chore: finalize file rename by removing old CoreTraidMath.py

Complete the file rename by removing the old file with typo.

This commit ensures the git history properly tracks the rename from
CoreTraidMath.py to CoreTradeMath.py.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

CLAUDE.md

@@ -0,0 +1,151 @@
+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+This is a Python-based algorithmic trading system for financial markets that implements technical indicator analysis, signal generation, decision making, and risk management. It integrates with Tinkoff Invest API for market data and trading.
+
+## Development Setup
+
+### Environment Setup
+- Python 3.9-3.12 (managed via Poetry)
+- Install dependencies: `poetry install`
+- Activate virtual environment: `poetry shell`
+- Environment variables are in `.env` (contains Tinkoff API tokens)
+
+### Docker Development
+- Build image: `docker build -f dockerfiles/Dockerfile -t market-trade .`
+- Main Dockerfile uses Poetry 1.7.1 and Python 3.11
+- Requires SSH mount for private tinkoff-grpc dependency
+
+### Running Tests
+- Test files located in `market_trade/tests/`
+- Run test: `python market_trade/tests/test_decision.py`
+- Run specific test: `python market_trade/tests/test_dataloader.py`
+
+### Data Tools
+Scripts in `tools/` directory for data collection:
+- `save_currencies_data.py` - Collect currency market data
+- `save_shares_data.py` - Collect stock market data
+- `get_shares_stats.py` - Generate trading statistics
+- Usage: `python tools/<script_name>.py [options]`
+
+## Architecture
+
+### Core Trading Pipeline (docs/trading-flow.md)
+
+The system follows this data flow:
+1. **SELECT INSTRUMENT** - Choose trading instrument
+2. **GET_CANDLES(10000)** - Fetch historical candlestick data
+3. **RETRO TRAINING** - Backtest signals on historical data
+4. **STREAM PROCESSING**:
+   - Receive real-time market messages
+   - Accumulate data in sliding window
+   - Update window with each new message
+   - Generate trading signals
+
+### Module Structure
+
+#### `market_trade/core/` - Core Trading Logic
+
+**Signal Processing Chain:**
+1. **Indicators** (`indicators.py`, `indicators_v2.py`) - Technical indicator calculation
+   - Base class: `coreIndicator`
+   - Bollinger Bands: `ind_BB`
+   - All indicator classes (Ind_*.py): ADX, Alligator, DonchianChannel, Envelopes, Gator, Ishimoku, LRI, STD, Stochastic, bollingerBands
+
+2. **Signals** (`signals.py`, `signals_v2.py`) - Signal generation from indicators
+   - Base class: `coreSignalTrande` with three modes:
+     - `online` - Real-time signal generation
+     - `retro` - Expanding window backtesting
+     - `retroFast` - Sliding window backtesting
+   - Signal implementations: `signal_BB` (Bollinger Bands signal)
+   - Aggregator: `signalAgrigator` manages multiple signal instances
+
+3. **Decision Manager** (`decisionManager.py`, `decisionManager_v2.py`) - Trading decisions
+   - Class: `decsionManager`
+   - Combines signals from `signalAgrigator`
+   - Uses `trandeVoter` for probability matrix generation
+   - Methods:
+     - `getSignalTest()` - Test signal generation
+     - `generateMatrixProbability()` - Create probability matrices from backtest
+     - `getOnlineAns()` - Real-time decision making
+
+4. **Trade Voter** (`trandeVoter.py`) - Probability-based decision weighting
+   - Generates probability matrices from historical signal performance
+   - Weights multiple signals to produce final decision
+
+5. **Risk Manager** (`riskManager.py`) - Position sizing and risk controls
+   - Class: `riskManager`
+   - Combines signal decisions with risk parameters
+
+6. **Deal Manager** (`dealManager.py`) - Trade execution and management
+   - Class: `DealManager`
+   - Manages active positions and orders
+
+**Helper Modules:**
+- `CoreTradeMath.py` - Mathematical operations for indicators (moving averages, STD)
+- `CoreDraw.py` - Visualization utilities for indicators and signals
+
+#### `market_trade/data/` - Data Loading
+
+- `dataloader.py` - Contains `DukaMTInterface` class
+  - Converts Dukascopy format candlestick data to internal format
+  - Separates bid/ask candlesticks from multi-indexed CSV
+  - Handles both file paths and DataFrames
+
+#### `market_trade/tests/` - Testing
+
+- Test files demonstrate usage patterns:
+  - `test_decision.py` - Shows complete decision manager workflow with retro training
+  - `test_dataloader.py` - Data loading tests
+
+### External Dependencies
+
+- **tinkoff-grpc** - Private GitHub repo for Tinkoff Invest API integration
+  - Located at: `git@github.com:strategy155/tinkoff_grpc.git`
+  - Used in tools for market data collection
+- **Data Analysis**: pandas, numpy, scipy, matplotlib, plotly, mplfinance
+- **Web Scraping**: requests-html, beautifulsoup4, selenium
+- **Development**: JupyterLab (notebooks in `notebooks/`)
+
+## Key Constants (market_trade/constants.py)
+
+- `ROOT_PATH` - Project root directory
+- `CANDLESTICK_DATASETS_PATH` - Path to candlestick data: `data/candlesticks/`
+- `TEST_CANDLESTICKS_PATH` - Test dataset: `data/EURUSD_price_candlestick.csv`
+- `TINKOFF_TOKEN_STRING` - Production API token (from .env)
+- `SANDBOX_TOKEN_STRING` - Sandbox API token (from .env)
+- `TINKOFF_API_ADDRESS` - API endpoint: 'invest-public-api.tinkoff.ru:443'
+
+## Data Formats
+
+### Candlestick Data
+Expected DataFrame columns:
+- `date` - Timestamp
+- `open`, `high`, `low`, `close` - OHLC price data
+- For bid/ask data: Multi-indexed with ('bid'/'ask', 'open'/'high'/'low'/'close')
+
+### Signal Configuration Dictionary
+```python
+{
+    'signal_name': {
+        'className': signal_class,  # e.g., sig_BB
+        'indParams': {...},          # Indicator parameters
+        'signalParams': {            # Signal parameters
+            'source': 'close',       # Source price column
+            'target': 'close'        # Target price column for analysis
+        },
+        'batchSize': 30              # Window size
+    }
+}
+```
+
+## Development Notes
+
+- Code contains Russian comments and variable names (e.g., "агрегатор", "индикаторы")
+- Version 2 modules (`*_v2.py`) represent newer implementations
+- The system uses sliding window approach for real-time signal generation
+- Backtesting generates probability matrices that weight signal reliability
+- Data symlink: `data/` -> `/var/data0/markettrade_data`

dockerfiles/Dockerfile

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

dockerfiles/save_currencies_data.Dockerfile

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

market_trade/core/CoreDraw.py

@@ -15,7 +15,7 @@ import datetime
 import matplotlib.dates as mdates
 import matplotlib.pyplot as plt
 
-import mplfinance as mpf
+#import mplfinance as mpf
 
 import plotly
 #import plotly.plotly as py
@@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import plotly.express as px
 
 
@@ -79,9 +79,10 @@ class corePlt():
 
 
 class coreDraw():
-    def __init__(self, data=[],needShow=False):
+    def __init__(self, data=[],needShow=False,subplot_titles={}):
         self.data=self.getPlts(data)
         self.needShow=needShow
+        self.subplot_titles=subplot_titles
         self.ans=self.getAns()
         
