Getting Profit and Loss Make Easy From Binance

Overview

Getting Profit and Loss Make Easy From Binance

I have been in Binance Automated Trading for some time and have generated a lot of transaction records, so I want to see my historical profit and loss records (for each cryptocurrency). But Binance does not provide this information.

After searching for a period of time, various useful code sections were integrated, and then presented graphically with Poltly.

The usage is very simple, just follow the following operations to get the total profit and loss in historical.

Requirement

python-binance

pip install python-binance

plotly

pip install plotly==4.14.3

jupyter-dash

pip install jupyter-dash

Usage

from calcuation import profit_loss
from chart import RealizedProfitLoss
from binance.client import Client
import pandas as pd

key = 'Your API Key'
secret = 'Yout Secert Key'

client = Client(key, secret)

Get the profit and loss of BTCUSDT from 2020-01-01 to 2021-12-21

pnl = profit_loss(market='BNB-USDT', client=client, showlog=True)

output

Get the profit and loss chart of [crypto pair] every 30 days from 2020-01 to 2021-12

from datetime import datetime

dates_df = pd.DataFrame(index=[datetime(2020,1,1), datetime(2021,12,31)])
dates = dates_df.resample('d').first().index[::30]
profilio = []
for s in ['BTC-USDT', 'BNB-USDT', 'LINK-USDT', 'ADA-USDT', 'CAKE-USDT', 'UNI-USDT', 'ETH-USDT']:        
    for start_date, end_date in zip(dates[:], dates[1:]):        
        pnl = profit_loss(market=s, start_date=start_date.strftime("%Y-%m-%d"), end_date=end_date.strftime("%Y-%m-%d"), client=client)
        profilio.append({'date': end_date, 'symbol':s, 'pnl':pnl['total_profit(quote)']})    
    
profilio_df = pd.DataFrame(profilio)
profilio_df = profilio_df.rename({'symbol':'stock_id'}, axis='columns')    
RealizedProfitLoss(profilio_df).run_dash()

Pnl DashBoard

Automated Machine Learning with scikit-learn

auto-sklearn auto-sklearn is an automated machine learning toolkit and a drop-in replacement for a scikit-learn estimator. Find the documentation here

AutoML-Freiburg-Hannover 6.7k Jan 07, 2023
Distributed Evolutionary Algorithms in Python

DEAP DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data stru

Distributed Evolutionary Algorithms in Python 4.9k Jan 05, 2023
An MLOps framework to package, deploy, monitor and manage thousands of production machine learning models

Seldon Core: Blazing Fast, Industry-Ready ML An open source platform to deploy your machine learning models on Kubernetes at massive scale. Overview S

Seldon 3.5k Jan 01, 2023
Dieses Projekt ermöglicht es den Smartmeter der EVN (Netz Niederösterreich) über die Kundenschnittstelle auszulesen.

SmartMeterEVN Dieses Projekt ermöglicht es den Smartmeter der EVN (Netz Niederösterreich) über die Kundenschnittstelle auszulesen. Smart Meter werden

greenMike 43 Dec 04, 2022
Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models

Highly interpretable, sklearn-compatible classifier based on decision rules This is a scikit-learn compatible wrapper for the Bayesian Rule List class

Tamas Madl 482 Nov 19, 2022
Fit interpretable models. Explain blackbox machine learning.

InterpretML - Alpha Release In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be lig

InterpretML 5.2k Jan 09, 2023
Stacked Generalization (Ensemble Learning)

Stacking (stacked generalization) Overview ikki407/stacking - Simple and useful stacking library, written in Python. User can use models of scikit-lea

Ikki Tanaka 192 Dec 23, 2022
It is a forest of random projection trees

rpforest rpforest is a Python library for approximate nearest neighbours search: finding points in a high-dimensional space that are close to a given

Lyst 211 Dec 29, 2022
Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way

Apache Liminals goal is to operationalise the machine learning process, allowing data scientists to quickly transition from a successful experiment to an automated pipeline of model training, validat

The Apache Software Foundation 121 Dec 28, 2022
Spark development environment for k8s

Local Spark Dev Env with Docker Development environment for k8s. Using the spark-operator image to ensure it will be the same environment. Start conta

Otacilio Filho 18 Jan 04, 2022
Used Logistic Regression, Random Forest, and XGBoost to predict the outcome of Search & Destroy games from the Call of Duty World League for the 2018 and 2019 seasons.

Call of Duty World League: Search & Destroy Outcome Predictions Growing up as an avid Call of Duty player, I was always curious about what factors led

Brett Vogelsang 2 Jan 18, 2022
A Python package to preprocess time series

Disclaimer: This package is WIP. Do not take any APIs for granted. tspreprocess Time series can contain noise, may be sampled under a non fitting rate

Maximilian Christ 57 Dec 17, 2022
ETNA – time series forecasting framework

ETNA Time Series Library Predict your time series the easiest way Homepage | Documentation | Tutorials | Contribution Guide | Release Notes ETNA is an

Tinkoff.AI 675 Jan 08, 2023
Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies

Crypto-trading - ML techiques are used to forecast short term returns in 14 popular cryptocurrencies. We have amassed a dataset of millions of rows of high-frequency market data dating back to 2018 w

Panagiotis (Panos) Mavritsakis 4 Sep 22, 2022
Machine-Learning with python (jupyter)

Machine-Learning with python (jupyter) 머신러닝 야학 작심 10일과 쥬피터 노트북 기반 데이터 사이언스 시작 들어가기전 https://nbviewer.org/ 페이지를 통해서 쥬피터 노트북 내용을 볼 수 있다. 위 페이지에서 현재 레포 기

HyeonWoo Jeong 1 Jan 23, 2022
Predico Disease Prediction system based on symptoms provided by patient- using Python-Django & Machine Learning

Predico Disease Prediction system based on symptoms provided by patient- using Python-Django & Machine Learning

Felix Daudi 1 Jan 06, 2022
A collection of video resources for machine learning

Machine Learning Videos This is a collection of recorded talks at machine learning conferences, workshops, seminars, summer schools, and miscellaneous

Dustin Tran 1.5k Dec 29, 2022
Iterative stochastic gradient descent (SGD) linear regressor with regularization

SGD-Linear-Regressor Iterative stochastic gradient descent (SGD) linear regressor with regularization Dataset: Kaggle “Graduate Admission 2” https://w

Zechen Ma 1 Oct 29, 2021
A Pythonic framework for threat modeling

pytm: A Pythonic framework for threat modeling Introduction Traditional threat modeling too often comes late to the party, or sometimes not at all. In

Izar Tarandach 644 Dec 20, 2022
Optimal Randomized Canonical Correlation Analysis

ORCCA Optimal Randomized Canonical Correlation Analysis This project is for the python version of ORCCA algorithm. It depends on Numpy for matrix calc

Yinsong Wang 1 Nov 21, 2021