex'],
-            IndDict['BB']['drawFig']
-        )
-        self.getAns()
-        if mode=='retro':
-            self.analiz=self.analiz()
-        
-    def getAns(self):
-        if self.mode=='retro':
-            df=self.BB.base_df#.set_index('date')
-            #print (df)
-            df_ind=pd.DataFrame.from_dict({
-                'date': self.BB.ans['x'], 
-                'BB': self.BB.ans['BB'], 
-                'pSTD': self.BB.ans['pSTD'], 
-                'mSTD': self.BB.ans['mSTD'], 
-            })
-            self.df_ind=df_ind
-            #df_ind=df_ind.set_index('date')
-            #print(df_ind)
-            dictSig={}
-            ans_sig=[]
-            analizLst=[]
-            difSize=len(df)-len(df_ind)
-            for i in range(len(df_ind)):
-                #print(df.iloc[i+dif]['date'],df_ind.iloc[i]['date'])
-                if df.iloc[i+difSize]['close'] >df_ind.iloc[i]['pSTD']:
-                    ans_sig.append(1)
-                elif df.iloc[i+difSize]['close'] <df_ind.iloc[i]['mSTD']:
-                    ans_sig.append(-1)
-                else:
-                    ans_sig.append(0)
-                    
-            self.ans={
-                'signal':ans_sig,
-                'dttm':self.BB.ans['x']
-            }
-            
-
-                
-            
-        
-        return self.ans    
-    
-    def analiz(self):
-        df=self.BB.base_df
-        dictSig={}
-        ans_sig=self.ans['signal']
-        difSize=len(df)-len(self.df_ind)
-        analizLst=[]
-        for i in range(len(self.df_ind)-1):
-            if ans_sig[i]==1 and df.iloc[i+difSize]['close']<df.iloc[i+difSize+1]['high']:
-                analizLst.append(1)
-            elif ans_sig[i]==1 and df.iloc[i+difSize]['close']>=df.iloc[i+difSize+1]['low']:
-                analizLst.append(-1)
-            elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']>df.iloc[i+difSize+1]['low']:
-                analizLst.append(1)
-            elif ans_sig[i]==-1 and df.iloc[i+difSize]['close']<=df.iloc[i+difSize+1]['high']:
-                analizLst.append(-1)
-            else:
-                analizLst.append(0)
-                
-        self.analizLst=analizLst
-        
-        t=0
-        f=0
-        zeroNum=0
-        for i in self.analizLst:
-            if i==1:
-                t=t+1
-            if i==-1:
-                f=f+1
-            if i==0:
-                zeroNum=zeroNum+1
-                
-        ans={}
-        ans['chstota']=sum(self.analizLst)/len(self.analizLst)
-        ans['zeroNum %']=zeroNum/len(self.analizLst)
-        ans['t %']=t/len(self.analizLst)
-        ans['f %']=f/len(self.analizLst)
-        try:
-            ans['toch']=t/(t+f)
-        except:
-            pass
-
-        return ans

market_trade/signals/coreSignal.py

@@ -1,14 +0,0 @@
-class coreSignal():
-    def __init__ (self,
-                  #indDict={},
-                  name='NoName',
-                  mode='retro',
-                  target=None,
-                  valeType=None
-                 ):
-        self.ans=None
-        self.name=name
-        #self.indDict=indDict
-        self.mode=mode
-        self.target=target # nextBar
-        self.valeType=valeType #price o c h l 

market_trade/signals/readme.txt

@@ -1,13 +0,0 @@
-Signal_1
-  BB синал предсказывающий будет ли цена за следубщий бар больше\меньше цены закртыия текущего, если текущиий бар закрылся ниже\выше диапазона BB
-  
-  ind_params={'MeanType':'SMA','window':5,'valueType':'low','kDev':2}
-  #df_candle[:3000],ind_params,True,False,True
-  indEl1={
-      'df':df_candle[:3000],
-      'params':ind_params,
-      'needFig':False,
-      'showOnlyIndex':False,
-      'drawFig':True   
-  }
-  a=Signal_1.SignalB_B_1({'BB':indEl1})