         
@@ -156,11 +157,11 @@ class coreDraw():
             rows=maxRow,
             cols=maxCol,
             shared_xaxes=True,
-            vertical_spacing=0.02,
+            vertical_spacing=0.1,
             shared_yaxes=True,
-            horizontal_spacing=0.02,
+            #horizontal_spacing=0.02,
             #column_widths=[]
-        
+            subplot_titles=self.subplot_titles
         )
         
         
@@ -188,7 +189,7 @@ class coreDraw():
                         except:
                             colorType='normal'
                         colors=self.getBarColorList(i.df[j],colorType)
-                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors))
+                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors),row=i.row, col=i.col)
                 
         
         

market_trade/core/CoreTradeMath.py

@@ -0,0 +1,137 @@
+ 
+import pandas as pd
+import datetime
+import numpy as np
+import plotly as pl
+import plotly.graph_objs as go
+import matplotlib.pyplot as plt
+import math
+import scipy
+import random
+import statistics 
+
+
+import datetime 
+
+
+
+
+
+class CoreMath:
+    
+    def __init__(self, base_df, params=None):
+        default_params = {
+            'dataType':'ohcl',
+            'action': None,
+            'actionOptions':{}
+        }
+        self.base_df=base_df.reset_index(drop=True)
+        self.params=params if params is not None else default_params
+        if self.params['dataType']=='ohcl':
+            self.col=self.base_df[self.params['actionOptions']['valueType']]
+        elif self.params['dataType']=='series':
+            self.col=self.base_df
+        self.ans=self.getAns()
+        
+    
+    def getAns(self):
+        ans=None
+        
+        if self.params['action']=='findExt':
+            ans = self.getExtremumValue()
+        elif self.params['action']=='findMean':
+            ans = self.getMeanValue()
+        elif self.params['action']=='findSTD':
+            ans=self.getSTD()
+        
+            
+        return ans
+    
+    
+    def getExtremumValue(self):
+        ans=None
+        '''
+        actionOptions:
+            'extremumtype':
+                'min'
+                'max'
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+        '''
+        if self.params['actionOptions']['extremumtype']=='max':
+            ans=max(self.col)
+            
+        if self.params['actionOptions']['extremumtype']=='min':
+            ans=min(self.col)
+
+            
+        return ans
+     
+        
+    def getMeanValue(self):
+        '''
+        actionOptions:
+            'MeanType':
+                'MA'
+                'SMA'
+                'EMA'
+                'WMA'
+                --'SMMA'
+            'valueType':
+                'open'
+                'close'
+                'high'
+                'low'
+            'window'
+            'span'
+            'weights'
+        '''
+        ans=None
+        if self.params['actionOptions']['MeanType']=='MA':
+            ans = self.col.mean()
+        if self.params['actionOptions']['MeanType']=='SMA':
+            ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
+            #ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
+            
+        if self.params['actionOptions']['MeanType']=='EMA':
+            ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
+        if self.params['actionOptions']['MeanType']=='WMA':
+            try:
+                weights=self.params['actionOptions']['weights']
+            except KeyError:
+                weights=np.arange(1,self.params['actionOptions']['window']+1)
+            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
+
+        
+        
+        return(ans)
+    
+    def getSTD(self):
+        '''
+        actionOptions:
+            window
+        
+        
+        
+        
+        
+        '''
+        
+        ans=None
+
+
+        try:
+            window=self.params['actionOptions']['window']
+            ans=np.asarray([])
+            for i in range(len(self.col)-window+1):
+                ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
+            
+        except:
+            #window = len(self.col)
+            ans=np.std(self.col, ddof=1)
+
+        return ans
+        

market_trade/core/CoreTraidMath.py

@@ -1,127 +0,0 @@
-import numpy as np
-
-
-class CoreMath:
-
-    def __init__(self, base_df, params=None):
-        """
-        Этот класс нужен для того, чтобы проводить операции над датафреймами
-        :param base_df: pandas.DataFrame , датафрейм, над которым будут проведены математические операции
-        :param params: словарь, который определяет какие данные пришли в класс, и что с ними нужно делать, и как
-        """
-        if params is None:
-            params = {
-                'dataType': 'ohcl',
-                'action': None,
-                'actionOptions': {}
-            }
-
-        # нужно переопределить индексы, потому что нам ничего не известно об индексации входного файла
-        self.base_df = base_df.reset_index(drop=True)
-
-        self.params = params
-
-        # Эта часть определяет с какой частью данных нужно проводить вычисления
-        if self.params['dataType']=='ohcl':
-            self.col=self.base_df[self.params['actionOptions']['valueType']]
-        elif self.params['dataType']=='series':
-            self.col=self.base_df
-
-        # собственно производим вычисления
-        self.ans=self.getAns()
-
-    def getAns(self):
-        """
-        Эта функция занимается собственно рутингом вычислений, в зависимости от параметров
-        :return: ans, неопределенный тип данных, в заивисимости от action
-        """
-        ans=None
-
-        # в зависимости от параметра action производятся соответсвующие действия
-        if self.params['action']=='findExt':
-            ans = self.getExtremumValue()
-        elif self.params['action']=='findMean':
-            ans = self.getMeanValue()
-        elif self.params['action']=='findSTD':
-            ans = self.getSTD()
-
-        return ans
-
-
-    def getExtremumValue(self):
-        """
-        Эта функция возвращает экстремум произвольного типа внутри одного столбца
-        Тип контролируется разделом внутри словаря параметров `self.params` по ключу `actionOptions`:
-            'extremumtype': -- тип экстремума
-                'min'
-                'max'
-        :return ans, экстремум произвольного типа
-        """
-        ans=None
-        if self.params['actionOptions']['extremumtype']=='max':
-            ans=max(self.col)
-
-        if self.params['actionOptions']['extremumtype']=='min':
-            ans=min(self.col)
-
-        return ans
-
-    def getMeanValue(self):
-        """
-        Божественный код
-        Эта функция возвращает среднее значение одного из следующих типов.
-        Для определения типа используется словарь `self.params`, по ключу `actionOptions`, релевантные ключи выглядят
-        так:
-            'MeanType':
-                'MA' -- среднее по всему столбцу
-                'SMA'  -- скользящее среднее
-                'EMA' -- экспоненциальное скользящее среднее
-                'WMA' -- взвешенное скользящее среднее
-            'window' -- размер окна
-            'span' -- >=1 , аналог окна для экспоненциального среднего, чем он больше тем меньше коэффициент сглаживания
-            'weights' -- numpy.ndarray, список размером в параметр `window`, конкретные веса для каждого элемента
-        """
-
-        ans=None
-
-        if self.params['actionOptions']['MeanType']=='MA':
-            ans = self.col.mean()
-        if self.params['actionOptions']['MeanType']=='SMA':
-            ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
-        if self.params['actionOptions']['MeanType']=='EMA':
-            ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
-        if self.params['actionOptions']['MeanType']=='WMA':
-            try:
-                weights=self.params['actionOptions']['weights']
-            except KeyError:
-                weights=np.arange(1,self.params['actionOptions']['window']+1)
-            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
-
-        return ans
-
-    def getSTD(self):
-        '''
-        actionOptions:
-            window
-        
-        
-        
-        
-        
-        '''
-
-        ans=None
-
-
-        try:
-            window=self.params['actionOptions']['window']
-            ans=np.asarray([])
-            for i in range(len(self.col)-window+1):
-                ans=np.append(ans, np.std(self.col[i:i+window], ddof=1))
-
-        except:
-            #window = len(self.col)
-            ans=np.std(self.col, ddof=1)
-
-        return ans
-

market_trade/core/Ind_ADX.py

@@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 init_notebook_mode()
 
