moved things to a new place !
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docker-compose.yml
Normal file
0
docker-compose.yml
Normal file
@ -15,7 +15,7 @@ import datetime
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import mplfinance as mpf
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#import mplfinance as mpf
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import plotly
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#import plotly.plotly as py
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@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
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from plotly.subplots import make_subplots
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init_notebook_mode()
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import market_trade.core.CoreTraidMath
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import CoreTraidMath
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import plotly.express as px
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@ -79,9 +79,10 @@ class corePlt():
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class coreDraw():
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def __init__(self, data=[],needShow=False):
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def __init__(self, data=[],needShow=False,subplot_titles={}):
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self.data=self.getPlts(data)
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self.needShow=needShow
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self.subplot_titles=subplot_titles
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self.ans=self.getAns()
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@ -156,11 +157,11 @@ class coreDraw():
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rows=maxRow,
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cols=maxCol,
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shared_xaxes=True,
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vertical_spacing=0.02,
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vertical_spacing=0.1,
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shared_yaxes=True,
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horizontal_spacing=0.02,
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#horizontal_spacing=0.02,
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#column_widths=[]
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subplot_titles=self.subplot_titles
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)
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@ -188,7 +189,7 @@ class coreDraw():
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except:
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colorType='normal'
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colors=self.getBarColorList(i.df[j],colorType)
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fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors))
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fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors),row=i.row, col=i.col)
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@ -196,4 +197,4 @@ class coreDraw():
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ans=fig
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if self.needShow:
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plotly.offline.iplot(fig)
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return ans
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return ans
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@ -1,93 +1,102 @@
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import pandas as pd
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import datetime
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import numpy as np
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import plotly as pl
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import plotly.graph_objs as go
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import matplotlib.pyplot as plt
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import math
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import scipy
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import random
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import statistics
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import datetime
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class CoreMath:
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def __init__(self, base_df, params=None):
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"""
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Этот класс нужен для того, чтобы проводить операции над датафреймами
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:param base_df: pandas.DataFrame , датафрейм, над которым будут проведены математические операции
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:param params: словарь, который определяет какие данные пришли в класс, и что с ними нужно делать, и как
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"""
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if params is None:
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params = {
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'dataType': 'ohcl',
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'action': None,
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'actionOptions': {}
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}
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# нужно переопределить индексы, потому что нам ничего не известно об индексации входного файла
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self.base_df = base_df.reset_index(drop=True)
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self.params = params
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# Эта часть определяет с какой частью данных нужно проводить вычисления
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def __init__(self, base_df, params={
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'dataType':'ohcl',
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'action': None,
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'actionOptions':{}
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}
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):
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self.base_df=base_df.reset_index(drop=True)
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self.params=params
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if self.params['dataType']=='ohcl':
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self.col=self.base_df[self.params['actionOptions']['valueType']]
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elif self.params['dataType']=='series':
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self.col=self.base_df
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# собственно производим вычисления
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self.ans=self.getAns()
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def getAns(self):
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"""
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Эта функция занимается собственно рутингом вычислений, в зависимости от параметров
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:return: ans, неопределенный тип данных, в заивисимости от action
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"""
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ans=None
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# в зависимости от параметра action производятся соответсвующие действия
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if self.params['action']=='findExt':
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ans = self.getExtremumValue()
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elif self.params['action']=='findMean':
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ans = self.getMeanValue()
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elif self.params['action']=='findSTD':
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ans = self.getSTD()
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ans=self.getSTD()
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return ans
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def getExtremumValue(self):
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"""
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Эта функция возвращает экстремум произвольного типа внутри одного столбца
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Тип контролируется разделом внутри словаря параметров `self.params` по ключу `actionOptions`:
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'extremumtype': -- тип экстремума
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ans=None
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'''
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actionOptions:
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'extremumtype':
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'min'
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'max'
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:return ans, экстремум произвольного типа
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"""
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ans=None
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'valueType':
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'open'
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'close'
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'high'
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'low'
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'''
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if self.params['actionOptions']['extremumtype']=='max':
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ans=max(self.col)
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if self.params['actionOptions']['extremumtype']=='min':
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ans=min(self.col)
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return ans
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def getMeanValue(self):
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"""
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Божественный код
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Эта функция возвращает среднее значение одного из следующих типов.