market_trade/tests/test.py

@@ -0,0 +1,18 @@
+import market_trade.core.signals
+import market_trade.core.constants
+import pandas as pd
+
+
+if __name__ == '__main__':
+    df_candle = pd.read_csv(market_trade.core.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'}
+    b = market_trade.core.signals.signal_BB(data=df_candle[:99999],
+                  mode='retroFast',
+                  indParams=ind_params,
+                  signalParams=signalParams,
+                  batchSize=15
+                  )
+    a = b.getAns(df_candle[:99900])
+    print(b.analizMetrics)

market_trade/tests/test_dataloader.py

@@ -1,13 +1,13 @@
-import market_trade.dataloader
+import market_trade.core.dataloader
-import market_trade.constants
+import market_trade.core.constants
 
 
 def test_dataloader(data_path):
-    duka_interface = (market_trade.dataloader.DukaMTInterface(data_path))
+    duka_interface = (market_trade.core.dataloader.DukaMTInterface(data_path))
     print(duka_interface.ask_candlesticks)
 
 
 if __name__ == '__main__':
-    candlesticks_filepaths = [filepath for filepath in market_trade.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
+    candlesticks_filepaths = [filepath for filepath in market_trade.core.constants.CANDLESTICK_DATASETS_PATH.iterdir()]
     candlesticks_filepath = candlesticks_filepaths[0]
     test_dataloader(candlesticks_filepath)

poetry.lock

@@ -104,6 +104,28 @@ soupsieve = ">1.2"
 html5lib = ["html5lib"]
 lxml = ["lxml"]
 
+[[package]]
+name = "black"
+version = "22.6.0"
+description = "The uncompromising code formatter."
+category = "main"
+optional = false
+python-versions = ">=3.6.2"
+
+[package.dependencies]
+click = ">=8.0.0"
+mypy-extensions = ">=0.4.3"
+pathspec = ">=0.9.0"
+platformdirs = ">=2"
+tomli = {version = ">=1.1.0", markers = "python_full_version < \"3.11.0a7\""}
+typing-extensions = {version = ">=3.10.0.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+colorama = ["colorama (>=0.4.3)"]
+d = ["aiohttp (>=3.7.4)"]
+jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
+uvloop = ["uvloop (>=0.15.2)"]
+
 [[package]]
 name = "bleach"
 version = "5.0.0"
@@ -161,6 +183,17 @@ python-versions = ">=3.5.0"
 [package.extras]
 unicode_backport = ["unicodedata2"]
 
+[[package]]
+name = "click"
+version = "8.1.3"
+description = "Composable command line interface toolkit"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+colorama = {version = "*", markers = "platform_system == \"Windows\""}
+
 [[package]]
 name = "colorama"
 version = "0.4.4"
@@ -630,6 +663,14 @@ python-versions = "*"
 matplotlib = "*"
 pandas = "*"
 
+[[package]]
+name = "mypy-extensions"
+version = "0.4.3"
+description = "Experimental type system extensions for programs checked with the mypy typechecker."
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "nbclient"
 version = "0.6.4"
@@ -815,6 +856,14 @@ python-versions = ">=3.6"
 qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
 testing = ["docopt", "pytest (<6.0.0)"]
 
+[[package]]
+name = "pathspec"
+version = "0.9.0"
+description = "Utility library for gitignore style pattern matching of file paths."
+category = "main"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+
 [[package]]
 name = "pexpect"
 version = "4.8.0"
@@ -846,6 +895,18 @@ python-versions = ">=3.7"
 docs = ["olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinx-rtd-theme (>=1.0)", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "platformdirs"
+version = "2.5.2"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4)"]
+test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytest (>=6)"]
+
 [[package]]
 name = "plotly"
 version = "5.8.0"
@@ -1347,6 +1408,14 @@ async-generator = ">=1.10"
 trio = ">=0.11"
 wsproto = ">=0.14"
 