@@ -82,7 +82,7 @@ class ADXI:
             'action':'findMean',
             'actionOptions':{'MeanType':'EMA','span':10}
         }        
-        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)
+        ans=np.asarray(CoreTradeMath.CoreMath(ser,op).ans)
         #print(ans)
         #ans = np.asarray(ser.ewm(span=40,adjust=False).mean().to_list())
         #print(ans)

market_trade/core/Ind_Alligator.py

@@ -24,7 +24,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 
@@ -46,7 +46,7 @@ class Alligator:
                                  'valueType':self.options['valueType'],
                                  'window':self.options[keyAns]['window']}
             }
-        ans=CoreTraidMath.CoreMath(self.base_df,op).ans
+        ans=market_trade.core.CoreTradeMath.CoreMath(self.base_df,op).ans
         
         return ans
     

market_trade/core/Ind_DonchianChannel.py

@@ -24,7 +24,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 
@@ -62,9 +62,9 @@ class IDC:
         }
         
         for i in range(self.options['window'],len(self.base_df)-self.options['shift']+1):
-            ans['MaxExt'].append(CoreTraidMath.CoreMath(self.base_df[i-self.options['window']:i],opMax).ans)
+            ans['MaxExt'].append(CoreTradeMath.CoreMath(self.base_df[i-self.options['window']:i],opMax).ans)
             ans['x'].append(self.base_df['date'][i-1+self.options['shift']])
-            ans['MinExt'].append(CoreTraidMath.CoreMath(self.base_df[i-self.options['window']:i],opMin).ans)
+            ans['MinExt'].append(CoreTradeMath.CoreMath(self.base_df[i-self.options['window']:i],opMin).ans)
         return ans
     
     

market_trade/core/Ind_Envelopes.py

@@ -24,7 +24,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 class Envelopes:
@@ -64,7 +64,7 @@ class Envelopes:
         }
         if dictResp['MeanType']=='SMA':
 
-            y=CoreTraidMath.CoreMath(self.base_df,op).ans
+            y=market_trade.core.CoreTradeMath.CoreMath(self.base_df,op).ans
             ans['MainEnv']=y[:len(y)-self.options['shift']]
             ans['PlusEnv']=ans['MainEnv']*(1+self.options['kProc']/100)
             ans['MinusEnv']=ans['MainEnv']*(1-self.options['kProc']/100)

market_trade/core/Ind_Gator.py

@@ -24,7 +24,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 import market_trade.core.Ind_Alligator
 

market_trade/core/Ind_Ishimoku.py

@@ -23,9 +23,9 @@ import plotly.graph_objs as go
 # these two lines allow your code to show up in a notebook
 from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
-import CoreDraw
+import market_trade.core.CoreDraw
 init_notebook_mode()
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import plotly.express as px
 
 

market_trade/core/Ind_LRI.py

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

market_trade/core/Ind_STD.py

@@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 class ISTD:
@@ -53,7 +53,7 @@ class ISTD:
                     'actionOptions':{'valueType':self.options['valueType']}
                 }
             x=self.base_df['date'].to_list()
-        y= CoreTraidMath.CoreMath(self.base_df,op).ans
+        y= CoreTradeMath.CoreMath(self.base_df,op).ans
         ans={'y':y,'x':x}
 
         

market_trade/core/Ind_Stochastic.py

@@ -25,7 +25,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 class Stochastic:
@@ -69,7 +69,7 @@ class Stochastic:
             'action':'findMean',
             'actionOptions':{'MeanType':'SMA','window':self.options['windowSMA']}
         }        
-        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)        
+        ans=np.asarray(market_trade.core.CoreTradeMath.CoreMath(ser,op).ans)
         return ans
         #return np.convolve(col, np.ones(self.options['windowSMA']), 'valid') /self.options['windowSMA']
         

market_trade/core/Ind_bollingerBands.py

@@ -24,7 +24,7 @@ from plotly.offline import init_notebook_mode, iplot
 from plotly.subplots import make_subplots
 init_notebook_mode()
 
-import market_trade.core.CoreTraidMath
+import market_trade.core.CoreTradeMath
 import market_trade.core.CoreDraw
 
 
@@ -50,12 +50,12 @@ class BB:
                 'window':self.options['window']
             }
         }
-        ans['BB']=market_trade.core.CoreTraidMath.CoreMath(self.base_df,opMA).ans
+        ans['BB']=market_trade.core.CoreTradeMath.CoreMath(self.base_df,opMA).ans
         opSTD={'dataType':'ohcl',
             'action':'findSTD',
             'actionOptions':{'valueType':self.options['valueType'],'window':self.options['window']}
         }
-        ans['STD']=market_trade.core.CoreTraidMath.CoreMath(self.base_df,opSTD).ans
+        ans['STD']=market_trade.core.CoreTradeMath.CoreMath(self.base_df,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.base_df['date'][self.options['window']-1:].to_list())

market_trade/core/dealManager.py

@@ -0,0 +1,77 @@
+import pandas as pd
+import datetime
+import numpy as np
+import uuid
+
+
+class DealManager():
+    """Manages open trading positions and deal lifecycle.
+
+    Tracks active positions with their entry prices and quantities,
+    supporting both opening new positions and closing existing ones.
+    """
+
+    def __init__(self):
+        """Initialize DealManager with empty deals DataFrame."""
+        self.columns = ['uuid', 'figi', 'amount', 'startPrice']
+        self.deals = pd.DataFrame(columns=self.columns)
+        self.deals = self.deals.set_index('uuid')
+
+    def find_deal_by_price_and_figi(self, price: float, figi: str):
+        """Find existing deal by price and instrument identifier.
+
+        Args:
+            price: Entry price to search for.
+            figi: Financial Instrument Global Identifier.
+
+        Returns:
+            Deal UUID if found, None otherwise.
+        """
+        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 open_deal(self, figi: str, start_price: float, amount: int = 1) -> None:
+        """Open new deal or add to existing position.
+
+        If a deal with the same FIGI and price exists, adds to the amount.
+        Otherwise creates a new deal entry.
+
+        Args:
+            figi: Financial Instrument Global Identifier.
+            start_price: Entry price for the position.
+            amount: Number of units to trade (default 1).
+        """
+        desired_deal = self.find_deal_by_price_and_figi(start_price, figi)
+        if desired_deal is None:
+            new_deal_dict = {
+                'uuid': [str(uuid.uuid4())],
+                'figi': [figi],
+                'startPrice': [start_price],
+                'amount': [amount]
+            }
+
+            new_deal = pd.DataFrame.from_dict(new_deal_dict).set_index('uuid')
+            self.deals = pd.concat([self.deals, new_deal])
+        else:
+            self.deals.at[desired_deal, 'amount'] += amount
+
+    def close_deal(self, uuid_str: str, amount: int) -> None:
+        """Close deal partially or completely.
+
+        Args:
+            uuid_str: Deal UUID to close.
+            amount: Number of units to close.
+
+        Note:
+            If amount equals deal amount, removes deal entirely.
+            Otherwise decreases deal amount.
+        """
+        desired_deal = self.deals.loc[uuid_str]
+        if desired_deal.amount - amount == 0:
+            self.deals = self.deals.drop(labels=[uuid_str], axis=0)
+        else:
+            self.deals.at[uuid_str, 'amount'] -= amount

