oguzkirman Goto Github PK
Name: Oğuz Kırman
Type: User
Company: MGA-Soft
Twitter: OguzKirman
Location: Istanbul, Turkey
Name: Oğuz Kırman
Type: User
Company: MGA-Soft
Twitter: OguzKirman
Location: Istanbul, Turkey
Technical Indicators implemented in Python using Pandas
Stock Price Prediction with PCA and LSTM
Python library for high frequency portfolio analysis, intraday backtesting and optimization
📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.
PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).
An interactive graphing library for R
A pure Java backgammon game, written to be easily ported.
Determine optimal rebalancing of a passive stock portfolio.
Keep calm and optimize
Min Variance, Max Diversification, Risk Contribution Parity, Min CVaR
Heuristics for cardinality constrained portfolio optimisation
Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.
Mean variance portfolio optimization in C#
Neural Networks-Boosted Portfolio Optimization
Probabilistic Sharpe Ratio example in Python (by Marcos López de Prado)
Leslie Salt Data Set In 1968, the city of Mountain View, California, began the necessary legal proceedings to acquire a parcel of land owned by the Leslie Sal Company. The Leslie property contained 246.8 acres and was located right on the San Francisco Bay. The land had been used for salt evaporation and had an elevation of exactly sea level. However, the property was diked so that the waters from the bay park were kept out. The city of Mountain View intended to fill the property and use it for a city park. Ultimately,it fell into the courts to determine a fair market value for the property. Appraisers were hired, but what made the processes difficult was that there were few sales of byland property and none of them corresponded exactly to the characteristics of the Leslie property. The experts involved decided to build a regression model to better understand the factors that might influence market valuation. They collected data on 31 byland properties that were sold during the previous 10 years. In addition to the transaction price for each property, they collected data oina large number of other factors, including size, time of sale, elevation, location, and access to sewers. A listing of these data, including only those variables deemed relevant for this exercise. A description of the variables is provided below. Variable name Description Price Sales price in $000 per acre County San Mateo=0, Santa Clara =1 Size Size of the property in acres Elevation Average Elevation in foot above sea level Sewer Distance (in feet) to nearest sewer connection Date Date of sale counting backward from current time (in months) Flood Subject to flooding by tidal action =1; otherwise =0 Distance Distance in miles from Leslie Property (in almost all cases, this is toward San Francisco Discuss and Answer the following questions: 1. What is the nature of each of the variables? Which variable is dependent variable and what are the independent variables in the model? 2. Check whether the variables require any transformation individually 3. Set up a regression equation, run the model and discuss your results
Introduction to Pattern Sequence based Forecasting (PSF) algorithm in Python
PSO Algorithm with C#
Python copulas library for dependency modeling
Portfolio and risk analytics in Python
A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)
Constrained and Unconstrained Risk budgeting / risk parity allocation in Python
A simple python wrapper for the Firebase API.
Python for Random Matrix Theory: cleaning schemes for noisy correlation matrices.
Financial technical and fundamental analysis indicator library for pystockdb.
Systematic Trading in python
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.