Backend: Python
Frontend: PHP
Introduction to my Journey to Understanding Risk Tolerance
Understanding one's risk tolerance is crucial for successful investing. I embarked on a personal journey to discover my risk tolerance by studying historical market data, analyzing my reactions to market fluctuations, and seeking advice from financial experts. Through this process, I gained valuable insights into my risk appetite and built an investment approach that aligns with my financial goals and temperament.
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 as a distributed system to pull data from Yahoo Finance bypassing my daily limit set by Yahoo. 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 failed to account for the fact that I was biased from the get go.
My Framework as a Former Retail Trader
As a trader, my focus was on short-term to mid-term, low-cost investment strategies. I used a combination of Python, Pandas, and financial libraries like Pandas-Datareader and Yahoo Finance to collect historical market data and analyze various investment opportunities. The core principles of my trading framework include:
Gap Trading Strategy: I leverage price gaps (open and former close) to identify potential buying or selling opportunities in certain market conditions.
Mean Reversion Strategy: I explore the mean reversion concept, combining Bollinger Bands, RSI (Relative Strength Index), and EMAs (Exponential Moving Averages) to identify potential reversal points in stock prices.
Trend Trading Strategy: I use Heiken Ashi charts and trend analysis to identify stocks with strong price trends.
Lowest Point Strategy: This strategy involves identifying stocks that have reached their recent lowest points, using a combination of momentum and technical indicators.
Key Python Libraries Utilized
I utilize several powerful Python libraries to aid my research and analysis, including:
Scipy
Pandas
Matplotlib
Pandas-Datareader
YFinance
MySQL Connector
Talib
Numpy
BeautifulSoup
Requests
Csv
Statistics
itertools
Winsound (for sound notifications)
Let's Collaborate and Innovate Together
Feel free to explore the code in this repository and share your thoughts and suggestions. I'm always open to collaborations and discussions on investment strategies and trading algorithms. Let's combine our passion for technology to build cool projects together.
📈 Happy Investing! 🚀