market_trade/core/decisionManager.md

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

market_trade/core/decisionManager.py

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

market_trade/core/decisionManager_v2.py

@@ -0,0 +1,187 @@
+import os
+import pickle
+
+import pandas as pd
+import datetime
+import numpy as np
+
+from tqdm import tqdm
+
+from market_trade.core.indicators_v2 import ind_BB
+from market_trade.core.signals_v2 import sig_BB, SignalsAggregator
+from market_trade.core.dealManager import DealManager
+from market_trade.core.trandeVoter import TradeVoter
+from market_trade.core.riskManager import RiskManager
+
+
+class DecisionManager:
+    """Manages trading decisions based on signals, probability voting, and risk management.
+
+    Coordinates the entire decision-making pipeline:
+    1. Signals from indicators
+    2. Probability-based voting (TradeVoter)
+    3. Risk assessment (RiskManager)
+    4. Deal tracking (DealManager)
+
+    Example configuration:
+        sig_config = {
+            '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}
+                    }
+                }
+            }
+        }
+    """
+
+    def __init__(self, name: str, sig_dict: dict):
+        """Initialize DecisionManager with configuration.
+
+        Args:
+            name: Identifier for this decision manager instance.
+            sig_dict: Dictionary of signal configurations.
+        """
+        self.RM = RiskManager()
+        self.DM = DealManager()
+        self.TV = TradeVoter(name)
+        self.SA = SignalsAggregator(sig_dict)
+        self.sig_dict = sig_dict
+
+    def get_online_answer(self, signals_ans: dict, price: float) -> dict:
+        """Get trading decision for current market conditions.
+
+        Args:
+            signals_ans: Dictionary of signal data.
+            price: Current market price.
+
+        Returns:
+            Risk-adjusted decision dictionary.
+        """
+        probability_decision = self.TV.get_decision_by_signals(self.get_signals_answer(signals_ans))
+        rmd = self.RM.get_decision(
+            probability_decision=probability_decision,
+            price=price,
+            deals=self.DM.deals
+        )
+        return rmd
+
+    def get_signals_answer(self, signals_data_dict: dict) -> dict:
+        """Get answers from all configured signals.
+
+        Args:
+            signals_data_dict: Dictionary of signal data inputs.
+
+        Returns:
+            Dictionary of signal results.
+        """
+        return self.SA.get_answer(signals_data_dict)
+
+    def get_right_answer(self, value_1: float, value_2: float) -> str:
+        """Determine correct direction based on value comparison.
+
+        Args:
+            value_1: First value (current).
+            value_2: Second value (next).
+
+        Returns:
+            Direction: 'down' if value decreases, 'up' if increases, 'none' if same.
+        """
+        if value_1 > value_2:
+            ans = 'down'
+        elif value_1 < value_2:
+            ans = 'up'
+        else:
+            ans = 'none'
+
+        return ans
+
+    def get_retro_trend_answer(self, retro_template_dict: dict, data: pd.DataFrame, window: int) -> dict:
+        """Run retrospective analysis on historical data.
+
+        Slides a window through historical data to generate training data
+        for probability matrix generation.
+
+        Args:
+            retro_template_dict: Template defining signal structure.
+            data: Historical market data.
+            window: Size of sliding window.
+
+        Returns:
+            Dictionary with 'signalsAns' and 'rightAns' lists.
+        """
+        req_sig = {}
+        ans = {
+            'signalsAns': [],
+            'rightAns': []
+        }
+        target = ''
+
+        for k in tqdm(range(data.shape[0] - window - 1)):
+            for i in retro_template_dict.keys():
+                req_sig[i] = {'signalData': data[k:k+window], 'indicatorData': {}}
+                target = self.SA.signals[i].params['target']
+                for j in retro_template_dict[i]['indicatorData'].keys():
+                    req_sig[i]['indicatorData'][j] = data[k:k+window]
+
+            sig_ans = self.get_signals_answer(req_sig)
+            right_ans = self.get_right_answer(data[target][k], data[target][k+1])
+
+            ans['signalsAns'].append(sig_ans)
+            ans['rightAns'].append(right_ans)
+
+        return ans
+
+    def generate_matrix_probability_from_dict(self, dict_signals: dict) -> None:
+        """Generate probability matrices from retrospective signal data.
+
+        Args:
+            dict_signals: Dictionary containing 'signalsAns' and 'rightAns' from retro analysis.
+        """
+        self.TV.create_matrix_amounts(dict_signals['signalsAns'][0].keys())
+        for i in range(len(dict_signals['signalsAns'])):
+            self.TV.set_decision_by_signals(
+                signal_decisions=dict_signals['signalsAns'][i],
+                trande=dict_signals['rightAns'][i]
+            )
+        self.TV.generate_matrix_probability()
+
+    def create_dump(self, postfix: str = '') -> str:
+        """Save decision manager state to pickle file.
+
+        Args:
+            postfix: Optional postfix for filename.
+
+        Returns:
+            Absolute path to saved file.
+        """
+        data_dict = {
+            'RM': self.RM,
+            'DM': self.DM,
+            'TV': self.TV,
+            'SA': self.SA,
+            'sigDict': self.sig_dict
+        }
+        file_name = 'data_' + postfix + '.pickle'
+        with open(file_name, 'wb') as f:
+            pickle.dump(data_dict, f)
+
+        return os.path.abspath(file_name)
+
+    def load_dump(self, path: str) -> None:
+        """Load decision manager state from pickle file.
+
+        Args:
+            path: Path to pickle file.
+        """
+        with open(path, 'rb') as f:
+            data_dict = pickle.load(f)
+
+        self.RM = data_dict['RM']
+        self.DM = data_dict['DM']
+        self.TV = data_dict['TV']
+        self.SA = data_dict['SA']
+        self.sig_dict = data_dict['sigDict']