+[[package]]
+name = "typing-extensions"
+version = "4.3.0"
+description = "Backported and Experimental Type Hints for Python 3.7+"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "urllib3"
 version = "1.26.9"
@@ -1439,7 +1508,7 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
 [metadata]
 lock-version = "1.1"
 python-versions = ">=3.8,<3.11"
-content-hash = "2b9bb5eb71d49a52ff2c08d507d6fbbbd6eeacab53ad035a6835a6822a193a3d"
+content-hash = "60d90b1999e76733c2e6cda00baaf80ff041e0b89e355f9254e521af5f455f3e"
 
 [metadata.files]
 appdirs = [
@@ -1497,101 +1566,20 @@ beautifulsoup4 = [
     {file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30"},
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 ]
-bleach = [
+black = []
-    {file = "bleach-5.0.0-py3-none-any.whl", hash = "sha256:08a1fe86d253b5c88c92cc3d810fd8048a16d15762e1e5b74d502256e5926aa1"},
+bleach = []
-    {file = "bleach-5.0.0.tar.gz", hash = "sha256:c6d6cc054bdc9c83b48b8083e236e5f00f238428666d2ce2e083eaa5fd568565"},
-]
 bs4 = [
     {file = "bs4-0.0.1.tar.gz", hash = "sha256:36ecea1fd7cc5c0c6e4a1ff075df26d50da647b75376626cc186e2212886dd3a"},
 ]
-certifi = [
+certifi = []
-    {file = "certifi-2022.5.18.1-py3-none-any.whl", hash = "sha256:f1d53542ee8cbedbe2118b5686372fb33c297fcd6379b050cca0ef13a597382a"},
+cffi = []
-    {file = "certifi-2022.5.18.1.tar.gz", hash = "sha256:9c5705e395cd70084351dd8ad5c41e65655e08ce46f2ec9cf6c2c08390f71eb7"},
+charset-normalizer = []
-]
+click = [
-cffi = [
+    {file = "click-8.1.3-py3-none-any.whl", hash = "sha256:bb4d8133cb15a609f44e8213d9b391b0809795062913b383c62be0ee95b1db48"},
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+    {file = "click-8.1.3.tar.gz", hash = "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e"},
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 name = "markettrade"
 version = "0.1.0"
 description = ""
-authors = ["Mark <strategy155@gmail.com>"]
+authors = ["Mark <strategy155@gmail.com>", "Sasha <redsandybn@yandex.ru>"]
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 requests = "^2.27.1"
 selenium = "^4.1.5"
 jupyter = "^1.0.0"
+black = "^22.6.0"
 
 [tool.poetry.dev-dependencies]
 

tools/test_signal.py

@@ -1,37 +0,0 @@
-"""
-Signal_1
-BB
-синал
-предсказывающий
-будет
-ли
-цена
-за
-следубщий
-бар
-больше\меньше
-цены
-закртыия
-текущего, если
-текущиий
-бар
-закрылся
-ниже\выше
-диапазона
-BB
-"""
-
-import market_trade.signals.Signal1
-
-df_candle = []
-ind_params = {'MeanType': 'SMA', 'window': 5, 'valueType': 'low', 'kDev': 2}
-# df_candle[:3000],ind_params,True,False,True
-# window 5..100 (5) | valueType: 'open', 'low' | 'kdev' : 1..4 (0.1)
-indEl1 = {
-    'df': df_candle[:3000],
-    'params': ind_params,
-    'needFig': False,
-    'showOnlyIndex': False,
-    'drawFig': True
-}
-a = market_trade.signals.Signal1.SignalBollingerBands1({'BB': indEl1})