Best viewed with nbviewer - https://nbviewer.jupyter.org/ .
OVERVIEW To design and develop an intelligent systematic trading system leveraging the fundamentals of investing and algotrading.
SKILLS: Python, Understanding of libraries and Quantconnect
Phase 1: Foundation (2 weeks)
A :Fundamentals of Python
Learning Python, familiarise using Google Colab, Jupyter notebooks and VS Code. Github was used used for the report writing and storing the codes. After trying out and familiarizing with how to use Google Colab and Jupyter notebooks, and VS Code. I decided on using Jypyter notebook as it more intuitive and customisable. Notebook files from Phase1: 01-12 were written to practice using python for:
- Loading data
- Data resources: Yahoo Finance.
- download as .csv files (Colab, Jupyter)
- downloaded using pandas_data_reader (Jupyter, VSCode)
- downloaded using yfinance (Jupyter, VSCode)
- column reading, insert/delete column (Colab, Jupyter)
- Data resources: Yahoo Finance.
- Cleaning data
- normalizing
- convert string to datetime for computation
- Using Libraries & dependencies: pandas, numpy, matplotlib, sklearn, yfinance, ta, ta.utils.
- Creating Indicators (Technical, Fundamental ) by functions, libraries and Python
- Visualising data using matplotlib
Initially experimented using Google Colab, Jupyter and VSCode . Finally settled with using Jupyter Notebook for most python computation, storing in .ipynb files.
Files 01-12. Codes and files ran and used for calculations of important financial stock indicators:
- Var
- Covar
- Sharpe Ratio
- Beta
- SMA
- EMA
- MACD
- Bollinger Bands
- RSI
B: Fundamentals of Quantconnect Quantconnect is a very vast tool for developing trading algorithms. First phase is to complete all the botcamps within quantconnect to get familiar with syntax and functions of QC
- Understanding of Quant Connects Alpha stream
Deliverable:Successfully completed all the bootcamps