market_trade/core/indicators.py

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

market_trade/core/indicators_v2.py

@@ -0,0 +1,159 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTradeMath
+
+
+class CoreIndicator():
+    """Base class for technical indicators.
+
+    This class provides the foundation for implementing various technical
+    indicators used in trading signal generation.
+    """
+
+    def __init__(self, options: dict, data_type: str = None, predict_type: str = None, name: str = None):
+        """Initialize CoreIndicator with configuration options.
+
+        Args:
+            options: Dictionary containing indicator-specific parameters.
+            data_type: Type of data to process (e.g., 'ohlc'). Defaults to None.
+            predict_type: Type of prediction to make (e.g., 'trend'). Defaults to None.
+            name: Optional identifier. Defaults to None.
+        """
+        self.options = options
+        self.data_type = data_type  # ohlc
+        self.predict_type = predict_type  # trend
+
+    def get_answer(self, data: pd.DataFrame):
+        """Get indicator answer from data.
+
+        Args:
+            data: DataFrame containing market data.
+
+        Returns:
+            Calculated indicator values or "ERROR" if not implemented.
+        """
+        return "ERROR"
+
+
+class IndicatorsAggregator:
+    """Aggregates and manages multiple indicator instances.
+
+    Example usage:
+        indicators = {
+            'ind_BB': {
+                'className': ind_BB,
+                'params': {'MeanType': 'SMA', 'window': 15, 'valueType': 'close', 'kDev': 2.5}
+            }
+        }
+        data_dict = {
+            'ind_BB': df_candle[:1000]
+        }
+        aggregator = IndicatorsAggregator(indicators)
+        results = aggregator.get_answer(data_dict)
+    """
+
+    def __init__(self, ind_dict=None):
+        """Initialize aggregator with indicator dictionary.
+
+        Args:
+            ind_dict: Dictionary mapping indicator names to configurations.
+                Defaults to empty dict if not provided.
+        """
+        self.ind_dict = ind_dict if ind_dict is not None else {}
+        self.ind_instances = {}
+        self.ans = {}
+        self.create_indicators_instance()
+
+    def create_indicators_instance(self):
+        """Create instances of all configured indicators."""
+        for i in self.ind_dict.keys():
+            self.ind_instances[i] = self.ind_dict[i]['className'](self.ind_dict[i]['params'])
+
+    def get_answer(self, data_dict=None):
+        """Calculate answers from all indicators.
+
+        Args:
+            data_dict: Dictionary mapping indicator names to their data.
+                Defaults to empty dict.
+
+        Returns:
+            Dictionary of indicator results.
+        """
+        if data_dict is None:
+            data_dict = {}
+        ans = {}
+        for i in data_dict.keys():
+            ans[i] = self.ind_instances[i].get_answer(data_dict[i])
+        return ans
+
+
+class ind_BB(CoreIndicator):
+    """Bollinger Bands indicator implementation.
+
+    Calculates Bollinger Bands using moving average and standard deviation.
+
+    Required options:
+        MeanType: Type of moving average (e.g., 'SMA')
+        window: Period for calculations (int)
+        valueType: Price type to use ('low', 'high', 'open', 'close')
+        kDev: Standard deviation multiplier (float)
+    """
+
+    def __init__(self, options: dict, name=None):
+        """Initialize Bollinger Bands indicator.
+
+        Args:
+            options: Configuration parameters dictionary.
+            name: Optional identifier.
+        """
+        super().__init__(
+            options=options,
+            data_type='ohlc',
+            predict_type='trend',
+            name=name
+        )
+
+    def get_answer(self, data: pd.DataFrame):
+        """Calculate Bollinger Bands values.
+
+        Args:
+            data: DataFrame with OHLC price data.
+
+        Returns:
+            Dictionary containing:
+                - BB: Middle band (moving average)
+                - STD: Standard deviation
+                - pSTD: Upper band (BB + kDev * STD)
+                - mSTD: Lower band (BB - kDev * STD)
+                - x: Date array
+        """
+        data = data.reset_index(drop=True)
+        ans = {}
+
+        op_ma = {
+            'dataType': 'ohcl',
+            'action': 'findMean',
+            'actionOptions': {
+                'MeanType': self.options['MeanType'],
+                'valueType': self.options['valueType'],
+                'window': self.options['window']
+            }
+        }
+        ans['BB'] = market_trade.core.CoreTradeMath.CoreMath(data, op_ma).ans
+
+        op_std = {
+            'dataType': 'ohcl',
+            'action': 'findSTD',
+            'actionOptions': {
+                'valueType': self.options['valueType'],
+                'window': self.options['window']
+            }
+        }
+        ans['STD'] = market_trade.core.CoreTradeMath.CoreMath(data, op_std).ans
+        ans['pSTD'] = ans['BB'] + ans['STD'] * self.options['kDev']
+        ans['mSTD'] = ans['BB'] - ans['STD'] * self.options['kDev']
+        ans['x'] = np.array(data['date'][self.options['window']-1:].to_list())
+        self.ans = ans
+        return ans

market_trade/core/riskManager.py

@@ -0,0 +1,56 @@
+import pandas as pd
+import datetime
+import numpy as np
+import random
+
+
+class RiskManager:
+    """Manages risk assessment and position sizing for trading decisions.
+
+    Evaluates trading decisions from probability-based signals and applies
+    risk management rules including commission calculations and profit targets.
+    """
+
+    def __init__(self, commission: float = 0.04):
+        """Initialize RiskManager with commission rate.
+
+        Args:
+            commission: Commission rate as decimal (default 0.04 = 4%).
+        """
+        self.commission = commission
+
+    def get_decision(self, probability_decision: dict, price: float, deals: pd.DataFrame = None) -> dict:
+        """Evaluate trading decision with risk management rules.
+
+        Args:
+            probability_decision: Dictionary containing 'trande' direction from TradeVoter.
+            price: Current market price.
+            deals: DataFrame of active positions (optional).
+
+        Returns:
+            Dictionary with 'decision' ('buy', 'sell', 'none') and additional fields:
+                - For 'buy': includes 'amount' field
+                - For 'sell': includes 'deals' list of position UUIDs to close
+        """
+        ans = {}
+        ans['decision'] = 'none'
+
+        if probability_decision['trande'] == 'up':
+            ans['decision'] = 'buy'
+            ans['amount'] = 1
+
+        elif probability_decision['trande'] == 'none':
+            ans['decision'] = 'none'
+
+        elif probability_decision['trande'] == 'down':
+            if deals is not None:
+                for i in range(deals.shape[0]):
+                    ans['decision'] = 'none'
+                    ans['deals'] = []
+                    row = deals.iloc[i]
+                    # Check if position is profitable after commission
+                    if row.startPrice < price * pow(1 + self.commission, 2):
+                        ans['decision'] = 'sell'
+                        ans['deals'].append(row.name)
+
+        return ans

market_trade/core/signals.py

@@ -2,13 +2,12 @@ import pandas as pd
 import datetime
 import numpy as np
 
-import market_trade.core.CoreTraidMath as CoreTraidMath
-import market_trade.core.CoreDraw as CoreDraw
+import market_trade.core.CoreTradeMath
+import market_trade.core.CoreDraw
 from tqdm import tqdm
 
 from market_trade.core.indicators import *
 
-
 class coreSignalTrande():
     def __init__(self,
                  data=pd.DataFrame(),
@@ -17,138 +16,131 @@ class coreSignalTrande():
                  batchSize=None,
                  indParams=None,
                  signalParams=None,
-                 # needFig=False,
-                 # showOnlyIndex=False,
-                 # drawFig=False,
-                 # equalityGap=0
+                 #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
+        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)
+                    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)
+            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,
@@ -169,26 +161,90 @@ class signal_BB(coreSignalTrande):
                 )
         
         if self.indParams == None:
-            indParams = {'MeanType': 'SMA', 'window': 15, 'valueType': 'low', 'kDev': 2}
+            indParams={'MeanType':'SMA','window':15,'valueType':'low','kDev':2}
         else:
-            indParams = self.indParams
-        self.BB = ind_BB(
+            indParams=self.indParams
+        self.BB=ind_BB(
             data=data,
             options=indParams,
         )
         
