A collection of Python code projects, code shortcuts for future and current use.
A collection of algorithmic backtests and trading tools written in python.
N.B.: All backtests are simple and simply looking for return criteria to be fulfilled. Complex orders, size parlays, and defined stops are calculated in algorithm for deployment.
All backtests so far are using Open as the entry and Close as the exit as long as the signal is triggered or present.
Data: Gathered from Brokerage API using Oanda Forex Brokers, YFinance Python Module
Adjustments:
The idea behind these backtests is to look for exponential returns to then refine the system and iterate across various market conditions before adding complexity and retesting.
After retesting, and adjusting the dataframe with: Commissions, Slippage (*orders estimated to open price), Order size(Size parlaying can dramatically increase losses and profits, if done correctly, scaling can have exponential returns).
Add slippage and look at the average open range in order to simulate entering a market order at the open after the signal is present. By analyzing the range of the first 2 minutes of trading, one can estimate the slippage on entry price off this system.
**Work in progress
Algorithmic Backtests: This folder contains algorithmic backtest programs and projects.
Software: Python 3.11, VS Code, Jupyter Notebook
Languages: Python
Modules: Seaborn, Pandas, Yfinance, Matplotlib, numpy, Plotly, QuantStats, datetime
Tools : This portfolio contains tools and mini projects that enhance trading data systems.
Software: Python 3.11, VS Code, Jupyter Notebook
Languages: Python
Modules: Seaborn, Pandas, Yfinance, Matplotlib
Startup: I created a program that manages startup files on windows boot.
OS: Windows
Language: Python
Modules: winreg
Wolf Chart: Program that retrieves prices and charts them out using tkinter framework.
OS: Windows
Language: Python
Modules: yfinance, matplotlib, tkinter
Mortgage Calculator: Simple mortgage application with a tkinter framework.
OS: Windows
Language: Python
Modules: tkinter
This folder contains a collection of SQL and Data projects.
BankSQL: This project demonstrates the process of transforming a CSV file into a Looker Dashboard and SQL database. We can run queries on the data, and visualize these queries using Seaborn and Looker.
Additionally, the project generates an HTML file with the Looker dashboard embedded, which can be placed in an AWS S3 bucket for easy access and sharing.
Software: SQL,Seaborn, Python 3.11, VS Code, Jupyter Notebook
Languages: SQL, Python
Modules: Seaborn, Pandas, SQLite3, Matplotlib, Faker
ManhattanRE:This project aims to analyze and visualize Manhattan Rolling Sales Data from November 2022 to October 2023 using Python and Plotly.
Software: SQL,Plotly, Python 3.11, Tableau, VS Code, Jupyter Notebook
Languages: SQL, Python
Modules: Plotly, Pandas, SQLite3
SQL Zillow: Here we analyze Zillow Housing Data to check for highest cost locations, market trends and other patterns.
We will analyze Zillow's Top Tier Home Price Index and look for patterns and analyze data.
Software: SQL,Google Looker, Streamlit, GoogleSheets, Python 3.11, VS Code, Jupyter Notebook
Languages: SQL, Python
Modules: Seaborn, Pandas, SQLite3, Matplotlib
SQL Salary Data:This project is interesting in that it combines Python and SQL while generating fictional data using the Faker module.
We will generate information for a fictional survey which details salaries, survey questions, age and other data to then analyze and look for patterns in the data.
Software: SQL,Seaborn, Python 3.11, VS Code, Jupyter Notebook
Languages: SQL, Python
Modules: Seaborn, Pandas, SQLite3, Matplotlib, Faker