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Для определения типа используется словарь `self.params`, по ключу `actionOptions`, релевантные ключи выглядят
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так:
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'''
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actionOptions:
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'MeanType':
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'MA' -- среднее по всему столбцу
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'SMA' -- скользящее среднее
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'EMA' -- экспоненциальное скользящее среднее
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'WMA' -- взвешенное скользящее среднее
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'window' -- размер окна
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'span' -- >=1 , аналог окна для экспоненциального среднего, чем он больше тем меньше коэффициент сглаживания
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'weights' -- numpy.ndarray, список размером в параметр `window`, конкретные веса для каждого элемента
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"""
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'MA'
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'SMA'
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'EMA'
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'WMA'
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--'SMMA'
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'valueType':
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'open'
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'close'
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'high'
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'low'
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'window'
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'span'
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'weights'
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'''
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ans=None
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if self.params['actionOptions']['MeanType']=='MA':
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ans = self.col.mean()
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if self.params['actionOptions']['MeanType']=='SMA':
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ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
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#ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
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if self.params['actionOptions']['MeanType']=='EMA':
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ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
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if self.params['actionOptions']['MeanType']=='WMA':
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@ -97,8 +106,10 @@ class CoreMath:
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weights=np.arange(1,self.params['actionOptions']['window']+1)
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ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
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return ans
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return(ans)
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def getSTD(self):
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'''
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actionOptions:
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@ -109,7 +120,7 @@ class CoreMath:
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'''
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ans=None
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@ -117,11 +128,11 @@ class CoreMath:
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window=self.params['actionOptions']['window']
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ans=np.asarray([])
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for i in range(len(self.col)-window+1):
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ans=np.append(ans, np.std(self.col[i:i+window], ddof=1))
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ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
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except:
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#window = len(self.col)
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ans=np.std(self.col, ddof=1)
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return ans
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49
market_trade/core/dealManager.py
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49
market_trade/core/dealManager.py
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@ -0,0 +1,49 @@
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import pandas as pd
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import datetime
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import numpy as np
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import uuid
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class DealManager():
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def __init__(self):
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#self.commission=0.04
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self.columns=['uuid','figi','amount','startPrice']
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self.deals = pd.DataFrame(columns=self.columns)
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self.deals = self.deals.set_index('uuid')
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def findDealByPriceAndFig(self,price,figi):
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ans=None
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for i in range(self.deals.shape[0]):
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if self.deals.iloc[i].startPrice == price and self.deals.iloc[i].figi == figi:
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ans = self.deals.iloc[i].name
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break
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return ans
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def openDeal(self,figi,startPrice,amount=1):
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desiredDeal=self.findDealByPriceAndFig(startPrice,figi)
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if desiredDeal == None:
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newDealDict={
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'uuid':[str(uuid.uuid4())],
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'figi':[figi],
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'startPrice':[startPrice],
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'amount':[amount]
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}
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#newDealDict['profit']=[startPrice*pow(1+self.commission,2)]
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newDeal=pd.DataFrame.from_dict(newDealDict).set_index('uuid')
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self.deals=pd.concat([self.deals, newDeal])
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else:
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self.deals.at[desiredDeal,'amount'] += amount
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def closeDeal(self,uuid,amount):
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desiredDeal=self.deals.loc[uuid]
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if desiredDeal.amount - amount == 0:
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self.deals = self.deals.drop(labels = [uuid],axis = 0)
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else:
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self.deals.at[uuid,'amount'] -= amount
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#self.deals.loc[uuid].amount = desiredDeal.amount - amount
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116
market_trade/core/decisionManager.py
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116
market_trade/core/decisionManager.py
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import pandas as pd
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import datetime
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import numpy as np
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import pickle
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from signals import *
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from dealManager import *
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from trandeVoter import *
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from riskManager import riskManager
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class decsionManager():
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def __init__(self,name):
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self.name = name
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self.RM = riskManager()
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self.DM = DealManager()
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self.TV = trandeVoter(name)
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self.SA = signalAgrigator()
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pass
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#вытащенный из signalAgrigator метод теста для сигналов
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def getSignalTest(self,data: pd.DataFrame(),reqSig: dict, batchSize=30, dataType='candel') -> dict:
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self.SA.mode = 'retroFast'
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t.SA.createSingnalInstances(
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data = data,
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dictAgrigSignal = reqSig,
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dataType='candel',
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batchSize=30
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)
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ans = t.SA.getAns(data)
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return ans
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#метод для генерации матрицы вероятностей.