-    def getOnlineAns(self, data):
-        ans = 0
-        # print(data)
+    
+    def getOnlineAns(self,data):
+        ans=0
+        #print(data)
 
         self.BB.getAns(data)
-        # print(BB)
-        lastValue = data[self.signalParams['source']].to_list()[-1]
-        if lastValue > self.BB.ans['pSTD'][-1]:
-            ans = -1
-        elif lastValue < self.BB.ans['mSTD'][-1]:
-            ans = +1
+        #print(BB)
+        lastValue=data[self.signalParams['source']].to_list()[-1]
+        if lastValue>self.BB.ans['pSTD'][-1]:
+            ans=-1
+        elif lastValue<self.BB.ans['mSTD'][-1]:
+            ans=+1
         else:
-            ans = 0
+            ans=0
         
         return ans
+    
+class signalAgrigator:
+    """
+    dictAgrigSignal
+        key - name str
+        value - dict
+            className - class
+            indParams - dict
+            signalParams - dict
+            batchSize - int
+        
+    
+    """
+    def __init__(self,
+                 data=pd.DataFrame(),
+                 dictAgrigSignal={},
+                 mode='online',
+                 dataType='candel',
+                 batchSize=None
+                ):
+        self.createSingnalInstances(
+            data,
+            dictAgrigSignal,
+            dataType,
+            batchSize
+        )
+        self.mode=mode
+         
+         
+
+    def createSingnalInstances(
+        self,
+        data,
+        dictAgrigSignal,
+        dataType,
+        batchSize
+        ):
+        
+        ans={}
+        
+        for i in dictAgrigSignal:
+            ans[i]=dictAgrigSignal[i]['className'](
+                data=data,
+                dataType=dataType,
+                batchSize=batchSize,
+                indParams=dictAgrigSignal[i]['indParams'],
+                signalParams=dictAgrigSignal[i]['signalParams'],
+                mode=self.mode
+            )
+        self.signalsInstances = ans
+        return ans
+    
+    def getAns(self, data):
+        ans={}
+        
+        if self.mode == 'online':
+            for i in self.signalsInstances:
+                ans[i]=(self.signalsInstances[i].getAns(data))
+        elif self.mode == 'retroFast' or self.mode == 'retro':
+            for i in self.signalsInstances:
+                self.signalsInstances[i].getAns(data)
+                ans[i]=self.signalsInstances[i].analizMetrics
+        return ans

market_trade/core/signals_v2.py

@@ -0,0 +1,173 @@
+import pandas as pd
+import datetime
+import numpy as np
+
+import market_trade.core.CoreTradeMath
+from tqdm import tqdm
+
+from market_trade.core.indicators_v2 import IndicatorsAggregator, ind_BB
+
+
+class CoreSignalTrade:
+    """Base class for trading signals.
+
+    Provides foundation for generating trading signals based on technical indicators.
+    """
+
+    def __init__(self, name: str, req: dict, data_type: str):
+        """Initialize signal generator.
+
+        Args:
+            name: Signal identifier.
+            req: Configuration dictionary containing params and indicators.
+            data_type: Type of data to process (e.g., 'ohlc').
+        """
+        self.name = name
+        self.aggregate_indicators = self.create_indicators_instance(req)
+        self.params = req['params']
+        self.data_type = data_type
+
+    def create_indicators_instance(self, req: dict) -> IndicatorsAggregator:
+        """Create indicators aggregator from configuration.
+
+        Args:
+            req: Request dictionary containing indicators configuration.
+
+        Returns:
+            IndicatorsAggregator instance.
+        """
+        return IndicatorsAggregator(req['indicators'])
+
+    def get_indicator_answer(self, data_dict: dict) -> dict:
+        """Get answers from all indicators.
+
+        Args:
+            data_dict: Dictionary mapping indicator names to data.
+
+        Returns:
+            Dictionary of indicator results.
+        """
+        return self.aggregate_indicators.get_answer(data_dict)
+
+    def get_answer(self, data: pd.DataFrame, ind_data_dict: dict) -> dict:
+        """Get signal answer from data and indicator results.
+
+        Args:
+            data: Market data DataFrame.
+            ind_data_dict: Dictionary of indicator data.
+
+        Returns:
+            Signal answer (direction).
+        """
+        return self.get_signal_answer(data, self.get_indicator_answer(ind_data_dict))
+
+
+class sig_BB(CoreSignalTrade):
+    """Bollinger Bands signal generator.
+
+    Generates trading signals based on Bollinger Bands indicator:
+    - 'up' when price is below lower band
+    - 'down' when price is above upper band
+    - 'none' when price is within bands
+
+    Required indicator keys:
+        ind_BB: Bollinger Bands indicator
+    """
+
+    def __init__(self, name: str, req: dict):
+        """Initialize Bollinger Bands signal.
+
+        Args:
+            name: Signal identifier.
+            req: Configuration dictionary.
+        """
+        super().__init__(name, req, 'ohlc')
+
+    def get_signal_answer(self, data: pd.DataFrame, ind_ans_dict: dict) -> str:
+        """Calculate signal from Bollinger Bands.
+
+        Args:
+            data: Market data DataFrame.
+            ind_ans_dict: Dictionary containing indicator results.
+
+        Returns:
+            Signal direction: 'up', 'down', or 'none'.
+        """
+        last_value = data[self.params['source']].to_list()[-1]
+        if last_value > ind_ans_dict['ind_BB']['pSTD'][-1]:
+            ans = 'down'
+        elif last_value < ind_ans_dict['ind_BB']['mSTD'][-1]:
+            ans = 'up'
+        else:
+            ans = 'none'
+
+        return ans
+
+
+class SignalsAggregator:
+    """Aggregates and manages multiple signal generators.
+
+    Example usage:
+        sig_config = {
+            '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_data = {
+            'sig_BB': {
+                'signalData': df_candle[990:1000],
+                'indicatorData': {'ind_BB': df_candle[:1000]}
+            }
+        }
+
+        aggregator = SignalsAggregator(sig_config)
+        results = aggregator.get_answer(sig_data)
+    """
+
+    def __init__(self, req: dict):
+        """Initialize signals aggregator.
+
+        Args:
+            req: Dictionary mapping signal names to configurations.
+        """
+        self.signals = self.create_signals_instance(req)
+
+    def create_signals_instance(self, signals_dict: dict) -> dict:
+        """Create instances of all configured signals.
+
+        Args:
+            signals_dict: Dictionary of signal configurations.
+
+        Returns:
+            Dictionary of signal instances.
+        """
+        ans = {}
+        for i in signals_dict.keys():
+            ans[i] = signals_dict[i]['className'](name=i, req=signals_dict[i])
+        return ans
+
+    def get_answer(self, data_dict: dict) -> dict:
+        """Calculate answers from all signals.
+
+        Args:
+            data_dict: Dictionary mapping signal names to their data.
+                Each entry should contain 'signalData' and 'indicatorData'.
+
+        Returns:
+            Dictionary of signal results.
+        """
+        ans = {}
+        for i in data_dict.keys():
+            ans[i] = self.signals[i].get_answer(
+                data=data_dict[i]['signalData'],
+                ind_data_dict=data_dict[i]['indicatorData']
+            )
+        return ans

