nouaimiadil,AdNhub,github

python

Day-wise Python Learning resources from basic concepts to advanced Python applications such as data science and Machine learning. It also includes cheat-sheets, references which are logged daily to accelerate your learning.

python-binance

Binance Exchange API python implementation for automated trading

python-binance-chain

Binance Chain Exchange API python implementation for automated trading

python-data-analytics-and-visualization

Code repository for Python Data Analytics and Visualization by Packt

python-for-finance-second-edition

Python for Finance

python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

python-oracledb

Python driver for Oracle Database conforming to the Python DB API 2.0 specification. This is the renamed, new major release of cx_Oracle

python-stock-buy-indication-using-technical-indicators

Backend: Python Frontend: PHP. I spent the majority of my time coming up with strategies like Trend Trading, Mean Reversion, Gap Trading. It gave me a false sense of being an intellectual because it involved a fair bit of Mathematics, Programming and Data Visualization. I also tried to model some physics into the system as well. I did parallel programming and dabbled with the idea of using multiple smartphones to pull data from Yahoo Finance bypassing my daily limit set by Yahoo. I falsely assumed Jim SImmons in Renaissance Technologies used TA in the beginning for predicting the market. I learnt about the scientific gambling methods like Kelly Criteria. I understood the concept of Risk Management and stop losses and tried to make an automated system for it as well. In the end I did make a fully automated system that told me when to buy a stock although the basis of it was all wrong(In my opinion Technical Analysis has no edge to it). I spent so much time thinking TA was right that I was biased from the get go. I wasn’t making money and it really took a toll on me.

python-webapp

Tutorial on making a Webapp using Python, Flask, and mySQL.

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

pythonfinance--global-optimization-of-portfolios-a-rendezvous-with-vitaoptimum-plus

Portfolio optimization problem models have typically adopted solvers such as SciPy in the Python arena, that employs traditional methods to obtain the optimal portfolios. However, despite their sophistication, traditional methods suffer from pitfalls that can stifle its ability to handle complex problem models or yield efficient solutions. Heuristic solvers inspired by nature or adopting artificial intelligence methods, have quite often risen to the occasion by yielding near-optimal or acceptable solutions to those complex problem models, which traditional methods found difficult to handle, or even yielding efficient or practically prudent solutions when compared to those obtained by traditional methods for less difficult models. VitaOptimum Plus is a Python based non-traditional solver, that employs cutting edge technologies of Artificial Intelligence, Evolutionary Computation, Swarm Intelligence and Advanced Statistics, to arrive at the global optimum. This post investigates the potential of the traditional and heuristic solvers viz., optimize of SciPy and Ccs of VitaOptimum Plus respectively, to solve the Sharpe Ratio based Portfolio Optimization model

qq-pat

The qq-pat library provides you with an easy interface for the creation of graphs and the calculation of statistics for financial time series. It provides functionality similar to the R library PerformanceAnalystics in Python. It allows you to draw graphs, calculate general statistics and perform some advanced analysis such as Monte Carlo simulations and minimum variance portfolio optimizations.

radzen-examples

Radzen demos

stock-market-investment-1

This project is an attempt to utilize python in finance and investment sphere. Hence, I will apply stock market technical analysis using simple moving average investment strategy. I use real-time data of the most active stocks. It consists of 66 columns and 2196 entries with numerous variables such as date, stock symbol, open price, highest price, lowest price, closing price and traded volume. The source of data is yahoo finance.

stock_market_data_analysis

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

nouaimiadil Goto Github PK

AdNhub's Projects

Recommend Projects

Recommend Topics

Recommend Org