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def generateMatrixProbability(self,
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data: pd.DataFrame(),
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reqSig: dict,
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target: str,
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batchSize=30,
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#dataType='candel'
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):
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data=data.reset_index(drop=True)
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t.SA.createSingnalInstances(
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data = data,
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dictAgrigSignal = reqSig,
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dataType='candel',
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batchSize=batchSize
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)
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self.TV.createMatrixAmounts(reqSig.keys())
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for i in range(data.shape[0]-batchSize-1):
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sigAns=self.SA.getAns(data[i:i+batchSize])
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rightAns=self.getRetroStepAns(data[target][i],data[target][i+1])
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self.TV.setDecisionBySignals(self.KostilEbaniy(sigAns),rightAns)
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self.TV.generateMatrixProbability()
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#без коментариев блять
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def KostilEbaniy(self,d):
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ans={}
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for i in d.keys():
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if d[i] == 0:
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ans[i] = 'none'
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elif d[i] == 1:
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ans[i] = 'up'
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elif d[i] == -1:
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ans[i] = 'down'
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return ans
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#тож понятная хуита
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def getRetroStepAns(self, value1,value2):
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if value1 == value2:
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ans = 'none'
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elif value1 < value2:
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ans = 'up'
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else:
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ans = 'down'
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return ans
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#метод для онлай получения решения по сигналу
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def getSignal(self,data: pd.DataFrame(),reqSig: dict, dataType='candel') -> dict:
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data=data.reset_index(drop=True)
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self.SA.mode = 'online'
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t.SA.createSingnalInstances(
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data = data,
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dictAgrigSignal = reqSig,
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dataType='candel',
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batchSize=30
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)
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ans = t.SA.getAns(data)
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return ans
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#Создание сигналов. Вызывать перед getOnlineAns
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def crateSignals(self,data: pd.DataFrame(),reqSig: dict, dataType='candel'):
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data=data.reset_index(drop=True)
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self.SA.mode = 'online'
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t.SA.createSingnalInstances(
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data = data,
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dictAgrigSignal = reqSig,
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dataType='candel',
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batchSize=30
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)
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def getOnlineAns(self,data: pd.DataFrame(),price):
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sigAns = self.SA.getAns(data)
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prob = self.TV.getDecisionBySignals(sigAns)
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ans = self.RM.getDecision(sigAns,prob,price)
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return ans
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161
market_trade/core/decisionManager_v2.py
Normal file
161
market_trade/core/decisionManager_v2.py
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import os
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import pandas as pd
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import datetime
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import numpy as np
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from tqdm import tqdm
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from indicators_v2 import *
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from signals_v2 import *
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from dealManager import *
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from trandeVoter import *
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from riskManager import *
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import pickle
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class decsionManager:
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'''
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sigAgrReq = {
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'sig_BB':{
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'className':sig_BB,
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'params':{'source':'close','target':'close'},