market_trade/core/trandeVoter.py

@@ -0,0 +1,74 @@
+import pandas as pd
+import datetime
+import numpy as np
+#import random
+
+class TradeVoter():
+
+    def __init__(self, name):
+
+        self.name = name  # Instance identifier
+        self.trade_values_list = ['up', 'none', 'down']  # Valid trade directions
+        self.matrix_amounts = None  # Sum matrix for signal combinations
+        self.keys_matrix_amounts = None  # Matrix keys, technical field
+        self.matrix_probability = None  # Probability matrix for decision making
+
+
+    # Function to create DataFrame with specified columns and indices. Indices are unique combinations.
+    def create_df_by_names(self, names_index, column_names, default_value=0.0):
+        df = pd.DataFrame(dict.fromkeys(column_names, [default_value]*pow(3, len(names_index))),
+                          index=pd.MultiIndex.from_product([self.trade_values_list]*len(names_index), names=names_index)
+                        )
+        return df
+
+    # Create sum matrix with default value
+    def create_matrix_amounts(self, names_index: list) -> pd.DataFrame:
+        self.matrix_amounts = self.create_df_by_names(names_index, self.trade_values_list, 0)
+        self.keys_matrix_amounts = self.matrix_amounts.to_dict('tight')['index_names']
+        self.create_matrix_probability(names_index)
+        return self.matrix_amounts
+
+    # Create probability matrix with default value
+    def create_matrix_probability(self, names_index: list) -> pd.DataFrame:
+        self.matrix_probability = self.create_df_by_names(names_index, self.trade_values_list)
+        return self.matrix_probability
+
+    # Set values in sum matrix. signalDecisions - indicator values key:value; trande - actual value
+    def set_decision_by_signals(self, signal_decisions: dict, trande: str) -> None:
+        buff = []
+        for i in self.keys_matrix_amounts:
+            buff.append(signal_decisions[i])
+        self.matrix_amounts.loc[tuple(buff), trande] += 1
+
+    # Fill probability matrix with calculated values from sum matrix
+    def generate_matrix_probability(self) -> None:
+        for i in range(self.matrix_amounts.shape[0]):
+            print(self.matrix_amounts)
+            row_sum = sum(self.matrix_amounts.iloc[i]) + 1
+            self.matrix_probability.iloc[i]['up'] = self.matrix_amounts.iloc[i]['up'] / row_sum
+            self.matrix_probability.iloc[i]['none'] = self.matrix_amounts.iloc[i]['none'] / row_sum
+            self.matrix_probability.iloc[i]['down'] = self.matrix_amounts.iloc[i]['down'] / row_sum
+
+    # Get decision from probability matrix based on signal values
+    def get_decision_by_signals(self, signal_decisions: dict) -> dict:
+        ans = {}
+        splice_search = self.matrix_probability.xs(tuple(signal_decisions.values()),
+                                                       level=list(signal_decisions.keys())
+                                                      )
+        ans['probability'] = splice_search.to_dict('records')[0]
+        ans['trande'] = splice_search.iloc[0].idxmax()
+        return ans
+
+    # Get probability and sum matrices as dictionaries
+    def get_matrix_dict(self) -> dict:
+        ans = {}
+        ans['amounts'] = self.matrix_amounts.to_dict('tight')
+        ans['probability'] = self.matrix_probability.to_dict('tight')
+        return ans
+
+    # Set probability and sum matrices from dictionaries
+    def set_matrix_dict(self, matrix_dict: dict) -> dict:
+        if matrix_dict['amounts'] != None:
+            self.matrix_amounts = pd.DataFrame.from_dict(y['amounts'], orient='tight')
+        if matrix_dict['probability'] != None:
+            self.matrix_probability = pd.DataFrame.from_dict(y['probability'], orient='tight')

market_trade/data/dataloader.py

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

market_trade/tests/test.py

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

market_trade/tests/test_dataloader.py

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

market_trade/tests/test_decision.py

@@ -0,0 +1,64 @@
+from market_trade.core.decisionManager_v2 import DecisionManager
+from market_trade.core.indicators_v2 import ind_BB
+from market_trade.core.signals_v2 import sig_BB
+import market_trade.data.dataloader
+
+
+sig_agr_req = {
+    'sig_BB': {
+        'className': sig_BB,
+        'params': {'source': 'close', 'target': 'close'},
+        'indicators': {
+            'ind_BB': {
+                'className': ind_BB,
+                'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2.5}
+            }
+        }
+    },
+    'sig_BB_2': {
+        'className': sig_BB,
+        'params': {'source': 'close', 'target': 'close'},
+        'indicators': {
+            'ind_BB': {
+                'className': ind_BB,
+                'params': {'MeanType': 'SMA', 'window': 30, 'valueType': 'close', 'kDev': 2}
+            }
+        }
+    }
+}
+
+test = DecisionManager('Pipa', sig_agr_req)
+
+
+import pandas as pd
+
+df_candle = pd.read_csv("../../data/EURUSD_price_candlestick.csv")
+df_candle["date"] = df_candle["timestamp"]
+
+sig_agr_retro_template = {
+    'sig_BB': {
+        'signalData': None,
+        'indicatorData': {'ind_BB': None}
+    },
+    'sig_BB_2': {
+        'signalData': None,
+        'indicatorData': {'ind_BB': None}
+    }
+}
+
+retro_ans = test.get_retro_trend_answer(sig_agr_retro_template, df_candle[5000:6000].reset_index(drop=True), 40)
+
+test.generate_matrix_probability_from_dict(retro_ans)
+
+sig_agr_data = {
+    'sig_BB': {
+        'signalData': df_candle[990:1000],
+        'indicatorData': {'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2': {
+        'signalData': df_candle[990:1000],
+        'indicatorData': {'ind_BB': df_candle[:1000]}
+    }
+}
+
+test.get_online_answer(sig_agr_data, 0.0)

notebooks/RiskManager.ipynb

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

notebooks/autogen/Indicators.py

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

notebooks/autogen/RiskManager.py

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

notebooks/autogen/Signals.py

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

notebooks/autogen/Signals_v2.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[2]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import CoreTraidMath 
+import CoreDraw
+from tqdm import tqdm
+
+from indicators_v2 import *
+
+
+# In[3]:
+
+
+df_candle = pd.read_csv(r"../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[4]:
+
+
+class coreSignalTrande:
+    
+    def __init__(self, name: str, req: dict, dataType: str):
+        self.name = name
+        self.agrigateInds = self.createIndicatorsInstance(req)
+        self.params = req['params']
+        self.dataType = dataType
+        
+    
+    def createIndicatorsInstance(self,req: dict) -> dict:
+        return indicatorsAgrigator(req['indicators'])
+    
+    def getIndAns(self, dataDict: dict) -> dict:
+        return self.agrigateInds.getAns(dataDict)
+    
+    def getAns(self, data: pd.DataFrame(), indDataDict: dict) -> dict:
+        return self.getSigAns(data, self.getIndAns(indDataDict)) 
+        
+    
+    
+class sig_BB(coreSignalTrande):
+    """
+    ind keys:
+        ind_BB
+    """
+    
+    def __init__(self, name: str, req:dict):
+        super().__init__(name, req, 'ochl')
+    
+    def getSigAns(self, data: pd.DataFrame(), indAnsDict: dict) -> dict:
+
+        lastValue = data[self.params['source']].to_list()[-1]
+        if lastValue>indAnsDict['ind_BB']['pSTD'][-1]:
+            ans='down'
+        elif lastValue<indAnsDict['ind_BB']['mSTD'][-1]:
+            ans='up'
+        else:
+            ans='none'
+        
+        return ans
+        
+
+
+# In[5]:
+
+
+class signalsAgrigator:
+    
+    def __init__ (self,req:dict):
+        self.signals = self.createSignalsInstance(req)
+        
+    def createSignalsInstance(self, siganlsDict: dict) -> dict:
+        ans = {}
+        for i in siganlsDict.keys():
+            ans[i]=siganlsDict[i]['className'](name = i, req = siganlsDict[i])
+        return ans
+    
+    def getAns(self, dataDict: dict) -> dict:
+        ans = {}
+        for i in dataDict.keys():
+            ans[i] = self.signals[i].getAns(data = dataDict[i]['signalData'], 
+                                   indDataDict = dataDict[i]['indicatorData'])
+        return ans
+
+
+# In[ ]:
+
+
+
+
+
+# In[6]:
+
+
+sigreq= {
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+            }            
+        }
+    }
+    
+indReqDict ={'ind_BB':df_candle[:1000]}
+
+
+# In[7]:
+
+
+sigAgrReq = {
+    'sig_BB':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+            }            
+        }
+    },
+    'sig_BB_2':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+# In[ ]:
+
+
+
+
+
+# In[8]:
+
+
+ttt=signalsAgrigator(sigAgrReq)
+
+
+# In[9]:
+
+
+ttt.__dict__
+
+
+# In[10]:
+
+
+ttt.signals['sig_BB'].__dict__
+
+
+# In[11]:
+
+
+ttt.getAns(sigAgrData)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[12]:
+
+
+list({'ttt':2}.keys())[0]
+
+
+# In[13]:
+
+
+test = sig_BB('sig_BB', sigreq)
+
+
+# In[14]:
+
+
+test.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[15]:
+
+
+test.agrigateInds.__dict__
+
+
+# In[16]:
+
+
+ians = test.getIndAns(indReqDict)
+ians
+
+
+# In[17]:
+
+
+test.getAns(df_candle[:100],indReqDict)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/TrandVoter.py

