cacoderquan Goto Github PK

arctic

High performance datastore for time series and tick data

autoregressive-integrated-moving-average

play with ARIMA model

betty

Friendly English-like interface for your command line. Don't remember a command? Ask Betty.

biaxial-rnn-music-composition

A recurrent neural network designed to generate classical music.

blockchain

smart contract

chainlink

node of the decentralized oracle network, bridging on and off-chain computation

chia-blockchain

Chia blockchain python implementation (full node, farmer, harvester, timelord, and wallet)

data-preprocessing

deep_arb

A statistical arbitrage trading implementation using daily data and predictive models.

deep_trader

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

fate

An Industrial Level Federated Learning Framework

group-least-angle-regression

gym

A toolkit for developing and comparing reinforcement learning algorithms.

harmony

The core protocol of harmony

heap

jump-game

leetcode problem

leetcodeanimation

Demonstrate all the questions on LeetCode in the form of animation.（用动画的形式呈现解LeetCode题目的思路）

llmsherpa

Developer APIs to Accelerate LLM Projects

lstm

Long short-term memory (LSTM) model

mergets

merge two tables

nexchange-open-client-react

nginx-configparser

option-trading-strategy

playground

Play with neural networks!

predict-financial-recession

The major goal of this project is to predict financial re- cession given the frequencies of the top 500 word stems in the reports of financial companies. After applying various learning models, we can see that the prediction of financial recession by the bag of words has an accuracy of more than 90%. Hence, there is indeed a correlation between the two. Moreover, we have compared different learning models (ensemble methods with Decision Tree, SVM, and KNN) with various parameters to find the best model with a relatively high average accuracy and low variance of accuracy by cross-validation on the training data set. In addition, we have also tried several pre-processing methods (tf-idf, feature selection, and centroid-based clustering) to improve the accuracy of the learning models. In the end, the best model is Gradient Boosting with Decision Tree using the pre-processed tf-idf data set.

pyhawkes

Python framework for inference in Hawkes processes.

python-linear-programming

sentiment-analysis-on-the-rotten-tomatoes-movie-review-dataset

The Rotten Tomatoes movie review corpus is a collection of movie reviews collected by Pang and Lee in [2]. This corpus has been analysed in [3] where each sentence is parsed into its tree structure and each node is assigned a fine-grained sentiment label ranging from 1 − 5 where the numbers represent very negative, negative, neutral, positive and very positive respectively. In this paper we use this data on ath000 phrases and all the methods in this paper are assessed by training on a random subset of phrases (and their subphrases) of size approximately 4/5 of the data set and testing using the remaining 1/5. The idea is to use the non-associative functions and the parser trees structures to modify the feature vectors.

storing-and-loading-data-to-mongodb

subset

leetcode problem