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'indicators':{
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'ind_BB':{
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'className':ind_BB,
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'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}
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}
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}
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},
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'sig_BB_2':{
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'className':sig_BB,
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'params':{'source':'close','target':'close'},
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'indicators':{
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'ind_BB':{
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'className':ind_BB,
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'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
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}
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}
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}
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}
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sigAgrData = {
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'sig_BB':{
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'signalData': df_candle[990:1000],
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'indicatorData' :{'ind_BB': df_candle[:1000]}
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},
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'sig_BB_2':{
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'signalData': df_candle[990:1000],
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'indicatorData' :{'ind_BB': df_candle[:1000]}
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}
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}
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sigAgrRetroTemplate = {
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'sig_BB':{
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'signalData': None,
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'indicatorData' :{'ind_BB': None}
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},
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'sig_BB_2':{
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'signalData': None,
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'indicatorData' :{'ind_BB': None}
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}
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}
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'''
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|
||||
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']
|
||||
@ -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 CoreTraidMath
|
||||
import CoreDraw
|
||||
|
||||
class coreIndicator():
|
||||
def __init__(self,
|
||||
|
||||
89
market_trade/core/indicators_v2.py
Normal file
89
market_trade/core/indicators_v2.py
Normal file
@ -0,0 +1,89 @@
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import numpy as np
|
||||
|
||||
import 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']=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
|
||||
|
||||
29
market_trade/core/riskManager.py
Normal file
29
market_trade/core/riskManager.py
Normal file
@ -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
|
||||
|
||||
|
||||
@ -2,12 +2,11 @@ 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 CoreTraidMath
|
||||
import CoreDraw
|
||||
from tqdm import tqdm
|
||||
|
||||
from market_trade.core.indicators import *
|
||||
|
||||
from indicators import *
|
||||
|
||||
class coreSignalTrande():
|
||||
def __init__(self,
|
||||
@ -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
|
||||
112
market_trade/core/signals_v2.py
Normal file
112
market_trade/core/signals_v2.py
Normal file
@ -0,0 +1,112 @@
|
||||
import pandas as pd
|
||||
import datetime
|
||||
import numpy as np
|
||||
|
||||
import CoreTraidMath
|
||||
#import CoreDraw
|
||||
from tqdm import tqdm
|
||||
|
||||
from 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
|
||||
83
market_trade/core/trandeVoter.py
Normal file
83
market_trade/core/trandeVoter.py
Normal file
@ -0,0 +1,83 @@
|
||||
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]):
|
||||
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')
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -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'}
|
||||
|
||||
@ -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)
|
||||
1
market_trade/tests/test_decision.py
Normal file
1
market_trade/tests/test_decision.py
Normal file
@ -0,0 +1 @@
|
||||
im
|
||||
267
notebooks/autogen/Indicators.py
Normal file
267
notebooks/autogen/Indicators.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
45
notebooks/autogen/RiskManager.py
Normal file
45
notebooks/autogen/RiskManager.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
390
notebooks/autogen/Signals.py
Normal file
390
notebooks/autogen/Signals.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
249
notebooks/autogen/Signals_v2.py
Normal file
249
notebooks/autogen/Signals_v2.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
360
notebooks/autogen/TrandVoter.py
Normal file
360
notebooks/autogen/TrandVoter.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
427
notebooks/autogen/Voter_ne_tot.py
Normal file
427
notebooks/autogen/Voter_ne_tot.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
221
notebooks/autogen/coreDraw.py
Normal file
221
notebooks/autogen/coreDraw.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
145
notebooks/autogen/dealManager.py
Normal file
145
notebooks/autogen/dealManager.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
358
notebooks/autogen/decisionManager.py
Normal file
358
notebooks/autogen/decisionManager.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
382
notebooks/autogen/decisionManager_v2.py
Normal file
382
notebooks/autogen/decisionManager_v2.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
194
notebooks/autogen/indicators_v2.py
Normal file
194
notebooks/autogen/indicators_v2.py
Normal file
@ -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[ ]:
|
||||
|
||||
|
||||
|
||||
|
||||
26
poetry.lock
generated
26
poetry.lock
generated
@ -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"
|
||||
|
||||
@ -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]
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user