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

notebooks/autogen/Voter_ne_tot.py

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

notebooks/autogen/coreDraw.py

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

notebooks/autogen/dealManager.py

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

notebooks/autogen/decisionManager.py

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

notebooks/autogen/decisionManager_v2.py

@@ -0,0 +1,382 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[5]:
+
+
+import os
+
+import pandas as pd
+import datetime
+import numpy as np
+
+from tqdm import tqdm
+
+from indicators_v2 import *
+from signals_v2 import *
+from dealManager import *
+from trandeVoter import *
+from riskManager import *
+import pickle
+
+
+# In[6]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[7]:
+
+
+df_candle['close']
+
+
+# In[8]:
+
+
+class decsionManager:
+    '''
+sigAgrReq = {
+    'sig_BB':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}
+            }            
+        }
+    },
+    'sig_BB_2':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }     
+}    
+    
+    
+    
+    
+    
+    
+    '''
+    
+    
+    
+    
+    def __init__(self,name, sigDict: dict):
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalsAgrigator(sigDict)
+        self.sigDict = sigDict
+        
+        
+    def getOnlineAns(self, signalsAns: dict, price: float) -> dict:
+        probabilityDecsion = self.TV.getDecisionBySignals(self.getSignalsAns(signalsAns))
+        RMD = self.RM.getDecision(probabilityDecision=probabilityDecsion, price=price, deals = self.DM.deals)
+        return RMD
+        
+    def getSignalsAns(self, signalsDataDict: dict) -> dict:
+        return self.SA.getAns(signalsDataDict)
+    
+    def getRightAns(self,value_1, value_2):
+        
+        ans=''
+        
+        if value_1 > value_2:
+            ans = 'down'
+        elif value_1 < value_2:
+            ans = 'up'
+        else:
+            ans = 'none'
+        
+        return ans
+        
+    def getRetroTrendAns(self, retroTemplateDict: dict, data: pd.DataFrame(), window: int) -> list:
+        
+        reqSig={} 
+        ans = {
+            'signalsAns':[],
+            'rightAns':[]
+            
+        }
+        target = ''
+        
+        
+        for k in tqdm(range(data.shape[0]-window-1)):
+            for i in retroTemplateDict.keys():
+                reqSig[i] = {'signalData': data[k:k+window], 'indicatorData':{}}
+                target = self.SA.signals[i].params['target']
+                for j in retroTemplateDict[i]['indicatorData'].keys():
+                    reqSig[i]['indicatorData'][j] = data[k:k+window]
+                    
+            sigAns = self.getSignalsAns(reqSig)
+            rightAns = self.getRightAns(data[target][k], data[target][k+1])
+
+            ans['signalsAns'].append(sigAns)
+            ans['rightAns'].append(rightAns)
+
+        return ans
+ 
+
+    def generateMatrixProbabilityFromDict(self, dictSignals: dict) -> dict:
+        self.TV.createMatrixAmounts(dictSignals['signalsAns'][0].keys())
+        for i in range(len(dictSignals['signalsAns'])):
+            self.TV.setDecisionBySignals(signalDecisions = dictSignals['signalsAns'][i],
+                                        trande = dictSignals['rightAns'][i])
+        self.TV.generateMatrixProbability()
+        
+    def createDump(self,postfix='') -> str:
+        dataDict = {
+            'RM':self.RM,
+            'DM':self.DM,
+            'TV':self.TV,
+            'SA':self.SA,
+            'sigDict':self.sigDict
+        }
+        fileName='data_'+postfix+'.pickle'
+        with open(fileName, 'wb') as f:
+            pickle.dump(dataDict, f)
+            
+        return os.path.abspath(fileName)
+    
+    def loadDump(self,path: str) -> None:
+        
+        with open(path, 'rb') as f:
+            dataDict = pickle.load(f)
+        
+        self.RM = dataDict['RM']
+        self.DM = dataDict['DM']
+        self.TV = dataDict['TV']
+        self.SA = dataDict['SA']
+        self.sigDict = dataDict['sigDict']
+
+
+# In[9]:
+
+
+sigAgrReq = {
+    'sig_BB':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2.5}
+            }            
+        }
+    },
+    'sig_BB_2':{
+        'className':sig_BB,
+        'params':{'source':'close','target':'close'},
+        'indicators':{
+            'ind_BB':{
+                'className':ind_BB,
+                'params':{'MeanType':'SMA','window':30,'valueType':'close','kDev':2}
+            }            
+        }
+    }
+}
+
+sigAgrData = {
+    'sig_BB':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    },
+    'sig_BB_2':{
+        'signalData': df_candle[990:1000],
+        'indicatorData' :{'ind_BB': df_candle[:1000]}
+    }    
+}
+
+
+sigAgrRetroTemplate = {
+    'sig_BB':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    },
+    'sig_BB_2':{
+        'signalData': None,
+        'indicatorData' :{'ind_BB': None}
+    }     
+}
+
+
+# In[10]:
+
+
+test = decsionManager('Pipa', sigAgrReq)
+
+
+# In[11]:
+
+
+test.__dict__
+
+
+# In[12]:
+
+
+test.TV.__dict__
+
+
+# In[13]:
+
+
+test.SA.signals['sig_BB'].params['target']
+
+
+# In[14]:
+
+
+test.getSignalsAns(sigAgrData)
+
+
+# In[15]:
+
+
+#test.loadDump('C:\\Users\\Redsandy\\PyProj\\Trade\\MVP\\data_pupa.pickle')
+
+
+# In[16]:
+
+
+uuu = test.getRetroTrendAns(sigAgrRetroTemplate,df_candle[:5000],40)
+uuu
+
+
+# In[17]:
+
+
+test.generateMatrixProbabilityFromDict(uuu)
+
+
+# In[18]:
+
+
+test.TV.__dict__
+
+
+# In[19]:
+
+
+test.getOnlineAns(sigAgrData, 0.0)
+
+
+# In[20]:
+
+
+(test.DM.deals).shape
+
+
+# In[21]:
+
+
+test.createDump('pupa')
+
+
+# In[ ]:
+
+
+
+
+
+# In[22]:
+
+
+with open('C:\\Users\\Redsandy\\PyProj\\Trade\\MVP\\data_pupa.pickle', 'rb') as f:
+    data_new = pickle.load(f)
+data_new
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+

notebooks/autogen/indicators_v2.py

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

notebooks/dealManager.ipynb

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

poetry.lock

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

pyproject.toml

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