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30-days-of-react

30 Days of React challenge is a step by step guide to learn React in 30 days. This challenge needs an intermediate level of HTML, CSS, and JavaScript knowledge. It is recommended to feel good at JavaScript before you start to React. If you are not comfortable with JavaScript check out 30DaysOfJavaScript. This is a continuation of 30 Days Of JS.

advanced-java

😮 Core Interview Questions & Answers For Experienced Java(Backend) Developers | 互联网 Java 工程师进阶知识完全扫盲：涵盖高并发、分布式、高可用、微服务、海量数据处理等领域知识

amnezia-client

Amnezia VPN Client (Desktop+Mobile)

appliedstatistics

bayesian-regression-and-bitcoin

# Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions).

binance-api-postman

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bokeh

Interactive Data Visualization in the browser, from Python

boron

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catalyst

An Algorithmic Trading Library for Crypto-Assets in Python

ccxt

A JavaScript / Python / PHP cryptocurrency trading API with support for more than 120 bitcoin/altcoin exchanges

chatbox

Your Ultimate Copilot on the Desktop. Chatbox is a desktop app for GPT-4 / GPT-3.5 (OpenAI API) that supports Windows, Mac & Linux.

chatgpt-api

Node.js client for the official ChatGPT API. 🔥

chinese-llama-alpaca

中文LLaMA&Alpaca大语言模型+本地部署 (Chinese LLaMA & Alpaca LLMs)

chrome-charset

An extension used to modify the page default encoding for Chromium 55+ based browsers.

compound-liquidations

Aggregates liquidation opportunities on compound.finance.

core

🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

datashine

《Python统计与数据分析实战》课程代码，包含了大部分统计与非参数统计和数据分析的模型、算法。回归分析、方差分析、点估计、假设检验、主成分分析、因子分析、聚类分析、判别分析、对数线性模型、分位回归模型以及列联表分析、非参数平滑、非参数密度估计等各种非参数统计方法。

gpt-vup

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gpt_douyin

intellij-idea-tutorial

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java2019

latex-workshop

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node-binance-api

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numpy

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octobot

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pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

php

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pinia

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python-binance

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